Method and device for generating budgeting information, equipment and medium

Abstract

The invention belongs to the technical field of data processing, and relates to a budgeting information generation method and device, equipment and a medium, and the method comprises the steps: receiving a budgeting starting task of a target time period, including planning target data and historical budgeting data; the method comprises the steps of obtaining business unit rules, generating a budget filling template in combination with planning target data and historical budget data, sending the budget filling template to a plurality of subsystems, and after budget data returned by each subsystem based on the budget filling template is received, verifying and correcting by adopting a rule engine to obtain corrected budget data. And cutting the corrected budget data into time slice data through a time sequence simulation engine according to rolling prediction logic. And performing multi-scene simulation analysis on the time slice data by using an analysis and deduction algorithm to obtain an analysis result. And after obtaining budget approval data, adjusting an analysis result to obtain budget compilation information corresponding to the target time period. The generation efficiency and accuracy of the budgeting information can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular, to a budgeting information generation method and device, a computer device, and a storage medium. BACKGROUND

[0002] In the informatization process of enterprise budget management, the technical level of data collection, monitoring, and analysis links plays a crucial role in the efficiency and accuracy of budget management. However, there are significant deficiencies in current enterprise budget management in several key technical links.

[0003] In terms of data collection, the traditional method highly relies on manual offline reporting. This manual operation mode is not only inefficient, but also lacks effective data integration mechanisms, resulting in weak logical connections between budget subjects. More critically, the budget system and the financial system, business system have not achieved effective data connection. Each system forms a data island, leading to a large amount of manual reconciliation work during budget execution. Manual reconciliation not only consumes time, but also is prone to errors due to human negligence, seriously affecting the accuracy and timeliness of budget data. In terms of monitoring means, enterprises mainly rely on periodic reports, such as monthly or quarterly analysis reports. This periodic report monitoring method has obvious data feedback delay problems. Due to the inability to obtain real-time data, enterprises are difficult to timely discover abnormal situations in the budget execution process, and cannot quickly respond to potential risks, which may cause enterprises to miss the best opportunity to adjust budget strategies. In terms of analysis dimensions, existing technologies are limited to focusing on budget completion rates, i.e., simple comparison between actual execution and target budget. This single-dimensional analysis method fails to deeply explore business causes and cannot provide comprehensive and in-depth budget analysis results for enterprises, making it difficult for enterprises to make scientific and reasonable decisions.

[0004] In summary, the existing technology has technical problems such as inefficient collection of budget-related data involved in the enterprise budget management process, backward monitoring means, and single analysis dimension. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a budgeting information generation method and device, a computer device, and a storage medium to solve the problem of inefficient collection of budget-related data involved in the enterprise budget management process, backward monitoring means, and single analysis dimension in the existing technology.

[0006] In a first aspect, a budgeting information generation method is provided, which adopts the following technical solution:

[0007] The budget start task of the target time period is received, the budget start task including planning target data and historical budget data; a preset business unit rule is acquired, a budget report template including a logical association formula is generated based on the planning target data, the historical budget data and the business unit rule; the budget report template is sent to a plurality of subsystems to receive budget data returned by each of the subsystems based on the budget report template; a preset rule engine is used to check and correct the budget data to obtain corrected budget data; based on a preset rolling prediction logic, a preset time sequence simulation engine is used to cut the corrected budget data to obtain time slice data of the corrected budget data; a preset analysis and deduction algorithm is used to perform multi-scenario simulation analysis on the time slice data to obtain an analysis result; budget approval data is acquired, and the analysis result is adjusted based on the budget approval data to obtain budget compilation information corresponding to the target time period.

[0008] In a second aspect, a budget compilation information generation device is provided, which adopts the following technical solution:

[0009] A receiving module is configured to receive a budget start task of a target time period, the budget start task including planning target data and historical budget data.

[0010] An acquiring module is configured to acquire a preset business unit rule, and generate a budget report template including a logical association formula based on the planning target data, the historical budget data and the business unit rule.

[0011] A sending module is configured to send the budget report template to a plurality of subsystems to receive budget data returned by each of the subsystems based on the budget report template.

[0012] A checking module is configured to use a preset rule engine to check and correct the budget data to obtain corrected budget data.

[0013] A cutting module is configured to use a preset time sequence simulation engine to cut the corrected budget data based on a preset rolling prediction logic to obtain time slice data of the corrected budget data.

[0014] An analysis module is configured to use a preset analysis and deduction algorithm to perform multi-scenario simulation analysis on the time slice data to obtain an analysis result.

[0015] An adjusting module is configured to acquire budget approval data, and adjust the analysis result based on the budget approval data to obtain budget compilation information corresponding to the target time period.

[0016] In a third aspect, a computer device is provided, which adopts the following technical solution:

[0017] The budget starting task of the target time period is received, the budget starting task including planning target data and historical budget data; a preset business unit rule is acquired, a budget filling template including a logical association formula is generated based on the planning target data, the historical budget data and the business unit rule; the budget filling template is sent to a plurality of subsystems to receive budget data returned by each of the subsystems based on the budget filling template; a preset rule engine is used to check and correct the budget data to obtain corrected budget data; based on a preset rolling prediction logic, a preset time sequence simulation engine is used to cut the corrected budget data to obtain time slice data of the corrected budget data; a preset analysis and deduction algorithm is used to perform multi-scenario simulation analysis on the time slice data to obtain an analysis result; budget approval data is acquired, and the analysis result is adjusted based on the budget approval data to obtain budget compilation information corresponding to the target time period.

[0018] In a fourth aspect, a computer-readable storage medium is provided, and the following technical solutions are adopted:

[0019] The budget starting task of the target time period is received, the budget starting task including planning target data and historical budget data; a preset business unit rule is acquired, a budget filling template including a logical association formula is generated based on the planning target data, the historical budget data and the business unit rule; the budget filling template is sent to a plurality of subsystems to receive budget data returned by each of the subsystems based on the budget filling template; a preset rule engine is used to check and correct the budget data to obtain corrected budget data; based on a preset rolling prediction logic, a preset time sequence simulation engine is used to cut the corrected budget data to obtain time slice data of the corrected budget data; a preset analysis and deduction algorithm is used to perform multi-scenario simulation analysis on the time slice data to obtain an analysis result; budget approval data is acquired, and the analysis result is adjusted based on the budget approval data to obtain budget compilation information corresponding to the target time period.

[0020] Compared with the prior art, the embodiments of the present application have the following beneficial effects: in the data collection link, the budget initiation task containing the planning target data, the historical budget data and the industry benchmark data is received, and a budget filling template containing a logical association formula is generated by combining a preset business unit rule, so that the standardization and automation of budget filling are realized. The template is sent to multiple subsystems, so as to make each subsystem return budget data based on the unified template, break the data barrier between the budget system and the financial and business systems, effectively integrate cross-system data, solve the problems of low efficiency of traditional manual offline filling and weak logical association of budget subjects, greatly improve the efficiency and accuracy of budget-related data collection, and reduce the workload and error rate of manual reconciliation. In the monitoring aspect, the modified budget data is cut based on the rolling prediction logic and the time sequence simulation engine to obtain time slice data, which can track the budget execution progress in real time, discover abnormal situations in time, change the situation of data feedback delay caused by the dependence on periodic reports in the past, enable the enterprise to respond quickly to potential risks, and seize the best opportunity to adjust the budget strategy. In the analysis dimension, the preset analysis deduction algorithm is used to perform multi-scenario simulation analysis on the time slice data, deeply mine the business causes, and no longer be limited to simple budget completion rate comparison, so as to provide the enterprise with comprehensive and in-depth budget analysis results. Finally, the budget preparation information is obtained by adjusting the analysis results combined with the budget approval data, which effectively supports the enterprise to make scientific and reasonable decisions. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the schemes in the present application, the drawings needed in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0023] Figure 2 Flowchart of one embodiment of the budget preparation information generation method according to the present application;

[0024] Figure 3 is a structural schematic diagram of one embodiment of the budget preparation information generation device according to the present application;

[0025] Figure 4 is a structural schematic diagram of one embodiment of the computer device according to the present application. DETAILED DESCRIPTION

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terms used in the specification are intended to describe the particular embodiments and are not intended to limit the application; the terms "include" and "have" and their any variations used in the specification and the claims and the above description of drawings are intended to cover the non-exclusive inclusion; the terms "first", "second" and the like used in the specification and the claims and the above description of drawings are intended to distinguish different objects, not to describe a particular order.

