CN114997870A - Data processing method, data processing device, computer equipment and storage medium - Google Patents

Abstract

The embodiment of the application discloses a data processing method, a data processing device, computer equipment and a storage medium. The data processing method comprises the following steps: acquiring a settled transaction data set, wherein the settled transaction data set comprises N settled transaction data, each settled transaction data comprises transaction information and resource data types, the settled transaction data set corresponds to K resource data types, and N and K are positive integers; conducting profit and loss statistical processing on transaction information in the settled transaction data set to obtain profit and loss of each resource data type; when the profit and loss of M resource data types in the K resource data types is smaller than the profit and loss threshold value, acquiring the profit and loss reasons of the M resource data types, and generating profit and loss alarm information according to the profit and loss reasons of the M resource data types, wherein M is a positive integer; and outputting profit and loss alarm information. By adopting the method and the device, false alarm is avoided, and transaction data is monitored finely.

Description

Data processing method, apparatus, computer equipment and storage medium

technical field

The present application relates to the field of computer technology, and in particular, to a data processing method, apparatus, computer device, and storage medium.

Background technique

When a participant supports the transaction of resource data of multiple resource data types, the participant needs to monitor the overall profit and loss of all transactions. At present, the profit and loss of all transactions are calculated manually. Once abnormal profit and loss is found, an alarm is manually generated to notify the corresponding disposal personnel to deal with the abnormal situation.

The low degree of automated monitoring of transactions will lead to false alarms, and only monitoring the overall profit and loss of participating entities results in coarse monitoring granularity. Therefore, how to avoid false alarms and finely monitor transaction data is a current research hotspot.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a data processing method, apparatus, computer equipment, and storage medium, which can avoid false alarms and finely monitor transaction data.

On the one hand, the embodiments of the present application provide a data processing method, including:

Acquire a set of settled transaction data, the settled transaction data set includes N settled transaction data, each settled transaction data includes transaction information and resource data types, and the settled transaction data set corresponds to K resource data types , N, K are all positive integers;

Performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

When the profit and loss of M resource data types in the K resource data types is less than the profit and loss threshold, obtain the profit and loss reasons of the M resource data types, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, where M is a positive integer;

The profit and loss warning information is output.

On the one hand, the embodiments of the present application provide a data processing apparatus, including:

The acquisition module is used to acquire a set of settled transaction data, the set of settled transaction data includes N pieces of settled transaction data, each of the settled transaction data includes transaction information and resource data types, and the set of settled transaction data corresponds to K resource data types, N, K are positive integers;

A statistics module, configured to perform profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

The generating module is configured to obtain the profit and loss reasons of the M resource data types when the profit and loss of the M resource data types in the K resource data types is less than the profit and loss threshold, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, M is a positive integer;

An output module, configured to output the profit and loss alarm information.

An aspect of the embodiments of the present application provides a computer device, including a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the methods in the foregoing embodiments.

An aspect of the embodiments of the present application provides a computer storage medium, where the computer storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the methods in the foregoing embodiments are executed.

On the one hand, the embodiments of the present application provide a computer program product or computer program. The computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. When the computer instructions are executed by a processor of a computer device, the execution methods in the above embodiments.

In this application, the terminal device automatically performs statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type. Compared with only monitoring the total profit and loss of all resource data types, this application is for each The profit and loss of the resource data type, so the monitoring granularity of the transaction data is finer; in addition, in this application, the terminal device automatically calculates the profit and loss, and then generates alarm information, without manual participation. The high degree of automation can avoid false alarms; further , the alarm information of the present application also includes the reason for the smaller profit and loss of the resource data type, which can enrich the content contained in the alarm information.

Description of drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without any creative effort.

1 is a schematic diagram of a profit and loss provided by an embodiment of the present application;

2 is a schematic diagram of an overall service of data processing provided by an embodiment of the present application;

3 is a schematic flowchart of a data processing provided by an embodiment of the present application;

4 is a schematic diagram of a profit and loss indicator system provided by an embodiment of the present application;

5 is a schematic diagram of a display page provided by an embodiment of the present application;

6 is a schematic flowchart of a data processing provided by an embodiment of the present application;

7 is a schematic diagram of a 3-SIGMA algorithm provided by an embodiment of the present application;

8 is a schematic diagram of a display page provided by an embodiment of the present application;

9 is a schematic diagram of a display page provided by an embodiment of the present application;

FIG. 10 is a schematic diagram of a service handling page provided by an embodiment of the present application;

11 is a schematic diagram of a front-end framework provided by an embodiment of the present application;

12 is a schematic diagram of an intermediate layer framework provided by an embodiment of the present application;

13 is a schematic diagram of a system security framework provided by an embodiment of the present application;

14 is a system architecture diagram of a blockchain provided by an embodiment of the present application;

15 is a schematic flowchart of a data processing provided by an embodiment of the present application;

16 is a schematic structural diagram of a data processing apparatus provided by an embodiment of the present application;

FIG. 17 is a schematic structural diagram of a computer device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

This application can be applied to the risk perception and evaluation system of cross-border acquiring business. The risk perception module in the system is mainly used to calculate the profit and loss of each resource data type, the total profit and loss of the market, and find abnormal transaction objects (in this The transaction object in the application refers to the overseas merchant with consumption behavior), which can enable the relevant business personnel to perceive the risk situation in time; the evaluation module in the system is mainly used to find out the cause of the loss, issue the alarm information, and display the corresponding treatment according to the alarm information. page, so that business personnel can perform business operations on the disposal page, so as to improve the efficiency of business analysis and transaction disposal.

The following first describes the definition of cross-border acquiring and the definition of the profit and loss of resource data types when cross-border acquiring:

Cross-border acquiring means that after domestic individuals generate consumption at overseas merchants, participating entities collect RMB payment from domestic individuals, and then settle foreign exchange with overseas merchants through cooperative banks.

Due to the time difference between the user's consumption time and the settlement time with merchants, the consumption time exchange rate and settlement time exchange rate of the same resource data type may fluctuate greatly, resulting in participants needing more resource data (compared to transaction time) to purchase The resource data of the resource data type supported by the merchant is settled to the merchant, causing losses. For example: Mainland consumers spent a total of 100 million Hong Kong dollars at Merchant A in Hong Kong on T day, and the exchange rate of Hong Kong dollar to RMB at the time of consumption was 0.8840. Assuming that due to the impact of external events, the exchange rate of Hong Kong dollar to RMB at the time of settlement on T+1 is 0.9540, which will lead to a loss of 7 million for participating entities (100 million × (0.8840-0.9540) = -7 million).

As shown in FIG. 1 , which is a schematic diagram of a profit and loss provided by an embodiment of the present application, it can be seen from FIG. 1 that a transaction occurs at 02:00, the transaction is settled at 04:00, and when the transaction is completed The ratio (the ratio at 02:00) is lower than the ratio at settlement (the ratio at 04:00), so for the participants, this transaction will cause a loss.

To sum up, the definition of the profit and loss of the resource data type in cross-border acquiring is as follows (1):

Among them, Qi represents the total amount of resource data of the _ith resource data type, t _txn represents the exchange rate of the ith resource data type during transaction, and t _settlement represents the exchange rate of the ith resource data type during settlement.

Generally speaking, participating entities can support cross-border acquiring business of various resource data types, for example, it can support cross-border acquiring of RMB to HKD, can support cross-border acquiring of RMB to USD, and can support RMB to Japanese yen. Cross-border receipt, etc. In the existing technology, it is often only concerned with the overall profit and loss of the participating entities in the broader market, and the monitoring granularity of profit and loss is relatively coarse; and when abnormal profit and loss occurs, there is no supporting process plan such as broadcasting, change attribution, risk disposal, etc., and there is a lack of integrated disposal. platform.

