A budget management method, apparatus, device, and medium based on intelligent agents

Abstract

This invention discloses a budget management method, apparatus, device, and medium based on intelligent agents. The budget management method includes the following steps: determining the task requirements of the budget to be managed through a preset budget intelligent agent; determining budget indicator tasks related to the budget to be managed through the budget intelligent agent based on the task requirements; decomposing the budget indicator tasks through the budget intelligent agent to obtain multiple indicator sub-tasks; and processing each indicator sub-task by calling the corresponding indicator service through the budget intelligent agent to obtain the corresponding indicator processing results, which are used to adjust the budget to be managed. This method achieves task-driven dynamic budget control through a preset intelligent agent, significantly improving the accuracy of budget preparation and the real-time response capability of the execution process, ensuring that the entire budget execution process is efficiently aligned with corporate strategy, and forming a complete budget execution closed loop.

Description

Technical Field

[0001] This invention relates to the fields of enterprise management and artificial intelligence application technology, and in particular to a budget management method, device, equipment and medium based on intelligent agents. Background Technology

[0002] In the field of enterprise management, budget management is a core tool connecting strategic planning and business execution, playing a crucial role in optimizing resource allocation and achieving strategic goals. Current budget management primarily relies on manual periodic verification, manual Excel entry, and static report analysis. This approach is extremely inefficient in identifying risks or opportunities during budget execution, failing to provide a basis for timely budget optimization. Consequently, budget deviations are often discovered late and corrected reactively, missing the optimal opportunity for budget optimization. This also leads to untimely responses from budget managers to risks or problems, making it difficult to ensure a high degree of consistency between budget execution and corporate strategy. Summary of the Invention

[0003] This invention provides a budget management method, apparatus, device, and medium based on intelligent agents to solve the problem of high lag in budget management in the prior art, which makes it difficult to ensure the consistency between budget execution and corporate strategy.

[0004] In a first aspect, this application provides a budget management method based on an intelligent agent, comprising the steps of: determining the task requirements of a budget to be managed through a preset budget intelligent agent; determining budget indicator tasks related to the budget to be managed through the budget intelligent agent based on the task requirements; decomposing the budget indicator tasks through the budget intelligent agent to obtain multiple indicator sub-tasks; and processing the corresponding indicator service through the budget intelligent agent according to each indicator sub-task to obtain a corresponding indicator processing result, wherein the indicator processing result is used to adjust the budget to be managed.

[0005] Secondly, this application provides a budget management device based on an intelligent agent, comprising: a demand determination module, configured to determine the task requirements of a budget to be managed through a preset budget intelligent agent; a task determination module, configured to determine budget indicator tasks related to the budget to be managed through the budget intelligent agent based on the task requirements; a task decomposition module, configured to decompose the budget indicator tasks through the budget intelligent agent to obtain multiple indicator sub-tasks; and a task processing module, configured to process each indicator sub-task by calling a corresponding indicator service through the budget intelligent agent to obtain a corresponding indicator processing result, wherein the indicator processing result is used to adjust the budget to be managed.

[0006] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described agent-based budget management method.

[0007] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described agent-based budget management method.

[0008] The aforementioned technical solution for budget management based on intelligent agents, including a method, device, computer equipment, and storage medium, comprises the following steps: determining the task requirements of the budget to be managed through a pre-defined budget intelligent agent; determining budget indicator tasks related to the budget to be managed through the budget intelligent agent based on the task requirements; decomposing the budget indicator tasks through the budget intelligent agent to obtain multiple indicator sub-tasks; and processing each indicator sub-task by calling the corresponding indicator service through the budget intelligent agent to obtain the corresponding indicator processing results, which are then used to adjust the budget to be managed. This method achieves closed-loop management of requirement identification, task decomposition, and service collaboration through a budget intelligent agent, significantly improving the scientific nature of budget preparation, the dynamism of execution, and the accuracy of control, ensuring efficient alignment between the entire budget execution process and corporate strategy, and forming a complete closed loop of budget execution. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of a budget management method based on an intelligent agent according to an embodiment of the present invention; Figure 2 This is a specific flowchart of step S1 in a budget management method based on an intelligent agent according to an embodiment of the present invention; Figure 3 This is a specific flowchart of step S4 in a budget management method based on an intelligent agent according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the construction of an indicator attribution relationship model in a budget management method based on intelligent agents according to an embodiment of the present invention. Figure 5 This is a schematic diagram of a budget management device based on an intelligent agent according to an embodiment of the present invention; Figure 6This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] In one embodiment, such as Figure 1 As shown, an agent-based budget management method is provided, including the following steps: Step S1: Determine the task requirements for the budget to be managed through a preset budget agent.

