An Intelligent Manufacturing Equipment Control Method and System Based on Diffusion Large Model

Abstract

The present invention relates to the technical field of manufacturing equipment control, and particularly to an intelligent manufacturing equipment control method and system based on a diffusion large model. The method includes the following steps: acquiring multi-modal type data of intelligent manufacturing equipment; collecting time-series data based on the multi-modal type data of intelligent manufacturing equipment to generate an initial multi-modal data set; performing operation mode fusion on the initial multi-modal data set to generate multi-modal operation data of the manufacturing equipment; performing equipment virtual operation processing on the multi-modal operation data of the manufacturing equipment based on a preset diffusion large model to generate a device operation virtual state distribution map; and optimizing physical constraints on the initialized diffusion large model based on the device operation virtual state distribution map to generate a diffusion model matching the physical characteristics of the device. The present invention improves the accuracy and adaptability of intelligent manufacturing equipment control by combining a diffusion large model, multi-modal data fusion, incremental learning, and differential privacy protection.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and particularly to an intelligent manufacturing equipment control method and system based on a diffusion large model. Background Art

[0002] In the early stage, the control methods of intelligent manufacturing equipment mainly relied on traditional PLCs (Programmable Logic Controllers) and DCSs (Distributed Control Systems). These methods completed automated operations in the production process through pre-determined programs and fixed control logics, and were suitable for simple and repetitive manufacturing tasks. However, with the increasing complexity of manufacturing processes, the traditional control methods gradually showed the problem of insufficient flexibility. With the development of artificial intelligence technology, especially the application of deep learning and machine learning algorithms, the control of intelligent manufacturing equipment began to develop towards higher automation and intelligence. Machine learning algorithms can make predictions and decisions based on historical data, optimize the production process, and improve the operation efficiency and accuracy of equipment. In recent years, as a generative model, the diffusion large model has begun to be applied in intelligent manufacturing. The diffusion large model can generate complex structures and patterns in high-dimensional spaces and is suitable for the generation and optimization of multi-modal data such as images and videos. In the field of intelligent manufacturing, the control method based on the diffusion model can achieve more accurate equipment state prediction, fault diagnosis, and real-time optimization, and has shown great potential especially in the multi-dimensional data fusion and anomaly detection of complex systems. However, in current traditional intelligent manufacturing, equipment control strategies often rely on empirical rules or prior knowledge, making it difficult to achieve personalized optimization and long-term adaptation. At the same time, static control strategies are usually adopted, making it difficult to cope with real-time and dynamic changes during equipment operation, thus resulting in low accuracy and adaptability of intelligent manufacturing equipment control. Summary of the Invention

[0003] Based on this, it is necessary to provide an intelligent manufacturing equipment control method and system based on a diffusion large model to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent manufacturing equipment control method based on a diffusion large model, the method includes the following steps:

[0005] Step S1: Obtain multi-modal type data of intelligent manufacturing equipment; collect time-series data based on the multi-modal type data of intelligent manufacturing equipment to generate an initial multi-modal data set; perform operation mode fusion on the initial multi-modal data set to generate multi-modal operation data of manufacturing equipment;

[0006] Step S2: Perform device virtual operation processing on the multi-modal operation data of the manufacturing equipment based on a preset diffusion large model to generate a device operation virtual state distribution map; optimize the physical constraints of the initialized diffusion large model based on the device operation virtual state distribution map to generate a diffusion model matching the device physical characteristics; perform multiple rounds of model verification and testing on the diffusion model matching the device physical characteristics based on the device operation virtual state distribution map to generate an optimized set of device control strategies;

[0007] Step S3: Perform state matching analysis on the multi-modal operation data of the manufacturing equipment according to the candidate set of device control strategies to generate a dynamic control strategy; perform multi-device collaborative optimization on the intelligent manufacturing equipment based on the dynamic control strategy to generate dynamic device collaborative optimization data; collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set; perform diffusion model incremental learning and continuous optimization on the diffusion model matching the device physical characteristics through the incremental learning data set, so as to generate a long-term optimization control matrix for the device;

[0008] Step S4: Perform access control on the long-term optimization control matrix of the device and the diffusion model matching the device physical characteristics to generate device control differential privacy protection data; perform integrity verification on the device control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access policies of the long-term optimization control matrix of the device according to the security threat monitoring log to generate a final security optimization strategy to execute the intelligent manufacturing equipment control operation.

[0009] The present invention can comprehensively and accurately reflect the operating state of a device by acquiring multimodal data (such as temperature, vibration, sound, images, etc.) from multiple sensors. The fusion of multimodal data improves the accuracy of device fault detection and status monitoring, avoiding information deviation caused by a single data source. The acquisition of time-series data enables the dynamic tracking of the device's operating process, ensuring timeliness and integrity. Through the fusion of operating modes, the characteristics of different data sources can be integrated to form a more comprehensive set of operating data for manufacturing devices, which helps improve the quality of subsequent model training and analysis. The virtual operation processing of the device can generate a virtual state distribution map of the device, providing a data basis for further physical property analysis and optimization. The physical constraint optimization of the diffusion large model can make the generated model more in line with the working characteristics of the actual device, avoiding the problem of mismatch between the model and the physical characteristics of the device in traditional methods. Through multiple rounds of model verification and testing, the accuracy and reliability of the device control strategy are ensured, which helps optimize the control effect and improve the device performance. The dynamic control strategy is adjusted according to the real-time operating data of the device, ensuring the real-time adaptability and optimization ability of the device and enabling it to cope with changes in different operating states of the device. The collaborative optimization of multiple devices can synchronously optimize the working states of multiple devices, improve production efficiency, reduce conflicts between devices, and enhance the collaborative ability of the entire manufacturing system. Through incremental learning, the control model of the device is continuously updated and optimized based on new execution data, avoiding outdated control strategies and helping to achieve continuous improvement and optimization of the device during long-term operation. Differential privacy protection ensures the security of sensitive data in the intelligent manufacturing process, protects the enterprise's core technologies and business secrets, and prevents data leakage or abuse. Conducting integrity verification on device control data helps detect potential risks of data tampering and ensures the reliability of device control data. Dynamically adjusting storage and access policies based on security threat monitoring logs can optimize security policies in real time according to changes in security threats, improve the overall anti-attack ability of the system, and ensure the long-term safe and stable operation of intelligent manufacturing devices. Therefore, the present invention improves the accuracy and adaptability of intelligent manufacturing device control by combining the diffusion large model, multimodal data fusion, incremental learning, and differential privacy protection.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire multimodal type data of the intelligent manufacturing device;

[0012] Step S12: Extract high-frequency sensor signals, operation logs, images, and video stream data from the multimodal type data of the intelligent manufacturing device, and perform time-series data acquisition on the high-frequency sensor signals, operation logs, images, and video stream data to generate an initial multimodal data set;

[0013] Step S13: Perform temporal alignment on the initial multi-modal dataset to generate a multi-modal aligned dataset for manufacturing equipment; perform inter-modal calibration on the multi-modal aligned dataset for manufacturing equipment to generate a multi-modal fusion dataset;

[0014] Step S14: Extract cross-modal features from the multi-modal fusion dataset to obtain cross-modal feature data for manufacturing equipment, where the cross-modal feature data for manufacturing equipment includes frequency-domain features, spatial features, and semantic features; perform fusion processing on the cross-modal feature data for manufacturing equipment to generate a multi-modal feature tensor;

[0015] Step S15: Decompose and compress the multi-modal feature tensor to generate multi-modal operation data for manufacturing equipment.

[0016] By acquiring and extracting multi-modal type data (such as high-frequency sensor signals, operation logs, images, and video stream data) and performing temporal acquisition, the present invention can provide a comprehensive and rich data basis for subsequent analysis, covering multi-dimensional states and operation information of manufacturing equipment. Through temporal alignment and inter-modal calibration, the multi-modal data can be made consistent in the time and space dimensions, effectively reducing analysis errors caused by different modal characteristics or data source asynchronization, and enhancing the reliability and accuracy of the data. Extracting cross-modal features such as frequency-domain features, spatial features, and semantic features, and generating a multi-modal feature tensor through fusion processing, provides a high-level feature representation for subsequent analysis. This cross-modal feature extraction method can better capture the potential correlations between data and improve the information utilization efficiency. Decomposing and compressing the multi-modal feature tensor can not only reduce data redundancy and storage space requirements, but also extract refined multi-modal operation data, facilitating subsequent intelligent analysis and modeling, and improving data processing efficiency. The finally generated multi-modal operation data for manufacturing equipment provides accurate data support for intelligent manufacturing tasks such as equipment monitoring, anomaly detection, and optimization control, contributing to improving the operation efficiency, stability, and production quality of the equipment.

[0017] Preferably, step S2 includes the following steps:

[0018] Step S21: Initialize a preset diffusion large model based on the multi-modal operation data of the manufacturing equipment to generate an initialized diffusion large model; input the multi-modal operation data of the manufacturing equipment into the initialized diffusion large model for equipment virtual operation processing to generate a device operation virtual state distribution map;

[0019] Step S22: Optimize the physical constraints of the initialized diffusion large model based on the device operation virtual state distribution map to generate a diffusion model matching the physical characteristics of the device; use the diffusion model matching the physical characteristics of the device to perform multiple rounds of training on the multi-modal operation data of the manufacturing equipment to generate a candidate set of device control strategies;

[0020] Step S23: Use the candidate set of device control strategies to verify and test the diffusion model for matching device physical characteristics, and generate an optimized set of device control strategies.

[0021] In the present invention, by inputting the multi-modal operation data of manufacturing equipment into the initialized diffusion large model, a virtual state distribution map of equipment operation is generated. This data-driven virtual operation can accurately simulate the operation state of the equipment under different conditions, laying a foundation for subsequent optimization. By introducing physical constraint optimization, the real physical characteristics of the equipment are embedded into the diffusion model, thereby generating a diffusion model for matching device physical characteristics. This process can reduce model deviation, enhance the consistency between the results and the actual device behavior, and improve the credibility and interpretability of the model. Using the optimized diffusion model to perform multiple rounds of training on the multi-modal operation data to generate a candidate set of device control strategies. This iterative training method fully explores the potential features of the data, helps to generate diverse control strategies, and supports the requirements of complex manufacturing scenarios. By verifying and testing the candidate set of device control strategies, an optimized set of device control strategies is finally generated. This rigorous verification process ensures the reliability and applicability of the control strategies, helps to improve the operation efficiency and stability of the equipment. The finally generated optimized set of device control strategies provides accurate guidance for the intelligent control of equipment operation, can achieve dynamic optimization of the manufacturing process, reduce energy consumption, increase production capacity, and contribute to the overall upgrade of the intelligent manufacturing system. By introducing physical constraints and virtual operation into the model, the diffusion large model can not only adapt to the current equipment, but also has good scalability and can be applied to the optimization and control tasks of other similar equipment or scenarios.

