AI intelligent order sending system based on dynamic worker portrait and process progress

Abstract

The invention discloses an AI intelligent order sending system based on a dynamic worker portrait and a process progress, and the system comprises a labeling module which is used for disassembling the total process of a construction project, and labeling the required worker information corresponding to each process; the establishment module acquires worker information and establishes a worker portrait according to the worker information; the first order sending module is used for sending orders according to the worker portrait and the required worker information to obtain a first order sending result; a first updating module obtains the state information of the worker after the order is sent according to the first order sending result, and updates the worker portrait according to the state information to obtain a corrected worker portrait; obtaining progress information of each process after order dispatching and corresponding preset progress information; and the second updating module updates the first order sending result according to the corrected worker portrait, the progress information of each process and the corresponding preset progress information to obtain a second order sending result. And meanwhile, the order dispatching is dynamically optimized, so that the resource utilization rate is improved, and the management cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of construction allocation technology, and in particular to an AI-powered intelligent dispatching system based on dynamic worker profiles and work process progress. Background Technology

[0002] In construction project management, work order dispatch (i.e., matching and scheduling workers with construction procedures) is a crucial link affecting construction efficiency, schedule control, and project quality. Currently, work order dispatch in the construction industry is mostly based on traditional experience-based scheduling. While some projects have introduced simple information management methods, significant limitations remain: 1. Worker information management is fragmented. Existing methods mostly record static data such as basic worker identity information and skill certificates, failing to integrate and analyze dynamic information such as worker skill performance, construction efficiency, quality level, and real-time work status (e.g., workload, emergencies). This lack of integration prevents the formation of a comprehensive and dynamic worker profile, resulting in a lack of data support for assessing worker suitability during work order dispatch; 2. Work order decision-making lacks precise quantitative basis, and the work order dispatch process is often... Relying on the personal experience of managers makes it difficult to quantitatively assess the suitability of workers' skills, efficiency, and quality, which can easily lead to resource mismatch. 3. The dispatching results lack dynamic adjustment capabilities. Most of the current dispatching is a one-time scheduling. After dispatching, worker information is not updated according to changes in the workers' status during the construction process (such as skill improvement, efficiency fluctuations, and emergencies), nor is the dispatching plan optimized based on the deviation between the process progress and the preset progress. When the process is behind schedule or ahead of schedule, the dispatching cannot be adjusted in time to adapt to the process progress requirements, which can easily lead to project delays, resource waste, or poor process connection. Summary of the Invention

[0003] This invention aims to at least partially solve one of the technical problems in the aforementioned technologies. Therefore, the purpose of this invention is to propose an AI-powered intelligent dispatching system based on dynamic worker profiles and process progress, which improves dispatching accuracy, dynamically optimizes dispatching, increases resource utilization, and reduces management costs.

[0004] To achieve the above objectives, this invention proposes an AI-powered intelligent work order dispatching system based on dynamic worker profiles and process progress, comprising: The annotation module is used to break down the overall construction process and annotate the worker requirements for each process. Establish a module to acquire worker information and create worker profiles based on that information; The first dispatch module is used to dispatch orders based on worker profiles and demand worker information to obtain the first dispatch result. The first update module is used for: Obtain the worker's status information after dispatching an order based on the first dispatch result, update the worker profile based on the status information, and obtain the corrected worker profile. Obtain the progress information of each process after the order is dispatched, as well as the corresponding preset progress information; The second update module is used to update the first dispatch result based on the corrected worker profile, the progress information of each process and the corresponding preset progress information, so as to obtain the second dispatch result.

[0005] According to some embodiments of the present invention, the annotation module is used to decompose the overall construction process into three levels—unit project, sub-project, and item project—based on the WBS (Work Breakdown Structure), and to annotate the corresponding worker requirements for each level of process.

[0006] According to some embodiments of the present invention, a building module includes: The acquisition module is used to acquire worker information; The data processing module is used to preprocess worker information; the data preprocessing includes outlier identification and processing, and data format standardization. The generation module is used to construct a multi-dimensional tag system, mapping preprocessed worker information into profile tags; the profile tags include skill tags, efficiency tags, quality tags, and status tags; and worker profiles are built based on the profile tags.

