Project monitoring method and device for business system

Abstract

The invention discloses a project monitoring method and device for a business system, relates to the technical field of project management, and aims to improve the project monitoring precision, provide support for project management decision and meet the requirement for precise management and control of the health state of a project. According to the main technical scheme, the method comprises the steps of obtaining monitoring parameters of a target project under specified dimensions, wherein the specified dimensions comprise a progress dimension, a quality dimension, a contract dimension and a supplier dimension; determining a deviation analysis result of each parameter in the monitoring parameters based on an actual execution state and an expected standard of each parameter in the monitoring parameters; matching service attributes of the target project in a preset attribute table, and determining a single health score and a weight coefficient of each parameter in the monitoring parameters according to the service attributes and the deviation analysis result; according to the single-item health score and the weight coefficient, calculating a comprehensive health score of the target item; and generating a monitoring report of the target project based on the comprehensive health score, the single health score and the deviation analysis result.

Description

Technical Field

[0001] This application relates to the field of project management technology, and in particular to a project monitoring method and apparatus for a business system. Background Technology

[0002] As businesses continue to expand and digital transformation deepens, the projects supported by business systems are becoming increasingly diversified, encompassing multiple scenarios such as R&D, procurement, implementation, and supply chain collaboration. These projects generally exhibit characteristics such as cross-departmental collaboration, complex resource dependencies, and varying delivery cycles. Whether project progress meets plans, quality control is up to standard, contract performance is standardized, and supplier collaboration is stable directly impacts the efficiency of achieving business objectives and the prevention and control of operational risks. Therefore, conducting systematic and quantitative monitoring of projects from multiple dimensions has become a core requirement for ensuring smooth project progress and optimizing management decisions.

[0003] Currently, while existing business systems possess basic parameter collection and scoring functions for project monitoring, enabling them to initially acquire relevant data on dimensions such as progress and quality, and obtain project health scores through simple weighted calculations, the scoring logic of this monitoring method is not personalized to the project's business attributes. This makes it difficult to establish a dynamic correlation mechanism between business attributes, deviation quantification analysis, and health scores, resulting in insufficient accuracy in project monitoring. Consequently, it fails to provide effective support for project management decisions and cannot meet the enterprise's need for precise control over project health status. Summary of the Invention

[0004] In view of the above problems, this application provides a project monitoring method and device for a business system. The main purpose is to improve the accuracy of project monitoring, provide effective support for project management decisions, and meet the needs of enterprises for precise control over the health status of projects.

[0005] To solve the above-mentioned technical problems, this application proposes the following solution: Firstly, this application provides a project monitoring method for a business system, the method comprising: Obtain monitoring parameters for the target project under specified dimensions, including schedule, quality, contract, and supplier dimensions, with each dimension corresponding to at least one parameter; Based on the actual execution status and expected standards of each parameter in the monitoring parameters, the deviation analysis results of each parameter in the monitoring parameters are determined; The business attributes of the target project are matched in the preset attribute table, and the individual health score and weight coefficient of each parameter in the monitoring parameters are determined according to the business attributes and the deviation analysis results. The attribute table is used to record the attribute information associated with each project in the business system. Based on the individual health scores and the weighting coefficients, the comprehensive health score of the target item is calculated; A monitoring report for the target project is generated based on the comprehensive health score, the individual health scores, and the deviation analysis results.

[0006] Secondly, this application provides a project monitoring device for a business system, the device comprising: The acquisition unit is used to acquire monitoring parameters of the target project under specified dimensions. The specified dimensions include progress dimension, quality dimension, contract dimension and supplier dimension, and each dimension corresponds to at least one parameter. The determining unit is used to determine the deviation analysis results of each parameter in the monitoring parameters based on the actual execution status and expected standard of each parameter in the monitoring parameters obtained by the acquiring unit. The processing unit is used to match the business attributes of the target project in a preset attribute table, and determine the individual health score and weight coefficient of each parameter in the monitoring parameters based on the business attributes and the deviation analysis results obtained by the determining unit. The attribute table is used to record the attribute information associated with each project in the business system. The calculation unit is used to calculate the comprehensive health score of the target item based on the individual health score and the weighting coefficient obtained by the processing unit. A generation unit is used to generate a monitoring report for the target project based on the comprehensive health score obtained by the calculation unit, the individual health score, and the deviation analysis results obtained by the determination unit.

[0007] To achieve the above objectives, according to a third aspect of this application, a storage medium is provided, the storage medium including a stored program, wherein, when the program is executed, the device where the storage medium is located controls the execution of the project monitoring method of the business system described in the first aspect.

[0008] To achieve the above objectives, according to a fourth aspect of this application, a processor is provided for running a program, wherein the program executes the project monitoring method of the business system described in the first aspect.

[0009] Using the above technical solution, this application provides a project monitoring method and apparatus for a business system. First, it acquires monitoring parameters of the target project under specified dimensions, including progress, quality, contract, and supplier dimensions, with each dimension corresponding to at least one parameter. Then, based on the actual execution status and expected standards of each parameter in the monitoring parameters, it determines the deviation analysis results of each parameter. Next, it matches the business attributes of the target project in a preset attribute table, and determines the individual health score and weight coefficient of each parameter in the monitoring parameters based on the business attributes and deviation analysis results. The attribute table is used to record the attribute information associated with each project in the business system. Then, based on the individual health score and weight coefficient, it calculates the comprehensive health score of the target project. Finally, it generates a monitoring report of the target project based on the comprehensive health score, individual health scores, and deviation analysis results. The technical solution provided in this application achieves multi-dimensional quantitative monitoring by clearly defining monitoring parameters for four specified dimensions: progress, quality, contracts, and suppliers. This ensures the systematic and comprehensive nature of project monitoring. By determining deviation analysis results based on the actual execution status and expected standards of the monitoring parameters, the deviations are quantitatively broken down, providing a data foundation for subsequent accurate scoring. Furthermore, by matching the target project's business attributes with a preset attribute table and dynamically determining the individual health scores and weight coefficients of each parameter in conjunction with these business attributes and deviation analysis results, a dynamic correlation mechanism between business attributes, deviation quantitative analysis, and health scores is successfully established. This enables personalized adaptation of scoring logic and effectively improves the accuracy of project monitoring. Finally, by calculating a comprehensive health score and generating a monitoring report containing the comprehensive score, individual scores, and deviation analysis results, a comprehensive and accurate reference is provided for project management decisions, effectively meeting the enterprise's need for precise control over the project's health status.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This application provides a flowchart of a project monitoring method for a business system according to an embodiment of the present application. Figure 2 This application provides a flowchart of another project monitoring method for a business system. Figure 3 This paper shows a block diagram of a project monitoring device for a business system provided in an embodiment of this application; Figure 4 This paper illustrates a block diagram of a project monitoring device for another business system provided in an embodiment of this application. Detailed Implementation

