Standardized project management and intelligent professional scheduling system

Abstract

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

Description

Technical Field

[0001] This invention belongs to the field of project management and intelligent human resource scheduling technology, specifically involving a standardized project management and intelligent professional scheduling system. Background Technology

[0002] In the fields of project management and human resource allocation, with the increasing complexity of business scenarios and the refinement of professional division of labor, traditional management models are gradually revealing multi-dimensional technical bottlenecks, making it difficult to meet the needs of efficient, accurate, and dynamic project advancement. Specific shortcomings are as follows: In traditional project management, business process decomposition relies heavily on manual experience, lacking a unified algorithmic decomposition mechanism. This leads to significant differences in task steps across different projects and personnel, making it difficult to create standardized templates and resulting in poor reusability of subsequent tasks. Furthermore, the utilization of historical data on similar tasks is limited to simple statistics, failing to mine data correlation features through algorithms such as cluster analysis. This prevents the generation of structured task attribute data (such as unified dimensional definitions of skill requirements, time constraints, and quality standards). Moreover, task attribute weights are often fixed and cannot be dynamically adjusted based on real-time project complexity (such as the number of tasks, dependency density, and difficulty level) and resource scarcity (such as the supply-demand ratio of professionals). This results in insufficient accuracy of input data for subsequent personnel matching and scheduling planning, easily leading to resource misallocation.

[0003] Current personnel matching methods largely focus on "skill keyword matching," neglecting multi-dimensional characteristics such as years of experience, geographical location, historical execution quality, and cost suitability. This results in matching results that only meet basic skill requirements, but may hinder project progress due to insufficient experience, excessive commuting distance, or cost overruns. Furthermore, matching weights are mostly preset manually, lacking an adaptive adjustment mechanism based on historical matching results (such as task completion rate, customer satisfaction, and quality deviation). This prevents dynamic optimization of weight allocation based on the core needs of different projects (e.g., some projects prioritize quality, while others prioritize cost). Additionally, the lack of globally optimal matching logic built using models such as integer linear programming easily leads to the problem of "reasonable local matching but wasteful global resources," such as a professional being matched with a single high-priority task, causing multiple low-priority tasks to stall due to a lack of suitable personnel. Traditional scheduling often employs a "static time scheduling" model, failing to develop differentiated strategies based on task type (on-site / remote). For on-site tasks, it doesn't integrate real-time traffic data to optimize execution paths, easily leading to increased time costs due to unreasonable commuting routes. For remote tasks, it doesn't consider time zone differences and bandwidth resource allocation, easily resulting in collaboration delays and data transmission bottlenecks. Furthermore, the scheduling process doesn't adequately consider resource constraints (such as maximum workload for professionals and cognitive fatigue thresholds), potentially leading to personnel overload or resource idleness. Moreover, the lack of a skills supply and demand forecasting mechanism makes it impossible to identify skills gaps in project progress in advance, frequently resulting in "temporary staff shortages causing schedule delays." In addition, the integration of scheduling results with personal calendars is mostly manual synchronization, prone to time conflicts, further reducing execution efficiency.

[0004] Existing quality control measures largely focus on "post-delivery review," failing to utilize algorithms for real-time anomaly detection. This makes it difficult to promptly identify issues such as format deviations, missing content, and data errors in deliverables, leading to delayed problem rectification and increased rework costs. Furthermore, performance data from professionals (such as task completion quality and efficiency) lacks a structured feedback mechanism, hindering the iterative optimization of personnel matching weights and scheduling strategies. Additionally, skill graph updates rely on manual maintenance, failing to dynamically improve based on new skills or changes in skill relationships (such as increased frequency of collaborative use of two skills). Consequently, the system's matching and scheduling accuracy gradually declines over long-term use, making it difficult to adapt to evolving business scenarios.

[0005] In summary, the current field of project management and professional scheduling needs an integrated system that can achieve "task standardization - precise personnel matching - dynamic and efficient scheduling - continuous quality optimization" to solve the core problems of insufficient standardization, inaccurate matching, inefficient scheduling, and lack of optimization in the traditional model. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a standardized project management and intelligent professional scheduling system; The objective of this invention can be achieved through the following technical solutions: A standardized project management and intelligent professional scheduling system, characterized by comprising a project standardization module, a file matching module, a dynamic scheduling module, and a quality optimization module; The project standardization module uses a business process association rule mining algorithm to decompose business processes and form standardized task steps; it analyzes historical data of similar tasks through unsupervised clustering algorithm to generate structured data of steps; at the same time, it introduces a task attribute dynamic weight algorithm to dynamically adjust attribute weights based on real-time project complexity and resource scarcity. The file matching module constructs professional information files based on task data, uses a multi-criteria decision-weighted bipartite graph matching algorithm to extract feature vectors of professionals and corresponding positions, combines customer base weights and adaptive weight adjustment algorithms to generate corrected weights, outputs the final matching weights through the analytic hierarchy process, calculates the matching score, constructs a bipartite graph and integer linear programming model, and generates the optimal professional-position matching pair. The dynamic scheduling module receives the optimal matching pair of professionals and positions, optimizes the interval variable time window control strategy based on the resource constraint scheduling mechanism with the goal of minimizing project cycle, commuting cost and cognitive load; it uses the geographic clustering-improved path optimization algorithm to plan the execution path for on-site tasks, and the time zone alignment-bandwidth adaptive allocation algorithm for remote tasks, and predicts the skill gap based on the skill supply and demand prediction algorithm. The generated scheduling results are written into the personal calendar of the professional. The quality optimization module uses project quality standards as the monitoring benchmark, employs a deliverable anomaly detection algorithm to verify the compliance of task deliverables, collects performance data of professionals, introduces a time-series prediction optimization algorithm to analyze historical performance trends, dynamically corrects the professional skill confidence and matching weight, iteratively optimizes the cognitive load and time window control strategy, and improves the skill graph based on the graph incremental update algorithm.

