Project team intelligent dynamic optimization matching system and method based on artificial intelligence

Abstract

The invention discloses a project team intelligent dynamic optimization matching system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence, and the method specifically comprises the steps: triggering a team member matching degree evaluation process through monitoring the function adjustment, time node change and resource demand change of a project demand in real time, and setting a demand change threshold; in the evaluation process, collecting multidimensional data of team members, extracting features by adopting a principal component analysis method, constructing a causal feature matrix by utilizing a Bayesian model, and calculating the matching degree of the team members and project requirements; and according to a matching degree analysis result, the system intelligently adjusts team member configuration and task allocation, and performs fine adjustment on an adjustment result so as to improve project execution efficiency and quality.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence technology, and more specifically, provides an intelligent dynamic optimization matching system and method for a project team based on artificial intelligence. Background Art

[0002] In the existing project management process, the formation of project teams usually relies on the experience and subjective judgment of managers. This approach has many limitations. For example, it is unable to comprehensively and accurately consider factors such as team members' various skills, knowledge reserves, work styles, and personality traits. Moreover, as the project progresses, requirements may change. Traditional methods cannot quickly and effectively adjust team members dynamically to adapt to new project requirements. This may lead to problems such as inefficient team collaboration, delayed project schedules, and cost overruns.

[0003] For example, the Chinese patent application with publication number CN115062854A discloses a dynamic estimation method for enterprise innovation information matching projects, including: obtaining the innovation ratio difference by comparing the enterprise's own innovation parameter values ​​with the standard innovation parameter values ​​obtained from big data, positioning the enterprise's own capabilities in the same industry market, and realizing the quantification of the model data of the enterprise positioning, and then extracting the innovation optimization ratio for the selectable projects, determining the preliminary project direction after completing the element matching, incorporating the preliminary selected project innovation resource data into the original enterprise resource data for estimation, outputting the corresponding projects that meet the estimation requirements, and completely analyzing the introduction optimization ratio of each project through data quantification, thereby solving the problem that existing small and micro-sized technology enterprises cannot relatively accurately quantify their own positioning in the same market and have not accurately grasped the direction of introducing other innovation projects.

[0004] The above existing technologies have the following problems: quantitative analysis is used to determine the company's positioning in the market of similar industries and the optimization ratio of project introduction, but the model may not cover all factors that affect the company's innovation capabilities; it emphasizes data quantitative analysis, but may lack sufficient flexibility to cope with the rapidly changing market environment and emerging technologies; it is mainly aimed at small and micro technology companies, and its applicability and effectiveness for large enterprises or enterprises in different industries may need further verification. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes an artificial intelligence-based intelligent dynamic optimization matching system and method for project teams. It monitors the functional adjustments, time node changes and resource demand changes of project requirements in real time, and sets the demand change threshold to trigger the team member matching evaluation process; in the evaluation process, multi-dimensional data of team members are collected, and the principal component analysis method is used to extract features. The Bayesian model is used to construct a causal feature matrix, and the matching degree between team members and project requirements is calculated; according to the matching analysis results, the system intelligently adjusts the team member configuration and task allocation, and fine-tunes the adjustment results to improve the efficiency and quality of project execution.

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

[0007] AI-based intelligent dynamic optimization matching method for project teams, including:

[0008] Monitor changes in project requirements in real time and determine whether to trigger the team member matching assessment process based on preset requirement change thresholds; changes in project requirements include functional adjustments, time node changes, and resource requirement changes;

[0009] After the team member matching evaluation process is triggered, multidimensional data of the team members are collected, and based on the multidimensional data of the team members, a principal component analysis method is used to extract features from the multidimensional data of the team members, and a causal feature matrix is ​​constructed using a Bayesian model;

[0010] According to the causal characteristic matrix, the matching degree between team members and project requirements is calculated, and based on the matching analysis results, the team member configuration and task allocation are intelligently adjusted;

[0011] Fine-tune team member configuration and task allocation based on the adjusted team member configuration and task allocation.

[0012] Specifically, the specific steps of constructing the causal feature matrix using the Bayesian model include:

[0013] A1: After the team member matching assessment process is triggered, multi-dimensional data of team members is collected and pre-processed; the multi-dimensional data of team members includes work experience, skill level, educational background, personality traits, and teamwork ability;

[0014] A2: Determine the prior probability of the causal relationship, and use Bayes' theorem to calculate the updated posterior probability based on the prior probability of the causal relationship and the pre-processed multi-dimensional data of team members;

[0015] A3: Based on the updated posterior probability, a causal characteristic matrix between team members is constructed; the elements of the causal characteristic matrix represent the strength of the causal relationship between team members in different dimensions.

[0016] Specifically, the matching degree between team members and project requirements is calculated based on the causal feature matrix, and the configuration and task allocation of team members are intelligently adjusted based on the matching analysis results, including:

[0017] B1: Based on the pre-processed multi-dimensional data of team members, use machine learning algorithms to build employee capability profiles;

[0018] B2: Analyze the current project requirements information, combine the employee capability profile and the causal characteristic matrix, and use a matching algorithm to calculate the matching degree between team members and project requirements;

[0019] B3: Introducing predictive analysis technology to intelligently predict project progress and assess risks, identify inefficient configurations and risk points, and generate team optimization recommendations.

[0020] B4: Display matching results and team optimization configuration suggestions to project managers and team members in a visual form;

[0021] B5: The project manager and team members make corresponding adjustments to the team configuration based on the team optimization configuration suggestions.