This application proposes a solution based on the above background technology. This application not only determines the overall profit and loss of the participating entities, but also determines the profit and loss of each resource data type in a fine-grained manner (for example, determining the Hong Kong dollar, a resource data type, The profit and loss of a resource data type such as USD, and the profit and loss of a resource data type such as Japanese currency) are determined, and the cross-border acquiring business data can be monitored more accurately through granular refinement; Abnormal profit and loss, as well as finding the cause of profit and loss for abnormal profit and loss, generating alarms and displaying disposal pages, etc., form a business closed loop, which can improve the efficiency of transaction disposal; Transaction volume and unsettled transaction volume) and predicted transaction volume to detect abnormal merchants in time. Once abnormal merchants are found, users can be reminded to trade cautiously to protect the security of users’ assets.

Please refer to FIG. 2. FIG. 2 is a schematic diagram of an overall business of data processing provided by an embodiment of the present application. The present application involves two parts. That is to say, the profit and loss of each currency, the profit and loss of the market, the profit and loss of each time period, etc., according to the profit and loss threshold and the calculated profit and loss of each resource data type, the profit and loss of the large market, and the profit and loss of each time period to determine whether to generate an alarm; the other part is Use the offline transaction data on T-1 day to predict the predicted transaction volume of the transaction object on T day, and find out whether the transaction object is an abnormal transaction object through the transaction volume and predicted transaction volume in the real-time transaction data of T day. If it is an abnormal transaction object, an alarm for the abnormal transaction object is also required.

After the alarm is generated, the application can also analyze the abnormality from multiple dimensions to obtain the abnormal cause; and display the transaction trend, the ratio trend (ie, the exchange rate trend) and the profit and loss trend in the form of charts through the visualization platform. After analyzing the cause of the abnormality, an alarm is sent to the corresponding business personnel (that is, corresponding to the alarm broadcast in Figure 2). After the business personnel receives the alarm, they manually verify and process the business. Subsequently, the processing log can be recorded.

The data processing solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Referring to FIG. 3 , FIG. 3 is a schematic flowchart of data processing provided by an embodiment of the present application. The following embodiments are described with the server as the execution body (of course, the terminal device can also be described as the execution body), and the data processing solution includes the following steps:

Step S101, obtaining a set of settled transaction data, the set of settled transaction data includes N pieces of settled transaction data, each of the settled transaction data includes transaction information and resource data types, and the set of settled transaction data corresponds to K Resource data type, N is a positive integer.

Specifically, the server obtains a set of settled transaction data. The set of settled transaction data may be the transaction data in the aforementioned cross-border acquiring business. The set of settled transaction data includes N pieces of settled transaction data, each of which is settled transaction data. Both include transaction information and resource data types, and transaction information includes transaction data volume, transaction ratio, and settlement ratio.

Among them, the transaction ratio can be considered as the exchange rate when consumers make transactions at overseas merchants; the settlement ratio can be considered as the exchange rate when participants settle settlements with overseas merchants, and the resource data type refers to the resource data type for foreign exchange settlement, that is, the type of resource data supported by overseas merchants. Resource data type.

For example, mainland consumers spend a total of HKD 100 at Hong Kong Merchant A on T day, the exchange rate of HKD to RMB is 0.8840 at the time of consumption, and the exchange rate of HKD to RMB is 0.9540 at the time of settlement on T+1, then the corresponding settlement has been made. Transaction data can be expressed as: (100,0.8840,0.9540,HKD).

The settled transaction data set may correspond to K resource data types, of course, K is not greater than N.

It should be noted that, in order to reflect real-time performance, the data set of settled transactions may be acquired every 10 minutes. For example, if the current date is 2021/2/24, within this day, the set of settled transaction data within today is obtained every 10 minutes, and then the data set of each resource data type can be determined in real time according to the set of settled transaction data. profit and loss.

Of course, if the value of the interval is shorter, the real-time performance is stronger. For example, a set of settled transaction data is obtained every 1 minute, and then the solution of this application can be used to determine the profit and loss of each resource data type in a more real-time manner. .

It should be noted that the above-mentioned settled transaction data is the transaction data that has already settled foreign exchange. In other words, it is the transaction data that the participant has settled with the overseas merchant, but the transaction between the consumer and the overseas merchant occurs only after the settlement. It can be used before, and the rest is not limited.

In addition to the real currency type, the resource data type can also be the game currency type in the game application. For example, on a cross-game application player trading platform, one game application corresponds to one type of game currency, and the transaction ratio can be considered as the ratio between players in game application A and players in game application B when transactions occur; the settlement ratio can be considered as The ratio at which the participant pays the players in the game application B, and the resource data type refers to the game currency type corresponding to the game application.

Step S102, performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type.

Specifically, the server may divide the N settled transaction data into multiple unit sets according to the resource data type in each settled transaction data, and the settled transaction data in each unit set has the same resource data type.

For any unit set, the unit profit and loss of each settled transaction data in the unit set is calculated separately, and all unit profit and loss are superimposed to obtain the profit and loss of the resource data type corresponding to the unit set. The profit and loss of the resource data type here can be a positive number or a negative number. A positive number indicates that the resource data type is currently in a profitable state, and a negative number indicates that the resource data type is currently in a loss state.

For example, there are currently 4 settled transaction data, namely settled transaction data 1: (100, 0.8, 0.9, HKD), settled transaction data 2: (200, 0.7, 0.9, HKD), settled transaction Data 3: (100, 10, 9, USD), settled transaction data 4: (200, 10, 12, USD), then the above 4 settled transaction data can be divided into 2 unit sets, which are unit sets respectively 1: {settled transaction data 1: (100, 0.8, 0.9, HKD), settled transaction data 2: (200, 0.7, 0.9, HKD)}, and unit set 2: {settled transaction data 3: (100 ,10,9,USD), settled transaction data 4: (200,10,12,USD)}. For unit set 1: the unit profit and loss of settled transaction data 1 is: 100×(0.8-0.9)=-10, and the unit profit and loss of settled transaction data 2 is: 200×(0.7-0.9)=-40, so The profit and loss of the resource data type of HKD is -50; the profit and loss of the resource data type of USD can be calculated in the same way as above to be -30.

Optionally, the server may superimpose the profits and losses of the K types of resource data into a cumulative profit and loss, and the cumulative profit and loss can be considered as the general market profit and loss of the participating entities.

Optionally, each settled transaction data also includes a settlement timestamp, the settled transaction data set corresponds to P settlement time periods, and except for the last settlement time period, the duration of the rest of the settlement time periods is the same as the preset time period (preset). duration can be equal to 1 hour) is the same.

For example, the set of settled transaction data is the settled transaction data between 2021/2/24/00:00-2021/2/24/02:30. If the preset time period is 1 hour, the settled transaction data can be The transaction data set is divided into 3 settlement time periods, namely: 2021/2/24/00:00-2021/2/24/00:59, 2021/2/24/01:00-2021/2/24/ 01:59, 2021/2/24/02:00-2021/2/24/02:30.

The server may subdivide the above K unit sets into multiple sub-unit sets according to the settlement timestamp of each settled transaction data. The resource data types of the settled transaction data in each sub-unit set are the same, and the settlement timestamps are in the same during the settlement period. For a sub-unit set, the server counts the profit and loss of the settled transaction data in the sub-unit set, and takes the profit and loss as the profit and loss of the resource data type and settlement time period corresponding to the sub-unit set. A profit and loss can be calculated for all sub-unit sets, and the profit and loss of each resource data type in each settlement period can be obtained.

The server may also superimpose the profit and loss of all resource data types in each settlement period into the total profit and loss in each settlement period.

Summarizing the above process, the profit and loss of each resource data type is the profit and loss determined in the dimension of the resource data type, the total profit and loss in each settlement time period is the profit and loss determined in the dimension of the settlement time period, and the accumulated profit and loss is the participating main body market. Overall profit and loss.

By analyzing settled transaction data in different dimensions and granularities to determine profit and loss, it can better help participants analyze settled transaction data.

