Automobile market point transaction data prediction method and device and electronic equipment

Abstract

The embodiment of the invention discloses an automobile market point transaction data prediction method and device and electronic equipment. The method comprises the following steps: analyzing historical data of an automobile market, and calculating corresponding historical point compliance cost, historical point supply-demand ratio and historical point transaction average data based on different algorithms; and then, a point transaction prediction model of the automobile market is constructed through the calculation data, and point transaction prediction data corresponding to the target year can be obtained only by inputting existing data into the point transaction prediction model subsequently. By using the prediction model and continuously optimizing the prediction parameters according to the actual prediction condition of each year, the accuracy requirement of the point transaction prediction data result is met, and the strategic planning and healthy development of each automobile enterprise and the automobile industry are improved.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to the field of data analysis technology, and more particularly to a method, device and electronic device for predicting automobile market points transaction data. Background Art

[0002] In the automobile industry, in order to improve the energy-saving level of passenger cars, ease energy and environmental pressures, establish a long-term market-oriented mechanism for the coordinated development of energy-saving and new energy vehicles, and promote the healthy development of the automobile industry, the Ministry of Industry and Information Technology has formulated the "Parallel Management Measures for Passenger Car Enterprises' Average Fuel Consumption and New Energy Vehicle Credits". According to the requirements of the credit method, all fuel vehicles sold by each car company are calculated in the form of credits. If the credits are negative, the company must produce new energy vehicles and use the positive new energy credits to offset the negative average fuel consumption credits; if the credits are not enough to offset, it is necessary to purchase positive new energy credits from other companies to offset them.

[0003] Therefore, a method is needed to predict the automobile market credit transaction data so that each automobile enterprise can arrange production plans in advance according to the credit transaction prediction data. However, the existing automobile market credit transaction data prediction methods often make predictions based on market conditions based on human experience, resulting in inaccurate prediction results, which is not conducive to the strategic planning of enterprises, nor to the business expectations and healthy development of various aspects of the industry. Summary of the invention

[0004] The embodiments of this specification provide a method, device and electronic device for predicting automobile market points transaction data, and the technical solution is as follows: In a first aspect, an embodiment of the present specification provides a method for predicting automobile market points transaction data, the method comprising: Acquire historical data of the automobile market, wherein the historical data includes historical production efficiency data and market historical transaction data for representing each automobile enterprise in different years; Calculate the historical credit compliance cost corresponding to the historical production efficiency data based on a weight fusion algorithm, and determine the historical credit supply-demand ratio corresponding to the historical credit compliance cost; Calculate the average historical points transaction data corresponding to the market historical transaction data based on the mean algorithm; A point transaction prediction model for the automobile market is obtained based on training of calculation data, wherein the calculation data includes the historical point compliance cost and the historical point supply-demand ratio as input data, and the historical point transaction average data as output data; Based on the point transaction prediction model, the point transaction prediction data corresponding to the target year is obtained.

[0005] In a second aspect, a device for predicting automobile market points transaction data is provided, the device comprising: An acquisition module, used to acquire historical data of the automobile market, wherein the historical data includes historical production efficiency data and market historical transaction data for representing each automobile enterprise in different years; A fusion module, used to calculate the historical credit compliance cost corresponding to the historical production efficiency data based on a weighted fusion algorithm, and determine the historical credit supply-demand ratio corresponding to the historical credit compliance cost; A calculation module, used for calculating the average historical points transaction data corresponding to the market historical transaction data based on a mean value algorithm; A training module, used for training a prediction model of points transaction in the automobile market based on calculation data, wherein the calculation data includes the historical points compliance cost and the historical points supply-demand ratio as input data and the historical points transaction average data as output data; The prediction module is used to obtain the point transaction prediction data corresponding to the target year based on the point transaction prediction model.

