Budget motivation analysis method and device, electronic equipment and storage medium
Through simulated calculation and macroeconomic forecasting models, historical trend analysis and future prediction of enterprise budget factors are solved, and the budget system in the existing technology is difficult to achieve real-time adjustment and prediction, and the efficiency and credibility of budget preparation are improved.
Patent Information
- Application Number
- CN202311618000.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-29
- Publication Date
- 2025-05-30
AI Technical Summary
The existing budget system is difficult to achieve real-time dynamic adjustment, prediction and trend analysis of key budget factors, and fails to achieve intelligent budget preparation dominated by data, resulting in low budget operation efficiency and effectiveness of financial institutions.
By simulating the historical trend of budget factors and building a macroeconomic forecast model, predicting the company's future profit data, making budget decisions based on these data, and predicting and trend analysis of key factors.
It has improved the efficiency and credibility of budget preparation for financial institutions, realized intelligent budget preparation dominated by data, and met the needs of responding to turbulent financial markets.
Smart Images
Figure CN120069915A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of data processing, and in particular, to a budget driver analysis method, apparatus, electronic device, and storage medium. Background Art
[0002] The budget system is used to plan and control a company's financial expenditures and revenues, thereby helping the company achieve maximum benefits with limited resources. Budget preparation is to formulate a clear and executable financial plan, including the company's projected revenues, expenditures, and investments. Currently, most of the budget-related systems and technologies on the market are for post-facto preparation, and their presentation formats are mainly in tabular form. Users can increase or modify the budget by operating on the table, and the analysis is mainly based on basic statistical analysis methods such as year-on-year and month-on-month comparisons and growth rates. Most of them use a web-based system to informatize the entire budget process, and cannot perform real-time dynamic adjustment of key budget factors, nor can they predict and analyze trends of key factors, failing to achieve data-driven intelligent budget preparation. For example, the financial institution system in the energy industry is huge, and the bottom-up budget method results in low efficiency and poor effectiveness in calculating the overall company budget, making it difficult to meet the needs of the volatile financial market. Summary of the Invention
[0003] To overcome the deficiencies in the prior art, the present application provides a budget driver analysis method, apparatus, electronic device, and storage medium, which can use AI capabilities to predict and analyze trends of key factors, and better achieve data-driven intelligent budget preparation.
[0004] In a first aspect, the present application provides a budget driver analysis method, which is applied to a budget system. The method includes the following steps:
[0005] According to the obtained historical annual budget data of the enterprise and the set budget factors, simulate and calculate the historical trends of the budget factors; wherein, the historical annual budget data of the enterprise includes the budget amounts corresponding to the budget factors.
[0006] According to the selected target macroeconomic forecasting factors and the historical annual profit data of the enterprise, construct a macroeconomic forecasting model to predict the future profit data of the enterprise through the macroeconomic forecasting model; wherein, the future profit data of the enterprise includes the total enterprise budget and the total enterprise revenue.
[0007] Based on the historical trends of the budget factors obtained by simulation and the predicted future profit data of the enterprise, make a budget decision for the enterprise.
[0008] In a possible implementation manner, the total enterprise budget is calculated by the following method, including the following steps:
[0009] Extract the formula associated with the set budget factors from the set budget template;
[0010] Parse the extracted formula into executable code to form an enterprise algorithm library;
[0011] Calculate the total enterprise budget using the enterprise algorithm library and based on the budget amount corresponding to the set budget factors.
[0012] In a possible implementation manner, wherein, based on the obtained enterprise historical annual budget data and the set budget factors, use a time series model to simulate and calculate the historical trend of the budget factors.
[0013] In a possible implementation manner, select target macroeconomic forecasting factors in the following way, including the following steps:
[0014] Perform correlation calculations on a plurality of prepared macroeconomic forecasting factors, and screen out a plurality of to-be-determined target macroeconomic forecasting factors with the strongest correlations;
[0015] Perform addition and deletion operations on the to-be-determined target macroeconomic forecasting factors according to the received adjustment instructions to obtain the target macroeconomic forecasting factors.