[0022] Preferably, the physical constraint optimization of the initialized diffusion large model based on the virtual state distribution map of equipment operation includes:

[0023] Extract key operation state parameters from the data of the virtual state distribution map of equipment operation to generate data of the equipment operation characteristic matrix, where the extraction of key operation state parameters includes operation speed, temperature, and vibration frequency; perform physical constraint modeling on the data of the equipment operation characteristic matrix to generate data of a set of physical characteristic constraint functions;

[0024] Perform model constraint embedding processing on the data of the set of physical characteristic constraint functions to generate data of a preliminary constrained diffusion model; perform iterative training on the data of the preliminary constrained diffusion model to generate training data of the diffusion model for matching device physical characteristics;

[0025] Perform feedback optimization on the training data of the diffusion model for matching device physical characteristics, thereby generating a diffusion model for matching device physical characteristics.

[0026] The present invention extracts key operating state parameters from the data of the virtual state distribution map of equipment operation, including operating speed, temperature, vibration frequency, etc., which can comprehensively reflect the core operating characteristics of the equipment. These key parameters provide basic data for subsequent physical constraint modeling and optimization, ensuring the accuracy and pertinence of the optimization process. The extracted operating state parameters are converted into equipment operating characteristic matrix data, providing structured data support for physical constraint modeling. This matrix-form characteristic data helps to better understand the behavior patterns of the equipment under different operating conditions and facilitates multi-dimensional optimization. By performing physical constraint modeling on the equipment operating characteristic matrix data, a set of physical characteristic constraint function data is generated. This process can combine actual physical laws with equipment operation data, enhance the matching degree between the model and the actual situation, ensure that the model can follow physical constraints during execution, and improve feasibility. The set of physical characteristic constraint function data is subjected to model constraint embedding processing to generate preliminary constraint diffusion model data. The embedding of constraint conditions ensures that the diffusion model can not only work theoretically but also play a role in actual equipment operation, preventing the model from producing unrealistic prediction results and enhancing the stability and usability of the model. By performing iterative training on the preliminary constraint diffusion model data, equipment physical characteristic matching diffusion model training data is generated. This process can continuously optimize the model and improve the adaptability and prediction accuracy of the model to the actual equipment operation state. Iterative training ensures that the model can make effective predictions and controls under different equipment states. Finally, feedback optimization is performed on the equipment physical characteristic matching diffusion model training data to generate the final equipment physical characteristic matching diffusion model. Through the process of feedback optimization, the model continuously adapts to the changes of the equipment and different operating environments, and finally achieves high precision and stability, which can better support the intelligent control and optimized operation of the equipment. Through the generated physical characteristic matching diffusion model, a more accurate equipment control strategy can be realized, thereby improving the equipment operation efficiency, reducing the occurrence of faults, and enhancing the stability of the manufacturing process. The model can adapt to equipment state changes in real time and provide strong support for the intelligent manufacturing system.

[0027] Preferably, step S3 includes the following steps:

[0028] Step S31: Perform state matching analysis on the multi-modal operation data of the manufacturing equipment according to the candidate set of equipment control strategies to generate a dynamic control strategy; decompose the control instruction set based on the dynamic control strategy to generate an equipment control instruction set;

[0029] Step S32: Use the equipment control instruction set to operate the intelligent manufacturing equipment and collect feedback data to generate a control execution feedback matrix; adaptively update the dynamic control strategy in real time through the control execution feedback matrix to generate an adaptive dynamic control strategy;

[0030] Step S33: Based on the adaptive dynamic control strategy, perform multi-device collaborative optimization on the intelligent manufacturing equipment to generate dynamic device collaborative optimization data; collect and analyze the execution effects of the dynamic device collaborative optimization data to generate an incremental learning data set;

[0031] Step S34: Perform diffusion model incremental learning and continuous optimization on the device physical characteristic matching diffusion model through the incremental learning data set, so as to generate a long-term device optimization control matrix.

[0032] The present invention performs state matching analysis on multi-modal operation data based on a device control strategy candidate set to generate a dynamic control strategy. This method can generate an optimized control strategy in real time according to the state changes of the device, ensuring that the device can obtain the most suitable control instructions under different working conditions, thereby improving the operation efficiency and stability of the device. The control instruction set deconstruction process converts the dynamic control strategy into an executable device control instruction set, enabling the control strategy to be directly applied to device operations. The deconstructed instruction set can simplify the device operation process and improve the efficiency and accuracy of instruction execution. The dynamic control strategy is adaptively updated in real time through the control execution feedback matrix to ensure that the control strategy can be optimized and adjusted according to the actual operation feedback. This real-time optimization enables the device to continuously adapt to changes in the working environment, reduce errors and failures, and thus improve the stability and controllability of the system. Based on the adaptive dynamic control strategy, multi-device collaborative optimization is performed to generate dynamic device collaborative optimization data. Through the collaborative optimization among multiple devices, the overall production efficiency can be improved, conflicts and imbalances among devices can be reduced, and the manufacturing process can be made more coordinated and smooth. This step further improves the integration and intelligent level of the intelligent manufacturing system. Through the collection and analysis of execution effects, an incremental learning data set is generated, providing a basis for continuous improvement of the device physical characteristic matching diffusion model. Incremental learning can gradually incorporate new data and feedback, continuously improve the accuracy and adaptability of the model, and effectively avoid the obsolescence problem of traditional models. Through continuous optimization of the diffusion model by incremental learning, a long-term device optimization control matrix is finally generated. This process ensures that the control strategy of the device can maintain an optimized state in the long term, gradually adapt to the operation trend of the device and changes in the external environment, and ultimately achieve continuous performance improvement and efficient operation of the device. Through means such as feedback data, dynamic adjustment, and incremental learning, the entire Step S3 not only improves the optimization ability of a single device, but also enables the entire intelligent manufacturing system to have stronger self-adaptability and long-term optimization ability, can continuously learn and optimize the control strategy, and support a more intelligent and efficient production process.

[0033] Preferably, performing state matching analysis on the multi-modal operation data of the manufacturing equipment according to the device control strategy candidate set includes:

[0034] Screen the candidate set of device control strategies according to the operating status rules to obtain the device control operating status rules; conduct a real-time operation feature item-by-item comparison and analysis of the multi-modal operation data of the manufacturing equipment according to the device control operating status rules, so as to generate feature comparison and analysis data;

[0035] Calculate the cosine similarity of the feature comparison and analysis data to generate a status matching score matrix; extract the highest score from the status matching score matrix to obtain the optimal status matching score data; perform a strategy optimization process on the candidate set of device control strategies through the optimal status matching score data to generate the optimal control strategy set data;

[0036] Based on the time axis of the multi-modal operation data of the manufacturing equipment, perform a logical integration process on the optimal control strategy set data to generate dynamic control strategy data.

[0037] Through running state rule screening for the candidate set of device control strategies, the present invention can extract the rules most relevant to the running state of the device. This process ensures that the selection of control strategies is closely matched with the current running state of the device, thereby improving the accuracy and pertinence of control decisions. By comparing and analyzing multi-modal running data item by item according to the device control running state rules, characteristic comparison and analysis data is generated. This process can accurately reflect the degree of fit between the current state of the device and each control strategy through real-time comparison of various running characteristics of the device, providing a detailed basis for subsequent state matching and strategy optimization. Calculate the cosine similarity of the characteristic comparison and analysis data to generate a state matching score matrix. By calculating the cosine similarity, the similarity between the running characteristics of the device and the control strategies can be quantified, thereby achieving accurate matching. The cosine similarity is a commonly used and efficient similarity calculation method, which can effectively reduce errors and improve the accuracy of matching. Extract the highest score from the state matching score matrix to obtain the optimal state matching score data. This process can quickly screen out the strategy that best matches the current device state among multiple control strategies, ensuring the selection of the optimal strategy at the most appropriate time and improving the accuracy and efficiency of device operation. According to the optimal state matching score data, optimize the candidate set of control strategies to generate the optimized control strategy set data. This optimization process ensures the quality of the control strategy set and excludes inapplicable or inappropriate strategies, ensuring that the selected strategy can maximize the performance of the device. Based on the time axis of the device multi-modal running data, logically integrate the optimized control strategy set to generate dynamic control strategy data. This process not only considers the real-time nature of device operation, but also optimizes the order and logic of strategy execution according to the time series, thereby generating dynamic control strategy data that can cope with the continuous changes in the device state. By real-time matching, optimizing and integrating control strategies, the system can dynamically adjust control strategies according to the specific running state of the device. This process greatly improves the adaptability and response speed of the device, helps the device to always maintain the best running state under different running environments and conditions, thereby improving the overall running efficiency and intelligent level. Combining means such as characteristic comparison and analysis, cosine similarity calculation and generation of optimized strategies, the system can make accurate control decisions based on the real-time running data of the device, enabling the device to achieve efficient and intelligent automatic control in complex environments and further promoting the realization of intelligent manufacturing.

[0038] Preferably, step S33 includes the following steps:

[0039] Step S331: Based on the adaptive dynamic control strategy, perform task decomposition processing between devices on the intelligent manufacturing device to generate device task allocation matrix data, where the task decomposition processing between devices includes load capacity decomposition and resource occupancy decomposition;

[0040] Step S332: Coordinate the device operation parameters for the device task allocation matrix data to generate multi-device collaborative operation parameter set data;

[0041] Step S333: Perform dynamic execution simulation processing on the multi-device collaborative operation parameter set data to generate dynamic device collaborative optimization data; extract key collaborative indicators from the dynamic device collaborative optimization data to obtain multi-device key collaborative indicators, where the multi-device key collaborative indicators include inter-device latency, resource conflicts, or energy consumption deviation;

[0042] Step S334: Collect and analyze the execution effects of the dynamic device collaborative optimization data to generate an incremental learning data set.

[0043] The present invention performs task decomposition processing on devices through an adaptive dynamic control strategy to generate device task allocation matrix data. This process ensures the reasonable allocation of tasks among devices, taking into account the load capacity and resource occupancy of each device, avoiding overloading and resource waste, and improving the overall task execution efficiency of the system. Coordinate the device operation parameters through the device task allocation matrix data to generate multi-device collaborative operation parameter set data. This process promotes the coordination among devices. By optimizing the operation parameters, multi-devices can cooperate efficiently when working together, reducing conflicts between devices and improving the overall work efficiency. Perform dynamic execution simulation processing on the multi-device collaborative operation parameter set data to generate dynamic device collaborative optimization data. This simulation processing helps predict the execution effects of cooperation between different devices and timely discover potential optimization space. Through dynamic simulation, optimization adjustments can be made before actual operation, avoiding instability and inefficiency during system operation. By extracting key collaborative indicators from the dynamic device collaborative optimization data, step S333 can generate multi-device key collaborative indicators such as inter-device latency, resource conflicts, or energy consumption deviation. These indicators provide a quantitative basis for collaborative optimization between devices, helping to monitor and adjust the collaborative work efficiency during actual operation to ensure the efficient operation of devices. Through the collection and analysis of execution effects, an incremental learning data set is generated. This process can collect execution effect data in real time, provide data support for incremental learning, and continuously optimize the collaborative control strategy. Through incremental learning, the system can dynamically adjust the optimization plan according to the latest execution data, enhancing the system's adaptive ability and decision-making efficiency. Through means such as device task decomposition, collaborative operation parameter optimization, and incremental learning, the entire system can efficiently coordinate the operation of each device, reducing problems such as resource conflicts, latency, and energy consumption deviation. The efficient cooperation between devices will significantly improve the overall work efficiency and production capacity of the intelligent manufacturing system. By generating an incremental learning data set, the system can continuously optimize according to real-time feedback, thereby realizing the continuous improvement of the device collaborative control strategy. Incremental learning not only enhances the system's adaptive ability but also ensures the long-term effectiveness of device collaborative optimization.

