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

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

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

Technical Field

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

Background Art

There are many products in the cosmetics industry, and many raw materials are used to produce different products. The prices of raw materials are easily affected by market fluctuations, which may lead to some unreasonable or even illegal purchasing behaviors in the process of purchasing raw materials.

Currently, the prices of different raw materials can be collected on various procurement information websites, and market research can be conducted to carry out subsequent business processing, such as predicting reasonable procurement opportunities, uncovering illegal procurement behaviors, etc. However, this model relies on manual operations, has a large workload, low accuracy, and low overall efficiency.

Summary of the invention

In view of this, the present invention provides a business processing method, device and storage medium based on cosmetic raw materials, so as to improve the efficiency of executing business processing based on the price of cosmetics.

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

Collect the first price data of historical futures for raw materials of cosmetics;

Calculating a moving average of similarities and differences for the raw materials of the cosmetics according to the first price data;

Calculating a random index for the raw material of the cosmetic according to the first price data;

Calculating a trend indicator for the raw material of the cosmetic 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 similarity-difference moving average, the stochastic index and the trend index;

Perform business processing on the raw materials of the cosmetics according to the business report.

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

A price data collection module, used to collect the first price data of historical futures for raw materials of cosmetics;

A similarity and difference moving average calculation module, used for calculating the similarity and difference moving average of the raw materials of the cosmetics according to the first price data;

a random index calculation module, used for calculating a random index for the raw material of the cosmetic according to the first price data;

a homeopathic index calculation module, used for calculating the homeopathic index of the raw material of the cosmetics according to the first price data;

A business report generating module, for generating a future futures business report for the raw materials of the cosmetics based on the first price data and according to the similarity-difference moving average, the stochastic index and the trend index;

A business processing execution module is used to execute 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, the electronic device comprising:

at least one processor; and

a memory communicatively connected to the at least one processor; wherein,

The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the business processing method based on cosmetic raw materials as described in the first aspect 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 business processing method based on cosmetic raw materials as described in the first aspect above.

A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the business processing method based on cosmetic raw materials as described in the first aspect above.

In this embodiment, the first price data of historical futures of cosmetic raw materials are collected; the moving average of similarities and differences of the cosmetic raw materials is calculated based on the first price data; the random index is calculated based on the first price data for the cosmetic raw materials; the trend index is calculated based on the first price data for the cosmetic raw materials; on the basis of the first price data, a business report of future futures is generated for the cosmetic raw materials based on the moving average of similarities and differences, the random index and the trend index; and business processing is performed on the cosmetic raw materials based on the business report. This embodiment fully mines the historical price characteristics of the cosmetic raw materials, and predicts the future price changes of the cosmetic raw materials. It has a high degree of automation, greatly reduces the frequency of manual operations, has a small workload, and has high accuracy, which can effectively improve the efficiency of subsequent business processing.

It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG1 is a flow chart of a business processing method based on cosmetic raw materials provided in Embodiment 1 of the present invention.

FIG. 2 is a schematic diagram of the structure of a business processing device based on cosmetic raw materials provided in Embodiment 2 of the present invention.

FIG3 is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

Embodiment 1

Referring to FIG1 , a flowchart of a business processing method based on cosmetic raw materials provided by Embodiment 1 of the present invention is shown. The method can be executed by a business processing device based on cosmetic raw materials. The business processing device based on cosmetic raw materials can be implemented in the form of hardware and/or software. The business processing device based on cosmetic raw materials can be configured in an electronic device. As shown in FIG1 , the method includes:

Step 101: Collect historical first futures price data for raw materials of cosmetics.

From a product perspective, cosmetics can be divided into outer packaging materials, inner packaging materials and active ingredients. Therefore, the raw materials used to produce outer packaging materials, inner packaging materials and active ingredients can be monitored.

In this embodiment, the first price data of the raw materials of cosmetics in each historical period (such as 1 trading day) can be subscribed from the futures website, or the API (Application Programming Interface) provided by the futures website can be called to collect the first price data of the raw materials of cosmetics in each historical period (such as 1 trading day).