[0007] According to some embodiments of the present invention, the acquisition module includes: The first acquisition submodule is used to acquire the static basic information of workers; the static basic information includes identity and qualifications, and basic attributes; the identity and qualifications include name, age, length of service, skill certificates, and training records; the basic attributes include gender, health status, and permanent construction area; The second acquisition submodule is used to acquire dynamic construction information of workers; the construction information includes skill performance, efficiency data, and quality data. The third acquisition submodule is used to acquire the real-time status information of workers; the real-time status information includes task status, load level, and emergency status.

[0008] According to some embodiments of the present invention, the first dispatch module includes: The first analysis module is used to analyze the information on workers in need and obtain quantitative indicators of demand corresponding to the worker profile. The quantitative indicators of demand include core skill requirements, efficiency threshold requirements, quality standard requirements, and number of workers and time window requirements. The filtering module is used to filter candidates based on worker profiles, using quantitative demand indicators as filtering criteria. The scoring module is used to quantitatively score each candidate based on the scoring model, including skill suitability, efficiency matching, quality fit, and status availability. The scoring results are sorted from high to low, and the first order assignment result is obtained based on the sorting results and the number of people required.

[0009] According to some embodiments of the present invention, the second update module includes: The module is used to locate and adjust the trigger point for dispatching orders based on the progress information of each process and the corresponding preset progress information. The first determination module is used to establish a multi-dimensional dynamic matrix of the process urgency of the trigger point and the worker suitability of the corrected worker profile, and to determine the adjustment candidate set based on the multi-dimensional dynamic matrix; The processing module is used to update the first dispatch result based on the adjusted candidate set to obtain the second dispatch result.

[0010] According to some embodiments of the present invention, the building module includes: The first calculation module is used to calculate the difference between the progress information of each process and the corresponding preset progress information. The difference is divided by the preset progress information to obtain the process progress deviation coefficient. The second determination module is used to classify and determine the deviation level based on the process progress deviation coefficient and the preset deviation level, and to use the process with the deviation level greater than the preset deviation level threshold as the dispatch adjustment trigger point.

[0011] According to some embodiments of the present invention, the first determining module includes: The second calculation module is used to determine whether the trigger point is a delayed process, calculate the weight of the delayed process in the total project duration and the process schedule deviation coefficient; calculate the urgency score based on the weight in the total project duration and the deviation level; urgency score = weight × (1 + absolute value of process schedule deviation coefficient × 2); determine whether the trigger point is a leading process, and calculate the available worker resources in the leading process; The third calculation module is used to calculate worker suitability from multiple dimensions based on the released worker resources and the revised worker profile; The selection module is used to generate a four-quadrant dynamic matrix with process urgency as the vertical axis and worker suitability as the horizontal axis; from the four-quadrant dynamic matrix, quadrants with urgency greater than the preset urgency and suitability greater than the preset suitability are selected, and adjustment candidate sets are selected from them.

[0012] According to some embodiments of the present invention, a verification module is used to verify the second dispatch result, determine whether worker adaptation is complete, and generate a skills gap analysis report when it is determined that worker adaptation is not complete, and push it to the engineering management terminal.

[0013] This invention proposes an AI-powered intelligent dispatching system based on dynamic worker profiles and process progress. By quantitatively matching dynamic worker profiles with worker information required for each process, it achieves precise adaptation of worker skills, efficiency, and quality levels to process requirements, thus improving dispatching accuracy. Through real-time updates to worker profiles and monitoring of process progress deviations, the system promptly adjusts dispatching plans, effectively addressing changes in worker status and fluctuations in process progress. This ensures processes proceed according to preset schedules, reduces the risk of project delays, and achieves dynamic optimization of dispatching. By quantitatively assessing worker suitability and availability, it enables rational allocation of worker resources, reducing idle high-efficiency workers and overloaded low-efficiency workers, thereby improving the overall utilization efficiency of construction resources. The system transforms dispatching decisions from relying on personal experience to data-driven AI intelligent decision-making, reducing the subjectivity and error rate of manual dispatching and lowering the management costs of construction scheduling.

