A full life cycle management application system and method for simultaneous progress of funds and projects

Abstract

The present invention discloses a full life cycle management application system and method for funds and project progress in the same progress, which specifically relates to the technical field of project management, and is used to solve the problem of lack of coupling consistency management of multi-level structures of funds allocation and project progress in the prior art. The system divides the project progress into three levels: sub-project level, work package level and task level, generates project progress data sets of each level and corresponding funds allocation level data, realizes multi-level data synchronization through mapping management, analyzes the coupling relationship between funds and project progress using a hierarchical aggregation algorithm, and generates synchronization status data; detects levels with inconsistent funds and progress status using a difference analysis algorithm, identifies abnormal level nodes based on probability distribution and propagation path analysis, calibrates funds and progress data in abnormal status, dynamically updates the management status of the project full life cycle, and effectively improves the synchronization efficiency of funds and project progress.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and more specifically, to a full life cycle management application system and method for co-processing funds and projects. Background Art

[0002] The full life cycle management of funds and projects in the same process is a comprehensive management method for synchronizing the flow of funds with the progress of projects in the project management process. Its core is to cover the entire process of project establishment, approval, execution, monitoring to completion, coordinate fund allocation and project progress in real time, and ensure the efficiency and accuracy of resource allocation. However, it usually relies on independent fund management systems and project progress management systems to run separately, and connect the data of the two through manual or simple interfaces.

[0003] In the existing technology, the multi-level structure of fund allocation and project progress lacks consistent coupling management, which makes it difficult to match the fund flow and progress status in real time during the project execution. When the project execution layer lags behind or advances, the upper management level cannot adjust the fund allocation strategy in time, which leads to data gaps and waste of resources, seriously affecting the overall project management effect.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a full life cycle management application system and method for funds and projects in the same process to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A full life cycle management application method for funds and projects in the same process, comprising the following steps:

[0008] S1: Divide the project progress into three levels: sub-project level, work package level and task level, and generate a project progress data set corresponding to each level based on the actual execution status of each level;

[0009] S2: Based on the project progress data set, generate the corresponding fund allocation level data, and map the fund allocation level with the project progress level;

[0010] S3: Analyze the coupling relationship between fund allocation data and project progress data through hierarchical aggregation algorithm to generate synchronization status data of funds and project progress at each level;

[0011] S4: Detect abnormal states in the coupling relationship between funds and progress through a difference analysis algorithm, and mark the levels where the funding allocation status is inconsistent with the project progress status;

[0012] S5: For the levels where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify the abnormal level nodes;

[0013] S6: Calibrate the marked abnormal level nodes, project progress data in abnormal status, and fund allocation data.

[0014] In a preferred embodiment, the project progress is divided into three levels: sub-project level, work package level and task level, and based on the actual execution status of each level, a project progress data set corresponding to each level is generated, specifically:

[0015] Determine the hierarchical division rules of the project progress, including the three levels of sub-project level, work package level and task level and their corresponding hierarchical division conditions;

[0016] Extract project structure information, including project goals, time nodes, and subtask divisions;

[0017] Based on the extracted project structure information and hierarchical division rules, the project is divided into sub-project level, work package level and task level;

[0018] Collect and record actual performance data at all levels;

[0019] Based on the actual execution data of the recorded levels, a project progress data set corresponding to each level is generated.

[0020] In a preferred embodiment, based on the project progress data set, corresponding fund allocation level data is generated, and the fund allocation level is mapped to the project progress level, specifically:

[0021] Formulate funding allocation rules based on the project schedule hierarchy, including funding allocation standards at the sub-project level, work package level and task level;

[0022] Based on the project progress data set, generate the fund allocation hierarchical data corresponding to the sub-project level, work package level and task level according to the fund allocation rules;

[0023] According to the corresponding relationship between the levels, the fund allocation level data and the project progress level data are mapped to form a level mapping table.

[0024] In a preferred embodiment, the coupling relationship between the fund allocation data and the project progress data is analyzed by a hierarchical aggregation algorithm to generate synchronization status data of funds and project progress at each level, specifically:

[0025] Extract funding allocation data and project progress data from the hierarchical mapping table, and mark the characteristic fields of each level according to the sub-project level, work package level and task level;

[0026] The funding allocation data and project progress data are integrated into a feature matrix by level, where each row represents a level unit and each column represents a funding allocation value and a project progress value;

[0027] Based on the feature matrix, the weight allocation rules of the hierarchical aggregation algorithm are formulated according to the importance and resource allocation of the sub-project level, work package level and task level;

[0028] Through the hierarchical aggregation algorithm, the fund allocation data and project progress data at each level are comprehensively analyzed to calculate the coupling strength between the fund allocation data and the project progress data at each level;

[0029] Based on the coupling strength calculation results, the data on the synchronization status of funds and project progress at each level is generated, and the synchronization mark and coupling strength value are recorded.

[0030] In a preferred embodiment, the abnormal state in the coupling relationship between funds and progress is detected by a difference analysis algorithm, and the level where the fund allocation status is inconsistent with the project progress status is marked, specifically:

[0031] Extract the coupling strength value and synchronization mark of sub-project level, work package level and task level from the generated synchronization status data;

[0032] Based on the synchronization status data, combined with the funding allocation fields and project progress fields at each level, a difference analysis model is established to define the comparison rules and difference thresholds for funding and progress data;

[0033] For each hierarchical unit, compare the normalized values ​​of its fund allocation field and project progress field one by one; calculate the difference index of the hierarchical unit;

[0034] Through the difference analysis model, the funding and progress data are compared layer by layer, and the layers with coupling strength lower than the preset threshold are identified as the layers where the funding allocation status is inconsistent with the project progress status.

