Project information data evaluation method and system based on big data
By automatically collecting and analyzing data based on big data, we build financial, schedule, and personnel risk models, solving the problems of low efficiency and poor accuracy in existing technologies and achieving efficient and comprehensive project information data evaluation.
Patent Information
- Application Number
- CN202511213692.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-28
- Publication Date
- 2025-10-03
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing project information data assessment methods rely on manual collection and analysis, which are inefficient and inaccurate. They are unable to assess project risks in multiple dimensions, resulting in a lack of comprehensiveness and objectivity in the assessment results.
Adopting automated data collection, processing and analysis methods based on big data, by constructing mathematical models of financial, schedule and personnel risk coefficients, combined with data cleaning and preprocessing, key features are extracted for multi-dimensional risk assessment.
It improves assessment efficiency and accuracy, can quickly process large-scale data, cover key risk points and capture hidden risks, and provide a more comprehensive basis for project decision-making.
Smart Images
Figure CN120746751A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data risk assessment, and in particular to a project information data assessment method and system based on big data. Background Art
[0002] In today's society, various projects are booming, and the amount of project information data is also exploding. Project information data evaluation plays a vital role in project decision-making, planning, and implementation.
[0003] However, existing project information data evaluation methods have the following problems: 1. Most of them rely on manual collection and analysis of data, resulting in low evaluation efficiency, poor accuracy, and difficulty in processing large-scale complex data; 2. They are unable to evaluate project risks from multiple dimensions, resulting in a lack of comprehensiveness and objectivity in the evaluation results. Therefore, the present invention proposes a project information data evaluation method and system based on big data. Summary of the Invention
[0004] The purpose of the present invention is to provide a project information data evaluation method and system based on big data to solve the above technical problems.
[0005] A project information data evaluation method based on big data, the method comprising the following steps: Step S1, collecting project-related parameter data; Step S2: Clean and preprocess the collected data, remove duplicate data and noise data, fill in missing data, and standardize the data; Step S3: Analyze the processed data and extract key features related to project evaluation; Step S4: Evaluate the risk of the project based on the key features of the relevant data of the project to be evaluated.
[0006] As a further description of the technical solution of the present invention, the specific process of step S1 includes: For data collection, a data interface is established with the company's internal information system to obtain project parameter data in real time; The parameter data of the project includes: basic information, financial data, progress data and personnel data.
[0007] As a further description of the technical solution of the present invention, the working process of step S3 includes: Obtain project financial data information, including budget execution rate (BR), cost variance (CV), and unallocated costs; The budget execution rate is the ratio of actual expenditure to budget, and the cost deviation is the difference between budgeted cost and actual cost; Construct a mathematical model of the project financial risk coefficient, the expression is: ; Where, is the project financial risk coefficient, is the cost deviation, is the budget execution rate, is the unallocated cost, is the actual cost, For budget, 、 and are weight coefficients, and > > .
[0008] As a further description of the technical solution of the present invention, the working process of step S3 further includes: Divide the project schedule into n phases and obtain the time data information of n phases, including: duration, milestone achievement rate, schedule deviation and float time The duration is the total duration of each phase, the milestone achievement rate is the percentage of phase data completed as planned, the progress deviation is the difference between the planned progress and the actual progress of each phase, and the float time is the maximum time that a task can be delayed without affecting the total duration; Construct a mathematical model of project schedule risk coefficient, the expression is: ; Where S is the project schedule risk coefficient, is the actual duration of the i-th stage, The total estimated duration of the project, is the floating time of the i-th stage, Q is the milestone achievement rate, is the progress deviation of the i-th stage. When the i-th stage project is delayed, is the lag time. When the i-th stage project is advanced, is the negative number of the advance time, .