Please refer to FIG. 4. FIG. 4 is a schematic diagram of a profit and loss indicator system provided by an embodiment of the present application. The user transaction data and settlement data can be combined to correspond to the settled transaction data in the present application. The resource data type in the user transaction data can Write the corresponding settled transaction data to the corresponding channel. For the settled transaction data that occurred at night, the corresponding settled transaction data is written to the nighttime borrowing channel of the corresponding channel. After the settled transaction data is divided, the daily accumulated profit and loss of the resource data type can be obtained by calculating the profit and loss of the same resource data type. The daily cumulative profit and loss of all resource data types is superimposed into the broad market profit and loss, and the broad market profit and loss is the total profit and loss of the participating entities on that day.

The server can display the total profit and loss, accumulated profit and loss, and profit and loss of each resource data type on the display page; The duration of the time period is 1 hour, and you can know the total profit and loss of all resource data types in the current 1 hour) and accumulated profit and loss.

Of course, when displaying the profit and loss of each resource data type, the server may display the profit and loss of the K resource data types in the order of profit and loss from large to small (or from small to large).

Please refer to FIG. 5. FIG. 5 is a schematic diagram of a display page provided by an embodiment of the present application. On the display page, the hourly accumulated profit and loss of the broad market (the accumulated profit and loss in the current hour) and the daily accumulated profit and loss of the broad market can be displayed, In Fig. 5, the black-shaded portion represents the loss amount. It can be known that different loss amounts correspond to different levels of alarm lines. Of course, different levels of alarm lines generate different alarm information. For example, the alarm information corresponding to the first-level alarm line may be represented in orange font, and the second-level alarm information may be represented in red font.

Step S103, when the profit and loss of M resource data types in the K resource data types is less than the profit and loss threshold, obtain the profit and loss reasons of the M resource data types, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, where M is positive. Integer.

Specifically, the server obtains a preset profit and loss threshold, and when the profit and loss of M resource data types in the profit and loss of the K resource data types is smaller than the profit and loss threshold, the server obtains the profit and loss of each resource data type in the M resource data types The reason, M is a positive integer.

For any resource data type (referred to as the target resource data type) among the M resource data types, the specific process of determining the reason for the profit and loss of the target resource data type is as follows:

It can be seen from the foregoing that the set of settled transaction data corresponds to P settlement time periods, and the largest settlement time period in the P settlement time periods can be referred to as the second settlement time period. The settlement period is referred to as the first settlement period.

The server extracts the settled transaction data of the target resource data type within the first settlement time period (referred to as the first settled transaction data set) from the settled transaction data set, and extracts the target resource data type from the settled transaction data set Settled transaction data during the second settlement time period (referred to as a second set of settled transaction data). The server calculates the average value of the transaction ratios of the settled transaction data in the first settled transaction data set (referred to as the first transaction ratio average a1), and calculates the average value of the settlement ratios of the settled transaction data in the first settled transaction data set. The average value (referred to as the first settlement ratio average value b1 ) is calculated as the sum of the transaction data volumes of the settled transaction data in the first settled transaction data set (referred to as the first transaction total amount c1 ).

Similarly, the server calculates the average value of the transaction ratios of the settled transaction data in the second set of settled transaction data (referred to as the second transaction ratio mean a2), and calculates the average value of the settled transaction data in the second set of settled transaction data. The average value of the settlement ratios (referred to as the second average settlement ratio b2 ) is calculated as the sum of the transaction data volumes of the settled transaction data in the second set of settled transaction data (referred to as the second total transaction volume c2 ).

Calculate the first profit and loss s1 according to the following formula (2):

s1=c1*(a1-b1) (2)

Calculate the second profit and loss s2 according to the following formula (3):

s2=c2*(a2-b2) (3)

The server subtracts the second profit and loss s2 from the first profit and loss s1 to obtain the change in profit and loss. The calculation formula for the change in profit and loss is as follows: formula (4):

Δs=s2-s1 (4)

Calculate the transaction volume contribution rate p1 according to the following formula (5):

The ratio contribution rate p2 is calculated according to the following formula (6):

The server may combine the transaction volume contribution rate p1 and the ratio contribution rate p2 calculated above as the profit and loss cause of the target resource data type. By analyzing the calculation process of the above-mentioned profit and loss reasons, it can be known that the profit and loss reason is the contribution rate of the transaction volume factor to the overall profit and loss + the ratio of resource data types to the overall profit and loss.

The server may determine the profit and loss cause for each of the M resource data types in the same manner.

After the server determines the profit and loss cause of each resource data type in the M resource data types, it can generate profit and loss alarm information including the profit and loss cause of the M resource data types, so as to assist the business personnel to quickly locate the problem when dealing with the problem. reasons for optimal treatment. Alternatively, the server may generate corresponding profit and loss alarm information for each of the M resource data types, that is, M pieces of profit and loss alarm information will be generated.

Optionally, as described above, when the profit and loss of the resource data type is less than the profit and loss threshold, the profit and loss alarm information will be generated for the resource data type. It can be seen from the foregoing that not only the profit and loss of each resource data type, but also the total profit and loss of each settlement time period, and the accumulated profit and loss are calculated. Similarly, if the total profit and loss of any settlement time period in the P settlement time periods is calculated If the profit or loss is less than the first threshold, then analyze the resource data type (referred to as auxiliary resource data type) that causes the total profit and loss of any settlement period to be smaller than the first threshold, and then use the above method to determine the profit and loss reason for the auxiliary resource data type, Regenerate alarm information, where the alarm information includes the cause of profit and loss of the auxiliary resource data type.

Optionally, if the cumulative profit and loss is less than the second threshold, analyze the resource data type (referred to as auxiliary resource data type) that causes the cumulative profit and loss to be less than the second threshold, and then use the above method to determine the profit and loss reason for the auxiliary resource data type, Regenerate alarm information, where the alarm information includes the cause of profit and loss of the auxiliary resource data type.

It can be known that when it is necessary to determine the profit and loss cause of the accumulated profit and loss, or the profit and loss cause of each settlement period, it is necessary to pay attention to a certain resource data type in the end.

Step S104, outputting the profit and loss alarm information.

As can be seen from the above, this application not only calculates the total profit and loss of the participating entities, but also calculates the total profit and loss in each time period, the profit and loss of each resource data type, and the monitoring granularity of transaction data is finer; The display page displays the total profit and loss, cumulative profit and loss, and profit and loss of each resource data type in descending order, which can improve the display effect of profit and loss; further, the reason for profit and loss in this application is the transaction volume The contribution rate of factors to the overall profit and loss + the contribution rate of the ratio between resource data types to the overall profit and loss, and the analysis of the reasons for the profit and loss of resource data types is accurate.

Please refer to FIG. 6. FIG. 6 is a schematic flowchart of a data processing provided by an embodiment of the present application. The data processing solution includes the following steps:

Step S201, obtaining a set of settled transaction data, the set of settled transaction data includes N pieces of settled transaction data, each of the settled transaction data includes transaction information and resource data types, and the set of settled transaction data corresponds to K Resource data type, N, K are positive integers.

Step S202, performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type.

Step S203, when the profit and loss of M resource data types in the K resource data types is less than the profit and loss threshold, obtain the profit and loss reasons of the M resource data types, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, where M is positive. Integer, output the profit and loss alarm information.

Wherein, for the specific implementation manner of step S201-step S203, reference may be made to step S101-step S104 in the above-mentioned embodiment corresponding to FIG. 3 .

Step S204, acquiring target transaction data of U transaction objects, each target transaction data including transaction data volume.

Specifically, the server obtains the target transaction data of the U transaction objects on the T day. The settled transaction data in the foregoing may correspond to the U transaction objects. The transaction objects here may refer to overseas merchants or game players, and the current date is T. In other words, what the server obtains is the transaction data of U transaction objects on that day.

