A sub-item engineering closed-loop operation management system and method based on an internet of things

Abstract

The application relates to the field of engineering management, and discloses a sub-subtask engineering closed-loop operation management system and method based on the Internet of Things. The system comprises the following modules: a task classification module, which is used for classifying operation tasks of sub-subtask engineering; an adaptability evaluation module, which is used for calculating the adaptability of construction team members to the keyness, timeliness and authority of each type of operation task, and performing adaptability fusion to obtain a comprehensive evaluation adaptability; a member allocation module, which is used for performing collaborative analysis on each adaptability and social behavior characteristics of the construction team members based on a graph neural network, and optimizing the allocation result of the construction team members; and a closed-loop management module, which is used for dynamically adjusting the engineering operation mode according to the progress of the engineering operation task, and in combination with real-time environmental data of the engineering site and the allocation result of the construction team members. The application realizes accurate matching of engineering operation tasks, optimization of team cooperation and dynamic adaptation of the construction environment.

Description

Technical Field

[0001] The present invention relates to the field of engineering management, and in particular to an Internet of Things-based closed-loop operation management system and method for sub-projects. Background Art

[0002] A construction project management method uses a method to divide the entire project into multiple independent parts and projects based on their functions or work content, facilitating better construction management and control. It breaks down a large project into several smaller parts, each with its own independent construction objectives, requirements, and schedule.

[0003] Engineering Closed-Loop Operation Management (ECOM) ensures that all operational activities are successfully completed within predetermined goals, requirements, and standards through comprehensive, comprehensive monitoring and management of engineering projects. The core of this closed-loop system is continuous feedback and adjustment, ensuring continuous optimization from design to construction and maintenance, thus forming an effective closed-loop management system.

[0004] However, the existing engineering closed-loop operation management system still has the following shortcomings:

[0005] (1) Due to the complexity of construction tasks and the diversity of skills and experience of construction team members, it is difficult to achieve accurate matching in the task allocation process, which may lead to insufficient or excessive allocation of human resources, thereby affecting the quality and efficiency of task execution.

[0006] (2) The efficiency of collaboration between team members directly affects the smoothness of task execution. Under the traditional management model, the collaboration between team members may suffer from poor communication or poor coordination, resulting in a decrease in overall work efficiency.

[0007] (3) How to accurately dispatch personnel based on the timeliness of the work tasks and the members' ability to adjust their time so that the construction project can be completed on time is a complex scheduling problem.

[0008] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0009] In view of this, the purpose of this application is to provide a closed-loop operation management system and method for sub-projects based on the Internet of Things to overcome the above-mentioned technical problems existing in the existing related technologies.

[0010] To this end, the specific technical solutions adopted in the present invention are as follows:

[0011] According to one aspect of the present application, a split item engineering closed-loop operation management system based on Internet of Things is provided, comprising:

[0012] A task classification module is configured to classify the operation tasks of the split item engineering based on a clustering algorithm and operation task data collected by Internet of Things.

[0013] An adaptability evaluation module is configured to calculate the adaptability of the construction team members to the criticality, timeliness and authority of each type of operation task, and perform adaptability fusion to obtain a comprehensive evaluation adaptability.

[0014] A member allocation module is configured to perform collaborative analysis on the comprehensive evaluation adaptability, the adaptability of criticality, the adaptability of timeliness, the adaptability of authority and the social behavior characteristics of the construction team members based on a graph neural network, and optimize the allocation result of the construction team members.

[0015] A closed-loop management module is configured to dynamically adjust the engineering operation mode according to the progress of the engineering operation tasks, in combination with real-time environmental data of the engineering site and the allocation result of the construction team members.

[0016] Further, the classification of the operation tasks of the split item engineering based on a clustering algorithm and operation task data collected by Internet of Things comprises:

[0017] According to the split item engineering requirements of the project construction site, the installation positions of the Internet of Things monitoring devices are planned.

[0018] The related data of the operation tasks are collected by the Internet of Things monitoring devices, and the operation task characteristics are extracted from the related data of the operation tasks, wherein the related data of the operation tasks include basic information of the operation tasks, progress information of the operation tasks, task characteristic data and operation equipment information.

[0019] Based on a clustering algorithm and according to the operation task characteristics, the operation tasks are grouped, and the contour coefficient is used to evaluate the effect of clustering.

[0020] Further, the calculation of the adaptability of the construction team members to the criticality, timeliness and authority of each type of operation task, and the adaptability fusion to obtain a comprehensive evaluation adaptability comprises:

[0021] Based on a weighted average method, the adaptability of the construction team members to the criticality of each type of operation task is calculated in combination with the task implementation ability of the construction team members.

[0022] Based on a linear regression model, the adaptability of the construction team members to the timeliness of each type of operation task is calculated in combination with the time adjustment ability of the construction team members.

[0023] Using fuzzy logic principles, combined with the professional ability of the construction team members, the adaptability of the construction team members to each type of work task in terms of authority is quantified;

[0024] Based on the adaptability of the construction team members to each type of work task in terms of criticality, timeliness and authority, the comprehensive evaluation adaptability is calculated, and the formula of the comprehensive evaluation adaptability is:

[0025]

[0026] In the formula, G ij represents the comprehensive evaluation adaptability of the construction team member i on the work task j;

[0027] a1, a2 and a3 respectively represent the importance coefficients of criticality, timeliness and authority adaptability in the comprehensive evaluation adaptability;

[0028] δ1 and δ2 are both adjustment coefficients;

[0029] Key ij represents the adaptability of the construction team member i on the work task j in terms of criticality;

[0030] Time ij represents the adaptability of the construction team member i on the work task j in terms of timeliness;

[0031] Au ij represents the adaptability of the construction team member i on the work task j in terms of authority;

[0032] T j represents the timeliness score of the work task j, A ij represents the time adjustment ability of the construction team member i on the work task j, L i represents the project leadership ability score of the construction team member i.

