Building industry AI positioning system

Abstract

The invention relates to the technical field of AI positioning, and discloses a building industry AI positioning system, which comprises an AI task generation module, an AI positioning module, an AI positioning module and an AI positioning module, the worker capability vector construction module is used for constructing a multi-dimensional and updatable worker capability vector; the task intelligent recommendation and worker autonomous selection module is used for realizing a bidirectional collaborative task undertaking mode; the task receiving guarantee and dynamic activation module is used for constructing a multi-stage early warning mechanism based on retention duration; and the feedback optimization and system self-evolution module is used for constructing a closed-loop learning mechanism. According to the scheme, the problems of opaque dispatching, unfair distribution and the like in traditional construction management are solved, accurate digital positioning of personnel and construction tasks is realized, intelligent distribution and fair undertaking of the tasks are realized through AI task generation, worker capability vector matching and an autonomous selection mechanism, retention tasks are activated by adopting a hierarchical response strategy, and the construction efficiency is improved. Self-evolution is realized through closed-loop feedback, and intelligence, transparency and high efficiency of construction management are improved.

Description

Technical Field

[0001] This invention relates to the field of AI positioning technology, and more particularly to an AI positioning system for the construction industry. Background Technology

[0002] The AI ​​positioning system for the construction industry is an intelligent construction management platform that integrates building information modeling, artificial intelligence algorithms, Internet of Things sensing, and mobile terminal technologies. Its core objective is to achieve precise mapping and dynamic coordination of construction elements such as "people, machines, materials, methods, and environment" in the physical space.

[0003] However, while some projects in the construction industry have introduced AI positioning systems, their functions are mostly limited to basic levels such as personnel attendance and safety area monitoring, achieving only superficial physical positioning. They have failed to achieve true deep integration with construction task management, resulting in persistent problems such as "opaque task assignment and unfair distribution." In practice, task allocation power is highly concentrated in the hands of team leaders or foremen, relying on verbal assignments and personal relationships, lacking unified, open, and quantifiable decision-making criteria. Workers cannot know the task content and reward standards in advance and often have no choice but to passively accept assignments. This can even lead to phenomena such as "familiarity breeds favoritism" and "grouping together based on hometown ties." Workers with high skill matching and a positive attitude often struggle to obtain suitable tasks, while some workers... Being assigned low-value, high-intensity work for extended periods leads to psychological imbalance and decreased motivation. This closed, experience-based assignment model not only weakens workers' sense of participation and trust but also easily triggers internal conflicts, affecting team collaboration efficiency. More seriously, due to the lack of data recording and process traceability mechanisms, the task allocation process cannot be audited. Once delays or quality issues occur, responsibility is difficult to clarify, and management cannot identify whether there is resource misallocation or bias. In the long run, this not only hinders the full utilization of the abilities of highly skilled workers but also exacerbates the mobility and management difficulty of the labor force, ultimately resulting in low construction efficiency, large quality fluctuations, and uncontrolled costs, severely restricting the in-depth advancement of lean management and digital transformation in construction projects. Summary of the Invention

[0004] The purpose of this invention is to achieve precise matching and dynamic coordination of four-dimensional elements—human, task, space, and time—to enable transparent allocation, fair acceptance, and intelligent transfer of construction tasks, thereby comprehensively improving project management efficiency and worker enthusiasm.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The construction industry AI positioning system includes: an AI task generation module: based on building information modeling and construction schedule planning, it automatically decomposes and structures construction tasks through the integration of rule engines and machine learning algorithms; a worker capability vector construction module: by integrating historical task execution data and dynamic behavior analysis algorithms, it constructs multi-dimensional and updatable worker capability vectors; a task intelligent recommendation and worker autonomous selection module: based on multi-dimensional matching calculations of task demand vectors and worker capability vectors, combined with personalized recommendation algorithms and multi-condition screening mechanisms, it realizes a two-way collaborative task acceptance mode; a task acquisition guarantee and dynamic activation module: it constructs a multi-level early warning mechanism based on dwell time, and gradually activates task attractiveness through hierarchical and progressive intervention strategies; and a feedback optimization and system self-evolution module: by collecting task execution data, worker behavior feedback, and on-site performance indicators, it constructs a closed-loop learning mechanism, and uses machine learning models to dynamically optimize task decomposition rules, recommendation strategies, and incentive parameters, achieving continuous iteration and adaptive improvement of the system's decision-making capabilities.

[0007] As a preferred technical solution of the present invention, the AI ​​task generation module includes the following processes: Multi-source data access and preprocessing: Integrating multiple data sources and performing extraction, transformation, loading, cleaning, and standardization processing; Automatic task decomposition and logical reconstruction: Based on the spatiotemporal coupling relationship between building information model components and schedule plans, the system performs multi-dimensional task decomposition to obtain the smallest work unit. After completing the task decomposition, logical relationship reconstruction is performed to construct task association groups; Intelligent filling of task attributes: For each generated smallest work unit, the system automatically fills in structured attributes based on multi-source data and rule models; Task conflict detection and optimization: Before task release, task conflict detection and optimization are performed to make the task executable; Digital task card generation and release: Each smallest work unit is encapsulated as a digital task card and pushed to the task pool.