[0022] Specifically, the steps of B1 include:

[0023] B1.1: Obtain multidimensional data of the team members, extract features from the multidimensional data using principal component analysis, and generate team member feature data;

[0024] B1.2: Load the pre-built random forest model and train it using team member feature data.

[0025] B1.3: Use the trained random forest model to predict the multidimensional data of all team members, obtain quantitative results for each team member in different capability dimensions, and generate employee capability profiles.

[0026] Specifically, the specific steps of B2 include:

[0027] B2.1: Interpret the project requirements document and determine team member information based on project requirements;

[0028] B2.2: Obtain the employee capability profile, which includes information on the team members' work capabilities, strengths, and weaknesses;

[0029] B2.3: Based on the team member information and employee capability profiles, use a similarity-based matching algorithm to match project requirements with employee capability profiles;

[0030] B2.4: Based on the matching calculation results, obtain the matching degree between each team member and the project requirements;

[0031] B2.5: Evaluate the matching results and adjust and optimize the matching algorithm or employee capability profile based on the evaluation results.

[0032] Specifically, the specific steps of B3 include:

[0033] B3.1: Obtain project progress history data and multi-dimensional data of team members; the project progress history data includes task completion status, resource usage, and time progress;

[0034] B3.2: Establish a project progress forecast model based on historical project progress data and multi-dimensional team member data; the project progress forecast model is obtained through the critical path method and program review and evaluation technique;

[0035] B3.3: Input the current project's real-time progress data and the corresponding multi-dimensional data of current team members into the project progress forecasting model to perform project progress forecasting and risk assessment;

[0036] B3.4: Identify inefficient configurations and risk points based on the output of the project schedule forecasting model;

[0037] B3.5: Analyze team members' work efficiency and task allocation based on forecast results and risk assessment reports;

[0038] B3.6: Based on the analysis results, generate team optimization recommendations, including adjusting task allocation, increasing resource input, and training to enhance capabilities.

[0039] Specifically, the specific steps of B3.2 include:

[0040] B3.21: Obtain the project progress historical data and the team member multi-dimensional data, and perform pre-processing;

[0041] B3.22: Define all project activities, describe and define each activity, determine the dependencies and priorities between activities, and form an activity network diagram;

[0042] B3.23: Calculate the resources and duration required for each activity based on pre-processed multidimensional data of team members;

[0043] B3.24: Estimate the duration of the activities using the three-point estimation method, calculating the weighted average duration and standard deviation for each activity;

[0044] B3.25: Based on the activity network diagram and the duration of the activities, calculate the earliest start time, latest start time, earliest finish time, and latest finish time for each task and determine the critical path;

[0045] B3.26: Add the expected values ​​of all tasks on the critical path to obtain the total expected duration of the project. Calculate the variance of the total expected duration. Also, use the normal distribution to calculate the probability of completion at different time points based on the total expected duration and standard deviation.

[0046] B3.27: Load the pre-built linear regression model, combine the results of the critical path and three-point estimation method, train the pre-built linear regression model, and generate a project schedule prediction model.

[0047] Specifically, the specific process of fine-tuning team member configuration and task allocation includes:

[0048] C1: Based on the adjusted team member configuration and task allocation, combined with the project progress forecast results, monitor team member performance in real time and track team collaboration indicators;

[0049] C2: During the monitoring process, regularly collect feedback from team members and, based on this feedback and in conjunction with a dynamic optimization mechanism, fine-tune team member configuration and task allocation.

[0050] C3: The fine-tuned team member configuration and task allocation are brought back into the scope of real-time monitoring and tracking for continuous monitoring.

[0051] An AI-based intelligent dynamic optimization matching system for project teams, including: an evaluation trigger module, a matching degree calculation module, an intelligent adjustment module, and a fine-tuning and optimization module;

[0052] The evaluation trigger module is used to monitor the changes in project requirements in real time and determine whether to trigger the team member matching evaluation process based on a preset requirement change threshold;

[0053] The matching degree calculation module is used to collect multi-dimensional data of team members after triggering the team member matching degree evaluation process, and calculate the matching degree between team members and project requirements in combination with project requirements analysis;

[0054] The intelligent adjustment module is used to intelligently adjust team member configuration and task allocation based on the matching analysis results;

[0055] The fine-tuning and optimization module is used to fine-tune the team member configuration and task allocation based on the adjusted team member configuration and task allocation, combined with the project progress forecast and team member feedback.

[0056] Specifically, the matching degree calculation module includes: a data collection unit, a portrait construction unit, a matching unit, and a prediction analysis unit;

[0057] The data collection unit is used to collect multi-dimensional data of team members and perform pre-processing;

[0058] The portrait construction unit is used to construct an employee capability portrait using a machine learning algorithm based on the pre-processed multi-dimensional data of team members;

[0059] The matching unit is used to calculate the matching degree between team members and project requirements using a matching algorithm based on current project requirement information;

[0060] The predictive analysis unit, by introducing predictive analysis technology, makes intelligent predictions and risk assessments on project progress and provides team optimization configuration suggestions.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. The present invention proposes an intelligent dynamic optimization matching system for project teams based on artificial intelligence, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0063] 2. The present invention proposes an artificial intelligence-based intelligent dynamic optimization matching method for project teams. By monitoring changes in project requirements in real time and intelligently triggering the team member matching evaluation process, it combines multi-dimensional data of team members and machine learning algorithms to build employee capability portraits, thereby achieving precise matching of team members with project requirements, thereby effectively improving project execution efficiency and success rate; the method also introduces predictive analysis technology to intelligently predict and assess project progress, provide team optimization configuration suggestions, and can be fine-tuned based on real-time monitoring and team member feedback, forming a closed loop of continuous monitoring and optimization, further ensuring the stability and efficiency of the project team. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a schematic diagram of the project team intelligent dynamic optimization matching method based on artificial intelligence of the present invention;