[0033] Further, based on the weighted average method, combined with the task implementation ability of the construction team members, the adaptability of the construction team members to each type of work task in terms of criticality includes:

[0034] According to the influence of each type of work task on the completion degree of the sub-item engineering project, the criticality score of the corresponding type of work task is determined;

[0035] Obtain the professional ability of the construction team members associated with each type of work task and the experience matching of each type of work task;

[0036] In a weighted scoring manner, combined with the professional ability score and experience matching degree of the construction team members, the criticality score of the construction team members on each type of work task is calculated;

[0037] The criticality score of each type of work task given by the construction team member is divided by the criticality score of the corresponding type of work task to obtain the adaptability of the construction team member to the criticality aspect of each type of work task.

[0038] Further, based on the linear regression model, the adaptability of the construction team member to the timeliness aspect of each type of work task is calculated in combination with the time adjustment ability of the construction team member, which includes:

[0039] According to the execution time of each type of work task, the timeliness score of the corresponding type of work task is determined;

[0040] The historical completion time and time management ability of the construction team member in completing each type of work task are obtained, and the time adjustment ability of the construction team member for each type of work task is comprehensively obtained;

[0041] The timeliness score of each type of work task is normalized, and the time adjustment ability is standardized;

[0042] A linear regression model of timeliness adaptability is constructed:

[0043]

[0044] In the formula, represents the adaptability of the construction team member i to the timeliness aspect of the work task category j;

[0045] β0 represents the intercept term, and β1 and β2 are the regression coefficients of the work task timeliness score and the time adjustment ability of the construction team member, respectively;

[0046] T′ j represents the normalized timeliness score of the work task category j;

[0047] C′ i represents the standardized time adjustment ability of the construction team member i;

[0048] The linear regression model of timeliness adaptability is fitted by the least square method and using historical data, and the target vector is the historical completion time of the construction team member in completing each type of work task;

[0049] After obtaining the regression coefficients, the linear regression model of timeliness adaptability is substituted, and the adaptability of the construction team member to the timeliness aspect of each type of work task is calculated by the linear regression model of timeliness adaptability.

[0050] Further, using the fuzzy logic principle, the adaptability of the construction team member to the authority aspect of each type of work task is quantified in combination with the professional ability of the construction team member, which includes:

[0051] determining the authority score of the corresponding type of work task according to the complexity and technical difficulty of each type of work task;

[0052] obtaining the academic background and project leadership ability of the construction team member, and taking the academic background and project leadership ability as input variables of fuzzy logic reasoning;

[0053] converting the input variables into fuzzy membership values through a membership function; obtaining the fuzzy reasoning result of the construction team member for each type of work task according to the fuzzy membership values of the academic background and project leadership ability, and through all suitable fuzzy reasoning rules and through a weighted calculation method;

[0054] using a defuzzification method to convert the fuzzy reasoning result into the authority score of the construction team member for each type of work task;

[0055] dividing the authority score of the construction team member for each type of work task by the authority score of the corresponding type of work task to obtain the fitness of the construction team member in the authority aspect for each type of work task.

[0056] Further, based on the graph neural network, the comprehensive evaluation fitness, the fitness in the criticality aspect, the fitness in the timeliness aspect, the fitness in the authority aspect, and the social behavior characteristics of the construction team member are analyzed in collaboration, and the allocation result of the construction team member is optimized, including:

[0057] obtaining the social behavior characteristics of the construction team member, the social behavior characteristics including the collaboration efficiency between the construction team members;

[0058] taking the construction team member as a node, the node characteristics being the fitness in the criticality aspect, the fitness in the timeliness aspect, the fitness in the authority aspect, and the comprehensive evaluation fitness, and determining the edges and edge weights between the nodes according to the social behavior characteristics of the construction team member;

[0059] constructing a graph neural network structure, updating the node characteristics using a message passing mechanism, and the node output of the graph neural network being the final feature vector of each construction team member, the final feature vector including the fitness of the construction team member for a certain type of construction task and the social behavior characteristic information after the graph neural network processing, and the fitness including the fitness in the criticality aspect, the fitness in the timeliness aspect, the fitness in the authority aspect, and the comprehensive evaluation fitness;

[0060] constructing a loss function based on the gap between the fitness of the construction team member for a certain type of construction task and the corresponding construction task requirement, and the collaboration efficiency between the construction team members; taking the allocation of the work task to maximize the fitness score and improve the construction team collaboration efficiency as an optimization goal, using gradient descent to minimize the loss function, and obtaining a trained graph neural network model;

[0061] Through the trained graph neural network model, input the job task requirements, and output the final fitness score of each construction team member; and the construction team members are allocated according to the final fitness score.

[0062] Further, the formula of the loss function is:

[0063]

[0064] In the formula, L represents the total value of the loss function;

[0065] represents the fitness score of the construction team member i to the job task j at the kth iteration, and Ta(j) represents the target requirement of the job task j;

[0066] w ij represents the weighting coefficient of the job task j to the construction team member i;

[0067] represents the weight factor of the cooperation efficiency;

[0068] C(S it ) represents the cooperation efficiency between the construction team member i and the construction team member t;

[0069] n represents the number of construction team members, and m represents the number of job tasks.

[0070] Further, according to the progress of the engineering job task, and in combination with the real-time environmental data of the engineering site and the allocation result of the construction team personnel, the engineering job mode is dynamically adjusted, including:

[0071] According to the progress of the engineering job task, the allocation of the construction team personnel is adjusted;

[0072] According to the real-time environmental data of the engineering site and the progress of the engineering job task, the configuration of the engineering resources is adjusted;

[0073] The effect of the adjusted engineering job mode is obtained, and the difference before and after the adjustment is compared until the expected engineering job progress target is achieved.

[0074] According to another aspect of the present application, a sub-item engineering closed-loop operation management method based on Internet of Things is also provided, comprising:

[0075] Based on the clustering algorithm and the job task data collected by the Internet of Things, the job tasks of the sub-item engineering are classified;

[0076] The fitness of the construction team members to the keyness, timeliness and authority of each type of job task is calculated, and the fitness is fused to obtain the comprehensive evaluation fitness;

[0077] Based on the graph neural network, the adaptability of the comprehensive evaluation, the adaptability of the key aspect, the adaptability of the timeliness aspect, the adaptability of the authority aspect and the social behavior characteristics of the construction team members are collaboratively analyzed, and the allocation result of the construction team members is optimized.