[0027] Reference herein to "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive or alternative embodiments. It is expressly understood that the embodiments described herein are combinable with each other.

[0028] In order to make the person skilled in the art better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings below.

[0029] As shown in Figure 1 The system architecture 100 can include a terminal device 101, a network 102 and a server 103, and the terminal device 101 can be a notebook computer 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0030] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0031] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing, in addition to the notebook computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an electronic book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer and a desktop computer, etc.

[0032] The server 103 can be a server providing various services, for example, a background server providing support for a page displayed on the terminal device 101.

[0033] It should be noted that the budget preparation information generation method provided by the embodiments of the present application is generally executed by a server / terminal device, and accordingly, the budget preparation information generation apparatus is generally arranged in the server / terminal device.

[0034] It should be understood that, Figure 1 The number of terminal devices, networks and servers in

[0035] With reference to Figure 2 , a flow chart of one embodiment of the budget preparation information generation method according to the present application is shown. The budget preparation information generation method comprises the following steps:

[0036] Step S201, receiving a budget start task of a target time period, the budget start task comprising planning target data and historical budget data.

[0037] The target time period refers to a specific time span set in advance in the enterprise budget management work, which needs to be prepared, monitored and analyzed. The start and end time of the budget work is used to carry out targeted budget-related work. For example, the enterprise sets the next quarter as the target time period, and the subsequent budget work will be carried out around this quarter.

[0038] The budget start task can be initiated by the enterprise management or the budget management department, which is a set of instructions and data collection for triggering the budget preparation process. It is used to start the entire budget preparation process and provide a basic guide for subsequent budget work. For example, after the budget start task containing data such as target time period and planning target is issued, each related department starts to participate in budget preparation.

[0039] The planning target data is the budget target related data set by the enterprise for a specific target time period based on strategic planning, market forecasting and other factors. It is used to guide the direction of budget preparation and execution, and provides a target basis for setting budget data. For example, the enterprise sets a 20% increase in sales for the next quarter as the planning target data.

[0040] The historical budget data is the budget related data actually executed and recorded by the enterprise in the past target time period. It is used to provide reference and basis for current budget preparation, and helps the enterprise analyze budget execution trends and rules. For example, the budget execution data of the same season in the past three years can be used as historical budget data.

[0041] Step S202, obtain the preset business unit rules, and generate a budget reporting template containing a logical association formula based on the planning target data, the historical budget data and the business unit rules.

[0042] The business unit rules are the rules and constraints related to budget preparation set by the enterprise for different business units (such as departments, projects, etc.). It is used to regulate the budget preparation behavior of each business unit and ensure the rationality and consistency of the budget data. For example, it is stipulated that the cost proportion of a certain department should not exceed 10% of the total budget.

[0043] The logical association formula is a mathematical expression or logical rule set in the budget reporting template to reflect the logical relationship between budget subjects. It is used to ensure the accuracy and reasonableness of the budget data and avoid logical errors. For example, the formula

cost = raw material cost + labor cost + manufacturing expense

[0044] The budget reporting template is generated based on the planning target data, the historical budget data and the business unit rules, and contains the logical association formula. It is used to guide the budget data reporting of each subsystem and ensure the standardization and consistency of the data.

[0045] Step S203, send the budget reporting template to multiple subsystems to receive the budget data returned by each subsystem based on the budget reporting template.

[0046] The multiple subsystems refer to different functional modules or information systems involved in the enterprise budget management, such as financial systems, business systems, etc. It is used to realize the information processing and data storage of each business of the enterprise and provide the source for budget data collection.

[0047] The budget data is the various types of data related to the budget of the target time period returned by each subsystem based on the budget reporting template. It reflects the plans and expectations of the enterprise in the budget preparation process from the data content perspective, and represents the specific content of the enterprise budget. For example, the budget cost data reported by each department.

[0048] Step S204, using a preset rule engine, the budget data is checked and corrected to obtain the corrected budget data.

[0049] Among them, the rule engine is a kind of preset program module. For the received budget data is automatically compared and judged, to ensure that the data meet the established norms and standards. For example, check whether the budget data exceeds the set amount range, whether it meets the logical relationship between budget subjects, etc., in order to guarantee the accuracy and rationality of the budget data.

[0050] Among them, the check and correction refers to the use of rule engine to check the compliance of budget data, and adjust the data based on the check result. For finding and correcting errors and non-standard places in the budget data, so that the data meet the requirements of enterprise budget management.

[0051] Among them, the corrected budget data refers to the budget data obtained after the rule engine check and correction. Characterized by the data set that meets the requirements of enterprise budget rules and logic after being audited and adjusted.

[0052] Step S205, based on the preset rolling prediction logic, using the preset time sequence simulation engine to cut the corrected budget data, to obtain the time slice data of the corrected budget data.

[0053] Among them, the rolling prediction logic is a kind of logic rule system based on time series and business development trend, which dynamically predicts the corrected budget data. For predicting the changes of budget data in different time periods according to historical data and current situation, to help enterprises adjust budget strategy in time to adapt to market changes.

[0054] Among them, the time sequence simulation engine is a kind of tool or algorithm module based on the preset rolling prediction logic, which simulates and analyzes the corrected budget data in time dimension. For detailed analysis and simulation of the corrected budget data according to time sequence, to generate budget data slice at different time points, and provide data support for multi-scenario simulation analysis.

[0055] Among them, the cutting refers to the process of cutting the corrected budget data according to the preset time interval (such as monthly, quarterly) by using the time sequence simulation engine. For dividing the overall budget data into different time period data segments, to facilitate the separate analysis and comparison of the budget execution in different time periods.

[0056] Among them, the time slice data is the budget data segment divided by time interval after cutting the corrected budget data. For detailed analysis and monitoring of the budget execution in different time periods, to help enterprises understand the execution progress and effect of budget in different stages.

[0057] Step S206, using a preset analysis deduction algorithm, time slicing data is analyzed and simulated in multiple scenarios to obtain analysis results.

[0058] Wherein, the analysis deduction algorithm is a preset algorithm or model for analyzing and simulating time slicing data in multiple scenarios. It is used to simulate and analyze time slicing data from different angles and predict the execution results of the budget in different scenarios.

[0059] Wherein, the multi-scenario simulation analysis is a process of analyzing and simulating time slicing data in multiple hypothetical scenarios using the analysis deduction algorithm. It represents the operation of evaluating the execution effect of budget data in different situations.

[0060] Wherein, the analysis result is the conclusion and suggestion about the budget execution and risk after the multi-scenario simulation analysis of time slicing data.

[0061] Step S207, obtain budget approval data, adjust the analysis results based on the budget approval data, and obtain the budget preparation information corresponding to the target time period.

[0062] Wherein, the budget approval data is the approval opinion and related data given by the management layer or relevant departments of the enterprise after auditing the budget preparation information. It is used to adjust and confirm the budget preparation information to ensure the rationality and feasibility of the budget.