[0065] Figure 2 This is a flowchart of the principle of the project team intelligent dynamic optimization matching method based on artificial intelligence of the present invention;

[0066] Figure 3 This is the architecture diagram of the project team intelligent dynamic optimization matching system based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0067] Example 1

[0068] See also Figure 1 and Figure 2 The present invention provides an embodiment of an intelligent dynamic optimization matching method for project teams based on artificial intelligence, the method comprising steps S101 to S104, including the following steps:

[0069] S101: Real-time monitoring of project requirement changes, and determining whether to trigger a team member matching assessment process based on a preset requirement change threshold; the project requirement changes include function adjustments, time node changes, and resource requirement changes;

[0070] Furthermore, the specific steps of S101 include:

[0071] (1) Use project management tools to track and record changes in project requirements in real time, including the addition, modification, deletion, or priority adjustment of requirements;

[0072] (2) According to the scale, complexity and importance of the project, set N thresholds for requirement changes. These thresholds can be quantitative, such as the number, frequency or impact of requirement changes, or qualitative, such as the impact of requirement changes on the project critical path;

[0073] (3) When the monitored project requirement changes reach or exceed the preset requirement change threshold, the team member matching evaluation process is triggered. The evaluation process includes collecting multi-dimensional data of team members, building employee capability profiles, and calculating matching steps.

[0074] S102: After the team member matching evaluation process is triggered, multidimensional data of the team members are collected, and based on the multidimensional data of the team members, a principal component analysis method is used to extract features from the multidimensional data of the team members, and a causal feature matrix is ​​constructed using a Bayesian model;

[0075] S103: Calculating the matching degree between team members and project requirements based on the causal characteristic matrix, and intelligently adjusting team member configuration and task allocation based on the matching analysis results;

[0076] S104: Fine-tune the team member configuration and task allocation based on the adjusted team member configuration and task allocation.

[0077] Specifically, the overall implementation process includes:

[0078] Use project management tools to track and record changes in project requirements in real time. Set requirement change thresholds based on the project's scale, complexity, and importance. When the monitored requirement changes reach or exceed the preset requirement change thresholds, trigger the team member matching assessment process. Collect multidimensional data of team members, including skill lists, work efficiency, and workload. Use principal component analysis to extract features from multidimensional data of team members, and use Bayesian models to build a causal feature matrix to solve the problem of multicollinearity. Based on the pre-processed multidimensional data of team members, use machine learning algorithms to build employee capability profiles. Analyze current project requirement information, combine it with employee capability profiles, and use matching algorithms. Calculate the matching degree between team members and project requirements. At the same time, introduce predictive analysis technology to make intelligent predictions and risk assessments on project progress, identify potential inefficient configurations and risk points, and intelligently adjust team member configurations and task assignments based on matching degrees and optimized configuration suggestions. Under the adjusted team member configurations and task assignments, combine the project progress forecast results and use project management tools to monitor team members' work performance and team collaboration indicators in real time. Based on the monitoring results and feedback from team members, combined with the dynamic optimization mechanism, fine-tune the team member configurations and task assignments, and bring the fine-tuned team member configurations and task assignments back into the scope of real-time monitoring and tracking, forming a closed loop of continuous monitoring.

[0079] It is important to understand that changes in project requirements encompass multiple dimensions, including functional adjustments, timeline changes, and resource requirement changes. Functional adjustments may mean that the project's core objectives or deliverables have changed, and the skill sets of team members originally adapted to the old functional requirements may no longer be applicable. Timeline changes, whether advanced or delayed, will directly affect the team's work rhythm and task scheduling. Advancing may require team members to have stronger and more efficient execution capabilities, while delaying may require replanning resource allocation to avoid waste. Changes in resource requirements involve adjustments in manpower, material resources, and financial resources. For example, a sudden increase in demand for specific technical resources requires the participation of team members with corresponding technical capabilities. This method, through a real-time monitoring mechanism, can capture relevant information as soon as demand changes occur. The preset demand change threshold avoids overreaction to minor or insignificant demand fluctuations, ensuring the rationality and efficiency of process triggering. When the degree of demand change exceeds the demand change threshold, the team member matching assessment process is immediately triggered, buying valuable time for subsequent team optimization and adjustment, ensuring that the team can quickly respond to demand changes and minimize potential negative impacts. The effects of this link are reflected in many aspects: in terms of response speed, it has achieved a shift from post-remediation to real-time response, shortening the time interval between identifying demand changes and initiating adjustments; in terms of resource utilization, it has avoided ineffective work of team members due to failure to promptly discover demand changes, thereby improving resource utilization efficiency; in terms of project stability, timely process triggering provides a prerequisite for dynamic adjustments of the project team, helps maintain the stable progress of the project, and reduces various risks caused by improper team configuration.

[0080] The specific steps of constructing the causal feature matrix using the Bayesian model include:

[0081] A1: After the team member matching assessment process is triggered, multi-dimensional data of team members is collected and pre-processed; the multi-dimensional data of team members includes work experience, skill level, educational background, personality traits, and teamwork ability;

[0082] A2: Determine the prior probability of the causal relationship, and use Bayes' theorem to calculate the updated posterior probability based on the prior probability of the causal relationship and the pre-processed multi-dimensional data of team members;

[0083] Furthermore, the specific steps of A2 include:

[0084] (1) Make a preliminary estimate of the causal relationship between team members based on domain knowledge and obtain the prior probability;

[0085] (2) Obtain pre-processed multidimensional data of team members, and construct a Bayesian model based on the prior probability and the pre-processed multidimensional data of team members. The Bayesian model is used to describe the probability distribution of causal relationships between team members.