[0078] According to the progress of the engineering operation task, combined with the real-time environmental data of the engineering site and the allocation result of the construction team members, the engineering operation mode is dynamically adjusted.

[0079] The present application includes the following beneficial effects:

[0080] (1) The present application uses Internet of Things technology to collect operation task data in real time, classifies the tasks combined with clustering algorithm, and evaluates the classification effect, so as to divide the operation tasks into several categories. At the same time, through the weighted average method, linear regression model and fuzzy logic principle, combined with the professional ability, time adjustment ability and experience of the construction team members, the adaptability of the members to the task is quantitatively evaluated. Through the comprehensive evaluation of the construction team members from multiple dimensions (key, timeliness and authority), the task allocation is more accurate, which can maximize the advantages of each member, thereby improving the overall work efficiency and task completion degree of the team.

[0081] (2) The adaptability of the construction team members and the social behavior characteristics are collaboratively analyzed through the graph neural network, so as to optimize the task allocation. The graph neural network optimizes the cooperation and task allocation among members through the message passing mechanism, so that the efficiency of the construction team in collaborative work is significantly improved. Through optimizing the allocation of the construction team, the task conflict and resource waste are reduced, and the overall execution efficiency of the team is improved. By optimizing the loss function, the task allocation is ensured to maximize the efficiency while improving the overall team collaboration.

[0082] (3) Based on the real-time feedback of task progress, environmental data, personnel allocation and resource configuration, the construction operation mode is dynamically adjusted, and the real-time data and analysis results are continuously optimized. The closed-loop management mechanism of real-time feedback and continuous adjustment is realized, which ensures that the engineering operation can timely respond to various changes in the actual process, avoiding delay or resource waste. BRIEF DESCRIPTION OF DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0084] Figure 1This is a principle block diagram of a closed-loop operation management system for sub-projects based on the Internet of Things according to an embodiment of the present invention;

[0085] Figure 2 The present invention is a flowchart of a closed-loop operation management method for sub-projects based on the Internet of Things.

[0086] In the picture:

[0087] 1. Task classification module; 2. Fitness evaluation module; 3. Member allocation module; 4. Closed-loop management module. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0089] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0090] According to an embodiment of the present invention, a closed-loop operation management system and method for sub-projects based on the Internet of Things are provided.

[0091] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a closed-loop operation management system for sub-projects based on the Internet of Things is provided, including:

[0092] The task classification module 1 is used to classify the work tasks of the sub-projects based on the clustering algorithm and the work task data collected by the Internet of Things.

[0093] In one embodiment, based on a clustering algorithm and task data collected by the Internet of Things, the classification of task data for each sub-project includes:

[0094] Plan the installation location of the Internet of Things monitoring equipment based on the requirements of the sub-projects at the project construction site.

[0095] The related data of the work task is collected by the monitoring device of the Internet of Things, and the work task features are extracted from the related data of the work task. The related data of the work task includes basic information of the work task, progress information of the work task, task feature data and work equipment information. Specifically, it includes temperature and humidity, air pressure, air quality, noise, etc. Collect environmental data to monitor the influence of work conditions on task progress, especially those tasks that have important influence on timeliness and safety (such as concrete pouring requiring certain temperature and humidity conditions). Task progress information, such as task start time, expected completion time, actual completion time, feedback during task execution, etc. The running state of the equipment, equipment failure information, use time, etc. The state of the equipment can be monitored in real time through the sensor to ensure that the equipment completes the task within the appropriate time period.

[0096] Based on the clustering algorithm and according to the work task features, the work tasks are grouped, and the effect of clustering is evaluated using the silhouette coefficient to ensure that each task group has high cohesion and large external separation. The range of the silhouette coefficient is [-1, 1], and the value is closer to 1, indicating that the clustering result is better; the value close to -1 indicates that the clustering effect is not good. Select the appropriate clustering algorithm, such as K-means, DBSCAN or hierarchical clustering, etc. to group tasks

[0097] The monitoring device types include:

[0098] Temperature and humidity sensor, for tasks that require specific environmental conditions (such as concrete pouring, steel structure installation, etc.), install temperature and humidity sensors to monitor the construction environment. RFID tags and sensors, installed in the material storage area, real-time monitor the location, quantity, consumption, etc. of the materials. Video monitoring camera, installed in key areas of the construction site (such as electrical installation area, high-altitude work area, etc.), to ensure safety and progress monitoring. Accelerometer, tilt sensor, installed in some high-risk areas (such as high-altitude work, heavy equipment operation area) to monitor the stability of the equipment and the safety of the personnel. According to the execution location of each task, reasonably plan the location of the equipment. For example, steel structure installation and electrical equipment installation tasks are usually high, and related monitoring equipment needs to be installed.

[0099] Material storage area: track the consumption and storage status of materials in real time through RFID tags to ensure timely and effective material management.

[0100] The fitness evaluation module 2 is used to calculate the fitness of the construction team members in terms of criticality, timeliness and authority of each type of work task, and to fuse the fitness to obtain a comprehensive evaluation fitness.

[0101] In an embodiment, the adaptability of the construction team member to each type of work task is calculated in terms of criticality, timeliness and authority, and the adaptabilities are fused to obtain a comprehensive evaluation adaptability, which includes:

[0102] Based on the weighted average method, the adaptability of the construction team member to each type of work task is calculated in terms of criticality in combination with the task implementation ability of the construction team member. Based on the linear regression model, the adaptability of the construction team member to each type of work task is calculated in terms of timeliness in combination with the time adjustment ability of the construction team member. The adaptability of the construction team member to each type of work task is quantified in terms of authority in combination with the professional ability of the construction team member by using the fuzzy logic principle.

