CN111884803B - Data processing method based on graphical modeling result - Google Patents
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Abstract
The application discloses a data processing method based on a graphical modeling result, which comprises the following steps: the graphical model management client displays a graphical modeling interface to obtain a target data model; transmitting the target data model to a target node; generating and sending a target data model optimization instruction to the target node, wherein the target node correspondingly sends a data tracking object change instruction to a data tracking server; the data tracking server extracts transaction data, effective information mining is carried out on the transaction data according to a transaction action sequence in the target data model, and tracking data are generated according to a mining result and an evaluation scalar in the target data model; the data tracking server sends the tracking data to the target node; and the target node updates the tracking data to the graphical model management client. The invention can carry out intelligent data tracking.
Description
Technical Field
The application relates to the field of data processing, in particular to a data processing method based on a graphical modeling result.
Background
In the field of programmed trading of securities, futures, virtual money, and the like, it is necessary to perform modeling of a mathematical model using computer technology. However, the programmed trading of securities/futures is a cross field, and the computer programming capability required by model building is difficult for many practitioners, so that providing efficient and simple graphical model customization software for the practitioners to build related mathematical models becomes the dominant direction of software products oriented to the securities/futures market.
However, the mere provision of a modeling customization function is far from meeting the needs of practitioners for such software products, and further provides data processing capability on the basis of providing a graphical customization model function, so as to facilitate further providing modeling guidance for practitioners through information mining on relevant samples of mathematical models, which becomes the direction of the research and development of these software products.
Disclosure of Invention
The embodiment of the invention provides a data processing method based on a graphical modeling result.
A method of data processing based on graphical modeling results, the method comprising:
the graphical model management client displays a graphical modeling interface and obtains a target data model according to an operation result of the graphical modeling interface;
transmitting the target data model to target nodes, wherein the target nodes are model storage server nodes with model storage quota not reaching an upper limit value, the model storage servers are in a distributed structure, each model storage server node has model storage quota, and the target nodes generate unique serial numbers for the target data model;
the graphical model management client generates and sends a target data model optimization instruction to the target node, and the target node correspondingly sends a data tracking object change instruction to the data tracking server;
the data tracking server starts data tracking on the target data model based on the data tracking object changing instruction, extracts transaction data, performs effective information mining on the transaction data according to a transaction action sequence in the target data model, and generates tracking data according to a mining result and an evaluation scalar in the target data model, wherein the tracking data is used for optimizing execution parameters in the target data model;
the data tracking server sends the tracking data to the target node;
and the target node updates the tracking data to the graphical model management client.
Preferably, the graphical modeling interface can comprise an action selection module, an action parameter module, a condition description module, a model evaluation scalar module and a mathematical model generation module;
the condition description module is used for describing conditions for triggering transaction actions, the action selection module is used for selecting transaction actions, and the action parameter module is used for setting relevant execution parameters corresponding to the transaction actions;
the model evaluation scalar module is used for setting at least one evaluation scalar for evaluating the mathematical model, and the evaluation scalar is used for setting a specific evaluation reference for the mathematical model, so that a server can conveniently mine evaluation scalar related information according to samples, and further related data capable of optimizing the model can be obtained.
Preferably, the data tracking server starts data tracking on the target data model based on the data tracking object change instruction, and the data tracking server includes:
the data tracking server sends first reference value acquisition requests to all model storage server nodes to obtain first reference values sent by all the model storage server nodes;
the data tracking server extracts an effective reference value, the effective reference value is a first reference value which is not equal to a default value, and the effective reference values are sequenced according to the corresponding model storage server node identification numbers to obtain an effective reference value sequence;
enabling the hash value corresponding to the effective parameter value sequence to serve as a token generation code, generating corresponding communication sequence codes for each model storage server node storing the tracked mathematical model according to the token generation code, and further generating verification sequence codes, wherein the communication sequence codes are sent to the nodes, and the verification sequence codes are stored locally in the data tracking server;
and the model storage server node receiving the communication sequence code interacts with the data tracking server based on the communication sequence code.