[0007] Specifically, the unsupervised clustering algorithm first performs feature processing on the required professional skills, work patterns, and expected duration dimensions of historical similar tasks to eliminate the dimensional differences in the data. Then, it iteratively divides the data into clusters, introducing a silhouette coefficient during the clustering process. By calculating the average similarity of tasks within each cluster and the average distance between tasks in different clusters, clustering results that meet the criteria of compactness within clusters and separation between clusters are generated.

[0008] Specifically, the task attribute dynamic weighting algorithm first defines the dimensions for calculating real-time project complexity, including the total number of tasks currently pending, the density of dependencies between tasks, and the task difficulty coefficient. The comprehensive complexity value is obtained through a weighted summation. The weight allocation is set based on the degree of influence of the dimensions on project progress. The resource tension is quantified by statistically analyzing the ratio of the total number of professionals currently available for the project to the total number of professionals actually needed for the project. The attribute weights are adjusted according to the changes in the comprehensive complexity value and the resource tension, with the adjustment direction being positively correlated with the comprehensive complexity value and the resource tension.

[0009] Specifically, the business process association rule mining algorithm preprocesses historical business process data, including deduplication of process steps, labeling of temporal relationships, and filtering of abnormal processes; then, it sets support thresholds and confidence thresholds, and extracts association rules that meet the threshold conditions by iteratively calculating the association degree between steps; finally, it constructs a process dependency graph based on the association rules, and decomposes the business process according to the logical chain of the starting step, core step, and closing step.

[0010] Specifically, the adaptive weight adjustment algorithm generates corrected weights by: first, determining the scope of historical matching data collection, which includes matching results, task execution results, and customer feedback information; extracting three core evaluation indicators: historical matching success rate (the proportion of professionals who successfully complete the corresponding job tasks after matching); customer feedback satisfaction (classifying satisfaction levels based on customer evaluations of professional performance); and task quality score deviation (comparing the quality score of the professional's actual deliverables with the project's preset quality score standard to calculate the degree of deviation); and then modeling the correlation between the core evaluation indicators and weight adjustment based on a linear regression model to obtain the corresponding correction coefficients and calculate the final corrected weights.

[0011] Specifically, the process of constructing and setting constraints for the integer linear programming model of the file matching module is as follows: The optimization objective is to maximize the global matching effect between professionals and positions. Three hard constraints are set: single position matching constraint, each position is assigned one professional; professional overload constraint, the number of projects matched by a single professional in a single instance is based on a preset anti-overload threshold; on-site task geographical constraint, for positions performed on-site, the spatial distance between the professional's geographical location and the task execution location needs to be verified, and professionals who are outside the reasonable commuting range are not included in the matching candidate pool of the position; based on the hard constraints, an integer linear programming model is constructed to generate the optimal matching result that satisfies the hard constraints.

[0012] Specifically, the optimization of the interval variable time window control strategy adopts a rolling time domain scheduling method, which divides the entire project cycle into continuous time windows, each covering a fixed project progress cycle. Within each time window, task execution feedback data from the previous time window is collected, including the deviation between the actual completion time and the planned completion time, and the difference between the actual utilization of professional resources and the plan. Based on the feedback data, the causes of the deviations are analyzed, and the time window parameters are adjusted by rolling optimization window by window.

[0013] Specifically, in the cognitive load optimization of the dynamic scheduling module, the execution process of cognitive load calculation and the quantification method of each factor are as follows: First, define the constituent factors of the remote task cognitive load index, including task complexity coefficient, historical interruption frequency, and cross-time zone factor. The constituent factors participate in the index calculation according to preset weights. The task complexity coefficient is divided according to the types of professional skills required by the task. The historical interruption frequency is calculated by statistically analyzing the interruption records of professionals when remotely performing tasks in the past, and the average number of interruptions per unit time is calculated. The cross-time zone factor is divided according to the degree of difference between the time zone where the professional is located and the time zone where the task collaborator is located.

[0014] Specifically, the improved path optimization algorithm is a heuristic search-based path optimization algorithm that integrates real-time traffic data. The specific execution steps include: first, collecting algorithm input data, including the execution location coordinates of the on-site task, the current location coordinates of the professional, and real-time traffic congestion data; then, converting the real-time traffic congestion data into a congestion coefficient, constructing a path cost function, multiplying the physical distance by the congestion coefficient to obtain the actual travel distance affected by traffic, and then adding the estimated parking time; finally, traversing the travel paths through a heuristic search method, calculating the path cost function value, and selecting the execution path with the lowest cost, i.e., the shortest commuting time, that conforms to the road traffic rules.

[0015] Specifically, the skills supply and demand prediction algorithm adopts a gradient boosting-based tree prediction model. The training dataset of the tree prediction model includes a list of skills demanded in past projects, a list of skills supplied by professionals, and historical data on skills gaps. Then, the training data is preprocessed, including missing value imputation, outlier removal, and feature encoding. Next, the tree prediction model is constructed, and a decision tree is generated through iterative training. The decision tree learns the supply and demand relationship in the skills dimension, and finally, the prediction results of the decision tree are integrated through gradient boosting.