[0012] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0013] Currently, while existing business systems possess basic parameter collection and scoring functions for project monitoring, enabling them to initially acquire relevant data on dimensions such as progress and quality, and obtain project health scores through simple weighted calculations, the scoring logic of this monitoring method is not personalized to the project's business attributes. This makes it difficult to establish a dynamic correlation mechanism between business attributes, deviation quantification analysis, and health scores, resulting in insufficient accuracy in project monitoring. Consequently, it fails to provide effective support for project management decisions and cannot meet the enterprise's need for precise control over project health status.

[0014] Research has revealed that monitoring parameters can be acquired across multiple core business dimensions of a project. By comparing the actual execution status of these parameters with expected standards, quantitative deviation analysis results are determined. Then, based on a pre-defined set of business attribute information, project business attributes are matched. The scoring rules and weighting coefficients for each parameter are dynamically configured in conjunction with these business attributes and the deviation analysis results. This process calculates a comprehensive health score and generates a monitoring report integrating the score and deviation information. In this way, a dynamic correlation mechanism between business attributes, quantitative deviation analysis, and health scores is established, effectively improving the accuracy of project monitoring, providing comprehensive and precise support for project management decisions, and meeting the enterprise's need for precise control over the health status of projects.

[0015] Based on the above considerations, this application provides a project monitoring method for a business system. This method can improve the accuracy of project monitoring, provide effective support for project management decisions, and meet the enterprise's need for precise control over the health status of projects. The specific execution steps are as follows: Figure 1 As shown, it includes: 101. Obtain the monitoring parameters of the target project under the specified dimension.

[0016] The specified dimensions include schedule, quality, contract, and supplier dimensions, with each dimension corresponding to at least one parameter.

[0017] In this embodiment, core monitoring parameters for each dimension are predefined based on the project management specifications of the business system. Specifically, the core parameters for the schedule dimension can include the actual completion time of key milestones and the actual cumulative execution time. The core parameters for the quality dimension can include the actual quality inspection results (including the number of nonconformities and the pass rate) and the problem level assessment results (classified into three levels: core, critical, and general according to document standards). The core parameters for the contract dimension can include the actual funding allocation ratio and the actual number of deliverables. The core parameters for the supplier dimension can include the validity period of the supplier's qualifications and the number of supplier risk events.

[0018] By integrating with project management tools, financial systems, supplier credit platforms, and quality inspection systems through business system interfaces, structured data is captured in real time. For example, completion times for project milestones are obtained from project management tools, payment ratios from the financial system, and supplier risk records are synchronized from the credit platform. For subjective parameters not integrated into any system (such as quality issue level assessment results), quality inspection personnel input data using pre-set structured forms in the business system (the forms can be limited to fields such as "problem description," "level options," and "judgment basis"). After collection, outliers exceeding reasonable ranges are removed, such as payment ratios >150% or negative qualification validity periods. Pop-up prompts are triggered for missing parameters, such as the absence of a milestone completion time, allowing for resubmission after correction. Non-numerical data is converted into a calculable format. For example, core issues are assigned 3 points each, serious issues 2 points each, and qualification validity periods are converted to remaining days, ultimately outputting a set of structured parameters. For example: the progress dimension parameters are "requirement approval node completion time 2025-06-10, cumulative execution time 45 days"; the quality dimension parameters are "3 non-conformities, pass rate 94%, 1 serious problem".

[0019] 102. Based on the actual execution status and expected standards of each parameter in the monitoring parameters, determine the deviation analysis results of each parameter in the monitoring parameters.

[0020] In this embodiment, the actual execution status represents the true execution of the target project across the specified monitoring parameters during its actual progress. Specifically, it reflects the actual values ​​of each parameter during the project's execution, such as the actual completion time of key milestones in the schedule dimension, the actual pass rate in the quality dimension, the actual funding disbursement ratio in the contract dimension, and the number of risk events occurring in the supplier dimension. This directly reflects the project's current operational status. The expected standard, on the other hand, represents the pre-set benchmark requirements for each monitoring parameter to ensure the project achieves its preset goals. This includes planned values ​​established during the project planning phase (such as the completion time of key milestones and the planned quantity of deliverables), preset pass / fail thresholds (such as quality acceptance thresholds and thresholds for the number of risks agreed upon in cooperation agreements), and clear judgment rules (such as problem level classification rules), providing a unified and comparable benchmark framework for deviation calculation.

[0021] Matching the actual execution status with the expected standards can be achieved by linking the project's unique identifier to the pre-stored project plan data in the business system, clarifying the expected standards for each parameter, and forming a "parameter-actual value-standard value" correspondence table, as shown in Table 1: Table 1 Dimension Monitoring parameters Actual execution status (actual value) Expected standard (standard value) Progress dimension Requirement approval node completion time 2024-06-10 2024-06-05 Progress dimension Actual cumulative execution time 45 days 40 days Quality Dimensions pass rate 94% ≥95% Contractual Dimension Actual funding allocation ratio 60% 70% Supplier Dimension Number of risk events 1 instance (public opinion warning) ≤0 times After obtaining the actual values ​​of the actual execution status and the standard values ​​of the expected standards, deviation calculations can be performed based on the differences between the two to obtain the deviation analysis results for each parameter in the monitoring parameters. These deviation analysis results include, but are not limited to, deviation levels and deviation values. The deviation level can be implemented by pre-building a rule base, which records the deviation levels corresponding to different deviation ratios under different specified dimensions. The deviation value can be a specific difference or a deviation ratio calculated based on the difference.