Among them, the first price data includes the highest price, lowest price, opening price, closing price, trading volume, increase or decrease, transaction amount, turnover rate, etc. in each period.

Step 102: Calculate a moving average of similarities and differences for the raw materials of the cosmetics based on the first price data.

In this embodiment, some parameters (such as closing price, etc.) can be filtered out from the first price data to calculate the Moving Average Convergence and Divergence (MACD) for the cosmetic raw materials. MACD uses the aggregation and separation between the short-term (usually 12-day) exponential moving average and the long-term (usually 26-day) exponential moving average of the closing price in the first price data to make a technical indicator for judging the timing of purchasing cosmetic raw materials.

In a specific implementation, on the one hand, the first price data within X (such as 12) periods can be used to calculate the moving average of the cosmetic raw materials as a fast moving average (Fast EMA); on the other hand, the first price data within Y (such as 26) periods can be used to calculate the moving average of the cosmetic raw materials as a slow moving average (Slow EMA).

Subtract the slow moving average from the fast moving average to get the deviation value (DIF).

The first price data and deviation values within Z (such as 9) periods are used to calculate the similarity and difference moving averages of cosmetic raw materials. The discreteness and aggregation of the fast and slow moving averages represent the current long and short status and the possible development and change trend of the price index.

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

Step 103: Calculate a random index for the raw material of the cosmetics according to the first price data.

In this embodiment, some parameters (such as the highest price, the lowest price and the closing price, etc.) can be screened out from the first price data, and random indicators of the raw materials of cosmetics can be calculated to judge the market trading situation and price trend changes.

In the specific implementation, the stochastic indicators of the raw materials of cosmetics include K fast stochastic line (K line for short), D slow stochastic line (D line for short) and J deviation stochastic line (J line for short). Then, the first price data can be used to calculate the immature stochastic value (Raw Stochastic Value, RSV) of the current period, where RSV = (closing price - lowest price) / (highest price - lowest price) * 100.

The K fast random line of the previous period is linearly fused with the immature random value (i.e. weighted summation) to form the K fast random line of the current period.

The D slow stochastic line of the previous period and the K fast stochastic line of the current period are merged (i.e., weighted summation) to form the D slow stochastic line of the current period.

The difference between the K fast stochastic line of the current period and the D slow stochastic line of the current period is smoothed to obtain the J deviation stochastic line.

The value range of the stochastic indicator is between 0 and 100, and 80 and 20 are usually used as the overbought and oversold judgment thresholds. When it is above 80, it is considered to be in the overbought area, which may mean that the price is too high and there may be an opportunity for adjustment or reversal. When it is below 20, it is considered to be in the oversold area, which may mean that the price is too low and there may be an opportunity for a rebound or reversal.

Step 104: Calculate a trend index for the raw material of the cosmetics based on the first price data.

In this embodiment, some parameters (such as the highest price, the lowest price and the closing price, etc.) can be screened out from the first price data, and the Commodity Channel Index (CCI) of the cosmetic raw materials can be calculated. The CCI indicator can be used to measure whether the price of the cosmetic raw materials has exceeded the normal distribution range. It mainly measures the variability of prices that deviate from the normal range and is a type of overbought and oversold indicator, fluctuating 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.

The typical price is obtained by averaging the highest price, lowest price and closing price.

The moving average (MA) is obtained by averaging the closing prices over multiple periods.

The average of the difference between the moving average and the closing price over multiple periods is calculated to obtain the mean absolute deviation (MD).

Divide the difference between the typical price and the moving average by the mean absolute deviation and the preset coefficient (such as 0.015) to obtain the trend index of the cosmetic raw materials, that is, CCI = (TP-MA) ÷ MD ÷ 0.015.

Step 105: Based on the first price data, a futures business report for the raw materials of cosmetics is generated according to the similarity-difference moving average, the stochastic index and the trend index.