[0016] Specifically, the deliverable anomaly detection algorithm is based on an unsupervised learning algorithm of an autoencoder. First, feature extraction is performed on the deliverable data, converting format compliance, content completeness, data accuracy, and quality standard compliance into high-dimensional feature vectors. Then, the high-dimensional feature vectors are input into a pre-trained autoencoder to compress them into low-dimensional vectors. Finally, the low-dimensional vectors are reconstructed into high-dimensional feature vectors by a decoder. The reconstruction error is calculated, and if it exceeds a preset threshold, the deliverable is determined to be an anomaly.

[0017] Specifically, the graph incremental update algorithm sets update trigger conditions: adding new professional skill terms, changes in existing skill associations, and updates to the skill proficiency of professionals. When the update is executed, the association strength between the new skill and existing skills is first calculated using cosine similarity to generate the position of the new skill in the graph and its association with other skills. Then, the weights of nodes and edges in the skill graph are adjusted.

[0018] The beneficial effects of this invention are as follows: The system uses a "business process association rule mining algorithm + unsupervised clustering algorithm" to generate structured and standardized task steps by combining historical data of similar tasks. This unifies the definition and scope of task attributes (such as skill requirements, time constraints, and quality standards), reducing communication costs caused by non-standard task breakdown. At the same time, it introduces a dynamic weighting algorithm for task attributes, which can adjust attribute priorities based on real-time project complexity (such as the number of tasks and dependency density) and resource scarcity. This makes task management more aligned with actual project needs, reduces manual intervention costs, and improves management efficiency. The file matching module employs a "multi-criteria decision-making algorithm-weighted bipartite graph matching algorithm + integer linear programming model" to comprehensively extract multi-dimensional feature vectors of professionals (skills, experience, geography, cost, etc.) and positions (skill weights, quality requirements, urgency, etc.). Combined with an adaptive weight adjustment algorithm, it dynamically adapts to customer needs and historical matching results, avoiding the limitations of single-dimensional matching. The hard constraints of the integer linear programming model (such as overload prevention and on-site geographical adaptation) further ensure the feasibility of the matching results, reduce "mismatch" and "overload" problems, and make human resources and job requirements highly compatible, thereby improving the quality of task completion (such as reducing rework rates) and the utilization rate of professionals. The dynamic scheduling module, based on resource constraints, employs differentiated optimization strategies for on-site and remote tasks: on-site tasks utilize "geographic clustering + improved path optimization algorithm integrating real-time traffic" to shorten commuting time and costs; remote tasks reduce collaboration latency through "time zone alignment + adaptive bandwidth allocation algorithm"; simultaneously, it combines cognitive load calculation and skill supply and demand prediction to balance the workload of professionals (e.g., avoiding continuous overload of remote tasks), proactively address skill gaps, and synchronize scheduling results with personal calendars in real time to avoid time conflicts; overall, it achieves multi-objective optimization of "time-cost-load," significantly improving scheduling efficiency and reducing resource waste caused by commuting and time conflicts.

[0019] The quality optimization module monitors delivery compliance in real time through a "deliverable anomaly detection algorithm," shifting quality control from "post-audit" to "process monitoring," promptly identifying and correcting anomalies to reduce delivery risks. Simultaneously, it analyzes the historical performance of professionals through a time-series prediction optimization algorithm, providing feedback on iterative matching weights (such as adjusting skill confidence) and scheduling strategies (such as optimizing time window control). Combined with a graph incremental update algorithm, it dynamically improves skill relationships, forming a closed loop of "matching-scheduling-quality-optimization," continuously enhancing the system's long-term management capabilities and ensuring the stability and iterative nature of project delivery levels.

[0020] This system enables standardization, intelligentization, and closed-loop optimization of the entire project management process, effectively shortening project cycles, improving resource utilization and delivery quality. It is applicable to various standardized project scenarios involving cross-regional and multi-professional collaboration, and has significant practical value and promotional significance. Attached Figure Description

[0021] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0022] Figure 1 This is a system architecture diagram of a standardized project management and intelligent professional scheduling system according to the present invention; Figure 2 This is a flowchart illustrating the deliverable anomaly detection process of a standardized project management and intelligent professional scheduling system according to the present invention. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0024] Please see Figure 1-2 A standardized project management and intelligent professional scheduling system, comprising the following steps: A standardized project management and intelligent professional scheduling system, characterized by comprising a project standardization module, a file matching module, a dynamic scheduling module, and a quality optimization module; The project standardization module uses a business process association rule mining algorithm to decompose business processes and form standardized task steps; it analyzes historical data of similar tasks through unsupervised clustering algorithm to generate structured data of steps; at the same time, it introduces a task attribute dynamic weight algorithm to dynamically adjust attribute weights based on real-time project complexity and resource scarcity. The file matching module constructs professional information files based on task data, uses a multi-criteria decision-weighted bipartite graph matching algorithm to extract feature vectors of professionals and corresponding positions, combines customer base weights and adaptive weight adjustment algorithms to generate corrected weights, outputs the final matching weights through the analytic hierarchy process, calculates the matching score, constructs a bipartite graph and integer linear programming model, and generates the optimal professional-position matching pair. The dynamic scheduling module receives the optimal matching pair of professionals and positions, optimizes the interval variable time window control strategy based on the resource constraint scheduling mechanism with the goal of minimizing project cycle, commuting cost and cognitive load; it uses the geographic clustering-improved path optimization algorithm to plan the execution path for on-site tasks, and the time zone alignment-bandwidth adaptive allocation algorithm for remote tasks, and predicts the skill gap based on the skill supply and demand prediction algorithm. The generated scheduling results are written into the personal calendar of the professional. The quality optimization module uses project quality standards as the monitoring benchmark, employs a deliverable anomaly detection algorithm to verify the compliance of task deliverables, collects performance data of professionals, introduces a time-series prediction optimization algorithm to analyze historical performance trends, dynamically corrects the professional skill confidence and matching weight, iteratively optimizes the cognitive load and time window control strategy, and improves the skill graph based on the graph incremental update algorithm.