[0006] In a third aspect, an electronic device is provided, including a device processor and a memory; The device processor is connected to the memory; The memory is used to store executable program code; The device processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0007] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a device processor, cause the computer or the device processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0008] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least: In one or more embodiments of this specification, by analyzing the historical data of the automobile market, the corresponding historical points compliance cost, historical points supply-demand ratio and historical points transaction average data are calculated based on different algorithms. Then, the points transaction prediction model for the automobile market is constructed through the above calculation data. Subsequently, only the existing data needs to be input into the points transaction prediction model to obtain the points transaction prediction data corresponding to the target year. By using the prediction model and continuously optimizing the prediction parameters according to the actual prediction situation each year, the accuracy requirements of the points transaction prediction data results are met to enhance the strategic planning and healthy development of various automobile companies and the automobile industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0010] Figure 1 A schematic diagram of the system architecture of a method for predicting automobile market points transaction data provided by an embodiment of this specification; Figure 2 A flowchart of a method for predicting automobile market points transaction data provided by an embodiment of this specification; Figure 3 A schematic diagram of the structure of a device for predicting automobile market points transaction data provided by an embodiment of this specification; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0011] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0012] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. 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 limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0013] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present specification. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described with respect to some examples may be combined in other examples.

[0014] See also Figure 1 , Figure 1 A schematic diagram of the system architecture of a method for predicting automobile market points transaction data provided by an embodiment of this specification is shown.

[0015] like Figure 1 As shown, the system architecture of the automobile market points transaction data prediction method may at least include a terminal 10 , a server 20 and a network 30 .

[0016] The terminal 10 includes but is not limited to electronic devices such as smart phones, desktop computers, tablet computers, laptops, smart speakers, digital assistants, smart wearable devices, etc., and may also be software running on the above electronic devices, such as applications, etc. Optionally, the operating system running on the electronic device may include but is not limited to Android system, IOS system, Linux, Windows, etc. Optionally, the terminal 10 provides the user with a car market point transaction data prediction service, and the terminal 10 may obtain a car market point transaction data prediction instruction of an application program interface, and send a car market point transaction data prediction request to the server 20.

[0017] The server 20 can provide background services for the terminal 10. According to the automobile market points transaction data prediction request sent by the terminal 10, the server 20 will obtain a series of automobile market points transaction data prediction instructions, and the server 20 transmits the automobile market points transaction data prediction instructions to other terminals 10 through the network 30. Specifically, the server 20 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0018] The network 30 is used to provide a medium for a communication link between the terminal 10 and the server 20. The network 30 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0019] In addition, it should be noted that Figure 1 What is shown is only one system provided by the present disclosure. In practical applications, other systems may also be included, for example, more terminals may be included.

[0020] In the embodiments of the present specification, the terminal 10 and the server 20 may be directly or indirectly connected via wired or wireless communication, which is not limited in the present disclosure.

[0021] See next Figure 2 , Figure 2 The overall flow chart of a method for predicting automobile market points transaction data provided by an embodiment of the present specification is shown. The method for predicting automobile market points transaction data can be used in the server 20 .

[0022] like Figure 2 As shown, the automobile market points transaction data prediction method may at least include the following steps: Step 201: Obtain historical data of the automobile market.

[0023] The historical data include historical production performance data and market historical transaction data used to characterize each automobile enterprise in different years.

[0024] In the embodiments of this specification, in order to predict the automobile market credit transaction data, the automobile market historical data based on year records can be directly obtained from the website of the Ministry of Industry and Information Technology or the historical database. The historical data may include the historical production efficiency data of all automobile companies in different years, as well as market historical transaction data and other data. Among them, the historical production efficiency data of each automobile company includes the company's annual output, annual efficiency, production cost, energy consumption, R&D investment and other data. The market historical transaction data includes the number of automobile companies, actual credit transactions and other data.

[0025] Among them, according to the unique mapping relationship obtained from the public platform, a series of conversion correspondences between data and points can be obtained. As an example: producing a fuel vehicle corresponds to -3 points, producing a new energy vehicle corresponds to +1 point, and fuel consumption exceeding the standard fuel consumption threshold by 10% corresponds to -5 points, etc.

