Multi-dimensional project health assessment method and device, electronic equipment and storage medium

Abstract

The invention discloses a multi-dimensional project health assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining project associated data corresponding to a target project based on a project management tool, and determining cooperation feature data corresponding to the target project; constructing a structured data set corresponding to the target project according to the project associated data and the collaborative feature data; and determining the project health degree of the target project based on the structured data set and a preset evaluation dimension, and performing project management on the target project according to the project health degree. Based on the technical scheme, the association data and the cooperation data corresponding to the project are acquired, and the health evaluation is performed on the project according to the acquired data, so that a user can perform project management according to the health degree of the project, objective evaluation on the health of the project is realized, and the management efficiency of the project is improved.

Description

Technical Field

[0001] This invention relates to the field of project management technology, and in particular to a multi-dimensional project health assessment method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of computer technology, project development is now achieved through remote conferencing and collaboration systems.

[0003] However, the existing solution relies on manual reporting and explanation of project progress, which makes it difficult for managers to understand project-related information in a timely manner, hindering the control of project progress. Furthermore, the lack of an objective evaluation method for the project in the existing technical solution also prevents managers from accessing objective data. Summary of the Invention

[0004] This invention provides a multi-dimensional project health assessment method, device, electronic device, and storage medium. By acquiring relevant and collaborative data corresponding to the project, and evaluating the project's health based on the acquired data, users can manage projects according to their health status, achieving an objective evaluation of project health and improving project management efficiency.

[0005] According to one aspect of the present invention, a multi-dimensional project health assessment method is provided, the method comprising:

[0006] Based on project management tools, obtain project-related data corresponding to the target project, and determine the collaborative characteristic data corresponding to the target project;

[0007] Construct a structured dataset corresponding to the target project based on the project association data and the collaboration feature data;

[0008] The project health of the target project is determined based on the structured dataset and preset evaluation dimensions. Project management is then carried out on the target project based on the project health. The preset evaluation dimensions include schedule deviation rate, cost performance index, quality defect rate, risk exposure value, resource utilization rate, and team collaboration entropy value.

[0009] According to another aspect of the present invention, a multi-dimensional project health assessment device is provided, the device comprising:

[0010] The data extraction module is used to obtain project-related data corresponding to the target project based on project management tools, and to determine the collaborative feature data corresponding to the target project.

[0011] The data processing module is used to construct a structured dataset corresponding to the target project based on the project-related data and the collaboration feature data;

[0012] The health assessment module is used to determine the project health of the target project based on the structured dataset and preset assessment dimensions, and to manage the target project according to the project health. The preset assessment dimensions include schedule deviation rate, cost performance index, quality defect rate, risk exposure value, resource utilization rate, and team collaboration entropy value.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the multi-dimensional project health assessment method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the multi-dimensional project health assessment method described in any embodiment of the present invention.

[0018] The technical solution of this invention involves acquiring project-related data corresponding to a target project using a project management tool, and determining collaborative feature data corresponding to the target project; constructing a structured dataset corresponding to the target project based on the project-related data and the collaborative feature data; determining the project health level of the target project based on the structured dataset and preset evaluation dimensions; and managing the target project based on the project health level. Based on this technical solution, by acquiring project-related and collaborative data, and evaluating project health based on the acquired data, users can manage projects according to their health level, achieving an objective evaluation of project health and improving project management efficiency.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a multi-dimensional project health assessment method provided in an embodiment of the present invention;

[0022] Figure 2 This is a flowchart of a multi-dimensional project health assessment method provided in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a multi-dimensional project health assessment device provided in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] 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.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Figure 1This is a flowchart illustrating a multi-dimensional project health assessment method provided in an embodiment of the present invention. This embodiment is applicable to situations where the health status of a project is evaluated based on associated and collaborative data related to the project. The method can be executed by a multi-dimensional project health assessment device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method specifically includes the following steps:

[0028] S110. Obtain project-related data corresponding to the target project based on project management tools, and determine the collaborative feature data corresponding to the target project.

