Intelligent manufacturing production line supervision system based on AI

Abstract

The invention, which relates to the technical field of intelligent manufacturing, discloses an AI-based intelligent manufacturing production line supervision system comprising a data acquisition unit, an intelligent analysis cloud platform and a remote supervision terminal. The data acquisition unit is used for acquiring equipment monitoring data and operator video data on the intelligent manufacturing production line in real time and preprocessing the data; the intelligent analysis cloud platform is used for processing and analyzing the data by using an AI algorithm, identifying abnormal conditions in a production process, evaluating production efficiency, optimizing a production scheme and monitoring execution conditions of instructions; the remote supervision terminal is used for receiving the uploaded monitoring information of the production line, sending setting information to the intelligent analysis cloud platform and remotely accessing information stored in the intelligent analysis cloud platform, the equipment failure rate is reduced, meanwhile, based on the AI algorithm, equipment failure reasons and personnel abnormal operation can be rapidly and accurately recognized, and the safety of the production line is improved. And corresponding optimization suggestions and alarm prompts are given.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing technology, and specifically to an AI-based intelligent manufacturing production line supervision system. Background Art

[0002] Smart manufacturing production line refers to a production line that realizes automation and intelligent production by connecting equipment and exchanging data through technologies such as the Internet of Things, cloud computing, big data, and AI. The control of smart manufacturing production lines can reduce production costs by rationally allocating resources, optimizing scheduling, and reducing manpower input. Although the existing smart manufacturing production lines for mechanical processing can monitor the production process in real time, they lack effective prediction of equipment failures and supervision of personnel operating specifications, and are unable to evaluate the safety of processing production and optimize production. Summary of the invention

[0003] In view of this, the present application provides an AI-based intelligent manufacturing production line supervision system, which monitors the equipment health status and personnel operating specifications of the mechanical processing intelligent manufacturing production line through the AI-based intelligent manufacturing production line supervision system, thereby solving the technical problems that the existing mechanical processing intelligent manufacturing production line can monitor the production process in real time, but lacks effective prediction of equipment failures and supervision of personnel operating specifications, and is unable to evaluate the safety of processing production and optimize production.

[0004] To achieve the above object, the present invention provides the following technical solutions: An AI-based intelligent manufacturing production line supervision system, comprising: a data acquisition unit, an intelligent analysis cloud platform and a remote supervision terminal; The data acquisition unit is used to collect equipment monitoring data and operator video data on the intelligent manufacturing production line in real time and pre-process the data; The intelligent analysis cloud platform is connected to the data acquisition unit through wireless communication. The intelligent analysis cloud platform includes a data analysis unit and a decision control unit. The data analysis unit is used to receive data from the data acquisition unit, process and analyze the data using AI algorithms, identify abnormal conditions in the production process, and predict potential failures. It is also used to evaluate production efficiency and optimize production plans. The decision control unit is used to send control instructions to the actuators and acquisition terminals of the production line, receive feedback information returned by the actuators, monitor the execution of instructions, and ensure that the instructions are correctly executed. The remote monitoring terminal is connected to the intelligent analysis cloud platform via wireless communication. The remote monitoring terminal is used to receive uploaded monitoring information of the production line, send setting information to the intelligent analysis cloud platform, and remotely access the information stored in the intelligent analysis cloud platform, and visualize the information sent by the intelligent analysis cloud platform.

[0005] Furthermore, the data acquisition unit includes a device status acquisition module, a personnel information acquisition module and a 5G intelligent gateway module; The equipment status acquisition module includes an information acquisition module and a screening module. The information acquisition module is used to obtain equipment operation data and environmental change data on the physical production line through sensors, and to obtain a sensor parameter matrix by numbering the equipment and parameter data according to the acquired data set. The screening module is used to determine whether there is abnormal data in the sensor parameter matrix according to the variance method, and when there is abnormal data, the abnormal data is screened out; The personnel information acquisition module includes a camera module and a UWB positioning tag. The camera module is used to collect video image information of operators in the production workshop through the deployed camera components, and the UWB positioning tag is used to obtain the location information and movement trajectory information of the operators; Both the equipment status acquisition module and the personnel information acquisition module are connected to the 5G intelligent gateway module. The 5G intelligent gateway module includes a data aggregation module and an edge computing module. The data aggregation module is used to receive the collected data. The edge computing module is connected to the data aggregation module. The edge computing module is used to uniformly manage and process the received data and send it to the data analysis unit, and receive the operation instruction information sent by the decision control unit, and send it to the acquisition terminal of the equipment status acquisition module and the personnel information acquisition module after protocol conversion.