[0013] It's important to clarify that a budget intelligence agent refers to an AI proxy embedded in an enterprise's budget management process. It possesses capabilities such as natural language understanding based on large models, task reasoning, multimodal data analysis, and personalized interaction. Based on historical budget data, real-time operational indicators, and strategic goal documents, it can automatically identify and structure the task requirements for the budget to be managed, and then manage the budget. The budget to be managed refers to the enterprise's dynamically adjusted fund allocation plan within a specific period, covering key expenditure dimensions such as R&D, marketing, and human resources. Task requirements refer to the specific management objectives identified by the budget intelligence agent from user-inputted budget management requests or during the real-time execution of the budget to be managed. These include core requests such as budget deviation warnings, execution progress tracking, resource reallocation suggestions, and strategic alignment assessments. Budget management requests are budget management requests submitted by users in natural language, such as "Please compare the correlation between the actual expenditure of the Q1 marketing budget and the KPI completion rate" or "Warning of R&D expense overrun risk and recommendation of optimization solutions."

[0014] In this embodiment, the user inputs a budget management request through a dialog box. The budget agent receives the budget management request from the user in real time, parses the budget management request, and determines the task requirements.

[0015] Specifically, the budget intelligence agent analyzes budget management requests to determine task requirements. This involves: first, performing semantic parsing and intent recognition on the budget management request to extract keywords and business entities; then, using a knowledge graph to match enterprise strategic goals, historical execution data, and organizational structure constraints; and finally, outputting a structured list of task requirements, specifying warning thresholds, analysis dimensions, and delivery formats. For example, when a user requests a "Q2 marketing expense overrun warning," the budget intelligence agent automatically correlates the historical average of marketing expenses, the Q2 sales target achievement rate, and the industry expense ratio benchmark, and dynamically sets a floating warning threshold.

[0016] In some embodiments, the budget agent can continuously monitor the execution status of the budget to be managed based on an active detection mechanism to confirm the corresponding task requirements.

[0017] Therefore, before step S1, the process also includes: acquiring budget execution data during the execution of the budget to be managed through a budget intelligence agent. This budget execution data consists of full-volume budget execution data collected in real-time from a pre-defined data lake warehouse by the budget intelligence agent, including revenue data, cost data, and various expense data. This data is then cleaned, standardized, and mapped to a pre-defined budget dimension labeling system to ensure data traceability, comparability, and modelability.

[0018] It's important to note that the data lake warehouse serves as the data foundation for the budget intelligence agent. It integrates heterogeneous data from multiple sources, including financial systems, ERP, CRM, and BI platforms. Through a unified API gateway, it achieves millisecond-level data synchronization and semantic alignment, ensuring that budget execution data is aligned with corporate strategic goals in real time. This provides the budget intelligence agent with high-fidelity, low-latency decision-making support. The data lake warehouse comprises an indicator platform and an indicator analysis module. The indicator platform provides indicator data query, indicator drill-down query, indicator association model, and detailed data query services, providing underlying data support for the budget intelligence agent's indicator data query, analysis, and update tasks. The indicator analysis module provides indicator analysis and quick report services, used to generate budget execution trends, deviation analysis results, and standardized budget execution reports, supporting the budget intelligence agent's analytical and proactive service tasks. Proactive service tasks involve the budget intelligence agent proactively pushing budget execution anomaly warnings, deviation attribution suggestions, and optimization strategy recommendations to users based on historical feedback and indicator change patterns.

[0019] Furthermore, in step S1, task requirements are determined by a budget agent based on budget execution data.

