Distributed cooperative control method and system for silicone rubber glass fiber sleeve production equipment

Abstract

The invention discloses a distributed cooperative control method and system for silicone rubber glass fiber sleeve production equipment, and relates to the technical field of industrial automation control. Through the block chain technology, the non-tampering and credibility of the data are ensured, the problems that in a traditional method, the data are prone to being tampered, and a trust mechanism is missing are solved, efficient cooperation and task distribution between devices are achieved in combination with a multi-agent system, device operation parameters are optimized, the production efficiency and product quality are improved, and the method is suitable for large-scale popularization and application. A virtual model is constructed by using a digital twin technology, simulation verification is performed on optimized parameters, the achievement of a production target is further ensured, and the system dynamically adjusts equipment operation parameters and enhances the flexibility and dynamic response capability of the system through a global optimization algorithm and a feedback control mechanism.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation control technology, and in particular to a distributed collaborative control method and system for silicone rubber glass fiber sleeve production equipment. Background Art

[0002] Intelligent control of silicone rubber fiberglass sleeving production equipment is a key research area in high-end manufacturing, crucial for improving production efficiency and ensuring product quality. Currently, traditional centralized control methods face challenges in complex production environments, including low data processing efficiency, slow response speeds, and insufficient system flexibility, making them unable to meet dynamically changing production demands. Furthermore, existing solutions lack data security and device interoperability, limiting the level of intelligent production systems. Against this backdrop, distributed collaborative control has become a core challenge that urgently needs to be overcome. First, production equipment must collect and process massive amounts of multidimensional data in real time. However, traditional data management methods struggle to ensure data security and immutability, resulting in a lack of trust mechanisms. Unresolved data security issues further hinder efficient collaboration between devices, as each device relies on trusted data for interaction and decision-making. Inadequate collaborative mechanisms directly impact the system's ability to rapidly respond to dynamic production goals. Especially in complex production scenarios, devices struggle to autonomously adjust parameters to optimize overall performance. Summary of the Invention

[0003] The purpose of the present invention is to provide a distributed collaborative control method and system for silicone rubber glass fiber sleeve production equipment. Through the combination of blockchain, multi-agent system and digital twin technology, it solves the shortcomings of traditional control methods and realizes efficient collaboration and dynamic optimization of the production system.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] The present application provides a distributed collaborative control method for silicone rubber glass fiber sleeve production equipment, comprising the following steps:

[0006] Use blockchain technology to store real-time multidimensional data sets, synchronize distributed ledgers to determine whether the data has been tampered with, and obtain tamper-proof data records;

[0007] Obtain equipment status parameters from tamper-proof data records, use a multi-agent system to assign agent roles, and obtain a task allocation plan;

[0008] The distributed decision-making algorithm is executed by the multi-agent system to calculate the operating parameter adjustment value of each device and obtain the optimized device parameter set;

[0009] Get adjustment instructions from the optimized equipment parameter set, use digital twin technology to build a virtual model of the production equipment, simulate and verify the optimized equipment parameter set, and obtain virtual optimization results;

[0010] Using global optimization algorithm, combined with production objective function, calculate the optimal parameter combination and obtain global optimization parameters;

[0011] Obtain the final adjustment instructions from the global optimization parameters and send them to each device through the distributed control network, updating the operating status in real time, collecting new multi-dimensional data streams and judging the system dynamic response to obtain the updated production status;

[0012] A feedback control algorithm is used to analyze the deviation between the newly collected multidimensional data and the expected target, calculate the deviation correction value, and reallocate tasks through the multi-agent system to obtain the corrected task plan.

[0013] Furthermore, tamper-proof data records are obtained, including:

[0014] Obtain real-time multidimensional data sets through blockchain technology, use hash functions to generate hash timestamp records, store them in distributed ledgers, and obtain initial data records. When the data hash values ​​of each device node in the distributed ledger are consistent, the latest data records are obtained through the data synchronization protocol to confirm data consistency;

[0015] A consensus algorithm is used to verify the hash timestamp in the distributed ledger. If the verification is successful, the latest data record is stored through blockchain technology to obtain tamper-proof data.

[0016] The real-time processing module obtains data records in the distributed ledger and uses an encryption algorithm to generate a data check value to determine whether the data has been tampered with. When the data check value is consistent with the hash timestamp, the check value is synchronized through the inter-device communication protocol to confirm that the data record cannot be tampered with.

[0017] Based on the synchronized checksum value, the data consistency protocol is used to update the multidimensional data set to generate the final data record. The final data record is then stored through blockchain technology, and the hash timestamp in the distributed ledger is obtained to ensure that the data cannot be tampered with.

[0018] Furthermore, a task allocation plan is obtained, which specifically includes:

[0019] S21. Obtain device status parameters from tamper-proof data records, verify data integrity using blockchain technology, and obtain a trusted state parameter set. Then, initialize agent nodes through the multi-agent system, distribute the trusted state parameter set to each agent according to preset role assignment rules, and determine the agent role assignment table.

[0020] S22. Each agent performs distributed data analysis on the assigned parameter set according to the collaborative protocol, and uses a clustering algorithm to classify the device status to obtain a device status classification result. If an abnormal category is found in the device status classification result, the collaborative protocol triggers the task allocation mechanism to generate a preliminary task allocation plan. If there is no abnormality, the current task allocation is maintained.

