Project risk early warning method based on heterogeneous data aggregation and traffic light model

Abstract

The application provides a project risk early warning method based on heterogeneous data aggregation and traffic light model, relates to the technical field of project operation risk control, and comprises the following operation steps: S1: standardized collection and normalized processing of multi-project group multi-source heterogeneous data; S2: project risk coupling quantitative operation based on the standardized data set; S3: dynamic threshold self-adaptive calibration of the risk level of the multi-project group; S4: red-yellow-green three-color light risk state grading determination based on the dynamic threshold; S5: multi-project group global risk visualization integration and graded real-time early warning output. The application constructs a multi-project group global visualization grading early warning system, realizes the upgrading of risk control from single-point isolated analysis to global overall control. By integrating all risk data and abnormal data sources of the multi-project, distinguishing independent risks and group risks, and combining the red-yellow-green standardized grading model to realize differentiated early warning pushing, the overall risk distribution situation of the project group can be intuitively presented.

Description

Technical Field

[0001] This invention relates to the field of project operation risk management technology, and more specifically, to a project risk early warning method based on heterogeneous data aggregation and a traffic light model. Background Technology

[0002] In current multi-project portfolio operation risk early warning and management scenarios, the industry generally adopts traditional risk management methods such as single-dimensional data assessment, fixed-level threshold determination, and independent analysis of single projects. Most management methods rely solely on project progress data or financial data for unilateral risk assessment, failing to achieve effective integration of heterogeneous data across systems. This results in data on project progress execution status and financial operation status being disconnected, making it difficult to identify potential compound operational risks caused by the misalignment between progress and finance.

[0003] Meanwhile, existing risk level assessments all use uniform, fixed threshold standards, which cannot adapt to the differentiated management needs of project groups of different sizes and scales. This easily leads to the problem of underestimating risks in small projects and misjudging risks in large projects, resulting in poor accuracy in risk warnings. In addition, traditional management models only conduct isolated risk analysis on a single project, lacking the ability to integrate and visualize risks from a global perspective across multiple project groups. They cannot distinguish between single-point project risks and group-wide common risks, resulting in one-sided and delayed risk warnings that are difficult to support managers in conducting proactive risk correction and overall operational management.

[0004] Therefore, it is necessary to propose a project risk early warning method based on heterogeneous data aggregation and traffic light model to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to address the significant shortcomings in the flexibility, accuracy, and systematic nature of current overall management and control, which makes it unable to meet the real-time risk warning and management needs of large-scale multi-project groups.

[0006] To achieve the aforementioned objectives and address the aforementioned problems, this invention provides a project risk warning method based on heterogeneous data aggregation and a traffic light model, comprising the following steps: S1: Standardized collection and normalization of multi-project group heterogeneous data from multiple sources, real-time collection of cross-system progress heterogeneous data and financial heterogeneous data corresponding to multiple project groups and normalization processing of them. S2: Based on standardized datasets, the project risk coupling quantification calculation is performed on the standardized schedule risk coefficient and standardized financial risk coefficient of a single project using the project risk coupling quantification formula. This yields a comprehensive risk coupling degree for a single project that balances schedule and financial risks. The formula is as follows: ; in Risk coupling degree for a single project and a single statistical period; To standardize the project schedule risk coefficient; To standardize the financial risk coefficient of the project; Fixed weighting coefficients for schedule risks; Fixed weighting coefficients for financial risk; A fixed correction factor is applied to the risk deviation. S3: Adaptive calibration of dynamic threshold for risk levels in multi-project clusters. Based on the risk coupling degree of a single project within a single statistical period, and considering the differences in the number of projects in the multi-project cluster and the size of individual projects, a dynamic threshold calibration formula is used to adaptively correct the fixed benchmark threshold, resulting in a dynamic risk threshold that adapts to the overall characteristics of the current project cluster. The formula is as follows: ; in This represents the dynamic risk threshold for the current project portfolio. The initial fixed reference threshold for the system; This represents the total number of valid projects included in the project group within the current statistical period. For the first project group Size adjustment factor for each project; S4: Risk status classification and determination based on dynamic thresholds for red, yellow, and green LED lights. As the basis for judgment, To establish a standardized red, yellow, and green three-color risk status model for risk classification, a risk classification and determination of the operational risks of each project within a multi-project group was completed. S5: Global risk visualization integration and hierarchical real-time early warning output for multiple project groups, completing the global integration and statistics of single-point project risks and group risks of project groups, and presenting them dynamically through visualization and hierarchical early warning push.

