Bamboo chopstick production line data real-time intelligent scheduling management system

Abstract

The invention relates to the technical field of production line scheduling management, in particular to a bamboo chopstick production line data real-time intelligent scheduling management system which comprises a data acquisition module, an intelligent scheduling module, an anomaly detection module, an equipment collaboration and optimization module and a decision support module. The data acquisition module acquires data such as equipment operation state, production progress, raw material consumption and yield statistics; the intelligent scheduling module generates a production scheduling scheme by using a federated learning algorithm and integrating multiple factors; the anomaly detection module monitors anomaly by means of a depth auto-encoder; the equipment collaboration and optimization module optimizes equipment collaboration based on the two; and the decision support module generates a visual interface and an intelligent analysis report. Real-time intelligent management of the production process is achieved, production efficiency is effectively improved, resource utilization is optimized, product quality is guaranteed, management decision is assisted, intelligent development of the bamboo chopstick production industry is promoted, and enterprise competitiveness and sustainable development capacity are improved.

Description

Technical Field

[0001] The invention relates to the technical field of production line scheduling management, and in particular to a real-time intelligent scheduling management system for bamboo chopstick production line data. Background Art

[0002] In the field of bamboo chopstick production, with the continuous growth of market demand and increasingly fierce competition, improving production efficiency, reducing costs and ensuring product quality have become important challenges faced by enterprises. The traditional management method of bamboo chopstick production line has many limitations and is difficult to adapt to the needs of modern production;

[0003] Traditional scheduling methods often rely on manual experience and lack real-time and comprehensive grasp of equipment operation status, production progress, raw material consumption and other data. It is difficult for dispatchers to make scientific and reasonable decisions quickly in a complex production environment, resulting in uneven distribution of production tasks and low equipment utilization. For example, some equipment may be overly busy while other equipment may be idle for a long time, resulting in loss of production efficiency. At the same time, when faced with emergencies such as order changes and equipment failures, traditional scheduling methods cannot adjust production plans in a timely and effective manner, which can easily cause production chaos and affect order delivery; managers lack sufficient data support and scientific analysis tools when making decisions. Traditional production management methods cannot provide comprehensive and accurate production data in a timely manner, and managers find it difficult to deeply understand various problems and potential risks in the production process and make forward-looking decisions. For example, when deciding whether to add production line equipment, adjust production processes or purchase raw materials, due to the lack of detailed analysis and trend prediction of key indicators such as production efficiency, equipment utilization, and raw material consumption, decisions are often blind, which may lead to resource waste or aggravation of production bottlenecks. Therefore, a real-time intelligent scheduling management system for bamboo chopstick production line data is proposed to address the above problems. Summary of the invention

[0004] The object of the present invention is to provide a real-time intelligent scheduling and management system for bamboo chopstick production line data to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A real-time intelligent scheduling and management system for bamboo chopstick production line data, comprising:

[0007] Data acquisition module: It is set up on the bamboo chopstick production line and connected with sensors, PLC controllers and production equipment interfaces to collect real-time equipment operation status data, production progress data, raw material consumption data and output statistics during the bamboo chopstick production process;

[0008] Intelligent scheduling module: It communicates with the data acquisition module and uses the federated learning algorithm based on the collected equipment operation status data, production progress data, raw material consumption data and output statistics. It also considers the real-time status of the equipment, current production needs and task priorities, and automatically generates a production scheduling plan for the bamboo chopstick production line through intelligent calculations.

[0009] Abnormality detection module: communicates with the data acquisition module and uses deep autoencoder technology to analyze the collected data in real time to monitor abnormal conditions in the production process of bamboo chopsticks, including equipment failure, insufficient raw materials, and bottlenecks in the production process;

[0010] Equipment coordination and optimization module: It communicates with the intelligent scheduling module and the anomaly detection module, and uses the federated learning algorithm to optimize the collaborative working mode between the various devices in the bamboo chopstick production line based on the production scheduling plan generated by the intelligent scheduling module and the anomaly feedback from the anomaly detection module;

[0011] Decision support module: Communicates with the intelligent scheduling module, anomaly detection module, and equipment coordination and optimization module. Based on the data obtained from each module, it generates a visual interface for management personnel to view through big data analysis technology, and regularly generates intelligent analysis reports covering production efficiency, equipment utilization, and raw material consumption.

[0012] As a preferred solution, the intelligent scheduling module includes:

[0013] Multi-objective collaborative optimization unit: In the application of the federated learning algorithm, multiple equipment nodes or links involved in the scheduling of the bamboo chopstick production line are set as clients of the federated learning, and the production efficiency is set as , equipment utilization is set to , energy consumption is set to , Raw material waste is set to As a joint optimization target; each client Based on locally collected data Constructing a local objective function ,in, For Clients The decision variable vector, the overall multi-objective optimization objective function is expressed as: ,in, is the number of clients, For Clients The weights of the models are exchanged and updated among the clients through the federated learning mechanism to collaboratively optimize the various objectives.

[0014] Client The local objective function The construction method is as follows: with production efficiency For example, suppose the output per unit time is , the equipment running time is ,but , Represents the client production efficiency, Comprehensive consideration It is constructed with other local related factors and constraints according to preset rules and mathematical relationships to accurately reflect the local contribution to the overall multi-objective optimization;

[0015] Adaptive adjustment unit: collects production-related data from each client in real time and aggregates them into a real-time status data set for the production line , and set a predefined set of emergency adjustment rules ; When abnormal situations occur, such as urgent order insertion and sudden equipment failure, the global coordination mechanism based on federated learning is used. and , by updating the scheduling policy parameters of each client To adjust the production scheduling plan, the update process is expressed as ,in, It is an adaptive adjustment function based on federated learning;

[0016] For different types of abnormal situations, The corresponding specific adjustment strategy details are set in the system. In case of equipment failure, the system determines how to reallocate production tasks to other normal equipment according to the link where the faulty equipment is located and the remaining available equipment capacity. Through the coordination of federated learning, the adjustment strategy is sent to the corresponding client for execution.

[0017] Task priority determination unit: Based on the urgency of the delivery date of bamboo chopstick orders , Product type importance level and the adequacy of raw material supply To determine the priority of production tasks; each client Calculate the local priority score based on local relevant information , ,in, For Clients correspond , , The weight coefficient of

[0018] Global production task priority Through the aggregation mechanism of federated learning, the Get, using weighted average method ,in, For Clients The weight in the global priority calculation.

[0019] As a preferred solution, the anomaly detection module includes:

[0020] Anomaly recognition and positioning unit: Use deep autoencoders to process the collected data and convert the input data Mapped to hidden layer representation through encoder , and then reconstructed by the decoder to get the output , calculate the reconstruction error , set a reasonable threshold ,when When determining that the production link corresponding to the data has abnormal conditions;

[0021] Further analysis of hidden layer representation Based on the characteristic distribution of each device and each production link under normal conditions, the specific abnormal device or production link can be located;

[0022] Abnormal warning and feedback unit:

[0023] Once an abnormal situation is detected, an early warning signal containing the abnormality type, location, and severity is immediately generated and sent to the intelligent scheduling module and the equipment coordination and optimization module through the communication link;

[0024] Different levels of early warning prompts are set according to the severity of the abnormality. For minor abnormalities, prompt information is only displayed on the visual interface. For abnormalities that seriously affect production, sound and light alarms are triggered at the same time, and detailed abnormality information is pushed to the manager's terminal device in real time.

[0025] As a preferred solution, the equipment coordination and optimization module includes:

[0026] Inter-device task allocation optimization unit: Under the federated learning architecture, each device is regarded as a node participating in the collaboration. According to the production scheduling plan generated by the intelligent scheduling module, combined with the current real-time status of each device and the abnormal information fed back by the abnormal detection module, an optimization model for task allocation between devices is constructed; let the device set be , is the total number of equipment, and the production task set is , is the number of production tasks, defining the allocation matrix , where the element Representation Task Is it assigned to the device? , the goal is to find the optimal allocation matrix The optimization process is achieved by iteratively updating the allocation strategy among each device node through the federated learning algorithm, that is, each device node adjusts its corresponding value, is the index of the device, is the index of the production task;

[0027] Considering the heterogeneity of devices, Set the task processing capability vector , indicating its processing efficiency or capacity upper limit for different types of tasks. When optimizing task allocation, adding constraints ensures that the tasks are allocated to the device. The total amount of tasks does not exceed its processing capacity, that is ,in, Representation Task The workload, Indicates the device To the task Task Type The processing capacity limit of the system is determined to ensure the rationality and feasibility of task allocation;

[0028] Equipment operation parameter collaborative adjustment unit: Each device shares its own operation parameters through the federated learning mechanism, and builds an operation parameter collaborative adjustment model based on multi-device joint; The operating parameter vector is , by analyzing the correlation between different equipment operating parameters and their impact on overall production;

[0029] The gradient descent optimization algorithm is used to update the operating parameters of each device under the federated learning framework. The learning rate is set to , in each iteration, the device The operating parameter update rule is ,in, Indicates the device The updated operating parameter vector, is the learning rate, Represents the overall production line performance loss function About the device Run parameter vector The gradient of is the overall production line performance loss function.

[0030] As a preferred solution, the decision support module includes:

[0031] Real-time monitoring visualization unit: Build a visualization interface to display detailed status information of the bamboo chopstick production line in real time with intuitive charts, graphs, and data lists, covering equipment operation status, production progress, currently effective production scheduling plan, and equipment health status warning information;

[0032] Intelligent report generation unit: According to the preset time period, the unit uses big data analysis technology to conduct a comprehensive analysis of the historical production data and real-time data of the bamboo chopstick production line, and generates an intelligent analysis report covering production efficiency, equipment utilization, and raw material consumption;

[0033] In the intelligent analysis report, in addition to presenting the actual data and changing trends of each indicator, data mining algorithms are also used to explore potential problems, optimization directions and improvement suggestions in the production process.

