Collaborative Management Methods and Systems for Intelligent Industrial Worker Communities

Abstract

This invention discloses a collaborative management method and system for intelligent industrial worker communities, belonging to the field of intelligent management technology. The method includes: deploying IoT sensors within the worker community to continuously collect environmental data, worker feedback, and production management data; fusing and analyzing these data to generate dynamically updated worker group profiles and individual demand preference models; based on this model, mining behavioral chains and spatiotemporal trajectories to perform dynamic demand prediction for the worker group; and conducting demand collision analysis and adaptive scheduling management based on the prediction results and the current production plan. This invention solves the technical problem that existing worker community management models cannot accurately coordinate production needs with workers' living needs, leading to unreasonable resource allocation, low management efficiency, and insufficient worker satisfaction. It achieves the technical effect of dynamically matching worker needs with production scheduling through intelligent collaborative management, optimizing resource allocation, and improving management efficiency and worker satisfaction.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, specifically to a collaborative management method and system for intelligent industrial worker communities. Background Technology

[0002] In traditional industrial worker community management, the lack of effective data collection and analysis methods often leads to reliance on manual and static data, making it difficult to accurately grasp the diverse needs of workers. This results in irrational resource allocation and low management efficiency. Furthermore, the lack of an effective communication mechanism between workers and management hinders timely feedback on worker needs, impacting worker satisfaction and work efficiency. In addition, insufficient coordination between production plans and living services can easily lead to resource conflicts, affecting production progress and the stability of community operations. Summary of the Invention

[0003] This application provides a collaborative management method and system for intelligent industrial worker communities, which is used to solve the technical problems of existing worker community management models being unable to accurately coordinate production needs and workers' living needs, resulting in unreasonable resource allocation, low management efficiency, and insufficient worker satisfaction.

[0004] The first aspect of this application provides a collaborative management method for intelligent industrial worker communities. The method includes: continuously collecting community environmental data, worker feedback data, and production management data through an IoT sensor network deployed within the community; generating worker job and work group classification information based on the production management data; extracting worker behavior feature vectors based on the community environmental data; and extracting worker selection preference information based on the worker feedback data; fusing and analyzing the worker job and work group classification information, worker behavior feature vectors, and worker selection preference information to generate a dynamically updated worker group profile and individual demand preference model; mining behavioral chains and spatiotemporal trajectories based on the worker group profile and individual demand preference model, and performing dynamic demand prediction of the worker group based on the behavioral chains and spatiotemporal trajectories to generate worker demand prediction results; and performing demand collision analysis based on the worker demand prediction results and the current production plan, and performing adaptive scheduling management based on the demand collision results.

[0005] The second aspect of this application provides a collaborative management system for an intelligent industrial worker community. The system includes: a data acquisition module for continuously collecting community environmental data, worker feedback data, and production management data through an IoT sensor network deployed within the community; a multi-dimensional feature extraction module for generating worker job and work group classification information based on the production management data, extracting worker behavior feature vectors based on the community environmental data, and extracting worker selection preference information based on the worker feedback data; an information fusion and analysis module for fusing and analyzing the worker job and work group classification information, worker behavior feature vectors, and worker selection preference information to generate a dynamically updated worker group profile and individual demand preference model; a worker demand prediction module for mining behavioral chains and spatiotemporal trajectories based on the worker group profile and individual demand preference model, and performing dynamic demand prediction of the worker group based on the behavioral chains and spatiotemporal trajectories to generate worker demand prediction results; and an adaptive scheduling management module for performing demand collision analysis based on the worker demand prediction results and the current production plan, and performing adaptive scheduling management based on the demand collision results.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The collaborative management method and system for intelligent industrial worker communities provided in this application pertain to the field of intelligent management technology. It collects community environment, worker feedback, and production management data through an Internet of Things (IoT) sensor network, integrates these data to generate dynamically updated worker group profiles and individual demand preference models, mines behavioral chains and spatiotemporal trajectories for demand prediction, and combines this with production planning for adaptive scheduling management. This achieves efficient resource allocation and improved management effectiveness, solving the technical problems of existing worker community management models that cannot accurately coordinate production needs with workers' living needs, leading to unreasonable resource allocation, low management efficiency, and insufficient worker satisfaction. The system achieves the technical effect of dynamically matching worker needs with production scheduling through intelligent collaborative management, optimizing resource allocation, and improving management efficiency and worker satisfaction. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram of the collaborative management method for an intelligent industrial worker community provided in this application embodiment; Figure 2 This is a schematic diagram of the collaborative management system structure for an intelligent industrial worker community provided in an embodiment of this application.

[0009] Figure labeling: 11 Data acquisition module, 12 Multidimensional feature extraction module, 13 Information fusion analysis module, 14 Worker demand prediction module, 15 Adaptive scheduling management module. Detailed Implementation

[0010] This application provides a collaborative management method and system for intelligent industrial worker communities, which is used to solve the technical problems of existing worker community management models being unable to accurately coordinate production needs and workers' living needs, resulting in unreasonable resource allocation, low management efficiency, and insufficient worker satisfaction.

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0013] Example 1, as Figure 1 As shown, this application provides a collaborative management method for intelligent industrial worker communities, the method comprising: P10: Continuously collect community environmental data, worker feedback data, and production management data through an IoT sensor network deployed within the community.

[0014] Specifically, the first step is to plan and deploy a high-density IoT sensor network within the community. This network includes various types of sensors, with the specific deployment locations and quantities optimized based on the community's layout and functional areas. For example, in the dormitory area, each dormitory room is equipped with a temperature and humidity sensor and an air quality sensor to monitor indoor environmental parameters in real time; in public areas such as the canteen, activity rooms, and work areas, environmental monitoring equipment, including light intensity sensors and noise sensors, is installed at regular intervals, such as every 50 square meters, to ensure environmental comfort in public spaces.

[0015] Secondly, regarding the collection of community environmental data, the sensor network will collect data in real time on temperature, humidity, air quality (such as PM2.5 and CO2 concentrations), light intensity, and noise levels. These sensors transmit data to the community's central data processing center via low-power wireless communication technologies such as LoRa or NB-IoT. The data transmission frequency is adjusted according to the sensor type and application scenario; for example, temperature and humidity sensors upload data every 5 minutes, while air quality sensors upload data every 10 minutes to ensure data real-time performance and accuracy.

