An ai large model driven full-process intelligent industrial equipment system and an application method thereof

Abstract

The application discloses an AI large model driven full-process intelligent industrial equipment system and an application method thereof, and is characterized in that the application is applied to industrial equipment and comprises an integrated AI large model, a data acquisition and integration module, a real-time monitoring and analysis module, an automatic control module, an intelligent maintenance and predictive maintenance module, a supply chain optimization module and a user interaction module; a feedback mechanism is established; the integrated AI large model performs self-learning and iteration according to new data and user feedback information obtained from the industrial equipment through the data acquisition and integration module, the real-time monitoring and analysis module, the automatic control module, the intelligent maintenance and predictive maintenance module and the supply chain optimization module, so that the accuracy and efficiency of the integrated AI large model are improved; and the application realizes comprehensive intelligentization of a production process, improves production efficiency and product quality, and reduces operating costs.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment management system technology, and in particular to an AI-driven, large-scale model-driven, end-to-end intelligent industrial equipment system and its application method. Background Technology

[0002] Industrial equipment management systems play a crucial role in modern enterprises. Through functions such as real-time monitoring, predictive maintenance, and optimized resource allocation, they help enterprises improve equipment utilization and production efficiency. Digital transformation is one of the core trends in the development of equipment management systems. By adopting advanced digital technologies, such as the Internet of Things (IoT), sensor technology, and data analysis tools, enterprises can achieve real-time monitoring and analysis of equipment, effectively prevent equipment failures, and reduce production downtime.

[0003] However, industrial equipment management systems also face some problems and challenges in practical applications:

[0004] The sheer variety of equipment and the difficulty of management: Factories typically possess a large number of diverse pieces of equipment with varying performance characteristics, making management challenging. A comprehensive equipment file system needs to be established for categorized management.

[0005] Insufficient skill level of equipment maintenance personnel: Maintenance personnel need to have professional skills and knowledge, but in practice, insufficient skill level often leads to poor equipment maintenance results.

[0006] Frequent equipment failures affect production progress: Equipment failures not only lead to production interruptions but may also pose safety hazards, necessitating the establishment of a comprehensive equipment inspection system and preventative maintenance system.

[0007] Rapid equipment upgrades and replacements lead to high management costs: the introduction of new equipment is often accompanied by high costs and technical challenges, requiring the development of reasonable equipment upgrade plans and technical training.

[0008] Inadequate equipment safety management: The safe operation of equipment is directly related to production safety and the safety of employees' lives. However, in practice, due to inadequate safety management, there are potential safety hazards in the equipment.

[0009] Taking the production workshop of a small household appliance manufacturing enterprise as an example, the current situation is as follows: The production process of small household appliances involves a variety of equipment, such as injection molding machines, assembly lines, and testing equipment. The data generated by different equipment has different formats and types, making it difficult to collect and analyze them in a unified manner. There is a lack of effective data analysis tools, making it impossible to detect abnormalities and potential faults in the production process in a timely manner. The automated control of the equipment mostly adopts simple control algorithms, which are poorly adaptable to complex production environments and changing production requirements. They cannot automatically adjust control strategies according to the actual operating status of the equipment, resulting in low production efficiency. Equipment maintenance mainly relies on periodic inspections and emergency repairs at the end of the period and after failures occur. There is a lack of predictive maintenance methods, resulting in long periods of unexpected equipment downtime. Supply chain management is not optimized, and inventory backlogs and raw material shortages occur frequently, increasing production costs.

[0010] Intelligent industrial equipment is an important type of machinery in the manufacturing industry, playing a crucial role in intelligent manufacturing. Common intelligent industrial equipment includes laser cutters and 3D printers, both of which are essential in the intelligent manufacturing process.