[0034] It can be seen from the technical solution provided by the present invention that the real-time intelligent scheduling and management system for bamboo chopstick production line data provided by the present invention has the following beneficial effects:

[0035] The intelligent scheduling module integrates multi-source data and uses the federated learning algorithm to achieve accurate task allocation and parameter setting, continuously optimizes the scheduling plan to adapt to production changes and reduce waiting time. The equipment collaboration and optimization module optimizes the equipment collaboration mode through federated learning based on the scheduling plan and abnormal situations, improves equipment utilization, and achieves efficient production; the anomaly detection module uses deep autoencoders to monitor data in real time, promptly discovers and warns of anomalies, quickly locates and solves problems, and prevents quality problems; intelligent scheduling and equipment collaborative optimization ensure stable equipment operation and stable process parameters to ensure product quality consistency; the decision support module's visual interface displays the production status in real time, assists managers in making timely decisions, and the interactive function facilitates in-depth analysis of data; regularly generated intelligent analysis reports provide key indicators and optimization suggestions to help management optimization, and the data can be used for strategic planning and performance evaluation; the system integrates multiple factors to optimize scheduling and adapt to complex working conditions and diversified production needs. The federated learning algorithm combines global and local optimization to adapt to complex production environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention is a schematic diagram of the overall structure of a real-time intelligent scheduling and management system for bamboo chopstick production line data. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0039] like Figure 1As shown, an embodiment of the present invention provides a real-time intelligent scheduling and management system for bamboo chopstick production line data, including a data acquisition module, an intelligent scheduling module, an anomaly detection module, an equipment coordination and optimization module, and a decision support module.

[0040] In this embodiment, the data acquisition module is arranged on the bamboo chopstick production line, and is connected with the sensor, the PLC controller and the production equipment interface to collect the equipment operation status data, the production progress data, the raw material consumption data and the output statistics data in the bamboo chopstick production process in real time;

[0041] Further, the specific operation steps of the data acquisition module are as follows:

[0042] 1. Module connection and deployment

[0043] Installation and configuration: Install the data acquisition module at a suitable location on the bamboo chopstick production line, ensure that it is compatible with the production line's operating environment, and perform necessary initialization configurations, such as setting communication parameters, data storage paths, etc.;

[0044] Connect the sensor:

[0045] Identify key sensors: Identify various sensors used on the production line to monitor equipment operating status, production progress, raw material consumption and output statistics, such as temperature sensors, pressure sensors, position sensors, counters, etc.

[0046] Physical connection: Use appropriate cables to connect the data acquisition module to the output interfaces of various sensors to ensure a firm connection and stable signal transmission; for different types of sensors, you may need to use corresponding adapters or interface modules to achieve a correct connection;

[0047] Connect to PLC controller:

[0048] Determine the communication protocol: Understand the communication protocol of the PLC controller used in the production line. Common ones include Modbus, Profibus, Ethernet / IP, etc.

[0049] Establish a communication link: According to the selected communication protocol, use the corresponding communication cable (such as Ethernet cable, serial port cable, etc.) to connect the data acquisition module with the communication port of the PLC controller; configure the same communication parameters as the PLC controller in the data acquisition module, such as IP address, port number, baud rate, data bit, stop bit, check bit, etc., to ensure that the two can communicate normally;

[0050] Connecting to production equipment interface:

[0051] Analyze the device interface type: Check the interface type of each device on the bamboo chopstick production line, which may include RS-232 / RS-485 serial port, Ethernet interface, USB interface or other special interfaces;

[0052] Adapt the connection: For different interface types, select the appropriate connection method and conversion device (if necessary); for example, for a serial port interface, use a serial cable to connect directly; for an Ethernet interface, connect to the same LAN via a network cable; for a USB interface, use a USB data cable to connect, and ensure that the device driver has been correctly installed;

[0053] 2. Data collection and transmission

[0054] Equipment operation status data collection:

[0055] Sensor data reading: The data acquisition module reads various physical quantity data during the operation of the equipment in real time through connection with the sensor according to the preset sampling period, such as equipment temperature, vibration amplitude, motor speed, hydraulic pressure, etc.; these data can reflect the real-time working status of the equipment and provide a basis for equipment fault prediction and maintenance;

[0056] PLC data acquisition (optional): If some equipment operation status information is stored in the PLC controller, the data acquisition module communicates with the PLC and reads the relevant status registers or variable values, such as the equipment start / stop status, fault alarm signal, operation mode, etc., according to the agreed data format and address;

[0057] Data preprocessing: preliminary processing of the collected equipment operation status data, including data cleaning (removing outliers, noise, etc.), data conversion (converting analog quantities into digital quantities, converting physical quantities according to sensor characteristics, etc.) and data formatting to meet the requirements of subsequent analysis and processing;

[0058] Data transmission: The processed equipment operation status data is transmitted in real time to the intelligent scheduling module, anomaly detection module and other related system modules through wired or wireless communication methods (such as Ethernet, Wi-Fi, 4G / 5G, etc.) for further analysis;

[0059] Production progress data collection:

[0060] Identify progress monitoring points: determine the key links and monitoring points in the bamboo chopstick production process that can accurately reflect the production progress, such as the amount of raw material input, the number of semi-finished products processed, the number of finished product packaging, etc.; install corresponding sensors at these monitoring points or use the counting function of the production equipment to obtain progress data;

[0061] Data collection and calculation: The data collection module obtains relevant data from monitoring points in real time, such as counting the number of bamboo chopstick blanks passing through the photoelectric sensor installed on the conveyor belt, or reading the number of completed processing steps from the control system of the production equipment. According to the production process and flow, these data are comprehensively calculated to obtain accurate production progress information, such as the percentage of completed products in the total order volume, the progress percentage of the current production process, etc.

[0062] Progress data transmission: The calculated production progress data is transmitted to the relevant modules in a timely manner, so that the management personnel can understand the operation status of the production line in real time and adjust the production plan and scheduling strategy in time;

[0063] Raw material consumption data collection:

[0064] Install raw material monitoring equipment (optional): If the production line does not have its own raw material consumption monitoring function, you can install weighing sensors, liquid level sensors or flow sensors on the raw material storage containers (such as silos, storage tanks, etc.) to monitor the inventory or usage of raw materials in real time;

[0065] Data acquisition and calculation: The data acquisition module regularly reads the data of the raw material monitoring equipment, and calculates the raw material consumption rate and the cumulative total consumption per unit time by comparing it with the initial raw material quantity. For some production links that input raw materials in batches, the raw material consumption can also be calculated by recording the raw material input batches and the quantity of each batch;

[0066] Data transmission and storage: The raw material consumption data is transmitted to the corresponding system module for analysis and processing, and stored in the local database or cloud server for subsequent query and statistical analysis, providing data support for raw material procurement plans;

[0067] Production statistics collection:

[0068] Finished product counting and statistics: Install a counter at the finished bamboo chopsticks packaging stage or final output location or use the counting function of the automated packaging equipment to count the finished bamboo chopsticks in real time; the data acquisition module obtains the counter data and records the finished product output in each time period;

[0069] Data integration and transmission: Integrate production statistics with production progress data, equipment operation status data, etc. to form a complete production data record; transmit production statistics to the decision support module through network communication to generate production reports, analyze production efficiency, and evaluate the completion of production tasks;

[0070] 3. Data verification and storage (optional)

[0071] Data verification:

[0072] Integrity check: Check whether the collected data is complete and ensure that each data item has a corresponding collected value without omissions; for missing data, take corresponding processing measures based on the importance and recoverability of the data, such as supplementing default values, using interpolation algorithms for estimation, or marking it as abnormal data for subsequent processing;

[0073] Accuracy verification: Verify the accuracy of the collected data by comparing it with the preset reasonable data range, threshold or historical data; for data outside the normal range, conduct secondary confirmation or cross-validation of multiple data sources to determine its validity; if data abnormalities are found, issue an alarm in time and notify relevant personnel to check and handle;

[0074] Consistency check: Check whether there are logical contradictions or inconsistencies between data from different sources or at different collection times; for example, the operating status data collected by different sensors of the same equipment should be consistent with each other, and the production progress data should be logically consistent with the raw material consumption data and output statistics; if inconsistencies are found, further analyze the reasons and make data corrections or adjustments;

[0075] Data Storage:

[0076] Local storage: Store the verified collected data in the local storage device of the data acquisition module (such as hard disk, SD card, etc.), organize it according to a certain data structure and file format, so as to facilitate subsequent quick query and retrieval; at the same time, set the data storage cycle and capacity management strategy, and when the storage device space is insufficient, delete the old data or perform data backup according to certain rules;

[0077] Remote storage (optional): To ensure data security and scalability, the collected data can be transmitted to a remote server or cloud storage platform through the network for backup and storage; this not only prevents local data loss, but also facilitates data sharing and centralized management among multiple production lines or factories, providing support for enterprise-level data analysis and decision-making; during data transmission, encryption technology is used to ensure data security and prevent data leakage;

[0078] 4. System monitoring and maintenance

[0079] Operation status monitoring: Establish an operation status monitoring mechanism for the data acquisition module to monitor the module's own working status in real time, including the operating status of the hardware equipment (such as whether the sensor is working properly, whether the communication link is unobstructed, whether the storage device is available, etc.) and the operation status of the software program (such as whether the data acquisition process is running normally, whether the data transmission is timely and accurate, etc.); timely discover and display any abnormal situation or fault alarm information through the monitoring interface or log file;

[0080] Fault diagnosis and repair: When the monitoring system detects an abnormality or fault, it automatically starts the fault diagnosis program to analyze and troubleshoot possible causes of the fault; based on the diagnosis results, it provides corresponding solutions and operational suggestions, such as restarting the device, replacing faulty parts, adjusting communication parameters, etc.; for some common faults, the system can automatically perform repair operations; for more complex faults, it promptly notifies maintenance personnel to handle on-site, ensuring that the data acquisition module can quickly resume normal operation and reduce the interruption time of data acquisition caused by the fault;

[0081] Regular maintenance and calibration: Develop a regular maintenance plan for the data acquisition module, including cleaning, maintenance, inspection and calibration of hardware equipment, as well as upgrading and optimizing software programs; regularly calibrate sensors to ensure that their measurement accuracy meets production requirements; check the connection of communication cables and interfaces to prevent looseness or damage that affects data transmission; update the software of the data acquisition module to fix possible vulnerabilities and improve system performance and stability; during the maintenance process, record maintenance operations and equipment status changes in detail to provide a reference for subsequent maintenance decisions and fault analysis.