[0008] As a preferred technical solution of the present invention, the automatic task decomposition includes: spatial dimension decomposition: dividing the BIM model into grids according to buildings, floors, and construction sections to generate physical work units; time dimension alignment: associating the work breakdown structure tasks with BIM components to make the component construction time window consistent with the schedule plan; process granularity control: decomposing the construction process of each component into the smallest executable work unit according to the process standard library.

[0009] As a preferred technical solution of the present invention, in the intelligent task attribute filling process, the task attributes include task name, work area, required job type, suggested number of people, estimated working hours, required materials, skill requirements, deadline, reward amount and priority.

[0010] As a preferred technical solution of the present invention, the task conflict detection includes: resource conflict detection: quantitative early warning is performed through resource load modeling and histogram analysis to identify excessive demand for the same type of work or equipment in the same period; spatial conflict detection: potential interference in cross-operation areas is identified by using BIM clash detection function; process sequence verification: based on the built-in construction process rule library, the task dependency relationship is automatically verified through a logical reasoning engine to ensure that subsequent tasks are generated only after the preceding tasks are completed.

[0011] As a preferred technical solution of the present invention, the worker capability vector construction module includes worker capability vector dimensions such as job qualification certification status, skill proficiency, completion rate of complex tasks, adaptability to risk areas, compliance of material use, and emergency task response index.

[0012] As a preferred technical solution of the present invention, the intelligent task recommendation process includes: after the AI ​​task generation module completes the generation of digital task cards, the system extracts their key attributes and structures them into task requirement vectors; calls the ability vector of each worker in the worker ability vector construction module, performs initial screening based on the hard conditions of the task, and obtains a task candidate pool; based on the candidate pool, calculates the comprehensive matching score of each worker by weighting the dimensions of skill matching, difficulty matching, risk matching, and material control matching, and obtains the final matching score; sorts the matching scores from high to low and generates a personalized recommended task list for each worker as a priority reference for their task selection.

[0013] As a preferred technical solution of the present invention, in the task intelligent recommendation and worker autonomous selection module, workers can view and filter tasks through multiple dimensions, including personalized recommended task view, region, reward, working hours, difficulty and risk environment.

[0014] As a preferred technical solution of the present invention, the task assignment guarantee and dynamic activation module is equipped with a hierarchical response and dynamic activation mechanism, which includes: a dynamic reward enhancement mechanism: triggering a tiered reward increase strategy based on the task dwell time, automatically increasing the economic incentive of the task to enhance the willingness to accept it through preset progressive ratios and cost upper limit constraints; a positive incentive mechanism: enhancing the willingness to accept high-alert tasks and the long-term participation enthusiasm through non-material incentives combined with worker ability vector updates; a task attribute adaptive optimization mechanism: improving the system recommendation adaptability and worker psychological acceptability of dwelling tasks by dynamically relaxing skill matching thresholds, adjusting task tags, and enhancing task descriptions; and a re-decomposition mechanism: for high-alert tasks with long-term dwell time, activating a structured decomposition algorithm to further decompose tasks with excessive granularity and inherit the original task attributes to lower the acceptance threshold and activate task flow.

[0015] As a preferred technical solution of the present invention, in the feedback optimization and system self-evolution module, the system automatically collects key indicators including task retention rate, early warning trigger frequency, dynamic reward usage, task breakdown ratio, deviation between recommended matching degree and actual acceptance rate, and the changing trend of worker ability vector, constructs data collection, and performs multi-dimensional root cause analysis based on the data collection.

[0016] The present invention has the following beneficial effects:

[0017] 1. Solve the problems of opaque task assignment and unfair distribution: In the traditional model, task assignment relies on personal relationships. This solution automatically generates standardized digital task cards through an AI task generation module, and realizes a two-way collaborative mode of task finding and task selection through intelligent task recommendation and worker self-selection modules. All tasks are publicly released in the task pool, and workers can independently select based on multiple dimensions such as recommendation, region, and reward, completely breaking down information barriers and realizing openness, fairness and impartiality in the task assignment process;

[0018] 2. Overcoming the problems of low person-job matching and resource mismatch: Traditional management lacks quantitative assessment of workers' abilities. This solution uses a worker ability vector construction module to integrate multi-dimensional data such as job qualifications, skill proficiency, completion rate of complex tasks, and risk adaptability to build a dynamically updated digital profile of abilities. Combined with task demand vectors, multi-dimensional matching calculations are performed to ensure that highly skilled workers can undertake high-difficulty tasks, improve the accuracy of person-job matching, and avoid the waste of human resources.

[0019] 3. Improve task acceptance rate and ensure construction progress: In response to the problem of high-difficulty and harsh environment tasks being prone to delays, this solution adopts a graded early warning and gradual intervention mechanism through a task acquisition guarantee and dynamic activation module to lower the acceptance threshold. This mechanism effectively activates task flow and prevents progress delays while respecting workers' right to choose.

[0020] 4. Support full-process traceability and strengthen management audit and responsibility definition: All tasks, from generation, recommendation, receipt, execution to acceptance, are recorded in the system, supporting full-process traceability. In the event of quality problems or project delays, the responsible person and execution process can be accurately located, eliminating the phenomenon of unclear responsibilities and mutual shirking of responsibility, and improving the standardization and auditability of project management. Attached Figure Description

[0021] Figure 1 This is a structural diagram of the AI ​​positioning system for the construction industry proposed in this invention;

[0022] Figure 2 A flowchart for the AI ​​task generation module;

[0023] Figure 3 This is a structural diagram of the task acquisition guarantee and dynamic activation module. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0025] Please refer to Figure 1 The construction industry AI positioning system includes an AI task generation module, a worker capability vector construction module, a task intelligent recommendation and worker self-selection module, a task acquisition guarantee and dynamic activation module, and a feedback optimization and system self-evolution module. Through these modules, a digital positioning system with precise matching of people, tasks, space, and time as its core is constructed. This prevents problems such as opaque work assignment, unfair allocation, and resource misallocation in traditional construction management, and achieves transparent, intelligent, and humanized collaboration in construction management.