[0007] As a preferred technical solution of this application, step S1 specifically includes: S101: Directed real-time collection of multi-source heterogeneous data, establishing multi-business data docking channels, synchronously docking with project management system, financial accounting system, and cost control system, and uniformly collecting two types of core heterogeneous data for all project groups within the preset control scope; S102: Precise cleaning and dimension binding of heterogeneous data. Compliance filtering is performed on all collected raw heterogeneous data. At the same time, the project's unique code is used as the association index to bind the progress heterogeneous data and financial heterogeneous data corresponding to the same project dimension by dimension. S103: Heterogeneous data unified dimension normalization conversion. For the two different dimension systems of time dimension for heterogeneous progress data and monetary dimension for heterogeneous financial data, a unified standardization mapping process is performed. Through a linear normalization algorithm, all bound and completed valid progress data and valid financial data are uniformly mapped to the 0-1 standard value range.

[0008] As a preferred technical solution of this application, in S101, the heterogeneous progress data specifically refers to the original data used to characterize the project execution progress status, including the planned duration of each project, the percentage of actual completed progress, the number of days of lag in key nodes, and the remaining core workload. Financial heterogeneous data specifically refers to raw data used to characterize the financial operating status of a project, including real-time actual costs, approved budget costs, current period cash inflows, total accounts payable, and project capital occupancy rate.

[0009] As a preferred technical solution of this application, in S2, the formula is based on the 0-1 interval standard data after normalization processing in S103, and calculates the standardized project schedule risk coefficient and the standardized project financial risk coefficient respectively.

[0010] As a preferred technical solution of this application, in S3, the formula is based on the scale and differentiation characteristics of the project group, and is constructed based on the parameters of project quantity and project scale to dynamically calibrate the initial benchmark threshold, wherein the initial benchmark threshold is a system preset fixed parameter.

[0011] As a preferred technical solution of this application, step S4 specifically includes: S401: Green light safety status, when The dynamic yellow light threshold indicates that the project's operational risks are under control, the project's progress is well-matched with its financial expenditures, there are no emerging operational risks, and no risk warning intervention is required. S402: Yellow light warning status, when the dynamic yellow light threshold is ≤ The dynamic red light threshold indicates a potential operational risk for the project, triggering a mild risk warning and prompting staff to verify the original heterogeneous data and correct any deviations in advance. S403: Red Light Crisis State: When If the threshold for a dynamic red light is reached, it is determined that the project faces substantial operational risks, and there is a serious imbalance between progress execution and financial control, triggering a severe risk warning and a mandatory rectification order.

[0012] As a preferred technical solution of this application, step S5 specifically includes: S501: Global risk aggregation statistics for project groups. Taking the complete project group as the statistical unit, it summarizes the risk coupling degree value, three-color light risk level, abnormal progress heterogeneous data, and abnormal financial heterogeneous data of all valid projects, distinguishes between independent risks of single projects and common group risks of multiple project groups, and generates global project risk profile data. S502: Dynamic visualization of risk status. Based on global project risk profile data, it displays the red, yellow and green light status of each project in a multi-project group in real time in the form of dynamic charts, the real-time fluctuation trend of risk coupling degree values, and the distribution of abnormal data, intuitively presenting the overall operational risk distribution of the project group. S503: Differentiated graded early warning push: Corresponding to the three-color light risk level of S4, a graded early warning mechanism is implemented.

[0013] As a preferred technical solution of this application, in S503, a routine inspection warning is pushed to items in the yellow light warning state, and an emergency rectification warning is pushed to items in the red light crisis state.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: In the scheme of this application: 1. It has achieved deep integration and standardized reuse of multi-source heterogeneous data. Through targeted collection, precise cleaning and dimensional normalization of heterogeneous data from multiple systems, it has transformed differentiated project progress heterogeneous data and financial heterogeneous data into a unified standardized dataset that can be collaboratively computed. This has broken down the data barriers between progress control and financial control, allowing risk assessment to no longer rely on a single data dimension. It has provided a foundation for the accurate identification of complex business risks and avoided the one-sidedness problem caused by single data assessment from the source. 2. Through risk coupling quantitative calculations, accurate quantitative assessment of project composite risks was achieved. By combining weighted coupling calculations with risk deviation correction logic, the positive correlation between schedule and financial risks was taken into account, while eliminating risk misjudgments caused by anomalies in single-dimensional data. This allows for a true reflection of the overall operational risk status of the project, thereby effectively improving the authenticity and accuracy of risk quantitative assessment. 3. Adaptively adjust the risk assessment threshold based on the number of projects in the project group and the scale characteristics of individual projects to achieve differentiated and accurate risk level assessment, so that the risk assessment standard fits the actual project operation conditions; 4. A global, visualized, hierarchical early warning system for multiple project clusters was constructed, upgrading risk management from isolated single-point analysis to overall global control. By integrating all risk data and abnormal data sources from multiple projects, distinguishing between independent and group risks, and combining a standardized traffic light hierarchical model to achieve differentiated early warning pushes, the system can intuitively present the overall risk distribution of the project cluster. It can accurately locate specific risk hazards in individual projects and identify common operational problems across multiple project clusters, providing effective data support for staff to proactively correct deviations, accurately rectify issues, and achieve overall control, significantly improving the real-time management efficiency of operational risks across multiple project clusters. Attached Figure Description

