AI-based auxiliary equipment production data management method and system

Abstract

The invention discloses an AI-based auxiliary equipment production data management method and system, and particularly relates to the technical field of artificial intelligence, and the system comprises a monitoring area division module, a production data collection module, a production data preprocessing module, a production data analysis module, a production data comprehensive analysis module, a judgment module and a man-machine interaction module. Different monitoring areas are divided through the monitoring area division module, production equipment parameters are collected through the production data collection module, a production data comprehensive evaluation value is calculated through the production data preprocessing module, the production data analysis module and the production data comprehensive analysis module, and a data comprehensive evaluation standard value is established through the judgment module. The production data comprehensive evaluation index is compared with the production data comprehensive evaluation standard value, whether the production state is normal or not is judged, corresponding information is sent to related workers through the man-machine interaction module according to the judgment result, the production state can be effectively reflected, and powerful support is provided for decision making.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and more specifically, to an AI-based auxiliary equipment production data management method and system. Background Art

[0002] With the rapid development and popularization of artificial intelligence technology, its application in various fields is becoming more and more extensive, and the trend of intelligence is irreversible. In the field of equipment production data management, the introduction of AI technology has become an important way to improve management efficiency and optimize resource allocation. Traditional equipment production data management often relies on manual operation and experience judgment, and has problems such as inefficiency and proneness to errors. With the intensification of market competition and changes in customer needs, companies need to respond to market changes more quickly and accurately to improve production efficiency and product quality. The introduction of AI technology can effectively solve these problems and enhance the competitiveness of companies.

[0003] The traditional auxiliary equipment production data management system includes equipment information management module, production plan management module, quality management module, cost management module and system master control and safety management module. The equipment information management module is used to manage the static and dynamic information of the equipment. The production plan management module includes the functions of production plan preparation, scheduling, monitoring and dispatching. The quality management module includes quality inspection, quality plan, archive data, statistical analysis and other functions. The cost management module includes product cost accounting, cost analysis, cost control and other functions. The system master control and safety management module includes user help, data backup and recovery, user authority management, user password change control and other functions. Through the collaborative work of multiple modules, comprehensive management and optimization of production data are achieved.

[0004] However, in actual use, it still has some shortcomings, such as untimely data updates. The traditional auxiliary equipment production data management system relies on manual input and recording, with delayed data updates, and is unable to grasp the equipment status and production progress in real time, affecting the timeliness and accuracy of decision-making; it cannot intuitively reflect the production status. The traditional auxiliary equipment production data management system only collects and analyzes data in the production process, and cannot clearly and intuitively reflect the status of the production site, which is not conducive to the optimal management of equipment and the continuous improvement of production processes. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an AI-based auxiliary equipment production data management method and system, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: an AI-based auxiliary equipment production data management system, including an operation database, a central processing unit and a user information terminal, and also including a monitoring area division module, a production data acquisition module, a production data preprocessing module, a production data analysis module, a production data comprehensive analysis module, a judgment module and a human-computer interaction module.

[0007] The operation database is used to store all data of the auxiliary equipment production data management system and collect the data output by each module in real time; the central processor is used to control the instructions issued by all modules; the user information terminal is an information output device for receiving the auxiliary equipment production data management system;

[0008] Monitoring area division module: divide different monitoring area types, and divide auxiliary equipment into different monitoring areas according to the actual layout and equipment distribution of the production site;

[0009] Production data acquisition module: collects production data parameters in real time. Production data parameters refer to the relevant parameters of production auxiliary equipment during the working process, and transmits them to the production data preprocessing module;

[0010] Production data preprocessing module: establish equipment operation status parameter preprocessing model and management control parameter preprocessing model, perform data preprocessing on production data parameters, and pass them to the production data analysis module;

[0011] Production data analysis module: analyzes the data transmitted by the production data preprocessing module, including the equipment operation status analysis unit and the management control analysis unit, calculates the equipment operation status evaluation index and the management control evaluation index, and transmits it to the production data comprehensive analysis module;

[0012] Production data comprehensive analysis module: Comprehensively process and analyze the equipment operation status evaluation index and management control evaluation index, calculate the comprehensive evaluation value of production data, and pass it to the judgment module;

[0013] Judgment module: establishes a standard value for comprehensive evaluation of production data, compares the comprehensive evaluation index of production data with the standard value for comprehensive evaluation of production data, determines whether the production status is normal, and transmits it to the human-computer interaction module;

[0014] Human-computer interaction module: Based on the information transmitted by the judgment module, the corresponding alarm signal is transmitted to the relevant staff, and the corresponding optimization suggestions are generated through the AI ​​algorithm.