In practical applications, the first price data of futures of cosmetic raw materials in history is used as the main benchmark. The price change trend of futures of cosmetic raw materials is formally predicted based on its similarity and difference moving average, stochastic indicator and trend indicator, so as to generate a business report for reference of purchasers and supervisors.

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

Step 1051: extract price feature data based on trend from the first price data.

In this embodiment, the first price data can be preprocessed. When the preprocessing is completed, feature engineering is performed on the first price data according to the requirements of the Hidden Markov Model (HMM) to extract price feature data on the trend (i.e., rise and fall), for example, the logarithmic difference of price fluctuations in each period, the logarithmic change of trading volume, the percentage of price change between the first time range (such as 5 days) and the second time range (such as 20 days), trading volume pairs, and so on.

Furthermore, a Fourier transform may be performed on the first price data to convert it from the time domain to the frequency domain. In the frequency domain, the periodicity of the first price data may be determined based on the frequency and weight thresholds and periodic (such as seasonal) features may be extracted.

Step 1052: construct a feature matrix using the similarity-divergence moving average, stochastic indicator, trend indicator and price feature data.

In this embodiment, the moving average difference, the stochastic index, the trend index and the price characteristic data are written into a matrix as factors to obtain a characteristic matrix.

Step 1053: Input the feature matrix into a preset hidden Markov model to predict the probability that the second price data of the futures of the raw materials of cosmetics will present various trends in the next period.

In this embodiment, the hidden Markov model may be built in advance and the hidden Markov model may be trained.

The feature matrix is input into the hidden Markov model to predict the probability that the second price data of the futures of the raw materials of cosmetics will present various trends in the next cycle, for example, the probability of a sharp rise, the probability of a slow rise, the probability of a fluctuation, the probability of a slow fall, the probability of a sharp fall, and so on.

Hidden Markov models can help identify the implicit state in the first price data of cosmetic raw material futures and predict the future state of the market. That is, in the price trend analysis of cosmetic raw material futures, hidden Markov models can help identify different states of price fluctuations, such as rapid rise, rapid fall, slow rise, slow fall, and fluctuation, and judge the next most likely state of the current state of cosmetic raw material futures prices based on the state transfer matrix obtained by the hidden Markov model, so as to better understand the nature and rules of price trends. This helps purchasers and supervisors to formulate more reasonable business processing strategies and improve the quality of business processing.

Step 1054: extract multiple factors related to price in time from the first price data.

In this embodiment, feature engineering can be performed on the first price data of the historical futures of raw materials of cosmetics according to the requirements of the Facebook Prophet model (FBP), and a variety of factors related to the price in time can be extracted therefrom.

Exemplarily, the factors include time series data, timestamp, upper limit price, lower limit price, non-trading day, cyclical and seasonal patterns; then, preprocessing can be performed on the first price data, such as converting the time format, filling missing values, etc.

If the preprocessing is completed, Fourier transform is performed on the index data according to the maximum period of the first price data to obtain time series data so that the prophet model can better capture the periodicity of the data.

Query the timestamp of time series data.

The highest price in the first price data is amplified (eg, doubled) to obtain an upper limit price.

The lowest price in the first price data is reduced (eg, by half) to obtain a lower limit price.

Querying the first price data for non-trading days helps to detect the impact of holidays on the offset and slope.

Extracting periodicity from time series data.

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

Step 1056, input the probability and multiple factors into the preset prophet model, predict the second price data of the raw materials of cosmetics futures in multiple cycles in the future, the confidence interval of the second price data and the price cycle within the specified time range.

The probability and multiple factors are input into the preset prophet model. The prophet model uses an additive model to split the time-related series into components such as trend, seasonality and holiday effects, and detects and adapts to the trend changes of the time series, identifies mutations and changes in trends, and thus predicts the second price data of futures in multiple cycles in the future for cosmetic raw materials, the confidence interval of the second price data and the price cycle within a specified time range.

The time range may include historical periods and/or future periods.