[0011] Intelligent industrial equipment is a general term for various tools used in intelligent manufacturing, including various equipment, robots, sensors, etc. They can realize intelligent production processes, enhance production efficiency and quality, and reflect the level and trend of the entire manufacturing industry. It is also an important component of intelligent manufacturing. Summary of the Invention

[0012] To address the aforementioned technical problems, this invention provides an AI-driven, end-to-end intelligent industrial equipment system. The system, applied to industrial equipment, includes an integrated AI model, a data acquisition and integration module, a real-time monitoring and analysis module, an automated control module, an intelligent maintenance and predictive maintenance module, a supply chain optimization module, and a user interaction module. A feedback mechanism is established, whereby the integrated AI model learns and iterates based on new data and user feedback acquired from the industrial equipment through the data acquisition and integration module, the real-time monitoring and analysis module, the automated control module, the intelligent maintenance and predictive maintenance module, and the supply chain optimization module, thereby improving the accuracy and efficiency of the integrated AI model's operation.

[0013] The integrated AI big model is trained using deep learning and machine learning algorithms to process and analyze industrial equipment data.

[0014] The data acquisition and integration module collects data in real time from various sensors and data sources of industrial equipment and integrates it into a unified data platform.

[0015] The real-time monitoring and analysis module monitors and analyzes real-time data to promptly detect anomalies in the production process and predict potential failures.

[0016] The automated control module connects the integrated AI model to the industrial equipment to achieve automated control of the industrial equipment.

[0017] The intelligent maintenance and predictive maintenance module analyzes the status and performance data of industrial equipment through the integrated AI model, predicts the maintenance needs of industrial equipment, and reduces the unexpected downtime of industrial equipment.

[0018] The supply chain optimization module analyzes supply chain data through the integrated AI model to optimize inventory management and logistics scheduling, thereby reducing costs and improving response speed.

[0019] Users can monitor the operational status of the integrated AI model through the user interaction module and intervene as needed.

[0020] As an improvement to the AI-driven intelligent industrial equipment system of the present invention, the integrated AI model is equipped with a multimodal data processing module 305. The multimodal data processing module 305 processes and analyzes multimodal data from different types of sensors of different industrial equipment, including vision, sound, temperature and pressure, to obtain all operating status information of the industrial equipment.

[0021] As an improvement to the AI-driven intelligent industrial equipment system of the present invention, the integrated AI model is equipped with an adaptive learning module. The adaptive learning module automatically adjusts its parameters and algorithms based on new data obtained from the industrial equipment and user feedback information to adapt to the ever-changing production environment.

[0022] As an improvement to the AI-driven intelligent industrial equipment system of the present invention, the integrated AI model is equipped with a data security and privacy protection module. The data security and privacy protection module performs security processing and privacy protection on sensitive information in new data obtained from the industrial equipment and user feedback information.

[0023] As an improvement to the AI-driven intelligent industrial equipment system of the present invention, a modular design is adopted, and data from different industrial equipment or different production processes of the same industrial equipment are integrated into the integrated AI model through different modules.

[0024] As an improvement to the AI-driven intelligent industrial equipment system of the present invention, an expansion module is also provided, which expands and upgrades the system.

[0025] As an improvement to the AI-driven intelligent industrial equipment system of this invention, a cost-benefit analysis module is also provided. During the system design and implementation process, the cost-benefit analysis module performs a detailed cost-benefit analysis to ensure that the introduction of the system can bring actual economic benefits to the enterprise.

[0026] As an improvement to the AI-driven intelligent industrial equipment system of the present invention, a human-machine collaboration module is also provided, which coordinates the operation of the system and the user's operation.

[0027] This invention provides an application method for an AI-driven, large-scale model-based, fully intelligent industrial equipment system, characterized in that: applying the AI-driven, large-scale model-based, fully intelligent industrial equipment system according to any one of claims 1-8 includes the following steps:

[0028] The first step involves collecting data from various sensors and data sources of industrial equipment through a data acquisition and integration module. Appropriate sensors are configured according to the type and characteristics of the industrial equipment. Then, the collected data is transmitted to a unified data platform according to the data transmission protocol. During the data transmission process, the data is monitored in real time to ensure the integrity and accuracy of the data.