[0015] Preferably, the equipment operation status parameters include performance efficiency parameters and process control parameters, and the performance efficiency parameters include production efficiency, power consumption and time utilization rate, which are marked as η respectively. p , W Eand η t The process control parameters include equipment temperature, equipment pressure, equipment speed and equipment displacement, which are marked as T, F, R and X respectively.

[0016] Preferably, the management control parameters include equipment maintenance parameters and data security parameters. The equipment maintenance parameters include maintenance cycle, failure rate, equipment utilization rate and equipment maintenance cost rate, which are marked as t, n, and f , η c and η e ,Data security parameters include data encryption degree and data backup rate, marked as D e and η d .

[0017] Preferably, the equipment operation status parameters collected by the production data collection module are imported into the equipment operation status parameter preprocessing model to calculate the performance efficiency evaluation index and the process control evaluation index. The performance efficiency evaluation index is specifically expressed as:

[0018]

[0019] y1 represents the performance efficiency evaluation index in the production process, η p Represents the production efficiency in the production process, W E Indicates the amount of electricity consumed in the production process, η t represents the time utilization rate in the production process, α1, α2 and α3 represent the influence coefficients of production efficiency, power consumption and time utilization rate on the performance efficiency evaluation index respectively; the process evaluation index is specifically expressed as:

[0020]

[0021] y2 represents the process evaluation index in the production process, T represents the equipment temperature in the production process, F represents the pressure on the equipment in the production process, R represents the equipment speed in the production process, X represents the equipment displacement in the production process, β1, β2, β3 and β4 represent the influence coefficients of equipment temperature, equipment pressure, equipment speed and equipment displacement on the process evaluation index respectively, and b is a known constant.

[0022] Preferably, the management control parameters collected by the production data collection module are imported into the management control parameter preprocessing model to calculate the equipment maintenance evaluation index and the data security evaluation index. The equipment maintenance evaluation index is specifically expressed as:

[0023]

[0024] y3 represents the equipment maintenance evaluation index of production equipment, t represents the maintenance cycle of production equipment, η frepresents the failure rate of production equipment, η c represents the equipment utilization rate of production equipment, η e represents the equipment maintenance cost rate of production equipment, γ1, γ2, γ3 and γ4 represent the influence coefficients of maintenance cycle, failure rate, equipment utilization rate and equipment maintenance cost rate on equipment maintenance evaluation index respectively, and c1 is a known constant; the data security evaluation index is specifically expressed as:

[0025]

[0026] y4 represents the data security assessment index of production data, D e Indicates the degree of data encryption of production data, η d represents the data backup rate of production data, ε1 and ε2 represent the influence coefficients of data encryption degree and data backup rate on data security assessment index respectively.

[0027] Preferably, the equipment operation status analysis unit is used to establish an equipment operation status analysis model, import the performance efficiency evaluation index and the process control evaluation index into the equipment operation status analysis model, and calculate the equipment operation status evaluation index, which is specifically expressed as: y 运 represents the equipment operation status evaluation index in the production process, y1 represents the performance efficiency evaluation index in the production process, y2 represents the process evaluation index in the production process, μ1 and μ2 represent the influence coefficients of the performance efficiency evaluation index and the process evaluation index on the equipment operation status evaluation index of the production equipment respectively; the management control analysis unit is used to establish a management control analysis model, import the equipment maintenance evaluation index and the data security evaluation index into the management control analysis model, and calculate the management control evaluation index, which is specifically expressed as: y 管 represents the management control evaluation index in the production process, y3 represents the equipment maintenance evaluation index of production equipment, y4 represents the data security evaluation index of production data, μ3 and μ4 represent the influence coefficients of equipment maintenance evaluation index and data security evaluation index on management control evaluation index respectively.

[0028] Preferably, a production data comprehensive analysis model is established through a production data comprehensive analysis module, the equipment operation status evaluation index and the management control evaluation index are imported into the production data comprehensive analysis model, and the production data comprehensive evaluation value is calculated, which is specifically expressed as: φ represents the comprehensive evaluation value of production data in the production process, y 运 Represents the equipment operation status evaluation index in the production process, y 管 Represents the management control evaluation index in the production process.

[0029] Preferably, the production data comprehensive evaluation standard value is marked as φ标 , when φ>φ 标 When φ<φ, it means that the comprehensive evaluation value of production data is greater than the standard value of comprehensive evaluation of production data, and production is in normal state; when φ<φ 标 , it means that the comprehensive evaluation value of production data is less than the standard value of comprehensive evaluation of production data, and production is in an abnormal state.