Step 1057: Write the probability, the second price data, the confidence interval and the price cycle into the business report of the future futures of the raw materials of cosmetics.

In this embodiment, the probability, the second price data, the confidence interval and the price cycle data are combined, and some additional information is added, such as the index name, the exchange opening date, etc., and written into the business report of the future futures of cosmetic raw materials in the form of columns.

This embodiment combines the short-term price prediction of cosmetic raw materials by the hidden Markov model with the long-term price prediction of cosmetic raw materials by the prophet model, which can improve the comprehensiveness of information in business reports. In addition, the high accuracy of the prediction can improve the reference value of business reports and provide a data basis for formulating efficient business processing.

Step 106: Perform business processing on the raw materials of cosmetics according to the business report.

In this embodiment, the future changes of the raw materials of cosmetics in the business report can be analyzed, so as to formulate appropriate business processing for the raw materials of cosmetics.

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

If the correlation coefficient is greater than or equal to the preset first threshold, it means that the correlation coefficient is high, and it can be determined that the second price data corresponding to the raw materials of each pair of cosmetics are positively correlated, that is, the changing trend of the second price data of the raw materials of one cosmetic is the same as or similar to the changing trend of the second price data of the raw materials of another cosmetic.

If the correlation coefficient is less than or equal to the preset second threshold, indicating that the correlation coefficient is low, it can be determined that the second price data corresponding to the raw materials of each pair of cosmetics are negatively correlated, that is, the changing trend of the second price data of the raw materials of one cosmetic is opposite to the changing trend of the second price data of the raw materials of another cosmetic.

The first threshold is greater than the second threshold.

If it is monitored that the fluctuation range of the actual third price data of the raw materials of the current cosmetics is greater than or equal to the preset third threshold, it means that the fluctuation range of the actual third price data of the raw materials of the current cosmetics is relatively large, and the raw materials of other cosmetics that are positively correlated and/or negatively correlated will produce a relatively large fluctuation range. In this case, purchase reminder information is generated for the raw materials of other cosmetics that are positively correlated and/or negatively correlated with the raw materials of the current cosmetics, and the purchaser is prompted to start or stop purchasing the raw materials of other cosmetics.

In addition, when the price cycle reaches a peak and/or a trough, purchase reminder information is generated for the raw materials of the cosmetics, prompting the purchaser to start or stop purchasing other raw materials of the cosmetics.

Furthermore, the fourth price data of the actual purchase of the raw materials for cosmetics can be queried, and the second price data of the raw materials for the recommended purchase of cosmetics can be read from the purchase prompt information.

If the second price data is greater than the fourth price data, and the difference between the second price data and the fourth price data is greater than the preset fourth threshold, indicating that the fourth price data of the raw materials actually purchased for cosmetics is significantly higher than the second price data of the raw materials recommended for purchasing for cosmetics, it is determined that the behavior of purchasing raw materials for cosmetics is abnormal, and an alarm message is generated for the behavior of purchasing raw materials for cosmetics, prompting the supervisor to investigate the behavior of purchasing raw materials for cosmetics.

In this embodiment, the first price data of historical futures of cosmetic raw materials are collected; the moving average of similarities and differences of the cosmetic raw materials is calculated based on the first price data; the random index is calculated based on the first price data for the cosmetic raw materials; the trend index is calculated based on the first price data for the cosmetic raw materials; on the basis of the first price data, a business report of future futures is generated for the cosmetic raw materials based on the moving average of similarities and differences, the random index and the trend index; and business processing is performed on the cosmetic raw materials based on the business report. This embodiment fully mines the historical price characteristics of the cosmetic raw materials, and predicts the future price changes of the cosmetic raw materials. It has a high degree of automation, greatly reduces the frequency of manual operations, has a small workload, and has high accuracy, which can effectively improve the efficiency of subsequent business processing.