[0016] Meanwhile, worker feedback data is collected through smart interactive terminals and mobile applications set up within the community. Multiple smart interactive terminals are installed in public areas and dormitories, allowing workers to submit feedback on community living and working conditions. Additionally, a dedicated mobile application allows workers to submit feedback anytime, anywhere. This feedback includes evaluations of the cafeteria's food, needs regarding dormitory facilities, and suggestions for the work environment. To ensure data accuracy and usability, the feedback data undergoes preliminary analysis using natural language processing (NLP) technology to extract key information and sentiment. Specifically, a BERT-based text classification algorithm is used to perform sentiment analysis on worker feedback, assessing satisfaction and extracting specific suggestions. This process includes word segmentation, part-of-speech tagging, and sentiment analysis of the feedback text, ultimately generating structured feedback data stored in a database at the central data processing center.

[0017] Production management data collection is achieved through integration with the enterprise's production management system. Smart devices worn by workers, such as smart bracelets or work badges, integrate RFID or Bluetooth modules to record worker attendance and work location in real time. These smart devices send attendance and location data to the production management system at regular intervals, such as every 10 minutes. Simultaneously, the production management system synchronizes task allocation, progress updates, and other information to the IoT sensor network. After data cleaning and preprocessing, this data is used to generate worker performance profiles, supporting subsequent job classification and team management. The data cleaning process includes removing duplicate data, filling in missing values, and correcting outliers to ensure data integrity and accuracy.

[0018] Throughout the data acquisition process, the IoT sensor network transmits the collected data to the community's central data processing center in real time via wireless communication technology. Encryption techniques are employed during data transmission to ensure data security and privacy. For example, the AES-256 encryption algorithm can be used to encrypt the transmitted data, ensuring it is not stolen or tampered with during transmission. Simultaneously, to guarantee data integrity and accuracy, the sensor network features automatic calibration and fault detection capabilities. Upon detecting a sensor malfunction, the system immediately issues an alarm and notifies maintenance personnel via SMS or an application for handling. Furthermore, the system automatically activates backup sensors to ensure continuous data acquisition. The backup sensor switching process is completed within 5 minutes, ensuring uninterrupted data collection.

[0019] P20: Based on the production management data, generate worker job type and work group classification information; based on the community environment data, extract worker behavior feature vectors; and based on the worker feedback data, extract worker selection preference information.

[0020] Optionally, the collected production management data, community environment data, and worker feedback data can be processed to generate worker job type and work group classification information, worker behavior feature vectors, and worker selection preference information.

[0021] First, production management data, including worker attendance records, task assignments, work progress, and performance evaluations, is obtained from the enterprise's production management system. This data is imported into a data preprocessing module for cleaning and formatting, removing duplicate records, filling in missing values, and converting the data into a format suitable for analysis. Specifically, attendance records are converted into daily attendance time series, task assignments are extracted into task type and duration, work progress is quantified as task completion percentage, and performance evaluations are standardized to values ​​between 0 and 1. After preprocessing, clustering algorithms are used to classify workers by job type and work group. Key features, such as task type, work duration, skill level, and performance rating, are extracted from the production management data. For example, K-means clustering is used to classify workers, and the elbow rule is used to determine the optimal number of clusters (K value). This involves calculating the intra-cluster variance (Inertia) under different K values ​​and selecting the K value that causes the Inertia to begin to decrease significantly as the optimal number of clusters. Each cluster represents a specific job type or work group. Finally, worker job type and work group classification information is generated and stored in a database.

[0022] Simultaneously, worker behavior feature vectors are extracted based on the community environmental data. This data includes information such as workers' activity trajectories, dwell time, and facility usage frequency within the community. This process involves monitoring worker activities within the community using sensors and combining this data with various community environmental data, such as temperature, humidity, air quality, and noise levels, to analyze workers' activity patterns and behavioral characteristics under different environmental conditions. For example, temperature changes detected by sensors may lead workers to take frequent breaks at higher temperatures, or workers may prefer to work in more distant areas when air quality is poor. The system generates corresponding behavior feature vectors through real-time monitoring of worker behavior, recording information such as activity trajectories, dwell time, and activity frequency. These behavioral features help understand workers' work patterns and lifestyles under different environmental conditions, thus providing a basis for subsequent demand forecasting. Next, the extracted behavioral features are converted into numerical vectors; for example, activity trajectories can be represented by coordinate sequences, dwell time by duration, and facility usage frequency by number of uses. Finally, a behavior feature vector for each worker is generated and stored in a database for subsequent behavior analysis and demand forecasting.

[0023] Finally, based on worker feedback data, information on workers' preferences regarding work environment, living conditions, and catering services is extracted. Worker feedback data can be collected through various methods such as questionnaires, daily interactions, and mobile application feedback, covering workers' needs and preferences for facilities such as dormitories, canteens, and activity areas. For example, workers may provide feedback on their preferences for the taste of food offered in the canteen, or their preferred rest time arrangements. By analyzing this feedback, the system can determine workers' specific needs for different services and environments, providing a concrete basis for subsequent service optimization and resource allocation. For example, the feedback text is cleaned, including removing useless symbols and stop words, and performing word segmentation. Next, natural language processing techniques, such as the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm, are used to perform semantic analysis on the feedback text, calculate the importance of each word, extract keywords, identify workers' main needs and preferences, and perform deep semantic analysis on the feedback text to extract workers' specific preference information, such as preferences for catering types, rest times, and entertainment facilities. The analysis results are then transformed into structured preference information; for example, "hope to add more fitness equipment" is transformed into "high demand for fitness equipment." Ultimately, the selection preference information for each worker is generated and stored in a database for subsequent personalized services and demand forecasting.

[0024] After completing the above classification and extraction processes, the generated worker job and work group classification information, worker behavioral feature vectors, and worker selection preference information are integrated and stored in the central database of the community management system, forming a comprehensive worker information database. This database can be stored using a relational database (such as MySQL) or a non-relational database (such as MongoDB), the specific choice depending on the data structure and query requirements. The database design includes tables for basic worker information, job and work group classification, behavioral feature vectors, and selection preference information, providing a solid data foundation for subsequent worker profiling and demand forecasting.

[0025] P30: The worker job type and work group classification information, worker behavior feature vectors and worker selection preference information are integrated and analyzed to generate a dynamically updated worker group profile and individual demand preference model.