[0044] Preferably, step S334 includes the following steps:

[0045] Step S3341: Collect multi-dimensional execution effect data for the dynamic device collaborative optimization data to generate an initial device collaborative execution effect data set; perform an expected value comparison analysis on the initial device collaborative execution effect data set to generate execution deviation data;

[0046] Step S3342: Compare the execution deviation data with a preset standard execution deviation threshold. When the execution deviation data is greater than or equal to the preset standard execution deviation threshold, perform attribution analysis and optimization feedback on the execution deviation data to generate execution optimization feedback data;

[0047] Step S3343: When the execution deviation data is less than the preset standard execution deviation threshold, perform incremental learning data construction on the execution deviation data to generate an incremental learning data set.

[0048] Through multi-dimensional data collection, the present invention generates an initial device collaborative execution effect data set, which ensures that the execution effect of device collaborative optimization is comprehensively and accurately recorded. Next, an expected value comparison analysis is performed to generate execution deviation data, which helps to discover the gap between the actual execution effect and the expected target and provides a basis for subsequent optimization. When the execution deviation data is greater than or equal to the preset standard execution deviation threshold, attribution analysis and optimization feedback are performed on the execution deviation data. Through attribution analysis, the specific source of the deviation can be located and the corresponding links can be optimized and adjusted. The optimization feedback data can help improve the collaborative efficiency between devices and reduce unnecessary resource consumption or delays. When the execution deviation data is less than the preset threshold, incremental learning data construction is performed to generate an incremental learning data set, which ensures that the system can continuously learn according to the real-time execution situation, generate new learning data, and continuously optimize the control strategy. Incremental learning not only improves the adaptive ability of the system but also enables the intelligent manufacturing system to maintain high efficiency and stability during long-term operation. The incremental learning mechanism enables the system to dynamically adjust the strategy according to the latest execution data, thereby continuously optimizing the device collaborative control scheme. Through continuous learning and adjustment, the system can adapt to complex environmental changes and optimize the collaborative working efficiency of each device to ensure long-term high-efficiency operation. Through real-time monitoring and feedback of execution deviations, the system can timely discover and handle potential problems, thereby improving the response ability of the intelligent manufacturing system to the changing environment. At the same time, the mechanism of deviation analysis and optimization feedback also ensures the stability of the system and reduces the impact of unexpected events on the system operation. Through careful analysis and optimization of execution deviations, the collaborative accuracy between devices has been significantly improved. Through attribution analysis and optimization feedback, the task execution between devices becomes more accurate, reducing problems such as resource conflicts and delays during the collaboration process, thereby improving the overall work efficiency.

[0049] Preferably, step S4 includes the following steps:

[0050] Step S41: Perform distributed encrypted storage on the long-term optimization control matrix of the device and the diffusion model for matching the physical characteristics of the device to generate an encrypted storage mapping table; perform access control on the encrypted storage mapping table based on the differential privacy protection mechanism to generate device control differential privacy protection data;

[0051] Step S42: Perform integrity verification on the device control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access policies of the long-term optimization control matrix of the device according to the security threat monitoring log to generate a final security optimization policy for executing the intelligent manufacturing device control operation.

[0052] In the present invention, by distributing and encrypting the long-term optimization control matrix of the device and the diffusion model for matching the physical characteristics of the device, this method can effectively prevent single-point failures and unauthorized access, and improve the overall security and disaster tolerance of data storage. The encrypted storage mapping table provides support for the rapid positioning and management of data, while ensuring that the security of the stored data will not be affected by the leakage of the mapping relationship. The differential privacy protection mechanism provides an effective privacy protection method for the access of device control data, and can minimize the risk of privacy leakage while the data is being used. The device control differential privacy protection data restricts the exposure of sensitive data through access control, ensuring that only authorized users or devices can access the long-term optimization control matrix of the device. The integrity verification provides security guarantees for the device control differential privacy protection data, and can detect whether the data has been tampered with or attacked during storage and transmission. The dynamic adjustment of the storage and access policies based on the security threat monitoring log ensures that the system can optimize the policies in a timely manner according to the current security status, improving the execution reliability and security of the device control operation. The security threat monitoring log provides the ability to record and analyze potential threats in real time, helping to quickly discover and locate security hazards. By dynamically adjusting the storage and access policies, external or internal security threats can be quickly responded to, preventing data leakage or system intrusion. The final security optimization policy integrates the advantages of distributed encrypted storage, differential privacy protection, integrity verification, and dynamic adjustment of policies, providing an efficient and secure execution guarantee for the device control operation. The system can automatically adapt the policy according to the operation requirements and security situation, enabling the device control operation to maintain stability and efficiency during long-term operation. The combination of the differential privacy protection mechanism and the dynamic optimization policy improves privacy protection while minimizing the impact on system performance. The dynamic adjustment policy can intelligently allocate resources according to the threat level to ensure the balance between privacy protection and system performance. The application of distributed storage and dynamic optimization policy provides a basis for the efficient management of the long-term optimization control matrix of the device. This method not only ensures the long-term security of the data, but also provides convenience for subsequent data analysis and optimization.

[0053] In this specification, a control system for intelligent manufacturing equipment based on a diffusion large model is provided, which is used to execute the above-mentioned control method for intelligent manufacturing equipment based on a diffusion large model. The control system for intelligent manufacturing equipment based on a diffusion large model includes:

[0054] A modality fusion module, configured to obtain multi-modal type data of intelligent manufacturing equipment; perform time-series data acquisition based on the multi-modal type data of intelligent manufacturing equipment to generate an initial multi-modal data set; perform operation modality fusion on the initial multi-modal data set to generate multi-modal operation data of manufacturing equipment;

[0055] A virtual operation module, configured to perform device virtual operation processing on the multi-modal operation data of manufacturing equipment based on a preset diffusion large model to generate a device operation virtual state distribution map; perform physical constraint optimization on the initialized diffusion large model based on the device operation virtual state distribution map to generate a diffusion model matching the physical characteristics of the device; perform multiple rounds of model verification and testing on the diffusion model matching the physical characteristics of the device based on the device operation virtual state distribution map to generate an optimized set of device control strategies;

[0056] A device collaborative control module, configured to perform state matching analysis on the multi-modal operation data of manufacturing equipment according to a candidate set of device control strategies to generate a dynamic control strategy; perform multi-device collaborative optimization on intelligent manufacturing equipment based on the dynamic control strategy to generate dynamic device collaborative optimization data; perform execution effect acquisition and analysis on the dynamic device collaborative optimization data to generate an incremental learning data set; perform diffusion model incremental learning and continuous optimization on the diffusion model matching the physical characteristics of the device through the incremental learning data set, so as to generate a long-term optimization control matrix for the device;

[0057] A secure storage module, configured to perform access control on the long-term optimization control matrix of the device and the diffusion model matching the physical characteristics of the device to generate device control differential privacy protection data; perform integrity verification on the device control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access policies of the long-term optimization control matrix of the device according to the security threat monitoring log to generate a final security optimization strategy to execute the intelligent manufacturing equipment control operation.

[0058] The beneficial effects of the present invention are as follows: By integrating data from multiple sensor types (such as temperature, vibration, sound, etc.), various operating information of the device can be comprehensively captured, improving the accuracy and integrity of the device state. The acquisition of time-series data ensures real-time monitoring and long-term tracking of the device operating state, helping to identify potential faults and provide early warnings. Through the fusion of operating modes, data features from different sensors can be effectively integrated to generate high-dimensional multi-modal operating data, providing a high-quality data basis for subsequent analysis and modeling. Through virtual operation processing, the operating state of the device can be simulated without actual device operation. This helps to verify and optimize control strategies in a laboratory environment. By optimizing the physical constraints in the diffusion large model, the model is ensured to match the physical characteristics of the actual device, improving the realism and reliability of the model, and thus obtaining a more accurate device control strategy. Based on the virtual state distribution map of the device, multiple rounds of model verification can be carried out to continuously iterate and optimize the device control strategy, ensuring the effectiveness and stability of the strategy. By generating a dynamic control strategy through state matching analysis, it is ensured that the control strategy can respond to device changes in real time and optimize the device operation efficiency. By coordinating the operating states of multiple devices, conflicts between devices are reduced, and the overall efficiency and resource utilization rate of the entire production system are improved. Based on the acquisition and analysis of execution effects, an incremental learning data set is generated, and the device control model is continuously optimized, thereby achieving long-term optimized control and enhancing the continuous stability and high efficiency of the device. Through differential privacy protection measures, the privacy and security of device control data are guaranteed, sensitive data leakage is avoided, and the core technology of the enterprise is protected. By performing integrity verification on device control data, potential risks of data tampering or damage can be detected in a timely manner, thus ensuring the accuracy and reliability of the data. According to the security threat monitoring log, the storage and access strategies are dynamically adjusted to ensure the security of device control data, enhance the system's ability to resist security attacks, and ensure the safety and reliability of device control operations. Therefore, the present invention improves the accuracy and adaptability of intelligent manufacturing device control by combining the diffusion large model, multi-modal data fusion, incremental learning, and differential privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the step flow of a method for controlling an intelligent manufacturing device based on a diffusion large model;

[0060] Figure 2 For Figure 1 It is a schematic diagram of the detailed implementation step flow of step S2 in

[0061] Figure 3 For Figure 1 It is a schematic diagram of the detailed implementation step flow of step S3 in

[0062] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation mode

[0063] The technical method of the present invention patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in the present invention without creative work belong to the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0065] It should be understood that although terms such as "first" and "second" may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be called the second unit, and similarly the second unit may be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0066] To achieve the above object, please refer to Figures 1 to 3 , a control method for intelligent manufacturing equipment based on a diffusion large model, the method comprising the following steps:

[0067] Step S1: Obtain multi-modal type data of intelligent manufacturing equipment; collect time-series data based on the multi-modal type data of intelligent manufacturing equipment to generate an initial multi-modal data set; perform operation mode fusion on the initial multi-modal data set to generate multi-modal operation data of manufacturing equipment;

[0068] Step S2: Perform equipment virtual operation processing on the multi-modal operation data of manufacturing equipment based on a preset diffusion large model to generate a distribution map of virtual operation states of the equipment; optimize physical constraints on the initialized diffusion large model based on the distribution map of virtual operation states of the equipment to generate a diffusion model matching the physical characteristics of the equipment; perform multiple rounds of model verification and testing on the diffusion model matching the physical characteristics of the equipment based on the distribution map of virtual operation states of the equipment to generate an optimized set of equipment control strategies;

[0069] Step S3: Perform state matching analysis on the multi-modal operation data of manufacturing equipment according to the candidate set of device control strategies to generate dynamic control strategies; perform multi-device collaborative optimization on intelligent manufacturing equipment based on the dynamic control strategies to generate dynamic device collaborative optimization data; collect and analyze the execution effects of the dynamic device collaborative optimization data to generate an incremental learning data set; perform diffusion model incremental learning and continuous optimization on the device physical characteristic matching diffusion model through the incremental learning data set, so as to generate a long-term optimization control matrix for the device;

[0070] Step S4: Perform access control on the long-term optimization control matrix of the device and the device physical characteristic matching diffusion model to generate device control differential privacy protection data; perform integrity verification on the device control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access policies of the long-term optimization control matrix of the device according to the security threat monitoring log to generate a final security optimization strategy to execute the intelligent manufacturing equipment control operation.