[0086] (3) The pre-processed multidimensional data of team members is used as observation data, and the observation data is input into the Bayesian model. The updated posterior probability is calculated using the Bayesian theorem. The formula of the Bayesian theorem is: ,in, represents the posterior probability that hypothesis H holds true after observing event E, It represents the conditional probability of observing event E under the assumption that H is true, also known as the likelihood function. represents the prior probability that hypothesis H is true, represents the total probability of observing event E.

[0087] It is important to understand that the posterior probability reflects the real possibility of the causal relationship between team members under given observation data; in the scenario of team member matching evaluation, the causal relationship between team members can be regarded as hypothesis H, and the observed multidimensional data of team members can be regarded as event E. By calculating the posterior probability , can update the cognition of causal relationships between team members, and thus provide decision support for team member configuration and task allocation.

[0088] A3: Based on the updated posterior probability, a causal characteristic matrix between team members is constructed; the elements of the causal characteristic matrix represent the strength of the causal relationship between team members in different dimensions.

[0089] Furthermore, the specific steps of A3 include:

[0090] (1) Identify the dimensions of causal relationships between team members that need to be analyzed, such as skills complementarity, work experience, and communication skills;

[0091] (2) Extracting updated posterior probabilities from the Bayesian model ;

[0092] (3) Create an empty matrix, where the rows and columns represent team members, and the size of the matrix is ​​the square of the number of team members;

[0093] (4) Fill in the elements of the causal feature matrix according to the posterior probability, where each element in the causal feature matrix represents the strength of the causal relationship between the two team members on a certain dimension.

[0094] It should be understood that constructing a causal feature matrix is ​​a major innovation of this method, and using the Bayesian model to construct a causal feature matrix fully utilizes the advantages of the Bayesian method in dealing with uncertainty and causal inference. Determining the prior probability of causality is to make a preliminary judgment on the possible causal relationship between team members based on existing knowledge and experience. In a project team, there may be a causal relationship between different members in terms of work experience, skill level, etc. For example, experienced members may have a positive impact on the skill improvement of new members.

[0095] Furthermore, the causal feature matrix constructed based on the updated posterior probabilities has elements representing the strength of causal relationships between team members along different dimensions. This matrix clearly reveals the inherent connections and mutual influences between team members, providing an important causal basis for subsequent matching calculations and team configuration adjustments. For example, the causal feature matrix reveals a strong positive causal relationship between member A's technical skill level and member B's project execution efficiency. Therefore, in project configuration, assigning members A and B to related tasks may improve the overall team's work efficiency. This causal-based team analysis is more in-depth and reliable than traditional analysis based solely on correlation. It can help project managers better understand the internal operating mechanisms of the team and provide scientific decision-making support for optimizing team configuration.

[0096] The calculation of the matching degree between team members and project requirements based on the causal characteristic matrix and the intelligent adjustment of team member configuration and task allocation based on the matching analysis results include:

[0097] B1: Based on the pre-processed multi-dimensional data of team members, use machine learning algorithms to build employee capability profiles;

[0098] B2: Analyze the current project requirements information, combine the employee capability profile and the causal characteristic matrix, and use a matching algorithm to calculate the matching degree between team members and project requirements;

[0099] B3: Introducing predictive analysis technology to intelligently predict project progress and assess risks, identify inefficient configurations and risk points, and generate team optimization recommendations.

[0100] Inefficient allocation refers to a situation where resources are not fully utilized or optimal efficiency is not achieved in project resource allocation, task assignment, or process setup. This inefficiency stems from a variety of factors, including but not limited to: 1) Improper resource allocation, such as excessive resources being allocated to team members or departments, which are then underutilized, resulting in wasted resources; 2) Improper task allocation, such as a mismatch between team members' skills, experience, and expertise and their assigned tasks, leading to low efficiency; and 3) Improper process setup, such as redundant, repetitive, or unnecessary steps in the project process, resulting in reduced efficiency. Inefficient allocation not only impacts project schedule and efficiency but can also increase project risk and cost. Therefore, by introducing predictive analytics, we can intelligently predict project schedule, assess risk, and identify inefficient allocations and risk points. Based on this, we generate team optimization recommendations to improve resource allocation, task assignment, and process setup, thereby increasing project efficiency and success.

[0101] B4: Display matching results and team optimization configuration suggestions to project managers and team members in a visual form;

[0102] B5: The project manager and team members make corresponding adjustments to the team configuration based on the visually displayed team optimization configuration suggestions.

[0103] Furthermore, the specific steps of B5 include:

[0104] (1) Project managers and team members understand the team optimization configuration suggestions displayed by the visualization tool, including information such as the distribution of team members' capabilities, the degree of match between project requirements and team members' capabilities, and resource utilization;

[0105] (2) Based on the visually displayed information, the project manager analyzes the rationality and feasibility of the team optimization configuration suggestions, including comprehensive consideration of team member capabilities, project requirements, resource constraints, etc.

[0106] (3) Based on the analysis results, the project manager formulates a specific team configuration adjustment plan, including personnel adjustment and resource allocation. Personnel adjustment includes reallocating tasks and adjusting team member roles, and resource allocation includes increasing or decreasing resource input.

