A natural resource project budget management system based on performance big data

Abstract

The present invention relates to the technical field of budget management, and discloses a natural resource project budget management system based on performance big data. The system improves the accuracy and intelligence level of project budget management through the collaboration of multiple modules. Compared with traditional manual budget management and a single data analysis method, the system can more accurately identify potential risks and anomalies in project budgets through multi-dimensional data collection and comprehensive analysis, realize real-time tracking and positioning of problems, and help project managers make scientific decisions. At the same time, based on a visual feedback mechanism, managers can quickly understand data analysis results, optimize decision-making processes, and further improve the efficiency and flexibility of budget management. Compared with existing technical means, the system has achieved significant improvements in abnormal monitoring sensitivity, data analysis depth, and decision response speed, ensuring the rationality, compliance, and sustainability of project budgets.

Description

Technical Field

[0001] The present invention relates to the technical field of budget management, and in particular to a natural resource project budget management system based on performance big data. Background Art

[0002] Natural resource management is the cornerstone of the global sustainable development strategy, covering land, water, mineral resources, forests and other fields. Within this broad framework, project budget performance management, as an important tool, focuses on how to rationally allocate and use these resources to ensure the dual goals of economic benefits and ecological protection.

[0003] The current budget management system faces many challenges in actual operation. Many projects have overspending during budget execution, and the lack of effective real-time monitoring and feedback mechanisms makes it difficult for managers to detect and respond to potential problems in a timely manner. In addition, traditional systems often only focus on single-dimensional budget data, while ignoring other key factors such as interest feedback and project benefits, resulting in incomplete decision-making. This situation not only affects the efficiency of project implementation, but may also lead to a waste of resources.

[0004] Therefore, we proposed a natural resource project budget management system based on performance big data to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a natural resource project budget management system based on performance big data to solve the problem that many projects mentioned in the above background technology have overspending during budget execution, lack of effective real-time monitoring and feedback mechanism, making it difficult for managers to discover and respond to potential problems in a timely manner. In addition, traditional systems often only focus on budget data in a single dimension, while ignoring other key factors such as interest feedback and project benefits, resulting in incomplete decision-making. This situation not only affects the efficiency of project implementation, but may also lead to the problem of waste of resources.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a natural resource project budget management system based on performance big data, comprising a data acquisition module, a data processing module, a first data analysis module, a second data analysis module and a feedback module;

[0007] The data acquisition module is used to acquire data for the project budget, including basic data, environmental intervention data and income data, and generate a first data set, a second data set and a third data set;

[0008] The data processing module is used to preprocess and dimensionlessly transform the first data set, the second data set, and the third data set;

[0009] The first data analysis module is used to calculate the processed first data set, second data set and third data set to generate a project abnormality reference coefficient XSP, and compare the calculated project abnormality reference coefficient XSP with a preset first threshold value Y to generate a first comparison result, and determine whether the current project can be approved and whether data abnormality occurs according to the first comparison result;

[0010] The second data analysis module is used to integrate and calculate the processed first data set, the second data set and the third data set, so as to generate a basic data anomaly coefficient S1, an environmental intervention data anomaly coefficient S2 and an income data anomaly coefficient S3, and compare the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 with the preset basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3, respectively, so as to generate a second comparison result, and according to the second comparison result, track and locate the abnormal point in real time;

[0011] The feedback module is used to generate a visual report based on the first and second comparison results.

[0012] Preferably, the data acquisition module includes a first data acquisition unit, a second data acquisition unit and a third data acquisition unit;

[0013] The first data collection unit is used to obtain basic data of the project and organize it into a first data set, including a total budget amount, a budget utilization rate, and an expenditure compliance score;

[0014] The total budget amount is divided into the real-time total budget amount A and the historical total budget amount SA. The historical total budget amounts are recorded as SA1, SA2, SA3, ..., SAn according to the timestamp of the number of declarations.

[0015] The budget utilization rate is divided into real-time budget utilization rate B and historical budget utilization rate SB. The historical budget utilization rates are recorded as SB1, SB2, SB3, ..., SBn according to the timestamp of the number of declarations;

[0016] The expenditure compliance score is divided into the real-time expenditure compliance score C and the historical expenditure compliance score SC. The historical scores are recorded as SC1, SC2, SC3, ..., SCn according to the timestamp of the number of declarations.

[0017] The second data collection unit is used to obtain environmental intervention data of the project and organize it into a second data set, including resource consumption, ecological restoration cost and environmental impact rate;

[0018] Resource consumption is divided into real-time resource consumption D and historical resource consumption SD. The historical resource consumption is recorded as SD1, SD2, SD3, ..., SDn according to the timestamp of the number of declarations.

[0019] The ecological restoration cost is divided into real-time ecological restoration cost E and historical ecological restoration cost SE. The historical ecological restoration cost is recorded as SE1, SE2, SE3, ..., SEn according to the timestamp of the number of declarations;

[0020] The environmental impact rate is divided into real-time environmental impact rate F and historical environmental impact rate SF. The historical environmental impact rates are recorded as SF1, SF2, SF3, ..., SFn according to the timestamp of the number of declarations;

[0021] The third data collection unit is used to obtain the revenue data of the project and organize it into a third data set, including the benefit deviation rate, the efficiency index achievement rate and the benefit achievement cycle;

[0022] The benefit deviation rate is divided into real-time benefit deviation rate G and historical benefit deviation rate SG. The historical benefit deviation rates are recorded as SG1, SG2, SG3, ..., SGn according to the timestamp of the number of declarations;

[0023] The efficiency index achievement rate is divided into the real-time efficiency index achievement rate H and the historical efficiency index achievement rate SH. The historical efficiency index achievement rates are recorded as SH1, SH2, SH3, ..., SHn according to the timestamp of the number of declarations.

[0024] The benefit achievement cycle is divided into real-time benefit achievement cycle I and historical benefit achievement cycle SI. The historical benefit achievement cycles are recorded as SI1, SI2, SI3, ..., SIn according to the timestamp of the number of declarations.

[0025] Preferably, the first data analysis module includes a first data calculation unit and a first data parsing unit;

[0026] The first data calculation unit is used to integrate and calculate the preprocessed first data set, second data set and third data set, so as to generate a project abnormality reference coefficient XSP;

[0027] The first data parsing unit is used to compare the project abnormality reference coefficient XSP with a preset first threshold value Y, so as to generate a first comparison result.

