CN118863957A

CN118863957A - A business processing method, device and storage medium based on cosmetic raw materials

Info

Publication number: CN118863957A
Application number: CN202410941381.7A
Authority: CN
Inventors: 孙云起; 孙怀庆; 郑思红; 黄宇航; 梁焕秋; 刘伟龙; 申毅
Original assignee: Guangzhou Marumi Network Technology Co ltd; Guangdong Marubi Biological Technology Co Ltd
Current assignee: Guangzhou Marumi Network Technology Co ltd; Guangdong Marubi Biological Technology Co Ltd
Priority date: 2024-07-12
Filing date: 2024-07-12
Publication date: 2024-10-29

Abstract

The present invention discloses a business processing method, device and storage medium based on cosmetic raw materials, the method comprising: collecting the first price data of historical futures for the raw materials of cosmetics; calculating the moving average of similarities and differences for the raw materials of cosmetics based on the first price data; calculating the random index for the raw materials of cosmetics based on the first price data; calculating the trend index for the raw materials of cosmetics based on the first price data; generating a business report of future futures for the raw materials of cosmetics based on the moving average of similarities and differences, the random index and the trend index on the basis of the first price data; performing business processing on the raw materials of cosmetics based on the business report. This embodiment fully mines the price characteristics of the raw materials of cosmetics in history, thereby predicting the future price changes of the raw materials of cosmetics, with a high degree of automation, greatly reducing the frequency of manual operation, less workload, high accuracy, and effectively improving the efficiency of subsequent business processing.

Description

Service processing method, device and storage medium based on cosmetic raw materials

Technical Field

The invention relates to the technical field of cosmetics, in particular to a business processing method, equipment and storage medium based on cosmetic raw materials.

Background

The products in the cosmetic industry are numerous, raw materials for generating different products are numerous, and the price of the raw materials is easily influenced by market fluctuation, so that unreasonable purchasing behaviors, even illegal purchasing behaviors, can be generated in the process of purchasing the raw materials.

At present, prices of different raw materials can be collected on each purchasing information network, and the market is researched, so that subsequent business processing, such as reasonable purchasing timing prediction, illegal purchasing behavior mining and the like, is carried out, but the mode relies on manual operation, and is large in workload, low in accuracy and low in overall efficiency.

Disclosure of Invention

In view of the above, the present invention provides a business processing method, apparatus and storage medium based on cosmetic raw materials to improve efficiency of performing business processing based on price of cosmetics.

A first aspect of the present invention provides a business processing method based on cosmetic raw materials, comprising:

collecting first price data of historical futures for raw materials of cosmetics;

calculating a different movement average line for the raw materials of the cosmetics according to the first price data;

calculating a random index for the raw materials of the cosmetics according to the first price data;

Calculating a homeopathy index for the raw materials of the cosmetics according to the first price data;

Based on the first price data, generating a future futures business report for the raw materials of the cosmetics according to the different movement average line, the random index and the homeotropic index;

and executing business treatment on the raw materials of the cosmetics according to the business report.

A second aspect of the present invention provides a cosmetic raw material-based business processing apparatus comprising:

the price data acquisition module is used for acquiring first price data of historical futures for raw materials of cosmetics;

the different movement average line calculation module is used for calculating different movement average lines for the raw materials of the cosmetics according to the first price data;

The random index calculation module is used for calculating a random index for the raw materials of the cosmetics according to the first price data;

The homeopathy index calculation module is used for calculating a homeopathy index for the raw materials of the cosmetics according to the first price data;

the business report generation module is used for generating a business report of future futures for the raw materials of the cosmetics according to the different movement average line, the random index and the homeotropic index on the basis of the first price data;

And the business processing execution module is used for executing business processing on the raw materials of the cosmetics according to the business report.

A third aspect of the present invention provides an electronic device comprising:

at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the cosmetic raw material-based business processing method according to the first aspect described above.

A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the cosmetic raw material-based business processing method according to the first aspect described above.

A fifth aspect of the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements the cosmetic raw material based business process method as described in the first aspect above.