[0082] In this embodiment, the intelligent scheduling module is connected to the data acquisition module in communication. Based on the collected equipment operation status data, production progress data, raw material consumption data and output statistics, the federated learning algorithm is used, and the real-time status of the equipment, the current production demand and the task priority are comprehensively considered. The production scheduling plan for the bamboo chopstick production line is automatically generated through intelligent calculation. The intelligent scheduling module includes:

[0083] Multi-objective collaborative optimization unit: In the application of the federated learning algorithm, multiple equipment nodes or links involved in the scheduling of the bamboo chopstick production line are set as clients of the federated learning, and the production efficiency is set as , equipment utilization is set to , energy consumption is set to , Raw material waste is set to As a joint optimization target; each client Based on locally collected data Constructing a local objective function ,in, For Clients The decision variable vector, the overall multi-objective optimization objective function is expressed as: ,in, is the number of clients, For Clients The weights of the models are exchanged and updated among the clients through the federated learning mechanism to collaboratively optimize the various objectives.

[0084] Client The local objective function The construction method is as follows: with production efficiency For example, suppose the output per unit time is , the equipment running time is ,but , Represents the client production efficiency, Comprehensive consideration It is constructed with other local related factors and constraints according to preset rules and mathematical relationships to accurately reflect the local contribution to the overall multi-objective optimization;

[0085] Adaptive adjustment unit: collects production-related data from each client in real time and aggregates them into a real-time status data set for the production line , and set a predefined set of emergency adjustment rules ; When abnormal situations occur, such as urgent order insertion and sudden equipment failure, the global coordination mechanism based on federated learning is used. and , by updating the scheduling policy parameters of each client To adjust the production scheduling plan, the update process is expressed as ,in, It is an adaptive adjustment function based on federated learning;

[0086] For different types of abnormal situations, The corresponding specific adjustment strategy details are set in the system. In case of equipment failure, the system determines how to reallocate production tasks to other normal equipment according to the link where the faulty equipment is located and the remaining available equipment capacity. Through the coordination of federated learning, the adjustment strategy is sent to the corresponding client for execution.

[0087] Task priority determination unit: Based on the urgency of the delivery date of bamboo chopstick orders , Product type importance level and the adequacy of raw material supply To determine the priority of production tasks; each client Calculate the local priority score based on local relevant information , ,in, For Clients correspond , , The weight coefficient of

[0088] Global production task priority Through the aggregation mechanism of federated learning, the Get, using weighted average method ,in, For Clients Weight in the global priority calculation;

[0089] Furthermore, the specific operation steps of the intelligent scheduling module are as follows:

[0090] 1. Data collection and communication

[0091] Establish a stable connection: The intelligent scheduling module uses Ethernet, Wi-Fi or other applicable communication technologies to build a reliable communication link with the data acquisition module to ensure the stability and real-time performance of data transmission;

[0092] Real-time data acquisition: Continuously receive equipment operation status data (such as equipment temperature, speed, operating time, etc.), production progress data (the number of completed processes, remaining workload, etc.), raw material consumption data (real-time inventory of raw materials, consumption rate, etc.) and output statistics (the number of products produced, the output of products of different specifications, etc.) from the data acquisition module;

[0093] Data caching and sorting: temporarily store the received data in the local cache area and sort it by data type, time series, etc. to facilitate subsequent processing and analysis;

[0094] 2. Data preprocessing and feature engineering

[0095] Data cleaning:

[0096] Remove erroneous data points that may be generated during the collection process, such as values ​​that are clearly outside the normal range (for example, the device temperature is higher than the limit that the device can withstand);

[0097] Handle missing data and choose appropriate methods to fill in the missing data based on the importance and distribution of the data, such as using the mean, median, or a specific interpolation algorithm;

[0098] Data transformation and normalization:

[0099] Unify the conversion of data of different units and magnitudes, such as converting equipment operating time into a unified time format, and converting raw material consumption data into a unified mass or volume unit;

[0100] Normalize the data so that each eigenvalue is at the same order of magnitude. Common methods include minimum-maximum normalization or standardization to improve the computational efficiency and accuracy of subsequent algorithms.

[0101] Feature extraction and construction:

[0102] Mining key features related to production scheduling from raw data, such as the average load rate of equipment (calculated by equipment operating power and rated power), the production rhythm of each process on the production line (the time interval between two adjacent products completing the process), raw material inventory turnover rate (the ratio of raw material consumption rate to average inventory level), and the month-on-month growth rate of output;

[0103] Build new features based on production processes and business logic, such as combining equipment operating status and production progress data to calculate equipment utilization trends at different production stages; estimate product raw material unit consumption and change trends based on raw material consumption and output data, providing richer information for subsequent analysis and decision-making;

[0104] 3. Construction and initialization of federated learning model

[0105] Model architecture selection: According to the characteristics and optimization goals of the bamboo chopstick production line, select a suitable federated learning model architecture, such as distributed neural network, federated decision tree, etc.; take the distributed neural network as an example, determine the number of network layers, the number of neurons in each layer, the activation function and other hyperparameters, and build the basic model structure;

[0106] Client model initialization: Initialize the selected model architecture on each device node (client) participating in federated learning, assign the same initial model parameters to each client, and ensure that the model has a consistent starting point globally, facilitating subsequent collaborative training and optimization;

[0107] 4. Multi-objective collaborative optimization

[0108] Objective function setting:

[0109] Clarify joint optimization goals, such as production efficiency (P), equipment utilization (U), energy consumption (E), and raw material waste (W);

[0110] For each goal, a specific objective function expression is constructed by combining the preprocessed data and the actual production situation. Taking production efficiency as an example, production efficiency can be expressed as the ratio of the output of qualified products per unit time to the equipment operation time, that is, ,in, is the number of qualified products, is the equipment running time; at the same time, the impact of factors such as equipment failure, maintenance plan, process adjustment, etc. on production efficiency is considered and these factors are incorporated into the constraints of the objective function;

[0111] Assign a weight to each client's objective function ( ), the weights are determined based on the importance the enterprise attaches to each goal and the importance of the client in the production process; for example, for equipment clients in key production links, the weights of production efficiency and equipment utilization targets may be relatively high; while for equipment clients with high energy consumption, the weight of energy consumption targets is more prominent;

[0112] Local model training:

[0113] Each client is based on the locally collected data ( ), using the local objective function ( ) to train the local model; during the training process, the client calculates the error (loss function) between the model prediction value and the actual value based on its own data characteristics and objective function, and adjusts the parameters of the local model through the back propagation algorithm or other optimization algorithms ( ), to minimize the loss function;

[0114] During the training process, the client can use optimization algorithms such as stochastic gradient descent (SGD), Adagrad, and Adadelta to select the appropriate learning rate and number of iterations based on the distribution of the data and the complexity of the model, ensuring that the local model can effectively learn the patterns and rules in the local data while avoiding overfitting.

[0115] Model parameter upload and aggregation:

[0116] After a certain round of local training, each client uploads the parameters obtained from local model training (such as the weight matrix and bias vector in the neural network) to the central server or coordinator node;

[0117] The central server uses the FedAvg algorithm or other appropriate aggregation algorithms to aggregate the uploaded client parameters. During the aggregation process, the central server aggregates the client parameters according to the client weight ( ) Perform a weighted average of the parameters of each client to obtain the updated parameters of the global model; for example, for the weight parameters of the neural network , the weight after global update The calculation formula is: ,in, is the number of clients, Weight parameters uploaded by the client;

[0118] Global model parameter distribution and update:

[0119] The central server sends the aggregated global model parameters back to each client;

[0120] The client uses the received global model parameters to update the local model, so that the local model is synchronized with the global model and achieves collaborative optimization of the model on a global scale.