[0026] Furthermore, step P30 in this embodiment of the application also includes: P31: Based on the worker job type and work group classification information, workers are initially classified according to job type and work group; P32: Based on the initial classification, secondary clustering is performed based on the worker behavior feature vector to generate worker groups with similar behavior patterns; P33: A group behavior feature database is established for each worker group to form the worker group profile; P34: For each individual worker, based on the worker selection preference information and combined with the individual's feature offset in the worker group, the individual demand preference model is generated.

[0027] It should be understood that integrating and analyzing worker job type and work group classification information, worker behavior feature vectors, and worker selection preference information to generate dynamically updated worker group profiles and individual demand preference models is a key step in achieving precise management and personalized services.

[0028] First, preprocessed and preliminarily analyzed worker job and work group classification information, worker behavioral feature vectors, and worker selection preference information are retrieved from the database. This data will serve as the basis for subsequent fusion analysis. Next, based on the generated worker job and work group classification information, workers are initially classified by job and work group. For example, each worker's job and work group label is read from the database, a classification mapping table is created, and each worker is assigned to the corresponding job and work group category. For instance, all workers labeled "welder" and belonging to "work group one" are grouped into one category, and all workers labeled "assembler" and belonging to "work group two" are grouped into another category. The preliminary classification results are stored in a temporary data table for further analysis.

[0029] Next, based on the initial classification, secondary clustering is performed using worker behavior feature vectors to generate worker groups with similar behavioral patterns. For example, behavioral feature vectors for each worker are extracted from the database; these vectors contain information such as activity trajectory, dwell time, and facility usage frequency. Appropriate clustering algorithms, such as DBSCAN (density-based spatial clustering) or spectral clustering, are selected to accommodate the complexity and diversity of the behavioral feature vectors. Based on the initial classification, clustering analysis is performed separately for workers within each job type and shift. For example, for the "welder" category, workers are further divided into "frequent morning shift activity group" and "frequent evening shift activity group" based on their behavioral feature vectors. During the clustering process, clustering parameters, such as ε and MinPts for DBSCAN, are set to ensure the reasonableness and discriminative power of the clustering results. The secondary clustering results are stored in the database, with each cluster representing a worker group with similar behavioral patterns.

[0030] Next, a group behavior feature database is established for each worker group, forming a worker group profile. For each worker group, the mean and standard deviation of its behavior feature vector are calculated to quantify the group's typical behavior patterns. For example, the average dwell time and facility usage frequency of the group are calculated. High-frequency activity areas and frequently used facilities of the group are extracted to form the group's behavior feature database. For example, a certain group may spend a long time in the cafeteria and frequently use the gym. The group behavior feature database is combined with job and work group information to generate a detailed worker group profile. The profile includes the group's basic attributes, such as job type and work group; behavioral patterns, such as activity trajectory, dwell time, and facility usage frequency; and preference characteristics, such as high-frequency activity areas and frequently used facilities. The worker group profiles are stored in a database for subsequent demand forecasting and resource allocation.

[0031] Furthermore, for each individual worker, an individual demand preference model is generated based on the worker's choice preference information and combined with the individual's feature offset within their respective worker group. Specifically, firstly, the choice preference information for each worker is extracted from the database, reflecting their specific needs for community services and facilities. Then, the offset between each worker's individual behavioral feature vector and the behavioral feature database of their respective worker group is calculated. For example, if a worker spends significantly more time at the gym than the group average, it indicates a higher demand for gym facilities. Next, by combining this preference information with the worker's feature offset within their group, a personalized demand preference model is built for each worker. The model includes the individual's basic needs, such as food preferences and rest time requirements; personalized needs, such as high demand for specific facilities; and latent needs, such as implicit needs inferred from behavioral offsets. The individual demand preference model is stored in the database and associated with the worker group profile to comprehensively consider the needs of both the group and individuals when forecasting demand and allocating resources.

[0032] Furthermore, to ensure that worker profiles and individual demand preference models reflect the latest community conditions and worker needs, a dynamic update mechanism is implemented. Community environment data, worker feedback data, and production management data are collected and analyzed periodically (e.g., weekly). Based on the new data, worker job and shift classification information, worker behavioral characteristic vectors, and worker selection preference information are updated, dynamically adjusting and updating the worker profiles and individual demand preference models. The updated models and profiles replace the old versions, ensuring the system always bases its demand forecasting and management decisions on the latest data.

[0033] P40: Based on the worker group profile and individual demand preference model, mine the behavior chain and spatiotemporal trajectory, and perform dynamic demand prediction of the worker group based on the behavior chain and spatiotemporal trajectory to generate worker demand prediction results. Furthermore, based on the aforementioned worker group profile and individual demand preference model, and by mining behavioral chains and spatiotemporal trajectories, step P40 of this embodiment further includes: P41: Based on the worker group profile, extract the activity sequences of different worker groups within typical work and rest cycles to generate a behavior chain; P42: Based on the individual demand and preference model, identify the personalized nodes of workers in the activity sequence and map the personalized nodes to the community geographic space; P43: Overlay the behavior chain and personalized nodes on the community geographic space to generate a spatiotemporal trajectory that includes group commonalities and individual differences.

[0034] Optionally, based on worker group profiles and individual demand preference models, combined with common behavioral patterns and individual differences among workers, dynamic demand prediction of workers can be performed to generate worker demand prediction results.

[0035] First, based on worker profiles, the system extracts activity sequences of different worker groups within typical work-rest cycles and generates behavioral chains. An activity sequence refers to the work and rest patterns of a worker group within a certain time period, such as the behavior patterns within a day, a work week, or a specific work shift. Specifically, by analyzing the behavioral patterns in the worker profiles, the system identifies the main activity nodes for each group throughout the day, such as waking up, breakfast, going to work, lunch, leaving get off work, dinner, leisure activities, and sleeping. By calculating the average time and duration of each activity node, the system generates the behavioral chain for each worker group. For example, for a morning shift worker group, their behavioral chain is: waking up (6:00-6:30) → breakfast (6:30-7:00) → going to work (7:00-11:30) → lunch (11:30-12:30) → going to work (12:30-17:00) → dinner (17:00-18:00) → leisure activities (18:00-20:00) → sleeping (22:00-6:00). These behavioral chains reflect the activity patterns of the working class within typical work-rest cycles.