[0022] Based on Table 1, for the schedule dimension: the overdue time is calculated to be 5 days, and the overdue percentage = 5 / 40 × 100% = 12.5%. A rule base can be set to record the deviation level corresponding to the overdue percentage. For example, according to the rule base "overdue percentage 5%-15% is a level 2 deviation", the deviation level of the schedule dimension is determined to be level 2, and the deviation value is "overdue by 5 days, overdue percentage 12.5%"; for the quality dimension: the defect rate is calculated as... 1 - 94% = 6%, cumulative score for problem level = 1 × 2 = 2 points. Based on the rule base "defect rate 3%-10% is a Level 2 deviation," the quality dimension deviation level is determined to be Level 2, with a deviation value of "6% defect rate, 1 serious problem." For the contract dimension: calculate the payment deviation ratio -10%. Based on the rule base "absolute value of payment deviation ratio 10%-20% is a Level 2 deviation," the contract dimension deviation level is determined to be Level 2, with a deviation value of "payment deviation -10%." For the supplier dimension: 1 instance of risk exceeding the threshold. Based on the rule base "1 instance of risk exceeding the threshold is a Level 2 deviation," the supplier dimension deviation level is determined to be Level 2, with a deviation value of "1 public opinion warning." After obtaining the deviation analysis results, the deviation values ​​and deviation levels of each dimension, along with the unique identifier of the associated project, can be encapsulated into structured data and stored in the deviation result table of the project monitoring database. For example: the table structure can be Project ID, Dimension Name, Deviation Value, Deviation Level, and Calculation Time.

[0023] 103. Match the business attributes of the target project in the preset attribute table, and determine the individual health score and weight coefficient of each parameter in the monitoring parameters based on the business attributes and deviation analysis results. The attribute table is used to record the attribute information associated with each project in the business system.

[0024] In this embodiment, an attribute table is pre-built and stored in the project monitoring database. The attribute table fields include "Project ID, Project Type, Project Size, and Creation Time." Project types can be categorized as "Key Projects, Major Projects, and General Projects," or "R&D, Procurement, and Implementation." Project size is categorized by budget as "Large, Medium, and Small," and can be quantified based on factors such as investment cost and number of members. The target project's unique identifier is used to accurately match target attribute information in the attribute table. For example, "Project Type: Key Project; Project Size: Medium."

[0025] A pre-defined "type-scale-concern level" mapping rule (stored in a rule base) is established based on the business system. The concern level of the target project is then matched according to this rule. Specifically, major projects or procurement projects, which may involve supplier collaboration and contract fulfillment, and medium-sized projects, which require a balance between efficiency and risk, correspond to a "Level 2" concern level, i.e., medium concern. For example, R&D projects or key projects plus large projects can be defined as Level 1, procurement projects or major projects plus medium-sized projects as Level 2, and implementation projects or general projects plus small projects as Level 3.

[0026] Based on the level of concern, the corresponding configuration is retrieved from a pre-set rule base. This rule base not only pre-sets a base score of 100 for all concern levels (the final score after deductions), but also pre-sets a weight matrix for different concern levels. Specifically, a percentage-based baseline score is used, and a unified initial base score is pre-set for a specified dimension to obtain a basic health score, such as 100. Weight coefficients are set for different concern levels based on project characteristics; for example, for level two concern: schedule dimension 0.25, quality dimension 0.25, contract dimension 0.3, and supplier dimension 0.2. Combining the deviation analysis results from step 102, a dual deduction logic combining basic and additional deduction values ​​is adopted. The rule base can also pre-set different fixed deduction values ​​for different deviation levels. The corresponding fixed deduction value is matched according to the deviation level as the basic deduction value, and dynamic deduction values ​​are calculated for each dimension based on the deviation value as additional deduction values. For example, in the schedule dimension: additional deduction = overdue percentage × 0.3; in the quality dimension: additional deduction = defect rate × 1; in the contract dimension: additional deduction = |payment deviation percentage| × 0.4; in the supplier dimension: additional deduction = number of times risk exceeds threshold × 2. The final deduction value, i.e., the score reduction value, is determined based on the basic deduction value and the additional deduction value. By subtracting the score reduction value from the basic health score, the individual health score of the target project can be obtained.

[0027] 104. Calculate the comprehensive health score of the target item based on the individual health scores and weighting coefficients.

[0028] In this embodiment, the individual health score and weight coefficient determined in step 103 are retrieved, and the overall health score of the target item is obtained by calculating "overall health score = Σ (individual health score × corresponding weight coefficient)". This overall health score can be a specific numerical value, such as 88.0, or multiple intervals corresponding to different levels can be pre-constructed, such as 90-100 points for excellent, 80-89 points for good, 60-79 points for average, and <60 points for poor. The corresponding level is obtained by matching the numerical values ​​to the intervals.

[0029] To ensure the accuracy and rationality of the calculations, the weighting coefficients can be verified, such as by checking if the sum of the weighting coefficients is 1, to ensure the validity of the calculation logic. The comprehensive health score can also be verified, such as by checking if it falls within the 0-100 range; if outliers are found, they are automatically corrected. For example, if a score of 105 is due to a data error, it is automatically corrected to 100 and logged.

[0030] 105. Generate a monitoring report for the target project based on the comprehensive health score, individual health scores, and deviation analysis results.

[0031] In this embodiment, the monitoring report can adopt a structured module design of "core summary - dimension details - deviation source tracing - scoring explanation". The core summary section presents the project name, ID, monitoring period, comprehensive health score and health level, and marks the core deviation type, such as "comprehensive score 78 points (good), core deduction item is contract dimension payment deviation"; the dimension details section is displayed in columns according to the specified dimensions, and each column includes "single health score, weight coefficient, deviation analysis result, score calculation process", such as "quality dimension: score 82 points, weight 0.35, deviation result 'first-level deviation, defect rate 2%', calculation process '100 points - basic deduction 2 points - additional deduction 1 point = 82 points'"; the deviation source tracing section can be sorted by deviation level and lists "deviation dimension, deviation value, deviation level"; the scoring explanation section marks the comprehensive score calculation formula, the basis for determining the weight coefficient and the data source. The monitoring report supports automatic generation of multiple formats such as PDF, Word, and Excel. PDF is used for archiving, and Excel is used for data export and reuse. The file name is automatically named "Project ID_Monitoring Period_Report Type". It is pushed to the project manager and reviewer through both business system account and corporate email. At the same time, the project ID is associated and stored in the report archive library so that it can be searched and viewed by multiple conditions such as project ID, monitoring period, and health level in the future.