The target transaction data is the transaction data that the consumer and the transaction object have already generated transaction behavior, but the transaction object has not yet settled with the participating entity. For example, at 2021/2/24/12:00, the user purchased commodity A in Hong Kong merchant A, the price is 100 Hong Kong dollars, and the transaction ratio is 0.8; but the participating entity has not yet settled this transaction with Hong Kong merchant A Therefore, the target transaction data can be expressed as: (100, Hong Kong Merchant A, 0.8, 2021/2/24/12:00), since the settlement has not yet been settled, the corresponding settled transaction data cannot be generated at this time.

Step S205: Perform statistical processing on the transaction data volume in the target transaction data of the U transaction objects, obtain abnormal transaction objects in the U transaction objects, and generate transaction alarm information according to the abnormal transaction objects.

Specifically, the server may divide all target transaction data into U clusters according to different transaction objects, and the target transaction data in each cluster belong to the same transaction object.

The transaction data volume in the target transaction data in each cluster is superimposed as the cumulative transaction data volume of the transaction object corresponding to the cluster, that is, the unsettled cumulative transaction data volume of each merchant is obtained.

The server then obtains the predicted transaction data volume of each transaction object, in which the predicted transaction data volume of the transaction object on T day is predicted by the transaction object's real transaction data on T-1 day, and the real transaction data on T-1 day is T-1 Complete transaction data for the day.

The server regards a transaction object whose predicted transaction data volume is greater than the accumulated transaction data volume as an abnormal transaction object.

This application divides all transaction objects into 3 types (high activity type, medium activity type, etc.) from five aspects, including the total number of historical transaction days, the last transaction time, the average daily transaction size, transaction stability, and transaction activity. type and low activity type), each type matches the corresponding prediction scheme. Specifically, the Prophet algorithm is used to predict the transaction objects of the high activity type, the 3-SIGMA algorithm is used to predict the transaction objects of the medium activity type, and the dynamic industry average is used to predict the transaction objects of the low activity type.

The three forecasting schemes are described below. First, the complete historical transaction data of a certain transaction object on T-1 day (mainly using the transaction data volume in the historical transaction data) based on the Prophet algorithm will be explained to predict the transaction object. Predicted transaction data volume on day T:

According to the classical time series theory, Prophet divides the time series data into trend items, periodic items, seasonal items, and holiday items. The fitting of each item can be expressed as the following formula (7):

y(t)=g(t)+s(t)+h(t)+ε(t) (7)

Among them, g(t) represents the trend item, which represents the change trend of the time series on a non-periodic basis; s(t) represents the cyclic item, or seasonal item, generally in weeks or years or hours; h (t) represents the holiday item, indicating whether there is a holiday on that day; ε(t) represents the error item or the remaining item. The Prophet algorithm obtains the predicted value of the time series by fitting these items and finally adding them up.

Trend term model g(t): Piecewise linear or logistic growth curve trend:

The trend term based on logistic regression can be expressed as the following formula (8):

σ表示表示增长率的变化量，m为偏差参数；Among them, C(t) is the upper limit of business experience; k is the initial growth rate; α(t) is the indicator function,

σ represents the change in growth rate, and m is the deviation parameter;

The trend term based on linear regression can be expressed as the following formula (9):

g(t)=(k+α(t)σ*t+(m+α(t) ^T γ)) (9)

The meanings of the parameters in formula (9) are the same as those in formula (8).

Seasonal term model s(t): Use a Fourier series to model the seasonal component of each year:

Since the seasonal term describes the periodic behavior, the seasonal term model s(t) (Fourier series) can be comprehensively represented by the sine and cosine functions (the following formula (10)),

Empirically, N=10 for year-period series (P=365.25); N=3 for week-period series (P=7);

Formula (10) is further abstracted as: s(t)=X(t)β, where β～N(0,σ ² ). In the model, ^σ2 is controlled by the seasonality priority, and the larger the value of ^σ2 , the more obvious the effect of season.

The holiday term model h(t) uses dummy variables to simulate the weekly periodic component, which can be expressed by the following formula (11):

Among them, k～N(0, v ² ), v is the priority of holidays, and the default value is 10. When the value of v is larger, it means that the holiday has a greater impact on the model.

Next, it is explained based on the 3-SIGMA algorithm and the complete historical transaction data of a certain transaction object on T-1 day (mainly using the transaction data volume in the historical transaction data) to predict the predicted transaction data volume of the transaction object on T day. :

The 3-SIGMA model data needs to obey a normal distribution. Under the 3σ principle, if the outlier exceeds 3 times the standard deviation, it can be regarded as an outlier. The probability of plus or minus 3σ is 99.7%. Therefore, the mean value u and the standard deviation σ are first calculated based on the transaction data volume in the complete historical transaction data on T-1 day, and u+3σ is taken as the predicted transaction data volume of the transaction object on T day.

Please refer to FIG. 7. FIG. 7 is a schematic diagram of a 3-SIGMA algorithm provided by an embodiment of the present application. It can be seen from FIG. 7 that an error equal to ±3σ is usually regarded as the limit error. The probability other than ±3σ is only 0.27%, and it is very unlikely to occur in a limited number of measurements, so there is a 3σ criterion. If the absolute value νi of the residual error of a certain measurement value in a set of measurement data is greater than 3σ, the measurement value is a bad value, that is, it corresponds to the accumulated transaction data volume exceeding the predicted transaction volume in the present application.

Finally, it is explained that the dynamic industry average is used to predict the predicted transaction data volume of a certain transaction object on T day:

The server obtains the historical transaction data of other transaction objects in the same industry as the transaction object to be predicted on day T-1, and takes the average of the transaction data volume in the obtained historical transaction data as the predicted transaction data volume of the transaction object to be predicted on day T.

So far, the server has determined the abnormal transaction objects from the U transaction objects, and the server can directly generate transaction alarm information including the abnormal transaction objects to remind the business processing personnel to pay special attention to these abnormal transaction objects.

Optionally, after the abnormal transaction object is determined, it may be further determined whether to generate transaction alarm information and the transaction alarm based on variables such as the transaction volume level, the risk rating of the transaction object, the accumulated transaction data volume and the deviation degree of the predicted transaction data volume. The corresponding disposition part of the information. The specific process is described by the following algorithm:

Step S206, outputting the transaction alarm information.

Optionally, the server may also sort the cumulative transaction data volume of the U transaction objects to obtain a sorting result, where the sorting result includes U transaction objects and the cumulative transaction data volume of each transaction object, and the U transaction objects The cumulative transaction data volume of is arranged in order from large to small (or from small to large). The server displays the sorting results on the display page, so that business personnel can quickly locate transaction objects with a large amount of accumulated transaction data.

Please refer to FIG. 8. FIG. 8 is a schematic diagram of a display page provided by an embodiment of the present application. By counting the cumulative transaction volume of each transaction object (the cumulative transaction volume may correspond to the cumulative transaction data volume of the present application), the Display multiple transaction objects in order from large to small, and the cumulative transaction volume of each transaction object.

Optionally, each target transaction data also includes a transaction timestamp and a transaction ratio. The server may divide all the target transaction data into multiple target transaction data sets according to the transaction timestamps of the target transaction data of the U transaction objects. The transaction timestamps in the target transaction data in a target transaction data set are in the same transaction time period. For example, consider one hour as a trading time period.

The server multiplies the transaction data volume in each target transaction data by the transaction ratio to obtain the converted transaction volume of each target transaction data. This is because different target transaction data correspond to different resource data types. Through the transaction data volume and transaction ratio The resource data type of the converted transaction volume obtained by multiplying it is the same (for example, the resource data type of the converted transaction volume is RMB type). The server may superimpose the converted transaction volume of the target transaction data in the same target transaction data set to obtain the transaction exposure of the transaction time period corresponding to the target transaction data set. The trading exposures of all trading time periods are superimposed into cumulative trading exposures, and the trading exposures and cumulative trading exposures of each trading period are displayed on the display page. Alternatively, only the cumulative trading exposure and the trading exposure of the largest trading period among multiple trading periods can be displayed on the display page.