[0009] As a further description of the technical solution of the present invention, the working process of step S3 also includes: Obtain professional skills risk factors, job stability risk factors, team stability risk factors, and personnel performance risk factors; The professional skills risk factor is the matching degree between the professional skills certificates of team members and project requirements; the work stability risk factor is the support degree of the work experience of team members for the project; the team stability risk factor is the turnover of team members; and the personnel performance risk factor is the work performance of team members. Construct a mathematical model of project personnel risk coefficient, the expression is: ； Where, is the project personnel risk factor, is the professional skill risk factor, is the job stability risk factor, Team stability risk factors and is the personnel performance risk factor, 、 、 and is the weight coefficient.
[0010] As a further description of the technical solution of the present invention, the mathematical model expression of the professional skill risk factor is: , where O is the matching score of professional skills certificate, and *100%, is the number of professional skill books related to the project, The total number of professional skill books held by team members. When the total number of professional skill books held by team members is 0, Recorded as 0, The reference value of the professional skills certificate matching score set by the system; The mathematical model expression of the work stability risk factor is: ,in, The average salary and experience of team members, The average work experience reference value set for the system; The mathematical model expression of the team stability risk factor is: , where L is the staff turnover rate, and *100%, is the number of employees lost in a certain period of time, is the total number of team members, Reference value of staff turnover rate set for the system; The mathematical model expression of the personnel performance risk factor is: ,in, Give the team members an average performance score. The average performance score reference value set by the system.
[0011] As a further description of the technical solution of the present invention, the working process of step S4 includes: The project financial risk coefficient, project schedule risk coefficient, and project personnel risk coefficient are compared with the corresponding thresholds set by the system. If any of the risk coefficients is greater than or equal to the corresponding threshold, it means that the project is at risk. If any of the risk coefficients is lower than the corresponding threshold, a mathematical model of the project's potential risk coefficient is constructed, and the expression is: ; Where, 、 and These are the thresholds for the project financial risk factor, project schedule risk factor, and project personnel risk factor set by the system. 、 and The system sets the difference reference value respectively. 、 and is the weight coefficient, is the potential risk factor of the project; Will Compared with the potential risk coefficient threshold set by the system, if If the potential risk coefficient is greater than or equal to the potential risk factor threshold set by the system, it means that the project has potential risks.
[0012] A project information data evaluation system based on big data, the system comprising: Data acquisition module, used to collect project-related parameter data; The data processing module is used to clean and preprocess the collected data, remove duplicate data and noise data, fill in missing data, and standardize the data; Data analysis module, used to analyze the processed data and extract key features related to project evaluation; The risk assessment module is used to assess the risk of a project based on the key features of the relevant data of the project to be assessed.
[0013] Beneficial effects of the present invention: 1. Improve evaluation efficiency: Through automated data collection, processing and analysis, manual intervention is greatly reduced, the efficiency of project information data evaluation is improved, and large-scale project data can be processed quickly.
[0014] 2. Improve assessment accuracy: Evaluate risks from multiple dimensions including finance, schedule, and personnel, covering key project risk points, and introduce potential risk factors to capture hidden risks, making assessment results more comprehensive and objective, and providing a more comprehensive basis for project decision-making. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The present invention will be further described below with reference to the accompanying drawings.
[0016] Figure 1 It is a partial flow chart of the project information data evaluation method based on big data provided by the present invention. DETAILED DESCRIPTION
[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.
[0018] See also Figure 1 As shown, the present invention is a project information data evaluation method based on big data, the method comprising the following steps: Step S1, collecting project-related parameter data; Step S2: Clean and preprocess the collected data, remove duplicate data and noise data, fill in missing data, and standardize the data; Step S3: Analyze the processed data and extract key features related to project evaluation; Step S4: Evaluate the risk of the project based on the key features of the relevant data of the project to be evaluated.