[0026] Specifically, please refer to the appendix. Figure 2 The AI ​​task generation module, based on Building Information Modeling (BIM) and construction schedule planning, integrates a rule engine and machine learning algorithms to automate the breakdown and structured representation of construction tasks. Its core objective is to transform the macro-level construction plan into standardized digital task cards with multi-dimensional attributes such as job type, work hours, materials, and skill requirements. These cards serve as the data foundation for subsequent worker assignment, intelligent recommendation, and resource scheduling, supporting the operation of a decentralized work assignment model. Specifically, it includes the following steps:

[0027] Step 1: Multi-source data access and preprocessing: The system first integrates the following data sources and performs standardization processing:

[0028] BIM Model: Import 3D models in IFC or Revit format and extract component hierarchical information, including component type, geometric dimensions, material specifications, reinforcement information, etc.

[0029] Construction schedule planning: Access Microsoft Project or Primavera P6 files, parse the Work Breakdown Structure (WBS) and Critical Path (CPM), and obtain the planned start time, duration, and logical relationships of each task;

[0030] Bill of Materials: Obtain the theoretical material requirements for each component from the BIM quantity calculation module or the Enterprise Resource Planning (ERP) system;

[0031] Process Standards Library: Built-in enterprise-level database of "Construction Process Standards", which includes the work process, quality acceptance standards and safe operating procedures for each process.

[0032] After the above data is cleaned through an extraction-transformation-loading process, it is uniformly mapped to the platform's task semantic model to form a structured knowledge graph, providing a data foundation for task generation.

[0033] Step 2, Automatic Task Decomposition and Logic Restructuring: Based on the spatiotemporal coupling relationship between BIM components and the schedule, the system performs task decomposition, specifically including:

[0034] Spatial dimension decomposition: The BIM model is divided into grids according to buildings, floors, and construction sections to generate physical work units;

[0035] Time dimension alignment: Link WBS tasks with BIM components in 4D (3D+Time) to ensure that the construction time window of each component is consistent with the schedule plan;

[0036] Process granularity control: Based on the process standard library, the construction process of each component is decomposed into the smallest executable work unit (ATU). For example, "reinforcement binding of beams and slabs on the second floor of Building 3" is broken down into: installation of main beam reinforcement; binding of stirrups; installation of joint reinforcement; and self-inspection before acceptance.

[0037] The process employs a rule-based reasoning engine, combined with graph neural networks, to perform semantic understanding of the component connection relationships, ensuring that the disassembly logic conforms to the construction process flow.

[0038] After task decomposition, the system further reconstructs logical relationships. Based on process dependence, spatial proximity, and resource continuity, it identifies the smallest collaboratively executable work units and constructs task association groups. For example, it marks the rebar installation process of the same component as a "rebar construction group," or organizes the formwork erection tasks of adjacent areas into a "standard floor formwork flow package." This group structure does not change the independent attributes and state management of the smallest work unit; it only serves as an auxiliary logical unit for intelligent recommendation and construction organization. It supports the system to perform batch recommendations or progress coordination on a group basis, achieving a unity of refined management and efficient construction.

[0039] Step 3, Intelligent Task Attribute Population: For each generated ATU, the system automatically populates the following structured attributes based on multi-source data and rule models:

[0040] property Generation method Task Name The naming convention of "Building-Floor-Component Code-Process" is adopted, such as: 3#-2F-BL1-Main Reinforcement Installation, to ensure uniqueness and traceability. Work area Linked BIM spatial codes, supporting highlighting in the digital twin platform. Required job types Based on the matching of work process types, such as rebar tying → rebar worker, formwork erection → carpentry, it supports the marking of joint tasks by multiple trades. Recommended number of people By accessing a historical project database and employing a regression model, and inputting component volume, reinforcement density, and construction period requirements, prediction results are obtained. Estimated working hours Calculated based on the "time consumed per unit of work" model Required materials Extract component bill of materials from BIM quantity survey results, bind it to the enterprise's material coding system, generate a structured material requirement table, and support real-time linkage with the inventory management system. Skill Requirements Skill tag sets are automatically generated based on the difficulty level of the work process and a database of technological standards. For example: high reinforcement density → high-precision binding; complex nodes → blueprint reading ability; prestressed construction → specialized operation certificate. The tags are derived from the "Classification Standard for Construction Workers' Skills". Deadline Extract the latest completion time of the task from the construction schedule and convert it to a local timestamp to ensure consistency with the overall project control plan. Reward Amount Calculated based on estimated working hours, unit price of each type of work, and risk level. Priority Automatically determine based on the task's status on the critical path

[0041] The calculation model for the time consumed per unit of work is as follows:

[0042] ;

[0043] Among them: Q i For the i-th type of project quantity, such as the tonnage of steel bars; t i The standard working hours per unit of project volume; Ccomplexity is the complexity coefficient, determined by AI based on node density and cross-operations; and Cenvironment is the environmental coefficient, such as high temperature or high-altitude operations.