[0029] Project management tools can be software tools used to coordinate and manage projects. The target project can be understood as a project requiring a health assessment; it can be a physical construction project, such as a power grid development project, or a software development project. Project-related data can be data directly related to the project, such as project budget data and resource usage. Collaboration characteristic data can be the characteristics of collaborative data generated during communication among members of the target project's project team.

[0030] Specifically, project management tools are used to extract relevant data closely related to the target project from the project database, including project progress, resource allocation, and task dependencies. Collaborative characteristics corresponding to the target project are then identified by recognizing dimensions such as communication frequency, task handover patterns, and problem-solving coordination among project members. For example, analyzing member activity in task discussion forums assesses the level of team collaboration; and evaluating the smoothness of collaboration is based on task completion time differences.

[0031] Based on the above technical solution, determining the collaborative feature data corresponding to the target project includes: acquiring an unstructured document corresponding to the target project; and determining the collaborative feature data corresponding to the target project based on the unstructured document.

[0032] The unstructured documents include at least one of the following: meeting minutes, emails, and task comments.

[0033] Specifically, collect unstructured documents related to the target project, including at least one type: meeting minutes, emails, or task comments. Obtain these unstructured documents using project management tools, and then process them using natural language processing (NLP) techniques. Utilize text preprocessing techniques such as word segmentation, part-of-speech tagging, and named entity recognition to transform the documents into structured information that computers can understand. Based on a pre-defined collaborative feature keyword library and semantic analysis model, extract collaboration-related feature data from the documents, such as discussions about task allocation in meeting minutes, collaborative problem-solving approaches in emails, and communication and feedback on work progress in task comments.

[0034] Based on the above technical solution, the step of determining the collaborative feature data corresponding to the target project from the unstructured document includes: extracting document feature data from the unstructured document based on natural language processing; and determining the collaborative feature data corresponding to the target project based on the document feature data.

[0035] The document feature data includes collaboration frequency, keyword repetition rate, and response time. Collaboration feature data includes collaboration density, information redundancy, and response latency index. Collaboration density is the ratio of effective communication counts to total communication counts; information redundancy is the ratio of the proportion of repeated keywords to the total number of keywords; the response latency index is the ratio of average response time to standard response time. Collaboration density = effective communication counts / total communication counts. Information redundancy = repeated keyword proportions / total number of keywords. Response latency index = average response time / standard response time.

[0036] Specifically, natural language processing (NLP) techniques are used to deeply analyze unstructured documents to extract document feature data. For collaboration frequency, text analysis is used to statistically analyze the frequency of expressions related to project collaboration, such as "jointly completed" and "collaborative processing," to measure the frequency of collaboration. For keyword repetition rate, keywords closely related to project collaboration are selected, and their repetition rate in the documents is calculated. Response time is estimated from document timestamps and content associations, such as the time interval between email exchanges and task comments. Collaboration feature data is determined based on the extracted document feature data. Then, after standardizing the collaboration frequency, collaboration density is calculated by combining the number of documents and the scale of participants, visually representing the closeness of project collaboration. Information redundancy is assessed based on keyword repetition rate; a higher repetition rate may indicate greater information redundancy. The response latency index is calculated comprehensively from response time to measure the timeliness of responses in project collaboration. For example, collaboration features are obtained from unstructured project data through text feature extraction technology. The extracted features include at least data related to collaboration frequency, information interaction completeness, and response timeliness. Based on information entropy theory, a quantitative index of collaboration status (team collaboration entropy value) is constructed. By calculating collaboration density (the ratio of effective interactions to total interactions), information redundancy (the proportion of duplicate information), and response delay index (the ratio of actual response time to standard response time), the team collaboration entropy value is obtained by substituting these values ​​into the entropy value calculation model. The higher the entropy value, the higher the degree of disorder in team collaboration.