[0063] Wherein, the adjustment refers to the process of modifying and improving the analysis results according to the budget approval data. It is used to make the budget preparation information more consistent with the actual situation and management requirements of the enterprise, to ensure that the budget can be smoothly executed and achieve the expected goal.

[0064] Wherein, the budget preparation information is the final determination data and related documents about the budget of the target time period, obtained after a series of processes such as receiving tasks, data collection, verification and correction, analysis and prediction, and approval adjustment. It is used to guide the budget execution and management of the enterprise in the target time period, and provides budget basis and control standard for the business activities of the enterprise.

[0065] The budget filling template containing the logical association formula is generated by receiving the budget starting task containing the planning target data, the historical budget data and the industry benchmark data, and combining the preset business unit rules. The standardization and automation of the budget filling are realized. The template is sent to multiple subsystems, so that the budget data is returned based on the unified template, the data barriers between the budget system and the financial and business systems are broken, the cross-system data is effectively integrated, the problems of low efficiency of traditional manual offline filling and weak logical association of budget subjects are solved, the efficiency and accuracy of the budget related data collection are greatly improved, and the workload and error rate of manual reconciliation are reduced. In the monitoring aspect, the time slice data is obtained by cutting the corrected budget data based on the rolling prediction logic and the time sequence simulation engine, the budget execution progress can be tracked in real time, abnormal conditions can be found in time, the situation of relying on periodic reports to cause data feedback delay is changed, the enterprise can make a quick response to potential risks, and the best opportunity to adjust the budget strategy is grasped. In the analysis dimension, the preset analysis deduction algorithm is used for multi-scenario simulation analysis of the time slice data, the business causes are deeply mined, and the budget analysis result is no longer limited to simple budget completion rate comparison. Comprehensive and in-depth budget analysis results are provided for the enterprise. Finally, the budget preparation information is obtained by adjusting the analysis result combined with the budget approval data, which effectively supports the enterprise to make scientific and reasonable decisions.

[0066] In some optional implementation manners of the embodiment, in step S202, the budget filling template containing the logical association formula is generated based on the planning target data, the historical budget data and the business unit rules, and specifically includes the following steps.

[0067] The text analysis is performed on the planning target data to obtain a strategic indicator list, statistical analysis is performed on the historical budget data to generate a historical data feature report, the business unit rules are classified to obtain constraint rules and calculation rules, a template framework design document is generated based on the strategic indicator list, the historical data feature report, the constraint rules and the calculation rules, and the logical association formula is obtained and embedded into the template framework design document to generate the budget filling template.

[0068] The text analysis refers to a process of in-depth analysis of the planning target data by using natural language processing and semantic understanding techniques. The strategic indicator information is extracted from the planning target data to provide strategic guidance for subsequent budget preparation.

[0069] The strategic indicator list is a set of key indicators reflecting the strategic direction and target of the enterprise extracted from the planning target data through text analysis. The strategic focus and direction of budget preparation are determined, and the allocation of budget resources in different business fields is guided.

[0070] Among them, statistical analysis is the process of collecting, sorting, calculating and analyzing historical budget data using statistical methods. It is used to discover rules and trends from historical budget data, providing historical reference and experience for budget preparation.

[0071] Among them, the historical data feature report is generated based on statistical analysis of historical budget data, and is a document that describes the characteristics and rules of historical budget data in detail.

[0072] Among them, the constraint rule is a mandatory provision and restriction condition that must be followed by the business unit in the budget preparation and execution process.

[0073] Among them, the calculation rule is a mathematical formula or logical rule used to calculate and derive budget data during budget preparation.

[0074] Among them, the template framework design document is a framework document of the budget reporting template designed based on information such as the strategic indicator list, the historical data feature report, the constraint rule, and the calculation rule.

[0075] In an example, the budget filling template generation process can be started when the enterprise conducts annual budgeting. First, receive the annual budget start task, which contains planning target data (such as the text content mentioned in the enterprise strategic planning document, such as "this year, the market share is to be increased to 25%, and the R&D investment proportion is to be 18%"), historical budget data (detailed budget data of each department's expenses and income in the past three years), and industry benchmark data (average profit margin, expense rate, and other data of similar size enterprises in the same industry). Then, text analysis is performed on the planning target data. Using natural language processing technology, key strategic information such as "market share increase by 25%" and "R&D investment proportion by 18%" is extracted from the above strategic planning text to form a list of strategic indicators. These indicators clearly indicate the strategic direction of the enterprise's annual budget, and subsequent budgeting will be carried out around these targets. At the same time, statistical analysis is performed on the historical budget data. Using statistical methods, calculate the average, standard deviation, and growth rate of each department's expense budget, analyze the historical fluctuations of the expense budget, and the change of budget proportion of different business types, and generate a historical data feature report. For example, it is found that the R&D department's expense budget has been growing by 15% on average each year for the past three years, which provides a reference for formulating this year's R&D budget. Then, obtain the preset business unit rules and classify them into constraint rules and calculation rules. Constraint rules such as "the sales department's expense budget cannot exceed 10% of its business income"; calculation rules such as "production cost = direct material cost + direct labor cost + manufacturing cost". Based on the strategic indicator list, historical data feature report, constraint rules, and calculation rules, generate a template framework design document. The document specifies that the budget filling template includes sales, R&D, production, and other departments, sets market share, R&D investment proportion, expense budget, and other subjects, clearly defines the data types and filling formats of each subject, and the logical association method between different subjects. Finally, obtain the logical association formula, such as "department profit = department income - department cost - department expense", and embed it into the template framework design document to generate the budget filling template. The template not only meets the strategic goals of the enterprise, but also combines historical data and business rules, making it easy for each business unit to accurately fill in the budget data and improve the efficiency and accuracy of budgeting.

[0076] The embodiments of the present application can obtain a strategic indicator list through text analysis on planning target data, accurately extract key indicators such as market share and R&D investment proportion from enterprise strategic planning and other texts, and provide clear strategic direction for budget preparation to avoid blindness. Statistical analysis of historical budget data generates a historical data feature report, which can grasp the laws of cost fluctuations and business proportions, and provide reliable historical reference for budget preparation. The business unit rules are classified into constraint rules and calculation rules, the constraint rules guarantee budget compliance, and the calculation rules ensure accurate data calculation. Based on the above information, a template framework design document is generated, and a budget reporting template is generated by embedding logical correlation formulas, so that the template meets the strategic target, combines historical data and business rules, and the logical correlation of each budget subject is close. The template is then pushed to each subsystem to reduce manual offline reporting, break down data silos, avoid the tediousness and errors of manual reconciliation, and improve data collection efficiency and accuracy.

[0077] In some optional implementations, the budget initiation task further includes industry benchmark data, step S204, and a preset rule engine is used to verify and correct the budget data to obtain corrected budget data, which specifically includes the following steps:

[0078] The preset rule engine is used to verify the compliance of the budget data to obtain the verified budget data; real-time market data is obtained, a preset dynamic optimization engine is used to compare the industry benchmark data, the real-time market data and the verified budget data, and multi-dimensional budget optimization suggestions are generated; based on the budget optimization suggestions, the verified budget data is corrected to obtain the corrected budget data.

[0079] The industry benchmark data is budget-related data with universal reference value obtained by statistics and analysis of enterprises in the same industry within a similar target time period. It is used to provide external comparison and reference for enterprise budget preparation and help enterprises evaluate the rationality of their own budget.

[0080] The compliance verification refers to the process of using a preset rule engine to comprehensively check and verify the budget data according to a series of established rules and standards. It is used to ensure that the budget data meets the specification requirements of enterprise budget management in terms of format, content, logic, etc., and to avoid data errors and illegal reporting.

[0081] The real-time market data refers to data information reflecting the current market situation, trends and dynamics obtained in real time through various data collection channels.