[0029] In the second step, the multimodal data processing module 305 of the integrated AI big model processes the multimodal data, extracts the features of each modality and fuses them, and uses corresponding deep learning and machine learning algorithms for analysis according to different application requirements. For equipment fault prediction applications, fault prediction algorithms are used to analyze the status data of the equipment to obtain the time and probability of possible equipment failure.

[0030] Third, based on the analysis results of the integrated AI big model, the automation control module and the supply chain optimization module take corresponding actions. The analysis results of the automation control module indicate that the equipment needs to adjust its operating parameters. The control commands are sent to the actuators of the equipment through the industrial automation communication protocol to realize the automation control of the equipment. The analysis results of the supply chain optimization module indicate that the inventory needs to be adjusted. The inventory optimization algorithm and the logistics scheduling algorithm are used to optimize the supply chain and adjust the inventory level and logistics arrangement.

[0031] Fourth, the intelligent maintenance and predictive maintenance module formulates a maintenance plan based on the analysis results of the integrated AI model, predicts that the equipment will need maintenance in the future, and arranges maintenance personnel and necessary repair tools and parts in advance. The user monitors the system's operating status through the user interaction module and provides feedback information according to the actual situation. If the user finds that the prediction results are inaccurate, the user inputs the feedback information into the integrated AI model through the user interaction module, and the integrated AI model performs autonomous learning and iteration.

[0032] The AI-driven intelligent industrial equipment system and its application method described in this invention have the following advantages: a unified data platform can collect and integrate various data from different devices in real time, and through the integration of the AI ​​model, it can quickly and accurately analyze the data and promptly detect anomalies and potential faults in the production process; by integrating the AI ​​model and the automation control module, the control strategy can be automatically adjusted according to the actual operating status of the equipment, thereby improving production efficiency; the application of predictive maintenance methods greatly reduces unexpected equipment downtime, while supply chain optimization improves inventory backlog and raw material shortages, thus reducing production costs. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the integrated AI large model process in a preferred embodiment of the AI ​​large model-driven intelligent industrial equipment system and its application method of the present invention.

[0034] Figure 2 This is a flowchart illustrating a preferred embodiment of the AI-driven intelligent industrial equipment system and its application method of the present invention. Detailed Implementation

[0035] Below, in conjunction with the appendix Figure 1-2 The present invention will be further described in detail below with specific implementation methods. It should be noted that, without conflict, the technical features described below can be arbitrarily combined to form new embodiments.

[0036] In a preferred embodiment, the present invention provides an AI-driven intelligent industrial equipment system for the entire process, characterized in that it is applied to industrial equipment 2 and includes an integrated AI model 4, a data acquisition and integration module 3, a real-time monitoring and analysis module 5, an automated control module 6, an intelligent maintenance and predictive maintenance module 7, a supply chain optimization module 8, and a user interaction module 9. A feedback mechanism is established, wherein the integrated AI model 4 performs self-learning and iteration based on new data and user feedback information acquired from the industrial equipment 2 through the data acquisition and integration module 3, the real-time monitoring and analysis module 5, the automated control module 6, the intelligent maintenance and predictive maintenance module 7, and the supply chain optimization module 8, thereby improving the accuracy and efficiency of the integrated AI model 4's operation.

[0037] The integrated AI model 4 is trained using deep learning algorithms (such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and their variants, Long Short-Term Memory Networks (LSTM),) and machine learning algorithms (such as decision trees and support vector machines) to process and analyze data from industrial equipment 2. Its technical logic lies in using deep learning algorithms to automatically learn and extract features from a large amount of industrial equipment 2 data, and using machine learning algorithms to perform classification, regression, and other analyses on the data, thereby achieving accurate judgment and prediction of the operating status of industrial equipment 2.

[0038] The data acquisition and integration module 3 collects data in real time from various sensors (such as temperature sensors, pressure sensors, vibration sensors, etc. 301) and data sources (such as production databases, equipment logs, etc.) of the industrial equipment 2, and integrates it into a unified data platform. The data acquisition technologies used include sensor interface technology and data transmission protocols (such as TCP / IP, Modbus, etc.). Data integration employs technologies such as data cleaning and data standardization to ensure data quality and consistency.