[0037] S120. Construct a structured dataset corresponding to the target project based on the project association data and the collaboration feature data.

[0038] In this context, a structured dataset can be a collection of data obtained by storing the collected feature data in a structured manner.

[0039] Specifically, project-related data undergoes preprocessing to standardize data formats and remove duplicate, erroneous, and invalid information. Collaboration characteristic data, such as collaboration density, information redundancy, and response latency index, are numerically standardized to ensure uniformity of measurement. Then, based on a pre-designed structured dataset framework—which includes data fields such as basic project information, progress data, resource allocation, collaboration frequency, and keyword repetition rate—the preprocessed project-related data and collaboration characteristic data are populated into the structured dataset according to the designed framework. Furthermore, a data indexing mechanism can be established to improve data retrieval efficiency, and the constructed structured dataset is validated to check data integrity and accuracy, ensuring it accurately and comprehensively reflects the entire target project and provides reliable support for subsequent project analysis and decision-making.

[0040] S130. Determine the project health of the target project based on the structured dataset and preset evaluation dimensions, and manage the target project according to the project health.

[0041] The preset evaluation dimensions include schedule deviation rate, cost performance index, quality defect rate, risk exposure value, resource utilization rate, and team collaboration entropy value. Project health is an evaluation score used to quantify the health level of the target project.

[0042] Specifically, data related to each evaluation dimension is extracted from the structured dataset. For the schedule deviation rate, it is calculated by comparing actual and planned schedule data; the cost performance index is determined based on actual and budgeted cost analysis; the quality defect rate is obtained by statistically analyzing the number and proportion of defects in project deliverables; the risk exposure value is assessed by comprehensively considering the probability of risk occurrence and the degree of impact in the project risk database; resource utilization rate is calculated based on resource input and actual output; and the team collaboration entropy value is calculated using entropy models based on team collaboration-related data, such as communication frequency and task coordination. Then, based on the weights of each evaluation dimension, a weighted summation method is used to calculate the overall project health score. Based on the score, project health can be divided into different levels, such as healthy, sub-healthy, and unhealthy. Targeted management strategies are then developed based on the project health level: healthy projects are monitored, sub-healthy projects have their processes optimized, and unhealthy projects are intervened and adjusted promptly.

[0043] For example, schedule-related indicators quantify the degree of schedule deviation by comparing the budgeted cost of completed work with the budgeted cost of planned work; cost-related indicators quantify cost efficiency by comparing the budgeted cost of completed work with the actual cost invested; risk-related indicators calculate the quantitative impact value (risk exposure value) of a risk by considering its probability of occurrence, scope of impact, and response costs; resource-related indicators quantify resource utilization efficiency by comparing the actual amount of resources used with the total amount of available resources. Schedule Performance Index (SPI): SPI = Budgeted Cost of Completed Work (BCWP) / Budgeted Cost of Planned Work (BCWS). Used to measure the deviation between project schedule and plan; SPI < 1 indicates schedule lag, > 1 indicates ahead of schedule. Cost Performance Index (CPI): CPI = Budgeted Cost of Completed Work (BCWP) / Actual Cost (ACWP). Used to reflect cost efficiency; CPI < 1 indicates cost overrun, > 1 indicates cost savings. Quality Defect Rate = Number of Defects / Total Number of Deliverables × 100%, used to directly reflect the quality of project output and guide quality improvement measures. Risk Exposure Value: A quantified value that considers the probability of risk occurrence, its impact, and the cost of response. It is used to prioritize risks and guide resource allocation (e.g., high-exposure risks require immediate response). Resource Utilization Rate: Actual resource usage / Total available resources × 100%. Used to optimize resource allocation and avoid idleness or overload. Team Collaboration Entropy Value: Based on information entropy theory, it quantifies the degree of disorder in team collaboration. A higher entropy value indicates lower communication efficiency and higher collaboration disorder. Entropy Formula: S = −k∑ipilnpi, where k is a constant and pi is the weight of each collaboration feature.