[0016] In a possible implementation manner, wherein, perform Pearson correlation calculations on a plurality of prepared macroeconomic forecasting factors, and screen out a plurality of to-be-determined target macroeconomic forecasting factors with the strongest correlations.
[0017] In a possible implementation manner, the budget decision-making for the enterprise using the historical trend of the budget factors calculated by simulation and the predicted future profit data of the enterprise includes the following steps:
[0018] Obtain the future profit data of the enterprise corresponding to various target macroeconomic forecasting factors respectively, observe the influence of each target macroeconomic forecasting factor on the future profit data of the enterprise, and determine the final total budget of the enterprise;
[0019] Adjust the budget amount of the future budget factors of the enterprise according to the final total budget of the enterprise and the historical trend of the budget factors calculated by simulation.
[0020] In a possible implementation manner, the construction of a macroeconomic forecasting model according to the selected target macroeconomic forecasting factors and the enterprise historical annual profit data includes the following steps:
[0021] Use the selected target macroeconomic forecasting factors and the enterprise historical annual profit data as sample data, and divide the sample data into a training set and a validation set;
[0022] Using the training set to initialize the macroeconomic prediction model, determining at least one parameter in the initial macroeconomic prediction model, and obtaining a pre-trained macroeconomic prediction model;
[0023] Using the validation set to validate the pre-trained macroeconomic prediction model, and obtaining a validation result;
[0024] According to the validation result, updating at least one parameter in the pre-trained macroeconomic prediction model, and establishing a trained macroeconomic prediction model.
[0025] In a second aspect, the present application provides a budget driver analysis device, which is applied to a budget system. The device includes:
[0026] A simulation calculation module, configured to simulate and calculate the historical trend of the budget factor according to the obtained historical annual budget data of the enterprise and the set budget factor; wherein, the historical annual budget data of the enterprise includes the budget amount corresponding to the budget factor;
[0027] A prediction module, configured to construct a macroeconomic prediction model according to the selected target macroeconomic prediction factor and the historical annual profit data of the enterprise, so as to predict the future profit data of the enterprise through the macroeconomic prediction model; wherein, the future profit data of the enterprise includes the total budget of the enterprise and the total income of the enterprise;
[0028] A budget decision-making module, configured to make a budget decision for the enterprise based on the simulated historical trend of the budget factor and the predicted future profit data of the enterprise.
[0029] In a third aspect, the present application provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the budget driver analysis method according to any one of the first aspects are executed.
[0030] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the budget driver analysis method according to any one of the first aspects are executed.
[0031] A budget driver analysis method, device, electronic device, and storage medium provided in this embodiment simulate and calculate the historical trend of the budget factor according to the obtained enterprise historical annual budget data and the set budget factor; wherein, the enterprise historical annual budget data includes the budget amount corresponding to the budget factor; construct a macroeconomic prediction model according to the selected target macroeconomic prediction factor and the enterprise historical annual profit data to predict the enterprise future profit data through the macroeconomic prediction model; wherein, the enterprise future profit data includes the total enterprise budget and the total enterprise revenue; based on the historical trend of the budget factor obtained by simulation calculation and the predicted enterprise future profit data, make a budget decision for the enterprise. Thus, by using the prediction ability related to AI and replicating the chained calculation of multi-level complex formulas in Excel, it is possible to predict and analyze the future trend of key indicators, and based on the data-driven budget docking work, improve the budget operation efficiency and credibility of financial institutions. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.