[0082] The dynamic optimization engine is a software system or technical module based on a preset algorithm and model that can comprehensively analyze and process different types of data and automatically generate optimization suggestions based on the analysis results.

[0083] The budget optimization suggestion is based on comparative analysis of industry benchmark data, real-time market data, and the verified budget data, and is proposed to improve and optimize measures for problems and deficiencies in the enterprise budget preparation and execution process.

[0084] In an example, during the annual budget preparation process of an enterprise, the budget data can be optimized by using the scheme of the embodiment. Specifically, first, the budget data returned by each department based on the budget reporting template is received. These budget data contain the estimated values of each department for each item of expenses, income, etc. Then, the budget data is verified for compliance using a pre-set rule engine. Various rules are pre-set in the rule engine, for example, it is stipulated that the marketing expense budget of the sales department should not exceed 15% of its expected business income. When the budget data of the sales department is verified, if it is found that its marketing expense budget accounts for 18%, it is determined that the data is not compliant, and the system will mark the problem and prompt the relevant personnel. After verification, the verified budget data is obtained, ensuring that the budget data meets the enterprise's requirements in terms of format, logic, and rules. Then, real-time market data, such as current raw material market prices, competitor product prices, etc. are obtained. At the same time, industry benchmark data, such as the average cost rate and profit rate of similar-sized enterprises in the same industry, etc. are obtained. The industry benchmark data, real-time market data, and verified budget data are compared and analyzed using a pre-set dynamic optimization engine. For example, the dynamic optimization engine finds that the enterprise's raw material procurement budget is based on historical prices, while the current market price of raw materials has risen by 10%, and the industry benchmark data shows that enterprises in the same industry perform better in raw material procurement cost control. Based on these comparisons, multi-dimensional budget optimization suggestions are generated, such as suggesting to increase the raw material procurement budget to cope with the price increase, and optimizing the procurement process to reduce procurement costs. Finally, the verified budget data is corrected according to the generated budget optimization suggestions.

[0085] The embodiment of the present application can verify the compliance of the budget data through the rule engine, and control the quality from the data source. The rule engine accurately identifies the irregular items in the budget data according to the pre-set rules, and timely marks and feeds back, ensuring that the verified budget data meets the specification requirements of the enterprise budget management, and reducing the budget execution deviation caused by data errors. By obtaining real-time market data and using a dynamic optimization engine to compare and analyze the industry benchmark data, real-time market data, and verified budget data, the differences between market changes and enterprise budgets can be accurately captured, and multi-dimensional budget optimization suggestions can be generated to make the budget more in line with market reality. Based on the budget optimization suggestions, the budget data is corrected, and the corrected budget data fully considers market dynamics and industry benchmarks, which helps the enterprise to reasonably allocate resources and improve the budget execution effect.

[0086] In some optional implementations, in step S205, the modified budget data is cut based on a preset rolling prediction logic using a preset timing simulation engine to obtain time-slice data of the modified budget data, specifically including the following steps:

[0087] The modified budget data is standardized to obtain a standardized budget data set. Based on a preset rolling prediction logic, the standardized budget data set is cut according to a time granularity to obtain a time-slice data set. A preset timing simulation engine is used to predict the results of the time-slice data set to obtain a prediction result. The prediction result and the time-slice data set are verified and fused to obtain time-slice data.

[0088] The standardization process is used to eliminate differences between data, so that budget data of different sources and different formats can be compared and analyzed under the same standard, improving the comparability and usability of data.

[0089] The time-slice data set is a data set formed by cutting the standardized budget data set according to a certain time granularity based on a preset rolling prediction logic. It is used to facilitate the separate analysis, prediction and processing of budget data in different time periods, so as to better grasp the characteristics and trends of budget data in different time periods.

[0090] The result prediction refers to the process of using a preset timing simulation engine to analyze and simulate the time-slice data set to predict the development trend and possible results of budget-related data in the future.

[0091] The prediction result is a predictive conclusion about future budget-related data obtained by analyzing the time-slice data set using a preset timing simulation engine based on the result prediction process.

[0092] The result verification and fusion refers to the process of comparing, analyzing and integrating the prediction result and the time-slice data set to verify the accuracy of the prediction result, and fusing the verified result with the time-slice data set to obtain more comprehensive and accurate time-slice data.

[0093] In an example, during the quarterly budgeting and optimization process of an enterprise, the revised budget data can be standardized. The revised budget data contains cost budget data in different formats for different departments, such as some departments in "yuan" and some in "ten thousand yuan", and the date formats are also not uniform. Through standardization, all cost units are converted to "ten thousand yuan" and the date format is standardized to "YYYY-MM-DD". For example, a certain cost of the sales department originally recorded as 50,000 yuan is converted to 50,000 yuan after standardization, and the date "20240115" is converted to "2024-01-15". The standardized budget data set is finally obtained, ensuring consistent data formats and facilitating subsequent analysis. Next, based on the preset rolling prediction logic, the standardized budget data set is cut according to the monthly time granularity. If the standardized budget data set covers the entire quarter, cutting by month will result in 3 time slice data sets, each representing a monthly budget data, facilitating separate analysis of budget in different months. Then, a preset time series simulation engine is used to predict the results of the time slice data set. The time series simulation engine uses specific algorithms to predict the next monthly cost budget based on historical data and the current time slice data. For example, based on the first two months of sales cost budget time slice data, it is predicted that the third month of sales cost budget will reach 800,000 yuan. Finally, the prediction results and time slice data set are verified and fused. The predicted third month sales cost budget of 800,000 yuan is compared and verified with the actual sales cost data of the same period in the past. If the deviation is within a reasonable range (such as ±10%), the prediction result is fused with the time slice data set to obtain a time slice data set containing prediction data.

[0094] The embodiments of the present application can standardize the revised budget data, eliminate differences in format, unit, etc. of budget data from different sources, unify the data into a standard form, and form a standardized budget data set. This provides a unified basis for subsequent processing, facilitates comparison and analysis between data, and avoids analysis errors caused by data format problems. Based on the rolling prediction logic, the data set is cut by time granularity to obtain a time slice data set, which can refine the continuous budget data by time dimension, focus on the data characteristics of different time periods, and accurately grasp the distribution and trend of the budget in different stages. The time series simulation engine is used to predict the results of the time slice data set, which can use historical data and current data trends to predict future budget trends and obtain prediction results to provide a basis for early planning for the enterprise. The prediction results and time slice data set are verified and fused to ensure the accuracy of the prediction results and combine the prediction data with the actual data to obtain more comprehensive and accurate time slice data.

[0095] In some optional implementations, in step S206, the time slice data is subjected to multi-scenario simulation analysis by using a preset analysis deduction algorithm to obtain analysis results, specifically including the following steps:

[0096] Based on the planning target data, a plurality of scene types of scenes and variable adjustment rules corresponding to each scene are defined; based on the variable adjustment rules and the time slice data, a preset analysis deduction algorithm is used to simulate and analyze each scene to obtain analysis results.

[0097] Among them, the plurality of scene types are a set of scene categories divided and defined according to different business needs, market environment change factors, enterprise strategic directions and other dimensions from the planning target data.

[0098] Among them, the scene is a specific simulation environment constructed for a specific business situation or market condition under the framework of the plurality of scene types.

[0099] Among them, the variable adjustment rule is an adjustment strategy and method formulated for each variable in the scene based on the planning target data and the scene demand.