[0044] Based on the above technical solution, determining the project health of the target project based on the structured dataset and preset evaluation dimensions includes: determining the project cycle stage corresponding to the target project, and determining the evaluation weight corresponding to each preset evaluation dimension based on the project cycle stage; determining the evaluation score for each preset evaluation dimension based on the structured dataset; and summing the evaluation scores corresponding to each preset evaluation dimension based on the evaluation weight to determine the project health.

[0045] The project cycle phase can be the current stage of the target project, such as the initiation phase, planning phase, execution phase, monitoring phase, or closing phase.

[0046] Specifically, based on the project plan and actual progress, determine the project's current stage in the project cycle, such as the initiation phase, planning phase, execution phase, monitoring phase, or closing phase. It's important to note that different stages have different influencing factors on project success. Therefore, based on the project cycle stage, assign appropriate evaluation weights to pre-defined evaluation dimensions such as schedule deviation rate, cost-performance index, quality defect rate, risk exposure value, resource utilization rate, and team collaboration entropy value. For example, during the execution phase, more attention is paid to schedule and cost, so the weights of these two dimensions can be appropriately increased. Then, extract relevant data for each evaluation dimension from the structured dataset and determine the evaluation score for each dimension according to pre-defined standards. For example, for the schedule deviation rate, a higher score is given if the actual progress is ahead of schedule; if the cost-performance index is below 1, points are deducted based on the degree of deviation. Using a weighted summation method, multiply the evaluation score of each pre-defined evaluation dimension by its corresponding weight and sum them to obtain the overall project health score.

[0047] For example, a phase-adaptive weight allocation model is adopted. Based on the current lifecycle stage of the project (initiation, execution, and closure), the weight ratio of each evaluation indicator is automatically adjusted to adapt the weight configuration to the core project management needs of the corresponding stage. For instance, the model achieves dynamic adaptation through a weight adjustment function, which can output the weight ratio of each indicator according to the characteristics of the project stage. This ensures that the weight of resource and risk-related indicators is higher in the initiation stage than in other stages, the weight of schedule and cost-related indicators is higher in the execution stage than in other stages, and the weight of quality and delivery-related indicators is higher in the closure stage than in other stages. During the weight adjustment process, a phase sensitivity coefficient and a time decay coefficient are introduced. The coefficient values ​​can be fitted based on historical project data or calibrated in combination with project management experience to optimize the accuracy and adaptability of the weight adjustment.

[0048] Function formula: Wi(t) = Wbase,i*(1+αe−βt); where Wi(t) is the dynamic weight of indicator i at time t; Wbase,i is the base weight of indicator i; α is the stage sensitivity coefficient, controlling the magnitude of weight adjustment; β is the time decay coefficient, controlling the speed of weight adjustment; t is the current time of the project (usually normalized to [0,1], 0 for the start-up phase and 1 for the end-up phase). Initial reinforcement: When t approaches 0 (start-up phase), e−βt≈1, weight Wi(t)≈Wbase,i*(1+α), achieving short-term reinforcement of key indicators; Asymptotic convergence: When t increases (execution phase / end-up phase), e−βt→0, weight Wi(t)→Wbase,i, restoring the base weight; By adjusting α and β, the weight change curves for different stages can be customized (such as steep reinforcement or gentle transition). For example, in the initiation phase (t≈0): increase the weight of resource allocation (such as manpower and budget) and risk management; if Wbase,resources=0.2, α=0.3, then Wresources(0)=0.2*1.3=0.26; in the execution phase (t≈0.5): optimize the weight of schedule control (SPI) and cost performance (CPI); if Wbase,schedule=0.3, α=0.2, then Wschedule(0.5)=0.3*(1+0.2e−0.5β)≈0.36; in the closing phase (t≈1): increase the weight of quality acceptance and document integrity. Based on this, the initial values ​​of the stage sensitivity coefficient and time decay coefficient can be fitted by regression analysis based on the evaluation data and project results of similar historical projects; the initial coefficients can be empirically calibrated by combining factors such as project complexity, industry characteristics, and risk level; and the coefficient values ​​can be dynamically optimized by machine learning algorithms (such as reinforcement learning and supervised learning) to maximize the correlation between the weight allocation results and the project success probability.