[0033] Figure 1 Shows the flowchart of the budget driver analysis method according to an embodiment of the present application;
[0034] Figure 2 Shows the flowchart of selecting the target macroeconomic prediction factor according to an embodiment of the present application;
[0035] Figure 3 Shows the flowchart of calculating the total enterprise budget according to an embodiment of the present application;
[0036] Figure 4 Shows the flowchart of making a budget decision for the enterprise based on the historical trend of the budget factor obtained by simulation calculation and the predicted enterprise future profit data according to an embodiment of the present application;
[0037] Figure 5 Shows the structural block diagram of the budget driver analysis device according to an embodiment of the present application;
[0038] Figure 6 Shows the structural block diagram of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. It should be understood that the accompanying drawings in this application are only for the purposes of illustration and description, and are not used to limit the protection scope of this application. Additionally, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of this application. It should be understood that the operations in the flowchart may not be implemented in sequence, and steps without a logical context relationship may be reversed or implemented simultaneously. Furthermore, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of this application.
[0040] In addition, the described embodiments are only some embodiments of this application, rather than all embodiments. The components of the embodiments of this application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application that is required to be protected, but only represents the selected embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of this application.
[0041] It should be noted that the term "including" will be used in the embodiments of this application to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.
[0042] Based on the technical problems proposed in the background art, this application provides a budget driver analysis method, device, electronic device, and storage medium, which can use AI capabilities to predict and analyze key factors, better achieve data-driven intelligent budget preparation, and improve the budget operation efficiency and credibility of financial institutions.
[0043] See the attached Figure 1 to the specification. A budget driver analysis method provided by this application is applied to a budget system. The method includes the following steps:
[0044] S1. According to the obtained historical annual budget data of the enterprise and the set budget factors, simulate and calculate the historical trends of the budget factors; wherein, the historical annual budget data of the enterprise includes the budget amounts corresponding to the budget factors.
[0045] S2. According to the selected target macroeconomic prediction factors and the historical annual profit data of the enterprise, construct a macroeconomic prediction model to predict the future profit data of the enterprise through the macroeconomic prediction model; wherein, the future profit data of the enterprise includes the total budget of the enterprise and the total revenue of the enterprise.
[0046] S3. Based on the historical trend of the budget factors calculated by simulation and the predicted future profit data of the enterprise, make a budget decision for the enterprise.
[0047] In the embodiment of the present application, the budget driver analysis method can run on a terminal device or a server; among them, the terminal device can be a local application software. When the budget driver analysis method runs on the server, the budget driver analysis method can be implemented and executed based on a cloud interaction system, where the cloud interaction system at least includes a server and a client device (i.e., application software). Specifically, taking the application to the server as an example, when the budget driver analysis method runs on the server, it can use AI capabilities to predict and analyze key factors, and better realize intelligent budget preparation dominated by data-driven.
[0048] Specifically, in step S1, according to the obtained historical annual budget data of the enterprise and the set budget factors, use a time series model to simulate and calculate the historical trend of the budget factors. Among them, a time series refers to a sequence formed by arranging the values of the same statistical indicator in the order of their occurrence time. The main purpose is to predict the future based on existing historical data. The time series model can be an autoregressive model AR(p), a moving average model MA(q), an autoregressive moving average model ARMA(p, q), or an autoregressive integrated moving average model ARIMA(p, d, q). Since it is well-known to those skilled in the art to simulate and calculate the historical trend of the budget factors through a time series model and it is not the focus of this application, it will not be elaborated here. Thus, analyze the historical business data of the enterprise, analyze the annual trends of the budget factors, and realize the future prediction of the budget factors.
[0049] Among them, the budget factors include one or more of sales, procurement, salary, production, production cost, cash, and technological transformation measures. Each enterprise specifically sets budget factors according to its own actual management needs, and this application does not limit and fix this.
[0050] See the attached Figure 2 , in step S2, select target macroeconomic prediction factors in the following manner, including the following steps:
[0051] S201. Perform correlation calculations on a plurality of prepared macroeconomic prediction factors, and screen out a plurality of to-be-determined target macroeconomic prediction factors with the strongest correlation;
[0052] S202. Perform addition and deletion operations on the to-be-determined target macroeconomic prediction factors according to the received adjustment instruction to obtain the target macroeconomic prediction factors.