[0071] The present invention can comprehensively and accurately reflect the operating state of the device by acquiring multimodal data (such as temperature, vibration, sound, image, etc.) from multiple sensors. The fusion of multimodal data improves the accuracy of device fault detection and status monitoring, avoiding information deviation caused by a single data source. The acquisition of time-series data enables the dynamic tracking of the device's operating process, ensuring timeliness and integrity. Through the fusion of operating modes, the characteristics of different data sources can be integrated to form more comprehensive operating data of manufacturing devices, which helps to improve the quality of subsequent model training and analysis. The virtual operation processing of the device can generate a virtual state distribution map of the device, providing a data basis for further physical property analysis and optimization. The physical constraint optimization of the diffusion large model can make the generated model more in line with the working characteristics of the actual device, avoiding the problem of mismatch between the model and the physical characteristics of the device in traditional methods. Through multiple rounds of model verification and testing, the accuracy and reliability of the device control strategy are ensured, which helps to optimize the control effect and improve the device performance. The dynamic control strategy is adjusted according to the real-time operating data of the device, ensuring the real-time adaptability and optimization ability of the device and being able to cope with changes in the device under different operating states. The collaborative optimization of multiple devices can synchronously optimize the working states of multiple devices, improve production efficiency, reduce conflicts between devices, and enhance the collaborative ability of the entire manufacturing system. Through incremental learning, the control model of the device is continuously updated and optimized according to new execution data, avoiding outdated control strategies and helping to achieve continuous improvement and optimization of the device during long-term operation. Differential privacy protection ensures the security of sensitive data in the intelligent manufacturing process, protects the core technologies and business secrets of enterprises, and prevents data leakage or abuse. Conducting integrity verification on the device control data helps to detect potential data tampering risks and ensure the reliability of the device control data. Dynamically adjusting the storage and access policies based on the security threat monitoring logs can optimize the security policies in real time according to changes in security threats, improve the overall anti-attack ability of the system, and ensure the long-term safe and stable operation of intelligent manufacturing devices. Therefore, the present invention improves the accuracy and adaptability of intelligent manufacturing device control by combining the diffusion large model, multimodal data fusion, incremental learning, and differential privacy protection.

[0072] In the embodiment of the present invention, with reference to Figure 1 As described, it is a schematic diagram of the step flow of a method for controlling an intelligent manufacturing device based on a diffusion large model according to the present invention. In this example, the method for controlling an intelligent manufacturing device based on a diffusion large model includes the following steps:

[0073] Step S1: Acquire multimodal type data of the intelligent manufacturing device; perform time-series data acquisition based on the multimodal type data of the intelligent manufacturing device to generate an initial multimodal data set; perform operating mode fusion on the initial multimodal data set to generate multimodal operating data of the manufacturing device;

[0074] In the embodiments of the present invention, by identifying and determining the types of intelligent manufacturing equipment involved, it is ensured that the data modalities provided by different types of equipment are adapted. Intelligent manufacturing equipment includes robotic arms, numerically controlled machine tools, automated assembly lines, robots, etc., and the data collected through different sensors will have different modalities. The data of multiple data sources are synchronously collected through a data acquisition system (such as an edge computing node, an IoT platform, etc.). The acquisition frequency and method (such as timed acquisition, event-driven acquisition) of each data stream need to be configured according to the actual requirements of the equipment. Add timestamps to each data stream (such as temperature, pressure, acceleration, etc.) to ensure that the data of each modality can be aligned at the same moment; set a unified time reference so that the data of different devices or sensors can be effectively synchronized, avoiding data asynchronization caused by clock drift, acquisition delay, etc.; considering that the equipment operates under different working conditions, multi-level time granularity acquisition is required (for example, data acquisition with different time granularities according to the equipment cycle, operating status, or event trigger, etc.). Use appropriate data acquisition hardware (such as a multi-modal sensor network, a data acquisition card) and a real-time data processing platform (such as an industrial IoT platform, an edge computing device) to perform regular data acquisition, ensuring that the collected data meets the timing requirements. Monitor in real time the data quality problems that occur during the acquisition process, such as packet loss, incorrect data, timing disorder, etc., to ensure that the collected data is complete and effective. Perform necessary preprocessing on the collected multi-modal data for subsequent analysis. Common preprocessing steps include: eliminating sensor noise through methods such as filtering and smoothing; interpolating or filling in the missing data to ensure the integrity of the data set; for data with different dimensions (such as temperature, pressure, acceleration, etc.), perform normalization or standardization processing to make the data within the same dimension range. Store the preprocessed data in a suitable storage system (such as a database, cloud storage) in chronological order to form an initial multi-modal data set. This data set contains data of various modalities and has a unified time label, making subsequent processing and analysis more efficient. If supervised learning needs to be performed on the data, labels such as the operating status and fault status of the equipment need to be added to each data point or data segment. Integrate different modalities of data (such as sensor data, video data, environmental data, etc.) to form a unified, multi-dimensional equipment operation data set. Through this integration, the operating status and working conditions of the equipment can be more comprehensively reflected. There are differences in the time granularity, data format, and feature distribution of different modalities of data, so it is necessary to align and match them: align the data of all modalities based on the timestamp to ensure that different data in the same time period can correspond to each other; according to specific requirements, select appropriate feature fusion methods, such as data-level fusion, feature-level fusion, decision-level fusion, etc. Data-level fusion combines the original data, while feature-level fusion is performed after data processing.Assign different weights to each modal data, perform weighted averaging, and fuse them into a unified feature; use multimodal neural networks (such as deep neural networks, multi-input convolutional neural networks) for end-to-end feature learning and fusion; use statistical methods such as covariance matrix and principal component analysis (PCA) to reduce the dimension and fuse different modal data. After completing modal fusion, the generated multimodal operation data contains the integration of different modal information and can describe the overall operation of the equipment. At this time, different operating states of the equipment (such as normal operation, critical state, abnormal state, etc.) can be extracted and analyzed from these data. The equipment operation data after multimodal fusion is displayed through a visualization platform (such as a dashboard, 3D graphical interface, etc.), allowing users to monitor the working status of the equipment in real time.

[0075] Step S2: Based on the preset diffusion macro model, the multimodal operation data of the manufacturing equipment is processed for virtual operation to generate a virtual state distribution map of the equipment operation; based on the virtual state distribution map of the equipment operation, the initialized diffusion macro model is optimized for physical constraints to generate a diffusion model matching the physical characteristics of the equipment; based on the virtual state distribution map of the equipment operation, the equipment physical characteristics matching diffusion model is verified and tested for multiple rounds of models to generate an optimized set of equipment control strategies;

[0076] In the embodiments of the present invention, by selecting a suitable diffusion large model (such as diffusion neural network, generative adversarial networks (GANs), etc.) according to the operating characteristics of the device, this model can process multi-modal operating data and simulate the virtual state of the device through the latent features of the data. The role of the diffusion large model is to predict the performance of the device under different working states by simulating the multi-modal operating data of the device. The input data of the model includes the multi-modal operating data of the device, such as sensor data, video data, environmental data, etc. The output of the model is the virtual operating state of the device under different working conditions, reflecting various potential behaviors, working efficiency, failure probability, etc. of the device. Using the diffusion large model to perform virtual simulation processing on the multi-modal data of the device and simulate the performance of the device under different working states, this process can help identify the potential operating modes and states of the device. Especially when it is difficult to achieve with the real device in the experimental or production environment, the virtual processing can provide important prediction information. The input data flows through the diffusion model and after multiple layers of processing, the virtual operating state data of the device is generated. Each virtual state represents the working state and behavior performance of the device in different environments. Through the output of the model, a virtual state distribution map of the device is generated. The map shows the state distribution of the device under different conditions, reflecting the operating behavior mode of the device. This distribution map can intuitively show the performance of the device in normal, critical and failure states. The virtual state map is displayed through a visualization tool, highlighting the changes in key performance indicators (such as temperature, pressure, rotational speed, etc.) of the device under different states. Based on the actual physical characteristics of the device, appropriate physical constraint conditions are determined. Physical constraints include the geometric shape of the device, kinematic limitations, dynamic constraints, energy consumption limitations, etc. These constraint conditions reflect the actual physical limitations that the device is subject to when operating in the real environment. For example, the maximum load capacity of the device, the working temperature range, the maximum rotational speed, etc. Through optimization algorithms (such as constrained optimization, gradient-based optimization, genetic algorithms, etc.), physical constraints are introduced during the training process of the diffusion large model. This can be achieved by taking the physical constraints as the regularization term of the model, restricting the virtual states generated by the model not to exceed the working range of the actual device. Physical constraints can be directly optimized by adding penalty terms to the objective function or through constraint conditions, making the operating state of the device output by the model more in line with the real physical environment. After optimization with physical constraints, a new device model is obtained, and this model can better match the actual physical characteristics of the device. The optimized diffusion large model more accurately reflects the operating state of the device and can generate more realistic virtual operating data. To ensure the reliability and accuracy of the optimized diffusion model, it needs to be verified and tested in multiple rounds. The verification process includes the following steps: Select a set of representative device operating data as the verification set, and these data should cover different working conditions, loads, faults, etc. Use the verification set to evaluate the optimized diffusion large model and check the prediction accuracy of the model under different conditions.The evaluation metrics include prediction error, model robustness, response ability to abnormal states, etc. Through multiple rounds of verification tests, the parameters and architecture of the model are gradually optimized. After each round of verification, the model is fine-tuned according to its performance (such as error rate, convergence, etc.). Generally, the verification process involves the following steps: The first round of verification: The optimized model is tested on a small part of the verification data, and its prediction results and errors are analyzed; The second round of verification: The model parameters are adjusted and more data is tested to optimize the generalization ability of the model; Subsequent rounds: Through repeated training and testing, the model is continuously optimized to ensure its stable performance under different working states of the device. Through simulating different device working conditions (such as high load, low load, abnormal states, etc.) for comprehensive testing, it is ensured that the diffusion model can cover all operating modes of the device. The test cases can include: The device operation data under normal working conditions; The device response under critical working conditions; The simulation of abnormal situations (such as device failures, precursors of failures). Based on the diffusion large model after multiple rounds of verification, the final set of device control strategies is generated.