[0121] Through multiple iterations of the above-mentioned local training, parameter uploading, aggregation, and updating process, the model continuously learns and adapts to various data patterns on the production line, and gradually optimizes each objective function until the preset convergence condition is reached (such as the loss function value is lower than a certain threshold or the number of iterations reaches the upper limit). The model parameters obtained at this time are the results of multi-objective collaborative optimization for the bamboo chopstick production line;

[0122] 5. Adaptive adjustment strategy

[0123] Real-time data monitoring and collection:

[0124] The intelligent scheduling module continuously monitors the real-time status of the production line and collects the latest data from each client, including sudden equipment failure information (faulty equipment number, fault type, occurrence time, etc.), emergency order insertion (order number, product specifications, quantity, delivery date, etc.), real-time changes in equipment operating parameters (such as sudden increase or decrease in equipment load), and dynamic changes in raw material supply (such as raw material inventory below the safety threshold, delay in the arrival of new raw materials, etc.);

[0125] Integrate these real-time data into the production line real-time status data set ( ) to provide timely and accurate data support for subsequent adaptive adjustments;

[0126] Emergency adjustment rule matching:

[0127] According to the preset emergency adjustment rule set ( ), for the real-time status dataset ( ) to quickly match and identify abnormal situations in the rule set ( ) has formulated detailed response strategies and adjustment rules for different types of abnormal situations, such as:

[0128] When a device fails, the rules stipulate that the tasks on the failed device will be reallocated to other normal devices based on the process where the failed device is located, the processing capacity of the remaining available devices, and the task priority; at the same time, the operating parameters of the relevant equipment will be adjusted to meet the needs of the new tasks and ensure the continuity of the production process;

[0129] For urgent order insertion, the rules determine that the production plan should be adjusted according to the order priority, product type and delivery date, without affecting the production progress of the original order, to give priority to the production of urgent orders; this may involve measures such as reallocating raw materials, adjusting equipment scheduling, and optimizing production processes to ensure that urgent orders can be delivered on time;

[0130] Scheduling strategy parameter update calculation:

[0131] When an abnormal situation is identified and the corresponding adjustment rules are matched, the global coordination mechanism based on federated learning calculates the new scheduling strategy parameters (g) of each client through a specific adaptive adjustment function (g) ); Adjustment function (g) according to the real-time status data set ( )、Emergency Adjustment Rule Set( ) and the current client's scheduling policy parameters ( ), taking into account various factors such as the remaining available time of the equipment, the urgency of the task, the inventory level of raw materials, etc., to determine how each client should adjust the production task allocation, equipment operating parameters and other strategic parameters under abnormal circumstances;

[0132] For example, in the case of equipment failure, the adjustment function (g) may calculate parameters such as the proportion of tasks associated with the failed equipment that should be allocated to other equipment and the operating speed adjustment coefficient of other equipment, so as to achieve reasonable transfer of production tasks and balanced operation of the production line;

[0133] Adjust strategy execution and feedback:

[0134] The updated scheduling policy parameters ( ) is sent to the corresponding client, and the client executes the adjusted production scheduling strategy according to the new parameters;

[0135] When executing the adjustment strategy, the client feeds back the execution status and production data to the intelligent scheduling module in real time, including the actual operation status of the equipment, the progress of task completion, the consumption of raw materials, etc.; based on the feedback information, the intelligent scheduling module continuously monitors the implementation effect of the adjustment strategy and determines whether the production process has returned to normal or is developing towards the expected goal;

[0136] If it is found that there are still problems in the adjusted production process or the expected effect is not achieved, the intelligent scheduling module will further optimize the adjustment strategy according to the actual situation and update the scheduling strategy parameters again until the production line can operate stably or meet emergency production needs under abnormal conditions; at the same time, the entire adaptive adjustment process is recorded and analyzed, and lessons learned are summarized to provide a reference for the handling of similar abnormal situations in the future, and to continuously improve the emergency adjustment rule set ( ) and adaptive adjustment mechanisms;

[0137] 6. Task priority determination:

[0138] Quantitative assessment of priority factors

[0139] Order delivery urgency ( ): Calculate the remaining time of each order from the delivery date. The shorter the remaining time, the more urgent the delivery date. The remaining time can be converted into a priority score by using the inverse of the time difference or other suitable functions, such as ,in, The remaining delivery time for the order; at the same time, considering the production cycle of the enterprise and the importance level of the order, the delivery urgency of different types of orders is differentiated and weighted to ensure that key orders receive higher priority;

[0140] Product Type Importance Level ( ): According to the company's product strategy and market demand, set importance level values ​​for different types of bamboo chopsticks products; for example, the importance level of high value-added, customized products can be set to a higher value, while the importance level of ordinary standard products is set to a lower value; in addition, combined with factors such as the product's profit contribution and market demand stability, the importance level is dynamically adjusted to accurately reflect the importance of the product in different periods;

[0141] Adequacy of raw material supply ( ): Real-time monitoring of raw material inventory levels and supply stability, and calculation of the number of days raw material inventory is available ( ,in, is the current raw material inventory quantity, is the average daily consumption rate of raw materials); when the number of days of raw material inventory available is lower than the safety threshold, it indicates that the raw material supply is tight and the priority of the corresponding production tasks should be increased; at the same time, considering the uncertainty factors of raw material supply (such as supplier delivery delay risk, raw material market price fluctuations, etc.), a comprehensive assessment of the adequacy of raw material supply is conducted to ensure that when there may be problems with raw material supply, the relevant production tasks are arranged in priority to avoid production interruptions due to raw material shortages;

[0142] Local precedence score calculation:

[0143] Each client calculates the local priority score based on the local production task information and the priority factors of the above quantitative evaluation ( ); The calculation formula is ,in, , , For client , , These weight coefficients are set according to the characteristics of the production link where the client is located and the production management strategy of the enterprise. For example, for a client that consumes a large amount of raw materials, The weight may be higher; for clients producing high value-added products, The weight of is relatively large;

[0144] When calculating the local priority score, the impact of factors such as the client's local device status, task progress, and staffing on the priority is fully considered to ensure that the priority score can accurately reflect the actual urgency and importance of the local production task;

[0145] Global priority aggregation and adjustment:

[0146] The intelligent scheduling module collects the local priority scores of each client through the aggregation mechanism of federated learning ( ), and use weighted average method to calculate the global production task priority ( ), the calculation formula is ,in, The weight of the client in the global priority calculation; weight The determination of the priority level takes into account the importance of the client in the production line, data quality, historical task completion status and other factors to ensure that the global priority level can reasonably reflect the production needs and task priority distribution of the entire production line;

[0147] Sort and adjust production tasks according to global priorities, give priority to high-priority tasks, and consider the relevance between tasks and resource constraints to ensure efficient and orderly production. During the production process, dynamically update task priorities according to actual conditions. For example, when urgent orders are inserted or raw material supply changes suddenly, timely re-evaluate and adjust task priorities to adapt to changes in the production environment.

[0148] 7.Generate and issue production scheduling plan

[0149] Task allocation and sorting based on optimization results:

[0150] According to the equipment operating parameters and task priority results obtained through multi-objective collaborative optimization, the most suitable equipment and execution time are allocated to each production task; in the task allocation process, the real-time status of the equipment (including whether the equipment is available, the equipment load, the equipment maintenance plan, etc.), the processing capacity of the equipment (such as the processing efficiency of the equipment for different types of tasks, the maximum processing capacity of the equipment, etc.) and the priority of the task are fully considered, and the greedy algorithm, dynamic programming algorithm or other appropriate task allocation algorithm are used to reasonably allocate the tasks to each device to achieve the goals of maximizing production efficiency, balancing equipment utilization, and minimizing energy consumption and raw material waste;

[0151] At the same time, the production tasks are sorted and the execution order of the tasks is determined; the sorting process comprehensively considers the production process constraints (such as certain processes must be carried out in a specific order), the equipment switching cost (the time and resources required for the equipment to switch from one task to another) and the correlation between tasks (such as the material transfer efficiency and time interval between the previous and next processes); by reasonably arranging the task sequence, the waiting time, equipment idle time and material handling time in the production process are reduced, and the overall production efficiency and smoothness of the production line are improved;

[0152] Generate detailed scheduling instructions:

[0153] Convert the determined task allocation and sorting results into specific scheduling instructions, including equipment start / stop instructions, equipment operating parameter setting instructions (such as equipment operating speed, temperature, pressure and other process parameters), task start / end time notifications, material delivery instructions (including raw material delivery quantity, delivery time and delivery destination, etc.) and personnel deployment instructions (such as arranging operators to designated equipment for production operations), etc.;

[0154] The format and communication protocol of the dispatching instructions should be compatible with the equipment control system and operator terminals on the production line to ensure that the instructions can be accurately transmitted and executed;

[0155] Plan issuance and execution monitoring:

[0156] The generated dispatching instructions are sent to each equipment node, material distribution system and operator terminal in real time through the communication network to ensure that each link in the production line can receive the instructions in time and execute the production tasks as required;

[0157] The intelligent scheduling module establishes a production process monitoring mechanism, tracks the execution of the scheduling plan in real time, collects feedback information such as equipment operation data, production progress data, material consumption data, etc., compares and analyzes it with the preset production plan and scheduling plan, and promptly discovers deviations and abnormalities in the execution process;

[0158] When deviations are found between the production process and the scheduling plan, such as equipment failures causing task delays, insufficient material supply affecting production progress, etc., the corresponding adjustment mechanism is immediately activated to dynamically optimize and adjust the scheduling plan according to the real-time situation, and the adjusted scheduling instructions are re-issued to ensure that the production process always proceeds smoothly towards the predetermined goal; at the same time, the execution status and adjustment process of the production scheduling plan are recorded and analyzed to provide data support and experience reference for the subsequent optimization and improvement of production scheduling;

[0159] 8. Solution evaluation and continuous optimization

[0160] Data collection and indicator calculation:

[0161] After the production scheduling plan is implemented for a period of time, the actual production data during this period is collected, including equipment operation data (such as equipment utilization rate, equipment failure rate, equipment operation time, etc.), production progress data (actual completion time of each process, product output, production cycle, etc.), raw material consumption data (actual total raw material consumption, raw material utilization rate, raw material waste rate, etc.) and order delivery data (order on-time delivery rate, number of delayed orders and delay time, etc.);

[0162] Based on the collected data, we calculate the evaluation indicators related to production scheduling, such as production efficiency (the ratio of actual output to theoretical maximum output), comprehensive utilization rate of equipment (the ratio of actual running time of each equipment to the total running time), energy consumption rate (the ratio of actual energy consumption to production output), raw material waste rate (the ratio of raw material waste to total raw material input), order delivery timeliness rate (the ratio of the number of orders delivered on time to the total number of orders), etc. These indicators fully reflect the execution effect of the production scheduling plan and the operational performance of the production line;

[0163] Program effect evaluation and analysis:

[0164] Compare and analyze the calculated evaluation indicators with the preset target values ​​or historical data to evaluate the pros and cons of the production scheduling plan; if each indicator reaches or exceeds the expected target, it means that the current scheduling plan is relatively effective; if some indicators do not meet expectations, such as low production efficiency, uneven equipment utilization, excessive energy consumption or delayed order delivery, it is necessary to conduct in-depth analysis of the reasons and find out the key factors affecting the production scheduling effect;