[0026] In one possible implementation, before calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm, the method further includes: Obtaining average fuel consumption data and average output data in the historical production efficiency data, and determining integral factors corresponding to the average fuel consumption data and the average output data, respectively, wherein the integral factors include positive integral factors and negative integral factors; Determining the enterprise classification of each of the automobile enterprises based on each of the integral factors, wherein the enterprise classification includes each of the leading enterprises and each of the ordinary enterprises; The calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm includes: The historical points compliance cost corresponding to the enterprise classification is calculated based on a weighted fusion algorithm.

[0027] In the embodiment of this specification, in order to calculate the historical integral compliance cost corresponding to the historical production efficiency data, the integral factors corresponding to each car company can be determined first, and then each car company can be classified, and the historical integral compliance cost of the average market can be determined by the classification of different car companies. First, the average fuel consumption data and average production data in the historical production efficiency data of each car company can be obtained first, and the integral factors corresponding to the data of each car company can be determined according to the preset fuel consumption-integral relationship and production-integral relationship. Among them, the integral factor includes a positive integral factor and a negative integral factor, and the types of produced cars include fuel vehicles and new energy vehicles. Therefore, the average production data can include fuel vehicle vehicle data and new energy production data, and the fuel consumption data and fuel vehicle production data both correspond to negative integral factors, and the new energy vehicle production data corresponds to positive integral factors. Next, each car company can be divided into fuel vehicle companies and new energy vehicle companies according to their main business scope. After obtaining the integral factors of each car company, as an example, in the new energy vehicle companies, they are arranged from large to small according to the integral factor size and accumulated. When the accumulated integral is greater than 75% of the total integral, the corresponding car company is called the head new energy vehicle company, and the remaining new energy vehicle companies are ordinary new energy vehicle companies. Similarly, among fuel vehicle companies, the integral factors are arranged from small to large and accumulated. When the accumulated integral is less than 75% of the total integral, the corresponding car company is called the leading fuel vehicle company, and the remaining fuel vehicle companies are ordinary fuel vehicle companies. Therefore, the overall classification of automobile companies can include leading companies and ordinary companies. When calculating the historical integral compliance cost in the subsequent calculation, the historical integral compliance cost can be directly calculated based on the weight fusion algorithm and the corporate classification of each car company.

[0028] Step 202: Calculate the historical credit compliance cost corresponding to the historical production efficiency data based on a weight fusion algorithm, and determine the historical credit supply-demand ratio corresponding to the historical credit compliance cost.

[0029] In the embodiments of this specification, in order to construct a point transaction prediction model, the point cost data corresponding to each car company can be determined based on the historical production data of each car company. Then, the corresponding weight of each car company is determined according to the type of car company. Further, the compliance cost data of each point is calculated based on the weight of each car company through a weight fusion algorithm to obtain the historical point compliance cost. Among them, when determining the historical point compliance cost, the historical point data corresponding to each car company can be determined at the same time, and then the historical point supply and demand ratio can be determined based on each historical point data.

[0030] In one possible implementation, the weight fusion algorithm is used to calculate the historical points compliance cost corresponding to the historical production efficiency data, including: Calculate average score data and average compliance cost data corresponding to the historical production efficiency data based on the enterprise classification; Determine the score weights corresponding to the leading enterprise and the ordinary enterprise based on the average score data of each of the automobile enterprises; Based on the point weights, weighted fusion calculations are performed on the average compliance cost data to obtain the historical point compliance costs for each historical year.