[0032] In addition, to achieve early warning for project monitoring, a scoring prediction model can be trained. This model can automatically output the scoring trend over a future time period by inputting data such as comprehensive health score and individual health score. By observing this trend, the future health status of the project can be known in advance. The output of this model can be added to the aforementioned monitoring report so that project leaders and reviewers can be informed in advance and make preparations.

[0033] Based on the above Figure 1As can be seen from the implementation method, the project monitoring method for a business system provided in this application achieves multi-dimensional quantitative monitoring by clearly defining monitoring parameters for four specified dimensions: progress, quality, contract, and supplier. This ensures the systematicness and comprehensiveness of project monitoring. By determining the deviation analysis results based on the actual execution status and expected standards of the monitoring parameters, the method achieves the quantitative decomposition of deviations, providing a data foundation for subsequent accurate scoring. Furthermore, by matching the business attributes of the target project with a preset attribute table and dynamically determining the individual health scores and weight coefficients of each parameter in conjunction with these business attributes and deviation analysis results, a dynamic correlation mechanism between business attributes, deviation quantitative analysis, and health scores is successfully established. This achieves personalized adaptation of scoring logic, effectively improving the accuracy of project monitoring. Finally, by calculating a comprehensive health score and generating a monitoring report containing the comprehensive score, individual scores, and deviation analysis results, the method provides a comprehensive and accurate reference for project management decisions, effectively meeting the enterprise's need for precise control over the health status of projects.

[0034] Furthermore, the preferred embodiments of this application are based on the above... Figure 1 Based on this, a detailed explanation of the project monitoring process for business systems is provided, including the specific steps as follows: Figure 2 As shown, it includes: 201. Obtain the monitoring parameters of the target project under the specified dimension.

[0035] This step combines the description of step 101 in the above method, and the same content will not be repeated here.

[0036] 202. Verify the validity of the actual execution status of each parameter in the monitoring parameters, and remove outliers from the actual execution status based on the verification results to obtain the processed actual execution status.

[0037] The actual execution status of each parameter in the monitoring parameters includes the actual completion time of key milestones, the actual cumulative execution time, the actual quality inspection results, the problem level assessment results, the actual funding allocation ratio, the actual number of deliverables, the validity period of supplier qualifications, and the number of supplier risk events. The actual completion time of key milestones and the actual cumulative execution time correspond to the progress dimension; the actual quality inspection results and the problem level assessment results correspond to the quality dimension; the actual funding allocation ratio and the actual number of deliverables correspond to the contract dimension; and the validity period of supplier qualifications and the number of supplier risk events correspond to the supplier dimension.

[0038] In this embodiment, considering the characteristics of the aforementioned multiple actual execution status parameters, a validity verification rule base is preset. For time-related parameters, such as actual key node completion time and supplier qualification validity period, the verification is that the actual key node completion time is greater than or equal to the project start time, and the supplier qualification validity period is in a valid date format (YYYY-MM-DD). Furthermore, the supplier qualification validity period cannot be more than 3 years earlier than the current system time to avoid invalid historical data. For duration / quantity-related parameters, such as actual cumulative execution time, actual number of deliverables, and number of supplier risk events, the verification value is ≥0, and the actual cumulative execution time cannot exceed twice the planned total execution time to avoid extreme abnormal data. The actual number of deliverables cannot exceed three times the planned number to exclude data entry errors. For ratio-related parameters, such as the actual funding allocation ratio, the verification value is within the range of [0, 150%]. Overpayment is allowed, but unreasonable ranges are restricted. For quality parameters, such as actual quality inspection results and problem level determination results, the actual quality inspection results must include three complete data items: "number of qualified items / number of unqualified items / pass rate", and the pass rate must be in the range of [0, 100%]. The problem level determination results must be selected from the preset options (core problem / serious problem / general problem).

[0039] Outliers exceeding reasonable limits or with incorrect formats are directly removed and marked as "data anomalies," such as a disbursement ratio of 180% or an illegal date format. Data slightly exceeding thresholds but logically reasonable is corrected according to rules; for example, if the actual cumulative execution time is 2.5 times the planned total execution time, it is corrected to 2 times the planned total execution time, and a correction log is recorded. For data failing integrity checks, such as missing quality inspection results for the number of non-conforming items, a system pop-up prompt is triggered, requiring data entry personnel to complete the data within 24 hours. If the data is not completed within the deadline, it is filled with "default values," such as a default pass rate of 0% and a default problem level of "general problem."

[0040] The data that has passed verification, been corrected for outliers, or been completed is organized into a structured dataset called "Processed Actual Execution Status". This dataset is associated with the project's unique identifier and the verification timestamp, and stored in the actual status table of the project monitoring database. The data format is shown in Table 2. Table 2 Project ID Actual execution status parameters Actual value after processing Verification status XM-2024-058 Actual critical node completion time 2024-06-10 Verification passed XM-2024-058 Actual funding allocation ratio 150% The revision has been approved (originally 180%). XM-2024-058 Problem level determination result Serious problems Supplementary enrollment passed 203. According to the specified dimensions, calculate the deviation between the actual value of the actual execution status after processing of each parameter in the monitoring parameters and the standard value of the expected standard, and obtain the deviation value and deviation level of each parameter.

[0041] The expected standards for each parameter in the monitoring parameters include the completion time of key project milestones, the total execution time of the project, the quality acceptance threshold, the problem level classification rules, the agreed funding allocation ratio, the planned number of deliverables, the supplier qualification standards, and the threshold for the number of risks agreed upon in the cooperation agreement. The completion time of key project milestones and the total execution time of the project correspond to the progress dimension; the quality acceptance threshold and the problem level classification rules correspond to the quality dimension; the agreed funding allocation ratio and the planned number of deliverables correspond to the contract dimension; and the supplier qualification standards and the threshold for the number of risks agreed upon in the cooperation agreement correspond to the supplier dimension.