[0039] The real-time monitoring and analysis module 5 monitors and analyzes real-time data to promptly detect anomalies in the production process and predict potential faults. The technologies employed include real-time data processing technologies (such as the streaming frameworks Flink and Spark Streaming), anomaly detection algorithms (such as statistical methods and machine learning-based methods), and fault prediction algorithms (such as time-series analysis-based methods and deep learning-based prediction methods).

[0040] The automated control module 6 connects the integrated AI big model 4 to the industrial equipment 2 to realize the automated control of the industrial equipment 2. The technologies used include industrial automation communication protocols (such as OPCUA, Profibus, etc.) and control algorithms 601 (such as PID control, fuzzy control, etc.). By receiving the decision instructions from the AI ​​big model, it performs precise control on the actuators (such as motors, valves, etc.) of the industrial equipment 2.

[0041] The intelligent maintenance and predictive maintenance module 7 analyzes the status and performance data of industrial equipment 2 through the integrated AI big model 4, predicts the maintenance needs of industrial equipment 2, and reduces the unexpected downtime of industrial equipment 2. Its technical logic is based on the analysis of historical data and real-time status data of industrial equipment 2, and uses machine learning and deep learning algorithms to build a maintenance prediction model, predicts the remaining service life of key components of the equipment, and arranges maintenance plans 701 in advance.

[0042] The supply chain optimization module 8 analyzes supply chain data through the integrated AI big model 4, optimizes inventory management and logistics scheduling 801, reduces costs and improves response speed. The technologies used include supply chain management system (SCM) integration technology, inventory optimization algorithms (such as the improved algorithm of the economic order quantity model EOQ), and logistics scheduling algorithms (such as the optimization algorithm of the vehicle routing problem VRP).

[0043] User 1 monitors the operating status of the integrated AI model 4 through the user 1 interaction module 9 and intervenes as needed. The user 1 interaction module 9 adopts graphical user interface (GUI) technology to provide an intuitive operation interface, making it convenient for user 1 to view the output results of the model (such as equipment operation status report, predictive maintenance plan 701, etc.) and input feedback information (such as correction of prediction results, adjustment of control strategy, etc.).

[0044] In a preferred embodiment, the integrated AI large model 4 is equipped with a multimodal data processing module 305305. This module processes and analyzes multimodal data from different types of sensors in different industrial equipment 2, including visual data (such as image data acquired by industrial cameras), sound data (such as noise data during equipment operation), temperature data, and pressure data, to obtain all operational status information of the industrial equipment 2. The technical logic involves using feature extraction and fusion techniques (such as principal component analysis (PCA) for feature extraction and deep neural networks for feature fusion) to uncover the correlations between different modal data, thereby gaining a comprehensive understanding of the operational status information of the industrial equipment 2.

[0045] In a preferred embodiment, the integrated AI large model 4 is equipped with an adaptive learning module. This module automatically adjusts its parameters and algorithms based on new data acquired from the industrial equipment 2 and feedback information from user 1 to adapt to the constantly changing production environment. Its technical logic is based on online learning algorithms (such as an online version of stochastic gradient descent) to continuously update the model's weights and parameters, enabling the model to adapt to new data distributions and production requirements in a timely manner.

[0046] In a preferred embodiment, the integrated AI big data model 4 is equipped with a data security and privacy protection module. This module performs secure processing and privacy protection on sensitive information in new data acquired from the industrial equipment 2 and user feedback information. The technologies employed include data encryption technologies (such as the symmetric encryption algorithm AES and the asymmetric encryption algorithm RSA), access control technologies (such as role-based access control RBAC), and data anonymization technologies to ensure the security and privacy of data during storage, transmission, and use.

[0047] In a preferred embodiment, a modular design is adopted, where data from different industrial equipment 2 or data from different production processes of the same industrial equipment 2 are integrated into the integrated AI model 4 through different modules. The technical logic is to achieve efficient data transfer and flexible model integration by defining clear module interfaces and data formats. For example, for different types of industrial equipment 2, such as machine tools and injection molding machines, their data acquisition modules will collect corresponding data according to the characteristics of the equipment, and then transmit it to the integrated AI model 4 for analysis through a unified data interface.