[0053] In step S201, the prepared macroeconomic prediction factors can be one or more of per capita GDP, inflation rate, consumer price index, population growth rate, total bank assets, total retail sales of consumer goods, industry export growth rate, raw material prices, and industry operation data. By performing Pearson correlation calculations based on multi-dimensional data, the multi-dimensional target macroeconomic prediction factors with the strongest positive and negative correlations are selected. Among them, the Pearson correlation calculation is a method for measuring the degree of correlation between two variables. The Pearson correlation coefficient is a value between 1 and -1, where 1 indicates a perfect positive correlation between variables, 0 indicates no linear correlation, and -1 indicates a perfect negative correlation. In one embodiment, 5-dimensional target macroeconomic prediction factors with the strongest positive and negative correlations can be selected through Pearson correlation calculations;
[0054] Among them, the reason why it is called the target macroeconomic prediction factor to be determined rather than the final target macroeconomic prediction factor is that it may be added, deleted, or modified by the enterprise's senior management according to the macroeconomic environment they understand. That is, in step S202, add or delete operations are performed on the target macroeconomic prediction factor to be determined according to the received adjustment instruction, so as to obtain the target macroeconomic prediction factor. After obtaining the target macroeconomic prediction factor, a macroeconomic prediction model is constructed using the target macroeconomic prediction factor and the enterprise's historical annual profit data to predict the enterprise's future profit data through the macroeconomic prediction model, and the corresponding target macroeconomic prediction factor is saved.
[0055] Among them, when constructing, training, and tuning the macroeconomic prediction model, the following steps are included: using the selected target macroeconomic prediction factor and the enterprise's historical annual profit data as sample data, and dividing the sample data into a training set and a validation set; using the training set to initialize the macroeconomic prediction model to determine at least one parameter in the initial macroeconomic prediction model, and obtaining a pre-trained macroeconomic prediction model; using the validation set to verify the pre-trained macroeconomic prediction model to obtain a verification result; according to the verification result, update at least one parameter in the pre-trained macroeconomic prediction model, such as automatically invoking linear regression, Knn regression, support vector machine regression algorithm, ridge regression, lasson regression, MLP regression, decision tree, extreme tree, XGBoost, random forest, AdaBoost, GradientBoost, so as to establish a trained macroeconomic prediction model to predict the enterprise's future profit data.
[0056] Among them, the enterprise's future profit data includes the enterprise's total budget and total revenue, and see the attached Figure 3 description. The enterprise's total budget is calculated in the following way, including the following steps:
[0057] P1. Extract the formula associated with the set budget factor from the set budget template;
[0058] P2. Parse the extracted formula into executable code to form an enterprise algorithm library;
[0059] P3. Use the enterprise algorithm library and calculate the total enterprise budget according to the budget amount corresponding to the set budget factor.
[0060] In steps P1 - P3, the budget template can be understood as the calculation rules for various businesses of the enterprise. Generally, it adopts the EXCEL table format, and various formulas built into the EXCEL table are used to conduct various calculations on relevant budget factors. In this application, relevant excel processing libraries in python are used, and combined with regular formulas, the formulas contained in each sheet of the EXCEL table are parsed into python executable code and stored to form the company method library, so as to quickly calculate the total enterprise budget according to the budget amount corresponding to the budget factor in the future.
[0061] For example, during application, after the user inputs the budget amounts corresponding to all budget factors or makes a floating adjustment to the budget amount corresponding to a certain budget factor, the total enterprise budget can be immediately output and visually displayed to the user.
[0062] See the attached Figure 4 In step S3, the use of the historical trend of the budget factor calculated based on the simulation and the predicted future profit data of the enterprise to make a budget decision for the enterprise includes the following steps:
[0063] S301. Obtain the future profit data of the enterprise corresponding to various target macroeconomic prediction factors respectively, observe the influence of each target macroeconomic prediction factor on the future profit data of the enterprise, and determine the final total enterprise budget;
[0064] S302. Adjust the budget amounts of the future budget factors of the enterprise according to the final total enterprise budget and the historical trend of the budget factor calculated based on the simulation.