[0028] Preferably, the first data calculation unit calculates and obtains the project abnormality reference coefficient XSP through the following steps;

[0029] S1.1. Calculate the project averages for the internal historical data of the first data set, the second data set, and the third data set, respectively, to generate the historical total budget amount average LPA, the historical budget utilization rate average LPB, the historical expenditure compliance score average LPC, the historical resource consumption average LPD, the historical ecological restoration cost average LPE, the historical environmental impact rate average LPF, the historical benefit deviation rate average LPG, the historical efficiency index achievement rate average LPH, and the historical benefit achievement cycle average LPI;

[0030] S1.2. Select the maximum values ​​of the historical data in the first data set, the second data set, and the third data set, and record them as the maximum value of the historical total budget amount SAmax, the maximum value of the historical budget utilization rate SBmax, the maximum value of the historical expenditure compliance score SCmax, the maximum value of the historical resource consumption SDmax, the maximum value of the historical ecological restoration cost SEmax, the maximum value of the historical environmental impact rate SFmax, the maximum value of the historical benefit deviation rate SGmax, the maximum value of the historical efficiency indicator achievement rate SHmax, and the maximum value of the historical benefit achievement period SImax;

[0031] S1.3. Integrate and calculate the real-time value, historical maximum value and historical average value of each parameter in the first data set, the second data set and the third data set, so as to generate the project abnormality reference coefficient XSP.

[0032] Preferably, the first comparison result generated by the first data parsing unit is as follows:

[0033] When XSP≤Y, it means the current project can be approved;

[0034] When XSP>Y, it means that the current project cannot be approved or the project needs data monitoring.

[0035] Preferably, the second data analysis module includes a second data calculation unit and a second data parsing unit;

[0036] The second data calculation unit is used to calculate the first data set, the second data set and the third data set respectively, so as to generate a basic data anomaly coefficient S1, an environmental intervention data anomaly coefficient S2 and an income data anomaly coefficient S3;

[0037] The second data analysis unit is used to compare the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 with the preset basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3 respectively, so as to generate a second comparison result.

[0038] Preferably, the second data calculation unit calculates and obtains the basic data abnormality coefficient S1, the environmental intervention data abnormality coefficient S2 and the income data abnormality coefficient S3 through the following steps;

[0039] S2.1. Calculate the window mean of historical data for the first data set, the second data set, and the third data set, thereby generating the window mean of the historical total budget amount PSA, the window mean of the historical budget utilization rate PSB, the window mean of the historical expenditure compliance score PSC, the window mean of the historical resource consumption PSD, the window mean of the historical ecological restoration cost PSE, the window mean of the historical environmental impact rate PSF, the window mean of the historical benefit deviation rate PSG, the window mean of the historical efficiency index achievement rate PSH, and the window mean of the historical benefit achievement period PSI, where the window value is set to m;

[0040] S2.2. Calculate the real-time values ​​and window means of various parameters in the first data set, the second data set and the third data set, thereby generating the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 respectively.

[0041] Preferably, the second comparison result generated by the second data parsing unit is as follows:

[0042] When S1≤R1, it means that there is no data anomaly in the basic data of the project. When S1>R1, it means that there is data anomaly in the basic data of the project and monitoring is required.

[0043] When S2≤R2, it means that the environmental intervention data of the project has no data anomaly. When S2>R2, it means that the environmental intervention data of the project has data anomaly and needs to be monitored.

[0044] When S3≤R3, it means that there is no data anomaly in the revenue data of the project. When S3>R3, it means that there is data anomaly in the revenue data of the project and it needs to be monitored.

[0045] Preferably, the visual report generated by the feedback module is as follows:

[0046] When XSP≤Y, check the second comparison result. If the basic data abnormal coefficient S1, the environmental intervention data abnormal coefficient S2, and the income data abnormal coefficient S3 in the second comparison result all exceed the basic data threshold R1, the environmental intervention threshold R2, and the income threshold R3, monitor all parameters;

[0047] When XSP>Y, check the second comparison result. If any of the basic data abnormal coefficient S1, the environmental intervention data abnormal coefficient S2, and the income data abnormal coefficient S3 in the second comparison result exceeds the basic data threshold R1, the environmental intervention threshold R2, and the income threshold R3, monitor the parameters in the data set to which the abnormal coefficient exceeding the threshold belongs;

[0048] When XSP>Y, check the second comparison result. If the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 in the second comparison result all exceed the basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3, monitor all data and suspend the project for inspection.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. This system improves the accuracy and intelligence of project budget management through the collaboration of multiple modules. Compared with traditional manual budget management and single data analysis methods, this system can more accurately identify potential risks and anomalies in project budgets through multi-dimensional data collection and comprehensive analysis, achieve real-time tracking and locate problems, and help project managers make scientific decisions. At the same time, based on the visual feedback mechanism, managers can quickly understand the results of data analysis, optimize the decision-making process, and further improve the efficiency and flexibility of budget management. Overall, the system has achieved significant improvements in abnormal monitoring sensitivity, data analysis depth, and decision-making response speed compared with existing technical means, promoted the intelligent and automated transformation of budget management, and ensured the rationality, compliance and sustainability of project budgets.

[0051] 2. The feedback module presents monitoring results through visual reports, allowing project managers to intuitively view abnormal data in budget execution. Charts and reports help decision makers quickly understand the status of the current project, especially when there are abnormalities in multiple dimensions, and can clearly show the severity of the abnormality, the data sets involved, and the specific issues that need attention. Compared with traditional text descriptions or numerical reports, visual reports not only improve decision transparency, but also greatly improve response efficiency, helping managers make decisions quickly and avoiding decision delays caused by information lags.

[0052] 3. The feedback module further improves the compliance and controllability of budget management by monitoring abnormal data that exceeds the threshold and triggering the project suspension inspection mechanism in serious abnormal situations. In traditional budget management, anomalies are usually discovered by manual judgment and periodic audits, which are often delayed and incomplete. The system can detect data anomalies in real time and take immediate response measures, avoiding the expansion of project budget deviations and ensuring the rational use of resources and compliance with the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a system flow chart of the present invention.