[0019] Through the above technical solution, the present invention obtains financial data, progress data and personnel data in real time through the enterprise's internal data interface, uses a deduplication algorithm to eliminate duplicate data, applies a filtering algorithm to remove noise data, uses a mean or interpolation method to fill in missing data, and standardizes the data to ensure consistency. Then, the budget execution rate, cost deviation and unallocated cost are obtained and substituted into the financial risk model to calculate the project's financial risk. Then, the milestone achievement rate, progress deviation and floating time are obtained, substituted into the progress risk model to calculate the project's progress risk. Then, the professional skills matching degree, average work experience, staff turnover rate and average performance are obtained, substituted into the personnel risk model to automatically select the project's personnel risk. If the financial, progress or personnel risk coefficient exceeds the threshold, a high-risk alarm is triggered. If all risk coefficients are lower than the threshold, the potential risk coefficient is calculated. If the potential risk coefficient exceeds the threshold, a potential risk is prompted. Risks are assessed from multiple dimensions of finance, progress and personnel, covering key risk points of the project, and potential risk coefficients are introduced to capture hidden risks.
[0020] As a further description of the technical solution of the present invention, the specific process of step S1 includes: For data collection, a data interface is established with the company's internal information system to obtain project parameter data in real time; The parameter data of the project includes: basic information, financial data, progress data and personnel data.
[0021] As a further description of the technical solution of the present invention, the working process of step S3 includes: Obtain project financial data information, including budget execution rate (BR), cost variance (CV), and unallocated costs; The budget execution rate is the ratio of actual expenditure to budget, and the cost deviation is the difference between budgeted cost and actual cost; Construct a mathematical model of the project financial risk coefficient, the expression is: ; Where, is the project financial risk coefficient, is the cost deviation, is the budget execution rate, is the unallocated cost, is the actual cost, For budget, 、 and are weight coefficients, and > > .
[0022] It should be noted that , ; Through the above technical solution, this embodiment provides a method for obtaining project financial risk. First, the cost deviation, budget execution rate and unallocated cost ratio are obtained. Then, the formula Calculate the project financial risk coefficient, where: It is the ratio of the absolute value of cost deviation to the budget, reflecting the severity of overspending / savings. The degree to which the budget execution rate deviates from 1 (BR>1 indicates overspending, BR<1 indicates savings), reflecting the stability of budget execution. It is the proportion of unallocated funds to total costs, reflecting the risk of capital planning.
[0023] As a further description of the technical solution of the present invention, the working process of step S3 further includes: Divide the project schedule into n phases and obtain the time data information of n phases, including: duration, milestone achievement rate, schedule deviation and float time The duration is the total duration of each phase, the milestone achievement rate is the percentage of phase data completed as planned, the progress deviation is the difference between the planned progress and the actual progress of each phase, and the float time is the maximum time that a task can be delayed without affecting the total duration; Construct a mathematical model of project schedule risk coefficient, the expression is: ; Where S is the project schedule risk coefficient, is the actual duration of the i-th stage, The total estimated duration of the project, is the floating time of the i-th stage, Q is the milestone achievement rate, is the progress deviation of the i-th stage. When the i-th stage project is delayed, is the lag time. When the i-th stage project is advanced, is the negative number of the advance time, .
[0024] Through the above technical solution, this embodiment provides a method for obtaining project schedule risk, dividing the project into stages, calculating the milestone achievement rate, schedule deviation and float time of each stage, and substituting them into the schedule risk model. Calculate project schedule risk, where The project schedule deviation reflects the severity of the overall delay. Provide buffer time for the project, reflecting the sustainability of the emergency buffer.
[0025] As a further description of the technical solution of the present invention, the working process of step S3 also includes: Obtain professional skills risk factors, job stability risk factors, team stability risk factors, and personnel performance risk factors; The professional skills risk factor is the matching degree between the professional skills certificates of team members and project requirements; the work stability risk factor is the support degree of the work experience of team members for the project; the team stability risk factor is the turnover of team members; and the personnel performance risk factor is the work performance of team members. Construct a mathematical model of project personnel risk coefficient, the expression is: ； Where, is the project personnel risk factor, is the professional skill risk factor, is the job stability risk factor, Team stability risk factors and is the personnel performance risk factor, 、 、 and is the weight coefficient.