[0031] In the embodiment of this specification, when determining the historical integral compliance cost corresponding to the historical production efficiency data, each automobile enterprise can be first classified into leading enterprises and ordinary enterprises. Then, the integral data of each automobile enterprise is obtained through the fuel consumption data and output data in the historical production efficiency data of each automobile enterprise, and the compliance cost data corresponding to the use of fuel-saving technology by each automobile enterprise is obtained based on the fuel-saving cost data, fuel consumption data before fuel saving, fuel consumption data after fuel saving, and the integral increment brought by the use of fuel-saving technology in the historical production efficiency data of each automobile enterprise. Then, after obtaining the integral data and compliance cost data of each automobile enterprise, the average integral data and average compliance cost data corresponding to the leading enterprises and ordinary enterprises are calculated by the average algorithm. Further, the average integral data ratio corresponding to the leading enterprises and ordinary enterprises is determined, and used as the integral weight corresponding to the leading enterprises and ordinary enterprises respectively. Finally, the average compliance cost data obtained is multiplied by the corresponding integral weight through the weight fusion algorithm and then added to obtain the historical integral compliance cost under each historical year.

[0032] As an example, in a certain historical year, the average compliance cost of the leading fuel vehicle companies was 2771.7 (yuan / cent), and the average compliance cost of ordinary companies was 3266.7 (yuan / cent). The point weight of the leading companies was 0.79, and the point weight of ordinary companies was 0.21. The historical point compliance cost in that historical year was 2771.7*(1-0.21)+3266.7*0.21=2796.6 (yuan / cent).

[0033] In one possible implementation, determining the historical points supply-demand ratio corresponding to the historical points compliance cost includes: Counting all the average score data of the automobile enterprises to obtain total score data; Determine the total points supply and the total points demand in the total points data, and determine the historical points supply-demand ratio for each historical year based on the total points supply and the total points demand.

[0034] In the embodiments of this specification, when determining the historical credit supply-demand ratio corresponding to the historical credit compliance cost, all average credit data of all automobile companies, including positive credit data and negative credit data, can be first counted and defined as total credit data. Then, all positive credit data are added to obtain the total credit supply in the total credit data. Similarly, all negative credit data are added to obtain the total credit demand. Finally, the two are proportionally calculated to obtain the historical credit supply-demand ratio for each historical year.

[0035] Step 203: Calculate the average historical points transaction data corresponding to the market historical transaction data based on the mean algorithm.

[0036] In the embodiments of this specification, after obtaining the historical market transaction data, the actual points transaction data of each car company in each historical year, the number of all car companies, the number of new energy car companies, etc. can be obtained from the data. Then, the actual points transaction data of each car company in the historical year is averaged according to the proportion of the number of car companies to obtain the average historical points transaction data, so as to construct the subsequent points transaction prediction model.

[0037] In one possible implementation, the calculating the historical point transaction average data corresponding to the market historical transaction data based on the mean algorithm includes: Obtaining the quantity data of automobile enterprises and the actual points transaction data in the historical transaction data of the market, and determining the target proportion of new energy automobile enterprises in the quantity data of automobile enterprises; Based on the target ratio, the average of each actual point transaction data is calculated to obtain the historical point transaction average data.

[0038] In the embodiments of this specification, the automobile enterprise quantity data and actual point transaction data in the market historical transaction data can be obtained first. The automobile enterprise quantity data includes the number of fuel vehicle enterprises, the number of new energy vehicle enterprises, and the actual point transaction data of each automobile enterprise in the historical year. Then, the average point transaction data corresponding to the fuel vehicle enterprises and the average point transaction data corresponding to the new energy vehicle enterprises are calculated based on the actual point transaction data. Further, the proportion of the number of new energy vehicle enterprises in the total number of automobile enterprises, that is, the target proportion, is determined. Finally, the average point transaction data of the two types of automobile enterprises are calculated according to the target proportion to obtain the historical point transaction average data.

[0039] As an example, when calculating the average point transaction data corresponding to new energy vehicle companies, the actual point transaction data corresponding to the three leading new energy vehicle companies can be selected for average calculation. For example: the actual point transaction data of Company A is 2,200 yuan / point, the actual point transaction data of Company B is 2,300 yuan / point, and the actual point transaction data of Company C is 2,100 yuan / point. Then the average point transaction data corresponding to the new energy vehicle companies is (2,200+2,300+2,100) / 3=2,200 yuan / point. The actual point transaction data corresponding to all new energy vehicle companies can also be averaged.