[0036] Next, based on the worker's individual needs and preferences model, the system identifies personalized nodes in the worker's activity sequence and maps these nodes to the community's geographic space. Personalized nodes refer to specific behavioral nodes in a worker's typical work-rest cycle, representing the worker's specific needs under certain environmental conditions or work states. For example, a worker might exhibit higher work efficiency within a specific temperature range or prefer rest during certain periods. These nodes represent the worker's personalized needs and behavioral differences. By analyzing each worker's activity sequence, the system can identify these personalized nodes and associate them with the worker's community geographic space. For example, one worker prefers to use the gym during leisure activities, while another prefers to use the library. The system combines these personalized nodes with geospatial information within the community to determine the specific location of each personalized node. For example, the gym is located in the northeast corner of the community, and the library is located in the southwest corner. In this way, each worker's personalized needs node can be mapped to the community's geographic space.

[0037] Next, the behavioral chains and personalized nodes are overlaid on the community's geographic space. This involves integrating the behavioral chains of each worker group with the personalized nodes of all workers within that group to generate a spatiotemporal trajectory that includes both group commonalities and individual differences. This trajectory reflects not only the common activity patterns of the worker group but also individual differences. For example, for the early shift worker group, their spatiotemporal trajectory is mainly concentrated between the work area and dormitory area during work hours, while during leisure time it is dispersed to personalized nodes such as the gym and library. The system uses Geographic Information System (GIS) technology to visualize these spatiotemporal trajectories on a community map, allowing managers to intuitively understand the workers' activity patterns and demand distribution.

[0038] Furthermore, based on the generated spatiotemporal trajectories, dynamic demand forecasting of the workforce can be performed. For example, by analyzing activity patterns within these spatiotemporal trajectories, the system can predict the workforce's needs over a future period. This includes, for instance, the demand for canteens, dormitories, or rest areas during a specific time period, and the capacity of a particular area (such as a work area or recreation area) to accommodate worker activities. By analyzing past data and spatiotemporal trajectories, the system can identify potential needs of the workforce in advance and adjust production schedules, resource allocation, and other management measures based on these forecasts.

[0039] For example, if the spatiotemporal trajectory shows a surge in demand for rest areas during lunch breaks for a particular group, the system can allocate rest area resources in advance based on this demand to avoid overcrowding or resource shortages. Similarly, if a group of workers is less efficient under certain environmental conditions, the system can also adjust work schedules based on this information to ensure maximum work efficiency.

[0040] Meanwhile, to ensure the accuracy and timeliness of predictions, it is necessary to regularly update the behavioral chain and spatiotemporal trajectory data. This means recalculating the behavioral chain and spatiotemporal trajectory based on the latest worker profiles and individual demand preference models, and updating the demand prediction results so that the system can promptly reflect changes in worker behavior and adjustments in demand, providing a basis for continuous optimization of community management.

[0041] Furthermore, dynamic demand prediction of the worker group based on behavioral chains and spatiotemporal trajectories is performed to generate worker demand prediction results. Step P40 in this embodiment of the application also includes: P44: Input the behavioral chain and the spatiotemporal trajectory into a pre-trained demand prediction model to predict the common service demand distribution of each worker group at different time periods; P45: Based on the individual demand preference model, assign individual preference weights to each demand in the common service demand distribution to generate a weighted comprehensive demand distribution; P46: Based on the capacity limitations of each functional area of ​​the community, perform constraint optimization on the comprehensive demand distribution to generate the worker demand prediction result.

[0042] It should be understood that the process of predicting the dynamic needs of the working class can be further refined. Specifically, firstly, behavioral chains and spatiotemporal trajectories are input into a pre-trained demand prediction model. This model is trained based on historical data and the behavioral patterns of the working class. Through the model's input, the system can identify the distribution of common service needs of the working class at different times. These common demand distributions include the intensity of workers' demand for various services during specific time periods. For example, the model might predict an increase in demand for canteen services during lunchtime or an increase in demand for dormitory services during rest periods after work.

[0043] The demand forecasting model employs a deep learning architecture, specifically a multi-layer Long Short-Term Memory (LSTM) network combined with fully connected layers. The input layer receives preprocessed behavioral chains and spatiotemporal trajectory data, which are converted into time-series format. Each time step includes activity node type and geospatial location information. The LSTM layer contains multiple units, each consisting of an input gate, a forget gate, and an output gate, capable of capturing long-term dependencies in the time series. The output of the LSTM layer is further passed to the fully connected layer, which maps the hidden states of the LSTM layer to the demand forecasting output space. Each neuron corresponds to a service demand type, and the output is non-linearly transformed using an activation function (such as ReLU) to ultimately generate a predicted value for each service demand type. The output layer outputs the common service demand distribution of various worker groups at different time periods. The output data is a demand matrix, where each row represents a worker group, each column represents a service demand type, and the elements of the matrix represent the predicted demand intensity.

[0044] During model training, historical behavior chains and spatiotemporal trajectory data can be used as input, while known service demand records are used as output labels for supervised learning. The dataset is divided into training and validation sets for model training and performance evaluation. Mean squared error (MSE) is used as the loss function to measure the difference between predicted and actual demand. The Adam optimizer is used for model training with a learning rate of 0.001 and a training period of 50 epochs. At the end of each epoch, the loss value on the validation set is calculated, and the learning rate is adjusted based on the change in the loss value to ensure model convergence. After training, the model's performance is evaluated using the validation set by calculating the mean squared error (MSE) and mean absolute error (MAE) between predicted and actual demand to assess the model's prediction accuracy.

[0045] During execution, the behavioral chains and spatiotemporal trajectories of the worker groups are first analyzed and fed into a pre-trained demand prediction model. At this stage, the model outputs predictions of common service demands for each worker group in different future time periods, covering the demand distribution for various facilities and services.

[0046] Next, based on the individual demand preference model, individual preference weights are assigned to each demand in the common service demand distribution. The individual demand preference model records each worker's personalized needs in specific services, such as canteen meals, dormitory environment, and entertainment facilities. For example, some workers may prefer high-protein diets, while others prefer lighter foods. The system extracts the preference weights for each worker from the individual demand preference model based on this preference information, assigning weights to each demand in the common demand distribution. This reflects the impact of individual differences on the overall demand distribution. Furthermore, a weighted calculation is used to combine worker preferences with group demand, generating a weighted comprehensive demand distribution. This ensures that each worker's personalized needs are taken into account, ensuring that demand forecasting considers not only the common needs of the group but also individual preference differences.