[0077] Step S3: Perform state matching analysis on the multi-modal operation data of the manufacturing device according to the candidate set of device control strategies to generate a dynamic control strategy; Based on the dynamic control strategy, perform multi-device collaborative optimization on the intelligent manufacturing device to generate dynamic device collaborative optimization data; Collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set; Through the incremental learning data set, perform diffusion model incremental learning and continuous optimization on the device physical characteristic matching diffusion model, so as to generate a long-term optimization control matrix for the device;

[0078] In the embodiments of the present invention, a candidate set containing multiple control strategies is constructed based on the multi-modal operation data of the device and the optimization objectives of the device. Each candidate strategy is predefined according to different working conditions and objectives, such as improving production efficiency, reducing energy consumption, extending the service life of the device, etc. The candidate set includes different control schemes, algorithms (such as PID control, fuzzy control, predictive control, etc.) and strategy combinations. Through multi-modal data (such as sensor data, environmental data, device feedback, etc.), the running state of the device is monitored in real time, and state matching analysis is performed on these data. The analysis process includes: sampling and feature extraction of the real-time data (temperature, pressure, rotational speed, vibration, etc.) of the device. These state data are matched with the candidate control strategies through machine learning methods (such as classifiers, clustering algorithms, etc.) to evaluate which control strategies are most suitable for the current device state. Techniques such as dynamic time warping (DTW) and similarity analysis are used to identify and match the current state with the best control strategy in the strategy candidate set. According to the results of the state matching analysis, the control strategy is selected or adaptively adjusted to generate a dynamic and real-time adaptive control strategy. The dynamic control strategy can be adjusted in real time according to the actual running state of the device, production objectives and environmental changes. In the process of intelligent manufacturing, multi-device collaborative optimization refers to synchronously controlling the behaviors of multiple devices to achieve the global optimal production benefit. This process considers factors such as the interaction between devices, resource sharing, task allocation, etc. Based on the dynamic control strategy, multiple devices are combined for collaborative optimization. An optimization model is constructed, and the goal of the model is to minimize the overall production cost, improve production efficiency or achieve other production objectives. The running data, task allocation, production scheduling information, etc. of multiple devices are input. Collaborative optimization algorithms (such as cluster optimization, game theory, distributed optimization, etc.) are used to realize the collaborative operation between multiple devices. The algorithm will achieve global optimization according to the dynamic control strategy of each device. According to the calculation results of the collaborative optimization, optimization data containing the specific execution plan of each device in the collaborative optimization process is generated. This data includes the working parameters of the device, task allocation, adjustment strategy, expected output, etc. After the implementation of multi-device collaborative optimization, real-time data collection is carried out to evaluate the execution effects of each device. These data include indicators such as production efficiency, energy consumption, device state, and product quality. Through sensors and data acquisition systems, the working state, output data, production efficiency, fault information, etc. of each device are obtained in real time. Through statistical analysis methods, the performance of the device after executing the dynamic collaborative optimization is evaluated to detect whether there are deviations or abnormal conditions. Common evaluation methods include performance comparison analysis, benefit analysis and risk assessment. According to the analysis results collected from the execution effects, an incremental learning data set is generated. The incremental learning data set contains new observation data, corrected control strategies and performance feedback data during the device execution process. This data set is the basis for further training of the machine learning model. The incremental learning method is used to continuously learn and optimize the large model for matching the diffusion of device physical characteristics.Incremental learning allows the model to update its parameters each time new data is acquired, without the need to retrain the entire model, thereby improving the learning efficiency of the model. Through methods such as online learning and incremental updates, newly collected data (such as real-time operation data, collaborative optimization data, etc.) is added to the diffusion model to continuously optimize the operation prediction and control strategies of the device. The parameters of the diffusion model are adjusted and optimized using the new data after incremental learning to ensure that the model can better adapt to the physical characteristics of the device and predict future device behavior. Through continuous optimization, the diffusion model can track the long-term changing characteristics of the device and gradually improve the accuracy of device state prediction. Each time optimization is performed, the model fits the new data to ensure the long-term effectiveness and sustainability of device operation. At regular intervals (or according to preset triggering conditions), the model is updated and fine-tuned to ensure that the model can always reflect the latest physical state and operation characteristics of the device. Through incremental learning and continuous optimization of the diffusion model, a long-term optimization control matrix for the device is finally generated. This matrix contains the optimization control strategies of the device under different operating conditions, including the best operating parameters, adjustment strategies, collaborative optimization results, etc. The control matrix forms the best control plan for each device in different states based on the long-term historical data, execution effects, collaborative optimization results, etc. of the device.

[0079] Step S4: Perform access control on the long-term optimization control matrix of the device and the diffusion model that matches the physical characteristics of the device to generate differentially private protected data for device control; perform integrity verification on the differentially private protected data for device control to generate a security threat monitoring log; dynamically adjust the storage and access policies of the long-term optimization control matrix of the device according to the security threat monitoring log to generate a final security optimization strategy to execute the intelligent manufacturing device control operation.

[0080] In the embodiments of the present invention, by designing a refined access permission management mechanism, it is ensured that only authorized users and devices can access the device long-term optimization control matrix and the physical property matching diffusion model. Role-based access control (RBAC) or attribute-based access control (ABAC) models can be used to manage permissions. All access operations need to pass multi-factor authentication (such as username / password, fingerprint recognition, dynamic passwords, etc.) to ensure the authenticity of the identity. At the same time, through refined permission allocation, different roles or users are ensured to only access the data they are authorized to. On the basis of access control, to protect the privacy of device control data, differential privacy technology needs to be adopted. The differential privacy mechanism can ensure that even if the data is accessed or analyzed multiple times, individual data points will not disclose sensitive information of individuals or devices. Process the device control differential privacy protected data, add noise to the key data of the control matrix data and the diffusion model, and ensure that for external visitors and analysts, the original control strategy or device parameters cannot be deduced from the data. The addition of noise should follow the ε-δ mechanism of differential privacy to ensure the strength and operability of privacy protection. Select an appropriate privacy budget (ε) to ensure that the privacy protection level complies with relevant regulatory requirements (such as GDPR or CCPA), while not affecting the effectiveness and accuracy of data analysis. Use an encryption hash algorithm (such as SHA-256) to hash the device control differential privacy protected data to generate a unique hash value for the data. Hash verification needs to be performed every time the data is read or updated to ensure that the data has not been tampered with. Combine public key infrastructure (PKI) and digital signature technology to generate digital signatures for each data change, which can ensure that the data has not been maliciously tampered with during transmission and storage, and at the same time provide a basis for subsequent security audits. Set up a security threat monitoring system to track all data access and modification behaviors in real time. The monitoring system can detect abnormal access behaviors (such as illegal access, permission overstepping, etc.) based on technologies such as behavior analysis and log auditing. When potential security threats are discovered, the system automatically generates detailed threat monitoring logs, including information such as timestamps, user identities, access operations, and descriptions of abnormal behaviors. This log provides important basis for subsequent security analysis and investigations. According to the security threat monitoring logs, evaluate the storage method of the device long-term optimization control matrix. If abnormal behaviors or potential attacks are found, the control matrix data needs to be migrated to a more secure storage environment (such as encrypted storage, private cloud with restricted access, etc.). Based on risk assessment, adjust the data storage strategy, such as storing sensitive control data at different levels, and adopting different security measures for control data at different levels (such as storing high-security-level data in restricted hardware devices). Encrypt the device control data stored in the cloud or external storage devices to prevent external attackers from obtaining the data through illegal means. According to the security threat monitoring logs, adjust the access control strategy in real time.For example, after detecting abnormal access behavior, immediately update access permissions or strengthen the authentication mechanism, such as enabling more stringent multi-factor authentication, enhancing permission auditing, etc. Machine learning algorithms can be combined to intelligently adjust access policies based on access patterns and historical security threat logs. For example, identify users who frequently access the device control matrix and dynamically adjust their access permissions to ensure the principle of least privilege. Based on all data access, storage, privacy protection measures, and monitoring logs, generate the final security optimization strategy. Automatically generate personalized security optimization strategies according to different device types, production requirements, and threat analysis, which can be achieved through a security policy engine or management platform, and dynamically adjust the strategy according to factors such as the operating state and usage environment of the device.

[0081] Preferably, step S1 includes the following steps:

[0082] Step S11: Obtain multi-modal type data of intelligent manufacturing equipment;

[0083] Step S12: Extract high-frequency sensor signals, operation logs, image, and video stream data from the multi-modal type data of intelligent manufacturing equipment, and perform time-series data acquisition on the high-frequency sensor signals, operation logs, image, and video stream data to generate an initial multi-modal data set;

[0084] Step S13: Perform time-series alignment on the initial multi-modal data set to generate a multi-modal aligned data set for manufacturing equipment; perform inter-modal calibration on the multi-modal aligned data set for manufacturing equipment to generate a multi-modal fusion data set;

[0085] Step S14: Extract cross-modal features of the multi-modal fusion data set to obtain cross-modal feature data for manufacturing equipment, where the cross-modal feature data for manufacturing equipment includes frequency-domain features, spatial features, and semantic features; perform fusion processing on the cross-modal feature data for manufacturing equipment to generate a multi-modal feature tensor;

[0086] Step S15: Decompose and compress the multi-modal feature tensor to generate multi-modal operation data for manufacturing equipment.

[0087] In the embodiments of the present invention, multi-modal data sources of intelligent manufacturing equipment are collected, including but not limited to high-frequency sensor signals, operation logs, image and video stream data, etc. Different sensors and device interfaces are used to obtain device status, environmental parameters, and real-time data during the production process. The signals collected from different sensors are processed in chronological order to ensure that various types of data (including sensor signals, operation logs, images, and video streams) can be analyzed uniformly on the time axis. For high-frequency sensor signals, the sampling frequency needs to be high enough to capture fine changes. The operation logs and image / video streams are captured synchronously to ensure that various types of data can cooperate effectively to form an initial multi-modal data set. The initial multi-modal data set is subjected to chronological alignment processing to ensure that data of different modalities are precisely matched in time, so that various types of data correspond to the correct timestamps. On the basis of chronological alignment, inter-modal calibration is performed to eliminate systematic errors from different sensors or data sources, so that the information content of each data modality is consistent. After calibration, a multi-modal aligned data set of the manufacturing equipment is generated. Cross-modal features are extracted from the multi-modal aligned data set, mainly including: extracting the frequency components of the signal through spectral analysis to help identify the dynamic characteristics of the equipment. Extracting the differences in spatial distribution by analyzing the performance of the equipment at different spatial positions. Extracting semantic information related to the equipment operation status and production process based on operation logs and image data. These cross-modal features are fused to generate a multi-modal feature tensor to represent the overall operation characteristics of the equipment. The generated multi-modal feature tensor is decomposed to identify potential low-dimensional feature representations, and at the same time data compression is performed to reduce redundant information. The compressed multi-modal data can be effectively stored and transmitted, and provide a concise and efficient input for subsequent equipment operation analysis.