Embodiment 2

Referring to FIG2 , a schematic diagram of the structure of a business processing device based on cosmetic raw materials provided by Embodiment 2 of the present invention is shown. As shown in FIG2 , the device includes:

The price data collection module 201 is used to collect the first price data of historical futures of the raw materials of cosmetics;

A moving average calculation module 202 for calculating the moving average of similarities and differences for the raw materials of the cosmetics according to the first price data;

A random index calculation module 203, used to calculate a random index for the raw material of the cosmetic according to the first price data;

A homeopathic index calculation module 204, configured to calculate a homeopathic index for the raw material of the cosmetic according to the first price data;

A business report generating module 205 is used to generate a futures business report for the raw materials of the cosmetics based on the first price data and according to the similarity-difference moving average, the stochastic index and the trend index;

The business processing execution module 206 is used to execute business processing on the raw materials of the cosmetics according to the business report.

In one embodiment of the present invention, the similarity-difference moving average calculation module 202 includes:

A fast moving average calculation module, used for calculating a fast moving average of the raw materials of the cosmetics using the first price data within X periods;

a slow moving average calculation module, used for calculating a slow moving average of the raw materials of the cosmetics using the first price data within Y periods;

A deviation value calculation module, used for subtracting the slow moving average from the fast moving average to obtain a deviation value;

A similarity-difference moving average generation module, used for calculating a similarity-difference moving average for the raw materials of the cosmetics using the first price data and the deviation value within Z periods;

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

In one embodiment of the present invention, the random indicators of the raw materials of the cosmetics include K fast random line, D slow random line and J deviation random line;

The random index calculation module 203 includes:

an immature random value calculation module, used to calculate the immature random value of the current period using the first price data;

A fast random line calculation module, used for linearly fusing the K fast random lines of the previous cycle with the immature random value into the K fast random lines of the current cycle;

A slow random line calculation module, used for fusing the D slow random line of the previous cycle with the K fast random line of the current cycle into the D slow random line of the current cycle;

The deviation random line calculation module is used to smooth the difference between the K fast random line of the current cycle and the D slow random line of the current cycle to obtain the J deviation random line.

In one embodiment of the present invention, the trend indicator calculation module 204 includes:

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

A typical price calculation module, used to calculate the average of the highest price, the lowest price and the closing price to obtain a typical price;

A moving average calculation module, used to calculate the average of the closing prices in multiple periods to obtain a moving average;

A mean absolute deviation calculation module is used to calculate the average of the difference between the moving average and the closing price in multiple periods to obtain a mean absolute deviation;

The trend index generating module is used to divide the difference between the typical price and the moving average by the average absolute deviation and a preset coefficient to obtain the trend index of the raw material of the cosmetic.

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

A price feature data extraction module, used for extracting price feature data in trend from the first price data;

A feature matrix construction module, used for constructing a feature matrix by combining the similarity-difference moving average, the stochastic indicator, the trend indicator and the price feature data;

A hidden Markov model calling module, used for inputting the feature matrix into a preset hidden Markov model to predict the probability that the second price data of the future futures of the raw materials of the cosmetics will present various trends in the next cycle;

A factor extraction module, used to extract a plurality of factors related to price in time from the first price data;

A prophet model calling module, used for inputting the probability and the multiple factors into a preset prophet model, predicting the second price data of the raw materials of the cosmetics in multiple future cycles, the confidence interval of the second price data and the price cycle within a specified time range;

A business report writing module is used to write the probability, the second price data, the confidence interval and the price cycle into the business report of the future futures of the cosmetic raw materials.

In one embodiment of the present invention, the factors include time series data, timestamp, upper price limit, lower price limit, non-trading day, cyclical and seasonal patterns;

The factor extraction module comprises:

A preprocessing module, used for performing preprocessing on the first price data;

A Fourier transform module, configured to perform Fourier transform on the index data according to the maximum period of the first price data to obtain time series data if the preprocessing is completed;

A timestamp query module, used to query the timestamp of the time series data;

An upper limit price calculation module, used to amplify the highest price in the first price data to obtain an upper limit price;

A lower limit price calculation module, used to reduce the lowest price in the first price data to obtain a lower limit price;

A non-trading day query module, used for querying the first price data for non-trading days;

A periodicity extraction module, used to extract periodicity from the time series data;

A seasonal pattern setting module is used to set a seasonal pattern for the first price data.