[0006] Furthermore, the intelligent analysis cloud platform also includes a database management module, which includes a device configuration management module and a data storage module. The device configuration management module is used to dynamically allocate and manage the device's gateway information, functional parameter information and demand division information, and the data storage module is used to store the data sent by the data acquisition unit and the data entered by the management user through the remote supervision terminal.

[0007] Furthermore, the data analysis unit includes an equipment failure analysis module, a personnel operation analysis module and an AI computing model module, and the equipment failure analysis module and the personnel operation analysis module are both connected to the AI ​​computing model module; The equipment fault analysis module includes a feature extraction module and a prediction and recognition module. The feature extraction module is used to extract features from the equipment health status monitoring signal and operating parameter data uploaded by the data acquisition unit using the signal feature extraction method in the AI ​​calculation model module. The prediction and recognition module is connected to the equipment fault analysis module. The equipment fault analysis module is used to identify the extracted features using the fault recognition method in the AI ​​calculation model module to determine whether the current state of the equipment is normal. If a fault occurs, the fault type and level are confirmed, and the fault development trend is predicted; The personnel operation analysis module includes an operation specification recognition module and an abnormal stay recognition module. The operation specification recognition module is used to extract action features of the operator's operation image information collected by the corresponding camera component, and adopts the template matching recognition method in the AI ​​calculation model module according to the extracted action features to determine whether the operator has abnormal operation actions. If there are abnormal operation actions, an alarm signal is sent to the remote supervision terminal. The abnormal stay recognition module is used to receive the positioning data fed back by each UWB positioning tag, and associate the positioning data fed back by each UWB positioning tag with the personnel list in timestamp order, and determine the length of time the UWB positioning tag stays in the key area of ​​the divided production line. If it exceeds the preset time threshold, an alarm signal is sent to the remote supervision terminal.

[0008] Furthermore, the data analysis unit further comprises the data analysis unit further comprises a production assessment module and a production optimization module; The production evaluation module includes an evaluation index module and a fuzzy evaluation module. The evaluation index module is used to update and manage the production evaluation indicators and obtain the indicator data corresponding to the indicators, and send the obtained indicator data to the fuzzy evaluation module. The fuzzy evaluation module uses the hierarchical entropy analysis method to comprehensively score the production process based on the indicator system constructed by the primary indicators and secondary indicators corresponding to the production events; The production optimization module is connected to the production evaluation module. The production optimization module is used to optimize and classify the periodic change data of each production evaluation indicator using the BP neural network in the AI ​​calculation model module, and match the production optimization suggestions in the expert solution library according to the optimization classification results.

[0009] Further, the decision control unit includes a production thread monitoring module, an abnormality reminder module and a decision module; The production thread monitoring module is used to monitor the execution of production instructions in the production process through heartbeat packets; the abnormal reminder module is connected to the production thread monitoring module, and the abnormal reminder module is used to record and output alarm signals when abnormalities occur in the execution of production instructions; the decision module is used to receive the setting information and decision feedback information sent by the remote supervision terminal, and send the parameter data carried by the setting information and decision feedback information to the acquisition terminal.

[0010] Further, the remote monitoring terminal includes a user module, a production line management module and a multifunctional interaction module; The user module includes a user registration and verification module, a role establishment module and a role authority management module. The user registration and verification module is used to establish a user account according to the user's basic information and preset role information, and to verify the user's login identity information. The role establishment module is used to establish role information including role description and role name. The role authority management module is connected to the role establishment module. The role authority management module is used to associate the role information with the operation authority of the bound role, and to add, delete, modify and query the established role information. The production line management module includes an equipment information management module, an alarm management module and a production information management module. The equipment information management module is used to obtain equipment information update instructions and equipment configuration instructions sent by users, and send the instruction information to the intelligent analysis cloud platform. The alarm management module is used to receive alarm information sent by the intelligent analysis cloud platform. The production information management module is used to receive the operation log information of operators sent by the intelligent analysis cloud platform; The multifunctional interactive module is used for managers to access data and to visualize production line supervision information. Furthermore, the multifunctional interactive module includes a touch display device and a CPU processing module. The touch display device is connected to the CPU processing module. The CPU processing module is used to receive mechanical processing production related information sent by the intelligent analysis cloud platform and send it to the touch display device. The touch display device is used to send relevant operation instruction information to the CPU processing module.