[0065] As described above, since the target macroeconomic prediction factors are regulated by the senior management of the enterprise, and different target macroeconomic prediction factors will result in different macroeconomic prediction models being trained, and thus the future profit data of the enterprise obtained will be different. Therefore, the senior management of the enterprise will utilize this function to observe the impact of each target macroeconomic prediction factor on the future profit data of the enterprise, so as to predict market trends and consumer behaviors, and determine the final total budget of the enterprise to be issued. In addition, the senior management of the enterprise will also observe the change trend of the total budget of the enterprise after floating adjustment of the budget amount corresponding to a certain budget factor, and then determine the budget amount allocated to each budget factor while meeting the final total budget of the enterprise, so as to achieve budget docking mainly driven by data.
[0066] In other embodiments, the budget driver analysis method proposed in this application can be applied to a subsidiary company under an enterprise, and the adjusted subsidiary company data can be merged in real time and summarized to a higher-level company to achieve an overall budget preparation mode.
[0067] It can be seen that the budget driver analysis method proposed in this application not only considers the external impact of macroeconomic factors on different industries, but also considers the internal impact of budget factor changes on the enterprise's revenue, forming a budget preparation method based on data drive. While improving the operation efficiency of budget preparation, it also improves the budget credibility of financial institutions.
[0068] Based on the same inventive concept, an embodiment of this application also provides a budget driver analysis device. Since the principle of solving problems by the device in the embodiment of this application is similar to that of the above-mentioned budget driver analysis method in the embodiment of this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.
[0069] As shown in the attached Figure 5 specification, this application also provides a budget driver analysis device, which is applied to a budget system. The device includes:
[0070] A simulation calculation module 501, configured to simulate and calculate the historical trend of the budget factor according to the obtained historical annual budget data of the enterprise and the set budget factor; wherein, the historical annual budget data of the enterprise includes the budget amount corresponding to the budget factor;
[0071] A prediction module 502, configured to construct a macroeconomic prediction model according to the selected target macroeconomic prediction factor and the historical annual profit data of the enterprise, so as to predict the future profit data of the enterprise through the macroeconomic prediction model; wherein, the future profit data of the enterprise includes the total budget of the enterprise and the total revenue of the enterprise;
[0072] A budget decision-making module 503 for making a budget decision for an enterprise based on the historical trends of the budget factors calculated by simulation and the predicted future profit data of the enterprise.
[0073] In some embodiments, the device further includes:
[0074] A calculation module for extracting formulas associated with the set budget factors from the set budget template according to the set budget template; parsing the extracted formulas into executable code to form an enterprise algorithm library; and calculating the total amount of the enterprise by using the enterprise algorithm library and according to the budget amount corresponding to the set budget factors.
[0075] In some embodiments, the simulation calculation module 501 simulates and calculates the historical trends of the budget factors by using a time series model based on the obtained historical annual budget data of the enterprise and the set budget factors.
[0076] In some embodiments, the prediction module 502 selects target macroeconomic prediction factors, including:
[0077] Performing correlation calculations on a plurality of prepared macroeconomic prediction factors, and screening out a plurality of to-be-determined target macroeconomic prediction factors with the strongest correlation;
[0078] Performing addition and deletion operations on the to-be-determined target macroeconomic prediction factors according to the received adjustment instructions to obtain the target macroeconomic prediction factors.
[0079] In some embodiments, the prediction module 502 performs Pearson correlation calculations on a plurality of prepared macroeconomic prediction factors and screens out a plurality of to-be-determined target macroeconomic prediction factors with the strongest correlation.
[0080] In some embodiments, the budget decision-making module 503 makes a budget decision for the enterprise based on the historical trends of the budget factors calculated by simulation and the predicted future profit data of the enterprise, including: obtaining the future profit data of the enterprise corresponding to various target macroeconomic prediction factors respectively, observing the influence of each target macroeconomic prediction factor on the future profit data of the enterprise, and determining the final total budget of the enterprise;
[0081] Adjusting the budget amount of the future budget factors of the enterprise according to the final total budget of the enterprise and the historical trends of the budget factors calculated by simulation.