[0040] Step 204: obtain a point transaction prediction model for the automobile market based on the calculation data training.

[0041] Among them, the calculation data includes the historical points compliance cost and historical points supply-demand ratio used as input data, and the historical points transaction average data used as output data.

[0042] In the embodiments of the present specification, after obtaining the calculation data through the above calculation steps, namely the historical points compliance cost, the historical points supply-demand ratio and the historical points transaction average data, since the points compliance cost and the points supply-demand ratio of each year determine the final points transaction data, an initial prediction model can be constructed first, and the first two can be used as inputs, and the final points transaction prediction data can be used as output to train the required points transaction prediction model, so as to subsequently predict the points transaction data in the target year.

[0043] Among them, when building the initial prediction model, you can choose machine learning regression models such as linear regression, ridge regression, LASSO, or you can choose CNN convolutional neural network deep learning model.

[0044] In one possible implementation, the point transaction prediction model for the automobile market is obtained by training based on calculation data, wherein the calculation data includes the historical point compliance cost and the historical point supply-demand ratio as input data, and the historical point transaction average data as output data, including: The calculation data is divided into training set data and validation set data, wherein the calculation data includes the historical points compliance cost and the historical points supply-demand ratio as input data, and the historical points transaction average data as output data; Constructing an initial point transaction prediction model for the automobile market, and training the initial point transaction prediction model based on the training set data to obtain a point transaction training model; The point transaction training model is optimized based on the verification set data to obtain an optimized point transaction prediction model.

[0045] In the embodiments of the present specification, the calculation data can be first divided into two parts of data, one part is the training set data, and the other part is the validation set data. In any training set data or validation set data, the historical points compliance cost and the historical points supply-demand ratio in a certain historical year are determined, and there will be a historical points transaction average data corresponding to it. Then, an initial points transaction prediction model for the automobile market can be constructed first, and then the historical points compliance cost and the historical points supply-demand ratio in the training set are used as input, and the corresponding historical points transaction average data is used as output to train its initial points transaction prediction model to obtain a points transaction training model. Furthermore, in order to improve the accuracy of the model prediction, the validation set data is used to optimize the points transaction training model in the same way to obtain an optimized points transaction prediction model.

[0046] As an example, the initial points trading prediction model constructed is: points trading prediction data Y = points compliance cost C + model coefficient k * points supply and demand ratio + adjustment coefficient b. The historical five-year data is brought into it, and after fitting, training, verifying the model and optimizing it, the optimized points trading prediction model is: points trading prediction data Y = points compliance cost C + 118 * points supply and demand ratio + 175.

[0047] Step 205: Obtain the point transaction prediction data corresponding to the target year based on the point transaction prediction model.

[0048] In the embodiments of the present specification, after obtaining the point transaction prediction model, the point transaction prediction data corresponding to the target year can be obtained by directly inputting the model input data corresponding to the current target year into the prediction model.

[0049] In one possible implementation, obtaining the point transaction prediction data corresponding to the target year based on the point transaction prediction model includes: Obtain the target points compliance cost and target points supply-demand ratio corresponding to the target year; Based on the point trading model, the point trading forecast data corresponding to the target point compliance cost and the target point supply-demand ratio is determined.

[0050] In the embodiment of the present specification, after obtaining all the automobile enterprise data for the target year, the target points compliance cost and target points supply-demand ratio corresponding to the target year are obtained by similarly performing the previous calculation steps, and both are input into the trained and optimized points transaction model to obtain the corresponding points transaction prediction data.

[0051] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] See next Figure 3 , Figure 3 The structure diagram of a vehicle market points transaction data prediction device provided by the embodiment of this specification is shown. It should be noted that: Figure 3 The automobile market points transaction data prediction device shown is used to execute the present application Figure 2 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 2 The embodiment shown.