[0021] S23. Based on the preliminary task allocation plan, a genetic algorithm is used to optimize the collaborative task allocation among devices to obtain an optimized task allocation plan. Device collaboration parameters are then extracted from the optimized task allocation plan and distributed to each device via a message passing interface to determine device collaborative execution instructions.

[0022] S24. Obtain the operation log of the equipment collaborative execution instruction, use time series analysis to verify the task execution effect, and obtain the verification result of the final task allocation plan.

[0023] Furthermore, the optimized device parameter set is obtained, specifically including:

[0024] S31. Acquire real-time data streams, perform distributed collection of operating parameters of each device through a multi-agent system, and then process them through a distributed decision-making algorithm to calculate adjustment values ​​of the operating parameters of each device to obtain an adjustment value set;

[0025] S32. When a parameter value in the adjustment value set exceeds a preset threshold, the adjustment value is normalized by the data processing module to obtain a normalized adjustment value set, and then the operating parameters of each device are updated using the multi-agent system to obtain an updated parameter set;

[0026] S33. Obtain dynamic indicators of the production target through the real-time data stream, determine whether the updated parameter set meets the production target, and obtain a satisfied state. If the satisfied state indicates that the parameters do not fully meet the production target, recalculate the adjustment value through the distributed decision algorithm to obtain a new adjusted value set;

[0027] S34. Repeatedly update the parameters and determine whether the target is satisfied based on the new adjustment value set to obtain the final optimized parameter set.

[0028] Furthermore, the virtual optimization results are obtained, including:

[0029] Obtain the optimized equipment parameter set, generate the adjustment instruction set by parsing the parameter set, confirm the integrity of the instruction set, collect real-time operation data from the production equipment, build a digital twin model, map the equipment operation status, and obtain the virtual operation status;

[0030] When the deviation between the virtual operating state and the real-time operating data exceeds a preset threshold, the digital twin model parameters are iteratively updated to adjust the model state and obtain a calibrated virtual operating state.

[0031] Using the calibrated virtual operating state, loading the adjustment instruction set, executing the parameter adjustment simulation, and obtaining the simulation operation results;

[0032] By analyzing the simulation results, extracting key performance indicators, judging whether the adjusted instruction set meets the optimization goal, and obtaining a verification result, if the verification result does not reach the preset optimization threshold, the adjusted instruction set is optimized through a genetic algorithm to generate a new instruction set to obtain the optimized adjusted instruction set;

[0033] According to the optimized adjustment instruction set, the digital twin model parameters are updated, the final simulation is performed, and the virtual optimization results are obtained.

[0034] Furthermore, the global optimization parameters are obtained, including:

[0035] Obtain production data, extract characteristic values ​​from efficiency indicators, quality indicators, and energy consumption indicators, and then use preset weight distribution to construct the objective function and determine the objective function expression;

[0036] Through the global optimization algorithm, the parameter combination is iteratively searched from the initial data set to obtain the candidate parameter set, and then the objective function value is calculated. When the function value meets the preset threshold, it is determined to be the optimal parameter. If it does not meet the threshold, the parameter combination is adjusted and the iteration continues;

[0037] Based on the optimal parameters, production control instructions are generated to obtain the optimized production configuration, obtain the optimized production data, calculate the efficiency indicators, quality indicators and energy consumption indicators, determine whether the production goals are met, and extract performance improvement features by comparing the data before and after optimization to obtain the final optimization results.

[0038] Furthermore, the updated production status is obtained, including:

[0039] Extract the final adjustment instructions from the global optimization parameters. Use the parameter extraction process to process the global optimization parameters through a preset extraction algorithm to obtain the final adjustment instructions. Then transmit the final adjustment instructions through the distributed control network. Use the instruction transmission mechanism. If the network bandwidth meets the preset threshold, the final adjustment instructions are distributed to each device to determine that the instruction transmission is complete.

[0040] Update the operating status of each device in real time, obtain the list of devices to which the instruction transmission has been completed, collect the real-time operating status of each device through the status update frequency configuration, obtain real-time operating status data, and then collect multi-dimensional data streams. For the real-time operating status data, adopt the data stream collection mechanism to obtain multi-dimensional data streams through the sensor interface to obtain a multi-dimensional data stream set;

[0041] Determine whether the multidimensional data flow set has achieved the dynamic response target, using the target judgment standard. If the key indicators in the multidimensional data flow set meet the preset threshold, it is confirmed that the dynamic response target has been achieved and the target achievement status is obtained;

[0042] Update the production status according to the target achievement status. Through the production status update mechanism, obtain the target achievement status and generate updated production status data. Then, use the feedback optimization algorithm to adjust the global optimization parameters to obtain the optimized global optimization parameters.

[0043] Furthermore, before obtaining tamper-proof data records, it also includes: obtaining multidimensional data streams from production equipment sensors, using edge computing nodes to preprocess the data, filtering noise data, generating standardized data packets, and obtaining real-time multidimensional data sets.

[0044] Furthermore, it also includes: obtaining adjusted operating parameters from the revised task plan, re-simulating and verifying through the digital twin model, updating the virtual model status, judging whether the production efficiency reaches the preset target, and obtaining the final optimized production parameters.

[0045] This application provides a distributed collaborative control system for silicone rubber glass fiber sleeve production equipment, which is used to implement a distributed collaborative control method for silicone rubber glass fiber sleeve production equipment, specifically including:

[0046] The data acquisition and preprocessing module collects multi-dimensional data streams from production equipment sensors, preprocesses them through edge computing, generates standardized data sets, and marks and processes abnormal data.