Preferably, the generating a corresponding communication sequence code for each model storage server node storing the tracked mathematical model according to the token generation code, and further generating a verification sequence code, includes:
obtaining construction factors according to the token generation code N, wherein the construction factors belong to N sets with the modulus of N, and the sets are obtained based on the congruence relation of the construction factors;
according to the construction factor atConstructing an algebraic expression;
randomly acquiring different self-variation values xiAccording to said parameter xiAnd said algebraic expression yields said parameter xiFactor y ofiWherein the parameter xiIs the same as the number of valid reference values;
according to xiObtain a communication half-code according to yiObtaining another communication half code to form a communication sequence code;
and obtaining a verification sequence code according to the obtained communication sequence code, storing the verification sequence code, and correspondingly sending the communication sequence code to a model storage server node.
Preferably, the model storage server node that receives the communication sequence code interacts with the data tracking server based on the communication sequence code, and includes:
a data tracking server acquires data tracking interaction requests sent by each model storage server node, wherein each data tracking interaction request carries a communication sequence code;
obtaining a plurality of communication code pairs according to each received communication sequence code, and calculating a target verification sequence code according to the communication code pairs;
and the data tracking server inquires whether a verification sequence code which is the same as the target verification sequence code exists locally, and if so, the data tracking server sends tracking data to the model storage server node, wherein the tracking data is used for optimizing a mathematical model participating in data tracking in the model storage server node.
Preferably, the tracking data is generated by the data tracking server according to the following method:
acquiring all mathematical models to be tracked;
extracting transaction data in a preset first time period, and mining effective information corresponding to each mathematical model to be tracked according to each mathematical model to be tracked;
and obtaining corresponding tracking data for the effective information corresponding to each mathematical model to be tracked. .
Preferably, the extracting transaction data in a preset first time period and mining effective information corresponding to each mathematical model to be tracked according to each mathematical model to be tracked includes:
extracting a transaction action sequence of the mathematical model to be tracked;
decomposing information capture parameter items according to the transaction action sequence, wherein the information capture parameter items comprise at least one group of information capture parameter pairs, and each group of information capture parameter pairs comprises a transaction occurrence condition, a transaction action and a transaction execution parameter range;
and filtering transaction data according to the information capture parameter item to obtain a transaction information set, wherein each piece of transaction information in the transaction information set points to the generated transaction data in a target transaction time period, and the target transaction time period is uniquely determined according to the information capture parameter item.
Preferably, the obtaining of the corresponding tracking data for the valid information corresponding to each mathematical model to be tracked includes:
calculating the correlation degree between the transaction information;
generating a plurality of classes to be evaluated according to the correlation degree between the transaction information, wherein the correlation degree of each transaction information in the same class to be evaluated is not less than a preset threshold value, and the correlation degrees of the transaction information respectively positioned in different classes to be evaluated are less than the preset threshold value;
calculating an evaluation scalar according to the transaction information in the class to be evaluated;
selecting a target class to be evaluated with the best evaluation scalar performance;
calculating an execution parameter expected value according to the transaction information in the target class to be evaluated;
calculating the difference value between the execution parameter expected value and the execution parameter in the tracking mathematical model to be evaluated;
and if the difference is larger than a preset threshold value, taking the expected value of the execution parameter as tracking data.
The embodiment of the invention provides a data processing method based on a graphical modeling result, which is characterized in that a mathematical model is established on a graphical model management client side based on a graphical component, after the mathematical model is uploaded to a model storage server node, the model storage server node and a data tracking root server perform safe interaction, and the mathematical model is updated according to an interaction result. The embodiment of the invention ensures that the data tracking server only carries out mathematical tracking on the mathematical model with the related authority by designing the details of safe interaction, ensures that the calculation tracking capability of the data tracking server is not leaked, achieves the aim of optimizing the execution parameters according to the big data by pushing the tracking result to the graphical model management client, and can provide modeling reference for practitioners.
Drawings
In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a data processing method based on graphical modeling results according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating a data tracking server initiating data tracking of the target data model based on the data tracking object change instruction according to an embodiment of the present application;
FIG. 3 is a flowchart of generating corresponding communication sequence codes for each model storage server node storing a tracked mathematical model based on the token generation code, and further generating a verification sequence code according to the embodiment of the present application;
FIG. 4 is a flowchart of a model storage server node receiving a communication sequence code, interacting with the data tracking server based on the communication sequence code;
FIG. 5 is a flowchart of extracting transaction data and mining valid information corresponding to each mathematical model to be tracked according to each mathematical model to be tracked, provided by an embodiment of the present application;
fig. 6 is a flowchart for obtaining corresponding tracking data for each mathematical model to be tracked according to the valid information corresponding to the mathematical model to be tracked provided in the embodiment of the present application.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, an embodiment of the present invention discloses a data processing method based on a graphical modeling result, as shown in fig. 1, the method includes:
s101, the graphical model management client displays a graphical modeling interface, and obtains a target data model according to an operation result of the graphical modeling interface.