[0026] As a further description of the technical solution of the present invention, the mathematical model expression of the professional skill risk factor is: , where O is the matching score of professional skills certificate, and *100%, is the number of professional skill books related to the project, The total number of professional skill books held by team members. When the total number of professional skill books held by team members is 0, Recorded as 0, The reference value of the professional skills certificate matching score set by the system; The mathematical model expression of the work stability risk factor is: ,in, The average salary and experience of team members, The average work experience reference value set for the system; The mathematical model expression of the team stability risk factor is: , where L is the staff turnover rate, and *100%, is the number of employees lost in a certain period of time, is the total number of team members, Reference value of staff turnover rate set for the system; The mathematical model expression of the personnel performance risk factor is: ,in, Give the team members an average performance score. The average performance score reference value set by the system.
[0027] Through the above technical solution, this embodiment provides a method for obtaining project personnel risks, which comprehensively considers personnel's professional skills, work experience, team stability, and performance. The professional skills risk factor reflects whether the personnel's professional skills meet the project requirements. The lower the match between the professional skills certificate and the project requirements, the higher the professional skills risk factor. The work experience risk factor reflects the degree of support of the personnel experience for the project. The less average work experience, the higher the work experience risk factor, and the risk of insufficient ability to handle complex problems may be faced. The team stability risk factor measures the mobility of team personnel. The higher the staff turnover rate, the higher the team stability risk factor, the worse the team stability, and the greater the risk. The personnel performance risk factor reflects the personnel's work performance. The lower the average performance score, the greater the personnel performance risk factor, and the greater the risk of affecting the project progress and quality.
[0028] As a further description of the technical solution of the present invention, the working process of step S4 includes: The project financial risk coefficient, project schedule risk coefficient, and project personnel risk coefficient are compared with the corresponding thresholds set by the system. If any of the risk coefficients is greater than or equal to the corresponding threshold, it means that the project is at risk. If any of the risk coefficients is lower than the corresponding threshold, a mathematical model of the project's potential risk coefficient is constructed, and the expression is: ; Where, 、 and These are the thresholds for the project financial risk factor, project schedule risk factor, and project personnel risk factor set by the system. 、 and The system sets the difference reference value respectively. 、 and is the weight coefficient, is the potential risk factor of the project; Will Compared with the potential risk coefficient threshold set by the system, if If the potential risk coefficient is greater than or equal to the potential risk factor threshold set by the system, it means that the project has potential risks.
[0029] Through the above technical solution, this embodiment first evaluates the project risk. If the financial, progress or personnel risk coefficient exceeds the threshold, a high risk alarm is triggered. If the financial, progress or personnel risk coefficient does not exceed the threshold, the formula Calculate the potential risk of the project. If the threshold is exceeded, a potential risk is indicated.
[0030] A project information data evaluation system based on big data, the system comprising: Data acquisition module, used to collect project-related parameter data; The data processing module is used to clean and preprocess the collected data, remove duplicate data and noise data, fill in missing data, and standardize the data; Data analysis module, used to analyze the processed data and extract key features related to project evaluation; The risk assessment module is used to assess the risk of a project based on the key features of the relevant data of the project to be assessed.
[0031] It should be noted that all weight coefficients in the present invention are empirical values, and the weight coefficients can be modified in combination with the characteristics of the type of data to be evaluated.
[0032] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.
Claims
1. A project information data evaluation method based on big data, characterized in that: The method comprises the following steps: Step S1, collecting project-related parameter data; Step S2: Clean and preprocess the collected data, remove duplicate data and noise data, fill in missing data, and standardize the data; Step S3: Analyze the processed data and extract key features related to project evaluation; Step S4: Evaluate the risk of the project based on the key features of the relevant data of the project to be evaluated.
2. The project information data evaluation method based on big data according to claim 1 is characterized in that: The specific process of step S1 includes: For data collection, a data interface is established with the company's internal information system to obtain project parameter data in real time; The parameter data of the project includes: basic information, financial data, progress data and personnel data.