[0100] In an example, when an enterprise conducts annual budgeting, different scenarios can be simulated and analyzed by the scheme of the embodiment. First, based on the planning target data, a plurality of scene types of scenes and variable adjustment rules corresponding to each scene are defined. It is assumed that the annual sales growth of 20% is set in the planning target data. The defined scene types include market expansion scenario, cost increase scenario and competition intensification scenario. In the market expansion scenario, the variable adjustment rule is: if 3 new sales regions are planned to be added, the sales expense is increased by 15%, and at the same time the sales growth is expected to be 25%; in the cost increase scenario, if the raw material price rises by 10%, the production cost increases by 8%, and the gross profit margin is expected to decrease by 3 percentage points; in the competition intensification scenario, if the competitor launches similar products and reduces the price by 10%, the product price of the enterprise needs to be reduced by 5%, and at the same time the market promotion expense is increased by 20%. Then, the time slice data of the revised budget data is obtained, for example, cut by quarter to obtain four quarterly time slice data, including the budget information of sales, cost, expense and the like of each quarter. Then, based on the variable adjustment rules and the time slice data, a preset analysis deduction algorithm is used to simulate and analyze each scene. Taking the market expansion scenario as an example, on the basis of the first quarter time slice data, the data is adjusted according to the rule that the addition of sales regions leads to an increase of 15% in sales expense and an increase of 25% in sales, and the analysis deduction algorithm is used to simulate the profit in the first quarter under the market expansion scenario. The four quarters are sequentially simulated and analyzed to obtain the analysis results such as profit and cash flow of each quarter under different scenarios.

[0101] The embodiments of the present application can define multiple scene types of scenes and corresponding variable adjustment rules based on planning target data, and can fully consider various complex situations that the enterprise may face in operation. Different scene types cover market, cost, competition and other factors, and the variable adjustment rules accurately correspond to the business variable change logic under each scene, making the simulation scene more close to the actual business environment, providing rich and reasonable analysis dimensions for comprehensive budget assessment. Combined with time slice data and variable adjustment rules, the analysis and deduction algorithm is used to simulate and analyze each scene, which can dynamically display the execution effect of the budget in different time periods and different scenes. The time slice data provides detailed budget information in the time dimension, the variable adjustment rule ensures that the simulation process conforms to the characteristics of each scene, and the analysis and deduction algorithm can efficiently process data and obtain accurate results.

[0102] In some optional implementations, step S207, adjusting the analysis result based on the budget approval data to obtain budgeting information corresponding to the target time period, specifically including the following steps:

[0103] A preset natural language technology is used to extract multiple key fields from the budget approval data; structured approval data is generated according to the multiple key fields, and adjustment rules are generated based on the structured approval data; preset budget constraint conditions are obtained, and the adjustment rules are verified based on the constraint conditions; if the verification is passed, the analysis result is adjusted based on the adjustment rules to obtain budgeting information corresponding to the target time period.

[0104] Among them, the natural language technology represents the use of computer algorithms and models to simulate human understanding, generation and operation capabilities of natural language, involving multiple aspects such as lexical analysis, syntactic analysis, semantic understanding, and sentiment analysis. It is used to accurately extract valuable information from unstructured budget approval data text, and convert the approval content expressed in human natural language into a data form that can be processed by a computer.

[0105] Among them, the multiple key fields are extracted from the budget approval data, and are a set of data elements with specific business meaning and identification function.

[0106] Among them, the structured approval data is a data set organized according to a predetermined data structure and format based on the multiple key fields.

[0107] Among them, the adjustment rule is generated based on the structured approval data, and is a set of logical criteria and operation instructions for adjusting the budget analysis result.

[0108] Among them, the budget constraint condition is a set of rules and restriction conditions for limiting and regulating budget adjustment, which is set in advance during the budgeting process.

[0109] The verification is based on a preset budget constraint condition, and is a process of checking and confirming the adjustment rule.

[0110] In an example, when the enterprise conducts quarterly budgeting, the technical solution of the embodiment can be used to analyze and process the budget approval data. First, the budget approval data is obtained, for example, an approval opinion is "agree to increase the R&D department equipment procurement budget to 800,000 yuan, and require that the marketing promotion expense ratio not exceed 15% of the total budget". The preset natural language technology is used to extract the fields of the approval data. Through technical means such as lexical analysis and semantic understanding, multiple key fields such as "R&D department", "equipment procurement budget", "800,000 yuan", "marketing promotion expense", "total budget", and "15%" are identified. Next, according to the extracted multiple key fields, structured approval data is generated. "R&D department" is taken as the approval department field, "equipment procurement budget" and "800,000 yuan" correspond to the specific approval item and amount fields, "marketing promotion expense ratio" and "15%" are another key approval condition field, and they are organized according to the predetermined data structure to form structured approval data. Then, the adjustment rule is generated based on the structured approval data. For the R&D department equipment procurement budget, the adjustment rule is to adjust the R&D department equipment procurement budget in the analysis result to 800,000 yuan; for the marketing promotion expense ratio, the adjustment rule is to ensure that the marketing promotion expense in the total budget does not exceed 15%. The preset budget constraint condition is obtained, such as the enterprise stipulates that the total budget of this quarter cannot exceed 5,000,000 yuan. Based on the constraint condition, the adjustment rule is verified to check whether the budget adjusted according to the adjustment rule meets the requirement that the total budget does not exceed 5,000,000 yuan. If the verification is passed, the analysis result obtained by the previous multi-scene simulation analysis is adjusted based on the adjustment rule. For example, if the original analysis result is that the marketing promotion expense is 900,000 yuan and the total budget is 480,000 yuan, but the total budget may exceed 5,000,000 yuan after the equipment procurement budget is adjusted, the marketing promotion expense needs to be further adjusted to meet the condition that the ratio does not exceed 15% and the total budget does not exceed 5,000,000 yuan, and finally the corresponding budgeting information of this quarter is obtained.

[0111] The budget approval data can be field extracted through natural language technology, key information can be accurately identified and separated from unstructured approval text, the problems of manual extraction errors and low efficiency are effectively solved, and the accuracy and comprehensiveness of key field extraction are ensured. Based on the extracted multiple key fields, structured approval data is generated, the originally disordered approval information is converted into an ordered and standardized data format, which is convenient for subsequent computer processing and analysis, and improves the usability and operability of the data. According to the structured approval data, adjustment rules are generated, and reasonable budget adjustment schemes can be automatically formulated according to the approval opinions, so that the budget adjustment is more scientific and reasonable. The adjustment rules are verified according to the budget constraint conditions, which can ensure that the adjustment rules meet the strategic goals and financial requirements of the enterprise, and avoid deviation of the budget adjustment. Finally, the analysis results are adjusted based on the verified adjustment rules, and accurate and reliable budget preparation information is obtained.

[0112] In some optional implementations, after the budget reporting template is sent to the multiple subsystems to receive the budget data returned by each subsystem based on the budget reporting template in step S203, the following steps are further included:

[0113] If no budget data is received within a preset time period, a target subsystem that does not return budget data is determined from the multiple subsystems; a preset exception diagnosis engine is used to analyze the delayed return event of the target subsystem, and a budget delay report is generated.

[0114] The preset time period is used to define an effective time window for receiving budget data. During the budget preparation process, the system will determine whether the budget data is received within the specified time according to this time period. It is used to monitor the progress of receiving budget data and ensure that the budget preparation work can proceed in an orderly manner according to the predetermined time plan.

[0115] The target subsystem represents a specific subsystem entity that does not return budget data within the specified time among the multiple subsystems.

[0116] The exception diagnosis engine represents a technical component with intelligent analysis and judgment capabilities for system exception events, which can analyze and judge the delay reply event of the target subsystem and other abnormal conditions according to the preset rules and algorithms. It is used to quickly locate the cause of system exceptions and provide a basis for subsequent exception handling.

[0117] The delay return event is caused by real-time monitoring of the system for budget data reception. When the subsystem does not return budget data within the preset time period, the record of this event is triggered.

[0118] The budget delay report is a document or data record generated by the abnormal diagnosis engine based on analysis of the delay return event of the target subsystem. The budget delay report is used to provide relevant personnel with detailed information about the budget delay, helping them understand the delay reasons and take appropriate measures.