[0053] like Figure 3 As shown, the automobile market points transaction data prediction device may at least include: An acquisition module 301 is used to acquire historical data of the automobile market, wherein the historical data includes historical production efficiency data and market historical transaction data for representing each automobile enterprise in different years; A fusion module 302 is used to calculate the historical credit compliance cost corresponding to the historical production efficiency data based on a weight fusion algorithm, and determine the historical credit supply-demand ratio corresponding to the historical credit compliance cost; A calculation module 303, used for calculating the historical point transaction average data corresponding to the market historical transaction data based on a mean value algorithm; A training module 304 is used to train and obtain a prediction model for points transaction in the automobile market based on calculation data, wherein the calculation data includes the historical points compliance cost and the historical points supply-demand ratio as input data and the historical points transaction average data as output data; The prediction module 305 is used to obtain the point transaction prediction data corresponding to the target year based on the point transaction prediction model.

[0054] In one possible implementation, the acquisition module 301 is specifically used for: Obtaining average fuel consumption data and average output data in the historical production efficiency data, and determining integral factors corresponding to the average fuel consumption data and the average output data, respectively, wherein the integral factors include positive integral factors and negative integral factors; Determining the enterprise classification of each of the automobile enterprises based on each of the integral factors, wherein the enterprise classification includes each of the leading enterprises and each of the ordinary enterprises; The calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm includes: The historical points compliance cost corresponding to the enterprise classification is calculated based on a weighted fusion algorithm.

[0055] In one embodiment, the fusion module 302 is specifically used for: Calculate average score data and average compliance cost data corresponding to the historical production efficiency data based on the enterprise classification; Determine the score weights corresponding to the leading enterprise and the ordinary enterprise based on the average score data of each of the automobile enterprises; Based on the point weights, weighted fusion calculations are performed on the average compliance cost data to obtain the historical point compliance costs for each historical year.

[0056] In one possible implementation, the fusion module 302 is further configured to: Counting all the average score data of the automobile enterprises to obtain total score data; Determine the total points supply and the total points demand in the total points data, and determine the historical points supply-demand ratio for each historical year based on the total points supply and the total points demand.

[0057] In one possible implementation, the calculation module 303 is specifically used for: Obtaining the quantity data of automobile enterprises and the actual points transaction data in the historical transaction data of the market, and determining the target proportion of new energy automobile enterprises in the quantity data of automobile enterprises; Based on the target ratio, the average of each actual point transaction data is calculated to obtain the historical point transaction average data.

[0058] In one embodiment, the training module 304 is specifically used to: The calculation data is divided into training set data and validation set data, wherein the calculation data includes the historical points compliance cost and the historical points supply-demand ratio as input data, and the historical points transaction average data as output data; Constructing an initial point transaction prediction model for the automobile market, and training the initial point transaction prediction model based on the training set data to obtain a point transaction training model; The point transaction training model is optimized based on the verification set data to obtain an optimized point transaction prediction model.

[0059] In one possible implementation, the prediction module 305 is specifically used for: Obtain the target points compliance cost and target points supply-demand ratio corresponding to the target year; Based on the point trading model, the point trading forecast data corresponding to the target point compliance cost and the target point supply-demand ratio is determined.

[0060] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0061] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0062] See next Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0063] like Figure 4 As shown, the electronic device 400 may include: at least one device processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0064] The communication bus 402 may be used to realize the connection and communication among the above-mentioned components.

[0065] The user interface 403 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0066] The network interface 404 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0067] Among them, the device processor 401 may include one or more processing cores. The device processor 401 uses various interfaces and lines to connect various parts within the entire electronic device 400, and executes various functions and processes data of the electronic device 400 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the device processor 401 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The device processor 401 can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the device processor 401, and it can be implemented separately through a chip.