[0107] (4) The project manager communicates with the team members to explain the reasons, purpose, and expected effects of the adjustment plan;

[0108] (5) After the team members reach a consensus, the project manager organizes and implements the adjustment plan, which involves specific operations such as task reallocation, role adjustment, and resource allocation;

[0109] (6) After the adjustment plan is implemented, the project manager continuously monitors the effect of the team configuration adjustment, which helps to promptly identify and resolve potential problems and ensure the effective implementation of the adjustment plan.

[0110] The specific steps of B1 include:

[0111] B1.1: Obtain multidimensional data of the team members, extract features from the multidimensional data using principal component analysis, and generate team member feature data;

[0112] Furthermore, the specific steps of B1.1 include:

[0113] (1) Obtain multidimensional data of team members and perform preprocessing;

[0114] (2) Calculate the covariance matrix of the preprocessed team member multidimensional data X , to measure the correlation between dimensions, where m represents the number of samples in X, represents the transposed matrix of X;

[0115] (3) Perform eigendecomposition on the covariance matrix , and get the eigenvalue And eigenvector v, and select the first k principal components according to the size of the eigenvalue;

[0116] (4) Use Project X onto these k principal components to obtain the feature data after dimensionality reduction and generate the feature data of team members ,in, Represents the eigenvector matrix, represented by composition, represents the feature vector.

[0117] B1.2: Load a pre-built random forest model and train it using the team member feature data. The random forest model is prior art in this field and does not constitute an inventive solution of this application, so it will not be described in detail here.

[0118] B1.3: Use the trained random forest model to predict the multidimensional data of all team members, obtain quantitative results for each team member in different capability dimensions, and generate employee capability profiles.

[0119] Furthermore, the specific steps of B1.3 include:

[0120] (1) Obtaining characteristic data of team members , and load the pre-built random forest model;

[0121] (2) Use the loaded random forest model to Make predictions and output quantitative results for each team member in different capability dimensions;

[0122] (3) Generate a capability profile for each team member based on the prediction results of the random forest model;

[0123] Organize the prediction results of the random forest model into a structured data format to ensure that the prediction value of each team member corresponds to their personal information;

[0124] Identify the competency dimensions that will be represented in the employee competency profile. These dimensions should correspond to the features that the random forest model uses for predictions.

[0125] Set quantitative standards for each capability dimension so that the prediction results can be converted into intuitive and comparable values ​​or grades;

[0126] Based on the capability dimensions and quantitative standards, data visualization tools are used to present the organized prediction data in a designed visualization format to generate a capability portrait for each team member.

[0127] Furthermore, the multi-dimensional data of all team members are predicted based on the trained random forest model to obtain quantitative results of each team member in different ability dimensions, and generate employee ability portraits. The employee ability portraits are no longer vague, qualitative descriptions, but comprehensive portrayals with specific quantitative indicators, including information such as the work ability, strengths, and weaknesses of team members. This quantitative ability portrait enables project managers to clearly understand the ability level of each member and provides an accurate yardstick for matching project needs.

[0128] The specific steps of B2 include:

[0129] B2.1: Interpret the project requirements document and determine the required team member information based on project requirements;

[0130] B2.2: Obtain the employee capability profile, which includes information on the team members' work capabilities, strengths, and weaknesses;

[0131] B2.3: Based on the team member information and employee capability profiles, use a similarity-based matching algorithm to match project requirements with employee capability profiles;

[0132] Furthermore, the specific steps of B2.3 include:

[0133] (1) Obtain employee capability profiles and perform pre-processing;

[0134] (2) Using cosine similarity matching algorithm , match the project requirements with the employee capability profiles and calculate the similarity score C between each team member and the project requirements, where represents the project demand vector, Represents the employee capability portrait vector, represents the dot product, Represents the modulus of a vector;

[0135] (3) Sort team members according to their similarity scores and select the team member with the highest score as the best candidate for the project.

[0136] B2.4: Based on the matching calculation results, obtain the matching degree between each team member and the project requirements;

[0137] B2.5: Evaluate the matching results and adjust and optimize the matching algorithm or employee capability profile based on the evaluation results.

[0138] Furthermore, the specific steps of B2.5 include:

[0139] (1) Obtain the matching results between team members and project requirements;

[0140] (2) Conduct quantitative evaluation of the matching results based on the established evaluation criteria to analyze the accuracy and reliability of the matching results and identify any possible errors or deviations;

[0141] (3) Analyze the evaluation results and identify any problems in the matching algorithm or employee capability profile;

[0142] (4) Based on the analysis results, adjust and optimize the cosine similarity matching algorithm, such as adjusting algorithm parameters and improving the algorithm model, and revise and improve the employee capability profile, such as updating the capability dimension and adjusting the quantitative standard;

[0143] (5) Re-run the matching algorithm to generate new matching results, evaluate the new matching results, and verify the optimization effect.

[0144] Furthermore, after obtaining the employee capability portrait, a similarity-based matching algorithm is used to match the project requirements with the employee capability portrait. The similarity-based matching algorithm can quantify the degree of match between each team member and the project requirements by calculating the similarity between the project requirement characteristics and the employee capability portrait characteristics. This matching calculation not only takes into account the fit between the individual employee's capabilities and the project requirements, but also combines the strength of the causal relationship between team members in the causal feature matrix to ensure that the matching results not only meet the individual capability requirements, but also give full play to the synergy between team members.