[0011] It can be seen from the above technical solution that the advantages of the present invention are: 1. In this application, the health data of equipment on the intelligent manufacturing production line and the video images of personnel operations are collected and processed by the data acquisition unit. The intelligent analysis cloud platform uses AI algorithms to process and analyze the data, identify abnormal conditions in the production process, and predict potential failures, thereby improving production safety while reducing equipment failure rates. Supervisors can also access the intelligent analysis cloud platform through remote supervision terminals to achieve remote supervision.

[0012] 2. This application uses AI algorithms to predict the operating status and health of equipment through real-time monitoring and analysis of equipment sensor data, detect potential problems in advance and perform preventive maintenance, thereby reducing equipment failure rate and production downtime. At the same time, the operating actions and trajectories of operators on the production line are monitored. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application.

[0014] Figure 1This is a schematic diagram of the structure of this application.

[0015] Figure 2 This is a structural diagram of the data acquisition unit of the present application.

[0016] Figure 3 This is a schematic diagram of the structure of the data analysis unit of the present application.

[0017] Figure 4 This is a schematic diagram of the structure of the remote monitoring terminal of this application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the implementation modes and the accompanying drawings. Here, the illustrative implementation modes and descriptions of the present application are used to explain the present application, but are not intended to limit the present application.

[0019] refer to Figures 1 to 4 ,like Figure 1 As shown, this embodiment provides an AI-based intelligent manufacturing production line supervision system, which is a device with a high degree of intelligence and rich display effects, specifically including: a data acquisition unit, an intelligent analysis cloud platform and a remote supervision terminal. The data acquisition unit is used to collect equipment monitoring data and operator video data on the intelligent manufacturing production line in real time, and pre-process the data. The intelligent analysis cloud platform is connected to the data acquisition unit through wireless communication. The intelligent analysis cloud platform includes a data analysis unit and a decision control unit. The data analysis unit is used to receive data from the data acquisition unit, use AI algorithms to process and analyze the data, identify abnormal conditions in the production process, and predict potential faults. It is also used to evaluate production efficiency and optimize production plans; the decision control unit is used to send control instructions to the actuator and acquisition terminal of the production line, and receive feedback information returned by the actuator, monitor the execution of the instructions, and ensure that the instructions are correctly executed. The remote supervision terminal is connected to the intelligent analysis cloud platform through wireless communication. The remote supervision terminal is used to receive the uploaded monitoring information of the production line, send setting information to the intelligent analysis cloud platform, and remotely access the information stored in the intelligent analysis cloud platform, and visualize the information sent by the intelligent analysis cloud platform.

[0020] Specifically, the data acquisition unit includes an equipment status acquisition module, a personnel information acquisition module and a 5G smart gateway module; the equipment status acquisition module includes an information acquisition module and a screening module. The information acquisition module is used to obtain equipment operation data and environmental change data on the physical production line through sensors, and to number the equipment and parameter data according to the acquired data set to obtain a sensor parameter matrix. The screening module is used to determine whether there is abnormal data in the sensor parameter matrix according to the variance method, and when there is abnormal data, the abnormal data is screened out; the personnel information acquisition module includes a camera module and a UWB positioning tag. The camera module is used to collect production vehicle information through the deployed camera components. The UWB positioning tag is used to obtain the operator's video image information during the operation, and the UWB positioning tag is used to obtain the operator's location information and movement trajectory information; the equipment status acquisition module and the personnel information acquisition module are both connected to the 5G intelligent gateway module. The 5G intelligent gateway module includes a data aggregation module and an edge computing module. The data aggregation module is used to receive the collected data. The edge computing module is connected to the data aggregation module. The edge computing module is used to uniformly manage and process the received data and then send it to the data analysis unit, and receive the operation instruction information sent by the decision control unit, and send it to the acquisition terminal of the equipment status acquisition module and the personnel information acquisition module after protocol conversion.

[0021] In this embodiment, the data acquisition unit is used to collect various data on the intelligent manufacturing production line in real time, including but not limited to equipment status, production progress, ambient temperature, humidity and video images during the production process. The module realizes data acquisition through sensor networks, industrial cameras and other devices, and sends the data to the intelligent analysis cloud platform. The intelligent analysis cloud platform is used to receive data from the data acquisition unit, process and analyze the data using AI algorithms, identify abnormal conditions in the production process, predict potential failures, evaluate production efficiency and optimize production processes.