[0047] The blockchain data storage and verification module uses blockchain technology to store multidimensional data sets, generate hash timestamp records, and ensure that data cannot be tampered with through consensus algorithms and data verification;

[0048] The multi-agent task allocation module obtains device status parameters from the blockchain, allocates tasks through the multi-agent system, and optimizes the task allocation scheme using clustering and genetic algorithms;

[0049] Distributed decision-making and parameter optimization module, based on the task allocation scheme, executes distributed decision-making algorithms through a multi-agent system to dynamically adjust equipment operating parameters and optimize production goals;

[0050] The digital twin simulation verification module builds a digital twin model of the production equipment and verifies the effect of parameter adjustments through simulation to ensure that the virtual operating status is consistent with the actual production target;

[0051] The global optimization module combines the production objective function and uses a global optimization algorithm to calculate the optimal parameter combination to improve production efficiency, quality and energy consumption performance;

[0052] The distributed control and dynamic response module converts global optimization parameters into adjustment instructions, sends them to devices through the distributed network, updates the operating status in real time, and determines the dynamic response effect;

[0053] The feedback control and task correction module uses feedback control algorithms to analyze deviations and calculate correction values ​​based on production status, and then redistributes tasks through a multi-agent system to optimize the production process.

[0054] The model update and verification module obtains operating parameters from the revised task plan, updates the digital twin model and simulates and verifies it again to ensure that production efficiency reaches the preset target.

[0055] The beneficial effects of the present invention are:

[0056] This invention uses blockchain technology to store and verify real-time multidimensional data sets, solving the problems of data susceptibility to tampering and lack of trust mechanisms in traditional centralized control methods. It uses hash timestamp recording and consensus algorithms to ensure data immutability and consistency, providing a reliable data foundation for efficient collaboration between devices. By allocating tasks and optimizing parameters through a multi-agent system, it further solves the problem of insufficient collaboration between devices and improves the intelligence level and responsiveness of the production system.

[0057] Combining global optimization algorithms with digital twin technology, through simulation verification and parameter optimization, we address the slow response and insufficient flexibility of traditional methods in complex production environments. We can adjust equipment operating parameters in real time to ensure that production efficiency and product quality meet preset targets. Multiple simulation verifications and parameter optimizations further enhance the stability and reliability of production efficiency, significantly improving the overall performance of the production system.

[0058] The use of distributed control networks and edge computing technologies solves the problem of insufficient adaptability of traditional centralized control methods in dynamic production environments. The system can collect and process multi-dimensional data streams in real time and quickly respond to dynamic changes in the production process. Through the update and verification of feedback control algorithms and digital twin models, the system can flexibly adjust task allocation and operating parameters to ensure the efficiency and stability of the production process and meet the industry's requirements for intelligent and dynamic response of production systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0060] Figure 1 A schematic flow chart of a distributed collaborative control method for silicone rubber glass fiber sleeve production equipment provided in Example 1 of the present application;

[0061] Figure 2A flow chart of a task allocation scheme obtained by a distributed collaborative control method for silicone rubber glass fiber sleeve production equipment provided in Example 1 of the present application;

[0062] Figure 3 A flow chart of the optimized equipment parameter set obtained by the distributed collaborative control method for the silicone rubber glass fiber sleeve production equipment provided in Example 1 of the present application;

[0063] Figure 4 This is a schematic structural diagram of the distributed collaborative control system for the silicone rubber glass fiber sleeve production equipment provided in Example 2 of the present application. DETAILED DESCRIPTION

[0064] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and systems consistent with certain aspects of the present application, as detailed in the appended claims.

[0065] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0066] The following describes in detail the specific implementation methods, features and effects of the present invention in conjunction with the accompanying drawings and preferred embodiments.

[0067] Example 1

[0068] See also Figure 1-Figure 3 This embodiment provides a distributed collaborative control method for silicone rubber glass fiber sleeve production equipment, including the following steps:

[0069] S1. Use blockchain technology to store real-time multidimensional data sets, generate hash-based timestamp records, synchronize them across devices through distributed ledgers, determine whether the data has been tampered with, and obtain tamper-proof data records;

[0070] Furthermore, tamper-proof data records are obtained, including:

[0071] Obtain real-time multidimensional data sets through blockchain technology, use hash functions to generate hash timestamp records, store them in distributed ledgers, and obtain initial data records. When the data hash values ​​of each device node in the distributed ledger are consistent, the latest data records are obtained through the data synchronization protocol to confirm data consistency;

[0072] A consensus algorithm is used to verify the hash timestamp in the distributed ledger. If the verification is successful, the latest data record is stored through blockchain technology to obtain tamper-proof data.

[0073] The real-time processing module obtains data records in the distributed ledger and uses an encryption algorithm to generate a data check value to determine whether the data has been tampered with. When the data check value is consistent with the hash timestamp, the check value is synchronized through the inter-device communication protocol to confirm that the data record cannot be tampered with.

[0074] Based on the synchronized checksum value, the data consistency protocol is used to update the multidimensional data set to generate the final data record. The final data record is then stored through blockchain technology, and the hash timestamp in the distributed ledger is obtained to ensure that the data cannot be tampered with.