[0088] As an example of the present invention, refer to Figure 2 shown, in this example, step S2 includes:

[0089] Step S21: Initialize a preset diffusion large model based on the multi-modal operation data of the manufacturing equipment to generate an initialized diffusion large model; input the multi-modal operation data of the manufacturing equipment into the initialized diffusion large model for device virtual operation processing to generate a device operation virtual state distribution map;

[0090] Step S22: Optimize the physical constraints of the initialized diffusion large model based on the device operation virtual state distribution map to generate a device physical characteristic matching diffusion model; use the device physical characteristic matching diffusion model to perform multiple rounds of training on the multi-modal operation data of the manufacturing equipment to generate a device control strategy candidate set;

[0091] Step S23: Verify and test the device physical characteristic matching diffusion model through the device control strategy candidate set to generate an optimized device control strategy set.

[0092] In the embodiments of the present invention, by obtaining multi-modal operation data of manufacturing equipment, including high-frequency sensor data, operation logs, images, video streams, etc., as the input of the diffusion large model. When initializing the diffusion large model, the equipment operation data is fused with the initial parameters of the model. At this time, the diffusion large model can already simulate different states and operating environments of the equipment. According to the characteristics of the equipment multi-modal operation data, the network structure and parameters of the diffusion large model are adjusted to make it adapt to the working mode and working conditions of the equipment. The initialized diffusion large model is used to perform virtual processing on the operation of the equipment, and the virtual state of the equipment under various working environments and conditions is deduced through the model, generating a virtual state distribution map of equipment operation. The map shows the operation state of the equipment under different working conditions and the corresponding probability distribution. The virtual state distribution map of equipment operation can be used to evaluate the performance of the equipment under different operating conditions and provide data support for subsequent optimization. Based on the virtual state distribution map of equipment operation, the diffusion large model is optimized by introducing physical constraint conditions of the equipment (such as equipment performance, limitation conditions, stability requirements, etc.). The goal of physical constraint optimization is to ensure that the equipment model conforms to the physical characteristics of the equipment during the simulation process and avoid generating virtual operation states that do not conform to the actual situation. The optimized diffusion large model will generate a diffusion model matching the physical characteristics of the equipment, which can more accurately simulate the operation state of the equipment in the actual production process. The diffusion model matching the physical characteristics of the equipment is used for multiple rounds of training, and different equipment control strategies are generated based on the model. In each round of training, by simulating the operation mode, working conditions and response results of the equipment, a candidate set of equipment control strategies is generated. These strategies include the optimal operation plans under different operation modes. The candidate set of control strategies covers a variety of equipment adjustment methods to ensure optimal control under different working conditions. The candidate set of equipment control strategies is applied to the diffusion large model for model verification and performance testing. During the model verification process, the effects of different control strategies are evaluated, the responses of the equipment under various working conditions are observed, and its performance is recorded. By comparing the verification results, those control strategies that can improve equipment performance, extend service life, reduce energy consumption or improve production efficiency are selected. Based on the model verification, the best-performing control strategies are screened out and combined into an optimized set of equipment control strategies. The optimized set of control strategies can dynamically adjust the operation mode of the equipment in actual production and automatically optimize the equipment performance according to different working environments and working conditions. These strategies can be implemented through an automated control system to ensure the stable operation and efficient production of the equipment during the production process.

[0093] Preferably, the physical constraint optimization of the initialized diffusion large model based on the virtual state distribution map of equipment operation includes:

[0094] Extract key operating state parameters from the data of the virtual state distribution map of equipment operation to generate equipment operation characteristic matrix data, where the extraction of key operating state parameters includes operating speed, temperature, and vibration frequency; perform physical constraint modeling on the equipment operation characteristic matrix data to generate physical characteristic constraint function set data;

[0095] Perform model constraint embedding processing on the physical characteristic constraint function set data to generate preliminary constraint diffusion model data; perform iterative training on the preliminary constraint diffusion model data to generate equipment physical characteristic matching diffusion model training data;

[0096] Perform feedback optimization on the equipment physical characteristic matching diffusion model training data to generate an equipment physical characteristic matching diffusion model.

[0097] In the embodiments of the present invention, by extracting key operating state parameters from the virtual state distribution map of equipment operation, these parameters generally include: speed data of the equipment under different working conditions, temperature change data of various parts of the equipment (such as motors, bearings, etc.), and vibration signals during the operation of the equipment, which can reflect the dynamic behavior of the equipment. Signal processing techniques (such as spectrum analysis, Fourier transform, etc.) are used to extract these important parameters from multi-modal data to ensure the capture of the key characteristics of the equipment. The extracted key operating state parameters (operating speed, temperature, vibration frequency) are organized into a matrix form to form equipment operation characteristic matrix data. The equipment operation characteristic matrix data is used to represent the multi-dimensional characteristics of the equipment under different operating states and can be used as the basis for physical constraint modeling. Based on the physical characteristics of the equipment (such as mechanical limitations, temperature limits, vibration tolerance, etc.), corresponding physical constraint models are established. The physical constraint modeling process involves the following: setting the speed range according to the maximum operating speed and working condition requirements of the equipment, setting the upper and lower temperature limits according to the thermal management requirements of the equipment, and defining the maximum tolerable vibration frequency according to the stability and safety requirements of the equipment. These physical constraint models are transformed into constraint functions to form a set of physical characteristic constraint function data. The generated set of physical characteristic constraint functions is embedded into the diffusion large model as the constraint conditions of the model. At this time, the output of the model not only depends on the input data but also needs to meet these physical constraint conditions. During the embedding process, optimization algorithms (such as Lagrange multiplier method, constraint optimization method, etc.) are used to combine the constraint functions with the objective function of the diffusion large model to ensure that the model output results conform to the physical constraints of the equipment. After the constraint embedding process, preliminary constraint diffusion model data is generated. At this time, the model can perform equipment state prediction and behavior simulation on the basis of meeting physical constraints. The preliminary constraint diffusion model data is used for multiple rounds of iterative training to optimize the model parameters to make it more accurate in simulating equipment behavior. During the training process, it not only depends on the historical data and virtual state distribution map of the equipment but also ensures that the model output meets the physical constraint conditions after each round of training. Through continuous training, the model can gradually adapt to the equipment operation characteristics under different working conditions and generate a more accurate diffusion model for matching the physical characteristics of the equipment. In each round of training, a corresponding training data set is generated for further optimization and tuning of the diffusion model. The training data set includes the operation data, constraint conditions, and optimization objectives of the equipment under various working conditions. Based on the training results, feedback optimization is performed on the diffusion model for matching the physical characteristics of the equipment. The feedback optimization process includes the following aspects: analyzing the errors of the model in predicting the equipment operation state, especially the parts that do not conform to the physical constraints. According to the feedback information, adjusting the model parameters and physical constraints to further optimize the accuracy of the model. Advanced optimization methods (such as gradient descent method, genetic algorithm, etc.) are used to accelerate the convergence speed of model training and avoid overfitting and underfitting phenomena. After feedback optimization, a diffusion large model that conforms to the physical characteristics of the equipment is finally generated.This model can accurately simulate the behavior of the device under actual working conditions and generate reasonable predictions of the device state under physical constraints. The final model can optimize the device control strategy in practical applications and support automatic adjustment and fault prediction in the intelligent manufacturing process.

[0098] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0099] Step S31: Perform state matching analysis on the multi-modal operation data of the manufacturing device according to the candidate set of device control strategies to generate a dynamic control strategy; decompose the control instruction set based on the dynamic control strategy to generate a device control instruction set;

[0100] Step S32: Use the device control instruction set to operate the intelligent manufacturing device and collect feedback data to generate a control execution feedback matrix; adaptively and real-time update the dynamic control strategy through the control execution feedback matrix to generate an adaptive dynamic control strategy;

[0101] Step S33: Perform multi-device collaborative optimization on the intelligent manufacturing device based on the adaptive dynamic control strategy to generate dynamic device collaborative optimization data; collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set;

[0102] Step S34: Perform diffusion model incremental learning and continuous optimization on the device physical property matching diffusion model through the incremental learning data set, so as to generate a long-term optimization control matrix for the device.

[0103] In the embodiments of the present invention, based on the candidate set of device control strategies, state matching analysis is performed with the multi-modal operation data of manufacturing devices. Matching algorithms (such as the least squares method, dynamic time warping algorithm, etc.) are used to evaluate the suitability between the current operating state of the device and the control strategy, and a state matching evaluation matrix is generated. According to the state matching evaluation results, the control strategies suitable for the current device state are screened and integrated to generate a dynamic control strategy. Based on the dynamic control strategy, high-level control objectives are decomposed into specific operation steps and instructions. The deconstruction process includes: extracting core control objectives (such as speed regulation, temperature control, fault prevention, etc.). Decomposing them into low-level executable device operation instructions (such as setting the rotation speed, adjusting the voltage, performing vibration correction, etc.). Finally, a device control instruction set is generated to provide an instruction basis for subsequent device operations. The intelligent manufacturing device is operated using the device control instruction set, and the operating state of the device is monitored in real time. Feedback data (including operation results, device responses, environmental changes, etc.) are collected through multi-modal sensors to generate a control execution feedback matrix. According to the control execution feedback matrix, the execution effect of the control strategy is analyzed, and its impact on device performance and operation objectives is evaluated. Adaptive algorithms (such as reinforcement learning, adaptive fuzzy control algorithm, etc.) are used to update the dynamic control strategy in real time. Through a feedback optimization process (such as adjusting strategy parameters, reallocating control priorities, etc.), an adaptive dynamic control strategy is generated to cope with changes in the device operating environment. Based on the adaptive dynamic control strategy, collaborative optimization of multiple intelligent manufacturing devices is carried out. Multi-device collaborative optimization algorithms (such as distributed optimization, collaborative game algorithms, etc.) are used to coordinate resource allocation, task allocation, and operation rhythm among multiple devices to generate dynamic device collaborative optimization data for evaluating and improving the collaboration efficiency between devices. By collecting and analyzing the execution effects of the dynamic device collaborative optimization data, the actual performance of the collaborative operation of the devices is obtained. The analysis content includes the collaboration efficiency between devices, operation stability, task completion degree, etc. The analysis results are organized into an incremental learning data set, which contains information such as device performance improvement requirements, new operation modes, and uncovered control strategies. The incremental learning data set is used for incremental learning of the device physical characteristic matching diffusion model. The incremental learning methods include: gradient accumulation: updating model parameters according to new data. Core sample selection: Selecting the samples that contribute the most to model improvement and adding them to the training. Model regularization: Preventing model overfitting caused by incremental learning. On the basis of incremental learning, the prediction ability and generalization performance of the diffusion model are further optimized. The optimization content includes: expanding the physical constraint range of the model to adapt to new working conditions. Improving the model's ability to handle non-linear characteristics and abnormal situations to generate the final long-term optimization control matrix for the device.