In this embodiment, first price data of historical futures is collected for raw materials of cosmetics; calculating different movement average lines for raw materials of the cosmetics according to the first price data; calculating a random index for raw materials of the cosmetics according to the first price data; calculating a homeopathy index for raw materials of cosmetics according to the first price data; based on the first price data, generating a future business report of the raw materials of the cosmetics according to the different movement average line, the random index and the homeopathic index; and performing business treatment on the raw materials of the cosmetics according to the business report. According to the method, the price characteristics of the raw material history of the cosmetics are fully excavated, so that future price changes of the raw materials of the cosmetics are predicted, the degree of automation is high, the frequency of manual operation is greatly reduced, the workload is small, the accuracy is high, and the efficiency of subsequent business processing can be effectively improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a business processing method based on a cosmetic raw material according to an embodiment of the present invention.

Fig. 2 is a schematic structural diagram of a business processing device based on cosmetic raw materials according to a second embodiment of the present invention.

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein are capable of being practiced otherwise than as specifically illustrated and described. 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 apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Referring to fig. 1, a flowchart of a cosmetic raw material-based business processing method according to an embodiment of the present invention is shown, and the method may be performed by a cosmetic raw material-based business processing device, which may be implemented in hardware and/or software, and the cosmetic raw material-based business processing device may be configured in an electronic device. As shown in fig. 1, the method includes:

and step 101, collecting first price data of historical futures for raw materials of cosmetics.

From the perspective of the product, cosmetics may be divided into an outer coating material, an inner coating material and an active ingredient, and then raw materials for producing the outer coating material, the inner coating material and the active ingredient may be monitored.

In this embodiment, the first price data of the raw materials of the cosmetics in each period (for example, 1 trade day) of the history may be subscribed from the futures website, or the first price data of the raw materials of the cosmetics in each period (for example, 1 trade day) of the history may be collected by calling an API (Application Programming Interface ) provided by the futures website.

Wherein the first price data includes the highest price, the lowest price, the open price, the close price, the volume of the transaction, the rise and fall, the transaction amount, the hand change rate, and the like in each period.

Step 102, calculating different movement average lines for raw materials of the cosmetics according to the first price data.

In this embodiment, a part of parameters (such as the closing price) can be selected from the first price data, different movement average lines (Moving Average Convergence AND DIVERGENCE, MACD) can be calculated for the raw materials of the cosmetics, and the MACD uses the aggregation and separation conditions between the short-term (usually 12 days) index movement average line and the long-term (usually 26 days) index movement average line of the closing price in the first price data to make a technical index for determining the time for purchasing the raw materials of the cosmetics.

In a specific implementation, on one hand, the first price data in X (e.g., 12) cycles may be used to calculate a moving average line for the raw material of the cosmetic product as a Fast moving average line (Fast EMA), and on the other hand, the first price data in Y (e.g., 26) cycles may be used to calculate a moving average line for the raw material of the cosmetic product as a Slow moving average line (Slow EMA).

Subtracting the slow movement average line from the fast movement average line to obtain a difference value (DIF).

And calculating different movement average lines for raw materials of cosmetics by using first price data and dispersion values in Z (e.g. 9) periods, and representing possible development trend of the current multiple empty state and price index by discrete and aggregation of the fast and slow movement average lines.

Wherein X, Y and Z are positive integers, Y is greater than X, and X is greater than Z.

Step 103, calculating a random index for the raw materials of the cosmetics according to the first price data.

In this embodiment, a part of parameters (such as the highest price, the lowest price, the closing price, etc.) may be selected from the first price data, and the raw materials of the cosmetics calculate random indexes to determine market buying and selling conditions and price trend changes.

In a specific implementation, the random index of the raw material of the cosmetic includes a fast K random line (K line for short), a slow D random line (D line for short) and a deviation J random line (J line for short), then the immature random value (Raw Stochastic Value, RSV) of the current period can be calculated using the first price data, where rsv= (closing price-lowest price)/(highest price-lowest price) ×100.

The K fast random line of the previous cycle is linearly fused (i.e., weighted summed) with the immature random value to the K fast random line of the current cycle.

And fusing (i.e. weighting and summing) the D slow random line of the previous period and the K fast random line of the current period to obtain the D slow random line of the current period.