[0165] Use data analysis methods (such as data mining, statistical analysis, etc.) to conduct in-depth mining of production data and analyze the correlation and influence of different factors, such as the relationship between equipment operating parameters and product quality, the relationship between production task allocation and equipment utilization, the relationship between raw material supply and production progress, etc.; through data analysis, determine the root cause of production scheduling problems and provide a basis for subsequent optimization and improvement;

[0166] Model and strategy optimization and adjustment:

[0167] According to the evaluation and analysis results, optimize and adjust the federated learning model and production scheduling strategy; if it is found that the model makes inaccurate predictions or cannot effectively optimize the objective function in some cases, it may be necessary to adjust the model structure (such as increasing or decreasing the number of neural network layers, adjusting the number of neurons, etc.), hyperparameters (such as learning rate, number of iterations, regularization coefficient, etc.) or optimization algorithms; at the same time, check the data preprocessing and feature engineering steps to ensure the data quality and feature validity of the input model;

[0168] For production scheduling strategies, based on the causes of the problems analyzed, targeted improvements can be made to the task allocation algorithm, priority determination rules, equipment collaborative working mode, etc. For example, if it is found that some equipment often has a backlog of tasks, resulting in excessive utilization, while other equipment has a low utilization rate, it may be necessary to adjust the task allocation strategy to distribute tasks more evenly; if the order delivery timeliness rate is low, it may be necessary to optimize the task priority determination method to highlight the impact of the urgency of the delivery date on the priority, or adjust the production plan to reserve enough production time in advance to deal with urgent orders;

[0169] Optimization solution implementation and verification:

[0170] Apply the optimized federated learning model and production scheduling strategy to actual production and implement a new round of production scheduling. During the implementation process, pay close attention to changes in the production process, collect relevant data, observe the changing trends of various evaluation indicators, and verify the effectiveness of the optimization measures.

[0171] If the optimization plan fails to achieve the expected results, it is necessary to re-evaluate and analyze, further identify problems, and adjust the optimization strategy until the best solution is found; through continuous evaluation, optimization, implementation and verification, the scientificity and rationality of the production scheduling plan will be continuously improved, and the overall operational efficiency and competitiveness of the bamboo chopstick production line will be improved;

[0172] Knowledge accumulation and experience inheritance:

[0173] In the process of program evaluation and continuous optimization, we should focus on accumulating knowledge and experience in production data, optimization strategies, problem-solving methods, etc.; establish a knowledge base or case library to organize and archive successful optimization cases and effective solutions so that they can be quickly referenced and learned when encountering similar problems in the future;

[0174] Regularly organize training and exchanges for production management personnel, technical personnel and operators to share experiences and lessons learned in the optimization process, improve the team's overall production scheduling management level and problem-solving ability; promote continuous improvement and innovative development of the company's production management through knowledge accumulation and experience inheritance.

[0175] In this embodiment, the abnormality detection module is connected to the data acquisition module in communication, and the collected data is analyzed in real time by using the deep autoencoder technology to monitor abnormal conditions in the production process of bamboo chopsticks, including equipment failure, insufficient raw materials, and bottlenecks in the production process. The abnormality detection module includes:

[0176] Anomaly recognition and positioning unit: Use deep autoencoders to process the collected data and convert the input data Mapped to hidden layer representation through encoder , and then reconstructed by the decoder to get the output , calculate the reconstruction error , set a reasonable threshold ,when When determining that the production link corresponding to the data has abnormal conditions;

[0177] Further analysis of hidden layer representation Based on the characteristic distribution of each device and each production link under normal conditions, the specific abnormal device or production link can be located;

[0178] Abnormal warning and feedback unit:

[0179] Once an abnormal situation is detected, an early warning signal containing the abnormality type, location, and severity is immediately generated and sent to the intelligent scheduling module and the equipment coordination and optimization module through the communication link;

[0180] According to the severity of the abnormality, different levels of early warning prompts are set. For minor abnormalities, only prompt information is displayed on the visual interface. For abnormalities that seriously affect production, sound and light alarms are triggered at the same time, and detailed abnormal information is pushed to the terminal device of the manager in real time;

[0181] Furthermore, the specific operation steps of the anomaly detection module are as follows:

[0182] 1. Data collection and transmission

[0183] Establish a communication link: The anomaly detection module establishes a stable communication connection with the data acquisition module through Ethernet, Wi-Fi or other reliable communication methods to ensure that data can be transmitted in real time and accurately;

[0184] Receive collected data: Continuously obtain equipment operation status data (such as equipment temperature, vibration, pressure, current, speed, etc.), production progress data (number of completed processes, product turnover time, etc.), raw material consumption data (raw material inventory changes, consumption rate, etc.) and output statistics (finished product output quantity, output of products of different specifications, etc.) during the bamboo chopstick production process from the data acquisition module;

[0185] Data caching and preprocessing: The received data is temporarily stored in the local cache area and preliminary data preprocessing is performed, including data cleaning (removing noise and outliers), data format conversion (unifying data types and encoding formats), data normalization (mapping data to a specific interval, such as [0, 1] or [-1, 1], to improve the efficiency of model training and analysis), etc., so that the data meets the input requirements of the deep autoencoder model;

[0186] 2. Deep Autoencoder Model Construction and Training

[0187] Model architecture design:

[0188] Determine the network structure of the deep autoencoder, including the number of input layers, hidden layers, and output layers and the number of nodes; determine the number of input layer nodes according to the number of features of the collected data. For example, if 10 parameters of the equipment operation status are collected, the number of input layer nodes is 10; the design of the hidden layer is determined according to the complexity of the data and the expressiveness of the model. Generally, a multi-layer structure can be adopted, such as 2-5 layers, with the number of nodes in each layer gradually decreasing to form a "funnel" shape to extract the key features of the data; the number of output layer nodes is the same as that of the input layer, which is used to reconstruct the input data;

[0189] Select a suitable activation function, such as ReLU (RectifiedLinearUnit) function for the hidden layer to introduce nonlinear factors and enhance the model's expressiveness. The output layer can select a suitable activation function according to the needs of reconstructed data, such as linear activation function for regression tasks (such as reconstructing equipment operating status data), or Sigmoid function for mapping output values ​​to the [0,1] interval (such as for judging abnormal probability).

[0190] Model training preparation:

[0191] Divide the preprocessed historical production data into a training set and a validation set, usually with 70%-80% allocated to the training set and the rest to the validation set; ensure that the training set can fully represent the data pattern under normal production conditions and cover data samples under various working conditions and production conditions;

[0192] Define a loss function to measure the difference between the model reconstructed data and the original data; the commonly used loss function is the mean square error (MSE) function. For multivariate data, MSE can calculate the average value of the reconstruction error of each variable. The formula is ,in, is the sample size, is the original data sample, Data samples reconstructed for the model;

[0193] Model training process:

[0194] Use the training set to train the deep autoencoder model, and adjust the model's weights and bias parameters through the back-propagation algorithm to minimize the loss function. During the training process, set appropriate training parameters such as learning rate (such as 0.001-0.01), number of iterations (such as 1000-5000 times) and batch size (such as 32-128). The learning rate determines the step size of each parameter update. If it is too large, the model may not converge, and if it is too small, the training process will be too long. The number of iterations controls the number of training rounds until the loss function value of the model on the training set no longer decreases significantly or reaches the preset iteration limit. The batch size affects the stability and efficiency of model training. The appropriate batch size can accelerate the training process and improve the generalization ability of the model.

[0195] After each round of training, use the validation set to evaluate the model, calculate the loss function value and other evaluation indicators on the validation set (such as accuracy, recall, etc., if applicable); observe the changing trend of the validation set indicators to determine whether the model is overfitting or underfitting; if the validation set loss function value continues to decrease, it means that the model is still learning effective features and has not yet reached the optimal state; if the validation set loss function value begins to rise, while the training set loss function value continues to decrease, overfitting may have occurred, and the model begins to over-learn the detailed features of the training set and loses the ability to generalize to new data; at this time, measures such as stopping training early, adding regularization terms (such as L1 or L2 regularization) or adjusting the model structure can be taken to prevent overfitting;

[0196] After multiple training and verification, until the performance of the model on the verification set is stable and meets the expected requirements, the model training process is completed and a trained deep autoencoder model is obtained;

[0197] 3. Real-time data monitoring and anomaly identification

[0198] Data input and reconstruction:

[0199] The real-time collected and preprocessed production data is input into the trained deep autoencoder model, and the encoder part of the model maps the input data to the hidden layer representation ( ), and then reconstructed by the decoder part to get the output data ( );

[0200] Reconstruction error calculation and anomaly judgment:

[0201] Calculate the reconstruction error between the reconstructed data and the original input data ( ), that is, calculate the square root of the sum of the squares of the differences between the two in each dimension; set a reasonable threshold , the threshold can be determined by analyzing the distribution of reconstruction errors on the training set and the validation set. For example, the 95% quantile or 99% quantile of the reconstruction error distribution of normal data is taken as the threshold. When the data is detected, there may be abnormalities in the production process corresponding to the data;

[0202] Abnormal location and analysis:

[0203] For data that is judged to be abnormal, further analysis of the model hidden layer representation The feature distribution of the hidden layer can be observed by using visualization techniques (such as drawing scatter plots after dimensionality reduction using principal component analysis (PCA) and feature heat maps) to observe the change pattern of hidden layer features, and locate the specific abnormal equipment or production link by combining the preset feature ranges of each device and each production link under normal conditions. For example, if it is found that the values ​​of several features corresponding to a certain device in the hidden layer representation are obviously deviated from the normal range, and the production link related to the device is in a key position in the production process, then it can be preliminarily determined that the device or its production link has an abnormality. At the same time, by comparing the differences between abnormal data and normal data in various features of the input layer, it is analyzed which parameter changes have a greater impact on the reconstruction error, so as to more accurately determine the root cause of the abnormality. For example, if the temperature of the device is too high, the feature representation of the device operation status data in the model may change significantly, thereby causing a large reconstruction error.