[0025] Specifically, the unsupervised clustering algorithm first performs feature processing on the required professional skills, work patterns, and expected duration dimensions of historical similar tasks to eliminate the dimensional differences in the data. Then, it iteratively divides the data into clusters, introducing a silhouette coefficient during the clustering process. By calculating the average similarity of tasks within each cluster and the average distance between tasks in different clusters, clustering results that meet the criteria of compactness within clusters and separation between clusters are generated.

[0026] Specifically, the task attribute dynamic weighting algorithm first defines the dimensions for calculating real-time project complexity, including the total number of tasks currently pending, the density of dependencies between tasks, and the task difficulty coefficient. The comprehensive complexity value is obtained through a weighted summation. The weight allocation is set based on the degree of influence of the dimensions on project progress. The resource tension is quantified by statistically analyzing the ratio of the total number of professionals currently available for the project to the total number of professionals actually needed for the project. The attribute weights are adjusted according to the changes in the comprehensive complexity value and the resource tension, with the adjustment direction being positively correlated with the comprehensive complexity value and the resource tension.

[0027] The real-time project complexity calculation dimensions include the total number of tasks currently pending, the density of dependencies between tasks (i.e., the number of pre- and post-tasks associated with a single task), and the task difficulty coefficient (a multi-level gradient divided according to the depth of professional skills required and the number of operational steps). A weighted summation method is used to obtain the comprehensive complexity value, with weight allocation based on the impact of each dimension on project progress. Secondly, the resource scarcity is quantified by calculating the ratio of the total number of professionals currently available for the project to the total number of professionals actually needed for the project; a lower ratio indicates higher scarcity. Finally, attribute weights are dynamically adjusted based on changes in complexity and scarcity values. The adjustment direction is positively correlated with complexity and scarcity; that is, the higher the complexity and scarcity, the greater the increase in the weight of key project attributes (such as professional skills and time constraints). Furthermore, the adjustment range of a single attribute weight does not exceed one-third of the initially set weight to avoid excessive weight fluctuations affecting subsequent matching accuracy.

[0028] Specifically, the business process association rule mining algorithm preprocesses historical business process data, including deduplication of process steps, labeling of temporal relationships, and filtering of abnormal processes; then, it sets support thresholds and confidence thresholds, and extracts association rules that meet the threshold conditions by iteratively calculating the association degree between steps; finally, it constructs a process dependency graph based on the association rules, and decomposes the business process according to the logical chain of the starting step, core step, and closing step.

[0029] The professional feature vector dimensions include: skill matching degree, calculated by comparing the overlap and proficiency between the professional's existing skills and the skills required for the position; years of experience, divided into different gradients based on the professional's cumulative time working in the corresponding field, with each gradient corresponding to a specific quantitative benchmark; geographical matching degree, divided into different intervals based on the spatial distance between the professional's residence area and the position's execution area, with each interval corresponding to a corresponding degree of fit; historical rating, based on client feedback from past projects, categorized into evaluation levels according to satisfaction and converted into quantitative values; and cost compliance, calculated by the ratio of the professional's expected compensation standard to the project's preset compensation budget, reflecting cost compliance. The availability of job features is measured by the proportion of time professionals can dedicate to the current project within a certain period, reflecting time adaptability. The job feature vector dimensions include: skill weight (set based on the urgency of the job's skill requirements), experience weight (set based on the job's experience requirements), geographic weight (determined by the influence of geographic factors based on the job's execution mode (on-site / remote), quality weight (set based on the project's quality standards for job deliverables), cost weight (set based on the project's overall budget control requirements for job compensation), and urgency weight (different urgency levels are assigned and weighted according to the job's task deadlines).

[0030] Specifically, the adaptive weight adjustment algorithm generates corrected weights by: first, determining the scope of historical matching data collection, which includes matching results, task execution results, and customer feedback information; extracting three core evaluation indicators: historical matching success rate (the proportion of professionals who successfully complete the corresponding job tasks after matching); customer feedback satisfaction (classifying satisfaction levels based on customer evaluations of professional performance); and task quality score deviation (comparing the quality score of the professional's actual deliverables with the project's preset quality score standard to calculate the degree of deviation); and then modeling the correlation between the core evaluation indicators and weight adjustment based on a linear regression model to obtain the corresponding correction coefficients and calculate the final corrected weights.

[0031] Specifically, the process of constructing and setting constraints for the integer linear programming model of the file matching module is as follows: The optimization objective is to maximize the global matching effect between professionals and positions. Three hard constraints are set: single position matching constraint, each position is assigned one professional; professional overload constraint, the number of projects matched by a single professional in a single instance is based on a preset anti-overload threshold; on-site task geographical constraint, for positions performed on-site, the spatial distance between the professional's geographical location and the task execution location needs to be verified, and professionals who are outside the reasonable commuting range are not included in the matching candidate pool of the position; based on the hard constraints, an integer linear programming model is constructed to generate the optimal matching result that satisfies the hard constraints.