[0035] In a preferred embodiment, for the level where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify abnormal level nodes, specifically:

[0036] Based on the probability distribution of the funding allocation field and project progress field of the hierarchical node, the entropy growth rate of each node is calculated;

[0037] Perform propagation analysis on the entropy growth rate of each node, evaluate the degree of uncertainty diffusion to directly adjacent nodes and indirectly related nodes, and calculate the cumulative impact value of the propagation path risk;

[0038] According to the entropy growth rate and the cumulative impact value of the propagation path risk, the abnormal level nodes are identified. The abnormal level nodes include core abnormal nodes and secondary abnormal nodes.

[0039] In a preferred embodiment, the expression of the cumulative impact value of the propagation path risk is: ;in, It is The cumulative impact value of the propagation path risk of the nodes at each level, Indicates The set of all upstream nodes of the level node, Indicates that from The nodes of the first level are propagated to the The propagation strength of nodes at each level, Indicates the number of a node in the hierarchical network. Indicates the number of the direct neighboring node of a hierarchical node in a hierarchical network.

[0040] In a preferred embodiment, the marked abnormal level nodes, the project progress data in the abnormal state and the fund allocation data are calibrated, specifically:

[0041] Extract the current status data of the fund allocation field and the project progress field and the related status data of the associated nodes from the marked abnormal level nodes;

[0042] Calculate the deviation between the current status and the expected status based on the historical data of the fund allocation field and the project progress field, combined with the planned data;

[0043] Calculate calibration adjustment parameters based on the deviation value, the adjustment parameters include the correction coefficient of the fund allocation ratio and the correction range of the progress completion time;

[0044] The fund allocation field and project progress field of the abnormal level node are corrected according to the calibration adjustment parameters, and the calibrated data is recorded.

[0045] On the other hand, the present invention provides a full life cycle management application system for funds and projects in the same process, including a project progress division module, a fund mapping generation module, a hierarchical coupling analysis module, an abnormal state detection module, an abnormal node identification module, and an abnormal data calibration module;

[0046] Project progress division module: divides the project progress into three levels: sub-project level, work package level and task level, and generates a project progress data set corresponding to each level based on the actual execution status of each level;

[0047] Generate fund mapping module: Generate corresponding fund allocation level data based on project progress data set, and map fund allocation level with project progress level;

[0048] Analysis of hierarchical coupling module: Analyze the coupling relationship between fund allocation data and project progress data through hierarchical aggregation algorithm, and generate synchronization status data of funds and project progress at each level;

[0049] Abnormal status detection module: detects abnormal status in the coupling relationship between funds and progress through a difference analysis algorithm, and marks the level where the fund allocation status is inconsistent with the project progress status;

[0050] Abnormal node identification module: For the levels where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify the abnormal level nodes;

[0051] Calibration of abnormal data module: calibrate the marked abnormal level nodes, project progress data in abnormal status and fund allocation data.

[0052] The technical effects and advantages of the full life cycle management application system and method of the present invention for funds and projects in the same process are as follows:

[0053] 1. The present invention provides an application method for the full life cycle management of the same progress of funds and projects. The method divides the project progress into three levels: sub-project level, work package level and task level, and generates a corresponding project progress data set based on the actual execution of each level. At the same time, the fund allocation level data is generated and mapped and managed, thereby realizing the coupling consistency management of funds and projects in a multi-level structure. The coupling relationship between funds and project progress is analyzed by a hierarchical aggregation algorithm, and synchronization status data of each level is generated. In combination with a difference analysis algorithm, the level where the funds and progress status are inconsistent can be quickly detected, effectively solving the problem of difficulty in real-time matching of fund flow and project progress status in the prior art. This method significantly improves the coordination efficiency of funds and project progress in complex management scenarios.

[0054] 2. The present invention further analyzes the funding and progress status through the probability distribution of hierarchical nodes, combines the cumulative risk impact value of the propagation path, accurately identifies abnormal hierarchical nodes, and calibrates the abnormal status. The calibrated project progress data and funding allocation data can dynamically update the management status of the project throughout its life cycle, thereby achieving synchronous adjustment of funding and projects, ensuring the efficiency and accuracy of resource allocation, improving the accuracy of project execution and the overall management effect, and providing a systematic and intelligent solution for complex project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a full life cycle management application method for funds and projects in the same process according to the present invention;

[0056] Figure 2 The present invention is a schematic diagram of the structure of a full life cycle management application system for funds and projects in the same process. DETAILED DESCRIPTION

[0057] 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. Example 1

[0058] Figure 1 The present invention provides a full life cycle management application method for funds and projects in the same process, which includes the following steps:

[0059] S1: Divide the project progress into three levels: sub-project level, work package level and task level, and generate a project progress data set corresponding to each level based on the actual execution status of each level.

[0060] S2: Based on the project progress data set, generate the corresponding fund allocation level data, and map the fund allocation level with the project progress level.

[0061] S3: Analyze the coupling relationship between fund allocation data and project progress data through a hierarchical aggregation algorithm to generate synchronization status data of funds and project progress at each level.

[0062] S4: Detect abnormal states in the coupling relationship between funds and progress through a difference analysis algorithm, and mark the levels where the funding allocation status is inconsistent with the project progress status.

[0063] S5: For the levels where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify abnormal level nodes.

[0064] S6: Calibrate the marked abnormal level nodes, project progress data in abnormal status, and fund allocation data.

[0065] The project progress is divided into three levels: sub-project level, work package level and task level. Based on the actual execution of each level, a project progress data set corresponding to each level is generated, specifically:

[0066] The hierarchical division rules for determining the project progress include three levels: sub-project level, work package level and task level and their corresponding hierarchical division conditions:

[0067] Based on the overall goal of the project, the project is divided into several sub-projects. The division of each sub-project includes the following conditions: the sub-project must have independent goals or phased results; the time span of each sub-project should match the key time nodes of the project; and there is relatively independent resource allocation between sub-projects.