[0075] The process of using an encryption algorithm to generate a data checksum includes: obtaining real-time, multi-dimensional data records stored in a distributed ledger. The data records contain key parameters during the operation of the production equipment, such as temperature, pressure, speed, and material thickness. Then, a suitable encryption algorithm (such as the SHA-256 hash algorithm) is selected to encrypt this data. The encryption algorithm generates a unique checksum (hash value) based on the content of the data. This checksum is unidirectional and unique, that is, the same input data will produce the same checksum, and any slight change in the data will cause a significant change in the checksum. By comparing the generated checksum with the hash timestamp record stored in the distributed ledger, it is possible to determine whether the data has been tampered with during transmission or storage.

[0076] Specifically, it achieves reliable storage and verification of real-time, multidimensional datasets for production equipment. Blockchain technology is used to generate hashed timestamp records, which are synchronized across devices via a distributed ledger to ensure data immutability and consistency. Consensus algorithms and data verification mechanisms further verify data integrity and authenticity, ultimately generating tamper-proof data records. This not only improves data security and credibility, but also provides an accurate and reliable data foundation for subsequent production process control, thereby enhancing the intelligence and collaborative capabilities of the entire production system.

[0077] S2. Obtain device status parameters from tamper-proof data records, use a multi-agent system to assign agent roles, and each agent analyzes the data according to a preset collaborative protocol to determine the collaborative task allocation between devices and obtain a task allocation plan;

[0078] Furthermore, a task allocation plan is obtained, which specifically includes:

[0079] S21. Obtain device status parameters from tamper-proof data records, verify data integrity using blockchain technology, and obtain a trusted state parameter set. Then, initialize agent nodes through the multi-agent system, distribute the trusted state parameter set to each agent according to preset role assignment rules, and determine the agent role assignment table.

[0080] S22. Each agent performs distributed data analysis on the assigned parameter set according to the collaborative protocol, and uses a clustering algorithm to classify the device status to obtain a device status classification result. If an abnormal category is found in the device status classification result, the collaborative protocol triggers the task allocation mechanism to generate a preliminary task allocation plan. If there is no abnormality, the current task allocation is maintained.

[0081] S23. Based on the preliminary task allocation plan, a genetic algorithm is used to optimize the collaborative task allocation among devices to obtain an optimized task allocation plan. Device collaboration parameters are then extracted from the optimized task allocation plan and distributed to each device via a message passing interface to determine device collaborative execution instructions.

[0082] S24. Obtain the operation log of the equipment collaborative execution instruction, use time series analysis to verify the task execution effect, and obtain the verification result of the final task allocation plan.

[0083] Specifically, by ensuring the credibility and integrity of data through blockchain technology and combining the distributed data analysis and optimization algorithms of the multi-agent system, intelligent and dynamic adjustment of task allocation is achieved, which not only improves the collaborative efficiency of production equipment, but also enhances the flexibility and responsiveness of the system, ultimately significantly improving the intelligence level of the production process and overall production efficiency.

[0084] S3. Based on the task allocation plan, the multi-agent system executes a distributed decision-making algorithm to calculate the operating parameter adjustment values ​​of each device. Based on the real-time data stream, it determines whether the parameters meet the production goals and obtains the optimized device parameter set.

[0085] Furthermore, the optimized device parameter set is obtained, specifically including:

[0086] S31. Acquire real-time data streams, perform distributed collection of operating parameters of each device through a multi-agent system, and then process them through a distributed decision-making algorithm to calculate adjustment values ​​of the operating parameters of each device to obtain an adjustment value set;

[0087] S32. When a parameter value in the adjustment value set exceeds a preset threshold, the adjustment value is normalized by the data processing module to obtain a normalized adjustment value set, and then the operating parameters of each device are updated using the multi-agent system to obtain an updated parameter set;

[0088] S33. Obtain dynamic indicators of the production target through the real-time data stream, determine whether the updated parameter set meets the production target, and obtain a satisfied state. If the satisfied state indicates that the parameters do not fully meet the production target, recalculate the adjustment value through the distributed decision algorithm to obtain a new adjusted value set;

[0089] S34. Repeatedly update the parameters and determine whether the target is satisfied based on the new adjustment value set to obtain the final optimized parameter set.

[0090] Specifically, through a multi-agent system and distributed decision-making algorithm, equipment operating parameters are collected and processed in real time, adjustment values ​​are dynamically calculated and normalized, and parameters are repeatedly optimized according to dynamic indicators of production targets. Ultimately, an optimized parameter set that meets production needs is obtained, significantly improving the accuracy and efficiency of the production process.

[0091] S4. Obtain adjustment instructions from the optimized equipment parameter set, use digital twin technology to build a virtual model of the production equipment, map the real-time operating status, verify the parameter adjustment effect through simulation, and obtain the virtual optimization result;

[0092] Furthermore, the virtual optimization results are obtained, including:

[0093] Obtain the optimized equipment parameter set, generate the adjustment instruction set by parsing the parameter set, confirm the integrity of the instruction set, collect real-time operation data from the production equipment, build a digital twin model, map the equipment operation status, and obtain the virtual operation status;

[0094] When the deviation between the virtual operating state and the real-time operating data exceeds a preset threshold, the digital twin model parameters are iteratively updated to adjust the model state and obtain a calibrated virtual operating state.