[0020] like Figure 2 As shown, determining task requirements based on budget execution data includes the following sub-steps: Step S11: The budget execution agent identifies the budget execution data to obtain the budget execution status of the budget to be managed.

[0021] Budget execution identification is based on the fusion analysis of multi-source heterogeneous data, combined with time series modeling and anomaly detection algorithms, to capture the dynamic fluctuations of budget execution in real time. Budget execution status refers to the execution status of the budget to be managed within the current period, including dimensions such as execution progress rate, deviation magnitude, and risk level.

[0022] For example, when a business unit's Q2 marketing expense execution rate reaches 118% and the deviation exceeds the ±15% threshold, the system immediately triggers a red alert and automatically links the business unit's execution data for the same period in the past three years, the industry average, and the Q2 sales target completion rate.

[0023] Step S12: Based on the budget execution status, determine the task requirements corresponding to the budget to be managed.

[0024] This can be achieved by pre-setting a mapping rule base between budget execution status and task requirements, allowing the budget agent to quickly match corresponding task requirements. For example, when the budget agent identifies an execution progress rate >120% and a deviation margin <-20%, it is determined to be a potential risk of fund stagnation, automatically triggering a task requirement for special analysis.

[0025] Furthermore, the mapping rule base supports dynamic learning and version iteration, and continuously optimizes rule weights based on historical task execution feedback. This ensures that the mapping rule base continuously adapts to changes in business scenarios as budget execution data accumulates and feedback loops, thereby improving the accuracy and timeliness of task requirement identification.

[0026] Furthermore, the budget agent can also collaborate with other agents. For example, if the budget agent identifies that the R&D budget execution progress rate in East China is only 63% and the deviation is +27%, it will immediately determine that there is a risk of execution delays, simultaneously push out task requirements for resource coordination and progress catch-up, and coordinate with the HR agent to activate a special talent scheduling plan to ensure that key R&D milestones are not delayed.

[0027] In this application, the task requirements of the budget to be managed are directly determined by the budget intelligence agent. The entire closed loop from data perception and status recognition to task requirement triggering can be completed without human intervention. This mechanism significantly improves the response speed and decision accuracy of budget management.

[0028] Step S2: Based on the task requirements, determine the budget indicator tasks related to the budget to be managed through the budget agent.

[0029] It should be noted that budget indicator tasks are specific executable indicator tasks decomposed by the budget intelligence agent based on task requirements. They cover quantifiable dimensions such as execution rate, deviation rate, and risk index, and support automatic classification and priority sorting by multi-dimensional tags such as business line, time period, and risk level.

[0030] In this embodiment, the budget intelligence agent automatically parses the associated budget indicator system based on task requirements, accurately locates the corresponding budget indicator tasks, and performs subsequent specific operations such as indicator data query, trend analysis, deviation attribution, threshold warning and dynamic adjustment.

[0031] Meanwhile, the budget intelligence agent dynamically verifies the logical consistency and business rationality of each indicator task. For example, when the "R&D input-output ratio" indicator is lower than the industry benchmark by 15% for two consecutive quarters, a deep attribution analysis task is automatically triggered. This task is then linked to collaborative indicators such as patent application volume, project completion rate, and technology transfer cycle for multi-dimensional cross-validation, accurately identifying bottlenecks in technology transfer efficiency. This process not only strengthens the causal relationship between indicators but also ensures that budget indicators and tasks are always aligned with business strategic goals by constructing a dynamic feedback loop for indicator tasks. This guarantees that budget resource allocation is always focused on the core path of improving innovation efficiency.

[0032] Step S3: Decompose the budget indicator task into multiple indicator sub-tasks through the budget agent.

[0033] It should be noted that the indicator subtask is an executable operation unit that further refines the budget indicator task, including but not limited to atomic-level actions such as indicator data query, analysis, update, early warning, attribution and adjustment, and supports parallel scheduling and status backtracking.

[0034] In this embodiment, the budget agent decomposes the budget indicator task into multiple atomic-level indicator sub-tasks, such as indicator data query task, indicator analysis task, and indicator update task, to support the budget agent in subsequently calling the corresponding indicator service to execute the corresponding indicator sub-tasks.