[0104] Preferably, the state matching analysis of the multi-modal operation data of manufacturing devices according to the candidate set of device control strategies includes:

[0105] Screen the candidate set of device control strategies according to the operating status rules to obtain the device control operating status rules; perform real-time operation feature-by-feature comparison and analysis on the multi-modal operation data of the manufacturing equipment according to the device control operating status rules, so as to generate feature comparison and analysis data;

[0106] Calculate the cosine similarity of the feature comparison and analysis data to generate a status matching score matrix; extract the highest score from the status matching score matrix to obtain the optimal status matching score data; perform strategy optimization processing on the candidate set of device control strategies through the optimal status matching score data to generate the preferred control strategy set data;

[0107] Perform logical integration processing on the preferred control strategy set data based on the time axis of the multi-modal operation data of the manufacturing equipment to generate dynamic control strategy data.

[0108] In the embodiment of the present invention, relevant operating status rules are extracted from the candidate set of device control strategies. The operating status rules define the range of device operating statuses applicable to different strategies, and usually include the following contents: speed range (such as low speed, medium speed, high speed). Temperature threshold (such as normal temperature, overheat, overcooling). Vibration frequency level (such as stable, micro-vibration, strong vibration). The goal of screening is to eliminate strategies that are not suitable for the current operating scenario, obtain the device control operating status rules, and provide constraint conditions for subsequent matching analysis. Extract key operating features from the multi-modal operation data of the manufacturing equipment, such as: speed (rotation speed, linear speed, etc. measured in real time). Temperature (temperature of the device surface, inside or environment). Vibration frequency (real-time vibration signal obtained through sensors). Compare and analyze the real-time extracted operating features with the operating status rules item by item. By calculating the feature difference amount, generate feature comparison and analysis data, including the deviation degree of each operating feature from the rule requirements. Apply the cosine similarity formula to the feature comparison and analysis data: ; where is the real-time operation feature vector, It is the eigenvector of the operation state rule. The calculated cosine similarity represents the matching degree between the current operation characteristics and each control policy rule. The matching scores of all policies are integrated into a state matching score matrix. Each row of the matrix corresponds to a set of device operation characteristics, and each column corresponds to a control policy. The highest score and its corresponding policy in each row are extracted from the state matching score matrix to generate the optimal state matching score data, which identifies the optimal control policy in the current operation state. According to the optimal state matching score data, the candidate set of device control policies is screened, low-matching policies are removed, and high-matching policies are retained to generate the preferred control policy set data. Based on the time axis of the multi-modal operation data of the manufacturing equipment, the preferred control policy set is logically integrated. The integration process includes: considering time correlation to ensure the continuity and compatibility between policies. Conflicting policies are eliminated, and policies with a wider coverage or stronger applicability are preferentially retained. The logically integrated preferred control policy set is transformed into dynamic control policy data. The dynamic control policy data is a control instruction framework that adapts to the device operation state in real time and lays the foundation for subsequent control instruction generation and device operation.

[0109] Preferably, step S33 includes the following steps:

[0110] Step S331: Based on the adaptive dynamic control policy, perform task decomposition processing between intelligent manufacturing devices to generate device task allocation matrix data, where the task decomposition processing between devices includes load capacity decomposition and resource occupancy decomposition;

[0111] Step S332: Coordinate the device operation parameters of the device task allocation matrix data to generate a multi-device collaborative operation parameter set data;

[0112] Step S333: Perform dynamic execution simulation processing on the multi-device collaborative operation parameter set data to generate dynamic device collaborative optimization data; extract key collaborative indicators from the dynamic device collaborative optimization data to obtain multi-device key collaborative indicators, where the multi-device key collaborative indicators include inter-device delay, resource conflict, or energy consumption deviation;

[0113] Step S334: Collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set.

[0114] In the embodiments of the present invention, based on device performance parameters (such as running speed, processing capacity, task capacity, etc.), the upper limit of the load capacity and the current load status of each device are calculated. The total task amount is proportionally allocated to different devices to generate load capacity decomposition data. The current resource occupancy of the devices (such as CPU usage rate, memory occupancy, energy consumption, etc.) is evaluated. According to the device resource occupancy status, the task allocation weights are adjusted to optimize the resource allocation efficiency. Combining the load capacity and resource occupancy decomposition data, device task allocation matrix data is generated, indicating the tasks allocated to each device and their priorities. The operation parameter requirements of each device, such as task execution time, resource call amount, communication bandwidth, etc., are extracted from the device task allocation matrix. According to the task dependency relationship and the communication requirements between devices, the device operation parameters are adjusted to ensure the coordination consistency between devices. The coordination content includes: ensuring the accuracy of the task sequence and timing dependency between devices, unifying the protocol standards for communication between devices, reducing latency, optimizing the resource allocation strategy, and avoiding conflicts. The optimized set of device operation parameters is output as the input for subsequent simulation and execution. Using the multi-device collaborative operation parameter set, the dynamic behavior of the devices during actual operation is simulated. The simulation process includes: simulation of the execution process after task decomposition, simulation of communication latency and data transmission between multiple devices, and prediction and analysis of energy consumption and resource utilization rate. The dynamic device collaborative optimization data is output, describing the overall performance of the device collaborative operation. The multi-device key collaborative indicators are extracted from the dynamic device collaborative optimization data, specifically including: the difference in task completion time between devices, the degree of competition for shared resources (such as network bandwidth, storage, etc.) between devices, and the difference between the actual energy consumption and the theoretical energy consumption of the devices. The actual operation data of the devices is monitored in real time, including task completion rate, device response time, energy consumption data, etc. The collected operation effect data is compared and analyzed with the dynamic device collaborative optimization data to evaluate the actual effect of the optimization scheme. The collected execution effect data is processed: the difference values of key indicators such as task completion rate, latency, and energy consumption are extracted. The reasons for not meeting the collaborative requirements during operation (such as communication bottlenecks, device performance limitations, etc.) are analyzed. The processed data is integrated into an incremental learning data set to provide support for the subsequent optimization of the device physical characteristic matching diffusion model.

[0115] Preferably, step S334 includes the following steps:

[0116] Step S3341: Collect multi-dimensional execution effect data from the dynamic device collaborative optimization data to generate an initial data set of device collaborative execution effects; conduct an expected value comparison analysis on the initial data set of device collaborative execution effects to generate execution deviation data;

[0117] Step S3342: Compare the execution deviation data with a preset standard execution deviation threshold. When the execution deviation data is greater than or equal to the preset standard execution deviation threshold, conduct attribution analysis and optimization feedback on the execution deviation data to generate execution optimization feedback data;

[0118] Step S3343: When the execution deviation data is less than the preset standard execution deviation threshold, construct incremental learning data from the execution deviation data to generate an incremental learning data set.

[0119] In the embodiment of the present invention, by monitoring and recording the dynamic device collaborative optimization data in real time, multi-dimensional data of device collaborative execution is collected, including: the time difference in task completion between devices, the actual energy consumption during device task execution, the proportion of bandwidth, storage, and computing resources used by the device, and the time interval from task allocation to execution completion. Integrate the above collected data to generate an initial data set of device collaborative execution effects. According to the preset performance goals and collaborative requirements, conduct comparative analysis on the initial data set of device collaborative execution effects and calculate the deviation. Compare the actual task completion time with the expected time to calculate the time deviation, compare the actual energy consumption with the expected energy consumption to calculate the energy consumption deviation, and statistically analyze the distribution of resource utilization rates to calculate the resource allocation deviation. Integrate the analysis results to generate execution deviation data, indicating the difference between the actual operation effect of the device and the expected value. Compare the execution deviation data with the preset standard execution deviation threshold: Determine that there is an obvious deviation in the device operation and further optimization is required; if the operation effect is close to the target, it can be used for incremental learning. For the scenario where the deviation exceeds the threshold, analyze the cause of the deviation: Analyze whether the performance of the device hardware or software meets the standard, identify resource competition or bottleneck problems, check the burden distribution of the task allocation strategy on the device, and evaluate the communication efficiency between devices. The attribution analysis can be achieved through methods such as data visualization, clustering analysis, or correlation calculation. For the results of the attribution analysis, generate an optimization feedback strategy, including: adjusting the task decomposition weight, optimizing the resource scheduling plan, improving the communication protocol or network configuration. Output the execution optimization feedback data to provide a reference for adjusting the device collaborative operation parameters. Screen the data with a deviation less than the preset standard threshold, and extract the key feature data in the stable operation scenario. Extract features from the screened execution deviation data to construct incremental learning samples, including: the change trend of task completion time and response speed, the time series distribution of device energy consumption, the resource occupancy ratio and balance. Organize and normalize the feature data to generate an incremental learning data sample set. Summarize the feature sample data to construct a complete incremental learning data set, providing data support for the continuous optimization and long-term learning of the diffusion model for device physical characteristics matching.

[0120] Preferably, step S4 includes the following steps:

[0121] Step S41: Perform distributed encrypted storage on the long-term optimization control matrix of the device and the device physical characteristic matching diffusion model to generate an encrypted storage mapping table; perform access control on the encrypted storage mapping table based on the differential privacy protection mechanism to generate device control differential privacy protection data;

[0122] Step S42: Perform integrity verification on the device control differential privacy protection data to generate a security threat monitoring log; dynamically adjust the storage and access policies of the long-term optimization control matrix of the device according to the security threat monitoring log to generate a final security optimization policy for executing intelligent manufacturing device control operations.

[0123] In the embodiment of the present invention, the long-term optimization control matrix of the device and the device physical characteristic matching diffusion model are subjected to data sharding processing, and divided into multiple sub-data units. According to the distributed storage network, the sub-data units are stored in different nodes to form the physical dispersion of the data. An independent encryption key is assigned to each sub-data unit, and an advanced symmetric or asymmetric encryption algorithm (such as AES or RSA) is used to encrypt the data to generate an encrypted storage mapping table of the sub-data unit, where the mapping table includes the sub-data unit identifier, the storage node location, and the corresponding encryption key. The mapping table is stored using distributed blockchain technology to ensure its immutability and high availability. Before accessing the encrypted storage mapping table, differential privacy processing is performed on the user request, and appropriate noise is added to protect data sensitivity. Calculate the privacy budget of the access request to ensure that the privacy leakage risk is controlled within an acceptable range. Define the access control policy, and allocate access permissions based on user authentication and permission levels. Combine the differential privacy mechanism to record and monitor the access operation to generate device control differential privacy protection data. Use a hash function (such as SHA-256) on the device control differential privacy protection data to generate a check value. Compare the hash value of the currently stored data with the hash value of the original record to detect whether the data has been tampered with. If the verification fails, trigger an exception handling process and record it in the security threat monitoring log. The security threat monitoring log records the following information: data access time, access user identity, exception type (such as tampering, unauthorized access, etc.), analyze the log data to identify potential security threats. According to the threat type marked in the security threat monitoring log, dynamically adjust the storage and access policies of the long-term optimization control matrix of the device: adjust the encryption algorithm or key update frequency, restrict the access permissions of high-risk users, and enhance the access audit frequency. Integrate all optimization measures to form a security optimization policy to ensure the security of the long-term optimization control matrix of the device in distributed storage. The security optimization policy includes: encryption algorithm update rules, differential privacy budget allocation policies, and execution frequencies of data integrity verification.