[0068] Under each sub-project, the sub-project is further divided into several work packages according to the principles of resource utilization and modularization of work content. The division conditions include: each work package must correspond to a clear work goal or deliverable; the work package must be directly related to the allocated resources (manpower, equipment, budget); the dependency relationship between work packages must be definable (such as sequential or parallel relationship).

[0069] Under the work package, the tasks are further refined to the specific task level. The criteria for dividing tasks include: each task must have a clear start time and completion time; each task must be assigned a specific executor; each task must have a standard for measuring the completion status, such as a "completion mark" or "acceptance criteria for deliverables."

[0070] Based on the above division rules of sub-project layer, work package layer and task layer, a standardized project level division document is integrated and generated, including: the basis and objectives of project level division; input data and output requirements of each level; logical relationships between levels (such as parent-child project relationships, resource dependencies).

[0071] Extract project structure information, including project goals, time nodes, and subtask divisions:

[0072] Among them, the project goal refers to the goal of the project and its description, the time node refers to the phase division of the project and the start and end time of each phase, and the subtask division refers to the list of subtasks and their parent-child relationship information.

[0073] The extracted information can be organized into structured data tables, including project hierarchy trees, timetables, and subtask relationship diagrams.

[0074] Based on the extracted project structure information and hierarchical division rules, the project is divided into sub-project level, work package level and task level:

[0075] Based on the extracted project structure information and hierarchical division rules, the division of sub-project level, work package level and task level is completed in sequence.

[0076] At the sub-project level, project objectives are divided into several sub-projects according to specific responsibility areas, and each sub-project is assigned a unique identifier.

[0077] At the work package level, the specific tasks in each sub-project are divided into several work packages according to the work modules, and the responsible person and resource allocation for each work package are recorded.

[0078] At the task level, the tasks within each work package are further broken down into executable task units, and the executor, estimated time, and completion criteria for each task are recorded.

[0079] Collect and record actual performance data at all levels:

[0080] The progress percentage, current status (in progress, completed, suspended) and estimated completion time of each sub-project.

[0081] Resource usage, task completion and whether there are any obstacles for each work package.

[0082] The actual completion time, task status and completion quality evaluation of each task.

[0083] Based on the actual execution data of the recorded levels, generate the project progress data set corresponding to each level:

[0084] Based on the collected actual execution data of each level, a project progress data set for each level is generated.

[0085] The dataset at the subproject level includes the current progress, status, and estimated completion time of each subproject. The dataset at the work package level includes the completion ratio, resource usage, and task completion of each work package. The dataset at the task level includes the execution time, status, and completion quality of each task.

[0086] The project progress data set is stored in a hierarchical structure, and the data at each level are associated with each other to form a complete set of project progress data sets for subsequent analysis.

[0087] Based on the project progress data set, the corresponding fund allocation level data is generated, and the fund allocation level is mapped to the project progress level, specifically:

[0088] Formulate funding allocation rules based on the project schedule hierarchy, including funding allocation standards at the sub-project level, work package level, and task level:

[0089] First, according to the hierarchical structure of the project schedule, the hierarchical rules for funding allocation are formulated. These rules include funding allocation standards at the subproject level, work package level, and task level. At the subproject level, the funding allocation rules are based on the resource requirements and duration of the subprojects, and the total project budget is allocated according to the weight ratio. The weight of each subproject is determined by the product of its resource share and duration ratio, which is used to calculate the allocated budget. At the work package level, the funding allocation rules are refined into an allocation mechanism centered on task complexity and resource requirements. Specifically, the number of tasks in a work package and the complexity of task dependencies directly affect its budget allocation ratio. At the task level, the funding allocation rules are further refined to allocate budgets based on the importance and difficulty of execution of tasks. Critical path tasks are given priority in budget support, while the budget allocation of ordinary tasks depends on their estimated completion time and resource requirements.

[0090] Based on the project progress data set, generate the fund allocation hierarchical data corresponding to the sub-project level, work package level and task level according to the fund allocation rules:

[0091] First, according to the allocation rules at the subproject level, the budget allocation of each subproject is calculated, and the results are recorded in the subproject level funding data table, which includes fields such as subproject ID, subproject name, budget amount, and allocation ratio. Next, enter the work package level, and within the budget range of each subproject, calculate the budget allocation of each work package according to the rules of the work package level, and generate the work package level funding data table, which records fields such as work package ID, subproject ID, work package name, budget amount, and allocation ratio. At the task level, the budget of each work package is refined according to the task level rules, and it is further allocated to each task, and the task level funding data table is generated, which records detailed information such as task ID, work package ID, task name, budget amount, task resource requirement, and task completion time.

[0092] According to the corresponding relationship between the levels, the fund allocation level data and the project progress level data are mapped to form a level mapping table:

[0093] The generation of the hierarchical mapping table depends on the hierarchical association between the subproject, work package and task layers. Specifically, the funding data at the subproject layer is matched with the subproject data at the project schedule layer through the subproject identifier to form a preliminary mapping relationship. The data at the work package layer is associated with the corresponding project schedule work package data through the work package identifier. The data at the task layer is connected with the project schedule task data through the task identifier, and finally a complete hierarchical mapping table is formed. The field design of the mapping table includes hierarchical identifiers (such as subproject identifiers, work package identifiers, task identifiers), funding allocation fields (such as subproject budgets, work package budgets, task budgets, budget amounts, used amounts and remaining amounts), funding usage fields (such as used amounts and remaining amounts) and project schedule fields (such as schedule status, current task status, and estimated completion time).