[0035] The indicator data query task initiates real-time data exploration of the data lake warehouse, automatically matching multi-source heterogeneous data table structures and accurately extracting time-series features and business context associated with budget indicators. The indicator analysis task integrates time-series modeling and attribution mapping technologies to dynamically identify structural deviations and cyclical fluctuations in budget execution, and automatically generate root cause hypotheses and verification paths. The indicator update task adjusts budget thresholds and weight configurations in real time based on analysis results, ensuring that budget strategies always adapt to dynamic business changes.

[0036] Step S4: Based on each indicator subtask, the budget agent calls the corresponding indicator service for processing to obtain the corresponding indicator processing results. The indicator processing results are used to adjust the budget to be managed.

[0037] It's important to note that the indicator service is a standardized capability module embedded within the budget intelligence system. It can directly connect to data lake warehouses, real-time stream computing engines, and AI model services, as well as heterogeneous data sources such as financial systems, ERP systems, and BI platforms. This enables millisecond-level extraction of indicator data, multi-source fusion, and semantic alignment. Based on a pre-defined business rule engine and lightweight inference model, it generates indicator processing results in real time. These results are structured data output after the corresponding indicator service executes its sub-tasks, including deviation attribution reports, trend prediction curves, dynamic threshold suggestions, and budget adjustment instructions, directly driving closed-loop optimization of the budget under management.

[0038] In this embodiment, for the indicator data query task, the indicator processing result includes the target indicator data. The corresponding step S4 includes: according to the indicator data query task, calling the indicator data query service through the budget agent to obtain the target indicator data, and then the target indicator data can be fed back to the user interaction terminal and presented to the user in the form of text or card.

[0039] It should be noted that the indicator data query service is a lightweight API service with multi-source adaptability. It can directly connect to heterogeneous data sources such as data lake warehouses, ERP, and BI platforms, and supports multiple access methods including SQL, GraphQL, and natural language queries. It automatically completes semantic parsing and field mapping to ensure that the query results are strictly consistent with the budget indicator definitions. Target indicator data refers to the raw numerical or structured datasets that the budget agent accurately extracts from heterogeneous data sources, conforming to the current budget indicator definitions and statistical dimensions.

[0040] In this embodiment, the indicator data query task calls the indicator data query service through the budget agent, connects to the indicator platform of the data lake warehouse, and obtains the target indicator data to support the accurate execution of subsequent indicator analysis and update tasks.

[0041] Furthermore, it's necessary to first obtain a list of metrics accessible to the user, verifying whether the user has the necessary access permissions for the corresponding metric data. If the user does have such access, a query request is sent to the metrics platform. The query statement is automatically constructed based on preset metric definitions and time dimensions, and the target metric data that meets the task requirements is returned in real time. If the user does not have the necessary access permissions, access control policies are automatically triggered, blocking the query request and returning a standardized permission denial response. The list of metrics accessible to the user is also displayed to remind the user to adjust the query scope or apply for the appropriate permissions, ensuring data security and compliance.

[0042] Furthermore, the indicator sub-tasks also include indicator analysis tasks, and the corresponding indicator processing results include budget execution trends and budget deviation warnings corresponding to the target indicator data.

[0043] It should be noted that the budget execution trend is a visualized trend map generated through time series modeling and multi-dimensional year-on-year and month-on-month analysis, based on historical execution data and real-time collected target indicator data. This map not only presents the linear changes in budget execution but also integrates seasonal fluctuations, business cycles, and external environmental factors to accurately identify execution inflection points and potential risk ranges. The budget deviation early warning system identifies and classifies responses to abnormal fluctuations in the budget execution trend. Early warning signals generated based on the deviation magnitude, duration, and coupling degree of related indicators can be pushed to user terminals in real time, reminding relevant personnel to intervene promptly.

[0044] like Figure 3 As shown, the execution of the indicator analysis task includes the following sub-steps: Step S41: Based on the indicator analysis task, the indicator analysis service is invoked through the budget agent to obtain the budget execution trend, and abnormal fluctuation nodes are identified based on the budget execution trend.

