Full-process intelligent industrial equipment system driven by AI large model and application method of full-process intelligent industrial equipment system

Abstract

The invention discloses an AI large model driven full-process intelligent industrial equipment system and an application method thereof, which are characterized by being applied to industrial equipment. Comprising 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. The integrated AI large model performs self-learning and iteration according to new data and user feedback information acquired 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; according to the integrated AI large model, the operation accuracy and efficiency of the integrated AI large model are improved, comprehensive intellectualization of the production process is achieved, the production efficiency and the product quality are improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment management systems, and in particular to a full-process intelligent industrial equipment system driven by an AI large model and an application method thereof. Background Art

[0002] Industrial equipment management systems play a vital role in modern enterprises. They help enterprises improve equipment utilization and production efficiency through functions such as real-time monitoring, predictive maintenance and optimized resource allocation. Digital transformation is one of the core trends in the development of equipment management systems. Enterprises use advanced digital technologies such as the Internet of Things (IoT), sensor technology and data analysis tools to 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] There are many types of equipment, which makes management difficult: Factories usually have a large number of equipment with various types and performances, which makes management difficult. It is necessary to establish a complete equipment file and classify and manage the equipment.

[0005] Insufficient skills of equipment maintenance personnel: Maintenance personnel need to have professional skills and knowledge, but in reality, insufficient skills often lead 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. It is necessary to establish a complete equipment inspection system and preventive maintenance system.

[0007] Equipment is updated quickly and management costs are high: The introduction of new equipment is often accompanied by high costs and technical difficulties, requiring the formulation of a reasonable equipment update plan 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 reality, due to inadequate safety management, there are 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, testing equipment, etc. The data formats and types generated by different equipment are different, making it difficult to collect and analyze them in a unified manner. There is a lack of effective data analysis tools, and it is impossible to detect abnormal conditions and potential failures in the production process in a timely manner; the automated control of the equipment mostly uses simple control algorithms, which have poor adaptability to complex production environments and changing production requirements, and are unable to automatically adjust control strategies according to the actual operating status of the equipment, resulting in low production efficiency; equipment maintenance mainly relies on regular inspections and emergency repairs at the end of the period and after a failure occurs. There is a lack of predictive maintenance methods, resulting in longer unexpected equipment downtime, insufficient optimization of supply chain management, and frequent inventory backlogs and raw material shortages, which increases production costs.

[0010] Smart industrial equipment is an important mechanical equipment in the manufacturing industry, and a single smart device that plays an important role in smart manufacturing. Common smart industrial equipment includes laser cutting machines and 3D printers, which are all needed in the smart manufacturing process.

[0011] Smart industrial equipment is a general term for various tools for smart manufacturing, including various devices, robots, sensors, etc. They can realize intelligent production processes and enhance production efficiency and quality. Smart industrial equipment can reflect the level and trend of development of the entire manufacturing industry and is also an important part of smart manufacturing. Summary of the invention

[0012] In order to solve the above-mentioned problems of the prior art, the present invention provides a full-process intelligent industrial equipment system driven by an AI big model, which is characterized in that it is applied to industrial equipment, including an integrated AI big model, a data acquisition and integration module, a real-time monitoring and analysis module, an automation control module, an intelligent maintenance and predictive maintenance module, a supply chain optimization module and a user interaction module, and establishes a feedback mechanism. The integrated AI big model performs self-learning and iteration based on the 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 automation control module, the intelligent maintenance and predictive maintenance module and the supply chain optimization module, so as to improve the accuracy and efficiency of the operation of the integrated AI big model:

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

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

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

[0016] The automation control module connects the integrated AI big model with industrial equipment to realize the automation control of industrial equipment;

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

[0018] The supply chain optimization module analyzes the supply chain data through the integrated AI big model, optimizes inventory management and logistics scheduling 801, reduces costs and improves response speed.

[0019] The user monitors the operating status of the integrated AI large model through the user interaction module and intervenes as needed.