[0032] Specifically, the optimization of the interval variable time window control strategy adopts a rolling time domain scheduling method, which divides the entire project cycle into continuous time windows, each covering a fixed project progress cycle. Within each time window, task execution feedback data from the previous time window is collected, including the deviation between the actual completion time and the planned completion time, and the difference between the actual utilization of professional resources and the plan. Based on the feedback data, the causes of the deviations are analyzed, and the time window parameters are adjusted by rolling optimization window by window.

[0033] In the dynamic scheduling module, the optimization of the interval variable time window control strategy adopts a rolling time domain scheduling method. The specific execution process is as follows: First, the entire project cycle is divided into multiple consecutive time windows, each covering a fixed project progress cycle. Within each time window, task execution feedback data from the previous time window is collected, including the deviation between the actual completion time and the planned completion time (earlier or later), and the difference between the actual use of professional resources and the plan. Then, the reasons for the deviation are analyzed based on the feedback data. If the delay is caused by the task difficulty exceeding expectations, the time allocation of similar tasks in subsequent time windows is adjusted. If the progress is delayed due to insufficient resources, the resource allocation plan is optimized. When adjusting the time window parameters, it is ensured that the adjusted duration does not deviate too much from the original planned duration to avoid a significant impact on the overall project cycle. Through rolling optimization window by window, the dynamic adaptation of the time window control strategy and the actual execution situation is achieved.

[0034] Specifically, in the cognitive load optimization of the dynamic scheduling module, the execution process of cognitive load calculation and the quantification method of each factor are as follows: First, define the constituent factors of the remote task cognitive load index, including task complexity coefficient, historical interruption frequency, and cross-time zone factor. The constituent factors participate in the index calculation according to preset weights. The task complexity coefficient is divided according to the types of professional skills required by the task. The historical interruption frequency is calculated by statistically analyzing the interruption records of professionals when remotely performing tasks in the past, and the average number of interruptions per unit time is calculated. The cross-time zone factor is divided according to the degree of difference between the time zone where the professional is located and the time zone where the task collaborator is located.

[0035] Specifically, the improved path optimization algorithm is a heuristic search-based path optimization algorithm that integrates real-time traffic data. The specific execution steps include: first, collecting algorithm input data, including the execution location coordinates of the on-site task, the current location coordinates of the professional, and real-time traffic congestion data; then, converting the real-time traffic congestion data into a congestion coefficient, constructing a path cost function, multiplying the physical distance by the congestion coefficient to obtain the actual travel distance affected by traffic, and then adding the estimated parking time; finally, traversing the travel paths through a heuristic search method, calculating the path cost function value, and selecting the execution path with the lowest cost, i.e., the shortest commuting time, that conforms to the road traffic rules.

[0036] Specifically, the skills supply and demand prediction algorithm adopts a gradient boosting-based tree prediction model. The training dataset of the tree prediction model includes a list of skills demanded in past projects, a list of skills supplied by professionals, and historical data on skills gaps. Then, the training data is preprocessed, including missing value imputation, outlier removal, and feature encoding. Next, the tree prediction model is constructed, and a decision tree is generated through iterative training. The decision tree learns the supply and demand relationship in the skills dimension, and finally, the prediction results of the decision tree are integrated through gradient boosting.

[0037] Specifically, the deliverable anomaly detection algorithm is based on an unsupervised learning algorithm of an autoencoder. First, feature extraction is performed on the deliverable data, converting format compliance, content completeness, data accuracy, and quality standard compliance into high-dimensional feature vectors. Then, the high-dimensional feature vectors are input into a pre-trained autoencoder to compress them into low-dimensional vectors. Finally, the low-dimensional vectors are reconstructed into high-dimensional feature vectors by a decoder. The reconstruction error is calculated, and if it exceeds a preset threshold, the deliverable is determined to be an anomaly.

[0038] The deliverable anomaly detection algorithm of the quality optimization module is an unsupervised learning algorithm based on an autoencoder. The specific detection process includes: First, feature extraction is performed on the deliverable data, converting "format compliance" (checking whether the deliverable meets the preset format standard), "content completeness" (checking whether the deliverable contains all required content), "data accuracy" (checking whether the data in the deliverable is correct), and "quality standard compliance" (checking whether the deliverable meets the quality requirements set by the project) into high-dimensional feature vectors to ensure comprehensive coverage of the key evaluation dimensions of the deliverable; then, the feature vectors are input into a pre-trained autoencoder model, which compresses the high-dimensional features into low-dimensional vectors through the encoder, and then reconstructs the low-dimensional vectors into high-dimensional features through the decoder; next, the reconstruction error between the reconstructed feature vector and the original feature vector is calculated. If the error value exceeds a preset threshold (this threshold is determined through training on a large amount of historical qualified deliverable data to ensure effective differentiation between qualified and unqualified deliverables), the deliverable is determined to be abnormal; finally, a re-inspection process is triggered, and the abnormal deliverable and error analysis results are fed back to the relevant reviewers for manual review and confirmation.

[0039] Specifically, the graph incremental update algorithm sets update trigger conditions: adding new professional skill terms, changes in existing skill associations, and updates to the skill proficiency of professionals. When the update is executed, the association strength between the new skill and existing skills is first calculated using cosine similarity to generate the position of the new skill in the graph and its association with other skills. Then, the weights of nodes and edges in the skill graph are adjusted.