[0049] Based on the above technical solution, the step of determining the evaluation score for each preset evaluation dimension based on the structured dataset includes: determining the quantized value for each preset evaluation dimension according to the structured dataset; and converting the quantized value of each preset evaluation dimension to a standard scoring range based on a nonlinear mapping function to determine the evaluation score for each preset evaluation dimension.

[0050] The nonlinear mapping function includes at least one of the following: an S-shaped curve, an exponential curve, or a piecewise function.

[0051] Specifically, raw data related to each preset evaluation dimension is extracted from the structured dataset. For example, the schedule deviation rate corresponds to the difference between actual and planned schedules, and the cost performance index is associated with actual and budgeted costs. Quantitative values ​​for each dimension are determined, and a non-linear mapping function is used to transform the quantified values ​​to a standard scoring range (e.g., 0-100 points) based on the characteristics of different evaluation dimensions. For dimensions like the schedule deviation rate, which has upper and lower limits and a gradually slowing trend, an S-curve mapping is used to make the score change stabilize when the quantified value approaches the critical value. For dimensions such as the cost performance index, which aim to highlight the impact of extreme values, an exponential curve is used to make the scores of quantified values ​​exceeding or falling below a specific value change significantly. For dimensions such as the quality defect rate, which require phased focus, a piecewise function is used to set different scoring rules based on different defect rate ranges.

[0052] It should be noted that the nonlinear mapping function uses at least one of the following: an S-curve, an exponential curve, or a piecewise function. By setting a key threshold (midpoint threshold) and a sensitivity coefficient (steepness coefficient), the rate of change of the quantitative value of the indicator near the key risk threshold is higher than the rate of change of the score in the non-risk range. Different evaluation indicators can be configured with independent function parameters, and the parameters can be dynamically adjusted with the project life cycle stage to adapt to the risk identification needs of different stages. The parameter configuration of the nonlinear mapping function includes: setting the key threshold (midpoint threshold): referring to the critical values ​​of risk occurrence in historical projects, industry standards, or project management specifications, determining the risk sensitivity critical point of each indicator; setting the sensitivity coefficient (steepness coefficient): setting a higher sensitivity coefficient for indicators with high risk impact, making the score change in the risk range more significant; dynamic parameter adjustment: lowering the key threshold during the project initiation stage to strengthen early risk identification, and adjusting the parameters according to risk management needs during the project execution and closing stages. For example, a dynamic scoring model is constructed by introducing an S-curve (Sigmoid function), where Scorei = 100 / (1 + e^(-k(xi-μ))); where xi is the original value of index i (such as defect rate, schedule deviation rate); μ is the midpoint of the curve (the threshold with the highest scoring sensitivity); k is the steepness coefficient of the curve (controlling the rate of change of the score); and Scorei is the mapped score (the range is usually normalized to [0,1] or [0,100]).

[0053] The technical solution of this invention involves acquiring project-related data corresponding to a target project using a project management tool, and determining collaborative feature data corresponding to the target project; constructing a structured dataset corresponding to the target project based on the project-related data and the collaborative feature data; determining the project health level of the target project based on the structured dataset and preset evaluation dimensions; and managing the target project based on the project health level. Based on this technical solution, by acquiring project-related and collaborative data, and evaluating project health based on the acquired data, users can manage projects according to their health level, achieving an objective evaluation of project health and improving project management efficiency.