3. The project information data evaluation method based on big data according to claim 2 is characterized in that: The working process of step S3 includes: Obtain project financial data information, including budget execution rate (BR), cost variance (CV), and unallocated costs; The budget execution rate is the ratio of actual expenditure to budget, and the cost deviation is the difference between budgeted cost and actual cost; Construct a mathematical model of the project financial risk coefficient, the expression is: ; Where, is the project financial risk coefficient, is the cost deviation, is the budget execution rate, is the unallocated cost, is the actual cost, For budget, 、 and are weight coefficients, and > > .
4. The project information data evaluation method based on big data according to claim 2 is characterized in that: The working process of step S3 further includes: Divide the project schedule into n phases and obtain the time data information of n phases, including: duration, milestone achievement rate, schedule deviation and float time The duration is the total duration of each phase, the milestone achievement rate is the percentage of phase data completed as planned, the progress deviation is the difference between the planned progress and the actual progress of each phase, and the float time is the maximum time that a task can be delayed without affecting the total duration; Construct a mathematical model of project schedule risk coefficient, the expression is: ; Where S is the project schedule risk coefficient, is the actual duration of the i-th stage, The total estimated duration of the project, is the floating time of the i-th stage, Q is the milestone achievement rate, is the progress deviation of the i-th stage. When the i-th stage project is delayed, is the lag time. When the i-th stage project is advanced, is the negative number of the advance time, .
5. The project information data evaluation method based on big data according to claim 2 is characterized in that: The working process of step S3 also includes: Obtain professional skills risk factors, job stability risk factors, team stability risk factors, and personnel performance risk factors; The professional skills risk factor is the matching degree between the professional skills certificates of team members and project requirements; the work stability risk factor is the support degree of the work experience of team members for the project; the team stability risk factor is the turnover of team members; and the personnel performance risk factor is the work performance of team members. Construct a mathematical model of project personnel risk coefficient, the expression is: ； Where, is the project personnel risk factor, is the professional skill risk factor, is the job stability risk factor, Team stability risk factors and is the personnel performance risk factor, 、 、 and is the weight coefficient.
6. The project information data evaluation method based on big data according to claim 5 is characterized in that: The mathematical model expression of the professional skill risk factor is: , where O is the matching score of professional skills certificate, and *100%, is the number of professional skill books related to the project, The total number of professional skill books held by team members. When the total number of professional skill books held by team members is 0, Recorded as 0, The reference value of the professional skills certificate matching score set by the system; The mathematical model expression of the work stability risk factor is: ,in, The average salary and experience of team members, The average work experience reference value set for the system; The mathematical model expression of the team stability risk factor is: , where L is the staff turnover rate, and *100%, is the number of employees lost in a certain period of time, is the total number of team members, Reference value of staff turnover rate set for the system; The mathematical model expression of the personnel performance risk factor is: ,in, Give the team members an average performance score. The average performance score reference value set by the system.
7. The project information data evaluation method based on big data according to claim 2 is characterized in that: The working process of step S4 includes: The project financial risk coefficient, project schedule risk coefficient, and project personnel risk coefficient are compared with the corresponding thresholds set by the system. If any of the risk coefficients is greater than or equal to the corresponding threshold, it means that the project is at risk. If any of the risk coefficients is lower than the corresponding threshold, a mathematical model of the project's potential risk coefficient is constructed, and the expression is: ； Where, 、 and These are the thresholds for the project financial risk factor, project schedule risk factor, and project personnel risk factor set by the system. 、 and The system sets the difference reference value respectively. 、 and is the weight coefficient, is the potential risk factor of the project; Will Compared with the potential risk coefficient threshold set by the system, if If the potential risk coefficient is greater than or equal to the potential risk factor threshold set by the system, it means that the project has potential risks.
8. A project information data evaluation system based on big data, the system being used to implement the project information data evaluation method based on big data according to any one of claims 1 to 7, characterized in that: The system comprises: Data acquisition module, used to collect project-related parameter data; The data processing module is used to clean and preprocess the collected data, remove duplicate data and noise data, fill in missing data, and standardize the data; Data analysis module, used to analyze the processed data and extract key features related to project evaluation; The risk assessment module is used to assess the risk of a project based on the key features of the relevant data of the project to be assessed.