[0042] In this embodiment, based on the parameter correspondence of a specified dimension, the pre-stored expected standard is retrieved through the unique identifier of the project, establishing a one-to-one mapping of "dimension-actual value-standard value" to ensure the accuracy of the calculation object, as shown in Table 3: Table 3 Specified dimension Actual execution status parameters Actual value after processing Corresponding to expected standard parameters Expected standard value Progress dimension Actual critical node completion time 2024-06-10 Planned key milestone completion time 2024-06-05 Progress dimension Actual cumulative execution time 45 days Total execution time of the plan 40 days Quality Dimensions Actual quality inspection results (pass rate) 94% Quality acceptance threshold setting ≥95% Quality Dimensions Problem level determination result One serious problem Problem Level Classification Rules Definition of a serious problem: Affects core functionality but is remediable. Contractual Dimension Actual funding allocation ratio 60% Funding allocation ratio 70% Contractual Dimension Actual number of deliverables 18 units Planned number of deliverables 20 units Supplier Dimension Supplier qualification validity period 2025-03-15 Supplier qualification standards Minimum validity period ≥ 6 months Supplier Dimension Number of supplier risk events 1 instance (public opinion warning) Cooperation agreement risk threshold ≤0 times Differential calculation logic is used according to specified dimensions to ensure accurate quantification of deviation values ​​and to closely match parameter characteristics, as detailed below: Schedule dimension: Overdue time = Actual critical node completion time - Planned critical node completion time; Overdue percentage = (Actual cumulative execution time - Planned total execution time) / Planned total execution time; Take "overdue percentage" as the core deviation value.

[0043] Quality Dimension: Defect Rate = 1 - Actual Pass Rate; Cumulative Score for Problem Level = Number of Serious Problems × Corresponding Score (Preset serious problems are assigned 2 points / problem); Defect Rate is taken as the core deviation value.

[0044] Contractual Dimension: Disbursement Deviation Ratio = Actual Disbursement Ratio - Agreed Disbursement Ratio; Delivery Deviation Quantity = Planned Deliverables Quantity - Actual Deliverables Quantity; The absolute value of the disbursement deviation ratio is taken as the core deviation value.

[0045] Supplier Dimension: Remaining Qualification Duration = Supplier Qualification Validity Period - Current System Time; Number of Risk Exceeding Thresholds = Number of Supplier Risk Events - Cooperation Agreement Risk Count Threshold; "Number of Risk Exceeding Thresholds" is taken as the core deviation value.

[0046] Based on a pre-defined rule library for different dimensions of deviation levels, core deviation values ​​are mapped to Level 1 / Level 2 / Level 3 deviations, corresponding to slight / moderate / severe deviations, respectively. Details are as follows: Schedule-related rules: Overdue rate <5% is Level 1 deviation, 5%-15% is Level 2 deviation, and >15% is Level 3 deviation; Quality-related rules: Defect rate <3% is Level 1 deviation, 3%-10% is Level 2 deviation, and >10% is Level 3 deviation; Contract-related rules: Absolute value of payment deviation rate <10% is Level 1 deviation, 10%-20% is Level 2 deviation, and >20% is Level 3 deviation; Supplier-related rules: Number of times risk exceeds the threshold = 0 is Level 1 deviation, = 1 is Level 2 deviation, and ≥2 is Level 3 deviation.

[0047] 204. Use the deviation value and deviation level as the results of the deviation analysis.

[0048] In this embodiment, the core deviation values ​​and deviation levels of each specified dimension are encapsulated into deviation analysis results. These encapsulated deviation analysis results are stored in the deviation analysis results table of the project monitoring database according to a hierarchical structure of "Project ID-Dimension-Deviation Information." The table fields include Project ID, specified dimension, core deviation value, deviation level, calculation time, and rule version number. Simultaneously, a data traceability link is established between the Project ID and the "Processed Actual Execution Status Table" and the "Expected Standard Table," forming a data traceability chain for subsequent individual health score calculations and monitoring report generation.

[0049] 205. Based on the unique identifier of the target project, match the corresponding target attribute information in the attribute table.

[0050] Among them, the target attribute information is used to characterize the project type and project scale of the target project.

[0051] Furthermore, before matching the business attributes of the target project in the preset attribute table and determining the individual health scores and weight coefficients of each parameter in the monitoring parameters based on the business attributes and deviation analysis results, the process also includes: extracting the attribute information of each project in the business system based on the project registration information of the business system. The attribute information includes type information and scale information; associating the attribute information with the unique identifier of each project and storing it in the attribute table of the project monitoring database.

[0052] In this embodiment, attribute information for each project is automatically extracted from the project registration information in the business system. The type information is pre-classified into standardized categories, such as key projects, major projects, and general projects, or R&D, procurement, and implementation projects. The scale information is categorized into large (≥10 million RMB), medium (1-10 million RMB), and small (<1 million RMB) based on project cost budget thresholds. The extracted attribute information is then associated with a unique project identifier and stored in the attribute table of the project monitoring database.

[0053] After receiving an attribute matching request for a target project, the business system can accurately locate the corresponding record from the attribute table using an SQL query, with the matching condition being "Project ID = Target Unique Identifier". If the match is successful, the target attribute information is directly returned: "Project Type = Main Project, Project Size = Medium". If the match fails, a system alarm is triggered, prompting "No target project attributes found, please supplement the registration", and the system redirects to the attribute supplementation interface, supporting manual entry and rematching. The system automatically verifies whether the returned target attribute information is complete, i.e., whether it simultaneously includes project type and project size. If any are missing, it is marked as "Attribute Incomplete", requiring supplementation before proceeding to subsequent steps.

[0054] 206. Determine the level of attention for the target project based on the project type and scale.

[0055] In this embodiment, a "type-scale-level of concern" mapping rule base can be preset. The core logic of this mapping rule is that the project type determines the core focus, and the project scale determines the intensity of concern. The specific rules are shown in Table 4 below: Table 4 Project Type Project Scale Attention Level Level Description (Core Focus Dimensions) Key projects Large Level 1 We pay close attention to quality and schedule (as these factors affect the implementation of core technologies). Key projects medium Level 2 The middle focuses on quality and schedule, while the weaker focuses on the contract. Main projects Large Level 1 Pay close attention to contracts and suppliers (involving large sums of money and performance risks). Main projects medium Level 2 China focuses on contracts and suppliers, and on quality. Main projects small Level 3 Weak focus on contracts and suppliers, strong focus on progress General items Large Level 2 China focuses on progress and quality (which involves cross-departmental collaboration). General items Medium / Small Level 3 We focus on all aspects, prioritizing timely delivery. By retrieving the target attribute information obtained in step 205 and accurately matching it with the mapping rule base, the attention level of the target project can be determined.

[0056] 207. Based on the level of attention, determine the basic health score and weight coefficient of each parameter in the monitoring parameters.