[0044] In addition, all attribute generation relies on structured input data to avoid manual intervention, support manual review and fine-tuning, and comply with the ISO19650 information management standard to ensure data interoperability with other smart construction site systems. It is worth noting that in order to ensure that the digital task cards are consistent with the actual construction site, the system establishes a change response mechanism. When design changes, schedule adjustments or model updates occur, the system automatically identifies the affected tasks and triggers the regeneration process.

[0045] Step 4, Task Conflict Detection and Optimization: To ensure the executable nature of the generated tasks, the system performs conflict detection and optimization before task release, specifically including the following aspects:

[0046] Resource conflict detection: Identify excessive demand for the same type of work or equipment within the same time period. Based on resource load curve analysis and resource histogram algorithm, arrange all generated tasks according to the time axis, and count the demand for specific work or equipment in each time period to form a resource demand curve. At the same time, the system accesses the project labor roster or equipment ledger to obtain the available supply of the resource. When the cumulative demand in a certain time period exceeds the supply threshold, it is determined to be a resource conflict.

[0047] Spatial conflict detection: Utilizing BIM clash detection function and combining it with task time windows, four-dimensional spatiotemporal conflict identification is performed to identify potential interference in overlapping work areas;

[0048] Process sequence verification: The system uses a rule engine or graph database to store process dependencies and verifies the rationality of the task sequence through a topological sorting algorithm. Specifically, the system has a built-in "Construction Process Standard" knowledge base to define the logical relationships between processes, traverse the task sequence, and check whether all the prerequisite tasks of the current task have been completed or arranged, ensuring that the subsequent tasks are generated only after the prerequisite tasks are completed.

[0049] For detected conflicts, the system generates a set of optimization suggestions and recommends the coordinated execution of related tasks in the form of task packages to improve resource utilization efficiency. For example:

[0050] Conflict Types Optimization suggestions Resource Exceeding Limit It is recommended to adjust the construction schedule and stagger task assignments; it is also recommended to merge similar tasks to improve resource utilization; additional labor is needed. Spatial interference It is recommended to adjust the work schedule and implement staggered construction; it is also recommended to break the task into smaller units and implement them in stages; high-risk areas should be marked and safety briefings should be conducted. Process inversion The system automatically adjusts the task order to ensure logical correctness; it prompts that if a prerequisite task is not completed, subsequent tasks will not be generated at this time.

[0051] Step 5: Digital Task Card Generation and Deployment: The system encapsulates each smallest job unit into a digital task card, whose data structure follows the JSON-LD format for easy semantic interoperability. After the task cards are automatically generated, they are pushed to the task pool. The task pool is a central task management area in the system where all generated tasks are centrally managed, including:

[0052] Release method: The task pool is deployed on a cloud server and made available to workers through a mobile application;

[0053] Worker access: After logging into the mobile app, workers can view all tasks in the task pool in real time, including recommended tasks, nearby tasks, high-reward tasks, etc.

[0054] Status Management: After a task is claimed, its status automatically changes from "pending" to "accepted". Once completed, it enters the "pending acceptance" status, achieving full-process visualization.

[0055] Worker Capability Vector Construction Module: This module constructs a structured worker capability vector aligned with the attributes of digital task cards, enabling a shift from traditional experience-based assignment to data-driven job matching. Its core objective is to quantify workers' actual performance in areas such as job qualifications, skill proficiency, completion rate of complex tasks, risk adaptability, material precision control, and emergency response. This results in a calculable, updatable, and matchable capability model, providing accurate input for subsequent intelligent task recommendations. The system does not rely on static registration information but rather evolves based on dynamic behavioral data of workers during task execution, ensuring that the capability profile accurately reflects current operational capabilities and that the job matching process is open, fair, and traceable. The worker capability vector includes the following dimensions:

[0056] Occupational qualification certification status: The system first digitally registers and continuously monitors the legal professional qualifications of each worker, and verifies them online with the public certificate database of the Ministry of Emergency Management or the Ministry of Housing and Urban-Rural Development to ensure the authenticity and validity of the information. After verification, the system records the occupational qualification status in the worker's competency vector and marks it as "valid" or "invalid" (including "expired" and "uncertified"). This status is automatically checked daily. Once the certificate is about to expire, the system sends a renewal reminder to the worker and team leader. If the certificate expires, the system will automatically prohibit the worker from undertaking the corresponding job task card until the qualification is renewed. This process is fully automated and has no human intervention, ensuring the rigid constraint of qualification compliance.

[0057] Skill proficiency: The system incorporates two dimensions, efficiency and quality, for comprehensive evaluation. In terms of efficiency, the system records the actual time a worker takes to complete each task and calculates the ratio of that time to the standard working hours. In terms of quality, the system calculates a quality score based on the quality inspection results. After each task is completed, the supervisor or quality inspector submits acceptance comments via mobile device. The final skill level score is a weighted average of the efficiency score and the quality score.