[0054] In the figure: 1. data acquisition module; 11. first data acquisition unit; 12. second data acquisition unit; 13. third data acquisition unit; 2. data processing module; 3. first data analysis module; 31. first data calculation unit; 32. first data parsing unit; 4. second data analysis module; 41. second data calculation unit; 42. second data parsing unit; 5. feedback module. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example 1: Please refer to Figure 1 , a natural resource project budget management system based on performance big data, including a data collection module 1, a data processing module 2, a first data analysis module 3, a second data analysis module 4 and a feedback module 5;

[0057] The data acquisition module 1 is used to acquire data for the project budget, including basic data, environmental intervention data and income data, and generate a first data set, a second data set and a third data set;

[0058] The data processing module 2 is used to preprocess and dimensionlessly transform the first data set, the second data set, and the third data set;

[0059] The first data analysis module 3 is used to calculate the processed first data set, second data set and third data set to generate a project abnormality reference coefficient XSP, and compare the calculated project abnormality reference coefficient XSP with a preset first threshold value Y to generate a first comparison result, and judge whether the current project can be approved and whether data abnormality occurs according to the first comparison result;

[0060] The second data analysis module 4 is used to integrate and calculate the processed first data set, the second data set and the third data set, so as to generate a basic data anomaly coefficient S1, an environmental intervention data anomaly coefficient S2 and an income data anomaly coefficient S3, and compare the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 with the preset basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3, respectively, so as to generate a second comparison result, and according to the second comparison result, track and locate the abnormal point in real time;

[0061] The feedback module 5 is used to generate a visual report based on the first and second comparison results.

[0062] In this embodiment: Data collection module 1 is responsible for comprehensive collection of various data of the project budget to ensure the integrity and multi-dimensionality of the data source. This module generates the first data set, the second data set and the third data set by collecting basic data, environmental intervention data and income data. Its main task is to ensure the accuracy and comprehensiveness of the data and provide reliable basic data support for subsequent analysis and processing. Through multi-dimensional data collection, the system can fully grasp the overall picture of the project, provide a solid foundation for subsequent budget evaluation and management, and effectively support the scientificity and accuracy of budget decisions.

[0063] Data processing module 2 is responsible for preprocessing and dimensionless conversion of the collected first data set, second data set and third data set. The preprocessing steps include data cleaning, noise removal and missing value filling to ensure that the data is neat and consistent, providing an accurate basis for subsequent analysis. The dimensionless conversion process standardizes data of different dimensions to make them comparable, thereby laying a unified foundation for further analysis and calculation. This processing process effectively eliminates the impact of data dimension differences, improves the reliability of data analysis and the accuracy of processing results.

[0064] The first data analysis module 3 performs an abnormal analysis of the project budget based on the processed data set. By calculating the first data set, the second data set and the third data set, the module generates a project abnormal reference coefficient XSP and compares it with the preset threshold value Y to determine whether the project budget meets the approval requirements or whether there is abnormal data. This analysis helps to identify potential problems in the project budget in a timely manner, such as budget overruns or data non-compliance, and then provides early warnings for managers to ensure that the project remains compliant and reasonable during the budget management process.

[0065] The second data analysis module 4 further deepens the data analysis. By integrating and calculating the basic data, environmental intervention data and income data, various abnormal coefficients are generated, including the basic data abnormal coefficient S1, the environmental intervention data abnormal coefficient S2 and the income data abnormal coefficient S3. By comparing these abnormal coefficients with various preset thresholds such as the basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3, the module can generate a second comparison result, thereby achieving real-time tracking and positioning of abnormal points. This function helps to accurately identify potential risks and helps managers take timely measures to reduce the financial risks caused by data anomalies.

[0066] Feedback module 5 is responsible for generating a visual report of the first comparison result and the second comparison result. Through charts, graphs, etc., feedback module 5 can present complex data analysis results to managers in an intuitive and easy-to-understand way, thereby improving the efficiency and quality of decision-making. The core task of this module is to ensure that the analysis results can be delivered in a timely manner and provide managers with clear and easy-to-operate feedback to help them respond quickly. Visual presentation not only enhances the transparency of budget management, but also improves the accuracy of decision-making and the timeliness of execution.

[0067] This system improves the accuracy and intelligence of project budget management through the collaboration of multiple modules. Compared with traditional manual budget management and single data analysis methods, this system can more accurately identify potential risks and anomalies in project budgets through multi-dimensional data collection and comprehensive analysis, achieve real-time tracking and location of problems, and help project managers make scientific decisions. At the same time, based on the visual feedback mechanism, managers can quickly understand the results of data analysis, optimize the decision-making process, and further improve the efficiency and flexibility of budget management. Overall, the system has achieved significant improvements in abnormal monitoring sensitivity, data analysis depth, and decision-making response speed compared to existing technical means, promoted the intelligent and automated transformation of budget management, and ensured the rationality, compliance, and sustainability of project budgets.

[0068] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a first data acquisition unit 11, a second data acquisition unit 12 and a third data acquisition unit 13;

[0069] The first data collection unit 11 is used to obtain basic data of the project and organize it into a first data set, including the total budget amount, budget utilization rate and expenditure compliance score;

[0070] The total budget amount is divided into the real-time total budget amount A and the historical total budget amount SA. The historical total budget amounts are recorded as SA1, SA2, SA3, ..., SAn according to the timestamp of the number of declarations.

[0071] The budget utilization rate is divided into real-time budget utilization rate B and historical budget utilization rate SB. The historical budget utilization rates are recorded as SB1, SB2, SB3, ..., SBn according to the timestamp of the number of declarations;

[0072] The expenditure compliance score is divided into the real-time expenditure compliance score C and the historical expenditure compliance score SC. The historical scores are recorded as SC1, SC2, SC3, ..., SCn according to the timestamp of the number of declarations.

[0073] The second data collection unit 12 is used to obtain the environmental intervention data of the project and organize it into a second data set, including resource consumption, ecological restoration cost and environmental impact rate;

[0074] Resource consumption is divided into real-time resource consumption D and historical resource consumption SD. The historical resource consumption is recorded as SD1, SD2, SD3, ..., SDn according to the timestamp of the number of declarations.

[0075] The ecological restoration cost is divided into real-time ecological restoration cost E and historical ecological restoration cost SE. The historical ecological restoration cost is recorded as SE1, SE2, SE3, ..., SEn according to the timestamp of the number of declarations;

[0076] The environmental impact rate is divided into real-time environmental impact rate F and historical environmental impact rate SF. The historical environmental impact rates are recorded as SF1, SF2, SF3, ..., SFn according to the timestamp of the number of declarations;

[0077] The third data collection unit 13 is used to obtain the income data of the project and organize it into a third data set, including the benefit deviation rate, the efficiency index achievement rate and the benefit achievement cycle;

[0078] The benefit deviation rate is divided into real-time benefit deviation rate G and historical benefit deviation rate SG. The historical benefit deviation rates are recorded as SG1, SG2, SG3, ..., SGn according to the timestamp of the number of declarations;

[0079] The efficiency index achievement rate is divided into the real-time efficiency index achievement rate H and the historical efficiency index achievement rate SH. The historical efficiency index achievement rates are recorded as SH1, SH2, SH3, ..., SHn according to the timestamp of the number of declarations.