[0015] Figure 1A flowchart of the project risk warning method based on heterogeneous data aggregation and traffic light model provided in this application. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0018] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the present invention can be combined with each other.

[0019] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0020] Please refer to Figure 1 The project risk warning method based on heterogeneous data aggregation and traffic light model includes the following steps: S1: Standardized collection and normalization of multi-project group heterogeneous data from multiple sources, real-time collection of cross-system progress heterogeneous data and financial heterogeneous data corresponding to multiple project groups and normalization processing of them. S2: Based on standardized datasets, the project risk coupling quantification calculation is performed on the standardized schedule risk coefficient and standardized financial risk coefficient of a single project using the project risk coupling quantification formula. This yields a comprehensive risk coupling degree for a single project that balances schedule and financial risks. The formula is as follows: ; in The risk coupling degree for a single project and a single statistical period is dimensionless and has a fixed value range of 0-1. To standardize the project schedule risk coefficient, the value range is 0-1; The standardized project financial risk coefficient has a value range of 0-1. A fixed weighting coefficient of 0.55 is set for schedule risk. A fixed weighting coefficient for financial risk is set, with a value of 0.45. A fixed correction factor for risk deviation is set, with a value of 0.2. By using weighting coefficients to achieve positive weighting of schedule risk and financial risk, we can align with the objective law that schedule delays in project operations inevitably lead to financial losses. By using deviation correction items to offset the assessment bias caused by serious discrepancies between schedule risk and financial risk values, we can output a coupling degree value that can truly reflect the overall operational risk of the project. S3: Adaptive calibration of dynamic threshold for risk levels in multi-project clusters. Based on the risk coupling degree of a single project within a single statistical period, and considering the differences in the number of projects in the multi-project cluster and the size of individual projects, a dynamic threshold calibration formula is used to adaptively correct the fixed benchmark threshold, resulting in a dynamic risk threshold that adapts to the overall characteristics of the current project cluster. The formula is as follows: ; in This represents the dynamic risk threshold for the current project portfolio. The initial fixed reference threshold for the system; This represents the total number of valid projects included in the project group within the current statistical period. For the first project group The scale adjustment factor for each project is determined based on the total project budget, with a uniform range of 0.8-1.2. S4: Risk status classification and determination based on dynamic thresholds for red, yellow, and green LED lights. As the basis for judgment, To establish a standardized red, yellow, and green three-color risk status model for risk classification, a risk classification and determination of the operational risks of each project within a multi-project group was completed. S5: Global risk visualization integration and hierarchical real-time early warning output for multiple project groups. It completes the global integration and statistics of single-point project risks and group risks of project groups. Through visual dynamic presentation and hierarchical early warning push, it realizes real-time visual management and early warning output of operational risks from the perspective of multiple project groups.

[0021] Furthermore, S1 specifically includes: S101: Directed real-time collection of multi-source heterogeneous data, establishing a second-level incremental data docking channel for multiple business systems, synchronously docking with project management system, financial accounting system, and cost control system, and uniformly collecting two types of core heterogeneous data for all project groups within the preset control scope; S102: Precise cleaning and dimension binding of heterogeneous data. Compliance filtering is performed on all collected raw heterogeneous data to uniformly remove null values, out-of-range extreme values, duplicate and redundant data, and invalid data cached by the system, while retaining valid raw data. At the same time, using the unique project code as the association index, the progress heterogeneous data and financial heterogeneous data corresponding to the same project are bound one-to-one, realizing precise matching of two-dimensional data of projects under multiple project groups, and completely avoiding data mismatch and data disconnection problems. S103: Heterogeneous data unified dimension normalization conversion. For the two different dimension systems of time dimension for heterogeneous progress data and monetary dimension for heterogeneous financial data, a unified standardization mapping process is performed. Through a linear normalization algorithm, all bound and valid progress data and valid financial data are uniformly mapped to the 0-1 standard numerical range, eliminating the technical barriers that prevent the coupling and calculation of different data dimensions. The final output is a standardized heterogeneous fusion dataset with unified format, unified dimension, and corresponding dimensions.