[0095] Using the calibrated virtual operating state, loading the adjustment instruction set, executing the parameter adjustment simulation, and obtaining the simulation operation results;

[0096] By analyzing the simulation results, extracting key performance indicators, judging whether the adjusted instruction set meets the optimization goal, and obtaining a verification result, if the verification result does not reach the preset optimization threshold, the adjusted instruction set is optimized through a genetic algorithm to generate a new instruction set to obtain the optimized adjusted instruction set;

[0097] According to the optimized adjustment instruction set, the digital twin model parameters are updated, the final simulation is performed, and the virtual optimization results are obtained.

[0098] Specifically, through digital twin technology and simulation verification, combined with genetic algorithm optimization and adjustment instruction sets, virtual optimization of production equipment operating parameters is achieved. This process can quickly and efficiently verify and optimize equipment parameter adjustment plans, reduce trial and error costs in actual production, improve production efficiency and product quality, and enhance the flexibility and adaptability of the production system.

[0099] S5. Based on the virtual optimization results, a global optimization algorithm is used to calculate the optimal parameter combination based on the production objective function F(x) = w1 efficiency + w2 quality + w3 energy consumption (w1, w2, w3 are weights, and x is the parameter set). The optimal parameter combination is determined to determine whether the preset threshold is met and the global optimization parameters are obtained.

[0100] Furthermore, the global optimization parameters are obtained, including:

[0101] Obtain production data, extract characteristic values ​​from efficiency indicators, quality indicators, and energy consumption indicators, and then use the preset weight distribution to construct the objective function F(x) = w1 efficiency + w2 quality + w3 energy consumption (w1, w2, w3 are weights, x is the parameter set) and determine the objective function expression;

[0102] Through the global optimization algorithm, the parameter combination is iteratively searched from the initial data set to obtain the candidate parameter set, and then the objective function value is calculated. When the function value meets the preset threshold, it is determined to be the optimal parameter. If it does not meet the threshold, the parameter combination is adjusted and the iteration continues;

[0103] Based on the optimal parameters, production control instructions are generated to obtain the optimized production configuration, obtain the optimized production data, calculate the efficiency index, quality index, and energy consumption index, and determine whether the production goals are met. By comparing the data before and after optimization, the performance improvement features are extracted to obtain the final optimization results.

[0104] Among them, based on the optimal parameters obtained by the global optimization algorithm, the system will parse these parameters and generate specific production control instructions. These instructions are sent to each production equipment through the distributed control network, guiding the equipment to adjust its operating status according to the optimized parameters, thereby achieving efficient coordination and performance improvement of the production process, and ultimately forming an optimized production configuration to ensure that indicators such as production efficiency, quality and energy consumption achieve the best balance.

[0105] Specifically, by combining the global optimization algorithm with the production objective function, we systematically analyze efficiency, quality and energy consumption indicators, and dynamically adjust the parameter combination to find the optimal solution. This process not only ensures the global optimization of production parameters, but also intuitively demonstrates the performance improvement effect by comparing the data before and after optimization, thereby significantly improving production efficiency, product quality and energy utilization efficiency, and realizing refined management and optimized control of the production process.

[0106] S6. Obtain the final adjustment instructions from the global optimization parameters and send them to each device through the distributed control network. Update the operating status in real time, collect new multi-dimensional data streams, determine whether the system has achieved the dynamic response target, and obtain the updated production status.

[0107] Furthermore, the updated production status is obtained, including:

[0108] Extract the final adjustment instructions from the global optimization parameters. Use the parameter extraction process to process the global optimization parameters through a preset extraction algorithm (such as a linear regression algorithm) to obtain the final adjustment instructions. Then transmit the final adjustment instructions through the distributed control network. Use the instruction transmission mechanism. If the network bandwidth meets the preset threshold, the final adjustment instructions are distributed to each device to determine that the instruction transmission is complete.

[0109] Update the operating status of each device in real time, obtain the list of devices to which the instruction transmission has been completed, collect the real-time operating status of each device through the status update frequency configuration, obtain real-time operating status data, and then collect multi-dimensional data streams. For the real-time operating status data, adopt the data stream collection mechanism to obtain multi-dimensional data streams through the sensor interface to obtain a multi-dimensional data stream set;

[0110] Determine whether the multidimensional data flow set has achieved the dynamic response target, using the target judgment standard. If the key indicators in the multidimensional data flow set meet the preset threshold, it is confirmed that the dynamic response target has been achieved and the target achievement status is obtained;

[0111] The production status is updated according to the target achievement status. The target achievement status is obtained through the production status update mechanism to generate updated production status data. Then, a feedback optimization algorithm (such as the gradient descent algorithm) is used to adjust the global optimization parameters to obtain the optimized global optimization parameters.

[0112] Specifically, the final adjustment instructions are extracted from the global optimization parameters and distributed to each device, the operating status is updated in real time and multi-dimensional data streams are collected to determine whether the dynamic response target has been achieved. Based on this, the production status is updated and the global parameters are optimized, realizing dynamic adjustment and optimization of the production process and improving the system's adaptability and intelligence.

[0113] S7. Based on the updated production status, a feedback control algorithm is used to analyze the deviation between the newly collected multidimensional data and the expected target, calculate the deviation correction value, and reallocate tasks through the multi-agent system to obtain a corrected task plan.