[0047] Next, the overall demand distribution is optimized based on the capacity limitations of each functional area within the community. Functional areas within the community, such as the canteen, gym, and supermarket, each have their own capacity limits. The system compares and analyzes the overall demand distribution with the capacity limits of each functional area, and adjusts the demand using optimization algorithms, such as linear programming or genetic algorithms. For example, if the predicted canteen dining demand exceeds the canteen's seating capacity, the system will reallocate dining times based on time windows and individual preference weights to ensure that demand is met within the capacity limits. The optimized overall demand distribution is the final worker demand prediction result. This result details the demand for different services from different worker groups at different times, while also considering the constraints of community resources. It provides a scientific basis for the rational allocation and management decisions of community resources, helping to improve community management efficiency and worker satisfaction.

[0048] P50: Based on the worker demand forecast results, a demand collision analysis is performed in conjunction with the current production plan, and adaptive scheduling management is carried out based on the demand collision results.

[0049] Furthermore, step P50 in this embodiment of the application also includes: P51: Align the worker demand forecast results with the current production plan in both time and space dimensions to identify resource utilization conflict periods and regions; P52: Establish scheduling constraints based on the resource utilization conflict periods and regions, constructing a scheduling optimization model with the objective function of maximizing resource utilization and demand satisfaction rate; P53: Solve the scheduling optimization model to generate a resource allocation strategy that includes specific scheduling objects, scheduling times, and scheduling quantities; P54: Execute adaptive scheduling management according to the resource allocation strategy. The adaptive scheduling management includes both living arrangements scheduling management and production scheduling management.

[0050] Specifically, the system first aligns worker demand forecasts with the current production plan across both time and space dimensions. Demand forecasts include the demand for different services from various worker groups at different times, while the production plan specifies worker working hours, task allocation, and work locations. The system matches the time periods of the demand forecasts with the working periods of the production plan through timeline alignment; simultaneously, it maps the service areas of the demand forecasts with the work areas of the production plan in the spatial dimension. Through comparative analysis, it identifies the time periods and areas of resource utilization conflicts. For example, if peak demand for cafeteria meals overlaps with workers' overtime hours within a certain time period, or if the demand for gym space in a certain area conflicts with the space required for production tasks, these are identified as resource utilization conflicts.

[0051] After identifying conflicting periods and areas in resource utilization, the system establishes scheduling constraints based on these conflict points, aiming to maximize resource utilization and demand satisfaction. Specifically, scheduling constraints include maximum capacity limits for each functional area, demand priorities for each worker group, and time window limits for production tasks. The objective function optimizes the scheduling strategy by maximizing resource utilization efficiency and meeting worker needs. For example, given limited canteen resources, the system prioritizes meeting workers' basic needs during lunchtime while avoiding overcrowding in the canteen area. Furthermore, the time window requirements for production tasks must be considered to ensure that production plans are completed on time, unaffected by scheduling changes. The objective function comprehensively considers efficient resource utilization and maximizing worker demand satisfaction, balancing the relationship between the two through weight allocation. For example, resource utilization has a weight of 0.6, and demand satisfaction has a weight of 0.4, ensuring that the optimization process fully utilizes resources while maximizing worker demand satisfaction.

[0052] Next, the scheduling optimization model is solved to generate a resource allocation strategy that includes specific scheduling objects, scheduling times, and scheduling quantities. The solution process can employ optimization algorithms such as linear programming, genetic algorithms, or simulated annealing. Taking linear programming as an example, the scheduling constraints and objective function are transformed into a system of linear equations, and the optimal solution is found using the simplex method. The solution result is a specific resource allocation strategy, which must clearly specify which workers are scheduled to which service areas at what times, and the number of workers scheduled. For example, the strategy might instruct 50 workers to be scheduled from the work area to the cafeteria for meals during a certain time period, or 30 workers to be scheduled from the dormitory area to the gym after get off work.

[0053] Finally, based on the resource allocation strategy, adaptive scheduling management is implemented, including living arrangements scheduling management and production scheduling management. Living arrangements scheduling management involves the allocation of resources for living service facilities such as canteens, supermarkets, and gyms to ensure that workers' needs are met during off-hours. For example, adjusting canteen opening hours according to the allocation strategy to increase the supply of seats during specific time periods; or rationally scheduling the use of gym equipment based on peak demand. Production scheduling management involves the allocation of resources within work areas to ensure the smooth progress of production tasks. For example, rationally scheduling overtime workers based on production plans and worker needs, optimizing personnel distribution within work areas, and avoiding resource conflicts in production areas.

[0054] This process enables adaptive scheduling management based on demand collision analysis. It can dynamically adjust scheduling strategies according to the conflict between worker demand forecasts and production plans to maximize resource utilization and worker demand satisfaction, ensure the rational allocation of production and living resources, and improve overall operational efficiency.

[0055] Furthermore, based on the resource allocation strategy, life scheduling management is carried out. Step P54 of this embodiment also includes: P54-1: Based on the resource allocation strategy, extract living resource scheduling information and calculate the expected load of each living facility; P54-2: When the expected load exceeds the preset load threshold, invoke the emergency plan to generate a first scheduling instruction; P54-3: When the expected load does not exceed the preset load threshold, generate a second scheduling instruction based on the resource allocation strategy.

[0056] In one possible embodiment of this application, the specific process of life scheduling management based on resource allocation strategy is further refined.

[0057] First, the system extracts information on the scheduling of living resources based on the resource allocation strategy, including the expected number of users and usage time for various living facilities such as canteens, gyms, and supermarkets at different times. Based on this information, the expected load of each living facility is calculated. Specifically, for each living facility, the system compares the number of users specified in the resource allocation strategy with the facility's capacity to calculate the expected load rate. For example, if the canteen's capacity is 200 people, and the resource allocation strategy indicates that 180 people need to eat during a certain time period, then the canteen's expected load rate is 90%.

[0058] When the calculated expected load exceeds the preset load threshold, the system invokes the emergency plan to generate the first dispatch instruction. The preset load threshold is set based on the facility's maximum capacity and safe operating standards, typically 80%-90% of the facility's capacity. For example, if the cafeteria's preset load threshold is 85%, and the calculated expected load rate is 95%, the system determines that the emergency plan needs to be activated. The emergency plan may include measures such as adding temporary dining areas, extending cafeteria opening hours, and adjusting meal schedules. Based on the specific content of the emergency plan, the system generates the first dispatch instruction, clearly indicating which specific measures to take and who is responsible for implementing them.