[0103] Based on the adaptability of the construction team member to each type of work task in terms of criticality, timeliness and authority, a comprehensive evaluation adaptability is calculated, and the formula of the comprehensive evaluation adaptability is:

[0104]

[0105] In the formula, G ij represents the comprehensive evaluation adaptability of the construction team member i on the work task j; a1, a2 and a3 respectively represent the importance coefficients of criticality, timeliness and authority adaptability in the comprehensive evaluation adaptability; δ1 and δ2 are both adjustment coefficients; Key ij represents the adaptability of the construction team member i in terms of criticality on the work task j; Time ij represents the adaptability of the construction team member i in terms of timeliness on the work task j; Au ij represents the adaptability of the construction team member i in terms of authority on the work task j; T j represents the timeliness score of the work task j, A ij represents the time adjustment ability of the construction team member i on the work task j, L i represents the project leadership ability score of the construction team member i.

[0106] In an embodiment, the adaptability of the construction team member to each type of work task is calculated in terms of criticality in combination with the task implementation ability of the construction team member based on the weighted average method, which includes:

[0107] According to the influence of each type of work task on the completion degree of the sub-project of the project, the criticality score of the corresponding type of work task is determined.

[0108] The professional ability of the construction team member associated with each type of work task and the experience matching situation with each type of work task are obtained.

[0109] In a weighted scoring manner, and in combination with the professional ability score and experience matching degree of the construction team members, the criticality score of the construction team members for each type of work task is calculated.

[0110] The criticality score of the construction team members for each type of work task is divided by the criticality score of the corresponding type of work task to obtain the adaptability of the construction team members to the criticality aspect of each type of work task.

[0111] Among them, according to the influence of each work task on the overall progress and completion of the project, the criticality score of each task is determined. Generally, tasks that affect the completion of the project will be given a higher criticality score. Influencing factors include task size, complexity, resource demand, time node, etc. For example, infrastructure construction tasks (such as foundation pouring, structural steel frame construction, etc.) usually have a higher criticality score, while decoration tasks (such as wall painting) may have a lower criticality score.

[0112] The professional ability score is a professional ability score evaluated for each worker according to their personal skills, work experience, and other factors. For example, worker A with rich foundation construction experience has a higher ability score (such as 8 / 10) on this type of task, but a lower ability score (such as 5 / 10) on electrical installation tasks. The experience matching degree is evaluated for each worker's experience matching degree for different tasks according to the matching degree between the worker's past experience and the requirements of each type of task.

[0113] By dividing the criticality adaptability score of the construction team members by the criticality score of the task, the adaptability of each worker to each type of work task is obtained. The adaptability value reflects the member's adaptability to a task in terms of criticality. If a worker has a higher adaptability to the criticality requirements of a task (i.e. has a higher professional ability and experience matching degree), the adaptability value is higher, and vice versa.

[0114] In one embodiment, based on a linear regression model, the time efficiency adaptability of the construction team members to each type of work task is calculated in combination with the time adjustment ability of the construction team members, which includes:

[0115] According to the execution time of each type of work task, the time efficiency score of the corresponding type of work task is determined.

[0116] Obtain the historical completion time of the construction team members to complete each type of work task and the time management ability, and comprehensively obtain the time adjustment ability of the construction team members for each type of work task.

[0117] The time efficiency score of each type of work task is normalized, and the time adjustment ability is standardized.

[0118] A linear regression model of time efficiency adaptability is constructed:

[0119]

[0120] Where, represents the adaptability of construction team member i to the timeliness of task category j; β0 represents the intercept term, β1 and β2 represent the regression coefficients of the task timeliness score and the construction team member's time adjustment ability, respectively, and are the effects of the task timeliness score and the member's time adjustment ability on the timeliness adaptability score; T′ j represents the normalized timeliness score of task category j; C′ i It represents the time adjustment ability of construction team member i after standardization.

[0121] The linear regression model of timeliness fitness is fitted using the least squares method and historical data, and the target vector is the historical completion time of each type of task completed by the construction team members.

[0122] The timeliness score of a task reflects its urgency and time constraints. Critical, high-priority tasks typically have higher timeliness scores. For example, a basic construction task like foundation pouring might have a timeliness score of 8 / 10, while some later finishing tasks might have a timeliness score of 5 / 10.

[0123] The Time Adjustment Ability Score calculates each worker's time adjustment ability based on their performance in past projects (e.g., whether they were able to adjust plans on time and complete tasks efficiently). This is measured by factors such as the worker's historical task completion time and the difference from the scheduled time. Standardized Time Adjustment Ability: This standardizes the worker's time adjustment ability and converts it into a standardized score for ease of subsequent calculations.

[0124] When using historical data to fit a linear regression model, the components of the historical data are:

[0125] Member historical completion time: The actual time each construction member took to complete similar tasks. This data reflects the actual time a member spent on a specific task, providing a true time performance indicator for the linear regression model.

[0126] Timeliness score: Each task has been assigned a timeliness score based on factors such as urgency and deadline. The timeliness score helps the model understand the time requirements of the task and is usually between 1 and 10, with higher values ​​indicating more urgent tasks.

[0127] Time adjustment capability of the member: this is the ability of the member to adjust and manage the time of the task, which is quantified by comprehensively considering the experience, past project completion, time management capability and other indicators of the member. The time adjustment capability is one of the key factors to predict whether the member can complete the task on time in the task.

[0128] These historical data will be used as input of the regression model. By fitting the regression model, the model can learn how to predict the timeliness adaptability score of the member for the new task according to the timeliness score of the task, the historical completion time of the member and the time adjustment capability, so as to help better task allocation.

[0129] After obtaining the regression coefficient, the linear regression model of timeliness adaptability is substituted, and the timeliness adaptability of the construction team member for each type of work task is calculated through the linear regression model of timeliness adaptability.

[0130] In one embodiment, the authority adaptability of the construction team member for each type of work task is quantified by using fuzzy logic principles combined with the professional ability of the construction team member, which includes:

[0131] According to the complexity and technical difficulty of each type of work task, the authority score of the corresponding type of work task is determined.

[0132] The academic background and project leadership ability of the construction team member are obtained, and the academic background and project leadership ability are used as input variables of fuzzy logic reasoning.

