Multi-agent profit forecasting method, device, electronic device and storage medium

Abstract

The present application provides a multi-subject profit forecasting method, device, electronic device and storage medium. The method includes: obtaining a forecast request and a forecast subject; obtaining the corresponding target profit forecasting rule according to the forecast request and the forecast subject; obtaining the basic data corresponding to the forecast request and the forecast subject, and determining the forecast result based on the basic data and the target profit forecasting rule; wherein the forecast subject includes: multiple sub-sectors and groups; the forecast request includes: annual profit forecast request, irregular profit forecast request and reverse forecast request. The present application can meet the diversified needs of multi-subject profit forecasting. Whether it is a refined forecast for a single sub-sector or a comprehensive forecast for the entire group, the system can provide accurate and personalized solutions to help companies better respond to market challenges and achieve profit goals.

Description

Technical Field

[0001] The present application relates to the technical field of financial management, and in particular to a multi-agent profit forecasting method, device, electronic device and storage medium. Background Art

[0002] In today's complex business environment, enterprises' demand for profit forecasting systems has far exceeded the traditional scope. Traditional profit forecasting systems are mainly based on historical data and use statistical models to perform trend analysis to predict possible future profits. However, this approach has shown its limitations in today's multi-subject and diversified needs.

[0003] First, the traditional profit forecasting system cannot meet the diversified profit needs of multiple entities. The profit sources of an enterprise may involve multiple business departments, product lines, and regional markets. Each entity has its own unique operating model and profit model, and its profit drivers are also different. Secondly, the traditional profit forecasting system cannot meet the needs of formulating tasks according to profit targets and cannot help enterprises achieve their profit targets. Therefore, a new type of profit forecasting system is needed to meet the diversified profit forecasting needs of multiple entities. Summary of the invention

[0004] The embodiments of the present application provide a multi-agent profit forecasting method, device, electronic device and storage medium to solve the problem of providing a new type of profit forecasting system to meet the diversified needs of multi-agent profit forecasting.

[0005] In a first aspect, an embodiment of the present application provides a multi-agent profit forecasting method, comprising:

[0006] Get the prediction request and prediction subject;

[0007] Acquire a corresponding target profit forecasting rule according to the forecasting request and the forecasting subject;

[0008] Acquire basic data corresponding to the forecast request and the forecast subject, and determine a forecast result based on the basic data and the target profit forecast rule;

[0009] The forecasting subjects include: multiple sub-sectors and groups; the forecasting requests include: annual profit forecasting requests, irregular profit forecasting requests and reverse forecasting requests.

[0010] In a possible implementation, the acquiring a corresponding target profit forecasting rule according to the forecasting request and the forecasting subject includes:

[0011] According to the prediction request and the prediction subject, obtaining the corresponding parameter type and the weight coefficient of the set parameter type, and determining the target profit prediction rule based on the parameter type and the weight coefficient of the set parameter type;

[0012] Among them, when the forecast request is the annual profit forecast request and the irregular profit forecast request, the parameter type corresponding to the same forecast subject and the weight coefficient of the set parameter type are the same; for the same forecast request, the parameter type and / or the weight coefficient of the set parameter type corresponding to different forecast subjects are different.

[0013] In a possible implementation, acquiring the corresponding parameter type and setting the weight coefficient of the parameter type according to the prediction request and the prediction subject includes:

[0014] When the forecast request is an annual profit forecast request or an irregular profit forecast request, the parameter types corresponding to the forecast subject include business and business cost, business volume, business unit price, period expenses and taxes corresponding to the business; wherein the business includes the main business and other businesses; the forecast subject has different business formats, and the corresponding main business and other business types are different; the set parameter types include: main business, and, some or all other businesses;

[0015] When the prediction request is a reverse prediction request, the parameter types corresponding to the prediction subject include profit targets, businesses and business costs, period expenses and taxes corresponding to the businesses; wherein, the businesses include main businesses and other businesses; the prediction subject has different business formats, and the corresponding types of main businesses and other businesses are different; the set parameter types include: main business, and, some or all other businesses.

[0016] In a possible implementation, determining the prediction result based on the basic data and the target profit prediction rule includes:

[0017] Selecting data of corresponding parameter types from the basic data according to the target profit prediction rule;

[0018] The prediction result is determined based on the data of each parameter type and the weight coefficient of the set parameter type.

[0019] In a possible implementation, the determining of the prediction result based on the data of each parameter type and setting the weight coefficient of the parameter type includes:

[0020] When the forecast request is an annual profit forecast request or an irregular profit forecast request, a weighted sum operation is performed based on the data of each parameter type and the weight coefficient of the set parameter type to determine the total profit as the forecast result.

[0021] When the prediction request is a reverse prediction request, the profit target is used as a reference quantity, and a weighted sum operation is performed on the data of each parameter type and the weight coefficient of the set parameter type to determine the business volume and unit price that make the reference quantity consistent with the summation result as the prediction result.

[0022] In a possible implementation manner, the acquiring basic data corresponding to the prediction request and the prediction subject includes:

[0023] When the forecast request is an annual profit forecast request, obtaining data of a first set year corresponding to the forecast subject;

[0024] When the forecast request is an irregular profit forecast request, obtaining data of the forecast subject in the year corresponding to the current date and before the current date;

[0025] When the prediction request is a reverse prediction request, data of a second set year corresponding to the prediction subject is obtained.