[0022] In the present application, the intelligent analysis cloud platform also includes a database management module, which includes a device configuration management module and a data storage module. The device configuration management module is used to dynamically allocate and manage the device's gateway information, functional parameter information, and demand division information. The data storage module is used to store the data sent by the data acquisition unit and the data entered by the management user through the remote supervision terminal.

[0023] The data analysis unit includes an equipment fault analysis module, a personnel operation analysis module and an AI computing model module. The equipment fault analysis module and the personnel operation analysis module are both connected to the AI ​​computing model module; the equipment fault analysis module includes a feature extraction module and a prediction recognition module. The feature extraction module is used to extract features from the equipment health status monitoring signal and operating parameter data uploaded by the data acquisition unit using the signal feature extraction method in the AI ​​computing model module. The prediction recognition module is connected to the equipment fault analysis module. The equipment fault analysis module is used to identify the extracted features using the fault recognition method in the AI ​​computing model module to determine whether the current state of the equipment is normal. If a fault occurs, the fault type and level are confirmed, and the fault development trend is predicted; The operator operation analysis module includes an operation specification recognition module and an abnormal stay recognition module. The operation specification recognition module is used to extract action features of the operator's operation image information collected by the corresponding camera component, and adopts the template matching recognition method in the AI ​​calculation model module according to the extracted action features to determine whether the operator has abnormal operation actions. If there are abnormal operation actions, an alarm signal is sent to the remote supervision terminal. The abnormal stay recognition module is used to receive the positioning data fed back by each UWB positioning tag, and associate the positioning data fed back by each UWB positioning tag with the personnel list in timestamp order, and determine the length of time the UWB positioning tag stays in the key area of ​​the divided production line. If it exceeds the preset time threshold, an alarm signal is sent to the remote supervision terminal.

[0024] The data analysis unit also includes a production evaluation module and a production optimization module; the production evaluation module includes an evaluation index module and a fuzzy evaluation module, the evaluation index module is used to update and manage the production evaluation indicators and obtain the indicator data corresponding to the indicators, and send the obtained indicator data to the fuzzy evaluation module, the fuzzy evaluation module uses the hierarchical entropy analysis method to comprehensively score the production process according to the indicator system constructed by the primary indicators and secondary indicators corresponding to the production events; the production optimization module is connected to the production evaluation module, the production optimization module is used to optimize and classify according to the periodic change data of each production evaluation indicator, using the BP neural network in the AI ​​calculation model module, and matching the production optimization suggestions in the expert solution library according to the optimization classification results.

[0025] In this embodiment, sensory data such as equipment failure, abnormal operation information of personnel, equipment use and environmental data are collected to build a secondary monitoring indicator system with dimensions such as production failure type, frequency, equipment use information, abnormal operation frequency of personnel, and location information. Based on the secondary monitoring dimension indicators, a primary monitoring indicator system with dimensions such as equipment, personnel and environment is established. The weight of each indicator is calculated using the hierarchical entropy analysis method to build a comprehensive production safety scoring model for the purpose of the probability and severity of safety risks. Specifically, , in, , , is the comprehensive score of the ith solution, The information entropy of the indicator is is the proportion of the jth indicator in the ith solution, n is the number of indicators, The weight of the jth indicator, is the ith value of the jth indicator. Finally, based on the production safety status, the comprehensive score is judged to belong to the risk status level, which includes four levels: ultra-high, high, medium, and low, to achieve real-time monitoring and early warning of production safety on the intelligent manufacturing production line of mechanical processing.

[0026] In this embodiment, the decision control unit is used to collect various data on the intelligent manufacturing production line in real time, including but not limited to equipment status, production progress, ambient temperature, humidity, and video images during the production process. The unit sends control instructions to the production line's actuators and acquisition terminals, such as PLCs (programmable logic controllers), robots, etc., and receives feedback information returned by the actuators. The unit is responsible for monitoring the execution of instructions, ensuring that the instructions are correctly executed, and adjusting instructions or issuing alarms when necessary.