[0058] Complex Task Completion Rate: High-difficulty construction tasks are automatically identified through the BIM model. For example, when the reinforcement density of a component exceeds 80 kg per cubic meter, or the number of node connections exceeds five, or special processes such as prestressing or irregular structures are involved, the system marks it as a "high-difficulty task." The complex task completion rate is the percentage of high-difficulty tasks that a worker passes on their first attempt out of the total number of high-difficulty tasks they undertake. For example, if a worker undertakes four high-difficulty tasks, passes three on their first attempt, and one requires rectification, their complex task completion rate is 75%. This indicator is entirely based on third-party quality inspection data, does not rely on subjective evaluation, and truly reflects the worker's ability to handle complex processes.

[0059] Adaptability to Risk Areas: Construction sites present various risky environments, including high-altitude, enclosed, and high-temperature conditions. The system analyzes workers' historical work patterns to automatically identify their adaptability to specific environments. For example, if a worker proactively accepts tasks in areas "above the 10th floor" or "rooftop" three times consecutively within the past month, the system will automatically label them as "adaptable to high-altitude work." Conversely, if they refuse tasks in enclosed spaces such as basements or shafts more than twice consecutively, the system will mark them as "avoiding enclosed spaces." These labels are automatically generated by the system based on actual behavior and can be dynamically updated. When a worker's behavior pattern changes, such as starting to accept and complete three enclosed space tasks, the system will automatically update the labels. This mechanism ensures that risk matching is based on actual behavior.

[0060] Material Usage Compliance: To prevent material waste and misreporting, material control accuracy is incorporated into capability assessment. Each task is linked to a material requirements list derived from BIM quantity calculations, including rebar type, length, and quantity. After workers complete a task, the system automatically compares their actual material requisition records with the planned task usage. Material usage compliance can be calculated using the following formula:

[0061] ;

[0062] For example, if the plan is to use 1 ton of steel bars, but the actual material requisition is 1.05 tons, the deviation rate is 5%, and the compliance rate is 95%. The system sets a compliance threshold of 95%, and anything higher than this value is considered a "high-precision operator".

[0063] Emergency Task Response Index: In urgent scenarios such as rushing to complete tasks or design changes, the system needs to identify workers with rapid response capabilities. The Emergency Task Response Index is calculated based on the worker's acceptance and completion of tasks with remaining timeframes of less than two hours. For example, the system calculates the percentage of such tasks accepted and the percentage of tasks completed on time over the past 30 days, weighting these two factors to arrive at a comprehensive index. For instance, an acceptance rate of 60% and an on-time completion rate of 40% are considered. When issuing emergency tasks, the system prioritizes pushing them to workers with high scores in this index, improving task acceptance efficiency.

[0064] In addition, a dynamic update and privacy protection mechanism for capability vectors is set up. All capability indicators are updated regularly, such as automatically at midnight every day, incorporating new behavioral data from the previous 24 hours. The system adopts a sliding window mechanism, for example, with data from the past 30 days accounting for 70% of the weight and historical data accounting for 30%, to ensure that capability vectors reflect the current status. If a worker has not performed a certain job for a long time, their corresponding skill proficiency will decrease monthly to prevent the phenomenon of holding a certificate but not being able to do the job.

[0065] To protect privacy, all raw data is stored on the project's local server and is not uploaded to the cloud. Workers can view their own ability vectors and the calculation logic of various scores through a mobile app, but they cannot view other people's data. Administrators can only view the overall distribution of the work group and cannot trace individual sensitive behaviors. The system provides ability growth curves to show the historical trend of various worker indicators, enhancing transparency and motivation.

[0066] The intelligent task recommendation and worker self-selection module is a core component of the construction industry AI positioning system, enabling human-machine collaboration and two-way selection. Its design goal is not only for the system to proactively recommend the most suitable tasks to workers, but also to empower workers with full autonomy. Based on the system's accurate recommendations, workers can actively filter and select the most suitable construction tasks according to their personal preferences, work pace, and career development needs. This module breaks the one-way nature of traditional assignment-based work, constructing a new operational model of "system recommendation + personal selection + autonomous decision-making." This ensures efficient matching of tasks and abilities while enhancing worker participation, satisfaction, and work enthusiasm, truly achieving a two-way integration of "task finding people" and "people selecting tasks." Specifically, it includes the following stages:

[0067] Phase 1: Generation and Matching Preparation of Task Requirement Vector: After the AI ​​task generation module completes the generation of the digital task card, the system immediately extracts its key attributes and structures them into a task requirement vector. This vector includes core information such as the type of work required for the task, skill requirements, construction difficulty level, environmental risks, material precision requirements, deadline urgency, and task reward amount. These attributes will serve as the basic input for subsequent matching calculations to ensure that the recommendation logic is strictly aligned with the actual needs of the task.

[0068] The second stage, personalized recommendation generation based on ability vectors: The system calls the ability vector of each worker in the worker ability vector construction module to start the matching calculation process:

[0069] First, the system performs an initial screening based on the task's hard requirements, excluding workers who do not meet the basic requirements. For example, if the task requires "steel rebar worker" and involves "high-altitude work", the system will only retain workers who hold valid steel rebar worker qualifications and have not been marked "avoid high-altitude work".

[0070] Based on the candidate pool, the system calculates the comprehensive matching score for each worker. This score is composed of multiple dimensions such as skill matching, difficulty matching, risk matching, and material control matching, and the final matching score is obtained.

[0071] The system sorts workers by matching degree from high to low and generates a personalized recommendation list for each worker, which serves as a priority reference for their task selection.