And smoothing the difference between the K fast random line of the current period and the D slow random line of the current period to obtain the J deviation random line.

The random index has a value ranging from 0 to 100, and typically 80 and 20 are used as overbuy and overstock judgment thresholds. Above 80, considered to be the overbuy area, may mean that the price is too high and that opportunities for adjustment or reversal may occur. Below 20, considered a overstock area, may mean that the price is too low and that the opportunity for rebound or reversal may occur.

Step 104, calculating a homeopathy index for the raw materials of the cosmetics according to the first price data.

In this embodiment, some parameters (such as the highest price, the lowest price, the closing price, etc.) may be selected from the first price data, the raw materials of the cosmetic calculate the homeopathic index (Commodity Channel Index, CCI), the CCI index may be used to measure whether the price of the raw materials of the cosmetic has exceeded the normal distribution range, and the variability outside the normal range of the price is mainly measured as one of overbuy overstock indexes, which fluctuates between positive infinity and negative infinity.

In a specific implementation, the highest price, the lowest price, and the closing price may be read from the first price data.

Average values are calculated for the highest price, the lowest price, and the closing price to obtain a typical price (TYPICAL PRICE).

The average value of the closing prices over a plurality of cycles is calculated to obtain a Moving Average (MA).

The average value is calculated for the difference between the moving average line and the closing price over a plurality of cycles, resulting in Mean absolute Deviation (MD).

The difference between the typical price and the moving average line is divided by the average absolute deviation and a preset coefficient (e.g., 0.015), respectively, to obtain a homeotropic index of the raw material of the cosmetic, i.e., cci= (TP-MA)/(MD)/0.015.

Step 105, based on the first price data, generating a future business report for the raw materials of the cosmetics according to the different movement average line, the random index and the homeotropic index.

In practical application, the first price data of future futures in raw materials of cosmetics is taken as a main standard, and the price change trend of the future futures in raw materials of cosmetics is corrected and predicted according to different movement average lines, random indexes and homeopathic indexes, so that a business report is generated and is referred to a buyer and a supervisor.

In one embodiment of the present invention, step 105 may include the steps of:

step 1051, extracting price characteristic data on trend from the first price data.

In this embodiment, the first price data may be preprocessed, and when the preprocessing is completed, feature engineering is performed on the first price data according to the requirements of the hidden markov model (Hidden Markov Model, HMM), and price feature data on trends (i.e., rises and falls) such as price fluctuation log difference, volume of transaction log change, price change percentage of a first time range (e.g., 5 days) and a second time range (e.g., 20 days), volume of transaction pair, and the like are extracted.

Further, for the first price data, fourier transformation may be performed thereon, which is converted from a time domain into a frequency domain, in which the periodicity of the first price data is determined and extracted according to a threshold of frequency and weight (e.g., seasonality).

Step 1052, constructing a feature matrix by using the different moving average line, the random index, the homeotropic index and the price feature data.

In this embodiment, the different movement average line, the random index, the homeotropic index and the price characteristic data are written into a matrix as factors to obtain a characteristic matrix.

Step 1053, inputting the feature matrix into a preset hidden Markov model, and predicting the probability that the second price data of the future futures of the raw materials of the cosmetics show each trend in the next period.

In this embodiment, the hidden markov model may be modeled in advance, and the hidden markov model may be trained.

The feature matrix is input into a hidden markov model, and the probability that the second price data of future futures of raw materials of cosmetics presents each trend in the next period is predicted, for example, the probability of rising suddenly, the probability of rising slowly, the probability of fluctuation, the probability of falling slowly, the probability of falling suddenly, and the like.

The hidden Markov model can help to identify hidden states in first price data of raw material futures of cosmetics and predict future states of markets, namely, in price trend analysis of the raw material futures of cosmetics, the hidden Markov model can help to identify different states of price fluctuation, such as a jerk state, a slow-rise state, a slow-fall state, a fluctuation state and the like, and judge the next most probable state of the current state of the price of the raw material futures of cosmetics according to a state transition matrix obtained by the hidden Markov model, so that the essence and rules of price trend are better understood. This helps the buyer and supervisor to formulate more reasonable business processing strategies, and improves the quality of business processing.