[0014] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a block diagram of an AI intelligent dispatching system based on dynamic worker profiles and process progress according to an embodiment of the present invention. Figure 2 This is a block diagram of a building module according to an embodiment of the present invention; Figure 3 This is a block diagram of an acquisition module according to an embodiment of the present invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] like Figure 1 As shown, this embodiment of the invention proposes an AI-powered intelligent work order dispatching system based on dynamic worker profiles and process progress, comprising: The annotation module is used to break down the overall construction process and annotate the worker requirements for each process. Establish a module to acquire worker information and create worker profiles based on that information; The first dispatch module is used to dispatch orders based on worker profiles and demand worker information to obtain the first dispatch result. The first update module is used for: Obtain the worker's status information after dispatching an order based on the first dispatch result, update the worker profile based on the status information, and obtain the corrected worker profile. Obtain the progress information of each process after the order is dispatched, as well as the corresponding preset progress information; The second update module is used to update the first dispatch result based on the corrected worker profile, the progress information of each process and the corresponding preset progress information, so as to obtain the second dispatch result.

[0019] The working principle of the above technical solution is as follows: Based on the annotation module, the overall construction process is structurally broken down, clarifying the specific worker requirements for each process and establishing a correspondence between processes and requirements. A module acquires worker information and creates worker profiles based on this information. The first dispatch module dispatches workers based on these profiles and the required worker information, resulting in the first dispatch result. The first update module acquires worker status information after dispatching based on the first dispatch result, such as real-time worker performance during construction (efficiency fluctuations, skill improvements, unexpected situations, etc.), and updates the worker profiles accordingly, generating a revised worker profile to ensure that the profile always reflects the worker's true status. Actual progress information for each process is collected and compared with preset progress information to identify processes that are ahead of schedule or behind schedule. The second update module updates the first dispatch result based on the revised worker profiles, the progress information of each process, and the corresponding preset progress information, resulting in the second dispatch result.

[0020] The beneficial effects of the above technical solution are as follows: Based on the quantitative matching of dynamic worker profiles and worker information required for each work process, precise matching of worker skills, efficiency, and quality levels with work process requirements is achieved, facilitating improved dispatch accuracy. By updating worker profiles in real time and monitoring work process progress deviations, dispatch plans can be adjusted promptly, effectively addressing changes in worker status and fluctuations in work process progress, ensuring that work processes proceed according to the preset schedule, reducing the risk of project delays, and achieving dynamic optimization of dispatch. By quantitatively assessing worker suitability and availability, rational allocation of worker resources is achieved, reducing idle high-efficiency workers and overloaded low-efficiency workers, thus improving the overall utilization efficiency of construction resources. Dispatch decisions are transformed from relying on personal experience to data-driven AI intelligent decision-making, reducing the subjectivity and error rate of manual dispatch and lowering the management costs of construction scheduling.

[0021] According to some embodiments of the present invention, the annotation module is used to decompose the overall construction process into three levels—unit project, sub-project, and item project—based on the WBS (Work Breakdown Structure), and to annotate the corresponding worker requirements for each level of process.

[0022] The working principle of the above technical solution is as follows: The annotation module is based on the structured decomposition logic of WBS (Work Breakdown Structure) to systematically break down and annotate the overall process of a construction project. Starting with the overall goal of the construction project, according to the WBS principle of "top-down, layer-by-layer decomposition," the overall process is decomposed into a three-level structure: Level 1 (Unit Project): Corresponding to an engineering unit with independent construction conditions and capable of forming an independent function; Level 2 (Sub-project): Sub-units under the unit project, divided according to professional nature or construction stage; Level 3 (Item Project): Specific construction links under the sub-project, divided according to construction technology, materials, or process type. For each decomposed process, the corresponding worker information is annotated according to its construction characteristics, and the demand information becomes increasingly precise with the level of refinement: Unit Project: Annotates the overall manpower scale requirement, core skill clusters, and time cycle requirements; Sub-project: Annotates the specific skill types, skill level requirements, and phased manpower requirements required for the sub-project; Item Project: Annotates the most detailed manpower requirements, including specific skills, efficiency thresholds, quality standards, real-time manpower, and time windows.

[0023] The beneficial effects of the above technical solution are as follows: By breaking down the complex overall construction process into three levels of WBS, the process management is transformed into sub-units with clear hierarchy and logical definition, making the process management more systematic, realizing the structuring and standardization of process requirements, and marking the corresponding worker information for each level of process, thereby improving the accuracy and granularity of the worker information.