[0080] The benefit achievement cycle is divided into real-time benefit achievement cycle I and historical benefit achievement cycle SI. The historical benefit achievement cycles are recorded as SI1, SI2, SI3, ..., SIn according to the timestamp of the number of declarations.

[0081] In this embodiment: Data collection module 1 comprehensively covers the key areas in project budget management by introducing multiple data collection units. Different from the traditional single financial data collection method, this module adopts a multi-dimensional data collection method, which not only covers the financial data of the project, but also includes environmental data and benefit data. This improvement can more comprehensively reflect the overall performance of the project, especially the balance between environmental protection and economic benefits, ensuring that managers can conduct comprehensive analysis from multiple dimensions and make more scientific decisions.

[0082] Each data unit not only collects real-time data, but also records historical data, and classifies it by the number of declarations and timestamps. In this way, the system can compare the changes between real-time data and historical data, provide more in-depth trend analysis, and help project managers identify dynamic changes in budget execution and resource consumption, and promptly warn of possible overspending risks and resource waste. This approach improves the flexibility and adaptability of budget management, allowing managers to adjust decisions in a timely manner to ensure the smooth progress of the project.

[0083] The historical data of each data collection unit is not only classified by timestamp, but also compared with the preset target values, such as the comparison between the budget utilization rate and the target budget utilization rate, and the resource consumption and the set standard. This combination of historical data and real-time data supports the system to conduct dynamic benefit evaluation, helping managers understand the performance of the project at different stages, and timely discover deviations and unreasonable budget use, thereby ensuring the rationality of budget and resource allocation and improving the overall project benefits.

[0084] The second data collection unit 12 strengthens the environmental protection goals in project budget management by acquiring environmental intervention data. This design enables project managers to not only effectively control economic benefits, but also fully grasp the environmental impact during project implementation, avoiding the problem of simply focusing on economic benefits while ignoring ecological protection. By comprehensively considering environmental impacts, the system can better balance resource consumption and ecological restoration costs, thereby promoting projects to meet sustainable development requirements while reducing environmental risks and promoting green development.

[0085] The third data collection unit 13 is responsible for collecting data related to project benefits. By recording these data, the system can monitor the actual benefits of the project in real time and evaluate the realization of its work efficiency. This design provides traceability of project benefits, supports managers to evaluate the execution efficiency of projects from multiple dimensions, and promptly discover potential problems. By comparing the real-time and historical benefit achievement cycles, managers can understand the deviation of project progress and ensure that the project is completed on time and with quality.

[0086] Compared with traditional budget management systems, data collection module 1 significantly improves the accuracy and comprehensiveness of budget management by comprehensively collecting real-time data and historical data, integrating key information from multiple dimensions such as finance, environment and benefits. This module not only focuses on the economic cost of the project, but also includes environmental benefits and project execution efficiency in the analysis to ensure the achievement of sustainable development goals. Through dynamic monitoring and trend analysis, project managers can identify and respond to potential risks in a timely manner, optimize resource allocation, and improve project execution efficiency and benefits. Data collection module 1 provides comprehensive intelligent support for the project, ensures budget compliance, and promotes the optimal allocation of project resources and maximizes benefits, providing a solid technical guarantee for the successful implementation of the project.

[0087] Example 3: Please refer to Figure 1 , the first data analysis module 3 includes a first data calculation unit 31 and a first data parsing unit 32;

[0088] The first data calculation unit 31 is used to perform integrated calculation on the preprocessed first data set, the second data set and the third data set, so as to generate a project abnormality reference coefficient XSP;

[0089] The first data parsing unit 32 is used to compare the project abnormality reference coefficient XSP with a preset first threshold value Y, so as to generate a first comparison result.

[0090] In this embodiment: the first data analysis module 3 integrates and calculates the preprocessed first data set, second data set and third data set by introducing the first data calculation unit 31 to generate the project abnormality reference coefficient XSP. The core improvement of this calculation process is that it can integrate multi-dimensional data such as finance, environment and benefits, and then calculate the abnormal reference coefficient that reflects the overall health status of the project. Compared with the traditional system that only relies on a single dimension of analysis, the integrated calculation can comprehensively evaluate the execution status of the project in multiple aspects and discover potential risks and unreasonable budget arrangements. This improvement provides managers with a global and comprehensive project health assessment tool.

[0091] The first data analysis unit 32 compares the project abnormality reference coefficient XSP with the preset first threshold value Y to generate a first comparison result. This comparison mechanism can intelligently detect abnormalities in the overall performance of the project to ensure that problems such as budget deviation, resource overspending, and benefit decline are discovered in a timely manner during the execution of the project budget. Compared with the traditional manual judgment method, the comparison method based on the preset threshold value not only improves the accuracy of abnormal warning, but also greatly improves the efficiency of decision-making. Managers can quickly locate problems based on the first comparison result and take corresponding countermeasures to avoid potential risks caused by management omissions during the execution of the project.

[0092] The first data calculation unit 31 and the first data analysis unit 32 are used in combination to improve the accuracy and sensitivity of the system's abnormal monitoring through the integrated calculation of multi-dimensional data and threshold comparison. Compared with the traditional manual budget review method, the system can automatically analyze and compare the real-time data of the project with the preset standards, and quickly find projects that do not meet the budget, have unreasonable resource allocation, or do not meet the efficiency standards. This efficient abnormal monitoring mechanism enables project managers to discover potential problems in the early stages of budget execution and adjust project progress and budget allocation in a timely manner, thereby avoiding waste of resources or low efficiency due to budget out of control.

[0093] By comparing the project abnormality reference coefficient XSP with the preset first threshold value Y, the first data analysis unit 32 provides clear decision support for the project manager. The results of this comparative analysis can not only help managers determine whether the project meets the approval requirements and whether there are data anomalies, but also provide a basis for subsequent budget adjustments, resource reconfiguration and other decisions. The precise data analysis provided by the system supports project managers to optimize resource allocation more accurately during the budget execution process and ensure that the project is successfully completed as expected.

[0094] Through the introduction of the first data analysis module 3, the system can integrate and calculate multi-dimensional data such as finance, environment and benefits, and perform intelligent anomaly detection based on preset thresholds. This improvement makes project management more efficient and accurate, and can promptly detect potential budget problems, resource waste or risk of declining benefits, providing real-time and scientific basis for decision-making, greatly improving the scientific nature of budget management and decision-making efficiency. Compared with traditional manual operations, the data-based automated analysis and early warning mechanism not only reduces the possibility of human errors, but also improves the transparency and operability of the entire project management process.