Step 1054 extracts a plurality of time-dependent price-related factors from the first price data.

In this embodiment, feature engineering may be performed on first price data of raw material history futures of cosmetics according to requirements of a precedent model (FBP), from which various factors related in time to price are extracted.

Illustratively, the factors include time series data, time stamps, upper limit prices, lower limit prices, non-trade days, periodicity, and seasonal patterns; then, preprocessing such as converting a time format, filling in missing values, etc. may be performed on the first price data.

If the preprocessing is completed, fourier transformation is performed on the index data according to the maximum period of the first price data, so that time series data are obtained, and the periodicity of the data can be better captured by the predictive model.

The time stamp of the time series data is queried.

And amplifying (e.g. doubling) the highest price in the first price data to obtain the upper limit price.

And (3) reducing (e.g. by half) the lowest price in the first price data to obtain a lower limit price.

Inquiring the first price data for the non-trade day, and detecting the influence factors of the holiday on the offset and the slope.

Periodicity is extracted from the time series data.

A seasonal pattern (multiple) is set for the first price data, which indicates that seasonal variation of the first price data is a multiplicative effect.

Step 1056, inputting the probability and various factors into a preset precedent model, predicting raw materials of cosmetics, predicting second price data of futures in a plurality of future periods, confidence intervals of the second price data and price periods in a specified time range.

The probability and various factors are input into a preset precedent model, the precedent model uses an addition model, a sequence related to time is split into trend, seasonal, holiday effect and other components, trend changes of the time sequence are detected and adapted, mutation conditions and speed change of the trend are identified, and accordingly the raw materials of the cosmetics are predicted to predict second price data of futures in a plurality of future periods, confidence intervals of the second price data and price periods in a specified time range.

Wherein the time range may include a historical period and/or a future period.

Step 1057, writing the probability, the second price data, the confidence interval and the price period into a business report of future futures of raw materials of cosmetics.

In this embodiment, the probability, the second price data, the confidence interval, the price period and other data are combined, and some additional information, such as an index name, a date of exchange market, and the like, is added, and written into a business report of future futures of raw materials of cosmetics in a column or other manner.

The method combines the short-term price prediction of the raw materials of the cosmetics by the hidden Markov model and the long-term price prediction of the raw materials of the cosmetics by the precedent model, can improve the information comprehensiveness of the business report, has high accuracy of prediction, can improve the reference value of the business report, and provides a data base for making efficient business processing.

And 106, executing business processing on the raw materials of the cosmetics according to the business report.

In this embodiment, future futures change conditions of raw materials of cosmetics in the business report may be analyzed, so as to formulate proper business treatment for raw materials of cosmetics.

In one business processing method, a correlation coefficient on a change trend, such as Pearson correlation coefficient, may be calculated for the second price data corresponding to the raw materials of the cosmetics.

If the correlation coefficient is greater than or equal to the preset first threshold value, which indicates that the correlation coefficient is high, it may be determined that there is a positive correlation between the second price data corresponding to the raw materials of the cosmetics in pairs, that is, the trend of change in the second price data of the raw material of one of the cosmetics is the same as or similar to the trend of change in the second price data of the raw material of the other cosmetic.

If the correlation coefficient is less than or equal to the preset second threshold value, which indicates that the correlation coefficient is low, it may be determined that there is a negative correlation between the second price data corresponding to the raw materials of the cosmetics in pairs, that is, the trend of change in the second price data of the raw material of one of the cosmetics is opposite to the trend of change in the second price data of the raw material of the other one of the cosmetics.

Wherein the first threshold is greater than the second threshold.

If the fluctuation range of the actual third lattice data of the raw materials of the current cosmetics is monitored to be larger than or equal to the preset third threshold value, the fluctuation range of the actual third lattice data of the raw materials of the current cosmetics is larger, and the corresponding positively and/or negatively related raw materials of other cosmetics can generate larger fluctuation range, purchase prompt information is generated for the raw materials of the other cosmetics positively and/or negatively related to the raw materials of the current cosmetics, and a purchaser is prompted to start or stop purchasing for the raw materials of the other cosmetics.