[0020] As an improvement of the full-process intelligent industrial equipment system driven by the AI ​​big model of the present invention, the integrated AI big model is provided with a multimodal data processing module 305, which 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 of the full-process intelligent industrial equipment system driven by the AI ​​big model of the present invention, the integrated AI big model is provided with an adaptive learning module, which automatically adjusts its parameters and algorithms according to the new data and user feedback information obtained from the industrial equipment to adapt to the ever-changing production environment.

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

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

[0024] As an improvement to the full-process intelligent industrial equipment system driven by the AI ​​big model of the present invention, an extensibility module is also provided, which expands and upgrades the system.

[0025] As an improvement of the full-process intelligent industrial equipment system driven by the AI ​​big model of the present 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 of the full-process intelligent industrial equipment system driven by the AI ​​big model of the present invention, a human-machine collaboration module is also provided, which coordinates system operation and user operation.

[0027] The present invention provides an application method of a full-process intelligent industrial equipment system driven by an AI large model, characterized in that: applying the full-process intelligent industrial equipment system driven by an AI large model according to any one of claims 1 to 8 comprises the following steps:

[0028] 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.

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

[0030] In the third step, according to the analysis results of the integrated AI large model, the automation control module and the supply chain optimization module take corresponding actions. The analysis results of the automation control module show that the equipment needs to adjust the operating parameters, and the control instructions are sent to the actuator of the equipment through the industrial automation communication protocol to realize the automatic control of the equipment. The analysis results of the supply chain optimization module show 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 arrangements.

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

[0032] The AI ​​big model-driven full-process intelligent industrial equipment system and its application method of the present invention have the following beneficial effects: the unified data platform can collect and integrate various data from different equipment in real time, and through the integration of the AI ​​big model, it can quickly and accurately analyze the data, and timely discover abnormal situations and potential faults in the production process; through the integration of the AI ​​big model and the cooperation of 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 means greatly reduces the unexpected downtime of equipment, and at the same time, supply chain optimization improves inventory backlogs and raw material shortages, thereby reducing production costs. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0034] Figure 2 This is a flow chart of a preferred embodiment of the full-process intelligent industrial equipment system driven by the AI ​​big model of the present invention and its application method. DETAILED DESCRIPTION

[0035] Below, combined with the attached Figure 1-2 As well as specific implementation methods, the present invention is further described. It should be noted that, under the premise of no conflict, the various technical features described below can be arbitrarily combined to form a new embodiment.

[0036] In a preferred embodiment, the present invention is an AI big model driven full-process intelligent industrial equipment system, characterized in that it is applied to industrial equipment 2, including an integrated AI big model 4, a data acquisition and integration module 3, a real-time monitoring and analysis module 5, an automation control module 6, an intelligent maintenance and predictive maintenance module 7, a supply chain optimization module 8 and a user 1 interaction module 9, and establishes a feedback mechanism. The integrated AI big model 4 performs self-learning and iteration based on the new data and user 1 feedback information obtained from the industrial equipment 2 through the data acquisition and integration module 3, the real-time monitoring and analysis module 5, the automation control module 6, the intelligent maintenance and predictive maintenance module 7 and the supply chain optimization module 8, so as to improve the accuracy and efficiency of the operation of the integrated AI big model 4:

[0037] The integrated AI large model 4 is trained by deep learning algorithms (such as convolutional neural network CNN, recurrent neural network RNN ​​and its variant long short-term memory network LSTM, etc.) and machine learning algorithms (such as decision tree, support vector machine, etc.) to process and analyze the data of industrial equipment 2. Its technical logic is to use deep learning algorithms to automatically learn and extract the features of a large amount of industrial equipment 2 data, and use machine learning algorithms to classify, regress and analyze the data, so as to achieve 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 industrial equipment 2, and integrates them into a unified data platform. The data acquisition technology used includes sensor interface technology and data transmission protocols (such as TCP / IP, Modbus, etc.). Data integration uses data cleaning, data standardization and other technologies to ensure data quality and consistency;

[0039] The real-time monitoring and analysis module 5 monitors and analyzes real-time data to promptly detect abnormal conditions in the production process and predict potential faults. The technologies used include real-time data processing technology (such as stream computing frameworks Flink, SparkStreaming, etc.), anomaly detection algorithms (such as statistical methods, machine learning methods, etc.) and fault prediction algorithms (such as time series analysis-based methods, deep learning-based prediction methods, etc.);