[0027] The decision control unit includes a production thread monitoring module, an abnormal reminder module and a decision module; the production thread monitoring module is used to monitor the execution of production instructions in the production process through heartbeat packets; the abnormal reminder module is connected to the production thread monitoring module, and the abnormal reminder module is used to record and output an alarm signal when an abnormality occurs in the execution of production instructions; the decision module is used to receive the setting information and decision feedback information sent by the remote supervision terminal, and send the parameter data carried by the setting information and decision feedback information to the acquisition terminal.

[0028] Specifically, the remote supervision terminal includes a user module, a production line management module and a multi-functional interaction module; the user module includes a user registration and verification module, a role establishment module and a role authority management module. The user registration and verification module is used to establish a user account based on the user's basic information and preset role information, and to verify the user's login identity information. The role establishment module is used to establish role information including a role description and a role name. The role authority management module is connected to the role establishment module. The role authority management module is used to associate the role information with the operating authority of the bound role, and to add, delete, modify and query the established role information; the production line management module includes an equipment information management module, an alarm management module and a production information management module. The equipment information management module is used to obtain equipment information update instructions and equipment configuration instructions sent by the user, and send the instruction information to the intelligent analysis cloud platform. The alarm management module is used to receive alarm information sent by the intelligent analysis cloud platform. The production information management module is used to receive operation log information of operators sent by the intelligent analysis cloud platform; the multi-functional interaction module is used for management personnel to interact with data access and visualize the production line supervision information. The multifunctional interactive module includes a touch display device and a CPU processing module. The touch display device is connected to the CPU processing module. The CPU processing module is used to receive mechanical processing production related information sent by the intelligent analysis cloud platform and send it to the touch display device. The touch display device is used to send relevant operation instruction information to the CPU processing module.

[0029] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An AI-based intelligent manufacturing production line supervision system, characterized in that: include: Data collection unit, intelligent analysis cloud platform and remote monitoring terminal; The data acquisition unit is used to collect equipment monitoring data and operator video data on the intelligent manufacturing production line in real time, and pre-process the data; The intelligent analysis cloud platform is connected to the data acquisition unit by wireless communication. The intelligent analysis cloud platform includes a data analysis unit and a decision control unit. The data analysis unit is used to receive data from the data acquisition unit, process and analyze the data using an AI algorithm, identify abnormal conditions in the production process, and predict potential failures. It is also used to evaluate production efficiency and optimize production plans. The decision control unit is used to send control instructions to the actuator and acquisition terminal of the production line, receive feedback information returned by the actuator, monitor the execution of the instructions, and ensure that the instructions are correctly executed. The remote monitoring terminal is connected to the intelligent analysis cloud platform via wireless communication. The remote monitoring terminal is used to receive uploaded monitoring information of the production line, send setting information to the intelligent analysis cloud platform, and remotely access the information stored in the intelligent analysis cloud platform, and visualize the information sent by the intelligent analysis cloud platform.

2. The AI-based intelligent manufacturing production line supervision system according to claim 1 is characterized in that: The data acquisition unit includes a device status acquisition module, a personnel information acquisition module and a 5G intelligent gateway module; The equipment status acquisition module includes an information acquisition module and a screening module. The information acquisition module is used to acquire equipment operation data and environmental change data on the physical production line through sensors, and to obtain a sensor parameter matrix by numbering the equipment and parameter data according to the acquired data set. The screening module is used to determine whether there is abnormal data in the sensor parameter matrix according to the variance method, and when there is abnormal data, the abnormal data is screened out; The personnel information acquisition module includes a camera module and a UWB positioning tag. The camera module is used to collect video image information of operators in the production workshop through the deployed camera components, and the UWB positioning tag is used to obtain the location information and movement trajectory information of the operators; The device status acquisition module and the personnel information acquisition module are both connected to the 5G intelligent gateway module. The 5G intelligent gateway module includes a data aggregation module and an edge computing module. The data aggregation module is used to receive the collected data. The edge computing module is connected to the data aggregation module. The edge computing module is used to uniformly manage and process the received data and then send it to the data analysis unit, and receive the operation instruction information sent by the decision control unit, and send it to the collection terminal of the device status acquisition module and the personnel information acquisition module after protocol conversion.

3. The AI-based intelligent manufacturing production line supervision system according to claim 2 is characterized in that: The intelligent analysis cloud platform also includes a database management module, which includes a device configuration management module and a data storage module. The device configuration management module is used to dynamically allocate and manage the device's gateway information, functional parameter information and demand division information, and the data storage module is used to store the data sent by the data acquisition unit and the data entered by the management user through the remote supervision terminal.