[0095] Example 4: Please refer to Figure 1 , the first data calculation unit 31 calculates and obtains the abnormal reference coefficient XSP of the project through the following steps;

[0096] S1.1. Calculate the project averages for the internal historical data of the first data set, the second data set, and the third data set, respectively, to generate the historical total budget amount average LPA, the historical budget utilization rate average LPB, the historical expenditure compliance score average LPC, the historical resource consumption average LPD, the historical ecological restoration cost average LPE, the historical environmental impact rate average LPF, the historical benefit deviation rate average LPG, the historical efficiency index achievement rate average LPH, and the historical benefit achievement cycle average LPI;

[0097] The specific calculation formula is as follows:

[0098]

[0099] Where: n is the total number of timestamps, i is the i-th timestamp;

[0100] SAi, SBi, SCi, SDi, SEi, SFi, SGi, SHi and SIi are the historical total budget amount, historical budget utilization rate, historical expenditure compliance score, historical resource consumption, historical ecological restoration cost, historical environmental impact rate, historical benefit deviation rate, historical efficiency index achievement rate and historical benefit achievement cycle at the i-th timestamp, respectively.

[0101] S1.2. Select the maximum values ​​of the historical data in the first data set, the second data set, and the third data set, and record them as the maximum value of the historical total budget amount SAmax, the maximum value of the historical budget utilization rate SBmax, the maximum value of the historical expenditure compliance score SCmax, the maximum value of the historical resource consumption SDmax, the maximum value of the historical ecological restoration cost SEmax, the maximum value of the historical environmental impact rate SFmax, the maximum value of the historical benefit deviation rate SGmax, the maximum value of the historical efficiency indicator achievement rate SHmax, and the maximum value of the historical benefit achievement period SImax;

[0102] S1.3. Integrate and calculate the real-time value, historical maximum value and historical average value of each parameter in the first data set, the second data set and the third data set, so as to generate the project abnormality reference coefficient XSP.

[0103] The specific calculation formulas are as follows:

[0104] XSP=a1×X1+a2×X2+a3×X3;

[0105]

[0106] Wherein: a1, a2, a3, b1, b2 and b3 are weight values, and the values ​​of a1, a2, a3, b1, b2 and b3 are adjusted and set by the user;

[0107] X1 is the comprehensive reference coefficient of basic data, which is obtained by integrating and calculating the first data set; X2 is the comprehensive reference coefficient of environmental intervention data, which is obtained by integrating and calculating the second data set; and X3 is the comprehensive reference coefficient of income data, which is obtained by integrating and calculating the third data set;

[0108] SAmax is the maximum historical total budget amount, SBmax is the maximum historical budget utilization rate, SCmax is the maximum historical expenditure compliance score, SDmax is the maximum historical resource consumption, SEmax is the maximum historical ecological restoration cost, SFmax is the maximum historical environmental impact rate, SGmax is the maximum historical benefit deviation rate, SHmax is the maximum historical efficiency indicator achievement rate, and SImax is the maximum historical benefit achievement cycle;

[0109] SAn, SBn, SCn, SDn, SEn, SFn, SGn, SHn and SIn are the historical total budget amount, historical budget utilization rate, historical expenditure compliance score, historical resource consumption, historical ecological restoration cost, historical environmental impact rate, historical benefit deviation rate, historical efficiency indicator achievement rate and historical benefit achievement cycle at the nth timestamp, respectively.

[0110] The first comparison result generated by the first data parsing unit 32 is as follows:

[0111] When XSP≤Y, it means the current project can be approved;

[0112] When XSP>Y, it means that the current project cannot be approved or the project needs data monitoring.

[0113] In this embodiment: the first data calculation unit 31 performs a systematic average calculation on the historical data through step S1.1 to generate the historical average values ​​of multiple key data items. This process can provide a benchmark point for each project data, helping the system to accurately evaluate the difference between the current performance and the historical performance of the project. Compared with the traditional single data comparison method, by calculating the average value of historical data, the system can more comprehensively and accurately grasp the overall trend of project execution and avoid erroneous abnormal judgments caused by data fluctuations at a certain moment. This not only enhances the grasp of historical data trends, but also provides project managers with clearer background information, so as to better judge the abnormal situation of the current project.

[0114] In step S1.2, the system further improves the sensitivity of anomaly identification by selecting the historical maximum value of each data set. By comparing with the historical maximum value, the system can identify extreme data points and their changing trends, thereby effectively capturing abnormal fluctuations in budget, resource consumption, and benefits. For example, when indicators such as project budget or resource consumption are close to the historical maximum value, the system will automatically warn managers of possible overspending or resource waste risks. This mechanism of comparison with historical extreme values ​​can detect potential problems more sensitively, helping project managers to take countermeasures in advance and prevent risks.

[0115] Step S1.3 calculates the project abnormality reference coefficient XSP by integrating the average value, maximum value and real-time data of historical data. This integration process combines historical data with real-time data to provide a comprehensive evaluation standard, so that abnormality identification no longer relies on data from a single dimension or a single time point. This approach can reflect the dynamic changes in project execution and comprehensively consider the actual situation in multiple dimensions such as budget, resources and benefits. For example, if the real-time utilization rate of the project budget far exceeds the historical average or is close to the historical maximum, the system can promptly detect the abnormal status of the project through the abnormal reference coefficient, thereby initiating monitoring and adjustment measures earlier and reducing the risk of project failure.

[0116] The first comparison result generated by the first data analysis unit 32 provides clear intelligent decision support for project approval and monitoring. Through automated comparison rules, the design can make quick decisions based on the abnormal reference coefficient XSP of the project and the first threshold Y, thereby avoiding subjective judgment and omissions in manual approval. If the abnormal reference coefficient exceeds the set threshold, the system will automatically mark the project as requiring further monitoring or adjustment, which improves the efficiency and accuracy of the approval process and ensures that project management is always under control.

[0117] The first data calculation unit 31 calculates the average and maximum values ​​of the historical data of the project and integrates it with the real-time data, providing a multi-dimensional data basis for project abnormality assessment. This mechanism significantly improves the accuracy of abnormal identification in terms of budget, resources and benefits. At the same time, the first data analysis unit 32 provides intelligent decision support for project approval and monitoring based on the comparative analysis of these calculation results. Through these improvements, the system can not only more accurately identify potential risks in the project, but also ensure that the project proceeds smoothly as expected, and ultimately achieve more scientific and efficient budget and resource management.