[0040] The automation control module 6 connects the integrated AI big model 4 with the industrial equipment 2 to realize the automation control of the industrial equipment 2. The adopted technologies include industrial automation communication protocol (such as OPC UA, Profibus, etc.) and control algorithm 601 (such as PID control, fuzzy control, etc.). By receiving the decision instructions of the AI ​​big model, the actuators (such as motors, valves, etc.) of the industrial equipment 2 are precisely controlled;

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

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

[0043] User 1 monitors the operating status of the integrated AI large 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, which facilitates 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 provided with a multimodal data processing module 305305, which processes and analyzes multimodal data from different types of sensors of different industrial equipment 2, including vision (such as image data collected by industrial cameras), sound (such as noise data when the equipment is running), temperature, and pressure, to obtain all operating status information of industrial equipment 2. Its technical logic is to mine the correlation between different modal data through feature extraction and fusion technology of different modal data (such as principal component analysis PCA for feature extraction, deep neural network for feature fusion), so as to fully understand the operating status information of industrial equipment 2.

[0045] In a preferred embodiment, the integrated AI large model 4 is provided with an adaptive learning module, which automatically adjusts its parameters and algorithms according to the new data obtained from the industrial equipment 2 and the feedback information from the user 1 to adapt to the ever-changing production environment. Its technical logic is based on an online learning algorithm (such as an online version of the stochastic gradient descent algorithm), which continuously updates the weights and parameters of the model so that the model can adapt to new data distribution and production requirements in a timely manner.

[0046] In a preferred embodiment, the integrated AI large model 4 is provided with a data security and privacy protection module, which performs secure processing and privacy protection on the new data obtained from the industrial equipment 2 and the sensitive information in the feedback information of the user 1. The technologies used include data encryption technology (such as symmetric encryption algorithm AES, asymmetric encryption algorithm RSA, etc.), access control technology (such as role-based access control RBAC) and data desensitization technology to ensure the security and privacy of data during storage, transmission and use.

[0047] In a preferred embodiment, a modular design is adopted, and the data of different industrial equipment 2 or the data of different production processes of the same industrial equipment 2 are integrated into the integrated AI large model 4 through different modules. Its technical logic is to achieve efficient data transmission and flexible integration of models 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 pass it to the integrated AI large model 4 for analysis through a unified data interface.

[0048] In a preferred embodiment, an extensibility module is also provided to expand and upgrade the system. Its technical logic is to facilitate adding new functional modules or upgrading existing modules during system operation by reserving interfaces and adopting an extensible architecture design (such as a microservice architecture). For example, when an enterprise introduces a new type of industrial equipment 2, it can be quickly integrated into the system through an extensibility module.

[0049] In the preferred embodiment, 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. Its technical logic is to quantitatively analyze the system construction cost (including hardware cost, software cost, labor cost, etc.) and expected benefits (such as improving production efficiency, reducing maintenance costs, optimizing supply chain costs, etc.), and use cost-benefit analysis models (such as net present value NPV, internal rate of return IRR, etc.) to evaluate the economic feasibility of the system.

[0050] In a preferred embodiment, a human-machine collaboration module is also provided, and the human-machine collaboration module coordinates the operation of the system with the operation of user 1. Its technical logic is to establish human-machine interaction rules and processes to ensure that user 1 can intervene in the operation of the system at an appropriate time, and the system can adjust the operation strategy in time according to the operation of user 1. For example, when the predictive maintenance module prompts that the equipment needs maintenance, but user 1 believes that the maintenance can be delayed according to the actual situation, the human-machine collaboration module will coordinate the relationship between the two, record the decision of user 1 and adjust the subsequent maintenance plan 701.

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

[0052] The first step is to collect data from various sensors and data sources of industrial equipment 2 through the data acquisition and integration module 3, configure corresponding 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 large model 4 processes the multimodal data, extracts the features of each modal data and fuses them, and uses corresponding deep learning and machine learning algorithms for analysis according to different application requirements. For equipment fault prediction applications, a fault prediction algorithm is used to analyze the status data of the equipment to obtain the time and probability of possible equipment failure.