[0026] In a possible implementation manner, the acquiring basic data corresponding to the prediction request and the prediction subject includes:

[0027] When the prediction subject is a sub-section, obtaining data corresponding to the sub-section;

[0028] When the prediction subject is a group, data of each sub-sector constituting the group is obtained.

[0029] In a second aspect, the embodiment of the present application provides a multi-agent profit forecasting device, including:

[0030] Demand acquisition module, used to obtain prediction requests and prediction subjects;

[0031] A rule acquisition module, used for acquiring corresponding target profit forecasting rules according to the forecasting request and the forecasting subject;

[0032] A data acquisition module, used to acquire basic data corresponding to the prediction request and the prediction subject, and determine a prediction result based on the basic data and the target profit prediction rule;

[0033] The forecasting subjects include: multiple sub-sectors and groups; the forecasting requests include: annual profit forecasting requests, irregular profit forecasting requests and reverse forecasting requests.

[0034] In a possible implementation, the rule acquisition module is specifically used to

[0035] According to the prediction request and the prediction subject, obtaining the corresponding parameter type and the weight coefficient of the set parameter type, and determining the target profit prediction rule based on the parameter type and the weight coefficient of the set parameter type;

[0036] Among them, when the forecast request is the annual profit forecast request and the irregular profit forecast request, the parameter type corresponding to the same forecast subject and the weight coefficient of the set parameter type are the same; for the same forecast request, the parameter type and / or the weight coefficient of the set parameter type corresponding to different forecast subjects are different.

[0037] In a possible implementation, the rule acquisition module is specifically used to:

[0038] When the forecast request is an annual profit forecast request or an irregular profit forecast request, the parameter types corresponding to the forecast subject include business and business cost, business volume, business unit price, period expenses and taxes corresponding to the business; wherein the business includes the main business and other businesses; the forecast subject has different business formats, and the corresponding main business and other business types are different; the set parameter types include: main business, and, some or all other businesses;

[0039] When the prediction request is a reverse prediction request, the parameter types corresponding to the prediction subject include profit targets, businesses and business costs, period expenses and taxes corresponding to the businesses; wherein, the businesses include main businesses and other businesses; the prediction subject has different business formats, and the corresponding types of main businesses and other businesses are different; the set parameter types include: main business, and, some or all other businesses.

[0040] In a possible implementation, the data acquisition module is specifically used to:

[0041] Selecting data of corresponding parameter types from the basic data according to the target profit prediction rule;

[0042] The prediction result is determined based on the data of each parameter type and the weight coefficient of the set parameter type.

[0043] In a possible implementation, the data acquisition module is specifically used to:

[0044] When the forecast request is an annual profit forecast request or an irregular profit forecast request, a weighted sum operation is performed based on the data of each parameter type and the weight coefficient of the set parameter type to determine the total profit as the forecast result.

[0045] When the prediction request is a reverse prediction request, the profit target is used as a reference quantity, and a weighted sum operation is performed on the data of each parameter type and the weight coefficient of the set parameter type to determine the business volume and unit price that make the reference quantity consistent with the summation result as the prediction result.

[0046] In a possible implementation, the data acquisition module is specifically used to:

[0047] When the forecast request is an annual profit forecast request, obtaining data of a first set year corresponding to the forecast subject;

[0048] When the forecast request is an irregular profit forecast request, obtaining data of the forecast subject in the year corresponding to the current date and before the current date;

[0049] When the prediction request is a reverse prediction request, data of a second set year corresponding to the prediction subject is obtained.

[0050] In a possible implementation, the data acquisition module is specifically used to:

[0051] When the prediction subject is a sub-section, obtaining data corresponding to the sub-section;

[0052] When the prediction subject is a group, data of each sub-sector constituting the group is obtained.

[0053] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0054] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0055] The embodiment of the present application provides a multi-subject profit forecasting method, device, electronic device and storage medium, by classifying the forecasting subject and forecasting request, wherein the forecasting subject includes: multiple sub-sectors and groups, and the forecasting request includes: annual profit forecasting request, irregular profit forecasting request and reverse forecasting request, so as to meet the system flexibility and customization capability. Obtain the forecasting request and the forecasting subject, and obtain the corresponding target profit forecasting rules according to the forecasting request and the forecasting subject, so as to provide accurate and personalized forecasting results according to different forecasting requests and forecasting subjects. Among them, the forecasting subject obtained can be each sub-sector under the group, or it can be the entire group itself. According to the forecasting subject, the corresponding target profit forecasting rules are obtained, which can adapt to the sum operation of the profit driving factors of each sub-sector and meet the forecasting demand processing of multiple levels and multiple subjects. Obtain the basic data corresponding to the forecasting request and the forecasting subject, provide the basis and basis for the profit forecast, and calculate and analyze the forecasting results based on the basic data and the target profit forecasting rules. Therefore, the present application can meet the diversified needs of multi-subject profit forecasting. Whether it is the refined forecasting of a single sub-sector or the comprehensive forecasting of the entire group, the system can provide accurate and personalized solutions to help enterprises better cope with market challenges and achieve profit goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art 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 labor.