In particular, the graphical management client may be a client held by a practitioner in the securities/futures related field, and the graphical management client may be used for constructing a mathematical model and providing relevant data for the practitioner through interaction with a server, wherein the data is obtained through information mining on a large number of samples.
Specifically, the graphical modeling interface may include an action selection module, an action parameter module, a condition description module, a model evaluation scalar module, and a mathematical model generation module.
The condition description module is used for describing conditions for triggering transaction actions, the action selection module is used for selecting transaction actions, and the action parameter module is used for setting relevant execution parameters corresponding to the transaction actions. Obviously, a transaction action sequence can be obtained by using the condition description module, the action selection module and the action parameter module for multiple times, and a mathematical model can be obtained according to the transaction action sequence.
For example, the condition (a) of the transaction action a is constructed based on the condition description module, the transaction action a is selected by the action selection module, and the execution parameter, element (a), related to the transaction action a is set by the action parameter module, so that one element condition (a) of the transaction action sequence can be obtained: element (A). Obviously, by using the module for multiple times, multiple elements can be obtained, and further a transaction action sequence can be generated.
The model evaluation scalar module is used for setting at least one evaluation scalar for evaluating the mathematical model, and the evaluation scalar is used for setting a specific evaluation reference for the mathematical model, so that a server can conveniently mine evaluation scalar related information according to samples, and further related data capable of optimizing the model can be obtained. Of course, not every mathematical model must have its corresponding evaluation scalar set, e.g., if the user does not set it himself, the data tracking server may use the default scalar for data tracking.
S102, transmitting the target data model to target nodes, wherein the target nodes are model storage server nodes with model storage limits not reaching an upper limit value, the model storage servers are of a distributed structure, each model storage server node has a model storage limit, and the target nodes generate unique serial numbers for the target data model.
And S103, the graphical model management client generates and sends a target data model optimization instruction to the target node, and the target node correspondingly sends a data tracking object change instruction to the data tracking server.
In fact, the method is a multi-terminal interaction system, each graphical model management client can customize at least one target data model and store the target data model to a certain target node, and each graphical model management client can select to optimize part or all of the at least one constructed target data model, so that a data tracking object of a data tracking server can be triggered to change by a target data model optimization instruction triggered by any graphical model management client.
For example, the data tracking object of the data tracking server at time A is
Node 1 (model A, model B, model C)
Node 76 (model 1, model 2, model 3)
A graphical management client requests to track the model toy, and the model toy in the node 314 triggers a data tracking object change instruction, and after the data tracking object is changed, the data tracking object of the data tracking server at the time point B is
Node 1 (model A, model B, model C)
Node 76 (model 1, model 2, model 3)
Node 314 (model toy)
S104, the data tracking server starts data tracking on the target data model based on the data tracking object changing instruction, extracts transaction data, conducts effective information mining on the transaction data according to a transaction action sequence in the target data model, and generates tracking data according to a mining result and an evaluation scalar in the target data model, wherein the tracking data is used for optimizing execution parameters in the target data model.
And S105, the data tracking server transmits the tracking data to the target node.
And S106, the target node updates the tracking data to the graphical model management client.
The data involved in the present application are all valid information related to transaction data and mathematical models, and therefore, to improve security, the data tracking server starts data tracking on the target data model based on the data tracking object change instruction, as shown in fig. 2, including:
s1, the data tracking server sends first reference value obtaining requests to all model storage server nodes to obtain first reference values sent by all the model storage server nodes.
Specifically, the method for acquiring the first reference value includes:
counting a mathematical model which is stored by a model storage server node and needs to be tracked;
if the number of the mathematical models to be tracked is 0, the first reference value is a default value;
if the number of the mathematical models to be tracked is nonzero, the first reference value is a numerical value uniquely mapped according to the serial number of each mathematical model to be tracked which is obtained through statistics.