[0045] It should be noted that the indicator analysis service is based on a built-in multimodal analysis engine, which integrates time-series forecasting models and causal inference algorithms to analyze the dynamic evolution characteristics of target indicator data in real time and accurately locate the causes and transmission paths of deviations. Abnormal fluctuation nodes refer to significant deviations in the budget execution trend that deviate from the preset execution threshold. The identification criteria comprehensively consider statistical significance, business sensitivity, and historical fluctuation baselines.

[0046] In this embodiment, based on the indicator analysis task, the budget agent calls the indicator analysis service and performs user data access permission verification. After successful verification, the indicator analysis module connected to the data lake warehouse generates a real-time visualized budget execution trend and analyzes the dynamic evolution characteristics of the target indicator data in real time based on the built-in multimodal analysis engine. This accurately locates the cause and transmission path of deviations, identifies abnormal fluctuation nodes, and provides precise anchor points for subsequent deviation warnings. The process of verifying user data access permissions is consistent with the aforementioned indicator query permission verification logic, and will not be described again here.

[0047] In some embodiments, an abnormal fluctuation node can be a significant inflection point in the budget execution trend that deviates from the baseline by more than a preset threshold and lasts for more than two statistical periods. Its judgment logic integrates a sliding window dynamic threshold algorithm and a multi-source indicator cross-validation mechanism to ensure that the identification results are both sensitive and robust, effectively avoiding the risk of misjudgment by a single indicator.

[0048] Furthermore, budget execution trends and abnormal fluctuations can be fed back to the user interface and used as a basis for proactive early warnings or subsequent indicator updates, supporting closed-loop management of budget execution.

[0049] Step S42: Based on the abnormal fluctuation nodes, the budget agent calls the indicator risk service to generate a budget deviation warning.

[0050] It should be noted that the indicator risk service is a core module that generates early warning signals based on the multi-dimensional feature vectors of abnormal fluctuation nodes, combined with the business rule engine and risk knowledge graph. Its output includes information such as deviation level (red / orange / yellow), scope of impact, confidence score and suggested actions, and supports early warning tracing and closed-loop tracking by organizational level, responsible entity or time dimension.

[0051] In this embodiment, the indicator risk service adopts a federated learning framework. Under the premise of ensuring data privacy, it collaborates with heterogeneous data sources from multiple departments to jointly model and realize the implicit sharing and dynamic aggregation of cross-domain risk characteristics, thereby significantly improving the generalization ability and business adaptability of the early warning. Its risk knowledge graph continuously integrates new industry regulatory rules, historical audit cases and internal risk control strategies of enterprises to ensure that the early warning logic is always iterated in sync with the latest compliance requirements and practical experience.

[0052] Furthermore, the budget intelligence agent can automatically trigger the early warning response process, accurately push the early warning signal to the corresponding user terminal, and link the budget execution system to automatically freeze high-risk payment instructions. Simultaneously, it can initiate root cause analysis and disposal plan matching to generate an intelligent decision report that includes historical similar cases, disposal path recommendations, and expected effect assessments, providing users with executable, verifiable, and reviewable closed-loop decision support.

[0053] Furthermore, the indicator analysis service can use indicator attribution relationship models to identify nodes with abnormal fluctuations.

[0054] like Figure 4 As shown, the construction of an indicator attribution model includes the following steps: Step S51: Decompose the preset total budget target into multiple levels to obtain budget sub-targets at multiple levels.

[0055] It should be noted that the overall budget indicators are a quantitative expression of the company's overall budget goals, representing the ultimate embodiment of the company's strategic intent. Their decomposition logic strictly follows a three-tiered transmission path of strategy—business—execution, ensuring that each level of sub-indicator carries clear strategic motivations and business value, forming a traceable, verifiable, and assessable indicator tree structure. Each level of budget sub-indicator uses causal graph modeling to clarify its mapping relationship with upstream driving factors and downstream business results, thereby supporting automatic backtracking and dynamic deduction of the attribution path.

[0056] Among them, the overall budget indicators are the core indicators of the enterprise-level budget, such as annual revenue targets, net profit margin, or per capita efficiency. The core indicators of the enterprise-level budget (such as revenue and net profit) are decomposed layer by layer into operating unit indicators (corresponding to the revenue and net profit of each business unit), business line indicators (a combination of product revenue, gross profit, sales expenses, etc.), product indicators (gross profit margin, sales volume, market share, etc.), and technology module indicators (R&D expenses, usage, gross profit margin, etc.), thereby constructing an indicator tree structure from strategic goals to technology execution.

