Financial project library dynamic optimization and budgeting intelligent association management system

Abstract

The invention discloses a financial project library dynamic optimization and budgeting intelligent association management system, which is characterized in that a multi-dimensional financial project attribute space and budgeting index mapping matrix is established, and a real-time policy factor and a financial operation situation perception model are combined; and automatic screening of items in a project library, dynamic adjustment of priorities and accurate matching of budgeting are realized. The system adopts a nonlinear coupling weight iteration algorithm and a budget deviation feedback closed-loop control mechanism, can maintain the strong relevance between a project library and budget data in multi-cycle financial planning, and improves the financial fund configuration efficiency and policy response speed.

Description

Technical Field

[0001] This invention relates to the field of financial management and budget preparation technology, specifically to a dynamic optimization and intelligent correlation management system for financial project databases and budget preparation. Background Technology

[0002] In the field of fiscal management and budget preparation, there has long been a disconnect between project databases and budget preparation. Traditional fiscal project databases mostly adopt a static classification method, archiving projects according to predetermined categories. This method is difficult to achieve accurate screening and dynamic sorting when the number of projects is large and their attributes are complex. Project prioritization often relies on human experience and is easily influenced by subjective factors, leading to a mismatch between fund allocation and actual needs. At the same time, the budget preparation process usually runs independently of the project database, with matching only occurring once during the preparation phase. The lack of real-time linkage and dynamic adjustment mechanisms results in idle or insufficient funds during budget execution.

[0003] While some existing systems have introduced simple data retrieval and matching functions, they have not established a deep correlation model between project attributes and budget indicators, nor can they automatically adjust project priorities based on policy changes and fiscal performance. Even those few systems that attempt to introduce weight calculations mostly employ linear weighting methods, which fail to reflect the complex relationships under multi-objective constraints and lack closed-loop feedback mechanisms to correct budget deviations. Furthermore, existing systems primarily use general-purpose encryption algorithms for security protection, failing to employ stronger domestically developed cryptographic algorithms tailored to the sensitivity of fiscal data, thus posing potential security risks.

[0004] In terms of fiscal operation trend perception, existing technologies mostly rely on single indicators or simple statistical models, lacking the ability to comprehensively analyze macroeconomic indicators and fiscal revenue and expenditure trends, making it difficult to accurately predict changes in project funding needs. This lack of predictive ability limits the foresight and adaptability of budget preparation, hindering the optimal allocation of resources in multi-cycle fiscal planning. Furthermore, existing systems generally lack anomaly detection and early warning mechanisms, failing to trigger timely review and intervention when significant deviations occur in project attributes or budget execution, increasing the risks of fiscal management.

[0005] In summary, existing fiscal project database management and budget preparation systems have significant shortcomings in dynamic optimization, intelligent correlation, closed-loop feedback, security protection, situational awareness, and anomaly early warning, failing to meet the requirements of modern fiscal management for efficiency, accuracy, security, and sustainability. Therefore, it is necessary to design a novel system capable of dynamically optimizing project ranking within a multi-dimensional attribute space, intelligently linking it with budget preparation, and achieving precise budget adjustments and secure control through closed-loop feedback and situational awareness, thereby comprehensively improving the efficiency of fiscal fund utilization and management. Summary of the Invention

[0006] The purpose of this invention is to provide a dynamic optimization and intelligent management system for fiscal project database and budget preparation.