[0024] like Figure 2 As shown, according to some embodiments of the present invention, the building module includes: The acquisition module is used to acquire worker information; The data processing module is used to preprocess worker information; the data preprocessing includes outlier identification and processing, and data format standardization. The generation module is used to construct a multi-dimensional tag system, mapping preprocessed worker information into profile tags; the profile tags include skill tags, efficiency tags, quality tags, and status tags; and worker profiles are built based on the profile tags.

[0025] The working principle of the above technical solution is as follows: The acquisition module comprehensively acquires worker information, such as static basic information, dynamic construction information, and real-time status information. The data processing module standardizes the acquired raw worker information, eliminating data noise and format differences. A multi-dimensional tagging system covers the tag dimensions of the worker's core attributes, including skill tags, efficiency tags, quality tags, and status tags. The pre-processed worker information is matched with specific tags in the tagging system, integrating all tags to form a tag-centric digital profile of the worker, intuitively presenting the worker's skill level, work efficiency, quality performance, and real-time status.

[0026] The beneficial effects of the above technical solution are as follows: Comprehensive data acquisition by the acquisition module improves the accuracy and completeness of worker information. Data preprocessing eliminates outliers and format differences, ensuring data accuracy and consistency. The multi-dimensional tagging system covers the core elements of worker-matched work processes, and the tags are generated based on specific data, transforming worker profiles from "fuzzy descriptions" into "quantified tag sets." Standardized data processing procedures and tagging mapping logic enable rapid conversion of new or changed worker information into tag updates, accurately reflecting the worker's true information. Worker profiles also facilitate improved accuracy in dispatching decisions.

[0027] like Figure 3 As shown, according to some embodiments of the present invention, the acquisition module includes: The first acquisition submodule is used to acquire the static basic information of workers; the static basic information includes identity and qualifications, and basic attributes; the identity and qualifications include name, age, length of service, skill certificates, and training records; the basic attributes include gender, health status, and permanent construction area; The second acquisition submodule is used to acquire dynamic construction information of workers; the construction information includes skill performance, efficiency data, and quality data. The third acquisition submodule is used to acquire the real-time status information of workers; the real-time status information includes task status, load level, and emergency status.

[0028] According to some embodiments of the present invention, the first dispatch module includes: The first analysis module is used to analyze the information on workers in need and obtain quantitative indicators of demand corresponding to the worker profile. The quantitative indicators of demand include core skill requirements, efficiency threshold requirements, quality standard requirements, and number of workers and time window requirements. The filtering module is used to filter candidates based on worker profiles, using quantitative demand indicators as filtering criteria. The scoring module is used to quantitatively score each candidate based on the scoring model, including skill suitability, efficiency matching, quality fit, and status availability. The scoring results are sorted from high to low, and the first order assignment result is obtained based on the sorting results and the number of people required.

[0029] The working principle of the above technical solution is as follows: The required worker information is analyzed to obtain quantitative indicators corresponding to worker profiles, including core skill requirements (clarifying the specific skill types and levels required for each process, corresponding to skill tags in the worker profile), efficiency threshold requirements (setting the minimum efficiency requirements for workers in each process, corresponding to efficiency tags in the worker profile), quality standard requirements (clarifying the bottom line for construction quality in each process, corresponding to quality tags in the worker profile), and number of workers and time window requirements (determining the number of workers required for each process and the time frame for completion, providing constraints for subsequent screening and ranking). Using the quantitative indicators as filtering conditions, candidates are selected based on worker profiles. After screening, only workers who simultaneously meet the constraints of core skills, efficiency, quality, status, and timeliness are retained, forming a candidate pool. Workers in the candidate pool are quantitatively scored across multiple dimensions, and the final dispatch targets are determined by ranking them according to their scores. The workers' skill suitability, efficiency matching, quality fit, and status availability are quantitatively scored, and the workers' comprehensive scores are calculated according to preset weights (e.g., skills 40%, efficiency 25%, quality 20%, status 15%). The scores are then ranked from highest to lowest. Based on the number of workers required for the process (e.g., 5 people), the top 5 workers are selected from the ranking results to determine the initial dispatch targets for that process, thus obtaining the first dispatch result.