[0030] Preferably, the information output device includes mobile phones of production-related staff, an auxiliary equipment production data management system interface, and a large electronic screen in the production workshop.

[0031] Preferably, an AI-based auxiliary equipment production data management method comprises the following steps:

[0032] S1: Different monitoring area types are divided through the monitoring area division module, and auxiliary equipment is divided into different monitoring areas according to the actual layout and equipment distribution of the production site;

[0033] S2: The production data acquisition module collects production data parameters in real time. The production data parameters refer to the relevant parameters of the production auxiliary equipment during the working process, including equipment operation status parameters and management control parameters, and transmits them to the production data preprocessing module;

[0034] S3: Based on the relevant parameters of the production auxiliary equipment during the working process, the equipment operation status parameter preprocessing model and the management control parameter preprocessing model are established through the production data preprocessing module, the production data parameters are preprocessed, and the data are transmitted to the production data analysis module;

[0035] S4: Analyze the data transmitted by the production data preprocessing module through the production data analysis module, including the equipment operation status analysis unit and the management control analysis unit, calculate the equipment operation status evaluation index and the management control evaluation index, and transmit it to the production data comprehensive analysis module;

[0036] S5: The equipment operation status evaluation index and the management control evaluation index are comprehensively processed and analyzed by the production data comprehensive analysis module, and the comprehensive evaluation value of the production data is calculated and transmitted to the judgment module;

[0037] S6: Establish a production data comprehensive evaluation standard value through the judgment module, compare the production data comprehensive evaluation index with the production data comprehensive evaluation standard value, judge whether the production status is normal, and pass it to the human-computer interaction module;

[0038] S7: Through the human-computer interaction module, according to the information transmitted by the judgment module, the corresponding alarm signal is transmitted to the relevant staff, and the corresponding optimization suggestions are generated through the AI ​​algorithm.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention monitors and collects relevant parameters in the production process in real time through the monitoring area division module and the production data collection module, pays attention to the status of production auxiliary equipment in real time, and transmits the collected data to the operation database in real time, which can timely and effectively reflect whether the current production status is abnormal, so that relevant staff can take corresponding measures in time;

[0041] 2. The present invention calculates the comprehensive evaluation value of production data through the production data preprocessing module, the production data analysis module, and the production data comprehensive analysis module, and establishes the comprehensive evaluation standard value of production data through the judgment module, compares the comprehensive evaluation index of production data with the comprehensive evaluation standard value of production data, judges whether the production status is normal, and sends relevant information to the information output device according to the judgment result to remind relevant personnel to make corresponding operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the present invention.

[0043] Figure 2 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] As attached Figure 2 An AI-based auxiliary equipment production data management system shown includes an operation database, a central processing unit and a user information terminal, and also includes a monitoring area division module, a production data acquisition module, a production data preprocessing module, a production data analysis module, a production data comprehensive analysis module, a judgment module and a human-computer interaction module.

[0046] The output end of the monitoring area division module is connected to the input end of the production data acquisition module by telecommunication, the input end of the production data acquisition module is connected to the output end of the production data preprocessing module by telecommunication, the output end of the production data preprocessing module is connected to the input end of the production data analysis module by telecommunication, the output end of the production data analysis module is connected to the input end of the production data comprehensive analysis module by telecommunication, the output end of the production data comprehensive analysis module is connected to the input end of the judgment module by telecommunication, the output end of the judgment module is connected to the input end of the human-computer interaction module by telecommunication, the output end of the central processing unit is connected to the input end of each module by telecommunication, and the output end of each module is connected to the input end of the operation database by telecommunication.

[0047] The operation database is used to store all data of the auxiliary equipment production data management system and collect the real-time output data of each module; the central processing unit is used to control the instructions issued by all modules; the user information terminal is an information output device for receiving the auxiliary equipment production data management system.

[0048] Furthermore, the information output devices include mobile phones of production-related staff, the production data management system interface of auxiliary equipment, and the large electronic screen in the production workshop.

[0049] The monitoring area division module is used to divide different monitoring area types and divide auxiliary equipment into different monitoring areas according to the actual layout of the production site and the distribution of equipment.

[0050] What needs to be specifically explained in this embodiment is that when dividing the monitoring area, it should be ensured that the monitoring area can fully cover all key equipment and production processes, and the monitoring area should be reasonably divided according to the equipment type, production process and functional requirements. First, a comprehensive survey of the production site should be conducted to understand the type, quantity, layout and production process of the equipment, determine the key links in the key equipment and production process, provide a basis for the division of the monitoring area, and divide the production site according to the survey results.