[0057] Furthermore, each level of decomposition incorporates a dynamic weight adjustment mechanism. This mechanism automatically calibrates the weights of each sub-indicator based on real-time business conditions, market environment changes, and historical execution deviations, ensuring that the indicator tree maintains strategic consistency and execution responsiveness in a dynamic environment. Simultaneously, the weight calibration process relies on the fusion analysis of multi-source time-series data, including industry prosperity indices, competitor dynamics, supply chain fluctuations, and internal execution data flows, ensuring that the indicator tree reflects both strategic rigidity and flexible responsiveness to uncertainty.

[0058] Furthermore, the indicator tree structure supports cross-level correlation analysis and penetrating early warning. When any sub-indicator experiences abnormal fluctuations, it can trace back along the causal graph to the root driving factor and forward extrapolate its transmission impact on upper-level indicators and ultimate strategic goals. This achieves a full-link intelligent response from "anomaly identification" to "root cause location" and then to "impact prediction," truly upgrading budget management from static reports to a dynamic command center. For example, when the gross profit margin of a product line suddenly drops by 5 percentage points, the system not only automatically triggers attribution analysis but also accurately identifies three driving factors: a 12% increase in upstream raw material costs, an 8% overspending on downstream promotional expenses, and the impact of price reductions by competitors. Simultaneously, it extrapolates the impact of this fluctuation on the annual net profit target, which reaches 2.3%, and then automatically generates corrective suggestions with priority ranking: first, optimize the procurement negotiation strategy to offset cost pressures; second, streamline ineffective promotional investments; and third, dynamically adjust regional pricing strategies to stabilize market share.

[0059] Step S52: Map the total budget target and the budget sub-targets at each level to obtain the corresponding target items.

[0060] It should be noted that an indicator item refers to the smallest manageable unit in which an indicator is implemented and executed in a specific business scenario. Its essence is to transform abstract indicators into specific business operations that are operable, traceable, and assessable.

[0061] By precisely binding the lowest-level technical module indicators with their corresponding indicator items, end-to-end penetrating management between total budget indicators and technical module indicators can be achieved, ensuring that each level of indicator can be traced back to specific projects, responsible persons, and execution nodes, thereby solidifying the granularity of budget execution.

[0062] Step S53: Associate each indicator item with its corresponding budget cost to obtain the indicator attribution relationship model.

[0063] It should be noted that the budget to be managed includes several different types of budget expenses. Budget expenses are the concrete expression of the indicator items in the financial dimension, and their composition directly reflects the resource consumption structure and cost drivers of the indicator items.

[0064] By precisely mapping each indicator item to its corresponding budget expense and deeply binding budget expenses to indicator items, the system can automatically identify the true contribution of each budget expense to the achievement of the target, thereby eliminating ineffective expenditures, identifying redundant costs, and supporting dynamic expense reallocation decisions.

[0065] Therefore, the indicator attribution model maps different budget targets to specific indicator items and associates various expenses with indicator items, forming a complete attribution chain of "company-operating unit-business line-product-technology module-project-expense". This constructs a three-in-one indicator attribution model of "indicator-project-expense", making the budget no longer an isolated accumulation of numbers, but a value network that carries strategic intentions, maps business actions, and drives resource allocation.

[0066] In this application, the indicator attribution model ensures that when any sub-indicator (such as the sales volume of a product) changes, its impact on the parent indicator (such as the revenue of the corresponding business line) can be traced in real time; conversely, when the parent indicator (such as the net profit of a business unit) changes, it can quickly identify which sub-indicators (such as the decline in product gross profit margin, the overspending of sales expenses, etc.) are causing it, providing core logical support for the tracing and optimization of budget execution.

[0067] For the indicator update task, the indicator risk service can be invoked through the budget agent, and the corresponding indicator processing result includes the updated budget indicator data.