[0030] The beneficial effects of the above technical solution are as follows: The first analysis module transforms vague worker demand information into quantifiable and comparable indicators, which directly map to worker profile tags, achieving precise matching between demand and profile. The filtering module improves filtering efficiency and accuracy, while the scoring module enhances the accuracy of the first dispatch result.

[0031] According to some embodiments of the present invention, the second update module includes: The module is used to locate and adjust the trigger point for dispatching orders based on the progress information of each process and the corresponding preset progress information. The first determination module is used to establish a multi-dimensional dynamic matrix of the process urgency of the trigger point and the worker suitability of the corrected worker profile, and to determine the adjustment candidate set based on the multi-dimensional dynamic matrix; The processing module is used to update the first dispatch result based on the adjusted candidate set to obtain the second dispatch result.

[0032] The working principle of the above technical solution is as follows: Based on the actual progress of each process after the execution of the first dispatch result, the construction module compares the actual progress information with the preset progress information to locate the key processes (trigger points) that require dispatch adjustment. A multi-dimensional dynamic matrix is ​​established regarding the process urgency of the trigger point and the worker suitability of the corrected worker profile to facilitate the determination of the adjustment candidate set. Specifically, the urgency of work processes is quantified by combining the weight of delayed trigger points in the total project duration (e.g., critical path processes have higher weights) and the degree of schedule deviation (the greater the deviation, the higher the urgency) to calculate an urgency score. A higher score indicates that the process requires priority resource allocation. Worker suitability is quantified by calculating the worker suitability score for delayed trigger point processes based on the corrected worker profile (reflecting the latest worker status) output by the first update module, considering dimensions such as skill matching, current efficiency, quality stability, and workload status (a higher score indicates that the worker is more suitable to support the process). Dynamic matrix construction and candidate set screening involve constructing a four-quadrant matrix with process urgency as the vertical axis and worker suitability as the horizontal axis, prioritizing combinations in the "high urgency + high suitability" quadrant, and also including idle workers released from ahead-of-schedule trigger points to form an adjustment candidate set (i.e., workers, target processes, and adjustment methods that need adjustment). For delay trigger points, select highly suitable workers from the adjustment candidate set and strengthen manpower by adding or transferring (transferring idle workers from advanced processes). At the same time, replace workers in the original dispatch that have decreased suitability (such as workers whose efficiency has decreased due to status changes). For advance trigger points: remove the released idle workers from the original dispatch and include them in the resource pool to be allocated, and prioritize matching them to delay trigger points. The final second dispatch result satisfies the principle of prioritizing process urgency and optimizing worker suitability, ensuring that the adjusted dispatch plan is highly compatible with the current worker status and process progress.

[0033] The beneficial effects of the above technical solution are as follows: By promptly capturing process progress deviations through trigger point location and dynamically optimizing work order assignment based on real-time worker status, the work order assignment plan is always synchronized with the actual construction situation, achieving dynamic adaptive adjustment of work order assignment. A multi-dimensional dynamic matrix provides quantitative basis for adjustments: action is taken only on processes (trigger points) that truly require adjustment, and the most suitable workers are prioritized for allocation, avoiding blind increases / deployments and improving the accuracy and targeting of adjustment decisions. By transferring idle workers from advanced processes to lagging processes, idle manpower is reduced, optimizing the dynamic balance and utilization efficiency of resources.

[0034] According to some embodiments of the present invention, the building module includes: The first calculation module is used to calculate the difference between the progress information of each process and the corresponding preset progress information. The difference is divided by the preset progress information to obtain the process progress deviation coefficient. The second determination module is used to classify and determine the deviation level based on the process progress deviation coefficient and the preset deviation level, and to use the process with the deviation level greater than the preset deviation level threshold as the dispatch adjustment trigger point.