In addition, when the peaks and/or troughs of the price cycle are reached, purchasing prompt information is generated for the raw materials of the cosmetics, and the purchasing prompt information is used for prompting the purchasing of the raw materials of other cosmetics or stopping purchasing.

Further, the fourth price data when the raw material of the cosmetic is actually purchased can be queried, and the second price data of the raw material of the proposed cosmetic is read from the purchase prompt information.

If the second price data is larger than the fourth price data and the difference between the second price data and the fourth price data is larger than a preset fourth threshold value, the fourth price data of the raw materials for actually purchasing cosmetics is obviously higher than the second price data of the raw materials for suggesting to purchase cosmetics, the abnormal behavior of the raw materials for purchasing cosmetics is determined, alarm information is generated for the behavior of the raw materials for purchasing cosmetics, and a supervisor prompts to investigate the behavior of the raw materials for purchasing cosmetics.

Example two

Referring to fig. 2, a schematic structural diagram of a service processing device based on cosmetic raw materials according to a second embodiment of the present invention is shown. As shown in fig. 2, the apparatus includes:

a price data collection module 201, configured to collect first price data of historical futures for raw materials of cosmetics;

a mean line of disparate movement calculation module 202 for calculating a mean line of disparate movement for the raw materials of the cosmetic product according to the first price data;

a random index calculation module 203, configured to calculate a random index for the raw materials of the cosmetic according to the first price data;

a homeopathy index calculating module 204, configured to calculate a homeopathy index for the raw materials of the cosmetics according to the first price data;

The service report generating module 205 is configured to generate a service report of future futures for the raw materials of the cosmetics according to the different movement average line, the random index and the homeopathic index on the basis of the first price data;

A business process execution module 206, configured to execute business process on the raw materials of the cosmetics according to the business report.

In one embodiment of the present invention, the different movement average line calculation module 202 includes:

A fast moving average line calculation module for calculating a fast moving average line for a raw material of the cosmetic using the first price data for X cycles;

a slow-movement average line calculation module for calculating a slow-movement average line for a raw material of the cosmetic using the first price data in Y cycles;

The deviation value calculation module is used for subtracting the slow-movement average line from the fast-movement average line to obtain a deviation value;

The different movement average line generation module is used for calculating different movement average lines for raw materials of the cosmetics by using the first price data and the deviation value in Z periods;

In one embodiment of the present invention, the random index of the raw material of the cosmetic comprises a K fast random line, a D slow random line, and a J bias random line;

the random index calculation module 203 includes:

An immature random value calculation module for calculating immature random value of the current period by using the first price data;

the fast random line calculation module is used for linearly fusing the K fast random lines of the previous period with the immature random values to form K fast random lines of the current period;

The slow random line calculation module is used for fusing the D slow random line of the previous period and the K fast random line of the current period into the D slow random line of the current period;

And the deviation random line calculation module is used for smoothing the difference between the K fast random line in the current period and the D slow random line in the current period to obtain a J deviation random line.

In one embodiment of the present invention, the homeowner index calculation module 204 includes:

the price data reading module is used for reading the highest price, the lowest price and the closing price from the first price data;

the typical price calculation module is used for calculating average values of the highest price, the lowest price and the closing price to obtain typical prices;

The moving average line calculating module is used for calculating an average value of the closing price in a plurality of periods to obtain a moving average line;

The average absolute deviation calculation module is used for calculating an average value of the difference values between the moving average line and the closing price in a plurality of periods to obtain an average absolute deviation;

and the homeopathy index generation module is used for dividing the difference value between the typical price and the moving average line by the average absolute deviation and a preset coefficient to obtain the homeopathy index of the raw materials of the cosmetics.

In one embodiment of the present invention, the service report generating module 205 includes:

the price characteristic data extraction module is used for extracting price characteristic data on trend from the first price data;

the characteristic matrix construction module is used for constructing a characteristic matrix from the different moving average line, the random index, the homeotropic index and the price characteristic data;

The hidden Markov model calling module is used for inputting the feature matrix into a preset hidden Markov model and predicting the probability that second price data of future futures of raw materials of the cosmetics show each trend in the next period;

a factor extraction module for extracting a plurality of factors related to price in time from the first price data;

The precedent model calling module is used for inputting the probability and a plurality of factors into a preset precedent model, predicting second price data of futures in a plurality of future cycles, confidence intervals of the second price data and price cycles in a specified time range;

And the business report writing module is used for writing the probability, the second price data, the confidence interval and the price period into a business report of the raw material future futures of the cosmetics.