[0118] Through the systematic analysis of historical data and the dynamic integration of real-time data, the system can more accurately evaluate various indicators such as project budget execution, resource consumption and benefits, further improving the scientific nature of project management. Compared with traditional manual judgment or single data comparison, this data-driven decision support system can significantly improve the efficiency, transparency and operability of project management. Project managers no longer make decisions based on experience and feelings alone, but make more accurate judgments based on the objective results of data analysis to ensure the efficient use of project budgets and resources.

[0119] Example 5: Please refer to Figure 1 , the second data analysis module 4 includes a second data calculation unit 41 and a second data parsing unit 42;

[0120] The second data calculation unit 41 is used to calculate the first data set, the second data set and the third data set respectively, so as to generate the basic data abnormality coefficient S1, the environmental intervention data abnormality coefficient S2 and the income data abnormality coefficient S3;

[0121] The second data analysis unit 42 is used to compare the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 with the preset basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3 respectively, so as to generate a second comparison result.

[0122] The second data calculation unit 41 calculates and obtains the basic data abnormality coefficient S1, the environmental intervention data abnormality coefficient S2 and the income data abnormality coefficient S3 through the following steps;

[0123] S2.1. Calculate the window mean of historical data for the first data set, the second data set, and the third data set, thereby generating the window mean of the historical total budget amount PSA, the window mean of the historical budget utilization rate PSB, the window mean of the historical expenditure compliance score PSC, the window mean of the historical resource consumption PSD, the window mean of the historical ecological restoration cost PSE, the window mean of the historical environmental impact rate PSF, the window mean of the historical benefit deviation rate PSG, the window mean of the historical efficiency index achievement rate PSH, and the window mean of the historical benefit achievement period PSI, where the window value is set to m;

[0124] S2.2. Calculate the real-time values ​​and window means of various parameters in the first data set, the second data set and the third data set, thereby generating the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 respectively.

[0125]

[0126]

[0127] Where: m is the window size, p is the pth timestamp;

[0128] SAp, SBp, SCp, SDp, SEp, SFp, SGp, SHp and SIp are the historical total budget amount, historical budget utilization rate, historical expenditure compliance score, historical resource consumption, historical ecological restoration cost, historical environmental impact rate, historical benefit deviation rate, historical efficiency index achievement rate and historical benefit achievement cycle at the pth timestamp, respectively.

[0129]

[0130] In the formula: c1, c2, c3, d1, d2, d3, e1, e2 and e3 are weight values, and the values ​​of c1, c2, c3, d1, d2, d3, e1, e2 and e3 are adjusted and set by the user.

[0131] A is the real-time total budget amount, B is the real-time budget utilization rate, C is the real-time expenditure compliance score, D is the real-time resource consumption, E is the real-time ecological restoration cost, F is the real-time environmental impact rate, G is the real-time benefit deviation rate, H is the real-time efficiency indicator achievement rate, and I is the real-time benefit achievement cycle;

[0132] PSA is the average of the historical total budget amount window, PSB is the average of the historical budget utilization rate window, PSC is the average of the historical expenditure compliance score window, PSD is the average of the historical resource consumption window, PSE is the average of the historical ecological restoration cost window, PSF is the average of the historical environmental impact rate window, PSG is the average of the historical benefit deviation rate window, PSH is the average of the historical efficiency index achievement rate window, and PSI is the average of the historical benefit achievement cycle window.

[0133] In this embodiment: the second data analysis module 4 generates the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 by calculating the first data set, the second data set and the third data set through the second data calculation unit 41. The core advantage of this improvement is that multiple key dimensions of project budget management are included in the anomaly detection range, ensuring that data analysis is not limited to a single dimension, but comprehensively covers the overall performance of the project. This all-round monitoring mechanism greatly improves the scientificity and comprehensiveness of project budget management, and helps to discover potential problems earlier and take timely countermeasures.

[0134] The second data calculation unit 41 uses window mean calculation to smooth historical data fluctuations and generate historical window means of multiple key indicators. By setting the window value m, the system can more flexibly balance short-term fluctuations and long-term trends to avoid misjudgments caused by short-term data fluctuations or accidental anomalies. Compared with traditional methods that rely only on historical averages, the window mean can be dynamically adjusted according to changes in recent data, improving the sensitivity and timeliness of data anomaly detection. This method can effectively identify persistent problems and promptly warn of risks such as resource overspending, environmental damage, or income deviations.

[0135] By comparing real-time data with the historical window average, the system can accurately assess whether there are any anomalies in the budget execution process of the project. This comparison mechanism allows project managers to track the dynamic changes of each dimension in real time and quickly identify which links have abnormal data that deviates from expectations. Compared with traditional single historical data comparison, this dynamic comparison method based on the window average can provide more accurate and sensitive anomaly identification and avoid missing potential risks.

[0136] The second comparison result generated by the second data analysis unit 42 clearly specifies when the basic data, environmental intervention data, and income data of the project need to be monitored. This decision-making mechanism enables project managers to identify anomalies in a timely manner and initiate monitoring to prevent the problem from further expanding. By setting clear thresholds and comparison rules, the system can quickly determine which data points need attention, avoiding the subjectivity and delay of human experience-based judgment in traditional methods, and improving the response speed and decision-making efficiency of project management.

[0137] Abnormal monitoring of environmental intervention data strengthens the consideration of sustainability in budget management. When abnormal environmental intervention data occurs, the system will promptly alert project managers to monitor. This mechanism ensures that the project maintains a balance between economic benefits and ecological protection, avoiding excessive resource consumption or ecological damage. In traditional budget management systems, environmental factors are often not fully considered, but through this innovative multi-dimensional abnormal analysis, the system can better promote the green development of projects.

[0138] The second data analysis module 4 enhances the accuracy and transparency of project data monitoring through precise anomaly detection and threshold comparison. Project managers can not only quickly obtain real-time warnings, but also clearly see the specific dimensions of data anomalies, helping them make more scientific and targeted decisions. Through this high-precision monitoring, project managers can better control risks during project execution, optimize budget and resource allocation, and increase the possibility of project success.

[0139] The second data analysis module 4 improves the anomaly detection capability of the project budget management system through multi-dimensional data calculation and dynamic evaluation mechanism. Compared with traditional systems, this module can not only cover multiple key areas such as budget, environment and benefits, but also provide more accurate anomaly judgment through real-time and historical data comparison. Through intelligent threshold comparison and monitoring trigger mechanism, the system can respond to problems in project management more promptly and flexibly, ensuring the efficiency and sustainability of project budget and resource allocation. This innovative design greatly enhances the intelligence and scientific nature of budget management while improving project decision-making efficiency, optimizing resource utilization and ensuring sustainable development of projects.