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

A correlation coefficient calculation module, used for calculating the correlation coefficient of the change trend of the second price data corresponding to the raw materials of the cosmetics in pairs;

a positive correlation determination module, configured to determine that the second price data corresponding to the raw materials of the cosmetics are positively correlated if the correlation coefficient is greater than or equal to a preset first threshold;

A negative correlation determination module, configured to determine that the second price data corresponding to the raw materials of the cosmetics are negatively correlated if the correlation coefficient is less than or equal to a preset second threshold value; and the first threshold value is greater than the second threshold value;

A first purchase reminder information generating module is used to generate purchase reminder information for other cosmetic raw materials that are positively correlated and/or negatively correlated with the current cosmetic raw material if it is monitored that the fluctuation range of the actual third price data of the current cosmetic raw material is greater than or equal to a preset third threshold value;

The second purchase reminder information generating module is used to generate purchase reminder information for the raw materials of the cosmetics when the peak and/or trough of the price cycle is reached.

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

An actual purchase price query module, used to query the fourth price data when the raw materials of the cosmetics are actually purchased;

A suggested purchase price query module, used for reading second price data of the raw materials of the cosmetics suggested for purchase from the purchase prompt information;

An abnormal alarm module is used to determine that the behavior of purchasing the raw materials of the cosmetics is abnormal if the second price data is greater than the fourth price data, and the difference between the second price data and the fourth price data is greater than a preset fourth threshold value, and generate an alarm message for the behavior of purchasing the raw materials of the cosmetics.

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

Embodiment 3

Referring to FIG. 3 , a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and/or claimed herein.

As shown in FIG3 , the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11 in communication, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can 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 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can 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 the bus 14.

A number of 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, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a business processing method based on cosmetic raw materials.

In some embodiments, the business processing method based on cosmetic raw materials can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and/or installed on 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 business processing method based on cosmetic raw materials described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the business processing method based on cosmetic raw materials in any other appropriate manner (for example, by means of firmware).

Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and/or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

Computer programs for implementing the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions/operations specified in the flow chart and/or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein may 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 the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the 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, voice input, or tactile input).

The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

Embodiment 4

An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the business processing method based on cosmetic raw materials as provided in any embodiment of the present invention.