[0007] To achieve the above objectives, this invention employs the following technical solution: a dynamic optimization and intelligent correlation management system for fiscal project databases and budget preparation, comprising a data acquisition module, a dynamic optimization module, a budget correlation module, a feedback control module, and a security module. The data acquisition module acquires fiscal project attribute data, policy factor data, and fiscal operation status data in real time. The dynamic optimization module prioritizes projects based on multidimensional attribute space modeling and a nonlinear coupled weight iterative algorithm. The budget correlation module constructs a mapping matrix between projects and budget indicators. The feedback control module uses a budget deviation feedback closed-loop control mechanism to adjust the budget preparation results in real time. The security module uses national cryptographic algorithms to encrypt and control data access. This system, with the core objective of dynamic optimization and intelligent correlation of fiscal project databases and budget preparation, constructs five collaborative functional modules. The data acquisition module is responsible for acquiring fiscal project attribute data, policy factor data, and fiscal operation status data in real time from the fiscal business system, policy release platform, and economic operation monitoring platform, ensuring the timeliness and completeness of the input information. The dynamic optimization module, relying on multidimensional attribute space modeling, transforms project attributes into high-dimensional feature vectors and uses a nonlinear coupled weight iterative algorithm to dynamically prioritize projects, enabling the ranking results to automatically adjust with changes in policy and the fiscal environment. The budget association module establishes a mapping matrix between projects and budget indicators to achieve precise matching between project needs and budget resources. The feedback control module introduces a closed-loop control mechanism for budget deviation feedback, continuously monitoring deviations and adjusting budget preparation results in real time during budget execution to ensure consistency between the budget and actual conditions. The security module employs national cryptographic algorithms to encrypt and control access to all financial data within the system, preventing unauthorized access and data leakage, and ensuring the security and compliance of financial information. This gives the system a complete closed-loop capability from data collection to dynamic optimization, budget association, feedback control, and security protection, ensuring intelligent association and dynamic optimization between the project database and budget preparation throughout the entire process, while also guaranteeing data security and laying a solid foundation for the collaborative operation of subsequent modules.

[0008] Furthermore, the multidimensional attribute space modeling method involves mapping multiple attributes of each fiscal project into high-dimensional feature vectors, which are then calculated using dynamic weights and attribute transformation functions. This method addresses various attributes of fiscal projects, such as project size, implementation period, policy alignment, and fiscal sustainability score. It normalizes the attribute values ​​of each project and maps them to a high-dimensional feature vector space. Each attribute value undergoes a non-linear transformation using a specific attribute transformation function to eliminate dimensional differences between attributes and enhance feature discriminative power. Dynamic weights are adjusted in real-time based on policy factors and fiscal operational trends, ensuring that the constructed feature vectors reflect the focus and priorities of the current fiscal management environment. This method allows the project's position in the feature space to intuitively reflect its comprehensive attributes, providing accurate data representation for subsequent prioritization and budget matching. It effectively solves the problem that traditional static classification cannot reflect the complex attributes of projects. Through high-dimensional feature vectors and dynamic weights, it makes project feature expression more comprehensive and accurate, providing reliable basic data for dynamic optimization and budget correlation.

[0009] Furthermore, a nonlinear coupled weight iterative algorithm is used to dynamically update attribute weights, and its iterative formula is based on a multi-objective optimization function. Designed for multi-objective optimization problems, the algorithm's objective function comprehensively considers three core factors: project importance, policy fit, and fiscal sustainability. In each iteration, the algorithm adjusts the weight values ​​based on the partial derivative of the objective function with respect to the weights, and uses an adaptive learning rate to ensure convergence speed and stability. This algorithm can capture the nonlinear interactions between attributes, making weight adjustments more consistent with the complex relationships in actual fiscal management, thereby achieving dynamic optimization of project priorities. Compared to the linear weighting method, this algorithm can more accurately reflect the complex coupling relationships between attributes, making project ranking more scientific and adaptable, and improving the accuracy and reliability of dynamic optimization.

[0010] Furthermore, the mapping matrix established by the budget association module reflects the correlation score between projects and budget indicators. The budget association module calculates the correlation score between each project and each budget indicator by analyzing the similarity between the project feature vector and the budget indicator feature vector, and by combining policy factor weighting. These scores constitute the elements of the mapping matrix, where rows represent projects, columns represent budget indicators, and the matrix values ​​reflect the intensity and degree of matching between the project's need for the budget. This mapping matrix provides a quantitative basis for budget preparation, enabling budget resources to be rationally allocated according to the importance and urgency of projects. This mapping mechanism realizes a quantitative correlation between the project database and budget preparation, making budget allocation more accurate, avoiding fund misallocation and waste, and improving the efficiency of fiscal fund utilization.