[0059] When the calculated expected load does not exceed the preset load threshold, the system generates a second scheduling instruction based on the resource allocation strategy. This second scheduling instruction is directly formulated according to the resource allocation strategy to ensure that the use of living facilities meets the expected arrangements. For example, if the expected load rate of the canteen is 70%, which does not exceed the preset 85% load threshold, the system generates a second scheduling instruction. This instruction adjusts the resource allocation of various living facilities according to the needs of the workers at different times. For example, the system may schedule canteen meal times according to a pre-determined plan, or arrange appropriate occupancy arrangements based on dormitory needs, ensuring that resources are allocated rationally while avoiding waste.

[0060] Through the above process, the system can ensure the rational allocation of living resources, avoid resource shortages due to excessive demand, and ensure that workers' living needs are met smoothly when the load does not exceed the threshold, thereby improving workers' satisfaction with living facilities.

[0061] Furthermore, based on the resource allocation strategy, production scheduling management is performed. Step P54 of this embodiment further includes: P54-4: Based on the resource allocation strategy, extract the peak demand periods for rest resources; P54-5: Based on the worker group profile and individual demand preference model, calculate the group fatigue index of each work group in the future period and identify the set of high-risk worker individuals; P54-6: Perform correlation analysis between the group fatigue index and the set of high-risk worker individuals and the peak demand periods for rest resources to generate a production collaboration safety early warning level; P54-7: Based on the production collaboration safety early warning level, generate a production scheduling instruction that includes shift adjustment suggestions, work intensity suggestions, and safety protection suggestions.

[0062] Optionally, the specific process for production scheduling management based on resource allocation strategies is further refined. First, the peak demand periods for rest resources are extracted based on the resource allocation strategy. This process involves analyzing the usage arrangements for rest areas in the resource allocation strategy to identify the time periods with the highest demand for rest resources in the future. For example, if analysis reveals that the demand for rest rooms peaks between 14:00 and 14:30, this period is marked as the peak demand period for rest resources, providing a basis for subsequent production scheduling management.

[0063] Next, based on worker profiles and individual demand preference models, the group fatigue index for each work group in the future is calculated, and a set of high-risk worker individuals is identified. Specifically, information such as work intensity and working hours for each work group is extracted from the worker profiles, and combined with work habits and health data from the individual demand preference model, the group fatigue index for each work group in the future is calculated. The fatigue index can be calculated based on multiple factors such as working hours, work intensity, and rest time, using a weighted summation method. For example, the weight of working hours is 0.4, the weight of work intensity is 0.3, and the weight of rest time is 0.3. Simultaneously, a threshold is set based on the fatigue index; if the fatigue index exceeds 0.7, the work group is considered to be in a state of high fatigue. This identifies a set of high-risk worker individuals whose fatigue index exceeds the threshold; these workers may require special attention and adjustments to their work arrangements.

[0064] Next, the system correlates the group fatigue index with the set of high-risk workers and the peak demand periods for rest resources. This correlation analysis identifies whether the peak fatigue periods of the worker group coincide with the peak demand periods for rest resources, thus determining whether special scheduling intervention is needed. For example, if a work group has a high group fatigue index during the peak demand period for rest resources, and there are many high-risk workers in that group, the system determines that there is a high risk to production coordination safety. Based on the degree of risk, different levels of production coordination safety warnings are generated, such as low risk (green warning), medium risk (yellow warning), and high risk (red warning).

[0065] Finally, based on the production collaboration safety warning level, production scheduling instructions are generated, including shift adjustment suggestions, work intensity suggestions, and safety protection suggestions. For example, for low-risk situations, the system suggests maintaining the current production plan but reminds workers to pay attention to fatigue and arrange appropriate short breaks. For medium-risk situations, the system generates shift adjustment suggestions, such as appropriately extending rest time or adjusting work intensity. For example, it suggests reducing work intensity from level 8 to level 6 and adding a 10-minute break. For high-risk situations, more stringent production scheduling instructions are generated, including shift adjustments, reduced work intensity, and enhanced safety protection. For example, it suggests moving high-risk workers from their current high-intensity work area to a low-intensity work area, while requiring increased safety protection measures, such as wearing additional protective equipment and assigning dedicated personnel to monitor their work status.

[0066] Through this process, production scheduling management can ensure that fatigue risks among workers are identified in a timely manner during production, and adjust work arrangements and rest strategies according to the actual situation, thereby optimizing production efficiency and worker safety, and reducing safety hazards and accidents caused by fatigue in production.

[0067] Furthermore, step P50 in this embodiment of the application also includes: P55: Collect actual community service data after the adaptive scheduling management is executed; P56: Calculate the prediction deviation between the worker demand prediction result and the actual community service data; P57: Optimize the demand prediction model based on the prediction deviation, and update the worker group profile and the individual demand preference model.

[0068] Specifically, the evaluation and feedback mechanism for the effectiveness of adaptive scheduling management can be further expanded to achieve continuous optimization of demand forecasting models and worker profiles.

[0069] Specifically, after implementing adaptive scheduling management, the system needs to collect real-time community service data. This data covers the actual usage of various living facilities and production areas, such as the actual number of diners in the canteen, the actual usage time of the gym, and the actual personnel distribution in the work area. This actual data provides a foundation for evaluating the accuracy of the demand forecasting model and reflects the actual worker demand after the scheduling management is implemented.

[0070] Next, the worker demand forecasts are compared with actual community service data to calculate the forecast bias. For example, for each service facility and production area, the demand forecasts are compared with the actual collected data to calculate the difference. Taking a canteen as an example, if the demand forecast shows 180 people dining during a certain period, while the actual number of diners is 190, the forecast bias is 10 people. Similarly, the forecast biases for all service facilities and production areas are summarized to generate a comprehensive forecast bias report. This report details the forecast bias values ​​for each facility and area, providing a quantitative basis for subsequent model optimization.

[0071] Next, based on the prediction deviation, the demand forecasting model is optimized, and the worker profile and individual demand preference model are updated. For example, the optimization of the demand forecasting model begins by using the prediction deviation data as a feedback signal to retrain the model or adjust its parameters. If a deep learning model, such as LSTM, is used, the prediction deviation can be fed back into the model through backpropagation to adjust the model's weights and bias parameters, thereby reducing future prediction errors. For instance, if the prediction deviation of the number of diners in the canteen is large, the system will increase the weights of canteen-related features, such as activity trajectories and work completion times, and retrain the model, thereby improving the model's accuracy in predicting future demand.