4. The AI-based intelligent manufacturing production line supervision system according to claim 1 is characterized in that: The data analysis unit includes an equipment failure analysis module, a personnel operation analysis module and an AI computing model module, and the equipment failure analysis module and the personnel operation analysis module are both connected to the AI ​​computing model module; The equipment fault analysis module includes a feature extraction module and a prediction and recognition module. The feature extraction module is used to extract features from the equipment health status monitoring signal and operating parameter data uploaded by the data acquisition unit using the signal feature extraction method in the AI ​​computing model module. The prediction and recognition module is connected to the equipment fault analysis module. The equipment fault analysis module is used to identify the extracted features using the fault recognition method in the AI ​​computing model module to determine whether the current state of the equipment is normal. If a fault occurs, the fault type and level are confirmed, and the fault development trend is predicted; The personnel operation analysis module includes an operation specification recognition module and an abnormal stay recognition module. The operation specification recognition module is used to extract action features of the operation image information of the operator collected by the corresponding camera component, and adopt the template matching recognition method in the AI ​​calculation model module to determine whether the operator has abnormal operation actions based on the extracted action features. If there is an abnormal operation action, an alarm signal is sent to the remote supervision terminal. The abnormal stay recognition module is used to receive the positioning data fed back by each UWB positioning tag, and associate the positioning data fed back by each UWB positioning tag with the personnel list in timestamp order, and determine the length of time the UWB positioning tag stays in the key area of ​​the divided production line. If it exceeds the preset time threshold, an alarm signal is sent to the remote supervision terminal.

5. The AI-based intelligent manufacturing production line supervision system according to claim 4 is characterized in that: The data analysis unit also includes a production assessment module and a production optimization module; The production evaluation module includes an evaluation index module and a fuzzy evaluation module. The evaluation index module is used to update and manage the production evaluation index and obtain the index data corresponding to the index, and send the obtained index data to the fuzzy evaluation module. The fuzzy evaluation module uses a hierarchical entropy analysis method to perform a comprehensive evaluation of the production process based on the index system constructed according to the primary index and secondary index corresponding to the production event; The production optimization module is connected to the production evaluation module. The production optimization module is used to optimize and classify the periodic change data of each production evaluation indicator using the BP neural network in the AI ​​calculation model module, and match the production optimization suggestions in the expert solution library according to the optimization classification results.

6. The AI-based intelligent manufacturing production line supervision system according to claim 1 is characterized in that: The decision control unit includes a production thread monitoring module, an abnormality reminder module and a decision module; The production thread monitoring module is used to monitor the execution of production instructions in the production process through heartbeat packets; the abnormal reminder module is connected to the production thread monitoring module, and the abnormal reminder module is used to record and output an alarm signal when an abnormality occurs in the execution of production instructions; the decision module is used to receive the setting information and decision feedback information sent by the remote supervision terminal, and send the parameter data carried by the setting information and decision feedback information to the acquisition terminal.

7. The AI-based intelligent manufacturing production line supervision system according to claim 1 is characterized in that: The remote monitoring terminal includes a user module, a production line management module and a multifunctional interaction module; The user module includes a user registration and verification module, a role establishment module and a role authority management module. The user registration and verification module is used to establish a user account according to user basic information and preset role information, and to verify the user's login identity information. The role establishment module is used to establish role information including role description and role name. The role authority management module is connected to the role establishment module. The role authority management module is used to associate the role information with the operation authority of the bound role, and to add, delete, modify and query the established role information. The production line management module includes an equipment information management module, an alarm management module and a production information management module. The equipment information management module is used to obtain equipment information update instructions and equipment configuration instructions sent by users, and send the instruction information to the intelligent analysis cloud platform. The alarm management module is used to receive alarm information sent by the intelligent analysis cloud platform. The production information management module is used to receive operation log information of operators sent by the intelligent analysis cloud platform; The multifunctional interaction module is used by management personnel to perform data access interaction and to visualize production line supervision information.

8. The AI-based intelligent manufacturing production line supervision system according to claim 7 is characterized in that: The multifunctional interactive module includes a touch display device and a CPU processing module. The touch display device is connected to the CPU processing module. The CPU processing module is used to receive mechanical processing production related information sent by the intelligent analysis cloud platform and send it to the touch display device. The touch display device is used to send relevant operation instruction information to the CPU processing module.

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