[0048] In a preferred embodiment, an extensibility module is also provided, which extends and upgrades the system. The technical logic behind this is to facilitate the addition of new functional modules or the upgrading of existing modules during system operation by reserving interfaces and adopting an extensible architectural design (such as a microservice architecture). For example, when an enterprise introduces a new type of industrial equipment, it can be quickly integrated into the system through the extensibility module.

[0049] In a preferred embodiment, a cost-benefit analysis module is also included. During the system design and implementation process, this module performs a detailed cost-benefit analysis to ensure that the system's introduction brings tangible economic benefits to the enterprise. The technical logic involves quantitatively analyzing the system's construction costs (including hardware costs, software costs, and labor costs) and expected benefits (such as increased production efficiency, reduced maintenance costs, and optimized supply chain costs), and using cost-benefit analysis models (such as Net Present Value (NPV) and Internal Rate of Return (IRR)) to assess the system's economic feasibility.

[0050] In a preferred embodiment, a human-machine collaboration module is also provided, which coordinates the system operation and user 1's operations. Its technical logic is to establish human-machine interaction rules and processes to ensure that user 1 can intervene in the system's operation at appropriate times, while the system can adjust its operating strategy promptly based on user 1's operations. For example, when the predictive maintenance module indicates that the equipment needs maintenance, but user 1 believes that maintenance can be delayed based on the actual situation, the human-machine collaboration module will coordinate the relationship between the two, record user 1's decision, and adjust the subsequent maintenance plan 701.

[0051] In a preferred embodiment, the present invention provides an application method for an AI-driven, fully intelligent industrial equipment system, characterized in that: applying the AI-driven, fully intelligent industrial equipment system according to any one of claims 1-8 includes the following steps:

[0052] The first step is to collect data from various sensors and data sources of industrial equipment 2 through data acquisition and integration module 3, configure appropriate sensors according to the type and characteristics of industrial equipment 2 (such as temperature sensors and pressure sensors for high temperature and high pressure equipment), and then transmit the collected data to a unified data platform according to the data transmission protocol. During the data transmission process, the data is monitored in real time to ensure the integrity and accuracy of the data.

[0053] In the second step, the multimodal data processing module 305 of the integrated AI big model 4 processes the multimodal data, extracts the features of each modality and fuses them, and uses corresponding deep learning and machine learning algorithms for analysis according to different application requirements. For equipment fault prediction applications, fault prediction algorithms are used to analyze the status data of the equipment to obtain the time and probability of possible equipment failure.

[0054] Third, based on the analysis results of the integrated AI model 4, the automation control module 6 and the supply chain optimization module 8 take corresponding actions. The analysis results of the automation control module 6 indicate that the equipment needs to adjust its operating parameters (such as the motor speed). The control module 6 sends control commands to the actuators of the equipment through the industrial automation communication protocol to achieve automated control of the equipment. The analysis results of the supply chain optimization module 8 indicate that the inventory needs to be adjusted (such as insufficient inventory of a certain raw material). The inventory optimization algorithm and logistics scheduling algorithm are used to optimize the supply chain and adjust the inventory level and logistics arrangement.

[0055] In the fourth step, the intelligent maintenance and predictive maintenance module 7 formulates a maintenance plan 701 based on the analysis results of the integrated AI model 4, predicting that the equipment will need maintenance in the future (e.g., a key component is expected to need replacement within 10 days), and arranges maintenance personnel and necessary repair tools and parts in advance. The user 1 monitors the system's operating status through the user 1 interaction module 9 and provides feedback information based on the actual situation. If the user 1 finds that the prediction result is inaccurate (e.g., the equipment does not actually need maintenance, but the prediction result is that it needs maintenance), the user 1 inputs the feedback information into the integrated AI model 4 through the user 1 interaction module 9, and the integrated AI model 4 performs autonomous learning and iteration.