[0011] Furthermore, the feedback control module employs a budget deviation feedback closed-loop control mechanism, whose budget adjustment formula incorporates proportional, integral, and derivative control. During budget execution, the feedback control module continuously compares the budget target with the actual execution results, calculating the budget deviation. Based on the proportional, integral, and derivative control algorithm, the system dynamically adjusts the budget preparation results according to the magnitude and trend of the deviation, ensuring that budget execution gradually approaches the target value. This closed-loop control mechanism can quickly respond to changes in fiscal operations, reducing lags and deviations in budget execution. This mechanism significantly improves the adaptability and enforceability of the budget, ensuring that the budget remains consistent with the target throughout the execution process and reducing the risk of budget failure due to environmental changes.

[0012] Furthermore, the situational awareness model predicts changes in project funding needs based on macroeconomic indicators and fiscal revenue and expenditure trends. The model collects macroeconomic indicators such as GDP growth rate, price index, and employment rate, along with fiscal revenue and expenditure data, and combines this with historical project funding needs to predict future changes in project funding needs through weighted fusion. The model considers both the inertia of historical demand and the impact of external economic factors, resulting in highly forward-looking and accurate predictions. This model enhances the forward-looking nature of budget preparation, enabling fiscal management to proactively allocate resources across multiple timeframes and optimize intertemporal fund allocation.

[0013] Furthermore, the security module employs the national cryptographic algorithm SM4 for block encryption of data and attaches an HMAC-SM3 message authentication code. The security module uses SM4 for block encryption during both data storage and transmission, with a 128-bit key and CBC encryption mode to ensure data confidentiality. Simultaneously, an HMAC-SM3 message authentication code is attached during data transmission to verify data integrity and authenticity, preventing data tampering or forgery. This high-strength domestic cryptographic algorithm safeguards the confidentiality, integrity, and tamper-proof capabilities of financial data, meeting the high standards of information security required by financial management.

[0014] Furthermore, a bidirectional data flow is established between the dynamic optimization module and the budget association module, achieving closed-loop control. Changes in project priorities output by the dynamic optimization module update the mapping matrix of the budget association module in real time, and changes in the mapping matrix trigger the feedback control module to adjust the budget, thus forming a bidirectional data flow and closed-loop control between the project library and budget preparation. This mechanism ensures the synchronous updating and high consistency of project priorities and budget allocation. The bidirectional data flow and closed-loop control enable high coordination among the various modules of the system, improving the linkage efficiency and consistency between the project library and budget preparation.

[0015] Furthermore, the system supports multi-cycle fiscal planning models and saves the status and results at the end of each cycle to train the situational awareness model parameters. The system can run across multiple fiscal planning cycles, automatically saving the project database status and budget execution results at the end of each cycle, and using historical data to train the situational awareness model parameters. This enables the model to adapt to changes in the fiscal environment and policies, achieving cross-cycle adaptive evolution. Multi-cycle support and adaptive model evolution give the system long-term operation and continuous optimization capabilities, adapting to the dynamic development of fiscal management.

[0016] Furthermore, the system includes an anomaly detection submodule to monitor sudden changes in project attributes, excessive budget deviations, and abnormal fluctuations in policy factors, triggering early warnings and manual reviews. This submodule analyzes the magnitude of changes in project attributes, budget execution deviations, and policy factor fluctuations in real time. When an anomaly exceeding a set threshold is detected, an early warning mechanism is immediately triggered, and a manual review process is initiated to ensure timely handling of anomalies and prevent risk escalation. The anomaly detection and early warning mechanisms significantly enhance the system's robustness and compliance, reducing potential risks in fiscal management.

[0017] This invention provides a dynamic optimization and intelligent correlation management system for fiscal project database and budget preparation, which has the following beneficial effects:

[0018] This system organically combines functional modules such as data collection, dynamic optimization, budget correlation, feedback control, and security protection to form a complete intelligent correlation management system for dynamic optimization of the fiscal project database and budget preparation, which has many significant advantages.