[0054] In one possible implementation of the present invention Figure 2 A flowchart of a multi-dimensional project health assessment method provided in an embodiment of the present invention is shown below. Figure 2 As shown, the project management of the target project based on the project health status in this embodiment further includes the following steps:

[0055] S210. Determine the project risk range corresponding to the target project based on the project health status.

[0056] The project risk range can be a pre-set data range corresponding to the project's health status. Project risks include high-risk, medium-risk, and low-risk ranges.

[0057] Specifically, a comprehensive analysis of project health is conducted to determine the numerical range and distribution characteristics. Based on industry experience and historical project data, project health thresholds are set, and projects are categorized into high, medium, and low risk zones according to these thresholds. For example, a project health score of 0-40 is defined as a high-risk zone, indicating serious problems in schedule, cost, quality, and other aspects, potentially facing failure. A score of 41-70 is a medium-risk zone, meaning the project has some issues, but with timely adjustments and optimization, there is still a good chance of achieving the goals. A score of 71-100 is a low-risk zone, indicating the project is generally operating well, with most indicators meeting expectations. After determining the risk zones, corresponding response strategies are developed for different risk levels. High-risk projects require immediate activation of emergency plans and a comprehensive investigation of the root causes of problems; medium-risk projects require enhanced monitoring and dynamic adjustments; and low-risk projects require continuous monitoring to ensure stable project progress and successful completion.

[0058] S220. Generate a risk warning corresponding to the target project based on the project risk range, and send the risk warning to the target user.

[0059] Risk warning is a notification message used to alert the target project to the risks involved. This notification message may include the risk items corresponding to the target project and the corresponding management strategies.

[0060] Specifically, a risk warning rule engine is constructed. Based on pre-set thresholds for high, medium, and low risk ranges, the project health assessment results are compared. When the project health falls into the high-risk range, a Level 1 red warning is triggered, indicating that the project is facing a serious crisis that may affect project delivery or cause significant losses. If it falls into the medium-risk range, a Level 2 yellow warning is triggered, indicating that there are potential problems in the project that require timely attention and handling. If it falls into the low-risk range, no warning is triggered, but continuous monitoring continues. Through the message push module of the project management tool, risk warning information is sent to target users in various forms such as email, SMS, and pop-ups. Target users can be managers associated with the target project.

[0061] Based on the above technical solution, the step of managing the target project according to the project health status includes:

[0062] Based on the project's health status, a visual evaluation interface corresponding to the target project is generated, enabling the target user to manage the project through the visual evaluation interface.

[0063] The visual evaluation interface can be a visualized webpage used to display evaluation information of the target project. The visual evaluation interface includes at least two interface levels.

[0064] Specifically, a layered architecture is adopted for the visual evaluation interface, constructing at least two interface layers. The first layer is the project overview layer, which uses intuitive charts, such as dashboards and bar charts, to display the overall health score and key indicators of the project, allowing users to quickly understand the overall status of the project. The second layer is the detailed analysis layer. Users can enter this layer by clicking on the corresponding chart elements in the first layer, and can view detailed data, trends, and risk range distribution for each preset evaluation dimension. The visual interface has interactive functions, allowing users to customize data filtering and adjust display dimensions.

[0065] For example, the multi-level visualization interface includes: Top-level interface: Displaying the overall health results and core dimension scores of the project using aggregated charts (such as radar charts and dashboards), and configuring early warning indicators based on health thresholds; Middle-level interface: Displaying the changing patterns of each indicator over time using trend charts (such as line charts and bar charts), marking nodes of abnormal indicator fluctuations and associating them with relevant event information; Bottom-level interface: Displaying the raw data and calculation process data of the indicators using detailed data tables, supporting historical data comparison queries and data source tracing. When the indicator score approaches the midpoint threshold (critical threshold), multi-level early warnings are triggered, with warning methods including reminder notifications and emergency emails; Based on the correlation analysis between team collaboration entropy value and schedule deviation rate and cost performance index, improvement suggestions are automatically generated, including collaboration optimization suggestions for teams with high entropy values ​​and resource adjustment suggestions for schedule or cost anomalies.