[0068] The memory 405 may include a RAM or a ROM. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 405 may optionally be at least one storage device located away from the aforementioned device processor 401. As Figure 4 As shown, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0069] Specifically, the device processor 401 may be used to call the automobile market points transaction data prediction application stored in the memory 405, and specifically perform the following operations: Acquire historical data of the automobile market, wherein the historical data includes historical production efficiency data and market historical transaction data for representing each automobile enterprise in different years; Calculate the historical credit compliance cost corresponding to the historical production efficiency data based on a weight fusion algorithm, and determine the historical credit supply-demand ratio corresponding to the historical credit compliance cost; Calculate the average historical points transaction data corresponding to the market historical transaction data based on the mean algorithm; A point transaction prediction model for the automobile market is obtained based on training of calculation data, wherein the calculation data includes the historical point compliance cost and the historical point supply-demand ratio as input data, and the historical point transaction average data as output data; Based on the point transaction prediction model, the point transaction prediction data corresponding to the target year is obtained.

[0070] As an option of the embodiment of this specification, before calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm, it also includes: Obtaining average fuel consumption data and average output data in the historical production efficiency data, and determining integral factors corresponding to the average fuel consumption data and the average output data, respectively, wherein the integral factors include positive integral factors and negative integral factors; Determining the enterprise classification of each of the automobile enterprises based on each of the integral factors, wherein the enterprise classification includes each of the leading enterprises and each of the ordinary enterprises; The calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm includes: The historical points compliance cost corresponding to the enterprise classification is calculated based on a weighted fusion algorithm.

[0071] As an option of the embodiment of this specification, the calculating the historical integral compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm includes: Calculate average score data and average compliance cost data corresponding to the historical production efficiency data based on the enterprise classification; Determine the score weights corresponding to the leading enterprise and the ordinary enterprise based on the average score data of each of the automobile enterprises; Based on the point weights, weighted fusion calculations are performed on the average compliance cost data to obtain the historical point compliance costs for each historical year.

[0072] As an option of the embodiment of this specification, the determining the historical points supply-demand ratio corresponding to the historical points compliance cost includes: Counting all the average score data of the automobile enterprises to obtain total score data; Determine the total points supply and the total points demand in the total points data, and determine the historical points supply-demand ratio for each historical year based on the total points supply and the total points demand.

[0073] As an optional embodiment of this specification, the calculating the historical point transaction average data corresponding to the market historical transaction data based on the mean algorithm includes: Obtaining the quantity data of automobile enterprises and the actual points transaction data in the historical transaction data of the market, and determining the target proportion of new energy automobile enterprises in the quantity data of automobile enterprises; Based on the target ratio, the average of each actual point transaction data is calculated to obtain the historical point transaction average data.

[0074] As an optional embodiment of this specification, the point transaction prediction model of the automobile market is obtained by training based on calculation data, and the calculation data includes the historical point compliance cost and the historical point supply-demand ratio used as input data, and the historical point transaction average data used as output data, including: The calculation data is divided into training set data and validation set data, wherein the calculation data includes the historical points compliance cost and the historical points supply-demand ratio as input data, and the historical points transaction average data as output data; Constructing an initial point transaction prediction model for the automobile market, and training the initial point transaction prediction model based on the training set data to obtain a point transaction training model; The point transaction training model is optimized based on the verification set data to obtain an optimized point transaction prediction model.

[0075] As an option of the embodiment of this specification, the step of obtaining the point transaction prediction data corresponding to the target year based on the point transaction prediction model includes: Obtain the target points compliance cost and target points supply-demand ratio corresponding to the target year; Based on the point trading model, the point trading forecast data corresponding to the target point compliance cost and the target point supply-demand ratio is determined.

[0076] The embodiments of this specification also provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0077] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0078] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0080] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0082] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0083] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0084] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for predicting automobile market points transaction data, characterized in that: The method comprises: Acquire historical data of the automobile market, wherein the historical data includes historical production efficiency data and market historical transaction data for representing each automobile enterprise in different years; Calculate the historical credit compliance cost corresponding to the historical production efficiency data based on a weight fusion algorithm, and determine the historical credit supply-demand ratio corresponding to the historical credit compliance cost; Calculate the average historical points transaction data corresponding to the market historical transaction data based on the mean algorithm; A point transaction prediction model for the automobile market is obtained based on training of calculation data, wherein the calculation data includes the historical point compliance cost and the historical point supply-demand ratio as input data, and the historical point transaction average data as output data; Based on the point transaction prediction model, the point transaction prediction data corresponding to the target year is obtained.