[0094] For example, the hierarchical mapping table can be understood by referring to Table 1 Hierarchical Mapping Example Table:

[0095] Table 1 Example of hierarchical mapping

[0096]

[0097] The coupling relationship between fund allocation data and project progress data is analyzed through a hierarchical aggregation algorithm to generate synchronization status data of funds and project progress at each level, specifically:

[0098] Extract the funding allocation data and project progress data from the hierarchical mapping table, and mark the characteristic fields of each level according to the sub-project level, work package level, and task level:

[0099] Obtain data from the subproject level, work package level, and task level from the level mapping table, and extract the funding allocation data and project progress data for each level. For each level, mark the characteristic fields separately. Among them, funding allocation data includes the budget amount, used amount, and remaining amount for each level; project progress data includes the current progress percentage, estimated completion time, and current status for each level. These characteristic fields are organized by level to ensure that the funding and progress information at each level are completely and completely associated.

[0100] For example, the characteristic fields of the tag are organized into the following structure:

[0101] Sub-project level characteristic fields: budget amount, amount used, and current progress percentage.

[0102] Work package characteristic fields: budget amount, remaining amount, current progress percentage.

[0103] Task-level characteristic fields: budget amount, amount used, and estimated completion time.

[0104] The funding allocation data and project progress data are integrated into a feature matrix by level, where each row represents a level unit and each column represents a funding allocation value and a project progress value:

[0105] The extracted funding allocation data and project progress data are integrated into a feature matrix. Each row of the feature matrix represents a hierarchical unit (such as a specific subproject, work package or task), and each column represents the value of a feature field. The representation of the feature matrix is ​​as follows:

[0106] ;in, is a feature matrix, representing the funding and progress data of all hierarchical units; Indicates the feature matrix Line The elements of the column represent The first level unit The value of the feature field; is the number of hierarchical units, that is, the number of rows in the feature matrix; is the number of feature fields, that is, the number of columns in the feature matrix.

[0107] Based on the feature matrix, the weight allocation rules of the hierarchical aggregation algorithm are formulated according to the importance and resource allocation of the sub-project level, work package level and task level:

[0108] According to the importance and resource allocation of each hierarchical unit, the weight allocation rules are defined for the hierarchical aggregation algorithm. The weight allocation rules are based on the following principles:

[0109] The sub-project level has the highest weight priority, followed by the work package level, and the task level has the lowest priority.

[0110] For fund allocation characteristic fields (such as budget amount and used amount), the weight value is proportional to the resource contribution.

[0111] For project schedule characteristic fields (such as current progress percentage and estimated completion time), the weight value is inversely proportional to the degree of progress completion.

[0112] The weight calculation formula is as follows:

[0113] ;in, Indicates The hierarchical unit is The weight value of the feature field; It is The amount of resources allocated to each level unit; It is The priority coefficient of a feature field ranges from zero to one, indicating the importance of the feature field.

[0114] The fund allocation data and project progress data at each level are comprehensively analyzed through the hierarchical aggregation algorithm, and the coupling strength between the fund allocation data and the project progress data at each level is calculated:

[0115] The hierarchical aggregation algorithm is used to process the feature matrix and calculate the coupling strength between the fund allocation data and the project progress data.

[0116] The calculation formula for the coupling strength value is: ;in, It is The coupling strength value of each level unit.

[0117] Based on the calculation results of coupling strength, generate the synchronization status data of funds and project progress at each level, and record the synchronization mark and coupling strength value:

[0118] Based on the coupling strength value, synchronization status data is generated, and the synchronization status data includes the following:

[0119] Synchronicity mark: According to the threshold range of the coupling strength value, each hierarchical unit is marked as synchronous or asynchronous. If the coupling strength value of a hierarchical unit is greater than its corresponding preset threshold, it is marked as synchronous, otherwise it is marked as asynchronous.

[0120] Coupling strength value: record the coupling strength value of each hierarchical unit as the basic data for subsequent analysis.

[0121] The preset threshold corresponding to the coupling strength value is determined based on historical data analysis and actual needs. By statistically analyzing the matching degree of funds and progress data at each level, a reasonable strength range is set to ensure the reliability of the synchronization state. For example, the distribution analysis method can be used to select the median of the historical coupling strength value or adjust the threshold according to business needs so that it can dynamically adapt to changes in the scale and complexity of different projects.

[0122] The difference analysis algorithm is used to detect abnormal states in the coupling relationship between funds and progress, marking the levels where the fund allocation status is inconsistent with the project progress status, specifically:

[0123] The coupling strength values ​​and synchronization marks at the sub-project level, work package level and task level are extracted from the generated synchronization status data.

[0124] Based on the synchronization status data, combined with the funding allocation fields and project progress fields at each level, a difference analysis model is established to define the comparison rules and difference thresholds for funding and progress data:

[0125] The variance analysis model is established through the following steps to quantitatively measure the deviation between the funding allocation field and the project progress field.

[0126] First, the funding allocation field and project progress field of each hierarchical unit are normalized so that different fields can be compared at the same level. The specific method is as follows:

[0127] For each field value, first calculate the maximum and minimum values ​​of the field in all hierarchical units.

[0128] The normalized value for each field is calculated by subtracting the minimum value from the actual value of the field and dividing the result by the difference between the maximum and minimum values.

[0129] The value range after normalization is fixed between zero and one, so that the calculation results of all fields have consistent dimensions, which facilitates subsequent difference analysis.

[0130] For each hierarchical unit, compare the normalized values ​​of its fund allocation field and project progress field one by one. Calculate the difference index of the hierarchical unit:

[0131] First, calculate the absolute difference between the fund allocation field and the project progress field field by field.

[0132] The absolute difference between the funding allocation field and the project progress field of all fields is accumulated and divided by the total number of fields to obtain the difference index of the hierarchical unit. This index is used to quantify the degree of deviation between the funding allocation status and the project progress status of the hierarchical unit.

[0133] The threshold of the difference index is determined by performing distribution analysis on historical data, and the statistical quantile of the difference index is usually selected as the threshold. Specific methods include:

[0134] Gather historical hierarchical unit data for multiple similar projects.