[0204] 4. Abnormal warning and feedback

[0205] Warning signal generation:

[0206] Once an abnormality is detected, the anomaly detection module immediately generates an early warning signal containing the type of abnormality (such as equipment failure, insufficient raw materials, bottlenecks in production links, etc.), the location of occurrence (specific equipment number, production process name, etc.), and the severity (graded according to the size of the reconstruction error or other relevant indicators, such as minor, moderate, and severe). The early warning signal uses a standardized data format to facilitate communication and interaction with other system modules.

[0207] Early warning information transmission:

[0208] The warning signal is sent to the intelligent scheduling module and the equipment coordination and optimization module through the communication link, so that these modules can obtain abnormal information in time and take corresponding countermeasures; at the same time, the warning information is displayed on the display screen or monitoring terminal set up at the production site to remind the on-site operators to pay attention to the occurrence of abnormal situations;

[0209] Warning prompt mode settings:

[0210] Different levels of early warning prompts are set according to the severity of the abnormality. For minor abnormalities (such as slight fluctuations in equipment operating parameters, but still within an acceptable range), only prompt information is displayed on the visual interface to attract the attention of relevant personnel, but it does not affect the normal production process. For abnormalities that seriously affect production (such as failure of key equipment, serious shortage of raw materials, etc.), sound and light alarms are triggered at the same time, and detailed abnormal information (including abnormal data details, possible impact range, recommended treatment measures, etc.) is pushed to the terminal device (such as mobile phones, computers, etc.) of managers in real time to ensure that managers can receive notifications and take emergency response measures as soon as possible to minimize the losses caused by abnormal situations to production.

[0211] 5. Model update and optimization

[0212] Data accumulation and labeling:

[0213] Continuously collect data during the production process, accumulate data on newly emerging abnormal situations and normal production conditions, accurately mark abnormal data, and record detailed information such as abnormality type, occurrence time, and handling process; these data will be used to update and optimize subsequent models to improve the model's ability to identify and accurately identify different abnormal situations;

[0214] Model periodic evaluation and update triggers:

[0215] Evaluate the performance of the deep autoencoder model regularly (e.g., weekly or monthly), and use newly accumulated data (including normal data and labeled abnormal data) to calculate the model's accuracy, recall, F1 value and other evaluation indicators in detecting anomalies; if a downward trend in model performance is found (e.g., decreased accuracy, increased missed or false positive rates), trigger the model update process;

[0216] Model retraining and optimization:

[0217] Merge the newly accumulated data with the original training data, re-divide the training set and validation set (the ratio can be adjusted appropriately according to the data distribution), and use the updated data set to retrain the deep autoencoder model; during the training process, you can try to adjust the model's hyperparameters (such as learning rate, hidden layer structure, etc.) or adopt more advanced training algorithms to further optimize the model performance; by continuously updating and optimizing the model, it can adapt to various changes and new abnormal patterns that may occur in the production process of bamboo chopsticks, and continuously improve the accuracy and reliability of anomaly detection.

[0218] In this embodiment, the equipment coordination and optimization module is connected to the intelligent scheduling module and the anomaly detection module in communication, and a federated learning algorithm is used to optimize the collaborative working mode between the various devices in the bamboo chopstick production line according to the production scheduling plan generated by the intelligent scheduling module and the anomaly feedback from the anomaly detection module. The equipment coordination and optimization module includes:

[0219] Inter-device task allocation optimization unit: Under the federated learning architecture, each device is regarded as a node participating in the collaboration. According to the production scheduling plan generated by the intelligent scheduling module, combined with the current real-time status of each device and the abnormal information fed back by the abnormal detection module, an optimization model for task allocation between devices is constructed; let the device set be , is the total number of equipment, and the production task set is , is the number of production tasks, defining the allocation matrix , where the element Representation Task Is it assigned to the device? , the goal is to find the optimal allocation matrix The optimization process is achieved by iteratively updating the allocation strategy among each device node through the federated learning algorithm, that is, each device node adjusts its corresponding value, is the index of the device, is the index of the production task;

[0220] Considering the heterogeneity of devices, Set the task processing capability vector , indicating its processing efficiency or capacity upper limit for different types of tasks. When optimizing task allocation, adding constraints ensures that the tasks are allocated to the device. The total amount of tasks does not exceed its processing capacity, that is ,in, Representation Task The workload, Indicates the device To the task Task Type The processing capacity limit of the system is determined to ensure the rationality and feasibility of task allocation;

[0221] Equipment operation parameter collaborative adjustment unit: Each device shares its own operation parameters through the federated learning mechanism, and builds an operation parameter collaborative adjustment model based on multi-device joint; The operating parameter vector is , by analyzing the correlation between different equipment operating parameters and their impact on overall production;

[0222] The gradient descent optimization algorithm is used to update the operating parameters of each device under the federated learning framework. The learning rate is set to , in each iteration, the device The operating parameter update rule is ,in, Indicates the device The updated operating parameter vector, is the learning rate, Represents the overall production line performance loss function About the device Run parameter vector The gradient of is the overall production line performance loss function;

[0223] Furthermore, the specific operation steps of the equipment coordination and optimization module are as follows:

[0224] 1. Data acquisition and preprocessing

[0225] Establish a communication connection:

[0226] The equipment coordination and optimization module uses Ethernet, Wi-Fi or specific industrial communication protocols to establish a reliable two-way communication link with the intelligent scheduling module and the anomaly detection module to ensure the stability and real-time performance of data transmission;

[0227] Receive scheduling plan data:

[0228] Obtain a detailed production scheduling plan from the intelligent scheduling module, including a list of equipment task assignments (clarifying the specific production tasks undertaken by each equipment, such as the process, quantity, and estimated processing time of bamboo chopsticks), equipment operating parameter setting values ​​(such as the specific values ​​of equipment speed, temperature, pressure and other process parameters), and task priority sorting (distinguishing between urgent tasks and routine tasks);

[0229] Receiving exception data:

[0230] Receive abnormal information sent by the abnormality detection module in real time, including equipment failure details (such as equipment name, number, fault code, and occurrence time), raw material shortage warning (type and quantity of short-supply raw materials, and estimated impact duration), and production bottleneck report (bottleneck link name, location, and comparison of current processing capacity with normal levels);

[0231] Data cleaning and integration:

[0232] Clean the acquired scheduling data and abnormal situation data to remove invalid, duplicate and erroneous data records; for example, correct abnormal data values ​​caused by sensor failure or communication interference, and delete data entries that are sent repeatedly;

[0233] Integrate the cleaned data according to device identification, time sequence, etc. to form a unified format data set to facilitate subsequent analysis and processing; for example, associate the task allocation information of a certain device with the corresponding operating parameter setting value of the device, and mark whether there are any abnormal situations and the type of abnormality;

[0234] 2. Build a federated learning model

[0235] Determine the model architecture:

[0236] According to the characteristics and optimization goals of the collaborative work of the bamboo chopstick production line equipment, select a suitable federated learning model architecture, such as a federated learning model based on a deep neural network or a federated decision tree model;

[0237] If a deep neural network is used, design the network structure, including the input layer (receiving feature data such as device status, task information, and abnormal identification), the hidden layer (used to extract data features and patterns, which can be multi-layered and the number of layers and nodes is determined according to the complexity of the data), and the output layer (outputting the optimization strategy for device collaboration, such as task adjustment suggestions, device parameter correction values, etc.);

[0238] Model initialization:

[0239] Initialize the selected model on each device node (client) participating in federated learning; assign the same initial model parameters to each client, such as the initial weights and bias values ​​of the neural network, to ensure that the model has a unified starting state globally, facilitating subsequent collaborative training and optimization;

[0240] 3. Optimize task allocation between devices

[0241] Local task allocation model training (client side):

[0242] Each device client builds a local task allocation model based on local data (including its own device status, received task information, and some shared production line data);

[0243] With the goal of minimizing the production cycle and maximizing equipment utilization, the local objective function is established by considering the real-time status of the equipment (such as the current load of the equipment, the remaining available time, the operating efficiency, etc.), the task characteristics (such as the urgency of the task, the difficulty of processing, the estimated processing time, etc.) and the impact of abnormal situations (such as the interruption of the task due to equipment failure, the adjustment of the task priority due to the shortage of raw materials);

[0244] Use gradient descent or other optimization algorithms to train the local model based on the local objective function and data, and optimize the model parameters (such as weights and biases in the neural network) so that the model can generate reasonable task allocation suggestions based on the input data;

[0245] Model parameter upload and aggregation (server side):

[0246] After a certain round of local training, each client encrypts the parameters of the local model (such as the weight matrix and bias vector of the neural network) and uploads them to the central server or coordinator node;

[0247] The central server uses the FedAvg algorithm or other aggregation algorithms to aggregate the uploaded client parameters. During the aggregation process, weights are assigned to the clients based on their importance in the production line, data quality, and other factors, and the updated parameters of the global model are calculated. For example, for key production equipment or clients with high data accuracy, their parameters are given higher weights during aggregation.

[0248] Global model parameter distribution and update (client):

[0249] The central server sends the aggregated global model parameters back to each client;

[0250] The client uses the received global parameters to update the local model, so that the local model is synchronized with the global model, and the collaborative optimization of the model is achieved globally.