[0051] The production data acquisition module is used to collect production data parameters in real time. Production data parameters refer to the relevant parameters of production auxiliary equipment during the working process, and pass them to the production data preprocessing module.

[0052] Furthermore, the equipment operation status parameters include performance efficiency parameters and process control parameters. The performance efficiency parameters include production efficiency, power consumption and time utilization rate, which are marked as η and η, respectively. p , W E and η t The process control parameters include equipment temperature, equipment pressure, equipment speed and equipment displacement, which are marked as T, F, R and X respectively.

[0053] What needs to be specifically explained in this embodiment is that the calculation method of production efficiency is production efficiency = output / input. Sensors are installed on the equipment end to monitor the operating status and power consumption of the equipment in real time. The equipment is used to record the time in different states, and the proportion of the time the equipment is in working state to the total time is calculated based on this to obtain the time utilization rate; corresponding sensors are used to monitor the temperature, pressure, rotation speed and displacement of the transmitted equipment in real time.

[0054] Furthermore, the management control parameters include equipment maintenance parameters and data security parameters. The equipment maintenance parameters include maintenance cycle, failure rate, equipment utilization rate and equipment maintenance cost rate, which are marked as t, η respectively. f , ηc and η e ,Data security parameters include data encryption degree and data backup rate, marked as D e and η d .

[0055] In this embodiment, it is necessary to specifically explain that the degree of data encryption is evaluated from the security of the encryption algorithm, the key length, the complexity of the encryption algorithm, and the data integrity. Different algorithm types have different security; the key length is one of the important indicators for measuring the degree of data encryption. A longer key length usually means higher security, because the computing resources and time required to crack a long key will increase significantly; the complexity of the same encryption algorithm is usually proportional to its security.

[0056] The production data preprocessing module is used to establish the equipment operation status parameter preprocessing model and the management control parameter preprocessing model, perform data preprocessing on the production data parameters, and pass them to the production data analysis module.

[0057] Furthermore, the equipment operation status parameters collected by the production data collection module are imported into the equipment operation status parameter preprocessing model to calculate the performance efficiency evaluation index and the process control evaluation index. The performance efficiency evaluation index is specifically expressed as:

[0058]

[0059] y1 represents the performance efficiency evaluation index in the production process, η p Represents the production efficiency in the production process, W E Indicates the amount of electricity consumed in the production process, η t represents the time utilization rate in the production process, α1, α2 and α3 represent the influence coefficients of production efficiency, power consumption and time utilization rate on the performance efficiency evaluation index respectively; the process evaluation index is specifically expressed as:

[0060]

[0061] y2 represents the process evaluation index in the production process, T represents the equipment temperature in the production process, F represents the pressure on the equipment in the production process, R represents the equipment speed in the production process, X represents the equipment displacement in the production process, β1, β2, β3 and β4 represent the influence coefficients of equipment temperature, equipment pressure, equipment speed and equipment displacement on the process evaluation index respectively, and b is a known constant.

[0062] Furthermore, the management control parameters collected by the production data collection module are imported into the management control parameter preprocessing model to calculate the equipment maintenance evaluation index and the data security evaluation index. The equipment maintenance evaluation index is specifically expressed as:

[0063]

[0064] y3 represents the equipment maintenance evaluation index of production equipment, t represents the maintenance cycle of production equipment, η f represents the failure rate of production equipment, η c represents the equipment utilization rate of production equipment, η e represents the equipment maintenance cost rate of production equipment, γ1, γ2, γ3 and γ4 represent the influence coefficients of maintenance cycle, failure rate, equipment utilization rate and equipment maintenance cost rate on equipment maintenance evaluation index respectively, and c1 is a known constant; the data security evaluation index is specifically expressed as:

[0065]

[0066] y4 represents the data security assessment index of production data, D e Indicates the degree of data encryption of production data, η d represents the data backup rate of production data, ε1 and ε2 represent the influence coefficients of data encryption degree and data backup rate on data security assessment index respectively.

[0067] The production data analysis module is used to analyze the data transmitted by the production data preprocessing module, including the equipment operation status analysis unit and the management control analysis unit, calculate the equipment operation status evaluation index and the management control evaluation index, and transmit it to the production data comprehensive analysis module.