[0035] The working principle of the above technical solution is as follows: The first calculation module calculates the difference between the progress information of each process and the corresponding preset progress information. The difference is divided by the preset progress information to obtain the process progress deviation coefficient; a positive value indicates that the progress is ahead of schedule, and a negative value indicates that the progress is behind schedule. Based on the absolute value of the deviation coefficient, the process progress deviation is classified into levels, and the dispatch adjustment trigger point is located. For deviation coefficients with an absolute value less than or equal to 5%, it is a minor deviation that does not require adjustment; for deviation coefficients with an absolute value greater than 5% but less than or equal to 20%, it is a moderate deviation; and for deviation coefficients with an absolute value greater than 20%, it is a severe deviation. The preset deviation level threshold is minor deviation.

[0036] The beneficial effects of the above technical solution are as follows: Based on the deviation coefficient, the schedule deviation can be quantified and accurately described. It accurately identifies key processes requiring adjustment, improving adjustment efficiency. The positive and negative values ​​of the deviation coefficient distinguish between schedule ahead (resources can be released) and schedule behind (resources need to be supplemented). The trigger points include both behind and ahead processes, taking into account both schedule ahead and behind, achieving bidirectional resource optimization.

[0037] According to some embodiments of the present invention, the first determining module includes: The second calculation module is used to determine whether the trigger point is a delayed process, calculate the weight of the delayed process in the total project duration and the process progress deviation coefficient; calculate the urgency score based on the weight in the total project duration and the deviation level; urgency score = weight × (1 + absolute value of process progress deviation coefficient × 2); determine whether the trigger point is a leading process, and calculate the available worker resources in the leading process; The third calculation module is used to calculate worker suitability from multiple dimensions based on the released worker resources and the revised worker profile; The selection module is used to generate a four-quadrant dynamic matrix with process urgency as the vertical axis and worker suitability as the horizontal axis; from the four-quadrant dynamic matrix, quadrants with urgency greater than the preset urgency and suitability greater than the preset suitability are selected, and adjustment candidate sets are selected from them.

[0038] The working principle of the above technical solution is as follows: First, determine whether the trigger point is a delayed process. For delayed processes, calculate the urgency score by combining its importance (weight) in the total project duration and the degree of schedule deviation (deviation coefficient). For advanced processes, calculate the available worker resources: combine the remaining workload of the process, the current worker load rate, and the status tags in the revised worker profile to determine the number and specific personnel of workers who can be transferred in advance. Based on the available worker resources output by the second calculation module and the revised worker profile generated by the first update module, quantify the worker's suitability for the delayed process from multiple dimensions, including skill matching, efficiency matching, quality fit, and status availability, and calculate the comprehensive suitability score. Using the process urgency as the vertical axis and the worker suitability as the horizontal axis, generate a four-quadrant dynamic matrix; select quadrants from the four-quadrant dynamic matrix where the urgency is greater than the preset urgency and the suitability is greater than the preset suitability, and select delayed process-worker combinations that simultaneously meet both thresholds, and include them in the adjustment candidate set (including workers to be added / transferred, target delayed processes, and adjustment quantities).

[0039] The beneficial effects of the above technical solution are: it enables precise quantification of process urgency, ensuring that resources are prioritized for key processes. Through multi-dimensional worker suitability calculation, it reduces the risk of resource mismatch; based on a four-quadrant matrix, it intuitively distinguishes different combinations of urgency and suitability, selecting only the optimal combination of "high urgency + high suitability" as the adjustment candidate set, avoiding ineffective adjustments to low urgency or low suitability combinations, and improving adjustment efficiency.

[0040] According to some embodiments of the present invention, a verification module is used to verify the second dispatch result, determine whether worker adaptation is complete, and generate a skills gap analysis report when it is determined that worker adaptation is not complete, and push it to the engineering management terminal.

[0041] The working principle and beneficial effects of the above technical solution are as follows: The optimized dispatch results undergo final verification to ensure the complete matching of process requirements with workers. When incomplete matching exists, a skills gap analysis report accurately informs management of the gap details, facilitating timely warnings of skills gaps and reducing schedule risks.

[0042] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An AI-powered intelligent order dispatching system based on dynamic worker profiles and process progress, characterized in that, include: The annotation module is used to break down the overall construction process and annotate the worker requirements for each process. Establish a module to acquire worker information and create worker profiles based on that information; The first dispatch module is used to dispatch orders based on worker profiles and demand worker information to obtain the first dispatch result. The first update module is used for: Obtain the worker's status information after dispatching an order based on the first dispatch result, update the worker profile based on the status information, and obtain the corrected worker profile. Obtain the progress information of each process after the order is dispatched, as well as the corresponding preset progress information; The second update module is used to update the first dispatch result based on the corrected worker profile, the progress information of each process and the corresponding preset progress information, so as to obtain the second dispatch result.