[0140] Example 6: Please refer to Figure 1 , the visualization report generated by feedback module 5 is as follows:

[0141] When XSP≤Y, check the second comparison result. If the basic data abnormal coefficient S1, the environmental intervention data abnormal coefficient S2, and the income data abnormal coefficient S3 in the second comparison result all exceed the basic data threshold R1, the environmental intervention threshold R2, and the income threshold R3, monitor all parameters;

[0142] When XSP>Y, check the second comparison result. If any of the basic data abnormal coefficient S1, the environmental intervention data abnormal coefficient S2, and the income data abnormal coefficient S3 in the second comparison result exceeds the basic data threshold R1, the environmental intervention threshold R2, and the income threshold R3, monitor the parameters in the data set to which the abnormal coefficient exceeding the threshold belongs;

[0143] When XSP>Y, check the second comparison result. If the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 in the second comparison result all exceed the basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3, monitor all data and suspend the project for inspection.

[0144] In this embodiment: the feedback module 5 provides accurate monitoring and response mechanisms for different situations by generating visual reports. When the project abnormality reference coefficient XSP is greater than the preset threshold, the system will automatically check the second comparison result of the second data analysis module 4, and decide whether to monitor the data according to the specific situation of the abnormality coefficient. This intelligent monitoring mechanism based on abnormal thresholds makes project management more automated and accurate, avoiding the delays and errors of traditional manual judgment. Managers can rely on real-time feedback from the system to make adjustments and decisions quickly, improving the response speed and decision-making efficiency of budget management.

[0145] The system adopts different monitoring strategies according to the abnormal coefficient values ​​of the basic data, environmental intervention data and income data in the second comparison result. If all parameters exceed the threshold, the system will comprehensively monitor all parameters; if only one parameter is abnormal, the system will monitor the parameter that exceeds the threshold separately; if all data anomalies exceed the threshold, the project will be suspended and checked. Through this multi-level monitoring strategy, feedback module 5 can achieve refined anomaly management and effectively identify and resolve potential risks. Compared with traditional single data monitoring, the system can flexibly adjust the response measures according to the severity of the data anomaly to ensure that the execution of the project budget is always under control.

[0146] The design of feedback module 5 makes the monitoring measures not just "one size fits all", but takes targeted measures according to the specific abnormal coefficient value. This flexibility greatly improves the accuracy and effectiveness of monitoring. For example, when an aspect of the project, such as the budget or environmental impact, is abnormal, the system will only monitor the relevant data set, avoiding unnecessary comprehensive monitoring and reducing the workload of managers. If the abnormal situation is serious, the system will also trigger a mechanism to suspend the project and conduct an inspection to ensure the compliance and risk control of the project execution. Compared with the traditional simple early warning mechanism, this hierarchical monitoring measure can accurately focus on risk points and avoid waste of resources and project delays.

[0147] Feedback module 5 presents monitoring results through visual reports, allowing project managers to intuitively view abnormal data in budget execution. Charts and reports help decision makers quickly understand the status of the current project, especially when there are abnormalities in multiple dimensions, and can clearly show the severity of the abnormality, the data sets involved, and the specific issues that need attention. Compared with traditional text descriptions or numerical reports, visual reports not only improve decision transparency, but also greatly improve response efficiency, helping managers make decisions quickly and avoiding decision delays caused by information lags.

[0148] Feedback module 5 further improves the compliance and controllability of budget management by monitoring abnormal data that exceeds the threshold and triggering the project suspension inspection mechanism in serious abnormal situations. In traditional budget management, anomalies are usually discovered by manual judgment and periodic audits, which are often delayed and incomplete. The system can detect data anomalies in real time and take immediate response measures, avoiding the expansion of project budget deviations and ensuring the rational use of resources and compliance with the project.

[0149] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0150] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A natural resource project budget management system based on performance big data, characterized by: It comprises a data acquisition module (1), a data processing module (2), a first data analysis module (3), a second data analysis module (4) and a feedback module (5); The data acquisition module (1) is used to acquire data for the project budget, including basic data, environmental intervention data and income data, and to generate a first data set, a second data set and a third data set; The data processing module (2) is used to preprocess and dimensionlessly transform the first data set, the second data set and the third data set; The first data analysis module (3) is used to calculate the processed first data set, the second data set and the third data set, so as to generate a project abnormality reference coefficient XSP, and compare the calculated project abnormality reference coefficient XSP with a preset first threshold value Y, so as to generate a first comparison result, and judge whether the current project can be approved and whether data abnormality occurs according to the first comparison result; The second data analysis module (4) is used to integrate and calculate the processed first data set, the second data set and the third data set, so as to generate a basic data anomaly coefficient S1, an environmental intervention data anomaly coefficient S2 and an income data anomaly coefficient S3, and compare the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 with the preset basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3, respectively, so as to generate a second comparison result, and according to the second comparison result, track and locate the abnormal point in real time; The feedback module (5) is used to generate a visual report based on the first and second comparison results.

2. According to claim 1, a natural resource project budget management system based on performance big data is characterized by: The data acquisition module (1) comprises a first data acquisition unit (11), a second data acquisition unit (12) and a third data acquisition unit (13); The first data collection unit (11) is used to obtain basic data of the project and organize it into a first data set, including a total budget amount, a budget utilization rate, and an expenditure compliance score; The total budget amount is divided into the real-time total budget amount A and the historical total budget amount SA. The historical total budget amounts are recorded as SA1, SA2, SA3, ..., SAn according to the timestamp of the number of declarations. The budget utilization rate is divided into real-time budget utilization rate B and historical budget utilization rate SB. The historical budget utilization rates are recorded as SB1, SB2, SB3, ..., SBn according to the timestamp of the number of declarations; The expenditure compliance score is divided into the real-time expenditure compliance score C and the historical expenditure compliance score SC. The historical scores are recorded as SC1, SC2, SC3, ..., SCn according to the timestamp of the number of declarations. The second data collection unit (12) is used to obtain environmental intervention data of the project and organize it into a second data set, including resource consumption, ecological restoration cost and environmental impact rate; Resource consumption is divided into real-time resource consumption D and historical resource consumption SD. The historical resource consumption is recorded as SD1, SD2, SD3, ..., SDn according to the timestamp of the number of declarations. The ecological restoration cost is divided into real-time ecological restoration cost E and historical ecological restoration cost SE. The historical ecological restoration cost is recorded as SE1, SE2, SE3, ..., SEn according to the timestamp of the number of declarations; The environmental impact rate is divided into real-time environmental impact rate F and historical environmental impact rate SF. The historical environmental impact rates are recorded as SF1, SF2, SF3, ..., SFn according to the timestamp of the number of declarations; The third data collection unit (13) is used to obtain the project's income data and organize it into a third data set, including the benefit deviation rate, the efficiency index achievement rate and the benefit achievement cycle; The benefit deviation rate is divided into real-time benefit deviation rate G and historical benefit deviation rate SG. The historical benefit deviation rates are recorded as SG1, SG2, SG3, ..., SGn according to the timestamp of the number of declarations; The efficiency index achievement rate is divided into the real-time efficiency index achievement rate H and the historical efficiency index achievement rate SH. The historical efficiency index achievement rates are recorded as SH1, SH2, SH3, ..., SHn according to the timestamp of the number of declarations. The benefit achievement cycle is divided into real-time benefit achievement cycle I and historical benefit achievement cycle SI. The historical benefit achievement cycles are recorded as SI1, SI2, SI3, ..., SIn according to the timestamp of the number of declarations.