[0019] First, the system employs a multi-dimensional attribute space modeling method for project attribute processing. This method maps multiple attributes of each fiscal project into high-dimensional feature vectors, and calculates these vectors using dynamic weights and attribute transformation functions. This eliminates dimensional differences between attributes and enhances feature discriminative power. This approach accurately characterizes the essential features of projects in complex attribute environments, providing a solid data foundation for subsequent prioritization and budget matching.

[0020] Secondly, the system introduces a nonlinear coupled weighted iterative algorithm for dynamically updating attribute weights. Based on a multi-objective optimization function, this algorithm comprehensively considers project importance, policy fit, and fiscal sustainability. By iteratively adjusting weights, it enables automatic optimization of project priority ranking in response to changes in the policy environment and fiscal situation. Compared to traditional linear weighting methods, this algorithm better reflects the complex relationships in reality, enhancing the scientific rigor and rationality of project selection.

[0021] Third, the system constructs a mapping matrix between projects and budget indicators in terms of budget correlation. Each element of this matrix reflects the correlation score between projects and budget indicators, and the score is obtained by weighting feature vector similarity with policy factors. This mapping mechanism achieves precise alignment between the project database and budget preparation, enabling budget resources to be rationally allocated according to the importance and urgency of projects, avoiding fund misallocation and waste.

[0022] Fourth, the system employs a budget deviation feedback closed-loop control mechanism, using proportional, integral, and derivative control algorithms to adjust the budget in real time. This mechanism dynamically corrects the budget preparation results based on deviations during budget execution, ensuring that budget execution more closely aligns with expected goals. This closed-loop control significantly improves the adaptability and enforceability of the budget, reducing the risk of budget failure due to environmental changes.

[0023] Fifth, in terms of situational awareness, the system has established a predictive model based on macroeconomic indicators and fiscal revenue and expenditure trends, which can predict changes in project funding needs in advance. This model combines the inertia of historical funding needs with the influence of external economic factors, making budget preparation more forward-looking and enabling the cross-period optimal allocation of resources in multi-cycle fiscal planning.

[0024] Sixth, in terms of security, the system employs the national cryptographic algorithm SM4 for block encryption of data, combined with HMAC-SM3 message authentication codes to ensure data integrity and tamper-proof capabilities. This robust security measure effectively prevents the leakage and tampering of financial data during storage and transmission, ensuring the compliance and security of financial management.

[0025] Seventh, the system implements bidirectional data flow between the dynamic optimization module and the budget association module, enabling adjustments to project priorities to be reflected in the budget mapping matrix in real time and triggering budget adjustments, thus forming a complete closed-loop control system. This bidirectional linkage mechanism ensures a high degree of consistency between the project database and budget preparation, improving the overall coordination and response speed of the system.

[0026] Eighth, the system supports multi-cycle fiscal planning models and automatically saves the project database status and budget execution results at the end of each cycle. Simultaneously, it trains situational awareness model parameters based on historical data, enabling the model to adaptively evolve across cycles. This feature allows the system to continuously accumulate experience and optimize its decision-making capabilities, adapting to the changes and challenges of long-term fiscal management.

[0027] Ninth, the system includes an anomaly detection submodule, which can monitor sudden changes in project attributes, excessive budget deviations, and abnormal fluctuations in policy factors in real time. Upon detecting anomalies, it triggers an early warning mechanism and initiates a manual review process. This anomaly early warning and intervention mechanism significantly improves the robustness and compliance of fiscal management and reduces fiscal risks caused by unforeseen circumstances.