2. The AI-powered intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 1, characterized in that, The annotation module is used to break down the overall construction process into three levels: unit project, sub-project, and item project, based on the WBS work breakdown structure, and to annotate the corresponding worker requirements for each level of process.

3. The AI-powered intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 1, characterized in that, Modules are created, including: The acquisition module is used to acquire worker information; The data processing module is used to preprocess worker information; the data preprocessing includes outlier identification and processing, and data format standardization. The generation module is used to construct a multi-dimensional tag system, mapping preprocessed worker information into profile tags; the profile tags include skill tags, efficiency tags, quality tags, and status tags; and worker profiles are built based on the profile tags.

4. The AI ​​intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 3, characterized in that, The acquisition module includes: The first acquisition submodule is used to acquire the static basic information of workers; the static basic information includes identity and qualifications, and basic attributes; the identity and qualifications include name, age, length of service, skill certificates, and training records; the basic attributes include gender, health status, and permanent construction area; The second acquisition submodule is used to acquire dynamic construction information of workers; the construction information includes skill performance, efficiency data, and quality data. The third acquisition submodule is used to acquire the real-time status information of workers; the real-time status information includes task status, load level, and emergency status.

5. The AI-powered intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 1, characterized in that, The first dispatch module includes: The first analysis module is used to analyze the information on workers in need and obtain quantitative indicators of demand corresponding to the worker profile. The quantitative indicators of demand include core skill requirements, efficiency threshold requirements, quality standard requirements, and number of workers and time window requirements. The filtering module is used to filter candidates based on worker profiles, using quantitative demand indicators as filtering criteria. The scoring module is used to quantitatively score each candidate based on the scoring model, including skill suitability, efficiency matching, quality fit, and status availability. The scoring results are sorted from high to low, and the first order assignment result is obtained based on the sorting results and the number of people required.

6. The AI-powered intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 1, characterized in that, The second update module includes: The module is used to locate and adjust the trigger point for dispatching orders based on the progress information of each process and the corresponding preset progress information. The first determination module is used to establish a multi-dimensional dynamic matrix of the process urgency of the trigger point and the worker suitability of the corrected worker profile, and to determine the adjustment candidate set based on the multi-dimensional dynamic matrix; The processing module is used to update the first dispatch result based on the adjusted candidate set to obtain the second dispatch result.

7. The AI ​​intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 6, characterized in that, Build modules, including: The first calculation module is used to calculate the difference between the progress information of each process and the corresponding preset progress information. The difference is divided by the preset progress information to obtain the process progress deviation coefficient. The second determination module is used to classify and determine the deviation level based on the process progress deviation coefficient and the preset deviation level, and to use the process with the deviation level greater than the preset deviation level threshold as the dispatch adjustment trigger point.

8. The AI ​​intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 7, characterized in that, The first determining module includes: The second calculation module is used to determine whether the trigger point is a delayed process, calculate the weight of the delayed process in the total project duration and the process progress deviation coefficient; calculate the urgency score based on the weight in the total project duration and the deviation level; urgency score = weight × (1 + absolute value of process progress deviation coefficient × 2); determine whether the trigger point is a leading process, and calculate the available worker resources in the leading process; The third calculation module is used to calculate worker suitability from multiple dimensions based on the released worker resources and the revised worker profile; The selection module is used to generate a four-quadrant dynamic matrix with process urgency as the vertical axis and worker suitability as the horizontal axis; from the four-quadrant dynamic matrix, quadrants with urgency greater than the preset urgency and suitability greater than the preset suitability are selected, and adjustment candidate sets are selected from them.

9. The AI-powered intelligent dispatching system based on dynamic worker profiles and process progress as described in claim 1, characterized in that, The verification module is used to verify the second dispatch result and determine whether the worker adaptation is complete. If it is determined that the worker adaptation is not complete, a skills gap analysis report is generated and pushed to the engineering management terminal.

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