The transaction exposure is for the participating entities, that is, consumers and overseas merchants have already made transactions, but the transaction data volume that the participating entities have not yet settled with overseas merchants can be called transaction exposure. The popular understanding of transaction exposure is participation. The risk that the entity needs to take, the greater the trading exposure, the greater the risk that the participating entity needs to take.

When the trading exposure of any one of the multiple trading time periods is greater than the first exposure threshold, or when the cumulative trading exposure is greater than the second exposure threshold, the server generates exposure warning information and sends a notification to the corresponding business personnel. Output the exposure warning information.

As shown in Figure 5, the display page can display the current cumulative exposure of the market within one hour, as well as the cumulative exposure of the market on the current day. When the hourly cumulative exposure is greater than the corresponding threshold, or the daily cumulative exposure is greater than the corresponding threshold, corresponding alarm information can be generated, and different levels of exceeding the threshold correspond to different levels of alarm information, and different levels of alarm information can help follow-up The business personnel sort out the processing priority for the alarm information.

Please refer to FIG. 9. FIG. 9 is a schematic diagram of a display page provided by an embodiment of the present application. The display page displays the corresponding exposure of each resource data type (foreign currency type) (which may be hourly cumulative exposure, or may be is the daily cumulative exposure), and the profit and loss for each resource data type (which can be hourly cumulative profit or loss, or it can be daily cumulative profit and loss). From the perspective of resource data types, analyze the risks borne by each resource data type, as well as the gains and losses of the broader market.

Among them, the exposure of each resource data type is to superimpose the conversion transaction volume of the target transaction data belonging to the same resource data type, and then the exposure corresponding to this resource data type can be obtained.

Optionally, when the server obtains profit and loss warning information, or obtains transaction warning information, or obtains exposure warning information, the server can display a business handling page corresponding to the warning information, so that business personnel can generate user information on the business handling page. Disposition instructions. Alternatively, the server may generate a service handling page, and push the service handling page to the corresponding service handling personnel, so that the corresponding service handling personnel can generate user handling instructions on the service handling page.

Please refer to FIG. 10. FIG. 10 is a schematic diagram of a business handling page provided by an embodiment of the present application. The business handling page corresponding to FIG. 10 is a business handling page for transaction alarm information, and the identifier of the transaction object can be displayed on the business handling page. , resource data types, forecast transaction volume and real-time cumulative transaction volume. Of course, the real-time cumulative transaction volume is greater than the predicted transaction volume. According to the information displayed on the business disposal page, the business personnel can select the corresponding disposal operations, and subsequently can generate user disposal instructions according to the disposal operations checked by the business personnel, and the server can execute the corresponding business operations based on the user disposal instructions.

As can be seen from the foregoing, the risk perception and assessment system for cross-border acquiring business can display the total profit and loss, cumulative profit and loss of each settlement period, and profit and loss of each resource data type, analyze the reasons for profit and loss, generate alarm information, and jump to business disposal page. The implementation of the above system in terms of visualization is described below:

The front-end is mainly for data visualization, which is convenient for analyzing, alarming and displaying key data. The process is started in the middle layer of node for data query and interface merging processing. Adopt egg frame. The technical framework involved is shown in Table 1 below:

Table 1

front-end framework Vue.js+Egg.js+elementUI Front-end chart library Ehcharts database Mysql

As shown in Figure 11, the vue framework based on the MVVM pattern design is used on the display side, the public component library elementUI and the chart library echart, and the public components of elementUI are used to integrate the business components with larger granularity. Relying on Echart's rich chart library, the data processed by the node layer is displayed.

Data display processing:

When it is necessary to display the ratio (including transaction ratio and settlement ratio), since the deviation of the ratio value is relatively small, it is necessary to clearly reflect the range of ups and downs on the display, and the ordinate shows the lowest and highest value of the exchange rate. The highest values are arranged according to the deviation, so that the effect of obvious fluctuation can be achieved.

When the total profit and loss of each settlement period needs to be displayed, or the trading exposure of each trading period needs to be displayed, the data on the abscissa may be dense. Under such conditions, the points on the abscissa should be spaced Processing, that is, firstly display some points of abscissa, hide some points of abscissa, when the cursor moves to the point of hidden abscissa, then display the point of hidden abscissa. To achieve the effect of displaying appropriate density and maintaining data trends.

In order to achieve a better visualization effect, some interfaces of the node layer are merged. The merged interface is the interface that can obtain the first screen data, and asynchronously requests the first screen data, and the non-first screen data is lazy loaded. After the interfaces are merged, the http requests of the first screen can be reduced, and the purpose of optimizing the first screen can be achieved.

As shown in Figure 12, the middle layer of Node adopts the egg framework, egg inherits the koa framework, and the middleware writing style selects the onion ring model to provide the upper layer with architectural capabilities and a highly extensible plug-in mechanism. object context), middleware (add one or more middleware), config (configure the default configuration items of the plugin itself in each environment). Iterative development of functions is carried out under a standardized directory structure, including routing layer, control layer, service layer, middleware, schedule, etc.

The risk perception and assessment system for cross-border acquiring business In the process of supporting the business, the middleware is responsible for the security filtering of interface requests and the permission control of requests, configure the API path for the interface in the routing layer, and write the interface of the business in the controller. The server layer writes sql statements to call the mysql database, and relies on egg's schedule to run scheduled tasks and query regularly to monitor abnormal business data and determine whether to generate alarm information, so that the business side can shorten the troubleshooting time.

As shown in Figure 13, the security and stability of the internal functions of the cross-border acquiring business risk perception and evaluation system are supported by four peripheral parts, including the FiT permission system, mobile analysis tools, aegis front-end monitoring, Generate an alert. Real-time performance and effectiveness can ensure the stability of the system and the stability of business data. In the risk perception and evaluation system of cross-border acquiring business, using the node plug-in in the egg framework does not affect the original business logic, and provides protection and monitoring for the system at the outer layer. With external protection, the internal functions can be carried out smoothly.

Please refer to FIG. 14. FIG. 14 is a system architecture diagram of a blockchain provided by an embodiment of the present application. The server in the foregoing embodiment can be node 1, or node 2, or node 3, or node 4 in FIG. 14, all nodes can be combined into a blockchain system, and each node includes a hardware layer, an intermediate layer, and an operating system layer. and application layer. As can be seen from Figure 14, the blockchain data stored by each node in the blockchain system is the same. It will be appreciated that the above-mentioned nodes may include computer equipment. The following embodiments are described with the target blockchain node as the execution subject, the target blockchain node is any node among multiple nodes in the blockchain system, and the target blockchain node may correspond to the server in the foregoing embodiments.

Please refer to FIG. 15 together. FIG. 15 is a schematic flowchart of a data processing provided by an embodiment of the present application. This embodiment mainly describes the combination of the monitoring of target transaction data and settled transaction data with blockchain technology. The data processing includes: Follow the steps below:

Step 301: Obtain a set of settled transaction data from the blockchain, where the set of settled transaction data includes N pieces of settled transaction data, each settled transaction data includes transaction information and resource data types, and the settled transaction data The data set corresponds to K resource data types, and N and K are both positive integers.

Specifically, when receiving the monitoring request, the target blockchain node extracts the block height carried by the monitoring request, reads the block corresponding to the block height on the blockchain, and extracts the block body from the block body. Read data from , as a collection of settled transaction data.

Among them, when the participant has settled with the cross-border merchant, the target blockchain node can store the settled transaction data on the blockchain in real time, and record the block height of the settled transaction data storage.

Step S302, performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

Step S303, when the profit and loss of M resource data types in the K resource data types is less than the profit and loss threshold, obtain the profit and loss reasons of the M resource data types, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, where M is positive. Integer, output the profit and loss alarm information.

Wherein, for the specific process of step S302-step S303, reference may be made to step S102-step S104 in the embodiment corresponding to FIG. 3 above.

Step 304: Package the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, profit and loss reasons for M resource data types, and profit and loss alarm information into blocks, and store the blocks in the on the blockchain.