[0054] In the third step, according to the analysis results of the integrated AI large 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 show that the equipment needs to adjust the operating parameters (such as the motor speed needs to be adjusted), and the control instructions are sent to the actuator of the equipment through the industrial automation communication protocol to realize the automatic control of the equipment. The analysis results of the supply chain optimization module 8 show that the inventory needs to be adjusted (such as insufficient inventory of a certain raw material), and the inventory optimization algorithm and logistics scheduling algorithm are used to optimize the supply chain and adjust the inventory level and logistics arrangements.

[0055] In the fourth step, the intelligent maintenance and predictive maintenance module 7 formulates a maintenance plan 701 according to the analysis results of the integrated AI large model 4, predicts that the equipment will require maintenance in the future (such as a key component is expected to be replaced within 10 days), and arranges maintenance personnel and required maintenance tools and accessories in advance. The user 1 monitors the operating status of the system through the user 1 interaction module 9 and provides feedback information based on actual conditions. If the user 1 finds that the prediction result is inaccurate (such as the equipment does not actually require maintenance, but the prediction result is that maintenance is required), the feedback information is input into the integrated AI large model 4 through the user 1 interaction module 9, and the integrated AI large 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 full-process intelligent industrial equipment 2 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 sensors and pressure sensors are installed on the injection molding machine to monitor the temperature and pressure changes during the injection molding process; vibration sensors are installed on the assembly line to monitor the vibration of the equipment during the assembly process, and the sensor communication protocol 302 is used to transmit the data collected by the sensor to a unified industrial big data platform 303, including but not limited to RS-232 communication protocol, RS-485 communication protocol, Modbus protocol, TCP / IP protocol and other methods, and the data is cleaned and standardized 304 to ensure the quality and consistency of the data; an integrated AI large model 4 is constructed, and deep learning algorithms such as convolutional neural network CNN and recurrent neural network RNN ​​and its variant long short-term memory network LSTM, as well as machine learning algorithms such as decision trees and support vector machines are used for training, and multimodal data processing module 305 is used to process multimodal data such as vision, sound, temperature, pressure, etc. from different sensors, extract the features of each modal data and fuse them to obtain all operating status information of small household appliance production equipment 30 6; the integrated AI big model 4 is connected to the small household appliance production equipment through the automation control module 6, and the OPCUA industrial automation communication protocol and PID control, fuzzy control and other control algorithms 601 are adopted to realize the automation control of the small household appliance production equipment. The intelligent maintenance and predictive maintenance module 7 uses the integrated AI big model 4 to analyze the status and performance data of the small household 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 household appliance production equipment and arrange maintenance plans in advance 701. The supply chain optimization module 8 uses the integrated AI big model 4 to analyze the supply chain data, and adopts inventory optimization algorithms and import and export and logistics scheduling algorithms to optimize inventory management and logistics scheduling 801, reduce costs and improve response speed. The user 1 interaction module 9 adopts the graphical user interface 1 GUI technology to provide an intuitive operation interface, which is convenient for the user 1 to view the operation status report and maintenance plan 701 of the small household appliance production equipment, and the user 1 can input feedback information according to the needs related to supply chain management and data collection.

[0057] The AI ​​big model-driven full-process intelligent industrial equipment system and its application method of the present invention have the following beneficial effects: the unified data platform can collect and integrate various data from different equipment in real time, and through the integration of the AI ​​big model, it can quickly and accurately analyze the data, and timely discover abnormal situations and potential faults in the production process; through the integration of the AI ​​big model and the cooperation of 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 means greatly reduces the unexpected downtime of equipment, and at the same time, supply chain optimization improves inventory backlogs and raw material shortages, thereby 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 limit the scope of implementation of the present invention. That is, all equivalent changes and modifications made within the scope of this application should still fall within the scope of the present invention.