[0119] In an example, in an enterprise annual budget preparation scenario, the system receives a budget start task for a target time period, which contains planning target data and historical budget data. Then, the system obtains preset business unit rules, generates a budget filing template containing a logical association formula, and sends the template to multiple subsystems, including a financial subsystem, a sales subsystem, and a production subsystem, to receive budget data returned by each subsystem based on the template. At the end of the preset time period, the system finds that budget data of the production subsystem has not been received. At this time, the system determines the production subsystem as the target subsystem that has not returned budget data from the multiple subsystems. Then, the system uses a preset abnormal diagnosis engine to analyze the delay return event of the production subsystem. The abnormal diagnosis engine first checks the network connection status of the production subsystem and finds that the network connection is normal. Then, the abnormal diagnosis engine checks the system resource occupation and finds that the CPU usage is too high, reaching more than 90%, which may cause the system to process the budget filing task slowly. Meanwhile, the engine checks the data transmission link and finds no obvious abnormalities. Based on the above analysis, the abnormal diagnosis engine generates a budget delay report. The report content includes: the target subsystem is the production subsystem; the delay return event occurs in the budget data receiving time period from January 1 to January 5; the preliminary diagnosis of the delay reason is that the CPU usage of the production subsystem is too high, affecting the processing speed of the budget filing task. In addition, the report also proposes a treatment suggestion, such as suggesting the production subsystem administrator to optimize the system, clean up unnecessary processes, and reduce the CPU usage to ensure that the subsequent budget data can be returned on time. Through this example, the system can timely find abnormal situations in the budget data receiving process and generate a report through the abnormal diagnosis engine to provide a basis for subsequent problem solving, ensuring the smooth progress of the budget preparation work.

[0120] The embodiments of the present application can accurately locate the problem source by determining the target subsystem that has not returned budget data from the multiple subsystems when budget data is not received within a preset time period. In a complex budget preparation system, multiple subsystems process budget filing tasks in parallel, and this technical feature can avoid blind investigation and quickly focus on a specific subsystem that has a problem, significantly improving the problem processing efficiency. The abnormal diagnosis engine can analyze the delay return event of the target subsystem, which can deeply analyze the delay reasons. The abnormal diagnosis engine detects the running state, data transmission link, and other aspects of the target subsystem based on preset rules and algorithms, and comprehensively and accurately finds out the key factors that cause the delay. Finally, the abnormal diagnosis engine generates a budget delay report to present the analysis results in a standardized and clear form.

[0121] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through computer readable instructions, and the computer readable instructions can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiment methods. Among them, the storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0122] It should be understood that although each step in the flowchart of the accompanying drawings is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise stated herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or sub-steps or stages of other steps.

[0123] Further referring to Figure 3 , as an implementation of the method shown in the above Figure 2 , the present application provides an embodiment of a budgeting information generation device, which corresponds to the method embodiment shown in Figure 2 , and the device can be specifically applied to various electronic devices.

[0124] As shown in Figure 3 , the budgeting information generation device 400 of the present embodiment includes a receiving module 401, an obtaining module 402, a sending module 403, a checking module 404, a cutting module 405, an analyzing module 406, and an adjusting module 407. Among them:

[0125] The receiving module 401 is configured to receive a budget start task for a target time period, and the budget start task includes planning target data and historical budget data.

[0126] The obtaining module 402 is configured to obtain a preset business unit rule, and generate a budget reporting template containing a logical association formula based on the planning target data, the historical budget data, and the business unit rule.

[0127] The sending module 403 is configured to send the budget reporting template to a plurality of subsystems to receive budget data returned by each subsystem based on the budget reporting template.

[0128] The checking module 404 is configured to check and correct the budget data by using a preset rule engine to obtain corrected budget data.

[0129] The cutting module 405 is configured to cut the corrected budget data by using a preset time sequence simulation engine based on a preset rolling prediction logic to obtain time slice data of the corrected budget data.

[0130] The analysis module 406 is configured to perform multi-scenario simulation analysis on the time slice data by using a preset analysis deduction algorithm to obtain an analysis result.

[0131] The adjustment module 407 is configured to obtain budget approval data, and adjust the analysis result based on the budget approval data to obtain budget compilation information corresponding to a target time period.

[0132] In the data collection link of the embodiment, a budget initiation task containing planning target data, historical budget data and industry benchmark data is received, and a budget reporting template containing a logical association formula is generated in combination with a preset business unit rule, so that the standardization and automation of budget reporting are realized. The template is sent to multiple subsystems, so that the subsystems return budget data based on the unified template, the data barriers between the budget system and the financial and business systems are broken, cross-system data is effectively integrated, the problems of low efficiency of traditional manual offline reporting and weak logical association of budget subjects are solved, the efficiency and accuracy of budget-related data collection are greatly improved, and the workload and error rate of manual reconciliation are reduced. In the monitoring aspect, the corrected budget data is cut based on the rolling prediction logic and the time sequence simulation engine to obtain time slice data, the budget execution progress can be tracked in real time, abnormal situations can be found in time, the situation of relying on periodic reports to cause delayed data feedback is changed, the enterprise can respond quickly to potential risks, and the best opportunity to adjust the budget strategy is grasped. In the analysis dimension, the time slice data is simulated and analyzed in multiple scenarios by using a preset analysis deduction algorithm, the business causes are deeply mined, and the analysis is no longer limited to simple budget completion rate comparison, comprehensive and in-depth budget analysis results are provided for the enterprise, and finally the budget compilation information is obtained by adjusting the analysis result in combination with the budget approval data, which effectively supports the enterprise to make scientific and reasonable decisions.

[0133] In an embodiment, the obtaining module 402 comprises:

[0134] The text analysis sub-module is configured to perform text analysis on the planning target data to obtain a strategic indicator list, and perform statistical analysis on the historical budget data to generate a historical data feature report.

[0135] The classification sub-module is configured to classify the business unit rule to obtain constraint rules and calculation rules.

[0136] The document generation submodule is configured to generate a template framework design document based on the strategic indicator list, the historical data feature report, the constraint rule, and the calculation rule.

[0137] The embedding submodule is configured to obtain a logical association formula, embed the logical association formula into the template framework design document, and generate a budget filing template.

[0138] The embodiments of the present application can obtain a strategic indicator list by performing text analysis on planning target data, accurately extract key indicators such as market share and R&D investment proportion from enterprise strategic planning and other texts, and clearly indicate the strategic direction for budget preparation to avoid blindness. The historical data feature report is generated by performing statistical analysis on historical budget data, and the law of cost fluctuation and business proportion can be mastered to provide reliable historical reference for budget preparation. The business unit rules are classified into constraint rules and calculation rules, the constraint rules ensure budget compliance, and the calculation rules ensure accurate data calculation. The template framework design document is generated based on the above information, and the logical association formula is embedded to generate a budget filing template, so that the template meets the strategic target, combines historical data and business rules, and the logical association of each budget subject is close. The template is subsequently pushed to each subsystem to reduce manual offline filing, break data silos, avoid the tediousness and errors of manual reconciliation, and improve data collection efficiency and accuracy.

[0139] In an embodiment, the verification module 404 includes:

[0140] The verification submodule is configured to perform compliance verification on the budget data by using a preset rule engine to obtain verified budget data.

[0141] The comparison submodule is configured to obtain real-time market data, and compare industry benchmark data, real-time market data, and verified budget data by using a preset dynamic optimization engine to generate multi-dimensional budget optimization suggestions.

[0142] The correction submodule is configured to correct the verified budget data based on the budget optimization suggestions to obtain corrected budget data.