3. According to claim 2, a natural resource project budget management system based on performance big data is characterized by: The first data analysis module (3) comprises a first data calculation unit (31) and a first data parsing unit (32); The first data calculation unit (31) is used to perform integrated calculation on the preprocessed first data set, the second data set and the third data set, so as to generate an item abnormality reference coefficient XSP; The first data analysis unit (32) is used to compare the project abnormality reference coefficient XSP with a preset first threshold value Y, thereby generating a first comparison result.

4. According to claim 3, a natural resource project budget management system based on performance big data is characterized in that: The first data calculation unit (31) calculates and obtains the abnormal reference coefficient XSP of the project through the following steps; S1.

1. Calculate the project averages for the internal historical data of the first data set, the second data set, and the third data set, respectively, to generate the historical total budget amount average LPA, the historical budget utilization rate average LPB, the historical expenditure compliance score average LPC, the historical resource consumption average LPD, the historical ecological restoration cost average LPE, the historical environmental impact rate average LPF, the historical benefit deviation rate average LPG, the historical efficiency index achievement rate average LPH, and the historical benefit achievement cycle average LPI; S1.

2. Select the maximum values ​​of the historical data in the first data set, the second data set, and the third data set, and record them as the maximum value of the historical total budget amount SAmax, the maximum value of the historical budget utilization rate SBmax, the maximum value of the historical expenditure compliance score SCmax, the maximum value of the historical resource consumption SDmax, the maximum value of the historical ecological restoration cost SEmax, the maximum value of the historical environmental impact rate SFmax, the maximum value of the historical benefit deviation rate SGmax, the maximum value of the historical efficiency indicator achievement rate SHmax, and the maximum value of the historical benefit achievement period SImax; S1.

3. Integrate and calculate the real-time value, historical maximum value and historical average value of each parameter in the first data set, the second data set and the third data set, so as to generate the project abnormality reference coefficient XSP.

5. According to claim 4, a natural resource project budget management system based on performance big data is characterized in that: The first comparison result generated by the first data parsing unit (32) is as follows: When XSP≤Y, it means the current project can be approved; When XSP>Y, it means that the current project cannot be approved or the project needs data monitoring.

6. According to claim 5, a natural resource project budget management system based on performance big data is characterized in that: The second data analysis module (4) comprises a second data calculation unit (41) and a second data parsing unit (42); The second data calculation unit (41) is used to calculate the first data set, the second data set and the third data set respectively, so as to generate a basic data anomaly coefficient S1, an environmental intervention data anomaly coefficient S2 and an income data anomaly coefficient S3; The second data analysis unit (42) is used to compare the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 with the preset basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3 respectively, so as to generate a second comparison result.

7. According to claim 6, a natural resource project budget management system based on performance big data is characterized by: The second data calculation unit (41) calculates and obtains the basic data abnormality coefficient S1, the environmental intervention data abnormality coefficient S2 and the income data abnormality coefficient S3 through the following steps; S2.

1. Calculate the window mean of historical data for the first data set, the second data set, and the third data set, thereby generating the window mean of the historical total budget amount PSA, the window mean of the historical budget utilization rate PSB, the window mean of the historical expenditure compliance score PSC, the window mean of the historical resource consumption PSD, the window mean of the historical ecological restoration cost PSE, the window mean of the historical environmental impact rate PSF, the window mean of the historical benefit deviation rate PSG, the window mean of the historical efficiency index achievement rate PSH, and the window mean of the historical benefit achievement period PSI, where the window value is set to m; S2.

2. Calculate the real-time values ​​and window means of various parameters in the first data set, the second data set and the third data set, thereby generating the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 respectively.

8. According to claim 7, a natural resource project budget management system based on performance big data is characterized in that: The second comparison result generated by the second data parsing unit (42) is as follows: When S1≤R1, it means that there is no data anomaly in the basic data of the project. When S1>R1, it means that there is data anomaly in the basic data of the project and monitoring is required. When S2≤R2, it means that the environmental intervention data of the project has no data anomaly. When S2>R2, it means that the environmental intervention data of the project has data anomaly and needs to be monitored. When S3≤R3, it means that there is no data anomaly in the revenue data of the project. When S3>R3, it means that there is data anomaly in the revenue data of the project and it needs to be monitored.

9. A natural resource project budget management system based on performance big data according to claim 8, characterized in that: The visual report generated by the feedback module (5) is specifically as follows: When XSP≤Y, check the second comparison result. If the basic data abnormal coefficient S1, the environmental intervention data abnormal coefficient S2, and the income data abnormal coefficient S3 in the second comparison result all exceed the basic data threshold R1, the environmental intervention threshold R2, and the income threshold R3, monitor all parameters; When XSP>Y, check the second comparison result. If any of the basic data abnormal coefficient S1, the environmental intervention data abnormal coefficient S2, and the income data abnormal coefficient S3 in the second comparison result exceeds the basic data threshold R1, the environmental intervention threshold R2, and the income threshold R3, monitor the parameters in the data set to which the abnormal coefficient exceeding the threshold belongs; When XSP>Y, check the second comparison result. If the basic data anomaly coefficient S1, the environmental intervention data anomaly coefficient S2 and the income data anomaly coefficient S3 in the second comparison result all exceed the basic data threshold R1, the environmental intervention threshold R2 and the income threshold R3, monitor all data and suspend the project for inspection.

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