[0251] Through multiple iterations of the above local training, parameter uploading, aggregation and updating process, the task allocation strategy is continuously optimized to make the task allocation between devices more reasonable and improve the overall production efficiency of the production line;

[0252] 4. Coordinated adjustment of equipment operating parameters

[0253] Run parameter adjustment model training locally (client):

[0254] Each device client builds a local device operating parameter collaborative adjustment model based on local device operating data (such as real-time device operating parameters, historical operating data, device performance indicators, etc.) and received abnormal situation information;

[0255] With the goal of minimizing energy consumption and ensuring stable product quality, the local objective function is established by considering the correlation between equipment operating parameters (such as the mutual influence between equipment speed and temperature and pressure), the current status of the equipment (such as whether there are hidden dangers of failure, the degree of equipment wear, etc.) and production task requirements (such as the restrictions on equipment parameters by product specifications);

[0256] Using the gradient descent optimization algorithm, the local model is trained according to the local objective function and data, and the model parameters are adjusted so that the model can generate appropriate equipment operation parameter adjustment plans based on the input data;

[0257] Parameter upload, aggregation and update (similar to task allocation optimization):

[0258] After a certain number of rounds of training, the client encrypts and uploads the gradient information related to the local model running parameters to the central server;

[0259] The central server decrypts and aggregates the uploaded gradient information (also considering the client weight), calculates the global gradient, and then updates the global model parameters based on the global gradient;

[0260] The central server sends the updated global parameters back to the client, and the client updates the local model accordingly, achieving coordinated optimization of equipment operating parameters on a global scale, ensuring that the operating parameters of each device match each other and improving the overall performance of the production line;

[0261] 5. Exception handling and coordinated adjustment

[0262] Abnormal situation analysis and strategy generation:

[0263] After receiving the abnormal situation data, the equipment coordination and optimization module will quickly conduct analysis and evaluation. For equipment failure, determine the type of failure, severity and scope of impact on the production process (such as which subsequent processes are affected and which equipment tasks need to be adjusted); for raw material shortages, evaluate the impact of the shortage on the production of different products and the possible production downtime; for production bottlenecks, analyze the causes of the bottlenecks (such as aging equipment, unreasonable processes, insufficient staffing, etc.) and the extent to which the bottlenecks restrict the efficiency of upstream and downstream equipment and the entire production line;

[0264] According to the results of abnormal analysis, combined with the current production scheduling plan and equipment status, formulate targeted collaborative adjustment strategies; for example, when equipment fails, determine which spare or idle equipment to transfer the tasks of the failed equipment to, and how to adjust the operating parameters of related equipment to adapt to the new tasks; when raw materials are in short supply, adjust the priority of production tasks, give priority to the production of products with low raw material consumption or that can be supported by inventory raw materials, and coordinate with the procurement department to speed up the procurement of raw materials; when there is a bottleneck in the production process, take measures such as optimizing the process parameters of the bottleneck equipment, adding temporary auxiliary equipment, or adjusting the task allocation of upstream and downstream equipment to alleviate the impact of the bottleneck on production;

[0265] Coordinated execution and monitoring:

[0266] Task reallocation and execution: Based on the formulated strategy, the optimized task allocation model is used to quickly calculate the new task allocation plan for each device under abnormal circumstances. In the process of task reallocation, the real-time status of the equipment (such as the remaining processing capacity of the available equipment, the load balancing of the equipment), the change of task priority (urgent tasks are given priority), and the production process constraints (to ensure the continuity and rationality of the production process) are fully considered. The adjusted task allocation plan is promptly issued to the control system of each device to ensure that the equipment can execute tasks according to the new plan.

[0267] Dynamic adjustment and execution of equipment parameters: Based on abnormal situations and adjustment strategies, the operating parameter collaborative adjustment model is used to calculate the operating parameter adjustment values ​​of each related equipment in real time. For example, when a task is transferred due to equipment failure, the operating parameters (such as speed, power, etc.) are appropriately increased according to the actual situation of the receiving task equipment to speed up production progress. When raw materials are in short supply, the equipment operating parameters (such as temperature, output, etc.) are reduced to reduce raw material consumption. When there is a bottleneck in the production process, the operating parameters of the bottleneck equipment and its upstream and downstream equipment are optimized to achieve a new balance in all links of the production line. The adjusted operating parameters are sent to the equipment in real time to achieve dynamic optimization of equipment operating parameters.

[0268] Collaborative adjustment monitoring: After implementing collaborative adjustment measures, continuously monitor the operating status of the production line, collect equipment operation data (such as equipment utilization, failure rate, energy consumption, etc.), production progress data (completion time of each process, product output, etc.) and product quality data (qualified rate, defective rate, etc.) in real time; by comparing the data before and after the adjustment, evaluate the effect of the collaborative adjustment measures and determine whether the production line has resumed normal operation or achieved the expected optimization goal;

[0269] Feedback and Optimization Loop:

[0270] Feedback the evaluation results of the collaborative adjustment effect to the intelligent scheduling module and the anomaly detection module to provide reference for subsequent decision-making; if the production line still has problems after adjustment or fails to achieve the expected results, such as low production efficiency, unbalanced equipment load or unstable product quality, further analyze the reasons based on the feedback information, and it may be necessary to optimize the adjustment strategy, federated learning model parameters or algorithms;

[0271] At the same time, we summarize the lessons learned from the process of exception handling and collaborative adjustment, store successful cases and optimization strategies in the knowledge base, provide a reference for handling similar abnormal situations in the future, continuously improve the equipment coordination and optimization mechanism, and improve the production line's ability to cope with abnormal situations and overall operating efficiency.

[0272] In this embodiment, the decision support module is connected to the intelligent scheduling module, the anomaly detection module, and the equipment coordination and optimization module. Based on the data obtained from each module, a visualization interface for management personnel to view is generated through big data analysis technology, and intelligent analysis reports covering production efficiency, equipment utilization, and raw material consumption are regularly generated. The decision support module includes:

[0273] Real-time monitoring visualization unit: Build a visualization interface to display detailed status information of the bamboo chopstick production line in real time with intuitive charts, graphs, and data lists, covering equipment operation status, production progress, currently effective production scheduling plan, and equipment health status warning information;

[0274] Intelligent report generation unit: According to the preset time period, the unit uses big data analysis technology to conduct a comprehensive analysis of the historical production data and real-time data of the bamboo chopstick production line, and generates an intelligent analysis report covering production efficiency, equipment utilization, and raw material consumption;

[0275] In the intelligent analysis report, in addition to presenting the actual data and change trends of each indicator, data mining algorithms are also used to discover potential problems, optimization directions and improvement suggestions in the production process;

[0276] Further, the specific operation steps of the decision support module are as follows:

[0277] 1. Data collection and integration

[0278] Establish communication connection and data acquisition:

[0279] The decision support module establishes real-time communication connections with the intelligent scheduling module, anomaly detection module, and equipment coordination and optimization module through stable network communication technologies (such as Ethernet, Wi-Fi, etc.) to ensure that data can be transmitted in a timely and accurate manner;

[0280] Obtain production scheduling related data from the intelligent scheduling module, including production task allocation plan, equipment operation plan, task priority information, etc.; obtain abnormal event data from the abnormality detection module, such as equipment failure records, raw material shortage warnings, production process bottleneck information, etc., as well as detailed descriptions of the time, location, severity, etc. of the abnormality; obtain equipment operation status data (such as real-time equipment operation parameters, equipment utilization, equipment failure repair status, etc.) and equipment collaborative work adjustment strategy execution result data from the equipment coordination and optimization module;

[0281] Data cleaning and conversion:

[0282] Clean the collected data to remove noise, erroneous data, and duplicate data; for example, verify and correct equipment operating parameter values ​​that are clearly beyond the normal range (such as excessively high temperature or low pressure), and delete duplicate data records caused by communication failures;

[0283] Convert data in different formats and units into a format suitable for analysis and processing, such as unifying the time format into a standard date and time format, unifying the units of equipment operating parameters into international standard units, and converting production data into the same counting unit (such as pieces, tons, etc.);

[0284] Data integration and association:

[0285] Integrate data from different modules according to the logical relationship of the production process and establish associations between data; for example, associate production task allocation information with the equipment operation data that executes the task, and match abnormal events with the affected production links and equipment, so as to fully understand the relationship between various factors in the production process and provide a basis for subsequent analysis and visualization;

[0286] 2. Real-time monitoring of visual interface construction and update

[0287] Visual interface design and layout:

[0288] Design the layout and display content of the visualization interface according to the needs of managers and the focus of production management; the interface can be divided into multiple areas, such as equipment operation status monitoring area, production progress display area, abnormal warning area, production index summary area, etc.;

[0289] In the equipment operation status monitoring area, the key operating parameters (such as temperature, speed, pressure, etc.), operating status (operating, stopped, fault, etc.) and equipment utilization trend of each equipment are displayed in real time in a graphical manner (such as dashboard, line chart, bar chart, etc.); in the production progress display area, the completion progress of each production task, the estimated completion time and the comparison with the planned progress are intuitively displayed in the form of progress bar, Gantt chart, etc.; the abnormal warning area prompts the current abnormal event with eye-catching colors (such as red for serious abnormalities and yellow for minor abnormalities) and flashing icons, and provides a link to the abnormal details; the production indicator summary area displays the current production efficiency, equipment utilization, raw material consumption and other key indicators in digital form;

[0290] Real-time data update and push:

[0291] Use data push technology or timed query mechanism to obtain the latest production data in real time and update it to the visual interface; ensure that managers can understand the real-time status of the production line in a timely manner and make accurate decisions; for example, update equipment operating parameters and production progress data every few seconds. When an abnormal event occurs, immediately display relevant information in the abnormal warning area and push notifications to managers;

[0292] Interactive function design:

[0293] Add interactive functions to the visualization interface to help managers gain in-depth understanding of production conditions. For example, set a click event so that when a manager clicks on a device icon or a production task progress bar, a detailed information window pops up to display the detailed data, history, and related operation logs of the device or task. Provide zoom and pan functions to view production data for different time periods or different device groups. Support data filtering and sorting functions so that managers can view data under specific conditions according to their needs, such as viewing the operating data of a specific device within a certain time period or sorting production tasks by production efficiency.