Specifically, the target blockchain node can calculate the total profit and loss of each settlement period, the cumulative profit and loss, the profit and loss of each resource data type, the profit and loss reasons for the M resource data types, and the profit and loss calculated by the intermediate process of generating profit and loss alarm information. The alarm information is stored in the block body, and the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, profit and loss reasons for M resource data types, and Merkel root of profit and loss alarm information for each settlement period are calculated, and obtain The hash value of the last block of the current blockchain, the target blockchain node stores the calculated Merkle root, the hash value of the last block of the current blockchain, and the current timestamp in the block header. The target blockchain node combines the block header and the block body into a block, stores the block in the blockchain maintained by the target blockchain node, and broadcasts the block to the rest of the nodes, so that the rest of the The nodes of the node add the block to the blockchains they maintain, so that the blockchains maintained by each node are synchronized.

Optionally, in addition to storing the total profit and loss, accumulated profit and loss, profit and loss of each resource data type, profit and loss reasons for M resource data types, and profit and loss alarm information in the blockchain for each settlement period, exceptions can also be stored on the blockchain. Transaction objects, transaction alarm information, transaction exposure for each transaction period, cumulative transaction exposure, and exposure alarm information are all stored on the blockchain.

As can be seen from the above, relying on the complete attributes and non-tampering attributes of the blockchain, it can be ensured that the settled transaction data set obtained by the target blockchain node is credible and has not been tampered with. Therefore, based on the statistics of the settled transaction data set The profit and loss of each resource data type, as well as the generated profit and loss reasons and profit and loss alarm information are also trustworthy, which can ensure the accuracy of the statistical results of the settled transaction data set.

Further, please refer to FIG. 16 , which is a schematic structural diagram of a data processing apparatus provided by an embodiment of the present application. As shown in FIG. 16 , the data processing apparatus 1 may be applied to the server or the target blockchain node in the embodiments corresponding to FIG. 1 to FIG. 15 . Specifically, the data processing device 1 may be a computer program (including program code) running in a computer device, for example, the data processing device 1 is an application software; the data processing device 1 corresponding steps in the method.

The data processing apparatus 1 may include: an acquisition module 11 , a statistics module 12 , a generation module 13 and an output module 14 .

The acquisition module 11 is used to acquire a set of settled transaction data, the set of settled transaction data includes N pieces of settled transaction data, and each of the settled transaction data includes transaction information and resource data types, and the set of settled transaction data Corresponding to K resource data types, N and K are positive integers;

A statistics module 12, configured to perform profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

The generating module 13 is configured to obtain the profit and loss reasons of the M resource data types when the profit and loss of the M resource data types in the K resource data types is less than the profit and loss threshold, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types , M is a positive integer;

The output module 14 is configured to output the profit and loss alarm information.

In a possible implementation manner, when the statistics module 12 is used to perform profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type, it is specifically used for:

dividing the settled transaction data set into K unit sets, and the resource data types in each unit set are the same;

Statistical processing of profit and loss is performed on the transaction information in each unit set to obtain the profit and loss of each resource data type.

In a possible implementation manner, each settled transaction data further includes a settlement timestamp, and the settled transaction data set corresponds to P settlement time periods;

The data processing apparatus 1 may further include: a first presentation module 15 .

The first display module 15 is used for performing profit and loss statistical processing on the transaction information in the settled transaction data corresponding to each settlement time period, to obtain the profit and loss of each resource data type in each settlement time period, and for each settlement time period. The profit and loss of all resource data types in the system is superimposed as the total profit and loss in each settlement period, and the profit and loss of each resource data type is superimposed into the cumulative profit and loss, and the total profit and loss, cumulative profit and loss of each settlement period and each Profit and loss for each resource data type.

In a possible implementation, the target resource data type is any resource data type among the M resource data types, the P settlement time periods include a first settlement time period and a second settlement time period, and the first settlement time period is adjacent to the second settlement time period, and the second settlement time period is the largest settlement time period among the P settlement time periods;

When the generating module 13 is used to determine the profit and loss cause of the target resource data type, it is specifically used for:

extracting a first settled transaction data set of the target resource data type within the first settlement time period from the settled transaction data set;

extracting a second set of settled transaction data of the target resource data type within the second settlement time period from the set of settled transaction data;

Analyzing and processing the first settled transaction data set and the second settled transaction data set to obtain a transaction volume contribution rate and a ratio contribution rate;

The transaction volume contribution rate and the ratio contribution rate are combined as the profit and loss reason for the target resource data type.

In a possible implementation, the transaction information includes transaction data volume, transaction ratio and settlement ratio;

When the generating module 13 is used to analyze and process the first settled transaction data set and the second settled transaction data set to obtain the transaction volume contribution rate and the ratio contribution rate, it is specifically used for:

The first transaction ratio mean is determined according to the transaction ratio in the first settled transaction data set, the first settlement ratio average is determined according to the settlement ratio in the first settled transaction data set, and the transaction data volume in the first settled transaction data set is determined determine the first transaction volume;

The second transaction ratio mean is determined according to the transaction ratio in the second settled transaction data set, the second settlement ratio average is determined according to the settlement ratio in the second settled transaction data set, and the transaction data volume in the second settled transaction data set is determined determining the second transaction volume;

The first profit and loss is determined according to the average value of the first transaction ratio, the average value of the first settlement ratio and the first transaction volume, the second profit and loss is determined according to the average value of the second transaction ratio, the average value of the second settlement ratio and the second transaction volume, and the second profit and loss Subtract the first profit and loss to get the change in profit and loss;

The transaction volume contribution rate is determined according to the second total transaction volume, the average first transaction ratio, the average first settlement ratio, the first profit and loss, and the change in profit and loss, and the ratio contribution rate is determined according to the second profit and loss and the transaction volume contribution rate.

In a possible implementation manner, the acquisition module 11 is further configured to acquire target transaction data of U transaction objects, and each target transaction data includes transaction data volume;

The statistics module 12 is further configured to perform statistical processing on the transaction data volume in the target transaction data of the U transaction objects, obtain abnormal transaction objects in the U transaction objects, and generate transaction alarm information according to the abnormal transaction objects ;

The output module 14 is further configured to output the transaction alarm information.

In a possible implementation manner, each target transaction data further includes a transaction time stamp and a transaction ratio; the data processing apparatus 1 further includes: a second display module 16 .

The second display module 16 is configured to divide the target transaction data into multiple target transaction data sets according to the transaction time stamps of the target transaction data, and the transaction time stamps in the target transaction data in the same target transaction data set are in the same transaction time period Inside;

The second display module 16 is further configured to determine the conversion transaction volume of each target transaction data according to the transaction data volume and transaction ratio in each target transaction data, and convert the conversion transaction volume of the target transaction data in the same target transaction data set Superimpose separately to obtain the trading exposure of each trading time period, superimpose the trading exposure of all trading time periods into cumulative trading exposure, and display the trading exposure and cumulative trading exposure of each trading time period on the display page. .

In a possible implementation manner, the generating module 13 is further configured to: when the trading exposure of any trading period in the multiple trading periods is greater than the first exposure threshold, or when the accumulated trading exposure is greater than When the second exposure threshold is reached, exposure alarm information is generated.

In a possible implementation manner, when the statistics module 12 is used to perform statistical processing on the transaction data volume in the target transaction data of the U transaction objects to obtain abnormal transaction objects among the U transaction objects, it is specifically used for :

Superimpose the transaction data volume in the target transaction data of each transaction object into the cumulative transaction data volume of each transaction object;

Obtain the predicted transaction data volume of each transaction object;

The transaction objects whose predicted transaction data volume is greater than the accumulated transaction data volume are regarded as abnormal transaction objects.

In a possible implementation manner, the data processing apparatus 1 may further include: a third presentation module 17 .

The third display module 17 is configured to sort the accumulated transaction data volumes of the U transaction objects to obtain a sorting result, and display the sorting result on the display page.

In a possible implementation manner, the data processing apparatus 1 may further include: a third presentation module 18 .