[0135] Statistics are collected on the distribution of the difference indicators of these data, and a fixed quantile (such as 90 percent) is selected as the threshold.

[0136] The selection of the threshold ensures a balance between the sensitivity and false positive rate of anomaly detection.

[0137] Among them, the difference index of the hierarchical unit is the output of the difference analysis model.

[0138] Through the difference analysis model, the funding and progress data are compared layer by layer, and the layers with coupling strength lower than the preset threshold are identified as the layers where the funding allocation status is inconsistent with the project progress status:

[0139] For each hierarchical unit, compare its difference index with its corresponding preset threshold:

[0140] If the difference index is greater than its corresponding preset threshold, the funding allocation status and project progress status of the hierarchical unit are judged to be inconsistent.

[0141] If the difference index is less than or equal to the corresponding preset threshold, the funding allocation status and project progress status of the hierarchical unit are judged to be normal.

[0142] For the level units whose detected funding allocation status is inconsistent with the project progress status, the following information is recorded:

[0143] Hierarchical identification: includes subproject identification, work package identification and task identification.

[0144] Difference index value: indicates the specific degree of deviation of the hierarchical unit.

[0145] Abnormal reason: List the specific deviation values ​​between the fund allocation field and the project progress field.

[0146] Among them, the preset threshold corresponding to the difference index is determined based on the statistical analysis of historical project data. Specifically, by analyzing the distribution range of the difference index in the normal hierarchical unit, an appropriate statistical quantile (such as the 90% quantile) is selected as the threshold to distinguish between normal and abnormal states, and ensure that it is suitable for dynamic adjustment needs of different project sizes and complexities.

[0147] For the levels where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify abnormal level nodes, specifically:

[0148] Based on the probability distribution of the funding allocation field and project progress field of the hierarchical node, the entropy growth rate of each node is calculated:

[0149] Extract the funding allocation field and project progress field of each node in the hierarchical network and construct the corresponding state distribution dataset.

[0150] The data for each node includes funding fields (such as budget, amount used, remaining amount) and progress fields (such as completion percentage, planned completion time, current status).

[0151] According to the topological structure of the hierarchical network, the association relationships between nodes are organized into an adjacency matrix for subsequent propagation analysis.

[0152] Probability distribution calculation is performed on the funding allocation field and project progress field of each node:

[0153] For each field, count its value distribution in the historical records and construct a probability distribution function.

[0154] The probability distribution function is used to calculate the initial entropy value of the node. The entropy value calculation formula is as follows:

[0155] ;in, Indicates The initial entropy value of the level nodes, Indicates The funding or progress field of the level node is the value of The probability of a state, Indicates The total number of possible values ​​for the funding or progress fields of each level node, Indicates the number of a node in the hierarchical network. Indicates the value status number of the fund allocation field or project progress field (used to distinguish the different possible value states of the field).

[0156] Simulation The change of the funding allocation field or project progress field status of each level node is detected, and its entropy value is recalculated.

[0157] The formula for calculating the entropy growth rate is as follows: ;in, Indicates The entropy growth rate of the nodes at each level, Indicates The entropy value after the state of the level node changes, Indicates the time interval between state changes.

[0158] The entropy growth rate of each node is analyzed for propagation, the degree of uncertainty diffusion to directly adjacent nodes and indirectly related nodes is evaluated, and the cumulative impact value of the propagation path risk is calculated:

[0159] Construct a propagation path network between hierarchical nodes:

[0160] Based on the adjacent matrix, the hierarchical nodes with higher entropy growth rate are taken as propagation sources, and their impact on nodes in other hierarchical levels is analyzed.

[0161] For Level nodes to The propagation path of the nodes at each level is used to calculate the propagation strength. The formula is as follows: ;in, Indicates that from The nodes of the first level are propagated to the The propagation strength of nodes at each level, Indicates The level nodes and The association weights between nodes at each level are Indicates the number of the directly adjacent node of a hierarchical node in a hierarchical network (with the same number as There is a direct or indirect relationship between the nodes.

[0162] For directly adjacent nodes (nodes directly associated with the propagation source), the magnitude of the entropy change is calculated and recorded as the direct propagation effect.

[0163] For indirectly connected nodes (nodes indirectly connected through intermediate nodes), the propagation strength of all upstream nodes is accumulated to calculate the cumulative impact value of the propagation path risk: ;in, It is The cumulative impact value of the propagation path risk of the nodes at each level, Indicates The set of all upstream nodes of the level node.

[0164] The larger the cumulative impact value of the propagation path risk, the more the target node is affected by the abnormal state of the upstream node, and the greater its overall risk exposure level in the network. This indicates that the abnormal state has a large cumulative effect at the target node along the propagation path, which may further deteriorate the funding and project progress status of the node, and even affect the normal operation of downstream nodes.

[0165] According to the entropy growth rate and the cumulative impact value of the propagation path risk, the abnormal level nodes are identified. The abnormal level nodes include core abnormal nodes and secondary abnormal nodes:

[0166] Identify core abnormal nodes: Filter the hierarchical nodes whose entropy growth rate is higher than the corresponding preset threshold and mark them as core abnormal nodes. Core abnormal nodes are usually the source of abnormal propagation or high entropy contribution nodes.

[0167] The hierarchical nodes in the propagation path are screened according to their cumulative impact values ​​of propagation path risks, and the hierarchical nodes whose cumulative impact values ​​of propagation path risks are significantly higher than the average level are selected as secondary abnormal nodes.