[0022] Furthermore, in S101, heterogeneous schedule data specifically refers to the raw data used to characterize the project execution schedule status, including the planned duration of each project, the percentage of actual progress completed, the number of days the key nodes are behind schedule, and the remaining core workload. Financial heterogeneous data specifically refers to the raw data used to characterize the project's financial operating status. Specifically, it includes the project's real-time actual cost, the project's approved budget cost, the amount of current cash received, the total amount of accounts payable, and the project's capital occupancy rate. All data adopts an incremental collection mode, updating only the data that changes within the period to ensure data real-time performance and collection efficiency.

[0023] Furthermore, in S2, the formula is based on the 0-1 interval standard data after normalization in S103, and calculates the standardized project schedule risk coefficient and the standardized project financial risk coefficient respectively. The larger the values ​​of the two types of coefficients, the higher the risk of the corresponding dimension.

[0024] Furthermore, in S3, the formula is based on the scale and differentiation characteristics of the project group, and is constructed based on the parameters of project quantity and project scale. The initial benchmark threshold is dynamically calibrated, where the initial benchmark threshold is a fixed parameter preset by the system.

[0025] Furthermore, S4 specifically includes: S401: Green light safety status, when The dynamic yellow light threshold indicates that the project's operational risks are under control, the project's progress is well-matched with its financial expenditures, there are no emerging operational risks, and no risk warning intervention is required. S402: Yellow light warning status, when the dynamic yellow light threshold is ≤ The dynamic red light threshold indicates potential operational risks for the project. At least minor anomalies in two categories—project delays and financial cost discrepancies—signify a potential for escalating risk, triggering a mild risk warning. This prompts staff to verify the original heterogeneous data and correct any deviations in advance. S403: Red Light Crisis State: When If the threshold for a dynamic red light is reached, it is determined that the project has substantial and significant operational risks, a serious imbalance between progress execution and financial fund management, and core hidden dangers such as serious delays in the construction period, project fund losses, and abnormal payment collection. This triggers a severe risk warning and mandates the issuance of rectification orders.

[0026] Furthermore, S5 specifically includes: S501: Global risk aggregation statistics for project groups. Taking the complete project group as the statistical unit, it summarizes the risk coupling degree value, three-color light risk level, abnormal progress heterogeneous data, and abnormal financial heterogeneous data of all valid projects, distinguishes between independent risks of single projects and common group risks of multiple project groups, and generates global project risk profile data. S502: Dynamic visualization of risk status. Based on global project risk profile data, it displays the red, yellow and green status of each project in a multi-project group in real time in the form of dynamic charts, the real-time fluctuation trend of risk coupling degree, and the distribution of abnormal data. It intuitively presents the overall operational risk distribution of the project group and realizes the visualization of risk status. S503: Differentiated graded early warning push: Corresponding to the three-color light risk level of S4, a graded early warning mechanism is implemented.

[0027] Furthermore, in S503, routine verification warnings are pushed to projects in the yellow light warning state, and emergency rectification warnings are pushed to projects in the red light crisis state. The warning information is simultaneously linked to the original progress and financial heterogeneous data of the corresponding projects, providing accurate traceability basis for risk rectification.

[0028] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0029] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the scope of the patent. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this invention.

Claims

1. A project risk early warning method based on heterogeneous data aggregation and a traffic light model, characterized in that, The following steps are included: S1: Standardized collection and normalization of multi-project group heterogeneous data from multiple sources, real-time collection of cross-system progress heterogeneous data and financial heterogeneous data corresponding to multiple project groups and normalization processing of them. S2: Based on standardized datasets, the project risk coupling quantification calculation is performed on the standardized schedule risk coefficient and standardized financial risk coefficient of a single project using the project risk coupling quantification formula. This yields a comprehensive risk coupling degree for a single project that balances schedule and financial risks. The formula is as follows: ； in Risk coupling degree for a single project and a single statistical period; To standardize the project schedule risk coefficient; To standardize the financial risk coefficient of the project; Fixed weighting coefficients for schedule risks; Fixed weighting coefficients for financial risk; A fixed correction factor is applied to the risk deviation. S3: Adaptive calibration of dynamic threshold for risk levels in multi-project clusters. Based on the risk coupling degree of a single project within a single statistical period, and considering the differences in the number of projects in the multi-project cluster and the size of individual projects, a dynamic threshold calibration formula is used to adaptively correct the fixed benchmark threshold, resulting in a dynamic risk threshold that adapts to the overall characteristics of the current project cluster. The formula is as follows: ； in This represents the dynamic risk threshold for the current project portfolio. The initial fixed reference threshold for the system; This represents the total number of valid projects included in the project group within the current statistical period. For the first project group Size adjustment factor for each project; S4: Risk status classification and determination based on dynamic thresholds for red, yellow, and green LED lights. As the basis for judgment, To establish a standardized red, yellow, and green three-color risk status model for risk classification, a risk classification and determination of the operational risks of each project within a multi-project group was completed. S5: Global risk visualization integration and hierarchical real-time early warning output for multiple project groups, completing the global integration and statistics of single-point project risks and group risks of project groups, and presenting them dynamically through visualization and hierarchical early warning push.

2. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 1, characterized in that, Specifically, S1 includes: S101: Directed real-time collection of multi-source heterogeneous data, establishing multi-business data docking channels, synchronously docking with project management system, financial accounting system, and cost control system, and uniformly collecting two types of core heterogeneous data for all project groups within the preset control scope; S102: Precise cleaning and dimension binding of heterogeneous data. Compliance filtering is performed on all collected raw heterogeneous data. At the same time, the project's unique code is used as the association index to bind the progress heterogeneous data and financial heterogeneous data corresponding to the same project dimension by dimension. S103: Heterogeneous data unified dimension normalization conversion. For the two different dimension systems of time dimension for heterogeneous progress data and monetary dimension for heterogeneous financial data, a unified standardization mapping process is performed. Through a linear normalization algorithm, all bound and completed valid progress data and valid financial data are uniformly mapped to the 0-1 standard value range.

3. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 2, characterized in that, In S101, the heterogeneous progress data specifically refers to the raw data used to characterize the progress status of the project, including the planned duration of each project, the percentage of actual progress completed, the number of days the key nodes are delayed, and the remaining core workload. Financial heterogeneous data specifically refers to raw data used to characterize the financial operating status of a project, including real-time actual costs, approved budget costs, current period cash inflows, total accounts payable, and project capital occupancy rate.

4. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 2, characterized in that, In S2, the formula is based on the 0-1 interval standard data after normalization in S103, and calculates the standardized project schedule risk coefficient and the standardized project financial risk coefficient respectively.

5. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 4, characterized in that, In S3, the formula is based on the scale and differentiation characteristics of the project group and is constructed based on the parameters of project quantity and project scale. The initial benchmark threshold is dynamically calibrated, where the initial benchmark threshold is a fixed parameter preset by the system.

6. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 4, characterized in that, Specifically, S4 includes: S401: Green light safety status, when The dynamic yellow light threshold indicates that the project's operational risks are under control, the project's progress is well-matched with its financial expenditures, there are no emerging operational risks, and no risk warning intervention is required. S402: Yellow light warning status, when the dynamic yellow light threshold is ≤ The dynamic red light threshold indicates a potential operational risk for the project, triggering a mild risk warning and prompting staff to verify the original heterogeneous data and correct any deviations in advance. S403: Red Light Crisis State: When If the threshold for a dynamic red light is reached, it is determined that the project faces substantial operational risks, and there is a serious imbalance between progress execution and financial control, triggering a severe risk warning and a mandatory rectification order.

7. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 6, characterized in that, The red light reference threshold is 0.7, the yellow light reference threshold is 0.4, and the green light reference threshold is 0.

8. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 6, characterized in that, Specifically, S5 includes: S501: Global risk aggregation statistics for project groups. Taking the complete project group as the statistical unit, it summarizes the risk coupling degree value, three-color light risk level, abnormal progress heterogeneous data, and abnormal financial heterogeneous data of all valid projects, distinguishes between independent risks of single projects and common group risks of multiple project groups, and generates global project risk profile data. S502: Dynamic visualization of risk status. Based on global project risk profile data, it displays the red, yellow and green light status of each project in a multi-project group in real time in the form of dynamic charts, the real-time fluctuation trend of risk coupling degree values, and the distribution of abnormal data, intuitively presenting the overall operational risk distribution of the project group. S503: Differentiated graded early warning push: Corresponding to the three-color light risk level of S4, a graded early warning mechanism is implemented.

9. The project risk early warning method based on heterogeneous data aggregation and traffic light model according to claim 7, characterized in that, In S503, a routine inspection warning is pushed to items in the yellow light warning state, and an emergency rectification warning is pushed to items in the red light crisis state.

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