[0057] Figure 1 This is a flowchart of a method for predicting profits of multiple entities provided in one embodiment of the present application;

[0058] Figure 2 It is a structural diagram of a multi-agent profit forecasting device provided by an embodiment of the present application;

[0059] Figure 3 It is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0060] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0061] The terms "first", "second", etc. in the specification and claims of the embodiments of the present application 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 numbers used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present application described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0062] Unless otherwise specified, the term "plurality" means two or more. The character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B. The term "and / or" is a description of the association relationship between objects, indicating that there can be three relationships. For example, A and / or B means: A or B, or, A and B.

[0063] The words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include the plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or including (comprising) refer to the existence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components and / or these groups. In the absence of more restrictions, the elements defined by the statement "including one..." do not exclude the existence of other identical elements in the process, method or device including the elements.

[0064] In this application, each embodiment may focus on the differences from other embodiments, and the same or similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts may refer to the description of the method part.

[0065] In the process of implementing the embodiments of the present application, it is found that the traditional profit forecasting system has many shortcomings, which are mainly manifested in the following two aspects:

[0066] First, the traditional profit forecasting system cannot meet the diversified profit needs of multiple entities. In a diversified market environment, the profit sources of an enterprise may involve multiple business departments, product lines, and even regional markets. Each entity has its own unique operating model and profit model, and its profit drivers are also different. Traditional profit forecasting systems can usually only provide a single, global profit forecast, but cannot be detailed to each entity, let alone meet the diversified needs of different entities.

[0067] Secondly, traditional profit forecasting systems also cannot meet the needs of setting tasks based on profit targets. In traditional forecasting systems, forecast results are often regarded as an end point rather than a starting point. However, in modern enterprises, forecasting profits is not only to understand possible future revenue situations, but more importantly to formulate corresponding business strategies and tasks based on forecast results. This requires the forecasting system to not only provide forecast results, but also provide actionable suggestions based on the forecast results to help enterprises achieve their profit targets.

[0068] Therefore, in order to meet the needs of modern enterprises, the embodiments of the present application provide a new type of profit forecasting system, which should be able to process complex data of multiple entities, provide fine-grained forecasting results, and provide actionable strategic recommendations based on the forecasting results to help enterprises achieve profit goals.

[0069] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0070] Figure 1 is a flowchart of a method for realizing profit forecasting of multiple entities provided by an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0071] S101, obtaining a prediction request and a prediction subject.

[0072] In the embodiment of the present application, the executor of the multi-agent profit forecasting method is a forecasting system, which is associated with a financial sharing system to obtain basic data required for profit forecasting from the financial sharing system.

[0073] Among them, the forecasting subjects include: multiple sub-sectors and groups; the forecasting requests include: annual profit forecasting requests, irregular profit forecasting requests and reverse forecasting requests.

[0074] In a specific embodiment, each sub-sector has different business types, including thermal power, heating, wind power, and natural gas. In other optional embodiments, depending on the business scale of the group, the sub-sector includes any two or more of thermal power, heating, wind power, and natural gas.

[0075] In other optional embodiments, the business types of each sub-sector are different, including any two or more of energy, transportation, water affairs, and urbanization construction.

[0076] For different prediction requests, the corresponding application scenarios and prediction contents are different, as follows:

[0077]

[0078]

[0079] As shown in the above table, forward forecasting is based on the accounting characteristics of income statement items and their relevance to operating activities. It uses different regulations to build forecasting models for different forecasting targets and forecasting subjects, thereby outputting a forecasted income statement.

[0080] Forward forecasts are divided into irregular profit forecasts and annual profit forecasts. The difference between the two forecasting methods is that irregular profit forecasts are used to predict the realization of profits in the current year. The forecasting model obtains data from historical financial statements and annual financial budgets, including actual numbers (i.e., the cumulative amount of the previous accounting period of irregular forecasts) and budget numbers (annual financial budget numbers). Combining the two, the forecast number can be calculated, that is, the annual budget number minus the actual number, and the value can be adjusted manually. Finally, the annual profit forecast report for this year is output.

[0081] The goal of reverse forecasting is to measure the business scale required to achieve a specific profit target. The forecasting model can take over the total profit output by the forward forecasting model and manually disassemble or modify its total profit. The forecasting model breaks down revenue and costs into quantity and price segments based on the characteristics of each main business. By adjusting the quantity, price, and other related and parallel items of the main business, the reverse forecast profit statement is finally output.

[0082] S102, obtaining corresponding target profit forecasting rules according to the forecasting request and the forecasting subject.

[0083] Among them, pre-establishing profit forecasting rules for forecasting requests can help companies respond quickly to forecasting needs and optimize resource allocation. After predicting changes in market demand, companies can adjust production plans and sales strategies in a timely manner to meet market demand. This can prevent overcapacity and ensure the stability of product supply. In addition, profit forecasting rules are set for different forecasting entities to adapt to the operating rules of each sub-sector and improve forecast accuracy.

[0084] Taking the annual profit forecast as an example, according to the different forecasting subjects, it is divided into sector annual profit forecast and group annual profit forecast.

[0085] Among them, the annual profit forecast of the sector is mainly for the profit forecast of each business sector within the enterprise. It helps the enterprise understand the profitability of each business sector and thus achieve effective allocation of resources. When conducting the annual profit forecast of the sector, the enterprise needs to fully consider the market environment, industry competition situation, policy factors and other influencing factors to ensure the accuracy and reliability of the forecast results.