Claims

1. A full-process intelligent industrial equipment system driven by an AI big model, characterized in that: Applied to industrial equipment, including integrated AI big model, data acquisition and integration module, real-time monitoring and analysis module, automation control module, intelligent maintenance and predictive maintenance module, supply chain optimization module and user interaction module, establish a feedback mechanism, the integrated AI big model performs self-learning and iteration based on the 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 automation control module, the intelligent maintenance and predictive maintenance module and the supply chain optimization module, so as to improve the accuracy and efficiency of the operation of the integrated AI big model: The integrated AI big model processes and analyzes industrial equipment data through deep learning and machine learning algorithm training; the data acquisition and integration module collects data from various sensors and data sources of industrial equipment in real time and integrates it into a unified data platform; The real-time monitoring and analysis module monitors and analyzes real-time data to promptly detect abnormal conditions in the production process and predict potential failures; The automation control module connects the integrated AI big model with industrial equipment to realize the automation control of industrial equipment; The intelligent maintenance and predictive maintenance module analyzes the status and performance data of industrial equipment through the integrated AI big model, predicts the maintenance needs of industrial equipment, and reduces the unexpected downtime of industrial equipment. The supply chain optimization module analyzes the supply chain data through the integrated AI big model, optimizes inventory management and logistics scheduling 801, reduces costs and improves response speed. The user monitors the operating status of the integrated AI large model through the user interaction module and intervenes as needed.

2. The full-process intelligent industrial equipment system driven by the AI ​​big model according to claim 1 is characterized in that: The integrated AI large model is provided with a multimodal data processing module, which 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.

3. The full-process intelligent industrial equipment system driven by the AI ​​big model according to claim 1 is characterized in that: The integrated AI big model is provided with an adaptive learning module, which automatically adjusts its parameters and algorithms according to the new data and user feedback information obtained from the industrial equipment to adapt to the ever-changing production environment.

4. The full-process intelligent industrial equipment system driven by the AI ​​big model according to claim 1 is characterized in that: The integrated AI big model is provided with a data security and privacy protection module, which performs security processing and privacy protection on the sensitive information in the new data and user feedback information acquired from the industrial equipment.

5. The full-process intelligent industrial equipment system driven by an AI large model according to any one of claims 1 to 4, characterized in that: By adopting a modular design, data of different industrial equipment or data of different production processes of the same industrial equipment are integrated into the integrated AI big model through different modules.

6. The AI ​​large model-driven full-process intelligent industrial equipment system according to claim 5 is characterized by: An expansibility module is also provided, and the expansibility module is used to expand and upgrade the system.

7. The AI ​​large model-driven full-process intelligent industrial equipment system according to claim 6 is characterized by: There is also a cost-benefit analysis module. During the system design and implementation process, the cost-benefit analysis module conducts a detailed cost-benefit analysis to ensure that the introduction of the system can bring actual economic benefits to the enterprise.

8. The AI ​​large model-driven full-process intelligent industrial equipment system according to claim 6 is characterized by: A human-machine collaboration module is also provided, which coordinates system operation with user operations.

9. An application method of a full-process intelligent industrial equipment system driven by an AI large model, characterized in that: The full-process intelligent industrial equipment system driven by the AI ​​big model described in any one of claims 1 to 8 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. In the second step, the multimodal data processing module of the integrated AI large model processes the multimodal data, extracts the features of each modal data and fuses them, and uses corresponding deep learning and machine learning algorithms for analysis according to different application requirements. For equipment fault prediction applications, a fault prediction algorithm is used to analyze the status data of the equipment to obtain the time and probability of possible equipment failure. In the third step, according to the analysis results of the integrated AI large model, the automation control module and the supply chain optimization module take corresponding actions. The analysis results of the automation control module show that the equipment needs to adjust the operating parameters, and the control instructions are sent to the actuator of the equipment through the industrial automation communication protocol to realize the automatic control of the equipment. The analysis results of the supply chain optimization module show 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 arrangements. In the fourth step, the intelligent maintenance and predictive maintenance module formulates a maintenance plan according to the analysis results of the integrated AI big model, predicts that the equipment will need maintenance in the future, and arranges maintenance personnel and required maintenance tools and accessories in advance. The user monitors the operating status of the system through the user interaction module and provides feedback information based on actual conditions. If the user finds that the prediction results are inaccurate, the user inputs the feedback information into the integrated AI big model through the user interaction module, and the integrated AI big model performs autonomous learning and iteration.

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