[0072] Phase Three: Multi-dimensional Task Display and Self-Selection Mechanism: The system publishes all pending tasks in a unified digital task pool. Workers can log in via a mobile app to view and filter tasks from multiple dimensions, enabling them to make independent decisions. These dimensions include:

[0073] Personalized task recommendation view: The system displays the top 10 tasks with the highest matching degree and annotates the matching reasons. This view provides workers with efficient decision support.

[0074] Filter by region: Workers can select their current construction area, and the system will automatically display available tasks in the vicinity of that area, reducing travel time and improving work efficiency;

[0075] Sort by reward: Workers can view the reward amount for all tasks, sort them from highest to lowest, and prioritize high-paying tasks.

[0076] Filter by time: Supports filtering short tasks (e.g., <2 hours) or long tasks (e.g., >4 hours), allowing workers to schedule tasks according to their own work rhythm;

[0077] Select by difficulty: Workers can set preferences, such as only displaying medium-difficulty tasks to avoid taking on work beyond their capabilities;

[0078] Filtering by risk environment: Workers can actively exclude environmental types they do not want to take on, such as high-altitude or enclosed spaces, and the system respects their personal wishes;

[0079] All filtering criteria can be combined. For example, a worker can set "recommended tasks + reward > 200 yuan + working hours < 3 hours", and the system will dynamically generate a list of tasks that meet the criteria.

[0080] Phase 4: Task Details Viewing and Receiving Decisions: Workers can click on any digital task card to view complete information, including work location, construction content and process requirements, required trades and skills, estimated working hours and deadline, material list and usage standards, quality acceptance standards, task reward amount and distribution rules, etc.

[0081] Workers can make a comprehensive judgment based on this information and decide whether to accept the task. Once the task is accepted, the system will immediately lock it to prevent others from accepting it repeatedly and generate an electronic work order.

[0082] Phase 5: Eligibility Rules and Fairness Guarantee Mechanism

[0083] To prevent tasks from being monopolized by a few workers, the system has set up rules to ensure fairness in task assignment. These rules include:

[0084] Daily limit: Each worker can accept a certain number of task cards per day to avoid overloading.

[0085] Cooldown mechanism: After completing a task, you need to wait for a period of time, such as 10 minutes, before you can accept the next one to ensure a reasonable rest;

[0086] Newcomer priority strategy: For tasks of moderate difficulty, the system can allocate a certain percentage to newcomers with lower skill levels but who actively take on tasks, to support their ability growth;

[0087] Unaccepted task alert: If a highly matched worker does not accept recommended tasks for an extended period of time, the system will send a reminder to them to improve the task acceptance rate.

[0088] Please refer to the appendix. Figure 3 Task Assignment Guarantee and Dynamic Activation Module: This module is a key guarantee mechanism in the construction industry AI positioning system to ensure efficient task flow. It aims to address the issue of prolonged delays in high-difficulty, unfavorable, or unattractive tasks that may occur under a worker-choice model. Its core purpose is not forced assignment, but rather to gradually activate task attractiveness through a tiered, progressive intervention strategy, guiding workers to voluntarily accept tasks. While respecting individual choice, this module gradually enhances task attractiveness through economic rewards and positive incentives, ensuring unimpeded construction progress and timely execution of every digital task. It is the core support for the system's robustness and practicality, specifically including:

[0089] Task Delay Monitoring and Intelligent Hierarchical Early Warning: The system adopts a dynamic delay threshold judgment model based on the urgency of task deadlines to monitor the claiming status of digital task cards in real time. Based on the time sensitivity level of the task in the project schedule, differentiated delay judgment benchmarks are set. When the time span from task release to unclaimed task reaches its corresponding threshold, the system automatically triggers a multi-level early warning mechanism. Early warning levels are divided into three levels according to the severity of delay: Level 1 warning indicates the task has entered the observation period; Level 2 warning indicates insufficient task attractiveness, requiring positive guidance and incentives for workers; Level 3 warning indicates long-term task delay, requiring the system to activate fallback measures.

[0090] For example, for urgent tasks (remaining time < 2 hours): if not claimed within 10 minutes of posting, it is marked as a Level 1 warning; if left unclaimed for 20 minutes, it is marked as a Level 2 warning; and if left unclaimed for 30 minutes, it is marked as a Level 3 warning. For general tasks (2-8 hours): if not claimed within 1 hour of posting, it is marked as a Level 1 warning; if left unclaimed for 2 hours, it is marked as a Level 2 warning; and if left unclaimed for 4 hours, it is marked as a Level 3 warning. For routine tasks (> 8 hours): if not claimed within 3 hours of posting, it is marked as a Level 1 warning; if left unclaimed for 4 hours, it is marked as a Level 2 warning; and if left unclaimed for 6 hours, it is marked as a Level 3 warning.

[0091] All warning statuses are identified by different colors in the task pool, such as yellow → orange → red, to facilitate quick identification by administrators.

[0092] Tiered Response and Dynamic Activation Mechanism: The system activates corresponding intervention strategies based on the warning level, forming a progressive response. Specifically:

[0093] The Level II early warning response mechanism includes:

[0094] Dynamic reward enhancement mechanism: The system initiates a tiered reward increase strategy, using economic leverage to enhance the attractiveness of tasks. The system presets a progressive reward ratio, and the reward increase is constrained by the cost control limit to prevent project incentive costs from getting out of control. The basic reward is calculated based on factors such as the BIM efficiency library, market unit price, and process difficulty coefficient. The reward is gradually increased based on the length of stay. For example, if the stay is 1 hour, the basic reward will be automatically increased by 10%; if the stay is 2 hours, the basic reward will be increased by another 15%; the maximum increase limit is no more than 30% of the basic reward.