[0133] The input variables are converted into fuzzy membership values by the membership function; according to the fuzzy membership values of the academic background and project leadership ability, and by all suitable fuzzy reasoning rules and by the way of weighted calculation, the fuzzy reasoning result of the construction team member for each type of work task is obtained.

[0134] The fuzzy reasoning result is converted into the authority score of the construction team member for each type of work task by using the defuzzification method.

[0135] The authority score of the construction team member for each type of work task is divided by the authority score of the corresponding type of work task, to obtain the authority adaptability of the construction team member for each type of work task.

[0136] Note that the authority score of each task is determined according to the technical difficulty and complexity of the task. For example, tasks with high technical requirements (such as high-voltage electrical equipment installation, complex structural steel frame construction) will have a higher authority score. In addition to the technical complexity of the task, whether the task requires high professional knowledge, the requirement for execution accuracy, and whether it involves the decision of the project leader will also affect the authority score. The authority score range is defined as a range of 0 to 10, with high technicality and high execution accuracy requirement tasks scoring high (such as 9 / 10), and relatively simple tasks scoring low (such as 5 / 10).

[0137] The adaptability of the construction team members in technical tasks is evaluated according to their academic background (such as whether they have a degree or certificate in the relevant field). Generally, workers with higher education or professional qualifications can obtain a higher academic background score. The project leadership ability of the worker is evaluated according to his project leadership experience, leadership ability, and performance in past projects.

[0138] The academic background and project leadership ability are converted into fuzzy membership values using membership functions. Common membership functions include: academic background membership: for example, member A's academic background (with an engineering degree) corresponds to a membership value of 0.9, while member B's membership value is 0.3 (with only a high school education). Project leadership ability membership: for example, member A has rich experience as a project manager, corresponding to a membership of 0.8, while member B is only an executive, with a membership of 0.4.

[0139] According to the rules of fuzzy logic reasoning, the membership of academic background and project leadership ability is combined with the authority score of certain work tasks. For example:

[0140] If the worker's academic background membership is high and the project leadership ability membership is high, he is more suitable for high-authority tasks. If the worker's academic background and project leadership ability membership are both low, he is less suitable. By weighting the membership of academic background and project leadership ability, the fuzzy reasoning result of the member for a certain type of work task is calculated, and according to the result of fuzzy reasoning, the fuzzy membership is converted into a specific numerical value through defuzzification methods (such as center average method, maximum membership method, etc.). For example, using the maximum membership method (i.e. selecting the specific score corresponding to the maximum membership value) to determine the authority score of the member for a certain task. For example, member A's fuzzy reasoning result is 0.85, and after using the maximum membership method, the member's authority score for electrical installation task is 8 / 10.

[0141] The member allocation module 3 is configured to perform collaborative analysis on the adaptability of the comprehensive evaluation, the adaptability of the criticality aspect, the adaptability of the timeliness aspect, the adaptability of the authority aspect, and the social behavior characteristics of the construction team members based on a graph neural network, and optimize the allocation result of the construction team members.

[0142] In one embodiment, the collaborative analysis on the adaptability of the comprehensive evaluation, the adaptability of the criticality aspect, the adaptability of the timeliness aspect, the adaptability of the authority aspect, and the social behavior characteristics of the construction team members based on the graph neural network, and the optimization of the allocation result of the construction team members include:

[0143] The social behavior characteristics of the construction team members are obtained, and the social behavior characteristics include the collaboration efficiency among the construction team members.

[0144] The construction team members are taken as nodes, the node characteristics are the adaptability of the criticality aspect, the adaptability of the timeliness aspect, the adaptability of the authority aspect, and the adaptability of the comprehensive evaluation, and the edges and edge weights between the nodes are determined according to the social behavior characteristics of the construction team members.

[0145] A graph neural network structure is constructed, a message passing mechanism is used to update the node characteristics, and the node output of the graph neural network is a final feature vector of each construction team member. The final feature vector includes the adaptability of the construction team member to a certain type of construction task and the social behavior characteristic information after the graph neural network processing, and the adaptability includes the adaptability of the criticality aspect, the adaptability of the timeliness aspect, the adaptability of the authority aspect, and the adaptability of the comprehensive evaluation.

[0146] A loss function is constructed based on the gap between the adaptability of the construction team member to a certain type of construction task and the corresponding construction task requirement, and the collaboration efficiency among the construction team members. The allocation of the work task is taken as an optimization target to maximize the adaptability score and improve the collaboration efficiency of the construction team. Gradient descent is used to minimize the loss function to obtain a trained graph neural network model.

[0147] Through the trained graph neural network model, the work task requirement is input, and the final adaptability score of each construction team member is output. The construction team members are allocated according to the final adaptability score, and the collaboration efficiency among the members is optimized.

[0148] In one embodiment, the formula of the loss function is:

[0149]

[0150] In the formula, L represents the total value of the loss function; represents the adaptability score of the construction team member i to the work task j at the kth iteration, Ta(j) represents the target requirement (such as criticality, timeliness, authority, etc.) of the work task j; wij represents the weighted coefficient of the construction team member i for the work task j, and represents the importance of the task. The determination of the weighted coefficient needs to comprehensively consider the criticality, timeliness, complexity, resource demand, and other factors of the task, as well as historical execution experience and the judgment of project management experts. Through reasonable combination of these factors, a suitable weighted coefficient is determined for each work task; λ represents the weight factor of the cooperation efficiency; C(S it ) represents the cooperation efficiency between the construction team member i and the construction team member t; n represents the number of construction team members, and m represents the number of work tasks.

[0151] It should be noted that the present application inputs multi-dimensional fitness characteristics into the graph neural network, which can help the model more accurately capture the different performances of team members in tasks and cooperation efficiency. This not only enhances the modeling ability of the model for complex tasks and team relationships, but also improves the accuracy of task allocation and team collaboration optimization. By considering the fitness characteristics of each member in multiple aspects, the network can better allocate tasks and improve the work efficiency of the entire construction team.