[0168] Among them, whether the cumulative impact value of the transmission path risk is significantly higher than that of the mean can be judged by statistical analysis methods. First, the mean and standard deviation of the cumulative impact value of the transmission path risk of all level nodes are calculated. Then a significance judgment threshold is set, for example, nodes that are "higher than the mean plus two times the standard deviation" are considered to be significantly higher than the average level. This threshold can exclude most nodes within the normal range and only retain nodes with obvious transmission path risks, which are marked as secondary abnormal nodes. This method is both scientific and adaptable, and can be dynamically adjusted according to specific project data.

[0169] The preset threshold corresponding to the entropy growth rate is determined through statistical analysis of historical data. The specific method is to calculate the distribution mean and standard deviation of the entropy growth rate of normal level nodes, and set the mean plus a fixed multiple of the standard deviation (such as twice) as the threshold to distinguish normal nodes from potential abnormal nodes.

[0170] Among them, the association weight is a numerical value that describes the strength of the association between two nodes in a hierarchical network, and is used to quantify the closeness of the relationship between nodes. The weight can be set based on a variety of factors such as funding dependence, task progress linkage, resource sharing, etc. It is usually calculated through historical data analysis or business rules and is used to evaluate the size of the influence between nodes.

[0171] An upstream node is a node that has a direct or indirect dependency on a target node in a hierarchical network, and is usually the input source of the target node. For example, in a fund flow, an upstream node may be the allocator of funds, and in a task schedule, an upstream node may be the predecessor of a target node task. The status change of an upstream node usually directly affects the operating status of the target node.

[0172] Directly adjacent nodes refer to nodes that are directly connected to the target node through an edge in the hierarchical network. These nodes have a direct funding or task relationship with the target node and are the first-level associated nodes that affect the state of the target node.

[0173] A hierarchical network refers to a topological structure consisting of hierarchical nodes and their relationships in funding and project schedule management. Each node represents a specific subproject, work package or task, and nodes are associated through funding flow, task dependency or resource sharing. The hierarchical network reflects the interactive relationship between different levels in project management and is an important basis for synchronous analysis of funding and schedule and identification of anomalies.

[0174] In this scenario, the hierarchical node refers to a basic unit in the funding and project progress management hierarchical network, representing an entity (or data unit) in a specific subproject, work package or task level. These nodes play a specific role in the entire hierarchical network and are used to describe the specific status of funding allocation and project progress. Take a specific project as an example:

[0175] Nodes at the sub-project level:

[0176] Identification: Sub-item number 001.

[0177] Funding status: budget amount is 1 million, 600,000 has been used, and the remaining amount is 400,000.

[0178] Progress status: completion percentage 70%, planned completion time 2024-12-31.

[0179] Nodes of the work package layer:

[0180] Identification: Work package number 001-A.

[0181] Funding status: budgeted amount is RMB 300,000, used amount is RMB 200,000, and remaining amount is RMB 100,000.

[0182] Progress status: completion percentage 50%, planned completion time 2024-11-30.

[0183] Nodes of the task layer:

[0184] Designation: Mission Number 001-A-1.

[0185] Fund status: budget amount is 100,000, used amount is 70,000, and remaining amount is 30,000.

[0186] Progress status: completion percentage 80%, planned completion time 2024-10-31.

[0187] Calibrate the marked abnormal level nodes, project progress data and fund allocation data in abnormal status, specifically:

[0188] Extract the current status data of the fund allocation field and project progress field and the related status data of the associated nodes from the marked exception level node:

[0189] Determine the abnormal level nodes that need to be calibrated, including core abnormal nodes and secondary abnormal nodes.

[0190] Extract the fund allocation field data of each abnormal level node, such as budget amount, used amount, and remaining amount.

[0191] Extract project progress field data for each exception level node, such as completion percentage, estimated completion time, and current status.

[0192] Obtain the relevant funds and progress status data of the upstream and downstream nodes directly associated with the abnormal level node to form a complete data set for reference during calibration.

[0193] Based on the historical data of the fund allocation field and the project progress field, combined with the planned data, calculate the deviation between the current status and the expected status:

[0194] For each abnormal level node, call the historical database to extract the historical status data of its fund allocation field and project progress field.

[0195] Based on the project plan data, determine the expected funding status and progress status of the node at the current point in time.

[0196] Calculate the deviation value for the Fund Allocation field:

[0197] Deviation value = current fund allocation status - planned fund allocation status.

[0198] Calculate the deviation value for the Project Schedule field:

[0199] Deviation value = current progress percentage - planned progress percentage.

[0200] The calculation results are stored in the input data set of the calibration model, and the deviation value of each abnormal level node is recorded.

[0201] The calibration adjustment parameters are calculated based on the deviation value. The adjustment parameters include the correction coefficient of the fund allocation ratio and the correction range of the progress completion time:

[0202] Based on the fund allocation deviation value, calculate the correction coefficient of the fund allocation ratio:

[0203] Correction factor = (Planned funding allocation - Current funding allocation) / Current funding allocation.

[0204] Based on the progress deviation value, calculate the correction range of the progress completion time:

[0205] Revised range = (Planned completion time – Current estimated completion time).

[0206] Combined with the data of the associated nodes, adjust the correction coefficient and correction range:

[0207] If the status of the associated node is abnormal, the correction coefficient and correction range are weighted to avoid diffusion effects.

[0208] The calibration adjustment parameters of each abnormal level node are recorded and stored in the calibration result database.

[0209] Correct the fund allocation field and project progress field of the abnormal level node according to the calibration adjustment parameters, and record the calibrated data:

[0210] Apply the calibration adjustment parameters to the Funds Allocation field:

[0211] Corrected fund allocation value = current fund allocation value + (current fund allocation value × correction factor).

[0212] To apply calibration adjustment parameters to the project progress fields:

[0213] Revised estimated completion time = current estimated completion time + revised scope.

[0214] Update the revised fund allocation data and progress status data to the node's storage location.

[0215] Mark the calibrated node status as "calibrated" and record the correction results in the calibration log for subsequent tracking and verification.