[0294] 3. Intelligent analysis report generation

[0295] Data statistics and analysis:

[0296] Conduct comprehensive statistical analysis on the integrated production data according to a predetermined time period (such as daily, weekly, and monthly); calculate production efficiency, that is, the ratio of qualified product output per unit time to the theoretical maximum output, analyze its changing trend in different time periods, and find out the factors affecting production efficiency, such as equipment failure, production process adjustment, and personnel operation level; calculate equipment utilization, calculate the percentage of actual operating time of each equipment to the planned operating time, evaluate the equipment's utilization efficiency and idleness, and analyze the relationship between equipment utilization and production task allocation; analyze raw material consumption, calculate the raw material consumption per unit product, compare the differences in raw material consumption between different products and different production batches, and find out the reasons for excessive or abnormal fluctuations in raw material consumption, such as changes in raw material quality and unreasonable production processes;

[0297] Problem discovery and optimization suggestions:

[0298] Use data mining algorithms (such as association rule mining, cluster analysis, decision tree algorithm, etc.) to conduct in-depth mining of production data to discover potential production problems and optimization opportunities; for example, through association rule mining, discover the relationship between certain equipment failure modes and specific production tasks or raw material batches, providing a basis for equipment maintenance and production plan adjustment; use cluster analysis to classify similar working conditions or product quality problems in the production process, find out the commonalities and differences of the problems, so as to take targeted improvement measures; use decision tree algorithm to analyze the impact of different production decisions on production indicators, and provide reference for optimizing production decisions;

[0299] Generate corresponding optimization suggestions based on data analysis results and discovered problems. For example, if it is found that the utilization rate of a certain equipment is low for a long time, it is recommended to adjust the production task allocation or consider equipment upgrades. If the raw material consumption is too high, it is recommended to optimize the production process, improve raw material quality control, or find alternative raw materials. For frequent abnormal events in the production process, formulate measures to strengthen equipment maintenance plans, optimize abnormal warning thresholds, or improve production processes.

[0300] Report generation and distribution:

[0301] Integrate statistical analysis results, problem mining findings and optimization suggestions to generate intelligent analysis reports, which include text descriptions, data charts, data tables, and specific steps and implementation plans for problem solutions and suggestions;

[0302] Intelligent analysis reports are regularly distributed to relevant managers by means of e-mail, internal enterprise system message push or generation of downloadable files, ensuring that managers can obtain the report content in a timely manner and make decisions and optimize management based on the information provided by the report; at the same time, the reports are stored in the enterprise data warehouse or document management system to facilitate historical data query and comparative analysis, providing data support for the continuous improvement of enterprise production management.

[0303] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time intelligent scheduling and management system for bamboo chopstick production line data, characterized by: include: Data acquisition module: It is set up on the bamboo chopstick production line and connected with sensors, PLC controllers and production equipment interfaces to collect real-time equipment operation status data, production progress data, raw material consumption data and output statistics during the bamboo chopstick production process; Intelligent scheduling module: It communicates with the data acquisition module and uses the federated learning algorithm based on the collected equipment operation status data, production progress data, raw material consumption data and output statistics. It also considers the real-time status of the equipment, current production needs and task priorities, and automatically generates a production scheduling plan for the bamboo chopstick production line through intelligent calculations. Abnormality detection module: communicates with the data acquisition module and uses deep autoencoder technology to analyze the collected data in real time to monitor abnormal conditions in the production process of bamboo chopsticks, including equipment failure, insufficient raw materials, and bottlenecks in the production process; Equipment coordination and optimization module: It communicates with the intelligent scheduling module and the anomaly detection module, and uses the federated learning algorithm to optimize the collaborative working mode between the various devices in the bamboo chopstick production line based on the production scheduling plan generated by the intelligent scheduling module and the anomaly feedback from the anomaly detection module; Decision support module: Communicates with the intelligent scheduling module, anomaly detection module, and equipment coordination and optimization module. Based on the data obtained from each module, it generates a visual interface for management personnel to view through big data analysis technology, and regularly generates intelligent analysis reports covering production efficiency, equipment utilization, and raw material consumption.

2. According to claim 1, a real-time intelligent scheduling and management system for bamboo chopstick production line data is characterized in that: The intelligent scheduling module includes: Multi-objective collaborative optimization unit: In the application of the federated learning algorithm, multiple equipment nodes or links involved in the scheduling of the bamboo chopstick production line are set as clients of the federated learning, and the production efficiency is set as , equipment utilization is set to , energy consumption is set to , Raw material waste is set to As a joint optimization target; each client Based on locally collected data Constructing a local objective function ,in, For Clients The decision variable vector, the overall multi-objective optimization objective function is expressed as: ,in, is the number of clients, For Clients The weights of the models are exchanged and updated among the clients through the federated learning mechanism to collaboratively optimize the various objectives. Client The local objective function The construction method is as follows: with production efficiency For example, suppose the output per unit time is , the equipment running time is ,but , Represents the client production efficiency, Comprehensive consideration It is constructed with other local related factors and constraints according to preset rules and mathematical relationships to accurately reflect the local contribution to the overall multi-objective optimization; Adaptive adjustment unit: collects production-related data from each client in real time and aggregates them into a real-time status data set for the production line , and set a predefined set of emergency adjustment rules ; When abnormal situations occur, such as urgent order insertion and sudden equipment failure, the global coordination mechanism based on federated learning is used. and , by updating the scheduling policy parameters of each client To adjust the production scheduling plan, the update process is expressed as ,in, It is an adaptive adjustment function based on federated learning; For different types of abnormal situations, The corresponding specific adjustment strategy details are set in the system. In case of equipment failure, the system determines how to reallocate production tasks to other normal equipment according to the link where the faulty equipment is located and the remaining available equipment capacity. Through the coordination of federated learning, the adjustment strategy is sent to the corresponding client for execution. Task priority determination unit: Based on the urgency of the delivery date of bamboo chopstick orders , Product type importance level and the adequacy of raw material supply To determine the priority of production tasks; each client Calculate the local priority score based on local relevant information , ,in, For Clients correspond , , The weight coefficient of Global production task priority Through the aggregation mechanism of federated learning, the Get, using weighted average method ,in, For Clients The weight in the global priority calculation.

3. The real-time intelligent scheduling and management system for bamboo chopstick production line data according to claim 1 is characterized by: The anomaly detection module comprises: Anomaly recognition and positioning unit: Use deep autoencoders to process the collected data and convert the input data Mapped to hidden layer representation through encoder , and then reconstructed by the decoder to get the output , calculate the reconstruction error , set a reasonable threshold ,when When determining that the production link corresponding to the data has abnormal conditions; Further analysis of hidden layer representation Based on the characteristic distribution of each device and each production link under normal conditions, the specific abnormal device or production link can be located; Abnormal warning and feedback unit: Once an abnormal situation is detected, an early warning signal containing the abnormality type, location, and severity is immediately generated and sent to the intelligent scheduling module and the equipment coordination and optimization module through the communication link; Different levels of early warning prompts are set according to the severity of the abnormality. For minor abnormalities, prompt information is only displayed on the visual interface. For abnormalities that seriously affect production, sound and light alarms are triggered at the same time, and detailed abnormality information is pushed to the manager's terminal device in real time.

4. The real-time intelligent scheduling and management system for bamboo chopstick production line data according to claim 1, characterized in that: The equipment coordination and optimization module includes: Inter-device task allocation optimization unit: Under the federated learning architecture, each device is regarded as a node participating in the collaboration. According to the production scheduling plan generated by the intelligent scheduling module, combined with the current real-time status of each device and the abnormal information fed back by the abnormal detection module, an optimization model for task allocation between devices is constructed; let the device set be , is the total number of equipment, and the production task set is , is the number of production tasks, defining the allocation matrix , where the element Indicates the task Is it assigned to the device? , the goal is to find the optimal allocation matrix The optimization process is achieved by iteratively updating the allocation strategy among each device node through the federated learning algorithm, that is, each device node adjusts its corresponding value, is the index of the device, is the index of the production task; Considering the heterogeneity of devices, Set the task processing capability vector , indicating its processing efficiency or capacity upper limit for different types of tasks. When optimizing task allocation, adding constraints ensures that the tasks are allocated to the device. The total amount of tasks does not exceed its processing capacity, that is ,in, Indicates the task The workload, Indicates the device To the task Type of task The processing capacity limit of the system is determined to ensure the rationality and feasibility of task allocation; Equipment operation parameter collaborative adjustment unit: Each device shares its own operation parameters through the federated learning mechanism, and builds an operation parameter collaborative adjustment model based on multi-device joint; The operating parameter vector is , by analyzing the correlation between different equipment operating parameters and their impact on overall production; The gradient descent optimization algorithm is used to update the operating parameters of each device under the federated learning framework. The learning rate is set to , in each iteration, the device The operating parameter update rule is ,in, Indicates the device The updated operating parameter vector, is the learning rate, Represents the overall production line performance loss function About the device Run parameter vector The gradient of is the overall production line performance loss function.

5. The real-time intelligent scheduling and management system for bamboo chopstick production line data according to claim 1 is characterized by: The decision support module includes: Real-time monitoring visualization unit: Build a visualization interface to display detailed status information of the bamboo chopstick production line in real time with intuitive charts, graphs, and data lists, covering equipment operation status, production progress, currently effective production scheduling plan, and equipment health status warning information; Intelligent report generation unit: According to the preset time period, the unit uses big data analysis technology to conduct a comprehensive analysis of the historical production data and real-time data of the bamboo chopstick production line, and generates an intelligent analysis report covering production efficiency, equipment utilization, and raw material consumption; In the intelligent analysis report, in addition to presenting the actual data and changing trends of each indicator, data mining algorithms are also used to explore potential problems, optimization directions and improvement suggestions in the production process.

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