[0145] The specific steps of B3 include:

[0146] B3.1: Obtain project progress history data and multi-dimensional data of team members; the project progress history data includes task completion status, resource usage, and time progress;

[0147] B3.2: Establish a project progress forecast model based on historical project progress data and multi-dimensional team member data; the project progress forecast model is obtained through the critical path method and program review and evaluation technique;

[0148] B3.3: Input the current project's real-time progress data and the corresponding multi-dimensional data of current team members into the project progress forecasting model to perform project progress forecasting and risk assessment;

[0149] Among them, the project progress prediction model automatically associates the configuration information of the current team members when making predictions; the configuration information of the current team members is realized by employee capability profiles and matching results.

[0150] B3.4: Based on the output of the project schedule forecasting model, identify potential inefficiencies and risk points;

[0151] B3.5: Analyze team members' work efficiency and task allocation based on forecast results and risk assessment reports;

[0152] B3.6: Based on the analysis results, generate team optimization recommendations, including adjusting task allocation, increasing resource input, and training to enhance capabilities.

[0153] The specific steps of B3.2 include:

[0154] B3.21: Obtain the project progress historical data and the team member multi-dimensional data, and perform pre-processing;

[0155] B3.22: Define all project activities, describe and define each activity, determine the dependencies and priorities between activities, and form an activity network diagram;

[0156] B3.23: Calculate the resources and duration required for each activity based on pre-processed multidimensional data of team members;

[0157] B3.24: Use the three-point estimation method Estimate the duration of the activities and calculate the weighted average duration and standard deviation of each activity, where Expressing optimism, represents the most likely time, represents the pessimistic time, and T represents the expected duration;

[0158] B3.25: Based on the activity network diagram and the duration of the activities, calculate the earliest start time ES, latest start time EF, earliest finish time LS, and latest finish time LF for each task to determine the critical path. The formula is: ,in, 、 、 、 They represent the earliest start time, latest start time, earliest finish time, and latest finish time of activity i, respectively. represents the maximum function, represents the minimum function, represents the earliest start time of activity j, represents the earliest completion time of activity l, represents the duration of activity i, represents the duration from activity j to activity i, represents the duration from activity i to activity l, i, j, l represent the activity index values ​​in the activity network diagram, l is the successor activity of i, and j is the predecessor activity of i;

[0159] B3.26: Add the expected values ​​of all tasks on the critical path to obtain the total expected duration of the project. Calculate the variance of the total expected duration. Simultaneously, use a normal distribution based on the total expected duration and standard deviation to calculate the probability of project completion at different time points. Variance calculation and normal distribution are prior art in this field and do not constitute the inventive solution of this application. These details are not detailed here.

[0160] B3.27: Load a pre-built linear regression model, combine the results of the critical path and three-point estimation method, train the pre-built linear regression model, and generate a project progress prediction model. The linear regression model is the existing technology content in this field and is not the inventive solution of this application, so it will not be elaborated here.

[0161] The specific process of fine-tuning team member configuration and task allocation includes:

[0162] C1: Based on the adjusted team member configuration and task allocation, combined with the project progress forecast results, use project management tools to monitor team member performance in real time and track team collaboration indicators.

[0163] Among them, project management tools are important tools that help project managers monitor team members' work performance in real time, track project progress and team collaboration indicators. Common project management tools include Jira, Trello, and Asana. Real-time monitoring refers to using the real-time data update function of project management tools to track team members' work progress and task completion, which helps project managers to promptly identify potential problems and take corresponding measures. Team collaboration indicators are important indicators for measuring the communication and collaboration efficiency between team members, including communication efficiency, problem-solving speed, team cohesion, task completion rate, and progress deviation.

[0164] Furthermore, the specific steps of C1 include:

[0165] (1) Adjust team member configuration and task allocation based on project requirements, team member capabilities and resource availability to ensure that each team member is clear about their responsibilities and tasks, as well as the timeline and priority of the tasks;

[0166] (2) Use project management tools or related methods to forecast project progress and set key milestones and checkpoints to monitor and evaluate project execution;

[0167] (3) Use the real-time monitoring function of project management tools to track the work progress and task completion of team members;

[0168] (4) Set up reminders and notifications to remind team members when a task or stage is about to end;

[0169] (5) Set team collaboration indicators and use the data analysis function of project management tools to track and evaluate team collaboration indicators.

[0170] C2: During the monitoring process, regularly collect feedback from team members and, based on this feedback and in conjunction with a dynamic optimization mechanism, fine-tune team member configuration and task allocation.

[0171] Furthermore, the specific steps of C2 include:

[0172] (1) At the beginning of the project, establish an effective feedback collection mechanism, such as regular meetings, anonymous surveys, and online feedback platforms, to ensure that team members understand how to provide feedback;

[0173] (2) Collect feedback from team members according to a predetermined schedule, such as weekly, to ensure that the feedback collected covers multiple aspects such as project progress, team configuration, and task allocation;

[0174] (3) Organize and analyze the collected feedback, identify common problems, key issues and improvement points, and evaluate the impact of the feedback on project progress and team efficiency;

[0175] (4) Based on the analysis results, combined with the actual situation of the project and the dynamic optimization mechanism, formulate a specific adjustment plan, which should clearly define the content, objectives, timetable and responsible persons of the adjustment;

[0176] (5) Gradually implement fine-tuning of team member configuration and task allocation according to the adjustment plan. At the same time, maintain communication with team members during the implementation process to ensure that they understand the purpose and expected results of the adjustment.

[0177] C3: Incorporate the fine-tuned team member configuration and task allocation back into the scope of real-time monitoring and tracking to form a closed loop of continuous monitoring.