In one embodiment of the invention, the factors include time series data, time stamps, upper limit prices, lower limit prices, non-trade days, periodicity, and seasonal patterns;

the factor extraction module comprises:

the preprocessing module is used for preprocessing the first price data;

The Fourier transform module is used for executing Fourier transform on the index data according to the maximum period of the first price data to obtain time sequence data if the preprocessing is completed;

the time stamp inquiring module is used for inquiring the time stamp of the time sequence data;

The upper limit price calculation module is used for amplifying the highest price in the first price data to obtain an upper limit price;

The lower limit price calculation module is used for reducing the lowest price in the first price data to obtain a lower limit price;

The non-trade day inquiring module is used for inquiring the non-trade day for the first price data;

a periodicity extraction module for extracting periodicity from the time series data;

and the seasonal mode setting module is used for setting a seasonal mode for the first price data.

In one embodiment of the present invention, the service processing execution module 206 includes:

The correlation coefficient calculation module is used for calculating correlation coefficients on the change trend of the second price data corresponding to the raw materials of the cosmetics;

The positive correlation determining module is used for determining positive correlation between the second price data corresponding to the raw materials of the cosmetics if the correlation coefficient is larger than or equal to a preset first threshold value;

The negative correlation determining module is used for determining negative correlation between the second price data corresponding to the raw materials of the cosmetics in pairs if the correlation coefficient is smaller than or equal to a preset second threshold value; the first threshold is greater than the second threshold;

The first purchase prompt information generation module is used for generating purchase prompt information for other raw materials of the cosmetics positively and/or negatively related to the raw materials of the cosmetics at present if the fluctuation range of the actual third lattice data of the raw materials of the cosmetics at present is monitored to be larger than or equal to a preset third threshold value;

and the second purchase prompt information generation module is used for generating purchase prompt information for the raw materials of the cosmetics when the peaks and/or the troughs of the price period are reached.

In one embodiment of the present invention, the service processing execution module 206 further includes:

The actual purchase price inquiring module is used for inquiring the fourth price data when the raw materials of the cosmetics are actually purchased;

the suggested purchase price inquiry module is used for reading second price data of raw materials suggested to purchase the cosmetics from the purchase prompt information;

And the abnormality alarm module is used for determining that the behavior of the raw material for purchasing the cosmetics is abnormal and generating alarm information for the behavior of the raw material for purchasing the cosmetics if the second price data is larger than the fourth price data and the difference value between the second price data and the fourth price data is larger than a preset fourth threshold value.

The business processing device based on the cosmetic raw materials provided by the embodiment of the invention can execute the business processing method based on the cosmetic raw materials provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the business processing method based on the cosmetic raw materials.

Example III

Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present invention is shown. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, blade servers, mainframes, and other appropriate computers. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as a business processing method based on cosmetic raw materials.

In some embodiments, the cosmetic raw material-based business process method may be implemented as a computer program, which is tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described cosmetic raw material-based business processing method may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the cosmetic raw material-based business process by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

Example IV

Embodiments of the present invention also provide a computer program product comprising a computer program which, when executed by a processor, implements the cosmetic raw material-based business processing method as provided by any of the embodiments of the present invention.

Computer program product in the implementation, the computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A business processing method based on cosmetic raw materials, characterized by comprising:

2. The method of claim 1, wherein calculating a different movement average line for the raw materials of the cosmetic product based on the first price data comprises:

calculating a fast moving average line for a raw material of the cosmetic using the first price data for X cycles;

calculating a slow movement average line for a raw material of the cosmetic using the first price data for Y cycles;

subtracting the slow-movement average line from the fast-movement average line to obtain a deviation value;

calculating a different movement average line for the raw material of the cosmetic using the first price data and the dispersion value in Z cycles;

3. The method of claim 1, wherein the random index of the raw material of the cosmetic comprises a kfast random line, a dslow random line, and a J bias random line;

the calculating the random index for the raw materials of the cosmetics according to the first price data comprises the following steps:

calculating an immature random value of the current period using the first price data;

linearly fusing the K fast random line of the previous period with the immature random value to form the K fast random line of the current period;

Fusing the D slow random line of the previous period and the K fast random line of the current period into the D slow random line of the current period;

And smoothing the difference between the K fast random line of the current period and the D slow random line of the current period to obtain a J deviation random line.