[0152] The social behavior characteristics (such as cooperation efficiency) are indicators of the collaboration between team members. Cooperation efficiency is evaluated through the cooperation effect of historical projects, such as task completion, quality, and communication and coordination ability between members. The value of cooperation efficiency is set to a value within the range of [0, 1], with 0 representing complete non-cooperation and 1 representing complete cooperation.

[0153] In the graph neural network, nodes exchange information with neighbor nodes through edges. Each node will pass its own features to its neighbor nodes and update according to the neighbor's features. The updated feature vector of the construction team member contains the criticality fitness, timeliness fitness, authority fitness, and social behavior characteristics after being processed by the graph neural network.

[0154] The closed-loop management module 4 is used to dynamically adjust the engineering work mode according to the progress of the engineering work task and in combination with the real-time environmental data of the engineering site and the allocation results of the construction team personnel.

[0155] In one embodiment, dynamically adjusting the engineering work mode according to the progress of the engineering work task and in combination with the real-time environmental data of the engineering site and the allocation results of the construction team personnel includes:

[0156] Adjusting the allocation of construction team personnel according to the progress of the engineering work task.

[0157] Adjusting the allocation of construction team personnel according to the progress of the engineering work task.

[0158] The effect of the adjusted engineering operation mode is obtained, and the difference before and after the adjustment is compared until the expected engineering operation progress goal is achieved.

[0159] When the progress of the engineering task is obtained, the execution of each task is tracked in real time through the Internet of Things device or the field management platform, including the start time, completion time, resource consumption, task interruption, etc. The actual progress is compared with the scheduled progress to calculate the delay or advance of the task. If the actual progress of the task lags behind the plan, corresponding adjustment measures need to be taken. At the same time, the Internet of Things device is used to collect real-time environmental data, monitor the influence of weather changes, environmental temperature and humidity, equipment failure, etc. on the task execution.

[0160] According to the progress of the engineering task, the construction team members are dynamically adjusted. If some tasks lag behind, more members with high timeliness adaptability can be allocated to speed up the task progress; if the task is ahead of schedule, some members can be transferred to other tasks that need support. According to the real-time environmental data, the resource allocation is dynamically adjusted. For example, in extreme weather, the construction plan needs to be adjusted or protective measures need to be increased; in the case of equipment failure, standby equipment needs to be quickly mobilized or maintenance needs to be arranged. The execution effect after the adjustment of the operation mode is evaluated, mainly focusing on the changes in task progress, construction quality, worker safety, cost control, etc.

[0161] As shown in Figure 2 According to another embodiment of the present application, a sub-task engineering closed-loop operation management method based on the Internet of Things is also provided, comprising:

[0162] S1, based on the clustering algorithm and the operation task data collected by the Internet of Things, the operation tasks of the sub-task engineering are classified.

[0163] S2, the adaptability of the construction team members to each type of operation task in terms of criticality, timeliness and authority is calculated, and the adaptability is fused to obtain a comprehensive evaluation adaptability.

[0164] S3, based on the graph neural network, the comprehensive evaluation adaptability, the adaptability in terms of criticality, the adaptability in terms of timeliness, the adaptability in terms of authority and the social behavior characteristics of the construction team members are collaboratively analyzed, and the allocation result of the construction team members is optimized.

[0165] S4, according to the progress of the engineering task, and combining the real-time environmental data of the engineering site and the allocation result of the construction team members, the engineering operation mode is dynamically adjusted.

[0166] In order to facilitate the understanding of the above technical solutions of the present application, the working principle of the present application in the actual process will be described in detail.

[0167] The present application is applied to large-scale facility construction projects, including but not limited to bridge construction, high-rise building construction, and other complex engineering projects.

[0168] For example, in a large-scale bridge construction project, different types of construction tasks need to be classified and managed. The project is divided into multiple stages such as infrastructure construction, steel structure installation, and concrete pouring.

[0169] I. Task Classification

[0170] (1) Equipment deployment and data collection

[0171] Install 50 temperature and humidity sensors distributed at different locations on the construction site, collecting data every hour. Configure 20 RFID tags to monitor material consumption in the material storage area. Install 10 video surveillance cameras in key areas to monitor construction progress and safety conditions in real time.

[0172] (2) Data processing and clustering analysis

[0173] The collected data includes temperature ranging from 15℃ to 30℃, humidity ranging from 40% to 70%, and air quality index fluctuating between 50 and 100. Use the K-means algorithm to classify the tasks, and through the silhouette coefficient evaluation, the average silhouette coefficient is 0.65, indicating good clustering effect. Specific case: distinguish concrete pouring tasks (environmental requirements: temperature 20℃±5℃, humidity 60%±10%) from other tasks to ensure the execution quality of tasks under specific environmental conditions. Steel structure installation tasks (environmental requirements: no strict environmental requirements) are assigned to a separate group.

[0174] II. Fitness evaluation module

[0175] Evaluate the fitness of construction team members for different types of tasks to optimize task allocation.

[0176] For example, for infrastructure construction tasks, the criticality score is determined to be 8 / 10 because this task directly affects the overall progress of the project. Member A has rich experience in foundation construction (professional ability score 8 / 10) and has a high matching degree in similar past projects (experience matching degree 9 / 10). The criticality fitness is calculated to be 0.9, i.e. (8+9) / 10, by using the weighted scoring method.

[0177] For example, the timeliness score of infrastructure construction tasks is 8 / 10, and member A's historical completion time is 95% of the estimated time (time adjustment ability score 0.95). Use a linear regression model to calculate the timeliness fitness, formula: fitness = 0.5 + 0.3 × 8 + 0.2 × 0.95, member A's timeliness fitness is 0.85.

[0178] For example, the authority score for the infrastructure task is 9 / 10, member A has a master's degree in the relevant field (academic background membership 0.9), and has rich experience as a project manager (project leadership membership 0.8). Applying fuzzy logic principles, combined with membership functions to calculate the authority fitness, the final score is 0.88.

[0179] The weight coefficient is set as:

[0180] Critical fitness: a1 = 0.5. Timeliness fitness: a2 = 0.3. Authority fitness: a3 = 0.2.