[0114] Furthermore, the revised task plan is obtained, which specifically includes:

[0115] Based on the updated production status, the feedback control algorithm is used to analyze the newly collected multidimensional data stream, compare the various parameters in the multidimensional data stream (such as temperature, pressure, speed, material thickness, etc.) with the preset production targets, and calculate the deviation between the actual value of each parameter and the target value;

[0116] Based on the deviation value, the corresponding correction value is calculated to adjust the operating parameters of the equipment. The correction value is then passed to the multi-agent system, and each agent re-evaluates its own task allocation based on the corrected parameters;

[0117] Through collaboration and negotiation between agents, each agent dynamically adjusts its task allocation based on the revised parameters and task requirements, resulting in a revised task plan. This ensures that the adjusted operating parameters of each device enable better collaboration, ultimately forming a revised task plan to optimize the efficiency and quality of the entire production process.

[0118] Specifically, the feedback control algorithm analyzes the deviation between the newly collected multi-dimensional data and the production target, calculates the correction value and transmits it to the multi-agent system. Each agent dynamically adjusts the task allocation based on the correction value and optimizes the collaborative work between equipment. This process realizes the real-time adjustment and optimization of production tasks, improves the flexibility and responsiveness of the production system, and significantly improves production efficiency and product quality.

[0119] Furthermore, before obtaining tamper-proof data records, it also includes: obtaining multidimensional data streams from production equipment sensors, including temperature, pressure, speed, and material thickness, using edge computing nodes to preprocess the data, filter noise data, generate standardized data packets, and obtain real-time multidimensional data sets.

[0120] Furthermore, a real-time multidimensional dataset is obtained, specifically including:

[0121] Acquire multidimensional data streams from production equipment sensors, including temperature, pressure, speed, and material thickness. Use edge computing nodes to preprocess the data, filter noise, and generate standardized data packets to obtain real-time multidimensional datasets. When any dimension in the multidimensional data stream exceeds a preset threshold, the dimension data is marked to generate an abnormally marked dataset.

[0122] Based on the abnormal labeled data set, the K-means clustering algorithm is used to classify the labeled data to obtain a set of abnormal data categories. The statistical characteristics of each category, including the mean and variance, are then obtained to generate an abnormal feature data set. When the variance in the abnormal feature data set is greater than the preset threshold, the sliding window method is used to perform time series analysis on the abnormal feature data set to obtain the abnormal change trend;

[0123] According to the abnormal change trend, the decision tree algorithm is used to classify the trend, determine whether the abnormality persists, and obtain the abnormal persistence judgment result. Based on the abnormal persistence judgment result, abnormal data processing instructions are generated and output to the edge computing node to obtain an updated real-time multidimensional data set.

[0124] Specifically, by preprocessing, anomaly marking, clustering analysis, feature extraction, trend analysis and anomaly processing the multi-dimensional data streams collected by production equipment sensors, we can effectively filter noise and identify abnormal data, ensure the quality and reliability of the data, and provide an accurate data basis for subsequent production control and optimization, thereby improving the stability and intelligence level of the production system.

[0125] Furthermore, it also includes: obtaining adjusted operating parameters from the revised task plan, re-simulating and verifying through the digital twin model, updating the virtual model status, judging whether the production efficiency reaches the preset target, and obtaining the final optimized production parameters.

[0126] Furthermore, the final optimized production parameters are obtained, including:

[0127] Obtain the adjusted operating parameters from the task plan, parse the parameter configuration, extract key operating indicators, and obtain the optimized operating parameter set. Then, load the optimized operating parameter set through the digital twin model, perform simulation verification, generate a virtual production scenario, and obtain simulation result data;

[0128] Extracting the virtual model state from the simulation result data, updating the operating state parameters of the virtual model, and obtaining the updated model state;

[0129] Calculate the production efficiency index based on the updated model state. If the efficiency index reaches the preset target, the current parameters are determined to be the optimized parameters. If not, adjust the parameters and return to the simulation for verification to obtain the optimized parameter set.

[0130] Extract key operating parameters from the optimized parameter set, generate parameter adjustment instructions, update the task plan configuration, and obtain an updated task plan;

[0131] The updated task plan is simulated and verified twice through the digital twin model to generate a production efficiency report, determine whether the efficiency is stable, obtain the final production parameters, extract the operation configuration from the final production parameters, verify it through the preset threshold, generate production deployment instructions, and determine the deployment status of the production parameters.

[0132] Specifically, by obtaining the adjusted operating parameters from the revised task plan and using the digital twin model for simulation verification, the system can dynamically update the virtual model status and evaluate whether the production efficiency reaches the preset target. This not only ensures the optimization of production parameters, but also further improves the stability and reliability of production efficiency through multiple simulation verifications and parameter adjustments. The final generated production deployment instructions provide verified optimal operating parameters for actual production, significantly improving the overall performance and intelligence level of the production system.

[0133] Example 2

[0134] See also Figure 4 This embodiment provides a distributed collaborative control system for silicone rubber glass fiber sleeve production equipment, which is used to implement a distributed collaborative control method for silicone rubber glass fiber sleeve production equipment, specifically including:

[0135] The data acquisition and preprocessing module collects multi-dimensional data streams from production equipment sensors, preprocesses them through edge computing, generates standardized data sets, and marks and processes abnormal data.