[0178] Example 2

[0179] See also Figure 3 Another embodiment provided by the present invention is an artificial intelligence-based intelligent dynamic optimization matching system for project teams, comprising:

[0180] Evaluation trigger module, matching calculation module, intelligent adjustment module, fine-tuning and optimization module;

[0181] The evaluation trigger module is used to monitor changes in project requirements in real time and determine whether to trigger the team member matching evaluation process based on the preset requirement change threshold;

[0182] The matching calculation module is used to collect multi-dimensional data of team members after the team member matching evaluation process is triggered, and calculate the matching degree between team members and project requirements in combination with project requirements analysis;

[0183] The intelligent adjustment module is used to intelligently adjust team member configuration and task allocation based on the matching analysis results, to achieve the best match between team members and project requirements and improve project execution efficiency;

[0184] The fine-tuning and optimization module is used to fine-tune the team member configuration and task allocation based on the adjusted team member configuration and task allocation, combined with project progress forecasts and team member feedback, to form a closed loop of continuous monitoring and optimization.

[0185] The evaluation trigger module includes: a demand monitoring unit and an evaluation trigger unit;

[0186] The demand monitoring unit is used to monitor changes in project requirements in real time, including changes in requirements and new requirements, to ensure that the system can detect changes in project requirements in a timely manner;

[0187] The evaluation trigger unit is used to determine whether to trigger the team member matching evaluation process based on the preset demand change threshold, ensuring that the matching evaluation process can be started when the demand change reaches a certain level.

[0188] The matching degree calculation module includes: data collection unit, portrait construction unit, matching unit, and prediction analysis unit;

[0189] Data collection unit, used to collect multidimensional data of team members and perform preprocessing;

[0190] The portrait construction unit is used to construct employee capability portraits based on pre-processed multi-dimensional data of team members using machine learning algorithms, converting complex multi-dimensional data into intuitive representations of employee capabilities to facilitate matching calculations;

[0191] The matching unit is used to combine the current project demand information, use the matching algorithm to calculate the matching degree between team members and project requirements, quantify the degree of fit between team members and project requirements, and provide a basis for intelligent adjustment;

[0192] The predictive analysis unit, through the introduction of predictive analysis technology, conducts intelligent predictions and risk assessments on project progress and provides team optimization configuration suggestions to identify potential problems in advance, optimize team configuration, and improve project success rates.

[0193] The fine-tuning and optimization module includes: monitoring and tracking unit, feedback unit, implementation and re-monitoring unit;

[0194] The monitoring and tracking unit is used to use project management tools to monitor team members' work performance and team collaboration indicators in real time, promptly identify problems in team work, and provide a basis for fine-tuning;

[0195] Feedback unit, used to regularly collect feedback from team members and process it in conjunction with the dynamic optimization mechanism to ensure that team members' feedback can be taken into consideration for optimization;

[0196] The implementation and re-monitoring unit is used to fine-tune team member configuration and task allocation based on feedback and real-time monitoring results, and include them in the scope of real-time monitoring and tracking again, forming a closed loop of continuous monitoring and optimization to ensure that the team is always in the best condition.

[0197] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention. These are all protected by the present invention.

[0198] If the technical solution disclosed herein involves personal information, the product using the technical solution disclosed herein has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution disclosed herein involves sensitive personal information, the product using the technical solution disclosed herein has obtained the individual's separate consent before processing the sensitive personal information and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the individual has entered the personal information collection scope and that personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information. The personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. The project team intelligent dynamic optimization matching method based on artificial intelligence is characterized by: include: Monitor changes in project requirements in real time and determine whether to trigger the team member matching assessment process based on preset requirement change thresholds; The changes in project requirements include functional adjustments, time node changes, and changes in resource requirements; After the team member matching evaluation process is triggered, multidimensional data of the team members are collected, and based on the multidimensional data of the team members, a principal component analysis method is used to extract features from the multidimensional data of the team members, and a causal feature matrix is ​​constructed using a Bayesian model; According to the causal characteristic matrix, the matching degree between team members and project requirements is calculated, and based on the matching analysis results, the team member configuration and task allocation are intelligently adjusted; Fine-tune team member configuration and task allocation based on the adjusted team member configuration and task allocation.

2. The method for intelligent dynamic optimization matching of project teams based on artificial intelligence according to claim 1, characterized in that: The specific steps of constructing the causal feature matrix using the Bayesian model include: A1: After the team member matching assessment process is triggered, multi-dimensional data of team members is collected and pre-processed; the multi-dimensional data of team members includes work experience, skill level, educational background, personality traits, and teamwork ability; A2: Determine the prior probability of the causal relationship, and use Bayes' theorem to calculate the updated posterior probability based on the prior probability of the causal relationship and the pre-processed multi-dimensional data of team members; A3: Based on the updated posterior probability, a causal characteristic matrix between team members is constructed; the elements of the causal characteristic matrix represent the strength of the causal relationship between team members in different dimensions.

3. The method for intelligent dynamic optimization matching of project teams based on artificial intelligence according to claim 2, characterized in that: The calculation of the matching degree between team members and project requirements based on the causal characteristic matrix and the intelligent adjustment of team member configuration and task allocation based on the matching analysis results include: B1: Based on the pre-processed multi-dimensional data of team members, use machine learning algorithms to build employee capability profiles; B2: Analyze the current project requirements information, combine the employee capability profile and the causal characteristic matrix, and use a matching algorithm to calculate the matching degree between team members and project requirements; B3: Introducing predictive analysis technology to intelligently predict project progress and assess risks, identify inefficient configurations and risk points, and generate team optimization recommendations. B4: Display matching results and team optimization configuration suggestions to project managers and team members in a visual form; B5: The project manager and team members make corresponding adjustments to the team configuration based on the team optimization configuration suggestions.