[0181] The two adjustment coefficients are 0.1 and 0.05 respectively.

[0182] The timeliness score of the infrastructure task is 8 / 10, and member A's time adjustment ability is 0.95, with a leadership score of 0.85. Substituting the formula for comprehensive evaluation fitness, the comprehensive evaluation fitness is 0.45 + 0.105 + 0.175 = 0.73.

[0183] Three, member allocation

[0184] Optimize the task allocation of construction team members using graph neural networks. Through historical project cooperation data, evaluate the collaboration efficiency between members. For example, the collaboration efficiency score of member A and member B is 0.85, indicating that the two people work in harmony and can complete tasks efficiently.

[0185] Graph neural network construction and optimization: all construction team members are nodes, and edge weights are determined according to collaboration efficiency. Assuming there are a total of 30 members, a graph neural network containing 30 nodes is constructed. After multiple iterations (such as 50 times), the loss function value decreases from the initial 150 to the final 30, indicating that the optimization effect is significant. Finally, member A is recommended to be responsible for the infrastructure task, and member B is recommended to be responsible for the steel structure installation task, because their respective fitness scores are high and their collaboration efficiency is good.

[0186] In task allocation, the loss function is used to measure the gap between task allocation and the target, which is to maximize task fitness and improve collaboration efficiency. The loss function includes two parts:

[0187] Task fitness gap: this part calculates the gap between task requirements and the fitness of each member, with the goal of ensuring that each member has the best fitness in task allocation.

[0188] Optimization of collaboration efficiency: this part calculates the collaboration efficiency between construction team members and hopes to maximize the overall collaboration efficiency.

[0189] The optimization goal of the loss function is:

[0190] Minimize the gap between member fitness and task requirements;

[0191] Improve the efficiency of collaboration among members, thereby optimizing the execution effect of the overall team.

[0192] Four, closed-loop management

[0193] Collect environmental data from the construction site every day, and find that the temperature suddenly rises to 35℃ on a certain day, exceeding the optimal temperature range for concrete pouring tasks. Adjust the work plan, increase the water sprinkling cooling measures, and deploy more members with high timeliness fitness to speed up the task progress.

[0194] Compare the task progress before and after adjustment, the original plan of concrete pouring task needs 7 days to complete, actually only uses 6 days, completes the task one day in advance. At the same time, due to the timely cooling measures, the quality problems caused by high temperature are avoided, and the construction quality is improved.

[0195] The present application includes:

[0196] (1) Through the cooperation analysis of fitness evaluation matrix and graph neural network, the ability of each member in keyness, timeliness, authority and the collaboration efficiency among team members are comprehensively considered, and the task allocation scheme is optimized. Through accurate multi-dimensional evaluation, the mismatch between member skills and task requirements can be effectively solved.

[0197] (2) Through the real-time collection of field data by Internet of Things technology, combined with data analysis and real-time feedback mechanism in the closed-loop management module, the construction operation mode can be dynamically adjusted, and the field changes can be quickly responded.

[0198] (3) The cooperation analysis mode of graph neural network considers the collaboration efficiency among construction team members, and optimizes the collaboration pairing of team members in the task allocation process, further improving the collaboration efficiency of construction team. This optimization can effectively reduce task conflicts and resource waste, improve the work coordination and overall execution of construction team.

[0199] (4) Through the combination of linear regression model and weighted average method, the timeliness fitness of members is evaluated combined with historical data, ensuring that members are reasonably scheduled according to their time adjustment ability and historical performance, thereby optimizing the execution time of construction tasks and ensuring the project to be completed on schedule.

[0200] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A closed-loop operation management system for sub-projects based on the Internet of Things, characterized by: include: The task classification module is used to classify the work tasks of each part of the project based on clustering algorithms and work task data collected by the Internet of Things; The fitness assessment module is used to calculate the fitness of construction team members in terms of the criticality, timeliness, and authority of each type of work task, and integrate the fitness to obtain a comprehensive fitness assessment; The member allocation module is used to obtain the social behavior characteristics of construction team members, including the collaboration efficiency among construction team members. Construction team members are regarded as nodes, and the node characteristics are the fitness in terms of criticality, timeliness, authority, and comprehensive evaluation. The edges and edge weights between nodes are determined according to the social behavior characteristics of construction team members. A graph neural network structure is constructed, and the node characteristics are updated using a message passing mechanism. The node output of the graph neural network is the final feature vector of each construction team member. The final feature vector includes the fitness and social behavior characteristic information of the construction team members for a certain type of construction task after being processed by the graph neural network. The fitness includes fitness in terms of criticality, timeliness, authority, and comprehensive evaluation. A loss function is constructed based on the gap between the fitness of construction team members for a certain type of construction task and the corresponding construction task requirements, as well as the collaboration efficiency among construction team members. The optimization goal is to maximize the fitness score and improve the collaboration efficiency of the construction team through the allocation of work tasks. Gradient descent is used to minimize the loss function to obtain a trained graph neural network model. The work task requirements are input into the trained graph neural network model, and the final fitness score of each construction team member is output. The construction team members are allocated according to the final fitness score. The formula of the loss function is: ； Where, L Represents the total value of the loss function; Construction team members i For homework tasks j In the k The fitness score at the iteration, Ta ( j ) indicates a job task j target requirements; w ij Indicates job tasks j For construction team members i The weighting coefficient of A weight factor representing collaboration efficiency; C ( S it ) indicates construction team members i and construction team members t The efficiency of collaboration between n represents the number of construction team members, m Indicates the number of job tasks; The closed-loop management module is used to dynamically adjust the engineering operation mode according to the progress of the engineering operation tasks, combined with the real-time environmental data of the engineering site and the allocation results of the construction team personnel.