2. The method according to claim 1, characterized in that Before calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm, the method further includes: Obtaining average fuel consumption data and average output data in the historical production efficiency data, and determining integral factors corresponding to the average fuel consumption data and the average output data, respectively, wherein the integral factors include positive integral factors and negative integral factors; Determining the enterprise classification of each of the automobile enterprises based on each of the integral factors, wherein the enterprise classification includes each of the leading enterprises and each of the ordinary enterprises; The calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm includes: The historical points compliance cost corresponding to the enterprise classification is calculated based on a weighted fusion algorithm.

3. The method according to claim 2, characterized in that The calculating the historical points compliance cost corresponding to the historical production efficiency data based on the weight fusion algorithm includes: Calculate average score data and average compliance cost data corresponding to the historical production efficiency data based on the enterprise classification; Determine the score weights corresponding to the leading enterprise and the ordinary enterprise based on the average score data of each of the automobile enterprises; Based on the point weights, weighted fusion calculations are performed on the average compliance cost data to obtain the historical point compliance costs for each historical year.

4. The method according to claim 3, characterized in that Determining the historical points supply-demand ratio corresponding to the historical points compliance cost includes: Counting all the average score data of the automobile enterprises to obtain total score data; Determine the total points supply and the total points demand in the total points data, and determine the historical points supply-demand ratio for each historical year based on the total points supply and the total points demand.

5. The method according to claim 1, characterized in that The calculating the historical point transaction average data corresponding to the market historical transaction data based on the mean value algorithm includes: Obtaining the quantity data of automobile enterprises and the actual points transaction data in the historical transaction data of the market, and determining the target proportion of new energy automobile enterprises in the quantity data of automobile enterprises; Based on the target ratio, the average of each actual point transaction data is calculated to obtain the historical point transaction average data.

6. The method according to claim 1, characterized in that The point transaction prediction model for the automobile market is obtained by training based on the calculation data, wherein the calculation data includes the historical point compliance cost and the historical point supply-demand ratio as input data, and the historical point transaction average data as output data, including: The calculation data is divided into training set data and validation set data, wherein the calculation data includes the historical points compliance cost and the historical points supply-demand ratio as input data, and the historical points transaction average data as output data; Constructing an initial point transaction prediction model for the automobile market, and training the initial point transaction prediction model based on the training set data to obtain a point transaction training model; The point transaction training model is optimized based on the verification set data to obtain an optimized point transaction prediction model.

7. The method according to claim 1, characterized in that The step of obtaining the point transaction prediction data corresponding to the target year based on the point transaction prediction model includes: Obtain the target points compliance cost and target points supply-demand ratio corresponding to the target year; Based on the point trading model, the point trading forecast data corresponding to the target point compliance cost and the target point supply-demand ratio is determined.

8. A device for predicting automobile market points transaction data, characterized in that: The device comprises: An acquisition module, used to acquire historical data of the automobile market, wherein the historical data includes historical production efficiency data and market historical transaction data for representing each automobile enterprise in different years; A fusion module, used to calculate the historical credit compliance cost corresponding to the historical production efficiency data based on a weighted fusion algorithm, and determine the historical credit supply-demand ratio corresponding to the historical credit compliance cost; A calculation module, used for calculating the average historical points transaction data corresponding to the market historical transaction data based on a mean value algorithm; A training module, used for training a prediction model of points transaction in the automobile market based on calculation data, wherein the calculation data includes the historical points compliance cost and the historical points supply-demand ratio as input data and the historical points transaction average data as output data; The prediction module is used to obtain the point transaction prediction data corresponding to the target year based on the point transaction prediction model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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