[0143] The embodiments of the present application can control quality from the data source by performing compliance verification on the budget data through the rule engine. The rule engine accurately identifies the irregular items in the budget data according to the preset rules, timely marks and feeds back, ensures that the verified budget data meets the standard requirements of enterprise budget management, and reduces the budget execution deviation caused by data errors. Real-time market data is obtained, and the dynamic optimization engine is used to compare and analyze the industry benchmark data, real-time market data and the verified budget data, which can sensitively capture the differences between market changes and enterprise budget, generate multi-dimensional budget optimization suggestions, and make the budget more in line with the actual market. The budget data is corrected based on the budget optimization suggestions, and the corrected budget data fully considers market dynamics and industry benchmarks, which helps enterprises to reasonably allocate resources and improve budget execution effect.

[0144] In an embodiment, the cutting module 405 includes:

[0145] The processing submodule is configured to perform standardization processing on the corrected budget data to obtain a standardized budget data set.

[0146] The cutting submodule is configured to cut the standardized budget data set according to a time granularity based on a preset rolling prediction logic to obtain a time slice data set.

[0147] The prediction submodule is configured to use a preset time series simulation engine to perform result prediction on the time slice data set to obtain a prediction result.

[0148] The fusion submodule is configured to perform result verification and fusion on the prediction result and the time slice data set to obtain a time slice data.

[0149] The embodiments of the present application can eliminate the differences in format, unit, etc. of budget data from different sources by performing standardization processing on the corrected budget data, unify the data into a standard form, and form a standardized budget data set. This provides a unified basis for subsequent processing, facilitates comparison and analysis between data, and avoids analysis errors caused by data format problems. The rolling prediction logic is used to cut the data set according to a time granularity to obtain a time slice data set, which can refine the continuous budget data according to the time dimension, focus on the data characteristics of different time periods, and accurately grasp the distribution and trend of the budget in different stages. The time series simulation engine is used to perform result prediction on the time slice data set, which can use historical data and current data trends to predict future budget trends and obtain a prediction result, providing a basis for early planning for enterprises. The result verification and fusion on the prediction result and the time slice data set can ensure the accuracy of the prediction result, and organically combine the prediction data with the actual data to obtain more comprehensive and accurate time slice data.

[0150] In an embodiment, the analysis module 406 includes:

[0151] define a plurality of scene types based on the planning target data, and define variable adjustment rules corresponding to each scene type;

[0152] The simulation analysis submodule is configured to simulate and analyze each scene based on the variable adjustment rules and the time-slice data, using a preset analysis deduction algorithm, to obtain an analysis result.

[0153] The embodiments of the present application can define a plurality of scene types based on the planning target data and corresponding variable adjustment rules, and can fully consider various complex situations that may be encountered in enterprise operation. Different scene types cover market, cost, competition and other factors, and the variable adjustment rules accurately correspond to the business variable change logic under each scene, so that the simulation scene is closer to the actual business environment, and provides rich and reasonable analysis dimensions for comprehensive evaluation of the budget. In combination with the time-slice data and the variable adjustment rules, the analysis deduction algorithm is used to simulate and analyze each scene, which can dynamically display the execution effect of the budget under different time periods and different scenes. The time-slice data provides detailed budget information in the time dimension, the variable adjustment rules ensure that the simulation process conforms to the characteristics of each scene, and the analysis deduction algorithm can efficiently process data and obtain accurate results.

[0154] In an embodiment, the adjustment module 407 comprises:

[0155] The extraction submodule is configured to extract a plurality of key fields from the budget approval data using a preset natural language technology.

[0156] The generation submodule is configured to generate structured approval data according to the plurality of key fields, and generate adjustment rules based on the structured approval data.

[0157] The verification submodule is configured to obtain preset budget constraint conditions, and verify the adjustment rules based on the constraint conditions.

[0158] The adjustment submodule is configured to, if the verification is passed, adjust the analysis result based on the adjustment rules to obtain budget preparation information corresponding to the target time period.

[0159] The budget approval data can be subjected to field extraction through natural language technology, key information can be accurately identified and separated from unstructured approval texts, the problems of easy errors and low efficiency in manual extraction can be effectively solved, and the accuracy and comprehensiveness of key field extraction are ensured. Structured approval data is generated based on the extracted multiple key fields, disordered approval information is converted into ordered and standardized data formats, subsequent computer processing and analysis are facilitated, and the usability and operability of the data are improved. Adjustment rules are generated according to the structured approval data, a reasonable budget adjustment scheme can be automatically formulated according to the approval opinions, the budget adjustment is more scientific and reasonable. The adjustment rules are verified according to the obtained budget constraint conditions, it is ensured that the adjustment rules meet the strategic goals and financial requirements of the enterprise, and deviation of the budget adjustment is avoided. Finally, the analysis results are adjusted based on the verified adjustment rules, and accurate and reliable budget preparation information is obtained.

[0160] In an embodiment, the budget preparation information generation apparatus 400 further comprises:

[0161] The determination module is configured to determine a target subsystem that does not return budget data from the multiple subsystems if budget data is not received within a preset time period.

[0162] The generation module is configured to analyze the delayed return event of the target subsystem by using a preset exception diagnosis engine, and generate a budget delay report.

[0163] When budget data is not received within a preset time period, the embodiment of the application can accurately locate the source of the problem by determining a target subsystem that does not return budget data from multiple subsystems. In a complex budget preparation system, multiple subsystems process budget reporting tasks in parallel, this technical feature can avoid blind investigation, quickly focus on a specific subsystem that has a problem, and significantly improve problem processing efficiency. The exception diagnosis engine can analyze the delayed return event of the target subsystem, and can deeply analyze the delay reasons. The exception diagnosis engine detects the running state, data transmission link and other aspects of the target subsystem based on preset rules and algorithms, and can comprehensively and accurately find out the key factors causing the delay. Finally, a budget delay report is generated, and the analysis results are presented in a standardized and clear form.

[0164] To solve the above technical problems, the embodiment of the application further provides a computer device. For details, please refer to Figure 4 , Figure 4 The basic structure block diagram of the computer device of the embodiment is shown in the figure.

[0165] The computer device 6 comprises a memory 61, a processor 62, and a network interface 63 which are communicatively connected by a system bus. It should be noted that the computer device 6 is only shown with the memory 61, the processor 62, and the network interface 63, but it should be understood that not all of the shown components are required to be implemented, and more or less components can be alternatively implemented. Among them, those skilled in the art can understand that the computer device herein is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0166] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device can interact with the user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, and the like.

[0167] The memory 61 comprises at least one type of readable storage medium, including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like. Of course, the memory 61 can also include both the internal storage unit and the external storage device of the computer device 6. In the present embodiment, the memory 61 is generally used to store an operating system and various application software installed in the computer device 6, such as computer readable instructions of the budgeting information generation method, and the like. In addition, the memory 61 can also be used to temporarily store various data that have been output or will be output.

[0168] The processor 62 may, in some embodiments, be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 62 is generally used to control the overall operation of the computer device 6. In the present embodiment, the processor 62 is configured to run computer readable instructions stored in the memory 61 or to process data, such as computer readable instructions of a method for generating budgeting information.