[0216] Among them, the calibrated project progress data and funding allocation data are used to dynamically update the status data in the project life cycle management, including key information in various stages such as project establishment, approval, execution, monitoring and completion. In the project establishment stage, the updated data can reflect more accurate budget allocation and initial schedule; in the approval and execution stages, the revised data provides a basis for real-time allocation of funds and dynamic adjustment of project progress; in the monitoring stage, the calibrated data can be used to generate abnormal alarms and risk assessment reports for timely intervention; in the project completion stage, the calibrated funding and progress data can provide a real reference for project summary and performance evaluation. Through this dynamic collaborative management of the entire process, real-time matching of funding flow and progress flow is achieved, ensuring the efficiency and accuracy of project execution. Example 2

[0217] The difference between Example 2 of the present invention and Example 1 is that this example introduces a full life cycle management application system for funds and projects in the same process.

[0218] Figure 2 A structural schematic diagram of a full life cycle management application system for funds and projects in the same process of the present invention is given, which includes a project progress division module, a fund mapping generation module, an analysis level coupling module, an abnormal state detection module, an abnormal node identification module and an abnormal data calibration module.

[0219] Project progress division module: divides the project progress into three levels: sub-project level, work package level and task level, and generates a project progress data set corresponding to each level based on the actual execution status of each level.

[0220] Generate funding mapping module: Based on the project progress data set, generate the corresponding funding allocation level data, and map the funding allocation level with the project progress level.

[0221] Analysis level coupling module: Analyze the coupling relationship between fund allocation data and project progress data through a hierarchical aggregation algorithm to generate synchronization status data of funds and project progress at each level.

[0222] Abnormal status detection module: detects abnormal status in the coupling relationship between funds and progress through a difference analysis algorithm, and marks the levels where the fund allocation status is inconsistent with the project progress status.

[0223] Abnormal node identification module: For the levels where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify abnormal level nodes.

[0224] Calibration of abnormal data module: calibrate the marked abnormal level nodes, project progress data in abnormal status and fund allocation data.

[0225] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0226] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0227] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0228] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0229] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0230] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0231] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0232] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0233] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0234] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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 full life cycle management application method for funds and projects in the same process, characterized in that: The steps include: S1: Divide the project progress into three levels: sub-project level, work package level and task level, and generate a project progress data set corresponding to each level based on the actual execution status of each level; The data set at the subproject level includes the current progress, status and estimated completion time of each subproject; the data set at the work package level includes the completion ratio, resource usage and task completion status of each work package; the data set at the task level includes the execution time, status and completion quality of each task; S2: Based on the project progress data set, generate the corresponding fund allocation level data, and map the fund allocation level with the project progress level; Calculate the budget allocation for each sub-project according to the allocation rules at the sub-project level and record it in the sub-project level funding data table, which includes the sub-project ID, sub-project name, budget amount and allocation ratio; Calculate the budget allocation of each work package according to the rules of the work package level within the budget range of each sub-project, and generate a work package level funding data table, which includes the work package ID, the sub-project ID, the work package name, the budget amount and the allocation ratio; According to the task-level rules, the budget of each work package is refined and further allocated to each task to generate a task-level funding data table, which includes the task ID, the work package ID, the task name, the budget amount, the task resource requirement and the task completion time; S3: Analyze the coupling relationship between fund allocation data and project progress data through hierarchical aggregation algorithm, and generate synchronization status data of funds and project progress at each level, specifically: Extract funding allocation data and project progress data from the hierarchical mapping table, and mark the characteristic fields of each level according to the sub-project level, work package level and task level; The funding allocation data and project progress data are integrated into a feature matrix by level, where each row represents a level unit and each column represents a funding allocation value and a project progress value; Based on the feature matrix, the weight allocation rules of the hierarchical aggregation algorithm are formulated according to the importance and resource allocation of the sub-project level, work package level and task level. The calculation formula of the weight in the weight allocation rule is as follows: ;in, Indicates The hierarchical unit is The weight value of the feature field; It is The amount of resources allocated to each level unit; It is The priority coefficient of the feature field indicates the importance of the feature field; the feature field includes the fund allocation feature field and the project progress feature field; The fund allocation data and project progress data at each level are comprehensively analyzed through the hierarchical aggregation algorithm, and the coupling strength between the fund allocation data and the project progress data at each level is calculated. The calculation formula of the coupling strength is: ;in, It is The coupling strength value of each level unit is Indicates the feature matrix Line The elements of the column, is the number of feature fields; Based on the coupling strength calculation results, generate the data on the synchronization status of funds and project progress at each level, and record the synchronization mark and coupling strength value; The identification logic of the synchronization identification is: if the coupling strength value of a certain level unit is greater than its corresponding preset threshold, it is marked as synchronized, otherwise it is marked as asynchronous; S4: Detect abnormal states in the coupling relationship between funds and progress through a difference analysis algorithm, and mark the levels where the funding allocation status is inconsistent with the project progress status; S5: For the levels where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify abnormal level nodes, specifically: Based on the probability distribution of the funding allocation field and project progress field of the hierarchical node, the entropy growth rate of each node is calculated; Among them, the logic for obtaining the entropy growth rate is: Calculate the initial entropy value of the node: ;in, Indicates The initial entropy value of the level nodes, Indicates The funding or progress field of the level node is the value of The probability of a state, Indicates The total number of possible values ​​for the funding or progress fields of each level node, Indicates the number of a node in the hierarchical network. Indicates the value status number of the fund allocation field or project progress field; Simulation The change of the funding allocation field or project progress field status of each level node and recalculate its entropy value; The formula for calculating the entropy growth rate is as follows: ;in, Indicates The entropy growth rate of the nodes at each level, Indicates The entropy value after the state of the level node changes, Indicates the time interval between state changes; Perform propagation analysis on the entropy growth rate of each node, evaluate the degree of uncertainty diffusion to directly adjacent nodes and indirectly related nodes, and calculate the cumulative impact value of the propagation path risk; Among them, the logic for calculating the cumulative impact value of the transmission path risk is: According to the topological structure of the hierarchical network, the association relationship between nodes is organized into an adjacent matrix. Based on the adjacent matrix, the hierarchical nodes with higher entropy growth rate are used as propagation sources. Level nodes to The propagation path of the nodes at each level is used to calculate the propagation strength: ;in, Indicates that from The nodes of the first level are propagated to the The propagation strength of nodes at each level, Indicates The level nodes and The association weights between nodes at each level are Indicates the number of the directly adjacent nodes of a hierarchical node in a hierarchical network; For directly adjacent nodes, the entropy change is calculated and recorded as the direct propagation effect; For indirectly related nodes, the propagation strength of all upstream nodes is accumulated to calculate the cumulative impact value of the propagation path risk: ;in, It is The cumulative impact value of the propagation path risk of the nodes at each level, Indicates The set of all upstream nodes of the level node; According to the entropy growth rate and the cumulative impact value of the propagation path risk, identify abnormal level nodes, which include core abnormal nodes and secondary abnormal nodes; Among them, identifying core abnormal nodes is to screen the hierarchical nodes whose entropy growth rate is higher than the corresponding preset threshold, and mark them as core abnormal nodes; the hierarchical nodes in the propagation path are screened according to their propagation path risk cumulative impact values, and the hierarchical nodes whose propagation path risk cumulative impact values ​​are significantly higher than the average level are selected as secondary abnormal nodes; S6: Calibrate the marked abnormal level nodes, project progress data in abnormal status, and fund allocation data.