[0086] The group's annual profit forecast is a profit forecast for the entire enterprise. It helps the enterprise grasp its overall profitability and provides strong support for its decision-making. The group's annual profit forecast needs to comprehensively consider the profitability of each business segment within the enterprise, as well as the impact of external market environment, policy factors, etc. on the overall profitability of the enterprise. In addition, the group's annual profit forecast should also pay attention to complex situations such as related transactions between enterprises and cross-industry operations to improve the accuracy of the forecast.

[0087] In irregular profit forecasts, enterprises need to adjust the forecast data in a timely manner according to the actual operating conditions and market environment. Compared with annual profit forecasts, irregular profit forecasts focus more on short-term profit performance in order to provide enterprises with timely and effective decision-making basis. At the same time, irregular profit forecasts should also follow the principle of consistency to ensure the authenticity and reliability of forecast data.

[0088] In different implementation cases, forward forecasting sets forecasting models for each item based on the accounting characteristics of the income statement items and whether they are related to business activities. Different regulations are used to output forecasted income statements based on different forecasting targets and forecasting subjects.

[0089] Annual profit forecasts can be divided into the following two categories according to different goals:

[0090] 1. Forecasting profit scale: Before preparing a budget, the annual profit forecasting rules can be used to forecast the entity's profit scale under specific business volume conditions.

[0091] 2. Check the rationality of the budget: After preparing the budget, the group introduces the budget data of the segment and verifies the rationality of the budget by adjusting the boundary conditions. For example, after introducing the parallel items of business volume and operating profit in the budget, the price set by the group is input, the profit scale is calculated through the profit forecasting rules, and the profit statement is output to evaluate the rationality of the budget.

[0092] In the process of irregular profit forecasting, the profit forecasting principles adopted are consistent with those used in the annual profit forecast. The main difference is that the object of the forecast is the profit realization status of the current year, and the range of data required in the forecasting process is different.

[0093] The reverse forecast profit forecasting rules can take over the total profit obtained by the forward forecast and disassemble or modify it. According to the characteristics of each main business, the income and cost are refined to the quantity and price level, and by adjusting the quantity and price and other related and parallel items of the main business, the reverse forecast profit statement is finally generated.

[0094] Among them, the profit prediction rules are prediction models trained in advance or the required data are obtained based on the prediction rules, and calculations are performed based on external prediction algorithms.

[0095] S103, acquiring basic data corresponding to the forecast request and the forecast subject, and determining the forecast result based on the basic data and the target profit forecasting rule.

[0096] In the embodiment of the present application, the basic data is derived from the financial sharing system, which contains data tables of sub-sectors at all levels of the group. In the process of profit forecasting, the account balances of the financial sharing system are integrated into the forecasting system through the integrated cloud, and the amounts are extracted according to dimensions such as organization, period, and account. There is a one-to-many mapping relationship between organizations and accounts, that is, a budget account needs to summarize the amounts of multiple organizations and multiple accounts. The period involves offset values. For example, if it is judged by the system period, the cumulative amount of the year from the previous month is used.

[0097] The financial sharing system extracts the values ​​of the required report items into the intermediate table through the two-open function and integrates them into the forecasting system. The profit forecast needs to be valued based on dimensions such as organization, period, and report item, covering both consolidated reports and individual reports. There is a one-to-many mapping relationship between organization and report item, that is, one budget item needs to summarize the values ​​of multiple organizations and multiple report items. The period involves offset value. If it is judged according to the system period, the value of the report of the previous month is used.

[0098] Among them, in different embodiments, the financial sharing system uses different methods to extract the values ​​of the required report items into the intermediate table through the two-open function.

[0099] In an optional embodiment, the financial sharing system extracts all data of the forecasting subject into an intermediate table through a two-open function, and the forecasting system retrieves values ​​from the intermediate table and performs forecast calculations according to the target profit forecasting rules corresponding to the forecasting request.

[0100] Optionally, the forecast result is determined based on the basic data and the target profit forecasting rule, including:

[0101] Select data of corresponding parameter type from basic data according to target profit forecasting rules;

[0102] The prediction result is determined based on the data of each parameter type and the weight coefficient of the set parameter type.

[0103] In other optional embodiments, the financial sharing system uses the second opening function to extract values ​​into the intermediate table based on the target profit forecasting rules and according to dimensions such as organization, period, and report item. The forecasting system directly determines the forecasting results based on the data in the intermediate table and the target profit forecasting rules.

[0104] In this embodiment, by classifying the forecast subject and the forecast request, wherein the forecast subject includes: multiple sub-sectors and groups, and the forecast request includes: annual profit forecast request, irregular profit forecast request and reverse forecast request, so as to meet the system flexibility and customization capability. Obtain the forecast request and the forecast subject, and obtain the corresponding target profit forecast rule according to the forecast request and the forecast subject, so as to provide accurate and personalized forecast results according to different forecast requests and forecast subjects. Among them, the forecast subject obtained can be each sub-sector under the group, or it can be the entire group itself. According to the forecast subject, the corresponding target profit forecast rule is obtained, which can adapt to the sum calculation of the profit driving factors of each sub-sector and meet the multi-level and multi-subject forecast demand processing. Obtain the basic data corresponding to the forecast request and the forecast subject, provide the basis and basis for profit forecasting, and calculate and analyze the forecast results based on the basic data and the target profit forecast rule. Therefore, this application can solve the problem of how to meet the diversified needs of multi-subject profit forecasting. Whether it is for the refined forecast of a single sub-sector or for the comprehensive forecast of the entire group, the system can provide accurate and personalized solutions to help enterprises better cope with market challenges and achieve profit goals.