In the process of implementation, the computer program product can be written in one or more programming languages or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A business processing method based on cosmetic raw materials, characterized by comprising: Collect the first price data of historical futures for raw materials of cosmetics; Calculating a moving average of similarities and differences for the raw materials of the cosmetics according to the first price data; Calculating a random index for the raw material of the cosmetic according to the first price data; Calculating a trend indicator for the raw material of the cosmetic 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 similarity-difference moving average, the stochastic index and the trend index; Perform business processing on the raw materials of the cosmetics according to the business report. 2. The method according to claim 1, characterized in that the step of calculating the moving average of similarities and differences of the raw materials of the cosmetics according to the first price data comprises: Calculating a fast moving average for the raw material of the cosmetic using the first price data within X periods; Calculating a slow moving average for the raw material of the cosmetic using the first price data within Y periods; Subtract the slow moving average from the fast moving average to obtain a deviation value; Calculate a similarity-difference moving average for the raw material of the cosmetic using the first price data and the deviation value within Z periods; Wherein, X, Y and Z are all positive integers, Y is greater than X, and X is greater than Z. 3. The method according to claim 1, characterized in that the random indicators of the raw materials of the cosmetics include K fast random line, D slow random line and J deviation random line; The step of calculating a random index for the raw material of the cosmetic according to the first price data includes: Calculate an immature stochastic value for the current period using the first price data; Linearly merge the K fast random line of the previous cycle and the immature random value into the K fast random line of the current cycle; Merge the D slow random line of the previous cycle and the K fast random line of the current cycle into the D slow random line of the current cycle; The difference between the K fast random line of the current period and the D slow random line of the current period is smoothed to obtain the J deviation random line. 4. The method according to claim 1, characterized in that the step of calculating the trend index for the raw material of the cosmetic according to the first price data comprises: Read the highest price, the lowest price and the closing price from the first price data; Calculate the average of the highest price, the lowest price and the closing price to obtain a typical price; Calculate the average of the closing prices in multiple periods to obtain a moving average; Calculate the average of the differences between the moving average and the closing price over multiple periods to obtain the average absolute deviation; The difference between the typical price and the moving average is divided by the mean absolute deviation and a preset coefficient to obtain a trend index of the raw material of the cosmetic. 5. The method according to any one of claims 1 to 4, characterized in that the step of generating a futures business report for the raw materials of the cosmetics based on the first price data and the similarity-difference moving average, the stochastic index and the trend index comprises: Extracting price characteristic data on trend from the first price data; Construct a feature matrix using the similarity-divergence moving average, the stochastic indicator, the trend indicator and the price feature data; Inputting the feature matrix into a preset hidden Markov model to predict the probability that the second price data of the futures of the raw material of the cosmetics will present various trends in the next cycle; extracting a plurality of factors related to price in time from the first price data; Input the probability and the multiple factors into a preset prophet model, predict the second price data of the raw materials of the cosmetics in multiple future cycles, the confidence interval of the second price data and the price cycle within a specified time range; The probability, the second price data, the confidence interval and the price cycle are written into a business report on future futures of the cosmetic raw materials. 6. The method according to claim 5, characterized in that the factors include time series data, timestamps, upper price limits, lower price limits, non-trading days, cyclical and seasonal patterns; The extracting of multiple factors related to price in time from the first price data includes: performing preprocessing on the first price data; If the preprocessing is completed, Fourier transform is performed on the index data according to the maximum period of the first price data to obtain time series data; Query the timestamp of the time series data; Amplifying the highest price in the first price data to obtain an upper limit price; Narrowing the lowest price in the first price data to obtain a lower limit price; Query the first price data for non-trading days; extracting periodicity from the time series data; A seasonal pattern is set for the first price data. 7. The method according to claim 5, characterized in that the performing business processing on the raw materials of the cosmetics according to the business report comprises: Calculating the correlation coefficient of the change trend of the second price data corresponding to the raw materials of the cosmetics in pairs; If the correlation coefficient is greater than or equal to a preset first threshold, it is determined that the second price data corresponding to the raw materials of the cosmetics are positively correlated with each other; If the correlation coefficient is less than or equal to a preset second threshold, it is determined that the second price data corresponding to the raw materials of the cosmetics are negatively correlated; the first threshold is greater than the second threshold; If it is monitored that the fluctuation range of the actual third price data of the raw material of the current cosmetics is greater than or equal to a preset third threshold, generating purchase prompt information for other raw materials of the cosmetics that are positively correlated and/or negatively correlated with the raw material of the current cosmetics; When the peak and/or trough of the price cycle is reached, purchase reminder information is generated for the raw materials of the cosmetics. 8. The method according to claim 7, characterized in that the performing business processing on the raw materials of the cosmetics according to the business report further comprises: Query the fourth price data when actually purchasing the raw materials of the cosmetics; Reading second price data of the raw material of the cosmetics recommended for purchase from the purchase prompt information; If the second price data is greater than the fourth price data, and the difference between the second price data and the fourth price data is greater than a preset fourth threshold, it is determined that the behavior of purchasing the raw materials for the cosmetics is abnormal, and an alarm message is generated for the behavior of purchasing the raw materials for the cosmetics. 9. An electronic device, characterized in that the electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the business processing method based on cosmetic raw materials 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, and when the computer program is executed by a processor, the business processing method based on cosmetic raw materials according to any one of claims 1 to 8 is implemented.