[0028] Tenth, in summary, this system integrates multiple innovative technologies, including multi-dimensional attribute modeling, nonlinear weight iteration, budget mapping matrix, closed-loop feedback control, situational awareness prediction, high-strength security protection, and anomaly early warning, to achieve intelligent correlation and dynamic optimization between the fiscal project database and budget preparation. This not only significantly improves the efficiency of fiscal fund utilization and policy response speed, but also enhances the security, foresight, and sustainability of fiscal management, providing new technical support and solutions for modern fiscal management. Attached Figure Description

[0029] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0030] Figure 1 This is a diagram illustrating the overall operational logic of the system of this invention. Figure 2 This is a flowchart of the data acquisition and modeling process of this invention; Figure 3 This is a flowchart illustrating the dynamic optimization and budget correlation of the present invention. Figure 4 This is a flowchart illustrating the budget execution and feedback control process of this invention. Figure 5 This is a flowchart illustrating the situational awareness and security process of this invention. Detailed Implementation

[0031] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0033] How to use:

[0034] 1. System initialization and data acquisition module usage

[0035] After system startup, the system first enters the data acquisition module to acquire real-time data on fiscal project attributes, policy factors, and fiscal operation status. Project attributes include scale, cycle, policy alignment, and fiscal sustainability score; policy factors are derived from the latest fiscal policy documents; and fiscal operation status data includes indicators such as revenue and expenditure and debt levels. The collected data is preprocessed and stored in the system database, providing raw input for subsequent multi-dimensional attribute space modeling.

[0036] 2. Use of multidimensional attribute space modeling methods

[0037] Using the collected project attribute data, a formula is constructed according to the feature vector. ;

[0038] Multiple attributes of each project are mapped to high-dimensional feature vectors. For the first The first project The original values ​​of each attribute, This is an attribute transformation function used to eliminate dimensional differences and enhance feature discriminative power. The weights are dynamic, and the initial values ​​can be set by the system default and adjusted according to policy factors.

[0039] 3. The nonlinear coupled weight iterative algorithm uses...

[0040] In the dynamic optimization module, a nonlinear coupled weight iterative algorithm is called to update the attribute weights. The iterative formula is as follows: ;

[0041] in The function is a multi-objective optimization function, comprehensively considering project importance, policy alignment, and fiscal sustainability. The system employs an adaptive learning rate. It iteratively updates the weights based on the latest policy and fiscal situation data until the weight changes are less than a preset threshold, thus obtaining a dynamically optimized project priority ranking.

[0042] 4. Budget association module usage

[0043] In the budget correlation module, a correlation score is calculated based on the similarity between the project feature vector and the budget indicator feature vector, combined with policy factor weighting. And establish a mapping matrix ;

[0044] This matrix reflects the degree of matching between each project and each budget indicator, providing a quantitative basis for budget preparation.

[0045] 5. Feedback control module usage

[0046] During budget execution, the feedback control module continuously compares the budget targets. Compared with actual execution results Calculate the deviation The budget is adjusted using a budget deviation feedback closed-loop control mechanism, and the formula is as follows: ;

[0047] in , , The system uses adjustable control coefficients to correct the budget preparation results in real time based on changes in deviation.

[0048] 6. Use of situational awareness models

[0049] The situational awareness model collects macroeconomic indicators and fiscal revenue and expenditure trend data, combines them with historical project funding needs, and uses forecasting formulas. ;

[0050] Forecast future project funding needs, including For the first A macroeconomic indicator at time The value, , It is a dynamic weight used to balance historical inertia and the influence of external factors.

[0051] 7. Security Module Usage

[0052] In the security module, the system uses the national cryptographic algorithm SM4 for block encryption of stored and transmitted financial data, with a key length of 128 bits and encryption mode of CBC. HMAC-SM3 message authentication code is attached during data transmission to ensure data confidentiality, integrity and tamper-proof.

[0053] 8. Use of bidirectional data flow and closed-loop control

[0054] The dynamic optimization module outputs changes in project priority, which update the mapping matrix of the budget association module in real time. Changes in the mapping matrix trigger the feedback control module to adjust the budget, forming a two-way data flow and closed-loop control between the project library and budget preparation, ensuring a high degree of consistency between the two.

[0055] 9. Use of multi-cycle fiscal planning model

[0056] The system supports multi-cycle operation. At the end of each cycle, the system automatically saves the project library status and budget execution results, and trains the situational awareness model parameters based on historical data, enabling the model to adaptively evolve across cycles and adapt to policy and environmental changes.