[0105] In a possible implementation, in step S102, obtaining a corresponding target profit forecasting rule according to the forecasting request and the forecasting subject includes:

[0106] According to the forecast request and the forecast subject, the corresponding parameter type and the weight coefficient of the set parameter type are obtained, and the target profit forecast rule is determined based on the parameter type and the weight coefficient of the set parameter type;

[0107] Among them, when the forecast request is an annual profit forecast request and an irregular profit forecast request, the parameter type corresponding to the same forecast subject and the weight coefficient of the set parameter type are the same; for the same forecast request, the parameter type and / or the weight coefficient of the set parameter type corresponding to different forecast subjects are different.

[0108] During the implementation process, the forecasting system builds a budget account system, selects the data source of each budget account, such as account and report item, at the front end through configurable business rules, and specifies the dimensions of the data source by setting the member range. The profit forecasting rules are built based on report templates and multidimensional databases to ensure consistency with the calculation logic of financial statements. The system receives profit element data such as business volume, revenue, cost, expense, tax, etc. based on historical financial reports or financial budgets, as well as manual total or percentage adjustments to the acquired data, and finally outputs the total profit forecast value.

[0109] Among them, according to the prediction request and the prediction subject, the corresponding parameter type and the weight coefficient of the set parameter type are obtained, that is, the data of parameter types such as business volume, income, cost, expense, tax, etc. are obtained, and the percentage obtained by manual adjustment is used as the weight coefficient.

[0110] Given the difference between irregular profit forecast and annual profit forecast, the former is based on the current year's profit realization status, that is, the forecast data range is different, therefore, the parameter types and weight coefficients corresponding to the same forecast subject remain consistent. In other words, in the irregular profit forecast process, the profit forecast principles adopted by the same forecast subject are consistent with the annual profit forecast.

[0111] For the same prediction request, different prediction entities may use different parameter types due to differences in business areas. Even if the parameter types are the same, the settings of the parameter types by different prediction entities may be different.

[0112] In this embodiment, corresponding target profit forecasting rules are formulated for different forecasting requests and forecasting subjects. These rules include parameter types and weight coefficients for setting parameter types. Different forecasting subjects are allowed to make adjustments based on their own characteristics and needs while ensuring consistency. This helps us better cope with various forecasting scenarios, improve the accuracy and reliability of forecasts, and provide strong support for corporate decision-making.

[0113] In a possible implementation, according to the prediction request and the prediction subject, the corresponding parameter type and the weight coefficient of the set parameter type are obtained, including:

[0114] When the forecast request is an annual profit forecast request or an irregular profit forecast request, the parameter types corresponding to the forecast subject include business and the business cost, business volume, business unit price, period expenses and taxes corresponding to the business; among them, business includes main business and other business; the forecast subject has different business formats, and the corresponding main business and other business types are different; the set parameter types include: main business, and, some or all other businesses;

[0115] When the forecast request is a reverse forecast request, the parameter types corresponding to the forecast subject include profit target, business and the business cost, period expenses and taxes corresponding to the business; among them, business includes main business and other business; the forecast subject has different business formats, and the corresponding types of main business and other business are different; the set parameter types include: main business, and, part or all of other businesses.

[0116] In a feasible embodiment, in order to improve the efficiency of data management and profit forecasting, a profit forecast report template is constructed. This template has optional contents such as business front-end table, amount front-end table, forecast result table, business forecast table, factor forecast table, business profit forecast summary table, sector profit forecast summary table, marginal profit table, safety margin profit table, etc. In order to meet the needs of profit forecasting, the budget account dimension at least includes the following metrics: amount, quantity, unit price and proportion. In addition, according to the business characteristics of the sub-sectors, business profit forecast report templates are set for thermal power, heating, wind power, natural gas, etc.

[0117] During the implementation process, the report template supports allocation according to organization, and the profit forecast template of the corresponding segment is allocated to each profit forecast entity.

[0118] In addition, the model report structure of the segment annual profit forecast and the group annual profit forecast is different, as follows:

[0119] (1) Segment annual profit forecast model report structure:

[0120] Segment business front-end table: The revenue and cost details of the main business of each segment using the quantity-price model are listed, including business volume, unit price, amount, revenue cost rate and other types of data. The data sources include the general ledger report and input of the financial sharing system. The revenue and cost of each type of business is obtained through the preset calculation rules in the table.

[0121] Segment amount front-end table: The main business income cost and other business income cost details of each segment applying the amount model are listed, and the amount, income cost rate and other types of data are listed. Historical data is obtained from the general ledger of the financial shared system as a reference value for prediction. By entering the adjustment value and combining the preset calculation rules for each item, the income cost amount of each type of business is obtained.

[0122] Front-end table for basic report items: Details of profit and loss statement items except operating income and operating costs. The data sources include general ledger reports and entries of the financial sharing system. The predicted amounts of various detailed items are obtained through the preset calculation rules in the table.

[0123] Profit forecast result table: obtain profit-related element data such as operating income, operating costs, and period expenses from three types of front-end tables, summarize them according to the corresponding relationship of the profit statement items, and output a forecast profit statement with separately listed items.

[0124] (2) Group annual profit forecast model report structure:

[0125] Group business front-end table, group amount front-end table: list the detailed items of operating income and operating costs of all segments, and merge some items that account for a small proportion or are not of concern and list them together.