The fourth display module 18 is configured to display a service handling page when receiving profit and loss alarm information or when receiving transaction alarm information, where the service handling page is used for receiving user handling instructions.

In a possible implementation, the set of settled transaction data is stored on a blockchain;

The data processing apparatus 1 may further include: a storage module 19 .

The storage module 19 is used to package the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, profit and loss reasons of M resource data types, and profit and loss alarm information into blocks, and store the blocks into blocks to the blockchain.

According to an embodiment of the present invention, each step involved in the method shown in FIG. 1 to FIG. 15 may be performed by each module in the data processing apparatus shown in FIG. 16 . For example, steps S101-S104 shown in FIG. 3 can be respectively performed by the acquisition module 11, the statistics module 12, the generation module 13, the output module 14, and the first display module 15 shown in FIG. 16; for another example, in FIG. 6 Steps S204-S206 shown in FIG. 16 can be performed by the acquisition module 11, the statistics module 12, the output module 14, the second display module 16, the third display module 17, and the fourth display module 18 shown in FIG. 16; For example, step S304 shown in FIG. 15 may be performed by the storage module 19 shown in FIG. 16 .

Further, please refer to FIG. 17 , which is a schematic structural diagram of a computer device provided by an embodiment of the present application. The server or target blockchain node in the embodiments corresponding to FIGS. 1 to 15 may be computer equipment 1000 . As shown in FIG. 17, computer device 1000 may include a user interface 1002, a processor 1004, an encoder 1006, and a memory 1008. Signal receiver 1016 is used to receive or transmit data via cellular interface 1010 , WIFI interface 1012 , . . . , or NFC interface 1014 . The encoder 1006 encodes the received data into a computer-processable data format. A computer program is stored in the memory 1008, and the processor 1004 is configured to perform the steps in any one of the above method embodiments through the computer program. The memory 1008 may include volatile memory (eg, dynamic random access memory DRAM), and may also include non-volatile memory (eg, one-time programmable read-only memory OTPROM). In some examples, memory 1008 may further include memory located remotely from processor 1004, which may be connected to computer device 1000 through a network. User interface 1002 may include: keyboard 1018 and display 1020.

In the computer device 1000 shown in FIG. 17 , the processor 1004 can be used to call the computer program stored in the memory 1008 to realize:

Acquire a set of settled transaction data, the settled transaction data set includes N settled transaction data, each settled transaction data includes transaction information and resource data types, and the settled transaction data set corresponds to K resource data types , N, K are all positive integers;

Performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

When the profit and loss of M resource data types in the K resource data types is less than the profit and loss threshold, obtain the profit and loss reasons of the M resource data types, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, where M is a positive integer;

The profit and loss warning information is output.

In one embodiment, the processor 1004 specifically performs the following steps when performing the profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type: The set is divided into K unit sets, and the resource data types in each unit set are the same;

Statistical processing of profit and loss is performed on the transaction information in each unit set to obtain the profit and loss of each resource data type.

In one embodiment, each settled transaction data further includes a settlement timestamp, and the set of settled transaction data corresponds to P settlement time periods;

Processor 1004 also performs the following steps:

Perform profit and loss statistical processing on the transaction information in the settled transaction data corresponding to each settlement time period, and obtain the profit and loss of each resource data type in each settlement time period;

Superimpose the profit and loss of all resource data types in each settlement period into the total profit and loss in each settlement period;

Superimpose the profit and loss of each resource data type into cumulative profit and loss;

The total profit and loss, cumulative profit and loss, and profit and loss of each resource data type for each settlement period are displayed on the display page.

In one embodiment, the target resource data type is any resource data type among M resource data types, and the P settlement time periods include a first settlement time period and a second settlement time period, and the first settlement time period and the second settlement time period The settlement time periods are adjacent, and the second settlement time period is the largest settlement time period among the P settlement time periods;

When the processor 1004 performs the determination of the profit and loss cause of the target resource data type, the following steps are specifically performed:

extracting a first settled transaction data set of the target resource data type within the first settlement time period from the settled transaction data set;

extracting a second set of settled transaction data of the target resource data type within the second settlement time period from the set of settled transaction data;

Analyzing and processing the first settled transaction data set and the second settled transaction data set to obtain a transaction volume contribution rate and a ratio contribution rate;

The transaction volume contribution rate and the ratio contribution rate are combined as the profit and loss reason for the target resource data type.

In one embodiment, the transaction information includes transaction data volume, transaction ratio and settlement ratio;

When the processor 1004 performs analysis and processing on the first settled transaction data set and the second settled transaction data set to obtain the transaction volume contribution rate and the ratio contribution rate, the processor 1004 specifically performs the following steps:

The first transaction ratio mean is determined according to the transaction ratio in the first settled transaction data set, the first settlement ratio average is determined according to the settlement ratio in the first settled transaction data set, and the transaction data volume in the first settled transaction data set is determined determine the first transaction volume;

The second transaction ratio mean is determined according to the transaction ratio in the second settled transaction data set, the second settlement ratio average is determined according to the settlement ratio in the second settled transaction data set, and the transaction data volume in the second settled transaction data set is determined determining the second transaction volume;

The first profit and loss is determined according to the average value of the first transaction ratio, the average value of the first settlement ratio and the first transaction volume, the second profit and loss is determined according to the average value of the second transaction ratio, the average value of the second settlement ratio and the second transaction volume, and the second profit and loss Subtract the first profit and loss to get the change in profit and loss;

The transaction volume contribution rate is determined according to the second total transaction volume, the average first transaction ratio, the average first settlement ratio, the first profit and loss, and the change in profit and loss, and the ratio contribution rate is determined according to the second profit and loss and the transaction volume contribution rate.

In one embodiment, the processor 1004 also performs the following steps:

Obtain the target transaction data of U transaction objects, each target transaction data includes the transaction data volume;

Perform statistical processing on the transaction data volume in the target transaction data of the U transaction objects, obtain abnormal transaction objects in the U transaction objects, and generate transaction alarm information according to the abnormal transaction objects;

The transaction warning information is output.

In one embodiment, each target transaction data further includes a transaction timestamp and a transaction ratio;

Processor 1004 also performs the following steps:

According to the transaction time stamp of the target transaction data, the target transaction data is divided into multiple target transaction data sets, and the transaction time stamps in the target transaction data in the same target transaction data set are in the same transaction time period;

Determine the conversion transaction volume of each target transaction data according to the transaction data volume and transaction ratio in each target transaction data;

The converted transaction volumes of the target transaction data in the same target transaction data set are superimposed respectively to obtain the transaction exposure of each transaction time period;

Overlay the trading exposures of all trading time periods into cumulative trading exposures;

On the display page, the trading exposure and cumulative trading exposure of each trading time period are displayed.

In one embodiment, the processor 1004 also performs the following steps:

When the trading exposure of any one of the multiple trading time periods is greater than the first exposure threshold, or when the accumulated trading exposure is greater than the second exposure threshold, the exposure warning information is generated.

In one embodiment, the processor 1004 specifically performs the following steps when performing statistical processing on the transaction data volume in the target transaction data of the U transaction objects to obtain abnormal transaction objects in the U transaction objects:

Superimpose the transaction data volume in the target transaction data of each transaction object into the cumulative transaction data volume of each transaction object;

Obtain the predicted transaction data volume of each transaction object;

The transaction objects whose predicted transaction data volume is greater than the accumulated transaction data volume are regarded as abnormal transaction objects.

In one embodiment, the processor 1004 also performs the following steps:

Sort the cumulative transaction data volume of the U transaction objects to obtain the sorting result;

The sorting results are displayed on the display page.

In one embodiment, the processor 1004 also performs the following steps:

When the profit and loss alarm information is received, or when the transaction alarm information is received, a business handling page is displayed, and the business handling page is used for receiving user handling instructions.

In one embodiment, the set of settled transaction data is stored on a blockchain;

Processor 1004 also performs the following steps:

Packing the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, profit and loss reasons of M resource data types, and profit and loss alarm information for each settlement period into blocks;

Store the block on the blockchain.