[0169] The network interface 63 may include a wireless network interface or a wired network interface, and is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0170] In the data collection link, the embodiments of the present application receive a budget initiation task containing planning target data, historical budget data and industry benchmark data, and generate a budget filling template containing a logical association formula in combination with a preset business unit rule, thereby realizing the standardization and automation of budget filling. The template is sent to multiple subsystems, prompting the subsystems to return budget data based on the unified template, breaking down the data barriers between the budget system and the financial and business systems, effectively integrating cross-system data, solving the problems of low efficiency of traditional manual offline filling and weak logical association of budget subjects, greatly improving the efficiency and accuracy of budget-related data collection, and reducing the workload and error rate of manual reconciliation. In the monitoring aspect, the modified budget data is cut based on the rolling prediction logic and time series simulation engine to obtain time-sliced data, which can track the budget execution progress in real time and timely detect abnormal situations, changing the situation of data feedback delay caused by relying on periodic reports, enabling the enterprise to respond quickly to potential risks and seize the best opportunity to adjust the budget strategy. In the analysis dimension, the preset analysis and deduction algorithm is used to perform multi-scenario simulation analysis on the time-sliced data, deeply mining the business causes, and no longer being limited to simple budget completion rate comparison, thereby providing the enterprise with comprehensive and in-depth budget analysis results. Finally, the analysis results are adjusted in combination with the budget approval data to obtain budgeting information, which effectively supports the enterprise to make scientific and reasonable decisions.

[0171] The present application also provides another implementation, i.e., a computer readable storage medium storing computer readable instructions, which can be executed by at least one processor to enable the at least one processor to perform the steps of the method for generating budgeting information as described above.

[0172] The embodiment of the application realizes the standardization and automation of budget filling by receiving a budget starting task containing planning target data, historical budget data and industry benchmark data, and generating a budget filling template containing a logical correlation formula in combination with a preset business unit rule in the data collection link. The template is sent to multiple subsystems to prompt the subsystems to return budget data based on the unified template, breaking the data barrier between the budget system and the financial and business systems, effectively integrating cross-system data, solving the problems of low efficiency of traditional manual offline filling and weak logical correlation of budget subjects, greatly improving the efficiency and accuracy of budget-related data collection, and reducing the workload and error rate of manual reconciliation. In the monitoring aspect, the budget data after correction is cut based on the rolling prediction logic and time sequence simulation engine to obtain time slice data, which can track the budget execution progress in real time and timely discover abnormal situations, changing the situation of data feedback delay caused by relying on periodic reports in the past, enabling the enterprise to respond quickly to potential risks and seize the best opportunity to adjust the budget strategy. In the analysis dimension, the preset analysis deduction algorithm is used to perform multi-scenario simulation analysis on the time slice data, deeply mining business causes, and no longer being limited to simple budget completion rate comparison, providing the enterprise with comprehensive and in-depth budget analysis results. Finally, the budget preparation information is obtained by adjusting the analysis results in combination with the budget approval data, which effectively supports the enterprise to make scientific and reasonable decisions.

[0173] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and a general hardware platform as required, and of course, they can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk), and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the methods of the various embodiments of the present application.

[0174] Obviously, the above-described embodiments are only some of the embodiments of the present application, not all the embodiments, and the preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be realized in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structure made by using the contents of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the scope of the patent protection of the present application.

[0175] The non-company software tools or components appearing in the embodiments of the present application are only illustrative and do not represent actual use.

Claims

1. A method of generating budgeting information, characterized by, The method comprises the following steps: receiving a budget starting task of a target period, the budget starting task comprising planning target data and historical budget data; obtaining preset business unit rules, and generating a budget filling template comprising a logical correlation formula based on the planning target data, the historical budget data and the business unit rules; sending the budget filling template to a plurality of subsystems to receive budget data returned by each subsystem based on the budget filling template; adopting a preset rule engine to check and correct the budget data to obtain corrected budget data; adopting a preset time sequence simulation engine to cut the corrected budget data based on a preset rolling prediction logic to obtain time slice data of the corrected budget data; adopting a preset analysis and deduction algorithm to perform multi-scenario simulation analysis on the time slice data to obtain an analysis result; obtaining budget approval data, and adjusting the analysis result based on the budget approval data to obtain budget compilation information corresponding to the target period.

2. The method of claim 1, wherein, The step of generating the budget filling template comprising the logical correlation formula based on the planning target data, the historical budget data and the business unit rules specifically comprises: performing text analysis on the planning target data to obtain a strategic indicator list, and performing statistical analysis on the historical budget data to generate a historical data feature report; classifying the business unit rules to obtain constraint rules and calculation rules; generating a template framework design document based on the strategic indicator list, the historical data feature report, the constraint rules and the calculation rules; obtaining a logical correlation formula, embedding the logical correlation formula into the template framework design document, and generating a budget filling template.

3. The method of claim 1, wherein, The budget starting task further comprises industry benchmark data; the step of adopting the preset rule engine to check and correct the budget data to obtain the corrected budget data specifically comprises: adopting a preset rule engine to perform compliance checking on the budget data to obtain checked budget data; obtaining real-time market data, and adopting a preset dynamic optimization engine to compare the industry benchmark data, the real-time market data and the checked budget data to generate multi-dimensional budget optimization suggestions; based on the budget optimization suggestions, correcting the checked budget data to obtain the corrected budget data.

4. The method of claim 1, wherein, The step of adopting the preset time sequence simulation engine to cut the corrected budget data based on the preset rolling prediction logic to obtain the time slice data of the corrected budget data specifically comprises: performing standardization processing on the corrected budget data to obtain a standardized budget data set; based on the preset rolling prediction logic, cutting the standardized budget data set according to a time granularity to obtain a time slice data set; adopting a preset time sequence simulation engine to perform result prediction on the time slice data set to obtain a prediction result; performing result verification and fusion on the prediction result and the time slice data set to obtain time slice data.

5. The method of claim 1, wherein, The step of performing multi-scenario simulation analysis on the time-slice data by using a preset analysis deduction algorithm to obtain an analysis result specifically includes: Defining multiple scene types of scenes and variable adjustment rules corresponding to each scene based on the planning target data; Performing simulation analysis on each scene by using a preset analysis deduction algorithm based on the variable adjustment rules and the time-slice data to obtain an analysis result.

6. The method of claim 1, wherein, The step of adjusting the analysis result based on the budget approval data to obtain budget compilation information corresponding to the target time period specifically includes: Extracting multiple key fields from the budget approval data by using a preset natural language technology; Generating structured approval data according to the multiple key fields, and generating adjustment rules based on the structured approval data; Obtaining a preset budget constraint condition, and verifying the adjustment rules based on the constraint condition; If the verification is passed, adjusting the analysis result based on the adjustment rules to obtain budget compilation information corresponding to the target time period.

7. The method of claim 1, wherein, After the step of sending the budget filing template to multiple subsystems to receive budget data returned by each subsystem based on the budget filing template, the method further includes: If the budget data is not received within a preset time period, determining a target subsystem that does not return the budget data from the multiple subsystems; Analyzing the delayed return event of the target subsystem by using a preset exception diagnosis engine to generate a budget delay report.

8. A budgeting information generation device characterized by comprising: The method includes: A receiving module configured to receive a budget start task of a target time period, the budget start task including planning target data and historical budget data; An obtaining module configured to obtain a preset business unit rule, and generate a budget filing template including a logical association formula based on the planning target data, the historical budget data, and the business unit rule; A sending module configured to send the budget filing template to multiple subsystems to receive budget data returned by each subsystem based on the budget filing template; A verifying module configured to verify and correct the budget data by using a preset rule engine to obtain corrected budget data; A cutting module configured to cut the corrected budget data by using a preset time sequence simulation engine based on a preset rolling prediction logic to obtain time-slice data of the corrected budget data; An analysis module configured to perform multi-scenario simulation analysis on the time-slice data by using a preset analysis deduction algorithm to obtain an analysis result; An adjustment module configured to obtain budget approval data, and adjust the analysis result based on the budget approval data to obtain budget compilation information corresponding to the target time period.

9. A computer device, comprising: A device includes a memory and a processor, the memory stores computer readable instructions, and the processor executes the computer readable instructions to implement the steps of the budget compilation information generation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer readable instructions, which, when executed by a processor, implement the steps of the budgeting information generation method according to any one of claims 1 to 7.

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