[0057] This step combines the description of step 103 in the above method, and the same content will not be repeated here.

[0058] It should be noted that a pre-defined "attention level - weight coefficient" mapping matrix can be used for the weight coefficients. The sum of the weight coefficients is strictly 1. Different attention levels correspond to different dimensional weight allocations, which align with the core attention dimensions in the level descriptions. The specific configuration is shown in Table 5. Table 5 Attention Level Progress dimension weight Quality dimension weights Contract Dimension Weight Supplier Dimension Weight Level 1 0.25 0.25 0.3 0.2 Level 2 0.25 0.25 0.3 0.2 Level 3 0.3 0.2 0.25 0.25 Based on the attention level obtained in step 206, the corresponding weight coefficient is automatically retrieved. Additionally, for specific scenarios, the weight can be adjusted within a range of ±10%.

[0059] 208. Based on the deviation analysis results of each parameter in the monitoring parameters, determine the score deduction value of each parameter in the monitoring parameters.

[0060] This step combines the description of step 103 in the above method, and the same content will not be repeated here.

[0061] Furthermore, based on the deviation analysis results of each parameter in the monitoring parameters, the specific execution process for determining the score deduction value of each parameter in the monitoring parameters is as follows: determine the basic deduction value of each parameter in the monitoring parameters according to the deviation level; determine the additional deduction value of each parameter in the monitoring parameters according to the deviation value; and sum the basic deduction value and the additional deduction value to obtain the score deduction value of each parameter in the monitoring parameters.

[0062] The basic deduction value is used to represent the standardized baseline penalty score corresponding to different deviation levels, while the additional deduction value is used to represent the extra penalty score corresponding to different deviation magnitudes under the same deviation level.

[0063] In this step, a multi-dimensional deduction rule base can be pre-set. The core logic of this rule base is that the deviation level determines the base deduction value, and the deviation value determines the additional deduction value. The rule base corresponds one-to-one with the specified dimensions. The specific rules are shown in Table 6. Dimension Deviation level Basic deduction value Additional deduction calculation formula Progress dimension Level 1 3 points Overdue percentage × 0.3 Progress dimension Level 2 8 points Overdue percentage × 0.3 Progress dimension Level 3 15 points Overdue percentage × 0.5 Quality Dimensions Level 1 2 points Defect rate × 1.0 Quality Dimensions Level 2 7 points Defect rate × 1.0 Quality Dimensions Level 3 12 points Defect rate × 1.5 Contractual Dimension Level 1 4 points Absolute value of disbursement deviation percentage × 0.4 Contractual Dimension Level 2 9 points Absolute value of disbursement deviation percentage × 0.4 Contractual Dimension Level 3 16 points The absolute value of the payment deviation ratio × 0.6 + the quantity of delivery deviation × 2 Supplier Dimension Level 1 3 points Number of times risk exceeds the threshold × 1.0 Supplier Dimension Level 2 8 points Number of times risk exceeds the threshold × 2.0 Supplier Dimension Level 3 14 points Number of times risk exceeded the threshold × 3.0 + Number of days the qualification expired × 0.1 By retrieving the deviation analysis results obtained in steps 203-204, the score deduction value of each parameter in the monitoring parameters can be determined.

[0064] 209. Calculate the difference between the basic health score and the score deduction value of each parameter in the monitoring parameters to obtain the individual health score of each parameter in the monitoring parameters.

[0065] In this embodiment, the difference between the basic health score (uniformly 100 points on a 100-point scale) determined in step 207 and the score deduction value calculated in step 208 is calculated. The specific formula is: Individual health score = Basic health score - Score deduction value. The calculation process is automatically executed by the business system and one decimal place is retained to ensure the accuracy of the score.

[0066] During the calculation process, dual validity checks can be triggered to avoid logical contradictions or data anomalies. These include range checks and consistency checks. Range checks: Whether a single health score falls within the range [0, 100]. If a score < 0 due to data anomalies, it is automatically corrected to 0; if it > 100 (e.g., a negative deduction value), it is automatically corrected to 100. Consistency checks: The basic health score from step 207 and the deduction value from step 208 are correlated to verify the correctness of the calculation logic "single health score = 100 - deduction value". If a calculation error occurs, a system alarm is triggered and the calculation is recalculated.

[0067] Each dimension's individual health score is stored in the individual score table of the project monitoring database according to the structure of "project ID-dimension-score details". The table fields include project ID, specified dimension, individual health score, basic health score, score deduction value, and calculation time. At the same time, the project ID is linked with the "weight configuration table" and "deviation analysis result table" to form a complete data traceability link of "attribute-deviation-deduction-score".

[0068] 210. Input the comprehensive health score and individual health scores into the pre-trained score prediction model to obtain the score prediction results.

[0069] The scoring prediction model is trained using historical comprehensive health scores and historical individual health scores of the target item as samples. The scoring prediction results are used to characterize the changing trends of comprehensive health scores and individual health scores over a specified future period.

[0070] In this step, the rating prediction model is a time-series prediction model (using the LSTM algorithm, adapted to the time-series characteristics of health scores). The training process is as follows: using historical monitoring data of the target project as training samples, the sample dimensions include "monitoring period, historical comprehensive health score, and historical single-item health score of specified dimensions", a total of nearly 12 monitoring periods (1 month per period) of valid data are collected to form 12 sets of training samples; the sample data are divided into training set and validation set in a 7:3 ratio, and the model is trained with the goal of "minimizing the mean square error between the predicted value and the actual value"; the trained model is packaged into the business system, which supports calling through project ID, and the preset prediction period is "1 future monitoring period (1 month)".

[0071] The individual health scores from step 209 and the comprehensive health score calculated in step 104 are retrieved as input data. A pre-trained score prediction model is invoked using the unique identifier of the target project. The input data is fed into the model, which analyzes the score change trend based on time-series characteristics and outputs the score prediction results for the next period. Specifically, this includes the predicted comprehensive health score, the predicted individual health scores, and a trend description. For example, if the comprehensive health score shows a slight downward trend, it is mainly due to the failure to promptly rectify payment deviations in the contract dimension, leading to continuous deductions; the supplier dimension score is relatively stable, while the quality and schedule dimensions show smaller declines. The score prediction results are associated with the project ID and stored in the prediction result table, while simultaneously synchronized to the project monitoring database for use in subsequent report generation.