Specifically, hash mapping may be used, and the embodiment of the present invention does not limit a specific mapping algorithm.
And S2, the data tracking server extracts an effective reference value, the effective reference value is a first reference value which is not equal to a default value, and the effective reference values are sequenced according to the corresponding model storage server node identification numbers to obtain an effective reference value sequence.
And S3, enabling the hash value corresponding to the effective parameter value sequence to serve as a token generation code, generating corresponding communication sequence codes for each model storage server node storing the tracked mathematical model according to the token generation code, and further generating a verification sequence code, wherein the communication sequence codes are sent to the nodes, and the verification sequence codes are stored locally in the data tracking server.
In one possible embodiment, the generating a corresponding communication sequence code for each model storage server node storing the tracked mathematical model according to the token generation code, and further generating a verification sequence code, as shown in fig. 3, includes:
s31, obtaining construction factors according to the token generation code N, wherein the construction factors belong to N sets with the modulus of N, and the sets are obtained based on the congruence relation of the construction factors.
S32, according to the construction factor atAnd constructing an algebraic expression.
Wherein, the algebraic formula
Where n and Δ are the security level parameter and the token generation code, respectively. The security level parameters may be preset in the data tracking server.
S33, randomly acquiring different self-variation values xiAccording to said parameter xiAnd said algebraic expression yields said parameter xiFactor y ofiWherein the parameter xiIs the same as the number of valid reference values.
S34. according to xiObtain a communication half-code according to yiAnother communication half-code is obtained, constituting a communication sequence code.
And S35, obtaining a verification sequence code according to the obtained communication sequence code, storing the verification sequence code, and correspondingly sending the communication sequence code to a model storage server node.
The calculation method of the verification sequence code is the same as the generation method of the target verification sequence code, and is not described herein after.
Obviously, the number of communication sequence codes is the same as the number of nodes of the receiving model storage server, and each node of the receiving model storage server can obtain a unique communication sequence code.
And S4, receiving the model storage server node of the communication sequence code, and interacting with the data tracking server based on the communication sequence code.
Specifically, the model storage server node that receives the communication sequence code interacts with the data tracking server based on the communication sequence code, as shown in fig. 4, including:
s41, the data tracking server acquires data tracking interaction requests sent by each model storage server node, and each data tracking interaction request carries a communication sequence code.
S42, obtaining a plurality of communication code pairs (x) according to the received communication sequence codesi，yi) And calculating a target verification sequence code according to the communication code pair.
Specifically, the calculation method of the target verification sequence code is as follows:
constructing a target form from individual communication code pairs
And constructing a key formula according to the target formula, wherein for any value of x in the key formula, the difference value between the corresponding value and the value of the target formula is a multiple of the token generation code, and determining a target verification sequence code according to the constant item of the key formula.
S43, the data tracking server inquires whether a verification sequence code identical to the target verification sequence code exists locally, and if the verification sequence code exists, the data tracking server sends tracking data to a model storage server node, wherein the tracking data is used for optimizing a mathematical model participating in data tracking in the model storage server node.
Specifically, the tracking data is generated by the data tracking server according to the following method:
and S10, acquiring all mathematical models to be tracked.
And S30, extracting transaction data in a preset first time period, and mining effective information corresponding to each mathematical model to be tracked according to each mathematical model to be tracked.
Specifically, in order to achieve the tracking effect, the preset first time period takes yesterday transaction data as a time period right boundary. For example, the preset first period of time may be the last three months, the last half year, and the last year. Of course, step S30 is repeatedly triggered to be performed every preset second time.
Specifically, the extracting transaction data in a preset first time period and mining effective information corresponding to each mathematical model to be tracked according to each mathematical model to be tracked includes, as shown in fig. 5:
s301, extracting a transaction action sequence of the mathematical model to be tracked.
S302, information capture parameter items are resolved according to the transaction action sequence, the information capture parameter items comprise at least one group of information capture parameter pairs, and each group of information capture parameter pairs comprises a transaction occurrence condition, a transaction action and a transaction execution parameter range.
The transaction execution parameter range is obtained by mapping the transaction execution parameters in the mathematical model, and the mapping method is calculated by the server and can also be set in advance, which is not described in detail herein.