[0082] In some embodiments, the prediction module 502 constructs a macroeconomic prediction model according to the selected target macroeconomic prediction factors and the historical annual profit data of the enterprise, including:
[0083] The selected target macroeconomic prediction factors and the historical annual profit data of the enterprise are used as sample data, and the sample data is divided into a training set and a validation set;
[0084] Using the training set to initialize the macroeconomic prediction model, at least one parameter in the initial macroeconomic prediction model is determined to obtain a pre-trained macroeconomic prediction model;
[0085] Using the validation set to verify the pre-trained macroeconomic prediction model to obtain a verification result;
[0086] According to the verification result, at least one parameter in the pre-trained macroeconomic prediction model is updated to establish a trained macroeconomic prediction model.
[0087] A budget driver analysis device provided by the present application, through a simulation calculation module, according to the obtained historical annual budget data of the enterprise and the set budget factors, simulates and calculates the historical trend of the budget factors; wherein, the historical annual budget data of the enterprise includes the budget amount corresponding to the budget factors; through a prediction module, according to the selected target macroeconomic prediction factors and the historical annual profit data of the enterprise, constructs a macroeconomic prediction model to predict the future profit data of the enterprise through the macroeconomic prediction model; wherein, the future profit data of the enterprise includes the total budget of the enterprise and the total income of the enterprise; through a budget decision-making module, based on the simulated historical trend of the budget factors and the predicted future profit data of the enterprise, makes a budget decision for the enterprise. Thus, by using the prediction ability related to AI and replicating the chained calculation of multi-level complex formulas in Excel, it is possible to predict and analyze the future trends of key indicators, and based on data-driven budget docking work, improve the budget operation efficiency and credibility of financial institutions.
[0088] Based on the same concept of the present invention, as shown in the attached Figure 6 FIG. The structure of an electronic device 600 provided by an embodiment of the present application includes: at least one processor 601, at least one network interface 604 or other user interfaces 603, a memory 605, and at least one communication bus 602. The communication bus 602 is used to realize the connection and communication between these components. The electronic device 600 optionally includes a user interface 603, including a display (such as a touch screen, LCD, CRT, holographic imaging or projector, etc.), a keyboard or a pointing device (such as a mouse, trackball, touchpad or touch screen, etc.).
[0089] The memory 605 may include a read-only memory and a random access memory, and provide instructions and data to the processor 601. A part of the memory 605 may also include a non-volatile random access memory (NVRAM).
[0090] In some embodiments, the memory 605 stores the following elements, executable modules, or data structures, or subsets thereof, or extended sets thereof:
[0091] The operating system 6051, which includes various system programs for implementing various basic services and handling hardware-based tasks;
[0092] The application program module 6052, which includes various application programs, such as a launcher, a MediaPlayer, a Browser, etc., for implementing various application services.
[0093] In the embodiments of the present application, by calling the programs or instructions stored in the memory 605, the processor 601 is used to execute the steps in a budget driver analysis method, and can use AI capabilities to predict and trend analyze key factors, better realizing data-driven intelligent budget preparation.
[0094] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps in the budget driver analysis method.
[0095] Specifically, the storage medium can be a general storage medium, such as a removable disk, a hard disk, etc. When the computer program on the storage medium is run, it can execute the above-mentioned budget driver analysis method.
[0096] In the embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For another example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.
[0097] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0098] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit.
[0099] If the function 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 such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.
[0100] Finally, it should be noted that the above embodiments are only specific implementation manners of the present application, used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed in the present application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application. All should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims
1. A budget driver analysis method, characterized in that, applied to a budget system, the method comprises the following steps: According to the obtained historical annual budget data of the enterprise and the set budget factors, simulate and calculate the historical trend of the budget factors; wherein, the historical annual budget data of the enterprise includes the budget amount corresponding to the budget factors; According to the selected target macroeconomic forecasting factors and the historical annual profit data of the enterprise, construct a macroeconomic forecasting model to predict the future profit data of the enterprise through the macroeconomic forecasting model; wherein, the future profit data of the enterprise includes the total enterprise budget and the total enterprise revenue; Based on the historical trend of the budget factors calculated by simulation and the predicted future profit data of the enterprise, make a budget decision for the enterprise.