2. According to claim 1, a full life cycle management application method for funds and projects in the same process is characterized in that: The project progress is divided into three levels: sub-project level, work package level and task level. Based on the actual execution of each level, a project progress data set corresponding to each level is generated, specifically: Determine the hierarchical division rules of the project progress, including the three levels of sub-project level, work package level and task level and their corresponding hierarchical division conditions; Extract project structure information, including project goals, time nodes, and subtask divisions; Based on the extracted project structure information and hierarchical division rules, the project is divided into sub-project level, work package level and task level; Collect and record actual performance data at all levels; Based on the actual execution data of the recorded levels, a project progress data set corresponding to each level is generated.

3. According to claim 1, a method for managing the entire life cycle of funds and projects in the same process, characterized in that: Based on the project progress data set, the corresponding fund allocation level data is generated, and the fund allocation level is mapped to the project progress level, specifically: Formulate funding allocation rules based on the project schedule hierarchy, including funding allocation standards at the sub-project level, work package level and task level; Based on the project progress data set, generate the fund allocation hierarchical data corresponding to the sub-project level, work package level and task level according to the fund allocation rules; According to the corresponding relationship between the levels, the fund allocation level data and the project progress level data are mapped to form a level mapping table.

4. According to claim 1, a method for managing the entire life cycle of funds and projects in the same process, characterized in that: The difference analysis algorithm is used to detect abnormal states in the coupling relationship between funds and progress, marking the levels where the fund allocation status is inconsistent with the project progress status, specifically: Extract the coupling strength value and synchronization mark of sub-project level, work package level and task level from the generated synchronization status data; Based on the synchronization status data, combined with the funding allocation fields and project progress fields at each level, a difference analysis model is established to define the comparison rules and difference thresholds for funding and progress data; For each hierarchical unit, compare the normalized values ​​of its fund allocation field and project progress field one by one; calculate the difference index of the hierarchical unit; Through the difference analysis model, the funding and progress data are compared layer by layer, and the layers with coupling strength lower than the preset threshold are identified as the layers where the funding allocation status is inconsistent with the project progress status.

5. According to claim 1, a method for managing the entire life cycle of funds and projects in the same process, characterized in that: Calibrate the marked abnormal level nodes, project progress data and fund allocation data in abnormal status, specifically: Extract the current status data of the fund allocation field and the project progress field and the related status data of the associated nodes from the marked abnormal level nodes; Based on the historical data of the fund allocation field and the project progress field, combined with the planned data, calculate the deviation value between the current status and the expected status; Calculate calibration adjustment parameters based on the deviation value, the adjustment parameters include the correction coefficient of the fund allocation ratio and the correction range of the progress completion time; The fund allocation field and project progress field of the abnormal level node are corrected according to the calibration adjustment parameters, and the calibrated data is recorded.

6. A full life cycle management application system for funds and projects in the same process, used to implement a full life cycle management application method for funds and projects in the same process as described in any one of claims 1 to 5, characterized in that: It includes project progress division module, fund mapping generation module, hierarchical coupling analysis module, abnormal state detection module, abnormal node identification module and abnormal data calibration module; Project progress division module: divides the project progress into three levels: sub-project level, work package level and task level, and generates a project progress data set corresponding to each level based on the actual execution status of each level; Generate fund mapping module: Generate corresponding fund allocation level data based on project progress data set, and map fund allocation level with project progress level; Analysis of hierarchical coupling module: Analyze the coupling relationship between fund allocation data and project progress data through hierarchical aggregation algorithm, and generate synchronization status data of funds and project progress at each level; Abnormal status detection module: detects abnormal status in the coupling relationship between funds and progress through a difference analysis algorithm, and marks the level where the fund allocation status is inconsistent with the project progress status; Abnormal node identification module: For the levels where the funding allocation status is inconsistent with the project progress status, the node status and propagation path are analyzed through the probability distribution of the funding allocation field and the project progress field of the level node to identify the abnormal level nodes; Calibration of abnormal data module: calibrate the marked abnormal level nodes, project progress data in abnormal status and fund allocation data.

Images