S303, transaction data are filtered according to the information capture parameter items to obtain a transaction information set, each piece of transaction information in the transaction information set points to the transaction data generated in a target transaction time period, and the target transaction time period is uniquely determined according to the information capture parameter items.
It is apparent that a series of actions defined in the sequence of transaction actions occur within the target transaction time period.
And S50, obtaining corresponding tracking data for the effective information corresponding to each mathematical model to be tracked.
Specifically, the obtaining of the corresponding tracking data for the valid information corresponding to each mathematical model to be tracked, as shown in fig. 6, includes:
and S501, calculating the correlation degree among the transaction information.
Specifically, the calculating the correlation between the transaction information includes:
s5011, calculating first correlation
Wherein, muα，σαThe average value and variance of the value of the target object, which may be a security or a future, corresponding to the trading information.
S5012, calculating second correlation
S5013. calculating fluctuation consistency C (alpha, beta)
And S5014, carrying out weighted summation according to the first correlation, the second correlation and the fluctuation consistency to calculate the correlation among the transaction information.
S502, generating a plurality of classes to be evaluated according to the correlation degree among the transaction information, wherein the correlation degree of each transaction information in the same class to be evaluated is not less than a preset threshold value, and the correlation degrees of the transaction information respectively positioned in different classes to be evaluated are less than the preset threshold value.
S503, calculating an evaluation scalar according to the transaction information in the class to be evaluated.
S504, selecting the target to-be-evaluated class with the best evaluation scalar expression.
And S505, calculating an execution parameter expected value according to the transaction information in the target class to be evaluated.
S506, calculating the difference value between the expected value of the execution parameter and the execution parameter in the tracking mathematical model to be evaluated.
And S507, if the difference value is larger than a preset threshold value, taking the expected value of the execution parameter as tracking data.
Obviously, this trace data is transmitted via the model storage server node to the graphical modeling management client for optimizing its local mathematical model.
The embodiment of the invention discloses a data processing method based on a graphical modeling result, which constructs a mathematical model based on a graphical component at a graphical model management client, uploads the mathematical model to a model storage server node, then carries out safe interaction between the model storage server node and a data tracking root server, and updates the mathematical model according to an interaction result. The embodiment of the invention ensures that the data tracking server only carries out mathematical tracking on the mathematical model with the related authority by designing the details of safe interaction, ensures that the calculation tracking capability of the data tracking server is not leaked, achieves the aim of optimizing the execution parameters according to the big data by pushing the tracking result to the graphical model management client, and can provide modeling reference for practitioners.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and server embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the partial description of the method embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A data processing method based on graphical modeling results is characterized by comprising the following steps:
the graphical model management client displays a graphical modeling interface and obtains a target data model according to an operation result of the graphical modeling interface;
transmitting the target data model to target nodes, wherein the target nodes are model storage server nodes with model storage quota not reaching an upper limit value, the model storage servers are in a distributed structure, each model storage server node has model storage quota, and the target nodes generate unique serial numbers for the target data model;
the graphical model management client generates and sends a target data model optimization instruction to the target node, and the target node correspondingly sends a data tracking object change instruction to the data tracking server;
the data tracking server starts data tracking on the target data model based on the data tracking object changing instruction, extracts transaction data, performs effective information mining on the transaction data according to a transaction action sequence in the target data model, and generates tracking data according to a mining result and an evaluation scalar in the target data model, wherein the tracking data is used for optimizing execution parameters in the target data model;
the data tracking server sends the tracking data to the target node;
the target node updates the tracking data to the graphical model management client;
the data tracking server starts data tracking of the target data model based on the data tracking object change instruction, and the data tracking server comprises:
the data tracking server sends first reference value acquisition requests to all model storage server nodes to obtain first reference values sent by all the model storage server nodes;
the data tracking server extracts an effective reference value, the effective reference value is a first reference value which is not equal to a default value, and the effective reference values are sequenced according to the corresponding model storage server node identification numbers to obtain an effective reference value sequence;
enabling the hash value corresponding to the effective parameter value sequence to serve as a token generation code, generating corresponding communication sequence codes for each model storage server node storing the tracked mathematical model according to the token generation code, and further generating verification sequence codes, wherein the communication sequence codes are sent to the nodes, and the verification sequence codes are stored locally in the data tracking server; and the model storage server node receiving the communication sequence code interacts with the data tracking server based on the communication sequence code.