[0126] Basic report item front-end table: lists the basic report items of all sections.

[0127] Profit forecast result table: obtain profit-related element data such as operating income, operating costs, and period expenses from three types of front-end tables, and summarize them according to the corresponding relationship between the income statement items.

[0128] Based on the model report structure of the above-mentioned annual profit forecast of the sector and the annual profit forecast of the group, the basic data required for irregular profit forecast and reverse forecast should be obtained and forecast should be made.

[0129] In the process of profit forecasting, businesses can be divided into main business and other businesses. According to the operating characteristics of the forecasting subject, the types of main business and other businesses are different. When a forecasting subject is involved in a variety of other businesses in addition to the main business, and there are non-profit businesses, in the profit forecast, the data of non-profit businesses do not need to be included in the calculation to reduce the burden of data processing and improve the forecasting efficiency. Therefore, when setting the weight coefficient, the following setting parameter types should be considered: main business, and some or all other businesses.

[0130] In this embodiment, the weight coefficient is reasonably set according to the characteristics of the forecast request and the forecast subject to ensure the accuracy of the forecast result. At the same time, attention is paid to the impact of the main business and other businesses, and adjustments are made according to the business format of the forecast subject. In this way, various forecasting needs can be better responded to and strong support can be provided to enterprises.

[0131] In a possible implementation, the prediction result is determined based on the data of each parameter type and the weight coefficient of the parameter type, including:

[0132] When the forecast request is an annual profit forecast request or an irregular profit forecast request, a weighted sum operation is performed based on the data of each parameter type and the weight coefficient of the set parameter type to determine the total profit as the forecast result.

[0133] When the forecast request is a reverse forecast request, the profit target is used as a reference quantity, and a weighted sum operation is performed on the data of each parameter type and the weight coefficient of the set parameter type to determine the business volume and unit price that make the reference quantity consistent with the summation result as the forecast result.

[0134] Based on the introduction of the forward forecast in the above embodiment, it can be known that the annual profit forecast and the irregular profit forecast are used to predict the profit scale of the whole year of the following year and the profit completion of the whole year in combination with the actual data of the historical period. Therefore, the profit forecast result is the sum of the profits of all months or some months in a year, which can be obtained by weighted summation based on the data of each parameter type and the weight coefficient of the set parameter type.

[0135] Different from annual profit forecasts and irregular profit forecasts, reverse forecasts aim to predict the business volume that should be achieved in order to achieve a certain profit target.

[0136] In a feasible embodiment, the reverse prediction mainly includes the following steps:

[0137] (1) Profit breakdown

[0138] Import or extract the profit target of each segment from the forward forecast result table, adjust the total profit target or the profit target proportion of each segment according to the actual situation, and set the profit target amount of each segment. Subsequent business forecasts are based on the profit targets of each segment after decomposition.

[0139] (2) Business Forecast

[0140] The business forecasting logic for a single item is: calculation result = variable calculation basis + basic variable value + variable adjustment value;

[0141] The variable calculation basis is the reference value of each report item, which is the actual number or budget number of the most recent year; the variable refers to the coefficient of each item forecast in the business forecast process, and the forecast result value is obtained by calculating with the calculation basis. The variable calculation basis can be introduced or entered.

[0142] Business forecast items are divided into operating profit related items and operating profit parallel items. Operating profit related items are profit elements that are linearly related to business scale, including operating income, operating costs, taxes and surcharges; operating profit parallel items are profit elements that are not linearly related to business scale and some fixed expenses, including period expenses, investment income, other income, etc.

[0143] (3) Factor prediction

[0144] Based on the business forecast results, the business volume and unit price are calculated backwards, and by adjusting the business volume or unit price, the revenue scale and profit scale under different operating volume and unit price boundary conditions are calculated.

[0145] In this embodiment, a highly efficient prediction method is implemented by performing a weighted summation operation on data of various parameter types and setting corresponding weight coefficients. When processing various types of prediction requests, the summation operation can provide accurate and reliable prediction results. In practical applications, the summation operation is used as an output or intermediate quantity according to the prediction request to meet the needs of forward prediction or reverse prediction, providing strong support for enterprises and organizations.

[0146] In a possible implementation, obtaining basic data corresponding to the prediction request and the prediction subject includes:

[0147] When the forecast request is an annual profit forecast request, obtaining data for a first set year corresponding to the forecast subject;

[0148] When the forecast request is an irregular profit forecast request, obtain the data of the forecast subject in the year corresponding to the current date and before the current date;

[0149] When the forecast request is a reverse forecast request, data of a second set year corresponding to the forecast subject is obtained.

[0150] Among them, the annual profit forecast aims to predict the overall profit situation in the following year. Therefore, it is necessary to combine the data of this year or this year and the set year before this year to predict the profit scale. The first set year is this year or the year specified by the user.

[0151] The irregular profit forecast is an irregular mid-year forecast, which needs to be combined with the actual data of the historical period to predict the full-year profit realization. Therefore, it is necessary to obtain the data of the forecast subject in the year corresponding to the current date and before the current date to determine the profit-related data available in the year, and focus on recent data to more accurately capture the short-term fluctuations in the profitability of the forecast subject.

[0152] The goal of reverse forecasting is to estimate the business scale required to achieve a specific profit target. By obtaining the historical data of the second set year corresponding to the forecast subject as a reference, it is helpful to understand how the forecast subject has responded to various market environments in the past, thereby providing a useful reference for future business scale decisions. Among them, the second set year can be selected from historical years according to the profit target.