It should be understood that the computer device 1000 described in the embodiments of the present application can execute the descriptions of the data processing methods in the foregoing embodiments corresponding to FIG. description, which will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated.

In addition, it should be pointed out here that the embodiment of the present application also provides a computer storage medium, and the computer storage medium stores the computer program executed by the aforementioned data processing apparatus 1, and the computer program includes program instructions, when When the processor executes the program instructions, it can execute the description of the data processing method in the foregoing embodiments corresponding to FIG. 1 to FIG. 15 , and therefore will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated. For technical details not disclosed in the computer storage medium embodiments involved in the present application, please refer to the description of the method embodiments of the present application. By way of example, program instructions may be deployed and executed on one computer device, or on multiple computer devices located at one site, or distributed across multiple sites and interconnected by a communication network to execute on multiple computer devices. Multiple computer devices in multiple locations and interconnected by a communication network can be combined into a blockchain network.

According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device can execute the methods in the foregoing embodiments corresponding to FIG. 1 to FIG. Repeat.

Those of ordinary skill in the art can understand that all or part of the process in the method of the above embodiment can be implemented by instructing the relevant hardware through a computer program, and the above program can be stored in a computer-readable storage medium. , may include the flow of the above-mentioned method embodiments. The above-mentioned storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM) or the like.

The above disclosures are only the preferred embodiments of the present application, and of course, the scope of the rights of the present application cannot be limited by this. Therefore, equivalent changes made according to the claims of the present application are still within the scope of the present application.

Claims (15)

1. a data processing method, is characterized in that, comprises: Acquire a set of settled transaction data, the settled transaction data set includes N settled transaction data, each settled transaction data includes transaction information and resource data types, and the settled transaction data set corresponds to K resource data types , N, K are all positive integers; Performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type; When the profit and loss of M resource data types in the K resource data types is less than the profit and loss threshold, obtain the profit and loss reasons of the M resource data types, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, where M is a positive integer; The profit and loss warning information is output. 2. The method according to claim 1, characterized in that, performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type, comprising: dividing the settled transaction data set into K unit sets, and the resource data types in each unit set are the same; Statistical processing of profit and loss is performed on the transaction information in each unit set to obtain the profit and loss of each resource data type. 3. The method according to claim 1, wherein each settled transaction data further comprises a settlement timestamp, and the settled transaction data set corresponds to P settlement time periods; The method also includes: Perform profit and loss statistical processing on the transaction information in the settled transaction data corresponding to each settlement time period, and obtain the profit and loss of each resource data type in each settlement time period; Superimpose the profit and loss of all resource data types in each settlement period into the total profit and loss in each settlement period; Superimpose the profit and loss of each resource data type into cumulative profit and loss; The total profit and loss, cumulative profit and loss, and profit and loss of each resource data type for each settlement period are displayed on the display page. 4. The method according to claim 3, wherein the target resource data type is any resource data type among the M resource data types, and the P settlement time periods include a first settlement time period and a second settlement time period , the first settlement time period is adjacent to the second settlement time period, and the second settlement time period is the largest settlement time period among the P settlement time periods; Identify profit and loss reasons for target resource data types, including: extracting a first settled transaction data set of the target resource data type within the first settlement time period from the settled transaction data set; extracting a second set of settled transaction data of the target resource data type within the second settlement time period from the set of settled transaction data; Analyzing and processing the first settled transaction data set and the second settled transaction data set to obtain a transaction volume contribution rate and a ratio contribution rate; The transaction volume contribution rate and the ratio contribution rate are combined as the profit and loss reason for the target resource data type. 5. The method according to claim 4, wherein the transaction information comprises transaction data volume, transaction ratio and settlement ratio; The said first settled transaction data set and the second settled transaction data set are analyzed and processed to obtain the transaction volume contribution rate and the ratio contribution rate, including: The first transaction ratio mean is determined according to the transaction ratio in the first settled transaction data set, the first settlement ratio average is determined according to the settlement ratio in the first settled transaction data set, and the transaction data volume in the first settled transaction data set is determined determine the first transaction volume; The second transaction ratio mean is determined according to the transaction ratio in the second settled transaction data set, the second settlement ratio average is determined according to the settlement ratio in the second settled transaction data set, and the transaction data volume in the second settled transaction data set is determined determining the second transaction volume; The first profit and loss is determined according to the average value of the first transaction ratio, the average value of the first settlement ratio and the first transaction volume, the second profit and loss is determined according to the average value of the second transaction ratio, the average value of the second settlement ratio and the second transaction volume, and the second profit and loss Subtract the first profit and loss to get the change in profit and loss; The transaction volume contribution rate is determined according to the second total transaction volume, the average first transaction ratio, the average first settlement ratio, the first profit and loss, and the change in profit and loss, and the ratio contribution rate is determined according to the second profit and loss and the transaction volume contribution rate. 6. The method of claim 1, further comprising: Obtain the target transaction data of U transaction objects, each target transaction data includes the transaction data volume; Perform statistical processing on the transaction data volume in the target transaction data of the U transaction objects, obtain abnormal transaction objects in the U transaction objects, and generate transaction alarm information according to the abnormal transaction objects; The transaction warning information is output. 7. The method according to claim 6, wherein each target transaction data further comprises a transaction timestamp and a transaction ratio; the method further comprises: According to the transaction time stamp of the target transaction data, the target transaction data is divided into multiple target transaction data sets, and the transaction time stamps in the target transaction data in the same target transaction data set are in the same transaction time period; Determine the conversion transaction volume of each target transaction data according to the transaction data volume and transaction ratio in each target transaction data; The converted transaction volumes of the target transaction data in the same target transaction data set are superimposed respectively to obtain the transaction exposure of each transaction time period; Overlay the trading exposures of all trading time periods into cumulative trading exposures; On the display page, the trading exposure and cumulative trading exposure of each trading time period are displayed. 8. The method of claim 7, further comprising: When the trading exposure of any one of the multiple trading time periods is greater than the first exposure threshold, or when the accumulated trading exposure is greater than the second exposure threshold, the exposure warning information is generated. 9. The method according to claim 6, wherein the statistical processing is performed on the transaction data volume in the target transaction data of the U transaction objects to obtain the abnormal transaction objects in the U transaction objects, comprising: Superimpose the transaction data volume in the target transaction data of each transaction object into the cumulative transaction data volume of each transaction object; Obtain the predicted transaction data volume of each transaction object; The transaction objects whose predicted transaction data volume is greater than the accumulated transaction data volume are regarded as abnormal transaction objects. 10. The method of claim 9, further comprising: Sort the cumulative transaction data volume of the U transaction objects to obtain the sorting result; The sorting results are displayed on the display page. 11. The method of claim 6, further comprising: When the profit and loss alarm information is received, or when the transaction alarm information is received, a business handling page is displayed, and the business handling page is used for receiving user handling instructions. 12. The method according to any one of claims 1-11, wherein the set of settled transaction data is stored on a blockchain; The method also includes: Packing the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, profit and loss reasons of M resource data types, and profit and loss alarm information for each settlement period into blocks; Store the block on the blockchain. 13. A data processing device, comprising: The acquisition module is used to acquire a set of settled transaction data, the set of settled transaction data includes N pieces of settled transaction data, each of the settled transaction data includes transaction information and resource data types, and the set of settled transaction data corresponds to K resource data types, N, K are positive integers; A statistics module, configured to perform profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type; The generating module is configured to obtain the profit and loss reasons of the M resource data types when the profit and loss of the M resource data types in the K resource data types is less than the profit and loss threshold, and generate profit and loss alarm information according to the profit and loss reasons of the M resource data types, M is a positive integer; An output module, configured to output the profit and loss alarm information. 14. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to execute any one of claims 1-12. A step of the method. 15. A computer storage medium, wherein the computer storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the computer device having the processor is executed. The method of any of claims 1-12.

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