2. The method of claim 1, wherein:
the graphical modeling interface comprises an action selection module, an action parameter module, a condition description module, a model evaluation scalar module and a mathematical model generation module;
the condition description module is used for describing conditions for triggering transaction actions, the action selection module is used for selecting transaction actions, and the action parameter module is used for setting relevant execution parameters corresponding to the transaction actions;
the model evaluation scalar module is used for setting at least one evaluation scalar for evaluating the mathematical model, and the evaluation scalar is used for setting a specific evaluation reference for the mathematical model, so that a server can conveniently mine evaluation scalar related information according to samples, and further related data capable of optimizing the model can be obtained.
3. The method of claim 1, wherein generating from the token generation code a corresponding communication sequence code for each model storage server node storing the tracked mathematical model and further generating a verification sequence code comprises:
obtaining construction factors according to the token generation code N, wherein the construction factors belong to N sets with the modulus of N, and the sets are obtained based on the congruence relation of the construction factors;
constructing an algebraic expression according to the construction factor a t;
randomly acquiring different self-variation values x i, and obtaining a dependent variable y i of the parameters x i according to the parameters x i and the algebraic expression, wherein the number of the parameters x i is the same as that of the effective reference values;
obtaining a communication half code according to x i, obtaining another communication half code according to y i to form a communication sequence code;
and obtaining a verification sequence code according to the obtained communication sequence code, storing the verification sequence code, and correspondingly sending the communication sequence code to a model storage server node.
4. The method of claim 1, wherein the model storage server node that receives the communication sequence code, interacting with the data tracking server based on the communication sequence code, comprises:
a data tracking server acquires data tracking interaction requests sent by each model storage server node, wherein each data tracking interaction request carries a communication sequence code;
obtaining a plurality of communication code pairs according to each received communication sequence code, and calculating a target verification sequence code according to the communication code pairs;
and the data tracking server inquires whether a verification sequence code which is the same as the target verification sequence code exists locally, and if so, the data tracking server sends tracking data to the model storage server node, wherein the tracking data is used for optimizing a mathematical model participating in data tracking in the model storage server node.
5. The method of claim 3, wherein:
the tracking data is generated by a data tracking server according to the following method:
acquiring all mathematical models to be tracked;
extracting transaction data in a preset first time period, and mining effective information corresponding to each mathematical model to be tracked according to each mathematical model to be tracked;
and obtaining corresponding tracking data for the effective information corresponding to each mathematical model to be tracked.
6. The method according to claim 5, wherein the extracting transaction data in the preset first time period and mining valid information corresponding to each mathematical model to be tracked according to each mathematical model to be tracked comprises:
extracting a transaction action sequence of the mathematical model to be tracked;
decomposing information capture parameter items according to the transaction action sequence, wherein the information capture parameter items comprise at least one group of information capture parameter pairs, and each group of information capture parameter pairs comprises a transaction occurrence condition, a transaction action and a transaction execution parameter range;
and filtering transaction data according to the information capture parameter item to obtain a transaction information set, wherein each piece of transaction information in the transaction information set points to the generated transaction data in a target transaction time period, and the target transaction time period is uniquely determined according to the information capture parameter item.
7. The method according to claim 6, wherein the obtaining of the corresponding tracking data for the valid information corresponding to each mathematical model to be tracked comprises:
calculating the correlation degree between the transaction information;
generating a plurality of classes to be evaluated according to the correlation degree between the transaction information, wherein the correlation degree of each transaction information in the same class to be evaluated is not less than a preset threshold value, and the correlation degrees of the transaction information respectively positioned in different classes to be evaluated are less than the preset threshold value;
calculating an evaluation scalar according to the transaction information in the class to be evaluated;
selecting a target class to be evaluated with the best evaluation scalar performance;
calculating an execution parameter expected value according to the transaction information in the target class to be evaluated;
calculating the difference value between the execution parameter expected value and the execution parameter in the tracking mathematical model to be evaluated;
and if the difference is larger than a preset threshold value, taking the expected value of the execution parameter as tracking data.
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