[0124] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0125] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A control method for intelligent manufacturing equipment based on a diffusion model, characterized in that: The following steps are involved: Step S1: Acquire multimodal type data of intelligent manufacturing equipment; Based on the multimodal data of intelligent manufacturing equipment, time series data is collected to generate an initial multimodal data set; Perform operation mode fusion on the initial multimodal data set to generate multimodal operation data of manufacturing equipment; Step S2: Based on the preset diffusion macro model, the multimodal operation data of the manufacturing equipment is processed for virtual operation to generate a virtual state distribution map of the equipment operation; based on the virtual state distribution map of the equipment operation, the initialized diffusion macro model is optimized for physical constraints to generate a diffusion model matching the physical characteristics of the equipment; based on the virtual state distribution map of the equipment operation, the equipment physical characteristics matching diffusion model is verified and tested for multiple rounds of models to generate an optimized set of equipment control strategies; Step S3: performing state matching analysis on the multimodal operation data of the manufacturing equipment according to the equipment control strategy candidate set to generate a dynamic control strategy; performing multi-equipment collaborative optimization on the intelligent manufacturing equipment based on the dynamic control strategy to generate dynamic equipment collaborative optimization data; Collect and analyze the execution effect of dynamic equipment collaborative optimization data to generate an incremental learning data set; use the incremental learning data set to perform incremental learning and continuous optimization of the diffusion model matching the equipment physical characteristics, thereby generating a long-term optimization control matrix for the equipment; Step S4: Perform access control on the device long-term optimization control matrix and the device physical characteristic matching diffusion model to generate device control differential privacy protection data; Perform integrity check on the differential privacy protection data of device control and generate security threat monitoring logs. Dynamically adjust the storage and access strategies of the equipment long-term optimization control matrix based on the security threat monitoring logs to generate the final security optimization strategy to execute intelligent manufacturing equipment control operations.

2. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire multimodal type data of intelligent manufacturing equipment; Step S12: extracting high-frequency sensor signals, operation logs, images and video stream data from the multimodal data of intelligent manufacturing equipment, and performing time series data collection on the high-frequency sensor signals, operation logs, images and video stream data to generate an initial multimodal data set; Step S13: performing time alignment on the initial multimodal data set to generate a multimodal alignment data set for manufacturing equipment; performing inter-modal correction on the multimodal alignment data set for manufacturing equipment to generate a multimodal fusion data set; Step S14: extracting cross-modal features of the multimodal fusion data set to obtain cross-modal feature data of manufacturing equipment, wherein the cross-modal feature data of manufacturing equipment includes frequency domain features, spatial features, and semantic features; fusing the cross-modal feature data of manufacturing equipment to generate a multimodal feature tensor; Step S15: Decompose and compress the multimodal feature tensor to generate multimodal operation data of the manufacturing equipment.

3. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Initializing a preset diffusion macromodel based on the multimodal operation data of the manufacturing equipment to generate an initialized diffusion macromodel; inputting the multimodal operation data of the manufacturing equipment into the initialized diffusion macromodel to perform equipment virtual operation processing to generate a virtual state distribution diagram of the equipment operation; Step S22: Based on the equipment operation virtual state distribution diagram, the initialized diffusion model is optimized by physical constraints to generate a diffusion model matching the equipment physical characteristics; the multi-modal operation data of the manufacturing equipment is trained multiple times by using the equipment physical characteristics matching diffusion model to generate a candidate set of equipment control strategies; Step S23: Model verification and testing are performed on the equipment physical characteristic matching diffusion model through the equipment control strategy candidate set to generate an optimized equipment control strategy set.

4. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 3 is characterized in that: The physical constraint optimization of the initialized diffusion model based on the virtual state distribution diagram of the equipment operation includes: Extract key operating state parameters from the equipment operation virtual state distribution diagram data to generate equipment operation characteristic matrix data, where the key operating state parameters extracted include operating speed, temperature, and vibration frequency; perform physical constraint modeling on the equipment operation characteristic matrix data to generate physical characteristic constraint function set data; Perform model constraint embedding processing on the physical characteristic constraint function set data to generate preliminary constraint diffusion model data; perform iterative training on the preliminary constraint diffusion model data to generate equipment physical characteristic matching diffusion model training data; Feedback optimization is performed on the equipment physical characteristic matching diffusion model training data to generate the equipment physical characteristic matching diffusion model.

5. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing state matching analysis on multimodal operation data of manufacturing equipment according to the equipment control strategy candidate set to generate a dynamic control strategy; deconstructing a control instruction set based on the dynamic control strategy to generate an equipment control instruction set; Step S32: using the device control instruction set to operate the intelligent manufacturing device and collect feedback data to generate a control execution feedback matrix; adaptively updating the dynamic control strategy in real time through the control execution feedback matrix to generate an adaptive dynamic control strategy; Step S33: performing multi-device collaborative optimization on intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate dynamic equipment collaborative optimization data; performing execution effect collection and analysis on the dynamic equipment collaborative optimization data to generate an incremental learning data set; Step S34: Perform incremental learning and continuous optimization of the diffusion model by matching the physical characteristics of the equipment with the diffusion model through the incremental learning data set, thereby generating a long-term optimization control matrix for the equipment.

6. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 5 is characterized in that: The state matching analysis of the multimodal operation data of manufacturing equipment based on the equipment control strategy candidate set includes: Screening the operation status rules of the equipment control strategy candidate set to obtain the equipment control operation status rules; performing a comparative analysis of the real-time operation characteristics of the multi-modal operation data of the manufacturing equipment item by item according to the equipment control operation status rules, thereby generating feature comparative analysis data; Calculate the cosine similarity of the feature comparison analysis data to generate a state matching score matrix; extract the highest score from the state matching score matrix to obtain the optimal state matching score data; perform strategy optimization processing on the device control strategy candidate set based on the optimal state matching score data to generate the optimal control strategy set data; Based on the time axis of the multimodal operation data of the manufacturing equipment, the optimal control strategy set data is logically integrated and processed to generate dynamic control strategy data.

7. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 5 is characterized in that: Step S33 includes the following steps: Step S331: performing inter-device task decomposition processing on the intelligent manufacturing equipment based on the adaptive dynamic control strategy to generate equipment task allocation matrix data, wherein the inter-device task decomposition processing includes load capacity decomposition and resource occupancy decomposition; Step S332: Coordinate the equipment operation parameters of the equipment task allocation matrix data to generate multi-equipment collaborative operation parameter set data; Step S333: dynamically executing simulation processing on the multi-device collaborative operation parameter set data to generate dynamic device collaborative optimization data; extracting key collaborative indicators from the dynamic device collaborative optimization data to obtain multi-device key collaborative indicators, wherein the multi-device key collaborative indicators include inter-device delay, resource conflict or energy consumption deviation; Step S334: collect and analyze the execution effect of the dynamic device collaborative optimization data to generate an incremental learning data set.

8. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 7 is characterized in that: Step S334 includes the following steps: Step S3341: collect multi-dimensional execution effect data of dynamic device collaborative optimization data to generate an initial data set of device collaborative execution effect; perform expected value comparison analysis on the initial data set of device collaborative execution effect to generate execution deviation data; Step S3342: comparing the execution deviation data with a preset standard execution deviation threshold, and when the execution deviation data is greater than or equal to the preset standard execution deviation threshold, performing attribution analysis and optimization feedback on the execution deviation data to generate execution optimization feedback data; Step S3343: When the execution deviation data is less than a preset standard execution deviation threshold, incremental learning data is constructed for the execution deviation data to generate an incremental learning data set.

9. The intelligent manufacturing equipment control method based on the diffusion large model according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Distributed encrypted storage is performed on the equipment long-term optimization control matrix and the equipment physical characteristic matching diffusion model to generate an encrypted storage mapping table; access control is performed on the encrypted storage mapping table based on a differential privacy protection mechanism to generate equipment control differential privacy protection data; Step S42: Perform integrity check on the device control differential privacy protection data and generate a security threat monitoring log; dynamically adjust the storage and access strategy of the device long-term optimization control matrix based on the security threat monitoring log, and generate a final security optimization strategy to execute intelligent manufacturing equipment control operations.

10. An intelligent manufacturing equipment control system based on a diffusion large model, characterized in that: The method for controlling intelligent manufacturing equipment based on a diffusion large model according to claim 1, wherein the control system for intelligent manufacturing equipment based on a diffusion large model comprises: The modal fusion module is used to obtain multi-modal data of intelligent manufacturing equipment; perform time series data collection based on the multi-modal data of intelligent manufacturing equipment to generate an initial multi-modal data set; perform operation modal fusion on the initial multi-modal data set to generate multi-modal operation data of manufacturing equipment; The virtual operation module is used to perform virtual operation processing on the multimodal operation data of the manufacturing equipment based on the preset diffusion macro model, and generate a virtual state distribution map of the equipment operation; perform physical constraint optimization on the initialized diffusion macro model based on the equipment operation virtual state distribution map, and generate a diffusion model matching the equipment physical characteristics; perform multiple rounds of model verification and testing on the equipment physical characteristics matching diffusion model based on the equipment operation virtual state distribution map, and generate an optimized set of equipment control strategies; The equipment collaborative control module is used to perform state matching analysis on the multimodal operation data of manufacturing equipment according to the candidate set of equipment control strategies to generate dynamic control strategies; perform multi-equipment collaborative optimization of intelligent manufacturing equipment based on dynamic control strategies to generate dynamic equipment collaborative optimization data; perform execution effect collection and analysis on dynamic equipment collaborative optimization data to generate incremental learning data sets; perform diffusion model incremental learning and continuous optimization on the equipment physical characteristics matching diffusion model through the incremental learning data sets, thereby generating a long-term optimization control matrix for the equipment; The secure storage module is used to perform access control on the equipment long-term optimization control matrix and the equipment physical characteristics matching diffusion model, generate equipment control differential privacy protection data; perform integrity verification on the equipment control differential privacy protection data, and generate security threat monitoring logs; dynamically adjust the storage and access strategies of the equipment long-term optimization control matrix according to the security threat monitoring logs, and generate the final security optimization strategy to execute intelligent manufacturing equipment control operations.

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