2. The closed-loop operation management system for sub-projects based on the Internet of Things according to claim 1 is characterized in that: The classification of the work tasks of the sub-projects based on the clustering algorithm and the work task data collected by the Internet of Things includes: Plan the installation location of IoT monitoring equipment based on the requirements of the sub-projects at the project construction site; Using IoT monitoring equipment to collect task-related data and extract task features from the task-related data, where the task-related data includes basic task information, task progress information, task feature data, and task equipment information; Based on the clustering algorithm and according to the characteristics of the job tasks, the job tasks are grouped, and the silhouette coefficient is used to evaluate the clustering effect.

3. The closed-loop operation management system for sub-projects based on the Internet of Things according to claim 1 is characterized in that: The calculation of the construction team members' adaptability to the criticality, timeliness, and authority of each type of task, and the integration of adaptability to obtain a comprehensive assessment of adaptability include: Based on the weighted average method and combined with the construction team members' task realization capabilities, the construction team members' adaptability to the critical aspects of each type of work task is calculated; Based on the linear regression model and combined with the time adjustment ability of construction team members, the adaptability of construction team members to the timeliness of each type of task is calculated; By using fuzzy logic principles and combining the professional capabilities of construction team members, we can quantify the adaptability of construction team members to the authority of each type of work task. Based on the construction team members' adaptability to the criticality, timeliness, and authority of each type of task, the comprehensive evaluation adaptability is calculated. The formula for the comprehensive evaluation adaptability is: ； Where, G ij Construction team members i In the homework task j Comprehensive evaluation fitness on a1, a2, and a3 represent the importance coefficients of criticality, timeliness, and authority fitness in the comprehensive evaluation of fitness, respectively; δ 1 and δ 2 are adjustment coefficients; Key ij Construction team members i In the homework task j Adaptability in key areas; Time ij Construction team members i In the homework task j Adaptability in terms of timeliness; Au ij Construction team members i In the homework task j Adaptability to authority; T j Indicates job tasks j Timeliness rating, A ij Construction team members i In the homework task j The ability to adjust time, L i Construction team members i Project leadership rating.

4. The closed-loop operation management system for sub-projects based on the Internet of Things according to claim 3 is characterized in that: The calculation of the construction team members' adaptability to the critical aspects of each type of task based on the weighted average method and combined with the construction team members' task realization capabilities includes: Determine the criticality score of each type of task based on its impact on the completion of each part of the project; Obtain the professional capabilities of construction team members associated with each type of work task and their experience matching with each type of work task; Calculate the criticality score of each construction team member for each type of task using a weighted scoring method, combined with the professional ability score and experience matching of the construction team members; The construction team members' criticality scores for each type of work task are divided by the criticality scores of the corresponding type of work tasks to obtain the construction team members' adaptability to the criticality of each type of work task.

5. The closed-loop operation management system for sub-projects based on the Internet of Things according to claim 3 is characterized in that: The calculation of the timeliness adaptability of construction team members to each type of task based on the linear regression model and the time adjustment ability of construction team members includes: Determine the timeliness score of each type of task based on the execution time of each type of task; Obtain the historical completion time and time management ability of construction team members for each type of task, and comprehensively obtain the time adjustment ability of construction team members for each type of task; Normalize the timeliness scores of each type of task and standardize the time adjustment ability; Construct a linear regression model of time-effectiveness fitness: ； Where, Construction team members i For job task categories j Adaptability in terms of timeliness; β 0 represents the intercept term, β 1 and β 2 are the regression coefficients of the timeliness score of the task and the time adjustment ability of the construction team members; Represents the normalized job task category j Timeliness rating; Represents standardized construction team members i Time regulation ability; The linear regression model of timeliness fitness is fitted using the least squares method and historical data, and the target vector is the historical completion time of each type of task completed by the construction team members; After obtaining the regression coefficient, it is substituted into the linear regression model of timeliness fitness, and the timeliness fitness of construction team members for each type of work task is calculated through the linear regression model of timeliness fitness.

6. The closed-loop operation management system for sub-projects based on the Internet of Things according to claim 3 is characterized in that: The method of using fuzzy logic principles and combining the professional capabilities of construction team members to quantify the adaptability of construction team members to the authority of each type of task includes: Determine the authority score for each type of task based on its complexity and technical difficulty; Obtain the academic background and project leadership ability of construction team members, and use the academic background and project leadership ability as input variables for fuzzy logic reasoning; The input variables are converted into fuzzy membership values ​​through membership functions. Based on the fuzzy membership values ​​of academic background and project leadership ability, all appropriate fuzzy inference rules are used and weighted calculation is performed to obtain the fuzzy inference results of construction team members for each type of work task. The defuzzification method is used to convert the fuzzy reasoning results into the authority scores of construction team members for each type of work task; The authority score of the construction team members for each type of work task is divided by the authority score of the corresponding type of work task to obtain the adaptability of the construction team members to the authority of each type of work task.

7. The closed-loop operation management system for sub-projects based on the Internet of Things according to claim 1 is characterized in that: The dynamic adjustment of the engineering operation mode according to the progress of the engineering operation task, combined with the real-time environmental data of the engineering site and the allocation results of the construction team personnel, includes: Adjust the allocation of construction team personnel according to the progress of engineering tasks; Adjust the allocation of engineering resources based on real-time environmental data at the engineering site and the progress of engineering tasks; Obtain the effect of the adjustment of the engineering operation mode and compare the differences before and after the adjustment until the expected engineering operation progress target is achieved.

8. A closed-loop operation management method for sub-item projects based on the Internet of Things, applied to a closed-loop operation management system for sub-item projects based on the Internet of Things according to any one of claims 1 to 7, characterized in that: include: Classify the work tasks of each sub-project based on clustering algorithms and work task data collected by the Internet of Things; Calculate the construction team members' adaptability to the criticality, timeliness, and authority of each type of task, and integrate the adaptability to obtain a comprehensive assessment of adaptability; Based on graph neural networks, we conduct collaborative analysis of comprehensive assessment fitness, criticality fitness, timeliness fitness, authority fitness, and the social behavior characteristics of construction team members, and optimize the allocation of construction team members. Dynamically adjust the engineering operation mode according to the progress of the engineering operation tasks, combined with the real-time environmental data of the engineering site and the allocation results of the construction team personnel.

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