[0040] This embodiment uses a company's "cross-regional IT system integration project" as an application scenario. This project requires the deployment, configuration, and operation and maintenance of network equipment for 10 branch offices, involving two types of tasks: "on-site equipment installation" and "remote system debugging." It requires the matching of three types of professionals: system architect, network engineer, and operation and maintenance specialist. The implementation process of this system is described in detail below: Business Process Decomposition and Clustering: Using a business process association rule mining algorithm, the "IT system integration" process is decomposed into seven task steps: "requirements survey → solution design → equipment procurement → on-site deployment → remote debugging → testing and acceptance → operation and maintenance delivery." Historical data from similar IT integration projects over the past three years (50 projects in total) are selected. A partition-based unsupervised clustering algorithm is used to normalize the features of three categories of data: "required professional skills," "work mode," and "estimated duration" (eliminating the difference in dimensions between "number of skills" and "number of days"), iteratively dividing the data into three clusters. Cluster 1 (Remote Task): Solution design and remote debugging (requires system architect / operations specialist, short duration, remote working mode). Cluster 2 (Field Task): Field Deployment (Requires a network engineer, moderate duration, on-site work mode); Cluster 3 (Comprehensive Task): Requirements gathering, testing and acceptance, operation and maintenance delivery (requires multi-role collaboration, long duration, and hybrid work mode). The silhouette coefficient was introduced for verification, and the average intra-cluster similarity was calculated to be 0.89 and the average inter-cluster distance was 0.72, which met the standard of "compact intra-cluster and separated inter-cluster". Structured task data (including task identifier, required skills, working mode, estimated time, etc.) was generated.

[0041] Dynamic weight adjustment: At the start of the project, there are 28 tasks to be processed (including 12 on-site deployments and 16 remote debuggings). The tasks have dense dependencies (e.g., "on-site deployment" depends on "equipment procurement") and the task difficulty coefficient is "high" (requiring cross-vendor equipment compatibility). The "comprehensive complexity value" obtained by weighted summation is 0.82. Currently, there are 8 professionals available (2 system architects, 3 network engineers, and 3 operations and maintenance specialists). The project requires 12 people, and the resource tension is 8 / 12≈0.67 (relatively high tension).

[0042] Based on the task attribute dynamic weighting algorithm, the weight of "professional skills matching degree" was increased from the initial 0.3 to 0.45, and the weight of "time constraint" was increased from 0.2 to 0.3, to ensure that subsequent matching prioritizes "skills adaptation" and "progress guarantee".

[0043] Professional Profile Building: Based on the task data output by the project's standardized module, build profiles for 8 professionals, as shown in the example below: Zhang San (System Architect): 5 years of experience (classified as "Intermediate / Senior Level"), proficient in "cloud architecture design and cross-regional system compatibility", geographical location: City A, historical rating: 4.8 (satisfaction level: "Excellent"), expected compensation in line with project budget, availability 80% for the next 30 days; Li Si (Network Engineer): 4 years of experience, proficient in routing and switching configuration and on-site troubleshooting; geographical area: City B (covering 3 branch offices); historical rating: 4.6; expected compensation meets budget; availability: 90% for the next 30 days. Wang Wu (Operations Specialist): 3 years of experience, proficient in "remote system monitoring and fault response", geographical location: City C, historical rating: 4.5, expected compensation is in line with budget, availability for the next 30 days: 85%.

[0044] Matching algorithm execution: Feature vector extraction: Professional feature vector (taking Zhang San as an example): [Skill matching degree 0.92 (overlap with "Solution Design" skills), experience years gradient 0.8, geographical matching degree 0.7 (City A covers 2 remote debugging tasks), historical score 0.96, cost compliance 1.0, availability 0.8]; Job feature vector (taking "Remote Debugging" as an example): [Skill weight 0.45, experience weight 0.2, geographical weight 0.15, quality weight 0.1, cost weight 0.05, urgency weight 0.05].

[0045] Weight Calculation: The customer base weights are set as "quality weight 0.3, skill weight 0.4". An adaptive weight adjustment algorithm is used, and historical matching data from the past year (30 sets in total) are selected to extract core evaluation indicators: historical matching success rate 0.85, customer feedback satisfaction "excellent" percentage 0.7, and task quality score deviation 0.08. A correction coefficient of 0.12 is obtained through a linear regression model. The final weight is calculated as: initial weight × (1 + 0.12). For example, the "skill weight" is corrected to 0.496.

[0046] Model Solution: Construct a professional-job bipartite graph (edge ​​weights are matching scores, Zhang San's matching score with "remote debugging" is 0.91), establish an integer linear programming model, and set hard constraints: ① "Remote debugging" only matches 1 maintenance specialist / architect; ② Each person matches ≤3 projects (to prevent overload); ③ "On-site deployment" only matches network engineers within the geographical coverage area (e.g., Li Si only matches 3 on-site tasks in City B); Solve the algorithm to obtain the optimal matching pairs: Zhang San → Solution Design (2 items) + Remote Debugging (3 items), Li Si → On-site Deployment in City B (3 items), Wang Wu → Remote Debugging in City C (4 items). Time window optimization: A rolling time-domain scheduling method is adopted, dividing the 60-day project cycle into 8 time windows (each window is 7-8 days). Within the first window: Feedback data collected: Zhang San's "scheme design" task was completed 1 day ahead of schedule (deviation -1 day), while Li Si's "on-site deployment" was delayed by 0.5 days due to traffic delays (deviation +0.5 days). Adjustment strategy: Extend the subsequent "remote debugging" time window from 5 days to 6 days (to reserve a buffer), and add 1 hour of traffic redundancy time for Li Si's "on-site deployment".