[0056] Taking the production workshop of a small household appliance manufacturing enterprise as an example, the application process of the intelligent industrial equipment system driven by a large artificial intelligence model of the present invention is as follows: According to the equipment type and process flow of the small household appliance production workshop, more than 301 sensors such as temperature sensors, pressure sensors, and vibration sensors are installed. For example, temperature and pressure sensors are installed on the injection molding machine to monitor temperature and pressure changes during the injection molding process; vibration sensors are installed on the assembly line to monitor equipment vibration during assembly. Sensor communication protocols 302, including but not limited to RS-232, RS-485, Modbus, and TCP / IP, are used to transmit the data collected by the sensors to a unified industrial big data platform 303, where data cleaning and standardization are performed 304 to ensure data quality and consistency. An integrated AI model 4 is constructed, trained using deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) and their variants, Long Short-Term Memory Networks (LSTM), as well as machine learning algorithms such as decision trees and support vector machines. A multimodal data processing module 305 processes multimodal data from different sensors, including visual, sound, temperature, and pressure data, extracts features from each modality, and fuses them to obtain all operational status information of the small appliance production equipment 30. 6; The integrated AI model 4 is connected to the small appliance production equipment through the automation control module 6. Using the OPCUA industrial automation communication protocol and control algorithms such as PID control and fuzzy control 601, the automated control of the small appliance production equipment is achieved. The intelligent maintenance and predictive maintenance module 7 uses the integrated AI model 4 to analyze the status and performance data of the small appliance production equipment, and uses machine learning and deep learning algorithms to build a maintenance prediction model to predict the service life of the small appliance production equipment and arrange maintenance plans in advance 701. The supply chain optimization module 8 uses the integrated AI model 4 to analyze supply chain data, and uses inventory optimization algorithms and import / export and logistics scheduling algorithms to optimize inventory management and logistics scheduling 801, reducing costs and improving response speed. The user interaction module 9 uses graphical user interface (GUI) technology to provide an intuitive operating interface, allowing users to view the operating status reports and maintenance plans 701 of the small appliance production equipment. Users can also input feedback information as needed related to supply chain management and data collection.

[0057] The AI-driven intelligent industrial equipment system and its application method described in this invention have the following advantages: a unified data platform can collect and integrate various data from different devices in real time, and through the integration of the AI ​​model, it can quickly and accurately analyze the data and promptly detect anomalies and potential faults in the production process; by integrating the AI ​​model and the automation control module, the control strategy can be automatically adjusted according to the actual operating status of the equipment, thereby improving production efficiency; the application of predictive maintenance methods greatly reduces unexpected equipment downtime, while supply chain optimization improves inventory backlog and raw material shortages, thus reducing production costs.

[0058] The present invention has been described in detail above. The above description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made in accordance with the scope of this application should still fall within the scope of the present invention.

Claims

1. An AI-driven, large-scale model-driven, end-to-end intelligent industrial equipment system, characterized in that: This system, applied to industrial equipment, includes an integrated AI model, a data acquisition and integration module, a real-time monitoring and analysis module, an automated control module, an intelligent maintenance and predictive maintenance module, a supply chain optimization module, and a user interaction module. A feedback mechanism is established, allowing the integrated AI model to learn and iterate based on new data and user feedback acquired from the industrial equipment through the data acquisition and integration module, the real-time monitoring and analysis module, the automated control module, the intelligent maintenance and predictive maintenance module, and the supply chain optimization module. This improves the accuracy and efficiency of the integrated AI model's operation. The integrated AI model also includes a multimodal data processing module. This module processes multimodal data from different types of sensors in various industrial equipment, including visual, sound, temperature, and pressure data. It extracts and fuses the features of each modality and, based on different application requirements, employs appropriate deep learning and machine learning algorithms for analysis. For equipment fault prediction applications, a fault prediction algorithm analyzes the equipment's status data to obtain the time and probability of potential equipment failures. The integrated AI big model is trained using deep learning and machine learning algorithms to process and analyze industrial equipment data. The data acquisition and integration module collects data in real time from various sensors and data sources of industrial equipment and integrates it into a unified data platform. The real-time monitoring and analysis module monitors and analyzes real-time data to promptly detect anomalies in the production process and predict potential failures. The automated control module connects the integrated AI model to the industrial equipment to achieve automated control of the industrial equipment. The intelligent maintenance and predictive maintenance module analyzes the status and performance data of industrial equipment through the integrated AI model, predicts the maintenance needs of industrial equipment, and reduces the unexpected downtime of industrial equipment. The supply chain optimization module analyzes supply chain data through the integrated AI big data model to optimize inventory management and logistics scheduling, reduce costs and improve response speed; Users can monitor the operational status of the integrated AI model through the user interaction module and intervene as needed.