2. The budget driver analysis method according to claim 1, characterized in that, The total enterprise budget is calculated in the following way, including the following steps: According to the set budget template, extract the formula associated with the set budget factors in the budget template; Parse the extracted formula into executable code to form an enterprise algorithm library; Utilize the enterprise algorithm library and calculate the total enterprise budget according to the budget amount corresponding to the set budget factors.
3. The budget driver analysis method according to claim 2, characterized in that, wherein, According to the obtained historical annual budget data of the enterprise and the set budget factors, use a time series model to simulate and calculate the historical trend of the budget factors.
4. The budget driver analysis method according to claim 3, characterized in that, The target macroeconomic forecasting factors are selected in the following way, including the following steps: Perform correlation calculations on a plurality of prepared macroeconomic forecasting factors, and screen out a plurality of to-be-determined target macroeconomic forecasting factors with the strongest correlation; Perform addition and deletion operations on the to-be-determined target macroeconomic forecasting factors according to the received adjustment instruction to obtain the target macroeconomic forecasting factors.
5. The budget driver analysis method according to claim 4, characterized in that, wherein, Based on Pearson correlation calculation for a plurality of prepared macroeconomic forecasting factors, screen out a plurality of to-be-determined target macroeconomic forecasting factors with the strongest correlation.
6. The budget driver analysis method according to claim 5, characterized in that, The making a budget decision for the enterprise by using the historical trend of the budget factors calculated by simulation and the predicted future profit data of the enterprise includes the following steps: Obtain the future profit data of the enterprise corresponding to various target macroeconomic forecasting factors respectively, observe the influence of each target macroeconomic forecasting factor on the future profit data of the enterprise, and determine the final total enterprise budget; According to the final total enterprise budget and the historical trend of the budget factors calculated by simulation, adjust the budget amount of the future budget factors of the enterprise.
7. The budget driver analysis method according to claim 6, characterized in that, The constructing a macroeconomic forecasting model according to the selected target macroeconomic forecasting factors and the historical annual profit data of the enterprise includes the following steps: The selected target macroeconomic prediction factors and the historical annual profit data of the enterprise are used as sample data, and the sample data is divided into a training set and a validation set; Using the training set to initialize the macroeconomic prediction model, at least one parameter in the initial macroeconomic prediction model is determined to obtain a pre-trained macroeconomic prediction model; Using the validation set to verify the pre-trained macroeconomic prediction model to obtain a verification result; According to the verification result, at least one parameter in the pre-trained macroeconomic prediction model is updated to establish a trained macroeconomic prediction model.
8. A budget driver analysis device, Characterized in that, Applied to a budget system, the device includes: A simulation calculation module for simulating and calculating the historical trend of the budget factor according to the obtained historical annual budget data of the enterprise and the set budget factor; wherein, the historical annual budget data of the enterprise includes the budget amount corresponding to the budget factor; A prediction module for constructing a macroeconomic prediction model according to the selected target macroeconomic prediction factors and the historical annual profit data of the enterprise to predict the future profit data of the enterprise through the macroeconomic prediction model; wherein, the future profit data of the enterprise includes the total budget of the enterprise and the total income of the enterprise; A budget decision-making module for making a budget decision for the enterprise based on the simulated historical trend of the budget factor and the predicted future profit data of the enterprise.
9. An electronic device, Characterized in that, It includes: A processor, a memory and a bus, the memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the budget driver analysis method according to any one of claims 1 to 7 are executed.
10. A computer-readable storage medium, Characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, the steps of the budget driver analysis method according to any one of claims 1 to 7 are executed.