4. The method of claim 1, wherein calculating a homeopathic index for raw materials of the cosmetic based on the first price data comprises:

reading the highest price, the lowest price and the closing price from the first price data;

Calculating average values of the highest price, the lowest price and the closing price to obtain a typical price;

calculating an average value of the closing price in a plurality of periods to obtain a moving average line;

Calculating an average value of the difference values between the moving average line and the closing price in a plurality of periods to obtain an average absolute deviation;

Dividing the difference between the typical price and the moving average line by the average absolute deviation and a preset coefficient to obtain a homeotropic index of the raw materials of the cosmetics.

5. The method according to any one of claims 1-4, wherein generating a future futures business report for the raw materials of the cosmetics according to the different movement average line, the random index and the homeopathic index based on the first price data comprises:

extracting price characteristic data on trend from the first price data;

constructing a feature matrix by the different mobile average line, the random index, the homeotropic index and the price feature data;

Inputting the feature matrix into a preset hidden Markov model, and predicting the probability that second price data of future futures of raw materials of the cosmetics show each trend in the next period;

Extracting a plurality of factors related to price in time from the first price data;

Inputting the probability and a plurality of factors into a preset precedent model, predicting second price data of futures in a plurality of future periods, confidence intervals of the second price data and price periods in a specified time range;

and writing the probability, the second price data, the confidence interval and the price period into a business report of future futures of raw materials of the cosmetics.

6. The method of claim 5, wherein the factors include time series data, time stamps, upper limit prices, lower limit prices, non-trade days, periodicity, and seasonal patterns;

The extracting a plurality of factors related to price in time from the first price data comprises:

performing preprocessing on the first price data;

If the preprocessing is completed, performing Fourier transform on the index data according to the maximum period of the first price data to obtain time sequence data;

Querying a timestamp of the time-series data;

amplifying the highest price in the first price data to obtain an upper limit price;

reducing the lowest price in the first price data to obtain a lower limit price;

inquiring the first price data for a non-trade day;

Extracting periodicity from the time series data;

and setting a seasonal mode for the first price data.

7. The method of claim 5, wherein said performing a business process on the raw materials of the cosmetic product in accordance with the business report comprises:

Calculating a correlation coefficient on a change trend of the second price data corresponding to the raw materials of the cosmetics;

If the correlation coefficient is larger than or equal to a preset first threshold value, determining positive correlation between the second price data corresponding to the raw materials of the cosmetics;

If the correlation coefficient is smaller than or equal to a preset second threshold value, determining negative correlation between the second price data corresponding to the raw materials of the cosmetics; the first threshold is greater than the second threshold;

If the fluctuation range of the actual third lattice data of the raw materials of the current cosmetics is monitored to be larger than or equal to a preset third threshold value, purchasing prompt information is generated for other raw materials of the cosmetics which are positively and/or negatively related to the raw materials of the current cosmetics;

and generating purchasing prompt information for the raw materials of the cosmetics when the peaks and/or the troughs of the price period are reached.

8. The method of claim 7, wherein said performing a business process on a raw material of the cosmetic product in accordance with the business report further comprises:

inquiring fourth-lattice data when raw materials of the cosmetics are actually purchased;

reading second price data of raw materials suggested to purchase the cosmetics from the purchase prompt information;

If the second price data is larger than the fourth price data and the difference value between the second price data and the fourth price data is larger than a preset fourth threshold value, determining that the raw material purchasing the cosmetics is abnormal, and generating alarm information for the raw material purchasing the cosmetics.

9. An electronic device, the electronic device comprising:

at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the cosmetic raw material-based business processing method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the cosmetic raw material-based business processing method according to any one of claims 1 to 8.