2. The AI-driven, large-scale intelligent industrial equipment system according to claim 1, characterized in that, The integrated AI big model is equipped with an adaptive learning module, which automatically adjusts its parameters and algorithms based on new data obtained from the industrial equipment and user feedback information to adapt to the constantly changing production environment.

3. The AI-driven, large-scale intelligent industrial equipment system according to claim 1, characterized in that, The integrated AI big model is equipped with a data security and privacy protection module, which performs security processing and privacy protection on sensitive information in new data obtained from the industrial equipment and user feedback information.

4. The AI-driven, large-scale intelligent industrial equipment system for the entire process according to any one of claims 1-3, characterized in that: The modular design allows data from different industrial equipment or different production processes of the same industrial equipment to be integrated into the integrated AI model through different modules.

5. The AI-driven, large-scale intelligent industrial equipment system according to claim 4, characterized in that: It also includes an expansion module for expanding and upgrading the system.

6. The AI-driven, large-scale intelligent industrial equipment system according to claim 5, characterized in that: It also includes a cost-benefit analysis module, which conducts detailed cost-benefit analysis during the system design and implementation process to ensure that the introduction of the system can bring actual economic benefits to the enterprise.

7. The AI-driven, large-scale intelligent industrial equipment system according to claim 5, characterized in that: It also includes a human-machine collaboration module, which coordinates system operation and user operation.

8. An application method for an AI-driven, large-scale model-driven, end-to-end intelligent industrial equipment system, characterized in that: The AI-driven, large-scale intelligent industrial equipment system according to any one of claims 1-7 comprises the following steps: The first step is to collect data from various sensors and data sources of industrial equipment through the data acquisition and integration module, configure the corresponding sensors according to the type and characteristics of the industrial equipment, and then transmit the collected data to a unified data platform according to the data transmission protocol. During the data transmission process, the data is monitored in real time to ensure the integrity and accuracy of the data. The second step involves the multimodal data processing module of the integrated AI big model processing the multimodal data, extracting the features of each modality and fusing them. According to different application requirements, corresponding deep learning and machine learning algorithms are used for analysis. For equipment fault prediction applications, fault prediction algorithms are used to analyze the status data of the equipment to obtain the time and probability of possible equipment failure. Third, based on the analysis results of the integrated AI big model, the automation control module and the supply chain optimization module take corresponding actions. The analysis results of the automation control module indicate that the equipment needs to adjust its operating parameters. The control commands are sent to the actuators of the equipment through the industrial automation communication protocol to realize the automation control of the equipment. The analysis results of the supply chain optimization module indicate that the inventory needs to be adjusted. The inventory optimization algorithm and the logistics scheduling algorithm are used to optimize the supply chain and adjust the inventory level and logistics arrangement. Fourth, the intelligent maintenance and predictive maintenance module formulates a maintenance plan based on the analysis results of the integrated AI model, predicts that the equipment will need maintenance in the future, and arranges maintenance personnel and necessary repair tools and parts in advance. The user monitors the system's operating status through the user interaction module and provides feedback information according to the actual situation. If the user finds that the prediction results are inaccurate, the user inputs the feedback information into the integrated AI model through the user interaction module, and the integrated AI model performs autonomous learning and iteration.

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