[0095] Positive incentive mechanism: Without interfering with workers' freedom of choice, increase their willingness to accept tasks that require them to stay in the field. This includes: Doubled skill growth points: Accepting tasks at this stage will earn them a certain number of growth points, which will be updated in conjunction with the worker's skill proficiency in their ability vector and the corresponding index for emergency tasks; Honor system support: The system sets up a list of workers who accept tasks that require them to stay in the field, and regularly publishes the list and awards them.

[0096] The task attribute adaptive optimization mechanism includes: Relaxing the matching threshold: automatically lowering the skill matching requirement, for example, from ≥90% to ≥80%, and recommending workers who have recently completed similar tasks and have high quality scores; for example, if the core tube rebar tying task is delayed, the system recommends workers who have completed shear wall tying and passed acceptance on the first attempt; Dynamic adjustment of task tags: if a task is not accepted due to tags such as "high altitude" or "enclosed space," the system automatically adds the "requires two-person collaboration" tag and recommends it to workers who already have stable partners; Enhanced task description: when the system detects an ambiguous description, it prompts managers to supplement with BIM screenshots, process videos, or acceptance standards.

[0097] The three-level early warning response mechanism includes: if a task remains unresolved for an extended period despite the aforementioned incentive mechanisms, the system determines that the low willingness to accept the task may be due to the initial granularity being too large. In this case, the task structured decomposition algorithm is activated to decompose the task into a more granular standard minimum work unit based on spatial, process, or material dimensions. For example, if the originally released task of "binding the steel reinforcement of the entire beam and slab" remains unresolved, the system will split it into sub-tasks such as "binding the 3-A axis segment" and "binding the 3-B axis segment" to lower the operational threshold. After decomposition, the sub-tasks inherit the attribute rules of the original task, allocate rewards according to the proportion of working hours, and reissue them as independent ATUs to the task pool for workers to choose to accept.

[0098] When a task reaches a stage where it cannot be broken down, manual intervention can be implemented to forcibly assign tasks and provide compensation and incentives.

[0099] Feedback Optimization and System Self-Evolution Module: By continuously collecting multi-source behavioral data throughout the entire task execution process, a self-evolution mechanism of "data-driven - root cause analysis - model tuning - dynamic iteration" is constructed. Its fundamental purpose is not only to discover deviations and bottlenecks in system operation, but also to promote the continuous optimization of front-end modules such as task generation, capability characterization, intelligent recommendation and dynamic activation. This enables the entire system to autonomously adapt and improve itself as the project progresses, worker behavior changes and management needs evolve. Through this module, the system upgrades from static rule operation to dynamic learning and evolution, achieving long-term sustainability of intelligent management.

[0100] During system operation, key indicators such as task retention rate, early warning trigger frequency, dynamic reward usage, task breakdown ratio, deviation between recommended matching degree and actual acceptance rate, and changing trend of worker capability vector are automatically collected daily. Data is aggregated on a weekly and monthly basis to provide a foundation for subsequent analysis. This data covers the entire lifecycle from task release to final closure, ensuring the completeness and representativeness of the analysis results.

[0101] Based on the collected data, the system initiates a multi-dimensional root cause analysis process, specifically:

[0102] For tasks that have been stuck for a long time, clustering algorithms are used to identify their common characteristics, such as whether they are concentrated in specific areas such as basements, specific environments such as high altitudes, specific processes such as complex node binding, or specific time periods such as nighttime construction. If a certain type of task is found to repeatedly enter the level 3 warning and ultimately rely on disassembly to complete, the system will determine that its task granularity design is unreasonable or the incentive mechanism is insufficient, and generate optimization suggestions.

[0103] The system diagnoses the discrepancy between the recommended matching degree and the actual acceptance behavior. For example, if a task is pushed to the top five workers with the highest matching degree, but no one accepts it, and it is eventually accepted by the tenth ranked worker, the system can locate the root cause of the problem by associating worker behavior logs, task attributes and incentive records.

[0104] By using machine learning models to track the evolution path of workers' ability vectors, the system can identify individual trends of ability decline or skill transformation. For example, if a steelworker has not undertaken any high-difficulty tasks in the past three months and his "complex task completion rate" continues to decline, the system will determine that his skills are at risk of degradation and suggest that project managers arrange targeted training or progressive task guidance.

[0105] Based on the root cause analysis results above, the system automatically generates multi-level optimization strategies and injects them back into the front-end module to achieve dynamic tuning of model parameters, including:

[0106] Task generation optimization: Adjust process granularity and time prediction model parameters;

[0107] Incentive strategy optimization: Dynamically adjust the increase and cap of rewards;

[0108] Recommendation algorithm parameter tuning: optimize matching degree weights, such as increasing risk weights and decreasing working time weights;

[0109] Capability vector update rule: Adjust the sliding window period or decay coefficient.