4. The method for intelligent dynamic optimization matching of project teams based on artificial intelligence according to claim 3, characterized in that: The specific steps of B1 include: B1.1: Obtain multidimensional data of the team members, extract features from the multidimensional data using principal component analysis, and generate team member feature data; B1.2: Load the pre-built random forest model and train it using team member feature data. B1.3: Use the trained random forest model to predict the multidimensional data of all team members, obtain quantitative results for each team member in different capability dimensions, and generate employee capability profiles.

5. The method for intelligent dynamic optimization matching of project teams based on artificial intelligence according to claim 4, characterized in that: The specific steps of B2 include: B2.1: Interpret the project requirements document and determine team member information based on project requirements; B2.2: Obtain the employee capability profile, which includes information on the team members' work capabilities, strengths, and weaknesses; B2.3: Based on the team member information and employee capability profiles, use a similarity-based matching algorithm to match project requirements with employee capability profiles; B2.4: Based on the matching calculation results, obtain the matching degree between each team member and the project requirements; B2.5: Evaluate the matching results and adjust and optimize the matching algorithm or employee capability profile based on the evaluation results.

6. The method for intelligent dynamic optimization matching of project teams based on artificial intelligence according to claim 5, characterized in that: The specific steps of B3 include: B3.1: Obtain project progress history data and multi-dimensional data of team members; the project progress history data includes task completion status, resource usage, and time progress; B3.2: Establish a project progress forecast model based on historical project progress data and multi-dimensional team member data; the project progress forecast model is obtained through the critical path method and program review and evaluation technique; B3.3: Input the current project's real-time progress data and the corresponding multi-dimensional data of current team members into the project progress forecasting model to perform project progress forecasting and risk assessment; B3.4: Identify inefficient configurations and risk points based on the output of the project schedule forecasting model; B3.5: Analyze team members' work efficiency and task allocation based on forecast results and risk assessment reports; B3.6: Based on the analysis results, generate team optimization recommendations, including adjusting task allocation, increasing resource input, and training to enhance capabilities.

7. The method for intelligent dynamic optimization matching of project teams based on artificial intelligence according to claim 6, characterized in that: The specific steps of B3.2 include: B3.21: Obtain the project progress historical data and the team member multi-dimensional data, and perform pre-processing; B3.22: Define all project activities, describe and define each activity, determine the dependencies and priorities between activities, and form an activity network diagram; B3.23: Calculate the resources and duration required for each activity based on pre-processed multidimensional data of team members; B3.24: Estimate the duration of the activities using the three-point estimation method, calculating the weighted average duration and standard deviation for each activity; B3.25: Based on the activity network diagram and the duration of the activities, calculate the earliest start time, latest start time, earliest finish time, and latest finish time for each task and determine the critical path; B3.26: Add the expected values ​​of all tasks on the critical path to obtain the total expected duration of the project. Calculate the variance of the total expected duration. Also, use the normal distribution to calculate the probability of completion at different time points based on the total expected duration and standard deviation. B3.27: Load the pre-built linear regression model, combine the results of the critical path and three-point estimation method, train the pre-built linear regression model, and generate a project schedule prediction model.

8. The method for intelligent dynamic optimization matching of project teams based on artificial intelligence according to claim 7, characterized in that: The specific process of fine-tuning team member configuration and task allocation includes: C1: Based on the adjusted team member configuration and task allocation, combined with the project progress forecast results, monitor team member performance in real time and track team collaboration indicators; C2: During the monitoring process, regularly collect feedback from team members and, based on this feedback and in conjunction with a dynamic optimization mechanism, fine-tune team member configuration and task allocation. C3: The fine-tuned team member configuration and task allocation are brought back into the scope of real-time monitoring and tracking for continuous monitoring.

9. An artificial intelligence-based intelligent dynamic optimization matching system for project teams, which is used to implement the artificial intelligence-based intelligent dynamic optimization matching method for project teams according to any one of claims 1 to 8, characterized in that: include: Evaluation trigger module, matching calculation module, intelligent adjustment module, fine-tuning and optimization module; The evaluation trigger module is used to monitor the changes in project requirements in real time and determine whether to trigger the team member matching evaluation process based on a preset requirement change threshold; The matching degree calculation module is used to collect multi-dimensional data of team members after triggering the team member matching degree evaluation process, and calculate the matching degree between team members and project requirements in combination with project requirements analysis; The intelligent adjustment module is used to intelligently adjust team member configuration and task allocation based on the matching analysis results; The fine-tuning and optimization module is used to fine-tune the team member configuration and task allocation based on the adjusted team member configuration and task allocation, combined with the project progress forecast and team member feedback.

10. The artificial intelligence-based project team intelligent dynamic optimization matching system according to claim 9, characterized in that: The matching degree calculation module includes: a data collection unit, a portrait construction unit, a matching unit, and a prediction analysis unit; The data collection unit is used to collect multi-dimensional data of team members and perform pre-processing; The portrait construction unit is used to construct an employee capability portrait using a machine learning algorithm based on the pre-processed multi-dimensional data of team members; The matching unit is used to calculate the matching degree between team members and project requirements using a matching algorithm based on current project requirement information; The predictive analysis unit, by introducing predictive analysis technology, makes intelligent predictions and risk assessments on project progress and provides team optimization configuration suggestions.

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