[0072] Meanwhile, based on actual community service data, the system adjusts the behavioral patterns and preference characteristics in the worker profile. For example, if actual data shows that a certain worker group's demand for gyms is higher than predicted during a specific time period, the system will increase the weight of gym usage frequency in the group's profile and adjust related behavioral patterns to make the worker profile more realistic and provide a more accurate basis for subsequent management decisions.

[0073] Furthermore, the system updates the preference weights in the individual needs preference model by incorporating actual community service data. For example, if a worker frequently uses the library in practice, but the model predicts a low preference, the system will increase the worker's preference weight for the library and adjust their individual needs preference model to more accurately reflect the worker's personalized needs.

[0074] This cyclical process forms a closed-loop feedback mechanism. This mechanism ensures that the system can be continuously adjusted and optimized based on actual operating conditions, improving the accuracy of demand forecasting and the scientific nature of management decisions, and providing continuous support for the efficient management of industrial worker communities and the improvement of worker satisfaction.

[0075] In summary, the embodiments of this application have at least the following technical effects: This application achieves accurate prediction of the dynamic needs of the worker group by integrating worker profiles with individual demand preference models and combining behavioral chains and spatiotemporal trajectory analysis, thereby improving the scientific and forward-looking nature of resource allocation. Adaptive scheduling management based on demand forecasts and production plans enables dynamic optimization of community resources, avoiding waste and improving resource utilization efficiency. Utilizing IoT sensor networks and intelligent algorithms, data-driven and automated management decisions are achieved, reducing manual intervention and significantly improving the efficiency and scientific nature of community management. Personalized services and precise demand responses enhance workers' sense of belonging and satisfaction. Coordinating production plans and living services reduces resource conflicts and management contradictions, ensuring smooth production and improving production efficiency and the overall stability of the project. By collecting actual data and calculating prediction deviations, the system can continuously optimize the demand forecasting model and worker profiles, possessing self-learning and improvement capabilities, and adapting to dynamic changes in the community and the evolution of worker needs.

[0076] It has achieved the technical effect of dynamically matching worker needs with production scheduling through intelligent collaborative management, optimizing resource allocation, and improving management efficiency and worker satisfaction.

[0077] Example 2, based on the same inventive concept as the collaborative management method for intelligent industrial worker communities in the foregoing examples, such as... Figure 2 As shown, this application provides a collaborative management system for an intelligent industrial worker community. The system and method embodiments in this application are based on the same inventive concept. The system includes: The data acquisition module 11 is used to continuously collect community environmental data, worker feedback data, and production management data through an IoT sensor network deployed in the community.

[0078] The multidimensional feature extraction module 12 is used to generate worker job type and work group classification information based on the production management data, extract worker behavior feature vectors based on the community environment data, and extract worker selection preference information based on the worker feedback data.

[0079] The information fusion analysis module 13 is used to fuse and analyze the worker job type and work group classification information, worker behavior feature vectors and worker selection preference information to generate dynamically updated worker group profiles and individual demand preference models.

[0080] The worker demand prediction module 14 is used to mine behavioral chains and spatiotemporal trajectories based on the worker group profile and individual demand preference model, and to perform dynamic demand prediction of the worker group based on behavioral chains and spatiotemporal trajectories, generating worker demand prediction results.

[0081] The adaptive scheduling management module 15 is used to perform demand collision analysis based on the worker demand prediction results and the current production plan, and to perform adaptive scheduling management based on the demand collision results.

[0082] Furthermore, the information fusion analysis module 13 is also used to perform the following steps: Based on the worker job type and work group classification information, workers are initially classified by job type and work group; based on the initial classification, secondary clustering is performed based on the worker behavior feature vector to generate worker groups with similar behavior patterns; a group behavior feature database is established for each worker group to form the worker group profile; for each individual worker, based on the worker selection preference information and combined with the individual's feature offset in the worker group, the individual demand preference model is generated.

[0083] Furthermore, the worker demand forecasting module 14 is also used to perform the following steps: Based on the worker group profile, activity sequences of different worker groups within typical work-rest cycles are extracted to generate behavior chains; based on the individual demand preference model, personalized nodes of workers in the activity sequences are identified and mapped to the community geographic space; the behavior chains and personalized nodes are overlaid on the community geographic space to generate a spatiotemporal trajectory that includes group commonalities and individual differences.

[0084] Furthermore, the worker demand forecasting module 14 is also used to perform the following steps: The behavioral chain and the spatiotemporal trajectory are input into a pre-trained demand prediction model to predict the distribution of common service demands for each worker group at different times. Based on the individual demand preference model, individual preference weights are assigned to each demand in the common service demand distribution to generate a weighted comprehensive demand distribution. Based on the capacity limitations of each functional area of ​​the community, the comprehensive demand distribution is constrained and optimized to generate the worker demand prediction result.

[0085] Furthermore, the adaptive scheduling management module 15 is also used to perform the following steps: Align the worker demand forecast results with the current production plan in both time and space dimensions to identify resource utilization conflict periods and regions. Based on these conflict periods and regions, establish scheduling constraints, using maximizing resource utilization and demand satisfaction as objective functions to construct a scheduling optimization model. Solve the scheduling optimization model to generate a resource allocation strategy that includes specific scheduling objects, scheduling times, and scheduling quantities. Execute adaptive scheduling management according to the resource allocation strategy. This adaptive scheduling management includes both living arrangements scheduling management and production scheduling management.

[0086] Furthermore, the adaptive scheduling management module 15 is also used to perform the following steps: Based on the resource allocation strategy, living resource scheduling information is extracted, and the expected load of each living facility is calculated. When the expected load exceeds a preset load threshold, the emergency plan is invoked to generate a first scheduling instruction. When the expected load does not exceed the preset load threshold, a second scheduling instruction is generated based on the resource allocation strategy.

[0087] Furthermore, the adaptive scheduling management module 15 is also used to perform the following steps: Based on the resource allocation strategy, the peak demand periods for rest resources are extracted; based on the worker group profile and individual demand preference model, the group fatigue index of each work group in the future period is calculated, and a set of high-risk worker individuals is identified; the group fatigue index and the set of high-risk worker individuals are correlated with the peak demand periods for rest resources to generate a production collaboration safety early warning level; based on the production collaboration safety early warning level, a production scheduling instruction containing shift adjustment suggestions, work intensity suggestions, and safety protection suggestions is generated.