[0047] Differentiated task scheduling: On-site task (Li Si): Geographic clustering was used to divide the three on-site tasks in City B into one cluster. Real-time traffic data (morning peak congestion coefficient 1.8, off-peak congestion coefficient 1.2) was integrated. A cost function was constructed using a path optimization algorithm based on heuristic search: path cost = physical distance × congestion coefficient + estimated parking time. After traversing three candidate paths, the optimal path "branch A → branch C → branch B" was selected, reducing the commute time from the original 4.5 hours to 3.2 hours.

[0048] Remote task (Zhang San / Wang Wu): Time zone alignment is adopted (scheduled uniformly according to the project core time zone 9:00-18:00), and Zhang San is allocated bandwidth priority "high" (to ensure cloud architecture design transmission); calculate cognitive load index: Zhang San's "remote debugging" task complexity coefficient 2 (requires 3 skills), historical interruption frequency 0.2 times / hour, cross time zone factor 1 (consistent with the branch office time zone), the initial index value is 0.68 and no adjustment is needed. Skills supply and demand forecasting: Using a tree-structured forecasting model based on gradient boosting, the system takes "Task skill requirements for the next 20 days (requiring 5 operations and maintenance specialists)" and "Current supply (3 operations and maintenance specialists)" as input, and outputs a skills gap of "2 operations and maintenance specialists". The system automatically triggers an alert, prompting HR to supplement recruitment.

[0049] Calendar Integration: Write the scheduling results to the enterprise calendar of Zhang San / Li Si / Wang Wu. For example, Li Si's "Deployment of Branch Office A in City B" is scheduled for "May 10, 9:00-12:00". This time period is locked to prevent conflicts with other on-site tasks. Zhang San's "Remote Debugging" task is allowed to partially overlap with the "Solution Design" task (15:00-16:00) from "May 12, 14:00-16:00".

[0050] Deliverable inspection: The "Solution Design Document" submitted by Zhang San was inspected using an unsupervised learning algorithm based on an autoencoder. Feature vectors were extracted: format compliance (0.98), content completeness (0.95), data accuracy (0.92), and quality standard compliance (0.96), and converted into a 128-dimensional vector. After the encoder reconstructs, the calculated reconstruction error is 0.07, which is lower than the preset threshold of 0.15, and is therefore deemed qualified. When testing the "on-site deployment report" of a certain branch, the reconstruction error is 0.21 (due to missing equipment model data), triggering a retest. After Li Si supplements the data, the error is reduced to 0.09, and is therefore deemed qualified.

[0051] Feedback and map updates: Performance data collected: Zhang San's task completion rate is 100% and customer satisfaction is 4.9; Wang Wu's task completion rate is 95% and customer satisfaction is 4.6. Through time series prediction optimization algorithm analysis, Zhang San's "remote debugging" efficiency is on the rise, so his "skill confidence" is increased from 0.92 to 0.95, and the matching weight is increased by 0.03. Skill graph update: The skill "Configuration of domestically produced equipment" was added to the project (first identification). The cosine similarity calculation showed that the association strength with "Network equipment deployment" was 0.83. A new node (weight 0.7) was added to the skill graph, and an edge (weight 0.65) was established with "On-site deployment" and "Remote debugging" to improve the skill association system.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A standardized project management and intelligent professional scheduling system, characterized in that, It includes a project standardization module, a file matching module, a dynamic scheduling module, and a quality optimization module; The project standardization module uses a business process association rule mining algorithm to decompose business processes and form standardized task steps; it analyzes historical data of similar tasks through unsupervised clustering algorithm to generate structured data of steps; at the same time, it introduces a task attribute dynamic weight algorithm to dynamically adjust attribute weights based on real-time project complexity and resource scarcity. The file matching module constructs professional information files based on task data, uses a multi-criteria decision-weighted bipartite graph matching algorithm to extract feature vectors of professionals and corresponding positions, combines customer base weights and adaptive weight adjustment algorithms to generate corrected weights, outputs the final matching weights through the analytic hierarchy process, calculates the matching score, constructs a bipartite graph and integer linear programming model, and generates the optimal professional-position matching pair. The dynamic scheduling module receives the optimal professional-job matching pair and optimizes the interval variable time window control strategy based on the resource constraint scheduling mechanism, with the goal of minimizing project cycle, commuting cost and cognitive load. For on-site tasks, a geographic clustering-improved path optimization algorithm is used to plan the execution path. For remote tasks, a time zone alignment-bandwidth adaptive allocation algorithm is used. Skill gaps are predicted based on a skill supply and demand prediction algorithm. The generated scheduling results are written into the personal calendar of the professionals. The quality optimization module uses project quality standards as the monitoring benchmark, employs a deliverable anomaly detection algorithm to verify the compliance of task deliverables, collects performance data of professionals, introduces a time-series prediction optimization algorithm to analyze historical performance trends, dynamically corrects the professional skill confidence and matching weight, iteratively optimizes the cognitive load and time window control strategy, and improves the skill graph based on the graph incremental update algorithm.

2. The system according to claim 1, characterized in that, The unsupervised clustering algorithm first performs feature processing on the required professional skills, work mode, and expected duration of historical similar tasks to eliminate the dimensional differences of the data. Then, it iteratively divides the data into clusters. During the clustering process, a silhouette coefficient is introduced. By calculating the average similarity of tasks within the clusters and the average distance between tasks in the clusters, clustering results that meet the criteria of compactness within clusters and separation between clusters are generated.