[0057] 10. Use of the anomaly detection submodule

[0058] The anomaly detection submodule monitors sudden changes in project attributes, budget deviations exceeding limits, and abnormal fluctuations in policy factors in real time. When a situation exceeding the set threshold is detected, an early warning mechanism is immediately triggered and a manual review process is initiated to ensure that anomalies are handled in a timely manner and to prevent risks from escalating.

[0059] Example:

[0060] Example 1: Complete Example of System Initialization and Data Acquisition Module

[0061] Upon initial deployment and startup, the system enters the initialization phase, first running the data acquisition module. This module connects to data sources related to fiscal project attributes, policy factors, and fiscal operational status through interfaces, acquiring project attribute data, policy factor data, and fiscal operational status data in real time. Project attributes include scale, implementation period, policy matching degree, and fiscal sustainability score; policy factors are derived from the latest published fiscal policy documents; and fiscal operational status data covers information such as revenue and expenditure and debt levels. The acquisition module performs time synchronization, format standardization, and validity verification on the multi-source data, storing the processed data in the system database. This process ensures that the starting data for system operation is consistent with the real fiscal environment, providing complete and real-time input data for subsequent multi-dimensional attribute space modeling, thereby guaranteeing the reliable operational foundation of the entire fiscal project database dynamic optimization and budget preparation intelligent correlation management system.

[0062] Example 2: Complete Implementation of Multidimensional Attribute Space Modeling and Nonlinear Coupled Weight Iterative Algorithm

[0063] After data collection is complete, the system enters the multi-dimensional attribute space modeling stage. Multiple attributes of each fiscal item are transformed using attribute transformation functions. Nonlinear transformation is performed to eliminate dimensional differences and enhance feature discrimination. Dynamic weights are then incorporated. According to the formula ;

[0064] Constructing high-dimensional feature vectors ,in For the first The first project The original values ​​of each attribute are then used. Subsequently, in the dynamic optimization module, a nonlinear coupled weight iterative algorithm is invoked, based on a multi-objective optimization function. According to the iterative formula ;

[0065] Update attribute weights, where An adaptive learning rate is used. The iterative process continues until the weight changes are less than a preset threshold, thereby obtaining a project priority ranking that reflects the current policy and fiscal situation. This embodiment ensures that project features are comprehensively represented and that the ranking results can be automatically optimized as the environment changes.

[0066] Example 3: Complete Implementation of Budget Association Module and Feedback Control Module

[0067] In the budget correlation module, the system calculates the similarity between the project feature vector and the budget indicator feature vector, and then combines this with policy factors to obtain a correlation score. According to the formula ;

[0068] Establish a mapping matrix The matrix rows represent projects, the columns represent budget targets, and the matrix values ​​reflect the intensity and degree of matching between the project's need for the budget. During the budget execution phase, the feedback control module continuously compares the budget targets. Compared with actual execution results Calculate the deviation The budget is adjusted using a budget deviation feedback closed-loop control mechanism, and the formula is as follows: ;

[0069] in , , This is an adjustable control factor. This embodiment achieves precise matching between the project library and budget preparation, and corrects the budget in real time during execution to ensure consistency between the budget and the target.

[0070] Example 4: Complete Implementation of Situational Awareness Model and Security Module

[0071] The situational awareness model collects macroeconomic indicators and fiscal revenue and expenditure trend data, combines them with historical project funding needs, and uses forecasting formulas. ;

[0072] Forecast future project funding needs, including For the first A macroeconomic indicator at time The value, , The dynamic weighting is used to balance historical inertia and the influence of external factors. In the security module, the system uses the national cryptographic algorithm SM4 for block encryption of stored and transmitted financial data, with a key length of 128 bits and CBC encryption mode. An HMAC-SM3 message authentication code is appended during data transmission. This embodiment enhances the forward-looking nature of budget preparation and ensures the confidentiality, integrity, and tamper-proof capability of data during storage and transmission.