[0088] Furthermore, the adaptive scheduling management module 15 is also used to perform the following steps: Collect actual community service data after the adaptive scheduling management is executed; calculate the prediction deviation between the worker demand prediction result and the actual community service data; optimize the demand prediction model based on the prediction deviation, and update the worker group profile and the individual demand preference model.

[0089] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0090] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0091] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A collaborative management method for intelligent industrial worker communities, characterized in that, The method includes: By deploying an IoT sensor network within the community, we continuously collect community environmental data, worker feedback data, and production management data. Based on the production management data, worker job type and work group classification information is generated; based on the community environment data, worker behavior feature vectors are extracted; based on worker feedback data, worker selection preference information is extracted. The worker job type and work group classification information, worker behavior feature vectors and worker selection preference information are integrated and analyzed to generate a dynamically updated worker group profile and individual demand preference model. Based on the worker group profile and individual demand preference model, behavioral chains and spatiotemporal trajectories are mined, and dynamic demand prediction of the worker group is performed based on behavioral chains and spatiotemporal trajectories to generate worker demand prediction results. Based on the worker demand forecast results, a demand collision analysis is performed in conjunction with the current production plan, and adaptive scheduling management is carried out based on the demand collision results.

2. The collaborative management method for intelligent industrial worker communities as described in claim 1, characterized in that, The worker job type and work group classification information, worker behavior feature vectors, and worker selection preference information are fused and analyzed to generate a dynamically updated worker group profile and individual demand preference model, including: Based on the worker job type and work group classification information, workers are initially classified according to job type and work group; Based on the initial classification, a second clustering is performed based on the worker behavior feature vectors to generate worker groups with similar behavior patterns. A group behavior feature database is established for each worker group to form a profile of the worker group; For each individual worker, based on the worker's selection preference information and combined with the individual's feature offset within the worker group, an individual demand preference model is generated.

3. The collaborative management method for intelligent industrial worker communities as described in claim 1, characterized in that, Based on the aforementioned worker group profile and individual demand preference model, behavioral chains and spatiotemporal trajectories are mined, including: Based on the worker group profile, the activity sequences of different worker groups during typical work and rest cycles are extracted to generate behavior chains; Based on the individual demand preference model, the individual nodes of workers in the activity sequence are identified, and the individual nodes are mapped to the community geographic space; The behavioral chain and personalized nodes are overlaid on the community's geographic space to generate a spatiotemporal trajectory that includes both group commonalities and individual differences.

4. The collaborative management method for intelligent industrial worker communities as described in claim 3, characterized in that, Perform dynamic demand forecasting for worker groups based on behavioral chains and spatiotemporal trajectories, and generate worker demand forecasting results, including: The behavioral chain and the spatiotemporal trajectory are input into a pre-trained demand prediction model to predict the distribution of common service demands of various worker groups at different times. Based on the individual demand preference model, individual preference weights are assigned to each demand in the common service demand distribution to generate a weighted comprehensive demand distribution; Based on the capacity limitations of each functional area in the community, the overall demand distribution is constrained and optimized to generate the worker demand prediction results.

5. The collaborative management method for intelligent industrial worker communities as described in claim 1, characterized in that, Based on the worker demand forecast results and in conjunction with the current production plan, adaptive scheduling management is performed, including: Align the worker demand forecast results with the current production plan in both time and space dimensions to identify periods and areas of resource utilization conflict; Based on the resource utilization conflict periods and regions, scheduling constraints are established, with the objective function of maximizing resource utilization and demand satisfaction rate, and a scheduling optimization model is constructed. Solve the aforementioned scheduling optimization model to generate a resource allocation strategy that includes specific scheduling objects, scheduling time, and scheduling quantity; Adaptive scheduling management is performed based on the resource allocation strategy.

6. The collaborative management method for intelligent industrial worker communities as described in claim 5, characterized in that, The adaptive scheduling management includes both living scheduling management and production scheduling management.

7. The collaborative management method for intelligent industrial worker communities as described in claim 6, characterized in that, Based on the aforementioned resource allocation strategy, life scheduling management is carried out, including: Based on the resource allocation strategy, extract living resource scheduling information and calculate the expected load of each living facility; When the expected load exceeds the preset load threshold, the emergency plan is invoked to generate a first scheduling instruction. When the expected load does not exceed the preset load threshold, a second scheduling instruction is generated based on the resource allocation strategy.

8. The collaborative management method for intelligent industrial worker communities as described in claim 6, characterized in that, Production scheduling management is performed according to the resource allocation strategy, including: Based on the resource allocation strategy, extract the peak demand periods for rest resources; Based on the worker group profile and individual demand preference model, calculate the group fatigue index of each type of work group in the future period, and identify the set of high-risk worker individuals. The fatigue index of the group and the set of high-risk workers are correlated with the peak demand period of rest resources to generate a production collaboration safety early warning level. Based on the aforementioned production collaboration safety early warning level, a production scheduling instruction is generated that includes shift adjustment suggestions, workload suggestions, and safety protection suggestions.

9. The collaborative management method for intelligent industrial worker communities as described in claim 4, characterized in that, The method further includes: Collect actual community service data after the adaptive scheduling management is executed; Calculate the prediction deviation between the worker demand forecast results and the actual community service data; Based on the prediction deviation, the demand prediction model is optimized, and the worker group profile and the individual demand preference model are updated.

10. A collaborative management system for intelligent industrial worker communities, characterized in that: The system includes: The data acquisition module is used to continuously collect community environmental data, worker feedback data, and production management data through an IoT sensor network deployed within the community. The multi-dimensional feature extraction module is used to generate worker job and work group classification information based on the production management data, extract worker behavior feature vectors based on the community environment data, and extract worker selection preference information based on worker feedback data. The information fusion and analysis module is used to fuse and analyze the worker job type and work group classification information, worker behavior feature vectors and worker selection preference information to generate dynamically updated worker group profiles and individual demand preference models. The worker demand prediction module is used to mine behavioral chains and spatiotemporal trajectories based on the worker group profile and individual demand preference model, and to perform dynamic demand prediction of the worker group based on behavioral chains and spatiotemporal trajectories, generating worker demand prediction results. The adaptive scheduling management module is used to perform demand collision analysis based on the worker demand forecast results and the current production plan, and to perform adaptive scheduling management based on the demand collision results.

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