[0110] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An AI positioning system for the construction industry, characterized in that, Includes the following modules: AI Task Generation Module: Based on building information modeling and construction schedule planning, it automatically decomposes and structures construction tasks by integrating rule engine and machine learning algorithm; Worker Capability Vector Construction Module: By integrating historical task execution data with dynamic behavior analysis algorithms, a multi-dimensional and updatable worker capability vector is constructed. Task intelligent recommendation and worker autonomous selection module: Based on multi-dimensional matching calculation of task demand vector and worker ability vector, combined with personalized recommendation algorithm and multi-condition screening mechanism, a two-way collaborative task acceptance mode is realized; Task Acquisition Guarantee and Dynamic Activation Module: Construct a multi-level early warning mechanism based on dwell time, and gradually activate the attractiveness of tasks through tiered and progressive intervention strategies; Feedback optimization and system self-evolution module: By collecting task execution data, worker behavior feedback and on-site performance indicators, a closed-loop learning mechanism is constructed. Machine learning models are used to dynamically optimize task decomposition rules, recommendation strategies and incentive parameters, so as to achieve continuous iteration and adaptive improvement of the system's decision-making capabilities.

2. The construction industry AI positioning system according to claim 1, characterized in that, The AI ​​task generation module includes the following process: Multi-source data access and preprocessing: Integrate multiple data sources and perform standardized processes such as extraction, transformation, loading, and cleaning; Automatic task decomposition and logical reconstruction: Based on the spatiotemporal coupling relationship between building information model components and schedule plans, the system performs multi-dimensional task decomposition to obtain the smallest work unit. After completing the task decomposition, it performs logical relationship reconstruction to build task association groups. Intelligent task attribute population: For each generated minimum job unit, the system automatically populates structured attributes based on multi-source data and rule models; Task conflict detection and optimization: Perform task conflict detection and optimization before task release to ensure task feasibility; Digital task card generation and distribution: Each smallest unit of work is encapsulated as a digital task card and pushed to the task pool.

3. The construction industry AI positioning system according to claim 2, characterized in that, The automatic task breakdown includes: Spatial dimension decomposition: The BIM model is divided into grids according to buildings, floors, and construction sections to generate physical work units; Time dimension alignment: Associate the work breakdown structure tasks with BIM components to align the component construction time window with the schedule plan; Process granularity control: Based on the process standard library, the construction process of each component is decomposed into the smallest executable work unit.

4. The construction industry AI positioning system according to claim 3, characterized in that, In the intelligent task attribute filling process, the task attributes include task name, work area, required job type, suggested number of people, estimated working hours, required materials, skill requirements, deadline, reward amount, and priority.

5. The construction industry AI positioning system according to claim 4, characterized in that, The task conflict detection includes: Resource conflict detection: Quantitative early warning is provided through resource load modeling and histogram analysis to identify excessive demand for the same type of work or equipment within the same time period; Spatial conflict detection: Utilize BIM's clash detection function to identify potential interference between overlapping work areas; Process sequence verification: Based on the built-in construction process rule library, the logical reasoning engine automatically verifies the task dependencies to ensure that subsequent tasks are generated only after the preceding tasks are completed.

6. The construction industry AI positioning system according to claim 5, characterized in that, In the worker capability vector construction module, the worker capability vector dimensions include job qualification certification status, skill proficiency, completion rate of complex tasks, adaptability to risk areas, compliance of material use, and emergency task response index.

7. The construction industry AI positioning system according to claim 6, characterized in that, The intelligent task recommendation process includes: Once the AI ​​task generation module completes the generation of the digital task card, the system extracts its key attributes and structures them into a task requirement vector. The ability vector of each worker in the worker ability vector construction module is called, and the task candidate pool is obtained by initial screening based on the hard conditions of the task. Based on the candidate pool, the comprehensive matching score of each worker is calculated by weighting the dimensions of skill matching, difficulty matching, risk matching, and material control matching, and the final matching score is obtained. Sort by matching degree from high to low, generate a personalized recommended task list for each worker as a priority reference for their task selection.

8. The construction industry AI positioning system according to claim 7, characterized in that, In the intelligent task recommendation and worker self-selection module, workers can view and filter tasks through multiple dimensions, including personalized recommended task view, region, reward, working hours, difficulty, and risk environment.

9. The construction industry AI positioning system according to claim 8, characterized in that, The task assignment guarantee and dynamic activation module is equipped with a tiered response and dynamic activation mechanism, which includes: Dynamic reward enhancement mechanism: Based on the task retention time, a tiered reward increase strategy is triggered. Through preset progressive ratios and cost cap constraints, the economic incentives for tasks are automatically increased to enhance the willingness to accept them. Positive incentive mechanism: By using non-material incentives and combining them with worker capability vector updates, we can enhance the willingness to undertake high-alert tasks and the long-term participation enthusiasm. Task attribute adaptive optimization mechanism: By dynamically relaxing skill matching thresholds, adjusting task tags, and enhancing task descriptions, the system recommendation adaptability and worker psychological acceptability of delayed tasks are improved. Re-decomposition mechanism: For high-alert tasks that have been stuck for a long time, a structured decomposition algorithm is activated to further decompose tasks that are too granular and inherit the original task attributes in order to lower the threshold for accepting tasks and activate task flow.

10. The construction industry AI positioning system according to claim 9, characterized in that, In the feedback optimization and system self-evolution module, the system automatically collects key indicators including task retention rate, early warning trigger frequency, dynamic reward usage, task breakdown ratio, deviation between recommended matching degree and actual acceptance rate, and changing trend of worker ability vector, constructs data collection, and performs multi-dimensional root cause analysis based on the data collection.

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