[0073] Example 5: Complete Implementation of the Bidirectional Data Flow and Closed-Loop Control, Multi-Period Planning and Anomaly Detection Submodule

[0074] The dynamic optimization module updates the mapping matrix of the budget association module in real time based on changes in project priority output. Changes in the mapping matrix trigger the feedback control module to adjust the budget, forming a two-way data flow and closed-loop control between the project library and budget preparation. The system supports multi-cycle operation. At the end of each cycle, the system automatically saves the project library status and budget execution results, and trains the situational awareness model parameters based on historical data, enabling the model to adaptively evolve across cycles. The anomaly detection submodule monitors sudden changes in project attributes, excessive budget deviations, and abnormal fluctuations in policy factors in real time. When a situation exceeding a set threshold is detected, an early warning mechanism is immediately triggered, and a manual review process is initiated. This embodiment ensures that project priorities and budget allocation are updated synchronously, maintaining long-term optimization capabilities and intervening promptly in abnormal situations, thereby improving the system's robustness and compliance.

[0075] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic optimization and intelligent management system for fiscal project databases and budget preparation, characterized in that: It includes a data acquisition module, a dynamic optimization module, a budget association module, a feedback control module, and a security module. The data acquisition module is used to acquire fiscal project attribute data, policy factor data, and fiscal operation status data in real time. The dynamic optimization module prioritizes projects based on multi-dimensional attribute space modeling and a nonlinear coupled weight iterative algorithm. The budget association module constructs a mapping matrix between projects and budget indicators. The feedback control module uses a budget deviation feedback closed-loop control mechanism to adjust the budget preparation results in real time. The security module uses national cryptographic algorithms to encrypt data and control access.

2. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The multidimensional attribute space modeling method includes mapping multiple attributes of each fiscal item to a high-dimensional feature vector. And through dynamic weights With attribute transformation function The calculation yields the following formula: ; in, For the first The first project The original values ​​of each attribute, It is a nonlinear transformation function used to eliminate dimensional differences and enhance feature discrimination.

3. The intelligent correlation management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The Nonlinear Coupled Weight Iterative Algorithm (NCWIA) is used to dynamically update attribute weights, and its iterative formula is as follows: ； in, The function is a multi-objective optimization function, comprehensively considering project importance, policy alignment, and fiscal sustainability. To achieve an adaptive learning rate, the iterative process continues until the weight change is less than a preset threshold.

4. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The mapping matrix established by the budget association module for: ； in, Indicates the first The project and the first The correlation score of each budget indicator is obtained by weighting the feature vector similarity with policy factors.

5. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The feedback control module employs a budget deviation feedback closed-loop control mechanism (BBFCM), and its budget adjustment formula is as follows: ； in, For budget deviation, , , The adjustable control coefficient enables dynamic and precise correction of the budget.

6. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The situational awareness model predicts changes in project funding needs based on macroeconomic indicators and fiscal revenue and expenditure trends. The prediction formula is as follows: ； in, For the first A macroeconomic indicator at time The value, , The weights are dynamic, reflecting the inertial influence of historical funding needs and the moderating effect of external factors.

7. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The security module uses the national cryptographic algorithm SM4 to encrypt data in blocks, with a key length of 128 bits and an encryption mode of CBC. In addition, an HMAC-SM3 message authentication code is attached during data transmission to ensure data integrity and prevent tampering.

8. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The dynamic optimization module and the budget association module form a bidirectional data flow, and the project priority output by the dynamic optimization module updates the mapping matrix in real time. The change in the mapping matrix triggers the feedback control module to adjust the budget, thus forming a closed-loop control system.

9. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The system supports a multi-cycle fiscal planning model. At the end of each planning cycle, it automatically saves the status of the project library and the budget execution results for that cycle, and trains the parameters of the situational awareness model based on historical data to achieve cross-cycle adaptive evolution of the model.

10. The intelligent association management system for dynamic optimization of the fiscal project database and budget preparation according to claim 1, characterized in that, The system also includes an anomaly detection submodule, which monitors sudden changes in project attributes, excessive budget deviations, and abnormal fluctuations in policy factors to trigger an early warning mechanism and initiate a manual review process, ensuring the robustness and compliance of the fiscal project database and budget preparation.

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