3. The system according to claim 1, characterized in that, The task attribute dynamic weighting algorithm first defines the dimensions for calculating real-time project complexity, including the total number of tasks to be processed, the density of dependencies between tasks, and the task difficulty coefficient, and obtains the comprehensive complexity value through a weighted summation method. The weight allocation is set based on the degree of influence of each dimension on project progress; the resource shortage is quantified by the ratio of the total number of professionals currently available for the project to the total number of professionals actually needed for the project; the attribute weights are adjusted according to the changes in the comprehensive complexity value and the resource shortage, and the adjustment direction is positively correlated with the comprehensive complexity value and the resource shortage.

4. The system according to claim 1, characterized in that, The business process association rule mining algorithm preprocesses historical business process data, including deduplication of process steps, annotation of temporal relationships, and filtering of abnormal processes. Then, it sets support thresholds and confidence thresholds, and extracts association rules that meet the threshold conditions by iteratively calculating the association degree between steps. Finally, it constructs a process dependency graph based on the association rules and decomposes the business process according to the logical chain of the starting step, core step, and closing step.

5. The system according to claim 1, characterized in that, The adaptive weight adjustment algorithm generates corrected weights by: first, determining the scope of historical matching data collection, which includes matching results, task execution results, and customer feedback; extracting three core evaluation indicators: historical matching success rate (the proportion of professionals who successfully complete the corresponding job tasks after matching); customer feedback satisfaction (classifying satisfaction levels based on customer evaluations of professional performance); and task quality score deviation (comparing the quality score of the professional's actual deliverables with the project's preset quality score standard to calculate the degree of deviation); and then modeling the correlation between the core evaluation indicators and weight adjustment based on a linear regression model to obtain the corresponding correction coefficients and calculate the final corrected weights.

6. The system according to claim 1, characterized in that, The process of constructing and setting constraints for the integer linear programming model of the file matching module is as follows: The optimization objective is to maximize the global matching effect between professionals and positions. Three hard constraints are set: single position matching constraint, each position is assigned one professional; professional overload constraint, the number of projects matched by a single professional in a single run is based on a preset anti-overload threshold; on-site task geographical constraint, for positions that are performed on-site, the spatial distance between the professional's geographical location and the task execution location needs to be verified, and professionals who are outside the reasonable commuting range are not included in the matching candidate pool of the position; based on the hard constraints, an integer linear programming model is constructed to generate the optimal matching result that satisfies the hard constraints.

7. The system according to claim 1, characterized in that, The optimization of the interval variable time window control strategy adopts a rolling time domain scheduling method, which divides the entire project cycle into continuous time windows, each covering a fixed project progress cycle. Within each time window, task execution feedback data from the previous time window is collected, including the deviation between the actual completion time and the planned completion time, and the difference between the actual utilization of professional resources and the plan. Based on the feedback data, the causes of the deviations are analyzed, and the time window parameters are adjusted by rolling optimization window by window.

8. The system according to claim 1, characterized in that, In the cognitive load optimization of the dynamic scheduling module, the execution process of cognitive load calculation and the quantification method of each factor are as follows: First, the constituent factors of the remote task cognitive load index are defined, including the task complexity coefficient, historical interruption frequency, and cross-time zone factor. The constituent factors participate in the index calculation according to preset weights. The task complexity coefficient is divided according to the types of professional skills required by the task. The historical interruption frequency is calculated by statistically analyzing the interruption records of professionals when remotely performing tasks in the past, and the average number of interruptions per unit time is calculated. The cross-time zone factor is divided according to the degree of difference between the time zone where the professional is located and the time zone where the task collaborator is located.

9. The system according to claim 1, characterized in that, The improved path optimization algorithm is a heuristic search-based path optimization algorithm that integrates real-time traffic data. The specific execution steps include: first, collecting algorithm input data, including the coordinates of the execution location of the on-site task, the coordinates of the professional's current location, and real-time traffic congestion data; then, converting the real-time traffic congestion data into a congestion coefficient, constructing a path cost function, multiplying the physical distance by the congestion coefficient to obtain the actual travel distance affected by traffic, and then adding the estimated parking time; finally, traversing the travel paths using a heuristic search method, calculating the path cost function value, and selecting the execution path with the lowest cost, i.e., the shortest commuting time, that conforms to road traffic rules.

10. The system according to claim 1, characterized in that, The skills supply and demand prediction algorithm adopts a gradient boosting-based tree prediction model. The training dataset of the tree prediction model includes a list of skills demand for past projects, a list of skills supply for professionals, and historical data on skills gaps. Then, the training data is preprocessed, including missing value imputation, outlier removal, and feature encoding. Next, the tree-like prediction model is constructed, and a decision tree is generated through iterative training. The decision tree learns the supply and demand relationship of the skill dimension, and finally the prediction results of the decision tree are integrated through gradient boosting.

11. The system according to claim 1, characterized in that, The deliverable anomaly detection algorithm is based on an unsupervised learning algorithm of autoencoder. First, feature extraction is performed on the deliverable data, converting format compliance, content completeness, data accuracy, and quality standard compliance into high-dimensional feature vectors. Then, the high-dimensional feature vectors are input into a pre-trained autoencoder to compress them into low-dimensional vectors. Finally, the low-dimensional vectors are reconstructed into high-dimensional feature vectors by a decoder. The reconstruction error is calculated, and if it exceeds a preset threshold, the deliverable is determined to be an anomaly.

12. The system according to claim 1, characterized in that, The graph incremental update algorithm sets update trigger conditions: adding new professional skill terms, changes in existing skill associations, and updates to the skill proficiency of professionals. When the update is executed, the association strength between the new skill and existing skills is first calculated using cosine similarity to generate the position of the new skill in the graph and its association with other skills. Then, the weights of nodes and edges in the skill graph are adjusted.

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