[0072] 211. Generate a monitoring report for the target item based on the comprehensive health score, individual health scores, deviation analysis results, and score prediction results.

[0073] This step combines the description of step 105 in the above method. It only requires the score prediction result to be included as one item in the generated monitoring report. The same content will not be repeated here.

[0074] Furthermore, as a response to the above Figure 1-2 The implementation of the method embodiment shown in this application provides a project monitoring device for a business system. This device is used to improve the accuracy of project monitoring, provide effective support for project management decisions, and meet the enterprise's need for precise control over the health status of projects. The embodiment of this device corresponds to the foregoing method embodiments. For ease of reading, this embodiment will not repeat the details of the foregoing method embodiments one by one, but it should be clear that the device in this embodiment can correspondingly implement all the contents of the foregoing method embodiments. Specifically, as shown... Figure 3 As shown, the device includes: The acquisition unit 31 is used to acquire monitoring parameters of the target project under specified dimensions. The specified dimensions include progress dimension, quality dimension, contract dimension and supplier dimension, and each dimension corresponds to at least one parameter. The determining unit 32 is used to determine the deviation analysis results of each parameter in the monitoring parameters based on the actual execution status and expected standard of each parameter in the monitoring parameters obtained by the acquiring unit 31. The processing unit 33 is used to match the business attributes of the target project in a preset attribute table, and determine the individual health score and weight coefficient of each parameter in the monitoring parameters according to the business attributes and the deviation analysis results obtained by the determining unit 32. The attribute table is used to record the attribute information associated with each project in the business system. The calculation unit 34 is used to calculate the comprehensive health score of the target item based on the individual health score and the weighting coefficient obtained by the processing unit 33. The generation unit 35 is used to generate a monitoring report for the target project based on the comprehensive health score obtained by the calculation unit 34, the individual health score, and the deviation analysis result obtained by the determination unit 32.

[0075] Furthermore, such as Figure 4 As shown, The actual execution status of each parameter in the monitoring parameters includes the actual completion time of key nodes, the actual cumulative execution time, the actual quality inspection results, the problem level judgment results, the actual funding allocation ratio, the actual number of delivered results, the validity period of supplier qualifications, and the number of supplier risk events. The expected standards for each parameter in the monitoring parameters include the completion time of key planned nodes, the total execution time of the plan, the quality acceptance threshold, the problem level classification rules, the agreed funding allocation ratio, the planned quantity of deliverables, the supplier access qualification standards, and the threshold for the number of risks agreed upon in the cooperation agreement. The actual key node completion time, the actual cumulative execution time, the planned key node completion time, and the planned total execution time correspond to the progress dimension; the actual quality inspection results, the problem level judgment results, the quality acceptance setting threshold, and the problem level classification rules correspond to the quality dimension; the actual funding allocation ratio, the actual number of deliverables, the agreed funding allocation ratio, and the planned number of deliverables correspond to the contract dimension; and the supplier qualification validity period, the number of supplier risk events, the supplier access qualification standards, and the cooperation agreement risk frequency threshold correspond to the supplier dimension.

[0076] Furthermore, such as Figure 4 As shown, the determining unit 32 includes: The verification module 321 is used to verify the validity of the actual execution status of each parameter in the monitoring parameters, and remove abnormal values ​​in the actual execution status according to the verification result to obtain the processed actual execution status. The first calculation module 322 is used to calculate the deviation between the actual value of the processed actual execution state obtained by the verification module 321 of each parameter in the monitoring parameters and the standard value of the expected standard according to the specified dimension, so as to obtain the deviation value and deviation level of each parameter. The confirmation module 333 is used to take the deviation value and the deviation level obtained by the first calculation module 332 as the deviation analysis result.

[0077] Furthermore, such as Figure 4 As shown, the device further includes: Extraction unit 36 ​​is used to extract the attribute information of each project in the business system based on the project registration information of the business system before the processing unit 33. The attribute information includes type information and scale information. The association unit 37 is used to associate the attribute information obtained by the extraction unit 36 ​​with the unique identifier of each project and store it in the attribute table of the project monitoring database.

[0078] Furthermore, such as Figure 4 As shown, the processing unit 33 includes: Matching module 331 is used to match corresponding target attribute information in the attribute table based on the unique identifier of the target project, wherein the target attribute information is used to characterize the project type and project scale of the target project; The first determining module 332 is used to determine the attention level of the target project based on the project type and project size obtained by the matching module 331; The second determining module 333 is used to determine the basic health score and the weight coefficient of each parameter in the monitoring parameters based on the attention level obtained by the first determining module 332. The third determining module 334 is used to determine the score deduction value of each parameter in the monitoring parameters based on the deviation analysis results of each parameter in the monitoring parameters obtained by the second determining module 333. The second calculation module 335 is used to calculate the difference between the basic health score obtained by the second determination module 333 and the score deduction value obtained by the third determination module 334 for each parameter in the monitoring parameters, so as to obtain the individual health score for each parameter in the monitoring parameters.

[0079] Furthermore, such as Figure 4 As shown, the third determining module 33 is specifically used for, Based on the deviation level, the basic deduction value of each parameter in the monitoring parameters is determined. The basic deduction value is used to represent the standardized bottom-line penalty score corresponding to different deviation levels. The additional deduction value for each parameter in the monitoring parameters is determined based on the deviation value. The additional deduction value is used to characterize the extra penalty points corresponding to different deviation amplitudes under the same deviation level. The base deduction value and the additional deduction value are summed to obtain the score deduction value for each parameter in the monitoring parameters.

[0080] Furthermore, such as Figure 4 As shown, the device further includes: Prediction unit 38 is used to input the comprehensive health score and the individual health score into a pre-trained score prediction model before the generation unit 35 to obtain a score prediction result. The score prediction model is trained based on the historical comprehensive health score and historical individual health score of the target item as samples. The score prediction result is used to characterize the change trend of the comprehensive health score and the individual health score in a future specified period. The generation unit 35 is specifically used for, The monitoring report for the target project is generated based on the comprehensive health score, the individual health score, the deviation analysis results, and the score prediction results obtained by the prediction unit 38.

[0081] Furthermore, embodiments of this application also provide a storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the above-described... Figure 1-2 The project monitoring method for the business system described in the document.