[0066] The technical solution of this invention, through the design of six-dimensional index quantification, dynamic weight adaptation, non-linear scoring and interactive visualization, and by integrating NLP, information entropy, dynamic algorithms and visualization technology, achieves intelligent and accurate project evaluation, adapts to the entire project lifecycle, meets the differentiated needs of high-level decision-making, mid-level analysis and execution level operations, reduces the risk of resource mismatch at the business level, shortens the problem location time, and improves the project success rate and decision-making efficiency.

[0067] Figure 3 This is a schematic diagram of the structure of a multi-dimensional project health assessment device provided in an embodiment of the present invention. Figure 3 As shown, the device includes: a data extraction module 310, a data processing module 320, and a health evaluation module 330.

[0068] The data extraction module 310 is used to obtain project-related data corresponding to the target project based on the project management tool, and to determine the collaborative feature data corresponding to the target project.

[0069] Data processing module 320 is used to construct a structured dataset corresponding to the target project based on the project-related data and the collaboration feature data;

[0070] The health assessment module 330 is used to determine the project health of the target project based on the structured dataset and preset assessment dimensions, and to manage the target project according to the project health. The preset assessment dimensions include schedule deviation rate, cost performance index, quality defect rate, risk exposure value, resource utilization rate and team collaboration entropy value.

[0071] Based on the above technical solution, the health evaluation module is used to determine the project cycle stage corresponding to the target project, and to determine the evaluation weight corresponding to each preset evaluation dimension based on the project cycle stage; to determine the evaluation score for each preset evaluation dimension based on the structured dataset; and to sum the evaluation scores corresponding to each preset evaluation dimension based on the evaluation weight to determine the project health.

[0072] Based on the above technical solution, the health evaluation module is used to determine the quantitative value of each preset evaluation dimension according to the structured dataset; and to convert the quantitative value of each preset evaluation dimension to a standard scoring range based on a nonlinear mapping function to determine the evaluation score of each preset evaluation dimension, wherein the nonlinear mapping function includes at least one of an S-curve, an exponential curve, or a piecewise function.

[0073] Based on the above technical solution, the data extraction module is used to obtain unstructured documents corresponding to the target project, wherein the unstructured documents include at least one type of meeting minutes, emails, and task comments; and to determine collaborative feature data corresponding to the target project based on the unstructured documents.

[0074] Based on the above technical solution, the data extraction module is used to extract document feature data from the unstructured document based on natural language processing, wherein the document feature data includes collaboration frequency, keyword repetition rate, and response time; and to determine collaboration feature data corresponding to the target project based on the document feature data, wherein the collaboration feature data includes collaboration density, information redundancy, and response latency index.

[0075] Based on the above technical solution, the health assessment module is used to determine the project risk range corresponding to the target project based on the project health level, wherein the project risk includes a high-risk range, a medium-risk range, and a low-risk range; generate a risk warning corresponding to the target project based on the project risk range, and send the risk warning to the target user.

[0076] Based on the above technical solution, the health evaluation module is used to generate a visual evaluation interface corresponding to the target project based on the project's health status, so that the target user can manage the project through the visual evaluation interface. The visual evaluation interface includes at least two interface levels.

[0077] The technical solution of this invention involves acquiring project-related data corresponding to a target project using a project management tool, and determining collaborative feature data corresponding to the target project; constructing a structured dataset corresponding to the target project based on the project-related data and the collaborative feature data; determining the project health level of the target project based on the structured dataset and preset evaluation dimensions; and managing the target project based on the project health level. Based on this technical solution, by acquiring project-related and collaborative data, and evaluating project health based on the acquired data, users can manage projects according to their health level, achieving an objective evaluation of project health and improving project management efficiency.