[0068] In this embodiment, the indicator update task identifies the update object and corresponding budget indicator through a budget intelligence agent, calls the indicator risk service to perform compliance verification of the update operation, and performs user data access permission verification. After the verification is passed, the indicator risk service writes the updated budget indicator data into the data lake warehouse to form a new budget baseline. The updated budget indicator data is then fed back to the user interface to complete the indicator update process. The new budget baseline is used for subsequent budget execution analysis and monitoring. This mechanism supports millisecond-level response, ensuring that the budget baseline is always synchronized with business dynamics. At the same time, relying on the version snapshot capability of the data lake warehouse, the historical trajectory and impact scope of each indicator change can be fully traced, providing an immutable and complete chain of evidence for budget auditing and review.

[0069] Furthermore, the budget intelligence agent can also push early warning information or optimization suggestions to users in the form of text, cards, etc., based on proactive service tasks (such as early warning of indicator changes and push of data reports) and combined with users' work habits (such as preferred communication time and tone preferences). This reminds relevant personnel to solve problems in budget execution in a timely manner, enabling them to adjust their work based on the early warnings or suggestions (such as optimizing project resource allocation and controlling expenses), thus ensuring the timeliness and accuracy of budget execution.

[0070] In some embodiments, the budget agent can automatically trigger adjustments to the budget to be managed based on the results of the above-mentioned indicator processing, and link the financial system to perform operations such as cost reallocation, resource rescheduling, or project priority adjustment, so as to ensure that budget execution and strategic goals are always in sync.

[0071] Furthermore, after adjusting the budget to be managed, the budget agent continuously collects new budget execution data and repeats the above steps S1-S4 to form a dynamic closed loop of budget execution, ensuring that the budget always converges towards the company's strategic goals.

[0072] In summary, the above-mentioned agent-based budget management method includes the following steps: determining the task requirements of the budget to be managed through a pre-set budget agent; determining budget indicator tasks related to the budget to be managed through the budget agent based on the task requirements; decomposing the budget indicator tasks through the budget agent to obtain multiple indicator sub-tasks; and processing each indicator sub-task by calling the corresponding indicator service through the budget agent to obtain the corresponding indicator processing results, which are then used to adjust the budget to be managed. This method achieves closed-loop management of requirement identification, task decomposition, and service collaboration through the budget agent, significantly improving the scientific nature of budget preparation, the dynamism of execution, and the accuracy of regulation. Furthermore, through real-time analysis and proactive reminders from the budget agent, enterprises can maintain budget resilience and strategic agility in a rapidly changing market environment, significantly improving the accuracy of budget preparation and the real-time responsiveness of the execution process. Simultaneously, by constructing a multi-level budget indicator attribution relationship, dynamic tracking of budget indicators, intelligent risk warning, and personalized proactive services are achieved, ensuring efficient alignment between the entire budget execution process and corporate strategy, forming a complete closed loop of budget execution.

[0073] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0074] In one embodiment, an agent-based budget management device is provided, which corresponds one-to-one with the agent-based budget management method described in the above embodiments. For example... Figure 5As shown, the agent-based budget management device includes a demand determination module 101, a task determination module 102, a task decomposition module 103, and a task processing module 104. Detailed descriptions of each functional module are as follows: The requirement determination module 101 is used to determine the task requirements of the budget to be managed through a preset budget agent.

[0075] The task determination module 102 is used to determine the budget indicator tasks related to the budget to be managed through the budget agent according to the task requirements.

[0076] The task decomposition module 103 is used to decompose the budget indicator task through the budget agent to obtain multiple indicator sub-tasks.

[0077] The task processing module 104 is used to process the corresponding indicator service through the budget agent according to each indicator sub-task, and obtain the corresponding indicator processing result. The indicator processing result is used to adjust the budget to be managed.

[0078] Specific limitations regarding the agent-based budget management device can be found in the limitations of the agent-based budget management method described above, and will not be repeated here. Each module in the aforementioned agent-based budget management device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0079] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an agent-based budget management method.

[0080] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the agent-based budget management method described in the above embodiments, for example... Figure 1 As shown in S1-S4, or Figures 2 to 4As shown, to avoid repetition, it will not be described again here. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in this embodiment of the agent-based budget management device, for example... Figure 5 The functions of the requirement determination module 101, task determination module 102, task decomposition module 103, and task processing module 104 shown are not described in detail here to avoid repetition.