[0153] In this embodiment, corresponding basic data is obtained according to different prediction requests. These three data acquisition methods have different focuses to better meet different business prediction requirements and ensure the accuracy and reliability of the prediction results.

[0154] In a possible implementation, obtaining basic data corresponding to the prediction request and the prediction subject includes:

[0155] When the prediction subject is a sub-sector, obtain the data of the corresponding sub-sector;

[0156] When the forecasting subject is a group, obtain the data of each sub-sector that constitutes the group.

[0157] In the process of implementing forecast analysis, when the forecast subject is a sub-sector, the relevant data of the sub-sector is obtained. By analyzing these data, the operating status of the sub-sector can be better understood, thus providing a strong basis for forecasting.

[0158] When the forecasting subject is a group, the data of each sub-segment within the group is obtained so that the mutual influence between the sub-segments can be fully considered in the analysis process, and the overall profit of the entire group can be comprehensively analyzed.

[0159] In this embodiment, basic data related to the forecast request and the forecast subject are obtained from multiple levels and angles to provide support for the forecast results, which helps to improve the accuracy of profit forecasts.

[0160] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0161] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.

[0162] Figure 3 : is a schematic diagram of the structure of a multi-agent profit forecasting device provided by an embodiment of the present application, such as Figure 3 For the sake of convenience, only the parts related to the embodiment of the present application are shown. Figure 3 As shown, the device comprises:

[0163] The demand acquisition module 201 is used to acquire the prediction request and the prediction subject;

[0164] A rule acquisition module 202, used to acquire corresponding target profit forecasting rules according to the forecasting request and the forecasting subject;

[0165] The data acquisition module 203 is used to acquire basic data corresponding to the forecast request and the forecast subject, and determine the forecast result based on the basic data and the target profit forecast rule;

[0166] Among them, the forecasting subjects include: multiple sub-sectors and groups; the forecasting requests include: annual profit forecasting requests, irregular profit forecasting requests and reverse forecasting requests.

[0167] In a possible implementation, the rule acquisition module 202 is specifically configured to:

[0168] According to the forecast request and the forecast subject, the corresponding parameter type and the weight coefficient of the set parameter type are obtained, and the target profit forecast rule is determined based on the parameter type and the weight coefficient of the set parameter type;

[0169] Among them, when the forecast request is an annual profit forecast request and an irregular profit forecast request, the parameter type corresponding to the same forecast subject and the weight coefficient of the set parameter type are the same; for the same forecast request, the parameter type and / or the weight coefficient of the set parameter type corresponding to different forecast subjects are different.

[0170] In a possible implementation, the rule acquisition module 202 is specifically configured to:

[0171] When the forecast request is an annual profit forecast request or an irregular profit forecast request, the parameter types corresponding to the forecast subject include business and the business cost, business volume, business unit price, period expenses and taxes corresponding to the business; among them, business includes main business and other business; the forecast subject has different business formats, and the corresponding main business and other business types are different; the set parameter types include: main business, and, some or all other businesses;

[0172] When the forecast request is a reverse forecast request, the parameter types corresponding to the forecast subject include profit target, business and the business cost, period expenses and taxes corresponding to the business; among them, business includes main business and other business; the forecast subject has different business formats, and the corresponding types of main business and other business are different; the set parameter types include: main business, and, part or all of other businesses.

[0173] In a possible implementation, the data acquisition module 203 is specifically configured to:

[0174] Select data of corresponding parameter type from basic data according to target profit forecasting rules;

[0175] The prediction result is determined based on the data of each parameter type and the weight coefficient of the set parameter type.

[0176] In a possible implementation, the data acquisition module 203 is specifically configured to:

[0177] When the forecast request is an annual profit forecast request or an irregular profit forecast request, a weighted sum operation is performed based on the data of each parameter type and the weight coefficient of the set parameter type to determine the total profit as the forecast result.

[0178] When the forecast request is a reverse forecast request, the profit target is used as a reference quantity, and a weighted sum operation is performed on the data of each parameter type and the weight coefficient of the set parameter type to determine the business volume and unit price that make the reference quantity consistent with the summation result as the forecast result.

[0179] In a possible implementation, the data acquisition module 203 is specifically configured to:

[0180] When the forecast request is an annual profit forecast request, obtaining data for a first set year corresponding to the forecast subject;

[0181] When the forecast request is an irregular profit forecast request, obtain the data of the forecast subject in the year corresponding to the current date and before the current date;

[0182] When the forecast request is a reverse forecast request, data of a second set year corresponding to the forecast subject is obtained.

[0183] In a possible implementation, the data acquisition module 203 is specifically configured to:

[0184] When the prediction subject is a sub-sector, obtain the data of the corresponding sub-sector;

[0185] When the forecasting subject is a group, obtain the data of each sub-sector that constitutes the group.