[0078] The multi-dimensional project health assessment device provided in the embodiments of the present invention can execute the multi-dimensional project health assessment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0079] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0080] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0081] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0082] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as multi-dimensional project health assessment methods.

[0083] In some embodiments, the multidimensional project health assessment method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the multidimensional project health assessment method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the multidimensional project health assessment method by any other suitable means (e.g., by means of firmware).

[0084] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0086] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0087] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0088] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0089] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0090] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0091] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A multi-dimensional project health assessment method, characterized in that, include: Based on project management tools, obtain project-related data corresponding to the target project, and determine the collaborative characteristic data corresponding to the target project; Construct a structured dataset corresponding to the target project based on the project association data and the collaboration feature data; The project health of the target project is determined based on the structured dataset and preset evaluation dimensions. Project management is then carried out on the target project based on the project health. The preset evaluation dimensions include schedule deviation rate, cost performance index, quality defect rate, risk exposure value, resource utilization rate, and team collaboration entropy value.

2. The method according to claim 1, characterized in that, The process of determining the project health of the target project based on the structured dataset and preset evaluation dimensions includes: Determine the project cycle stage corresponding to the target project, and determine the evaluation weight corresponding to each preset evaluation dimension based on the project cycle stage; The evaluation score for each preset evaluation dimension is determined based on the structured dataset. The health of the project is determined by summing the evaluation scores corresponding to each preset evaluation dimension based on the evaluation weights.

3. The method according to claim 2, characterized in that, The determination of the evaluation score for each preset evaluation dimension based on the structured dataset includes: The quantitative value for each preset evaluation dimension is determined based on the structured dataset. The quantified values ​​of each preset evaluation dimension are converted to a standard scoring range based on a nonlinear mapping function to determine the evaluation score for each preset evaluation dimension. The nonlinear mapping function includes at least one of an S-curve, an exponential curve, or a piecewise function.

4. The method according to claim 1, characterized in that, The determination of collaborative feature data corresponding to the target project includes: Obtain unstructured documents corresponding to the target project, wherein the unstructured documents include at least one of the following: meeting minutes, emails, and task comments; Based on the unstructured document, collaborative feature data corresponding to the target project is determined.

5. The method according to claim 4, characterized in that, The step of determining the collaborative feature data corresponding to the target project based on the unstructured document includes: Document feature data is extracted from the unstructured document based on natural language processing, wherein the document feature data includes collaboration frequency, keyword repetition rate, and response time; Based on the document feature data, collaborative feature data corresponding to the target project is determined, wherein the collaborative feature data includes collaboration density, information redundancy, and response latency index.

6. The method according to claim 1, characterized in that, The project management based on the project health status includes: Based on the project health status, a project risk range corresponding to the target project is determined, wherein the project risk includes a high-risk range, a medium-risk range, and a low-risk range; Based on the project's risk range, a risk warning corresponding to the target project is generated, and the risk warning is sent to the target user.

7. The method according to claim 1, characterized in that, The project management based on the project health status includes: Based on the project's health status, a visual evaluation interface corresponding to the target project is generated, so that the target user can manage the project through the visual evaluation interface. The visual evaluation interface includes at least two interface levels.

8. A multi-dimensional project health assessment device, characterized in that, include: The data extraction module is used to obtain project-related data corresponding to the target project based on project management tools, and to determine the collaborative feature data corresponding to the target project. The data processing module is used to construct a structured dataset corresponding to the target project based on the project-related data and the collaboration feature data; The health assessment module is used to determine the project health of the target project based on the structured dataset and preset assessment dimensions, and to manage the target project according to the project health. The preset assessment dimensions include schedule deviation rate, cost performance index, quality defect rate, risk exposure value, resource utilization rate, and team collaboration entropy value.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the multi-dimensional project health assessment method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the multi-dimensional project health assessment method according to any one of claims 1-7.

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