[0081] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the agent-based budget management method described above, for example... Figure 1 As shown in S1-S4, or Figures 2 to 4 As shown, to avoid repetition, it will not be described again here. Alternatively, when the computer program is executed by the processor, it implements the functions of each module / unit in this embodiment of the agent-based budget management device, for example... Figure 5 The functions of the requirement determination module 101, task determination module 102, task decomposition module 103, and task processing module 104 shown are not described again here to avoid repetition. The computer-readable storage medium can be non-volatile or volatile.

[0082] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0083] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0084] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0085] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A budget management method based on intelligent agents, characterized in that, Including the following steps: The task requirements for the budget to be managed are determined through a pre-set budget agent; Based on the task requirements, the budget agent determines the budget indicator tasks related to the budget to be managed. The budget agent decomposes the budget target task into multiple target sub-tasks. Based on each of the aforementioned indicator sub-tasks, the budget agent calls the corresponding indicator service for processing to obtain the corresponding indicator processing result, which is used to adjust the budget to be managed.

2. The budget management method according to claim 1, characterized in that, The indicator subtasks include indicator data query tasks, and the indicator processing results include target indicator data; the step of processing the corresponding indicator by calling the corresponding indicator service through the budget agent according to each indicator subtask to obtain the corresponding indicator processing results includes: Based on the indicator data query task, the target indicator data is obtained by calling the indicator data query service through the budget agent.

3. The budget management method according to claim 2, characterized in that, The indicator sub-tasks also include indicator analysis tasks, and the indicator processing results include budget execution trends and budget deviation warnings corresponding to the target indicator data; the step of processing the corresponding indicator by calling the corresponding indicator service through the budget agent according to each indicator sub-task to obtain the corresponding indicator processing results includes: According to the indicator analysis task, the indicator analysis service is invoked through the budget agent to obtain the budget execution trend, and abnormal fluctuation nodes are identified based on the budget execution trend; Based on the abnormal fluctuation nodes, the budget agent invokes the indicator risk service to generate the budget deviation warning.

4. The budget management method according to claim 3, characterized in that, The budget to be managed includes multiple different types of budget expenses. The indicator analysis service uses an indicator attribution model to identify the abnormal fluctuation nodes. The construction of the indicator attribution model includes the following steps: The preset total budget target is decomposed into multiple levels to obtain budget sub-targets. Map the total budget target and the sub-targets of each level of budget to projects to obtain the corresponding target projects; By associating each of the aforementioned indicator items with the corresponding budget expenses, the indicator attribution relationship model is obtained.

5. The budget management method according to claim 1, characterized in that, The budget management method also includes: The budget execution agent acquires budget execution data during the execution of the budget to be managed. The process of determining the task requirements for the budget to be managed through a pre-set budget agent includes: Based on the budget execution data, the task requirements are determined by the budget agent.

6. The budget management method according to claim 5, characterized in that, The step of determining the task requirements based on the budget execution data through the budget agent includes: The budget execution status of the budget to be managed is obtained by identifying the budget execution data through the budget intelligence agent. Based on the budget execution status, determine the task requirements corresponding to the budget to be managed.

7. The budget management method according to claim 1, characterized in that, The process of determining the task requirements for the budget to be managed through a pre-set budget agent includes: The system receives a budget management request from the user, parses the request through the budget agent, and determines the task requirements.

8. A budget management device based on intelligent agents, characterized in that, include: The requirement determination module is used to determine the task requirements of the budget to be managed through a preset budget agent; The task determination module is used to determine budget indicator tasks related to the budget to be managed through the budget agent based on the task requirements. The task decomposition module is used to decompose the budget indicator task by the budget agent to obtain multiple indicator sub-tasks; The task processing module is used to process the corresponding indicator service through the budget agent according to each indicator sub-task, and obtain the corresponding indicator processing result. The indicator processing result is used to adjust the budget to be managed.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the agent-based budget management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the agent-based budget management method as described in any one of claims 1 to 7.

Images