[0186] In this embodiment, by classifying the forecast subject and the forecast request, wherein the forecast subject includes: multiple sub-sectors and groups, and the forecast request includes: annual profit forecast request, irregular profit forecast request and reverse forecast request, so as to meet the system flexibility and customization capability. Obtain the forecast request and the forecast subject, and obtain the corresponding target profit forecast rule according to the forecast request and the forecast subject, so as to provide accurate and personalized forecast results according to different forecast requests and forecast subjects. Among them, the forecast subject obtained can be each sub-sector under the group, or it can be the entire group itself. According to the forecast subject, the corresponding target profit forecast rule is obtained, which can adapt to the sum calculation of the profit driving factors of each sub-sector and meet the multi-level and multi-subject forecast demand processing. Obtain the basic data corresponding to the forecast request and the forecast subject, provide the basis and basis for profit forecasting, and calculate and analyze the forecast results based on the basic data and the target profit forecast rule. Therefore, this application can solve the problem of how to meet the diversified needs of multi-subject profit forecasting. Whether it is for the refined forecast of a single sub-sector or for the comprehensive forecast of the entire group, the system can provide accurate and personalized solutions to help enterprises better cope with market challenges and achieve profit goals.

[0187] Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 3As shown, the electronic device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned profit prediction method embodiments of each multi-agent are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 2 The functions of each module are shown.

[0188] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 may be divided into Figure 2 The modules shown.

[0189] The electronic device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 3 and does not constitute a limitation of the electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0190] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0191] The memory 31 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the electronic device 3. The memory 31 is used to store the computer program and other programs and data required by the electronic device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0192] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0193] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0194] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0195] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0196] 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.

[0197] 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.

[0198] If the integrated module / 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 storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned profit forecasting method embodiments of each multi-subject can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0199] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-agent profit forecasting method, characterized in that: include: Get the prediction request and prediction subject; Acquire a corresponding target profit forecasting rule according to the forecasting request and the forecasting subject; Acquire basic data corresponding to the forecast request and the forecast subject, and determine a forecast result based on the basic data and the target profit forecast rule; The forecasting subjects include: multiple sub-sectors and groups; the forecasting requests include: annual profit forecasting requests, irregular profit forecasting requests and reverse forecasting requests; The obtaining a corresponding target profit forecasting rule according to the forecasting request and the forecasting subject includes: According to the prediction request and the prediction subject, obtaining the corresponding parameter type and the weight coefficient of the set parameter type, and determining the target profit prediction rule based on the parameter type and the weight coefficient of the set parameter type; Wherein, when the forecast request is the annual profit forecast request and the irregular profit forecast request, the parameter type and the weight coefficient of the setting parameter type corresponding to the same forecast subject are the same; for the same forecast request, the parameter type and / or the weight coefficient of the setting parameter type corresponding to different forecast subjects are different; The determining of the prediction result based on the basic data and the target profit prediction rule includes: Selecting data of corresponding parameter types from the basic data according to the target profit prediction rule; Determine the prediction result based on the data of each parameter type and the weight coefficient of the set parameter type; The method of determining the prediction result based on the data of each parameter type and setting the weight coefficient of the parameter type includes: When the forecast request is an annual profit forecast request or an irregular profit forecast request, a weighted sum operation is performed based on the data of each parameter type and the weight coefficient of the set parameter type to determine the total profit as the forecast result; When the prediction request is a reverse prediction request, the profit target is used as a reference quantity, and a weighted sum operation is performed on the data of each parameter type and the weight coefficient of the set parameter type to determine the business volume and unit price that make the reference quantity consistent with the summation result as the prediction result.

2. The method according to claim 1, characterized in that The obtaining, according to the prediction request and the prediction subject, a corresponding parameter type and a weight coefficient of the setting parameter type comprises: When the forecast request is an annual profit forecast request or an irregular profit forecast request, the parameter types corresponding to the forecast subject include business and business cost, business volume, business unit price, period expenses and taxes corresponding to the business; wherein the business includes the main business and other businesses; the forecast subject has different business formats, and the corresponding main business and other business types are different; the set parameter types include: main business, and, some or all other businesses; When the prediction request is a reverse prediction request, the parameter types corresponding to the prediction subject include profit targets, businesses and business costs, period expenses and taxes corresponding to the businesses; wherein, the businesses include main businesses and other businesses; the prediction subject has different business formats, and the corresponding types of main businesses and other businesses are different; the set parameter types include: main business, and, some or all other businesses.

3. The method according to claim 1, characterized in that The acquiring basic data corresponding to the prediction request and the prediction subject includes: When the forecast request is an annual profit forecast request, obtaining data of a first set year corresponding to the forecast subject; When the forecast request is an irregular profit forecast request, obtaining data of the forecast subject in the year corresponding to the current date and before the current date; When the prediction request is a reverse prediction request, data of a second set year corresponding to the prediction subject is obtained.

4. The method according to claim 1, characterized in that: The acquiring basic data corresponding to the prediction request and the prediction subject includes: When the prediction subject is a sub-section, obtaining data corresponding to the sub-section; When the prediction subject is a group, data of each sub-sector constituting the group is obtained.

5. A multi-agent profit forecasting device for executing the multi-agent profit forecasting method according to any one of claims 1 to 4, characterized in that: include: Demand acquisition module, used to obtain prediction requests and prediction subjects; A rule acquisition module, used for acquiring corresponding target profit forecasting rules according to the forecasting request and the forecasting subject; A data acquisition module, used to acquire basic data corresponding to the prediction request and the prediction subject, and determine a prediction result based on the basic data and the target profit prediction rule; The forecasting subjects include: multiple sub-sectors and groups; the forecasting requests include: annual profit forecasting requests, irregular profit forecasting requests and reverse forecasting requests.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running 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 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

Images