[0082] Furthermore, embodiments of this application also provide a processor for running a program, wherein the program executes the above-described... Figure 1-2 The project monitoring method for the business system described in the document.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0084] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0086] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0087] In addition, the memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0088] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0093] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0094] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A project monitoring method for a business system, characterized in that, The method includes: Obtain monitoring parameters for the target project under specified dimensions, including schedule, quality, contract, and supplier dimensions, with each dimension corresponding to at least one parameter; Based on the actual execution status and expected standards of each parameter in the monitoring parameters, the deviation analysis results of each parameter in the monitoring parameters are determined; The business attributes of the target project are matched in the preset attribute table, and the individual health score and weight coefficient of each parameter in the monitoring parameters are determined according to the business attributes and the deviation analysis results. The attribute table is used to record the attribute information associated with each project in the business system. Based on the individual health scores and the weighting coefficients, the comprehensive health score of the target item is calculated; A monitoring report for the target project is generated based on the comprehensive health score, the individual health scores, and the deviation analysis results.

2. The method according to claim 1, characterized in that, The actual execution status of each parameter in the monitoring parameters includes the actual completion time of key nodes, the actual cumulative execution time, the actual quality inspection results, the problem level judgment results, the actual funding allocation ratio, the actual number of delivered results, the validity period of supplier qualifications, and the number of supplier risk events. The expected standards for each parameter in the monitoring parameters include the completion time of key planned nodes, the total execution time of the plan, the quality acceptance threshold, the problem level classification rules, the agreed funding allocation ratio, the planned quantity of deliverables, the supplier access qualification standards, and the threshold for the number of risks agreed upon in the cooperation agreement. The actual key node completion time, the actual cumulative execution time, the planned key node completion time, and the planned total execution time correspond to the progress dimension; the actual quality inspection results, the problem level judgment results, the quality acceptance setting threshold, and the problem level classification rules correspond to the quality dimension; the actual funding allocation ratio, the actual number of deliverables, the agreed funding allocation ratio, and the planned number of deliverables correspond to the contract dimension; and the supplier qualification validity period, the number of supplier risk events, the supplier access qualification standards, and the cooperation agreement risk frequency threshold correspond to the supplier dimension.

3. The method according to claim 2, characterized in that, Based on the actual execution status and expected standards of each parameter in the monitoring parameters, the deviation analysis results of each parameter in the monitoring parameters are determined, including: The actual execution status of each parameter in the monitoring parameters is validated, and outliers in the actual execution status are removed based on the validation results to obtain the processed actual execution status. According to the specified dimension, the deviation between the actual value of the actual execution state of each parameter in the monitoring parameters after processing and the standard value of the expected standard is calculated to obtain the deviation value and deviation level of each parameter; The deviation value and the deviation level are used as the deviation analysis results.

4. The method according to claim 1, characterized in that, Before matching the business attributes of the target project in a preset attribute table, and determining the individual health score and weight coefficient of each parameter in the monitoring parameters based on the business attributes and the deviation analysis results, the method further includes: Based on the project registration information of the business system, the attribute information of each project in the business system is extracted, including type information and scale information; The attribute information is associated with the unique identifier of each project and stored in the attribute table of the project monitoring database.

5. The method according to claim 4, characterized in that, The business attributes of the target project are determined in a preset attribute table, and the individual health scores and weight coefficients of each parameter in the monitoring parameters are determined based on the deviation analysis results of the business attributes and each parameter in the monitoring parameters, including: Based on the unique identifier corresponding to the target project, the corresponding target attribute information is matched in the attribute table. The target attribute information is used to characterize the project type and project scale of the target project. Based on the project type and the project size, determine the level of attention for the target project; Based on the attention level, the basic health score and weight coefficient of each parameter in the monitoring parameters are determined respectively; Based on the deviation analysis results of each parameter in the monitoring parameters, determine the score deduction value of each parameter in the monitoring parameters; The difference between the basic health score and the score deduction value of each parameter in the monitoring parameters is calculated to obtain the individual health score of each parameter in the monitoring parameters.

6. The method according to claim 3, characterized in that, Based on the deviation analysis results of each parameter in the monitoring parameters, the score deduction value of each parameter in the monitoring parameters is determined, including: Based on the deviation level, the basic deduction value of each parameter in the monitoring parameters is determined. The basic deduction value is used to represent the standardized bottom-line penalty score corresponding to different deviation levels. The additional deduction value for each parameter in the monitoring parameters is determined based on the deviation value. The additional deduction value is used to characterize the extra penalty points corresponding to different deviation amplitudes under the same deviation level. The base deduction value and the additional deduction value are summed to obtain the score deduction value for each parameter in the monitoring parameters.

7. The method according to any one of claims 1-6, characterized in that, Before generating a monitoring report for the target item based on the comprehensive health score, the individual health scores, and the deviation analysis results, the method further includes: The comprehensive health score and the individual health score are input into a pre-trained score prediction model to obtain a score prediction result. The score prediction model is trained based on the historical comprehensive health score and historical individual health score of the target item as samples. The score prediction result is used to characterize the changing trend of the comprehensive health score and the individual health score in a future specified period. The process of generating a monitoring report for the target item based on the comprehensive health score, the individual health scores, and the deviation analysis results includes: The monitoring report for the target project is generated based on the comprehensive health score, the individual health scores, the deviation analysis results, and the score prediction results.

8. A project monitoring device for a business system, characterized in that, The device includes: The acquisition unit is used to acquire monitoring parameters of the target project under specified dimensions. The specified dimensions include progress dimension, quality dimension, contract dimension and supplier dimension, and each dimension corresponds to at least one parameter. The determining unit is used to determine the deviation analysis results of each parameter in the monitoring parameters based on the actual execution status and expected standard of each parameter in the monitoring parameters obtained by the acquiring unit. The processing unit is used to match the business attributes of the target project in a preset attribute table, and determine the individual health score and weight coefficient of each parameter in the monitoring parameters based on the business attributes and the deviation analysis results obtained by the determining unit. The attribute table is used to record the attribute information associated with each project in the business system. The calculation unit is used to calculate the comprehensive health score of the target item based on the individual health score and the weighting coefficient obtained by the processing unit. A generation unit is used to generate a monitoring report for the target project based on the comprehensive health score obtained by the calculation unit, the individual health score, and the deviation analysis results obtained by the determination unit.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to execute the project monitoring method of the business system as described in any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the project monitoring method of the business system as described in any one of claims 1 to 7.

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