[0136] The blockchain data storage and verification module uses blockchain technology to store multidimensional data sets, generate hash timestamp records, and ensure that data cannot be tampered with through consensus algorithms and data verification;

[0137] The multi-agent task allocation module obtains device status parameters from the blockchain, allocates tasks through the multi-agent system, and optimizes the task allocation scheme using clustering and genetic algorithms;

[0138] Distributed decision-making and parameter optimization module, based on the task allocation scheme, executes distributed decision-making algorithms through a multi-agent system to dynamically adjust equipment operating parameters and optimize production goals;

[0139] The digital twin simulation verification module builds a digital twin model of the production equipment and verifies the effect of parameter adjustments through simulation to ensure that the virtual operating status is consistent with the actual production target;

[0140] The global optimization module combines the production objective function and uses a global optimization algorithm to calculate the optimal parameter combination to improve production efficiency, quality and energy consumption performance;

[0141] The distributed control and dynamic response module converts global optimization parameters into adjustment instructions, sends them to devices through the distributed network, updates the operating status in real time, and determines the dynamic response effect;

[0142] The feedback control and task correction module uses feedback control algorithms to analyze deviations and calculate correction values ​​based on production status, and then redistributes tasks through a multi-agent system to optimize the production process.

[0143] The model update and verification module obtains operating parameters from the revised task plan, updates the digital twin model and simulates and verifies it again to ensure that production efficiency reaches the preset target.

[0144] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A distributed collaborative control method for silicone rubber glass fiber sleeve production equipment, characterized by: The steps include: Use blockchain technology to store real-time multidimensional data sets, synchronize distributed ledgers to determine whether the data has been tampered with, and obtain tamper-proof data records; Obtain equipment status parameters from tamper-proof data records, use a multi-agent system to assign agent roles, and obtain a task allocation plan; The distributed decision-making algorithm is executed by the multi-agent system to calculate the operating parameter adjustment value of each device and obtain the optimized device parameter set; Get adjustment instructions from the optimized equipment parameter set, use digital twin technology to build a virtual model of the production equipment, simulate and verify the optimized equipment parameter set, and obtain virtual optimization results; Using global optimization algorithm, combined with production objective function, calculate the optimal parameter combination and obtain global optimization parameters; Obtain the final adjustment instructions from the global optimization parameters and send them to each device through the distributed control network, updating the operating status in real time, collecting new multi-dimensional data streams and judging the system dynamic response to obtain the updated production status; A feedback control algorithm is used to analyze the deviation between the newly collected multidimensional data and the expected target, calculate the deviation correction value, and reallocate tasks through the multi-agent system to obtain the corrected task plan.

2. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: Obtain tamper-proof data records, including: Obtain real-time multidimensional data sets through blockchain technology, use hash functions to generate hash timestamp records, store them in distributed ledgers, and obtain initial data records. When the data hash values ​​of each device node in the distributed ledger are consistent, the latest data records are obtained through the data synchronization protocol to confirm data consistency; A consensus algorithm is used to verify the hash timestamp in the distributed ledger. If the verification is successful, the latest data record is stored through blockchain technology to obtain tamper-proof data. The real-time processing module obtains data records in the distributed ledger and uses an encryption algorithm to generate a data check value to determine whether the data has been tampered with. When the data check value is consistent with the hash timestamp, the check value is synchronized through the inter-device communication protocol to confirm that the data record cannot be tampered with. Based on the synchronized checksum value, the data consistency protocol is used to update the multidimensional data set to generate the final data record. The final data record is then stored through blockchain technology, and the hash timestamp in the distributed ledger is obtained to ensure that the data cannot be tampered with.

3. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: Get the task allocation plan, including: Obtain device status parameters from tamper-proof data records, use blockchain technology to verify data integrity, and obtain a trusted state parameter set. Then, initialize the agent nodes through the multi-agent system, distribute the trusted state parameter set to each agent according to the preset role allocation rules, and determine the agent role allocation table; Each agent performs distributed data analysis on the assigned parameter set according to the collaborative protocol, and uses a clustering algorithm to classify the equipment status to obtain the equipment status classification results. If there are abnormal categories in the equipment status classification results, the collaborative protocol triggers the task allocation mechanism to generate a preliminary task allocation plan. If there are no abnormalities, the current task allocation is maintained. Based on the preliminary task allocation plan, a genetic algorithm is used to optimize the collaborative task allocation between devices to obtain an optimized task allocation plan. The device collaboration parameters are then extracted from the optimized task allocation plan and distributed to each device through a message passing interface to determine the device collaborative execution instructions. Obtain the operation log of the equipment's collaborative execution instructions, use time series analysis to verify the task execution effect, and obtain the verification results of the final task allocation plan.

4. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: The optimized device parameter set is obtained, including: Obtain real-time data streams, collect the operating parameters of each device in a distributed manner through a multi-agent system, process them through a distributed decision-making algorithm, calculate the adjustment values ​​of each device's operating parameters, and obtain an adjustment value set; When the parameter value in the adjustment value set exceeds the preset threshold, the adjustment value is normalized by the data processing module to obtain a normalized adjustment value set, and then the multi-agent system is used to update the operating parameters of each device to obtain an updated parameter set; The dynamic indicators of production targets are obtained through real-time data streams, and the updated parameter set is judged to determine whether it meets the production targets and obtain the satisfied status. If the satisfied status indicates that the parameters do not fully meet the production targets, the adjustment values ​​are recalculated through the distributed decision-making algorithm to obtain a new set of adjusted values. According to the new adjustment value set, the parameters are repeatedly updated and the target satisfaction status is judged to obtain the final optimized parameter set.

5. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: Get the virtual optimization results, including: Obtain the optimized equipment parameter set, generate the adjustment instruction set by parsing the parameter set, confirm the integrity of the instruction set, collect real-time operation data from the production equipment, build a digital twin model, map the equipment operation status, and obtain the virtual operation status; When the deviation between the virtual operating state and the real-time operating data exceeds a preset threshold, the digital twin model parameters are iteratively updated to adjust the model state and obtain a calibrated virtual operating state. Using the calibrated virtual operating state, loading the adjustment instruction set, executing the parameter adjustment simulation, and obtaining the simulation operation results; By analyzing the simulation results, extracting key performance indicators, judging whether the adjusted instruction set meets the optimization goal, and obtaining a verification result, if the verification result does not reach the preset optimization threshold, the adjusted instruction set is optimized through a genetic algorithm to generate a new instruction set to obtain the optimized adjusted instruction set; According to the optimized adjustment instruction set, the digital twin model parameters are updated, the final simulation is performed, and the virtual optimization results are obtained.

6. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: Get the global optimization parameters, including: Obtain production data, extract characteristic values ​​from efficiency indicators, quality indicators, and energy consumption indicators, and then use preset weight distribution to construct the objective function and determine the objective function expression; Through the global optimization algorithm, the parameter combination is iteratively searched from the initial data set to obtain the candidate parameter set, and then the objective function value is calculated. When the function value meets the preset threshold, it is determined to be the optimal parameter. If it does not meet the threshold, the parameter combination is adjusted and the iteration continues; Based on the optimal parameters, production control instructions are generated to obtain the optimized production configuration, obtain the optimized production data, calculate the efficiency indicators, quality indicators and energy consumption indicators, determine whether the production goals are met, and extract performance improvement features by comparing the data before and after optimization to obtain the final optimization results.

7. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: Get the updated production status, including: Extract the final adjustment instructions from the global optimization parameters. Use the parameter extraction process to process the global optimization parameters through a preset extraction algorithm to obtain the final adjustment instructions. Then transmit the final adjustment instructions through the distributed control network. Use the instruction transmission mechanism. If the network bandwidth meets the preset threshold, the final adjustment instructions are distributed to each device to determine that the instruction transmission is complete. Update the operating status of each device in real time, obtain the list of devices to which the instruction transmission has been completed, collect the real-time operating status of each device through the status update frequency configuration, obtain real-time operating status data, and then collect multi-dimensional data streams. For the real-time operating status data, adopt the data stream collection mechanism to obtain multi-dimensional data streams through the sensor interface to obtain a multi-dimensional data stream set; Determine whether the multidimensional data flow set has achieved the dynamic response target, using the target judgment standard. If the key indicators in the multidimensional data flow set meet the preset threshold, it is confirmed that the dynamic response target has been achieved and the target achievement status is obtained; The production status is updated according to the target achievement status. The target achievement status is obtained through the production status update mechanism, and the updated production status data is generated. Then, the feedback optimization algorithm is used to adjust the global optimization parameters to obtain the optimized global optimization parameters.

8. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: Before obtaining tamper-proof data records, it also includes: obtaining multidimensional data streams from production equipment sensors, using edge computing nodes to pre-process the data, filtering noisy data, generating standardized data packets, and obtaining real-time multidimensional data sets.

9. The distributed collaborative control method for silicone rubber glass fiber sleeve production equipment according to claim 1, characterized in that: Also includes: The adjusted operating parameters are obtained from the revised task plan, and the digital twin model is used to simulate and verify them again. The virtual model status is updated to determine whether the production efficiency has reached the preset target and obtain the final optimized production parameters.

10. A distributed collaborative control system for silicone rubber glass fiber sleeving production equipment, used to implement the distributed collaborative control method for silicone rubber glass fiber sleeving production equipment according to any one of claims 1 to 9, characterized in that: include: The data acquisition and preprocessing module collects multi-dimensional data streams from production equipment sensors, preprocesses them through edge computing, generates standardized data sets, and marks and processes abnormal data. The blockchain data storage and verification module uses blockchain technology to store multidimensional data sets, generate hash timestamp records, and ensure that data cannot be tampered with through consensus algorithms and data verification; The multi-agent task allocation module obtains device status parameters from the blockchain, allocates tasks through the multi-agent system, and optimizes the task allocation scheme using clustering and genetic algorithms; Distributed decision-making and parameter optimization module, based on the task allocation scheme, executes distributed decision-making algorithms through a multi-agent system to dynamically adjust equipment operating parameters and optimize production goals; The digital twin simulation verification module builds a digital twin model of the production equipment and verifies the effect of parameter adjustments through simulation to ensure that the virtual operating status is consistent with the actual production target; The global optimization module combines the production objective function and uses a global optimization algorithm to calculate the optimal parameter combination to improve production efficiency, quality and energy consumption performance; The distributed control and dynamic response module converts global optimization parameters into adjustment instructions, sends them to devices through the distributed network, updates the operating status in real time, and determines the dynamic response effect; The feedback control and task correction module uses feedback control algorithms to analyze deviations and calculate correction values ​​based on production status, and then redistributes tasks through a multi-agent system to optimize the production process. The model update and verification module obtains operating parameters from the revised task plan, updates the digital twin model and simulates and verifies it again to ensure that production efficiency reaches the preset target.

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