[0068] Furthermore, the equipment operation status analysis unit is used to establish an equipment operation status analysis model, import the performance efficiency evaluation index and the process control evaluation index into the equipment operation status analysis model, and calculate the equipment operation status evaluation index, which is specifically expressed as: y 运 represents the equipment operation status evaluation index in the production process, y1 represents the performance efficiency evaluation index in the production process, y2 represents the process evaluation index in the production process, μ1 and μ2 represent the influence coefficients of the performance efficiency evaluation index and the process evaluation index on the equipment operation status evaluation index of the production equipment respectively; the management control analysis unit is used to establish a management control analysis model, import the equipment maintenance evaluation index and the data security evaluation index into the management control analysis model, and calculate the management control evaluation index, which is specifically expressed as: y 管 represents the management control evaluation index in the production process, y3 represents the equipment maintenance evaluation index of production equipment, y4 represents the data security evaluation index of production data, μ3 and μ4 represent the influence coefficients of equipment maintenance evaluation index and data security evaluation index on management control evaluation index respectively.

[0069] The production data comprehensive analysis module is used to comprehensively process and analyze the equipment operation status evaluation index and the management control evaluation index, calculate the comprehensive evaluation value of the production data, and pass it to the judgment module.

[0070] Furthermore, a comprehensive analysis model for production data is established through the comprehensive analysis module for production data, the equipment operation status evaluation index and the management control evaluation index are imported into the comprehensive analysis model for production data, and the comprehensive evaluation value of production data is calculated, which is specifically expressed as: φ represents the comprehensive evaluation value of production data in the production process, y 运 Represents the equipment operation status evaluation index in the production process, y 管 Represents the management control evaluation index in the production process.

[0071] The judgment module is used to establish a standard value for comprehensive evaluation of production data, compare the comprehensive evaluation index of production data with the standard value for comprehensive evaluation of production data, judge whether the production status is normal, and pass it to the human-computer interaction module.

[0072] Furthermore, the comprehensive evaluation standard value of production data is marked as φ 标 , when φ>φ 标 When φ<φ, it means that the comprehensive evaluation value of production data is greater than the standard value of comprehensive evaluation of production data, and production is in normal state; when φ<φ 标 , it means that the comprehensive evaluation value of production data is less than the standard value of comprehensive evaluation of production data, and production is in an abnormal state.

[0073] The human-computer interaction module is used to transmit corresponding alarm signals to relevant staff based on the information transmitted by the judgment module, and generate corresponding optimization suggestions through AI algorithms.

[0074] What needs to be specifically explained in this embodiment is that by comparing the comprehensive evaluation value of production data with the comprehensive evaluation standard value of production data, it is determined whether the production is in a normal state. If it is in a normal state, there is no need to issue an alarm, and the electronic screen in the production workshop will show that there is no abnormality. If it is in an abnormal state, an alarm message needs to be sent to the information output device to remind relevant personnel to deal with and adjust the production equipment in a timely manner.

[0075] like Figure 1 As shown, this embodiment provides an AI-based auxiliary equipment production data management method, the steps are as follows:

[0076] S1: Different monitoring area types are divided through the monitoring area division module, and auxiliary equipment is divided into different monitoring areas according to the actual layout and equipment distribution of the production site;

[0077] S2: The production data acquisition module collects production data parameters in real time. The production data parameters refer to the relevant parameters of the production auxiliary equipment during the working process, including equipment operation status parameters and management control parameters, and transmits them to the production data preprocessing module;

[0078] S3: Based on the relevant parameters of the production auxiliary equipment during the working process, the equipment operation status parameter preprocessing model and the management control parameter preprocessing model are established through the production data preprocessing module, the production data parameters are preprocessed, and the data are transmitted to the production data analysis module;

[0079] S4: Analyze the data transmitted by the production data preprocessing module through the production data analysis module, including the equipment operation status analysis unit and the management control analysis unit, calculate the equipment operation status evaluation index and the management control evaluation index, and transmit it to the production data comprehensive analysis module;

[0080] S5: The equipment operation status evaluation index and the management control evaluation index are comprehensively processed and analyzed by the production data comprehensive analysis module, and the comprehensive evaluation value of the production data is calculated and transmitted to the judgment module;

[0081] S6: Establish a production data comprehensive evaluation standard value through the judgment module, compare the production data comprehensive evaluation index with the production data comprehensive evaluation standard value, judge whether the production status is normal, and pass it to the human-computer interaction module;

[0082] S7: Through the human-computer interaction module, according to the information transmitted by the judgment module, the corresponding alarm signal is transmitted to the relevant staff, and the corresponding optimization suggestions are generated through the AI ​​algorithm.

[0083] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0084] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An AI-based auxiliary equipment production data management method, characterized in that: include: S1: Different monitoring area types are divided through the monitoring area division module, and auxiliary equipment is divided into different monitoring areas according to the actual layout and equipment distribution of the production site; S2: The production data acquisition module collects production data parameters in real time. The production data parameters refer to the relevant parameters of the production auxiliary equipment during the working process, including equipment operation status parameters and management control parameters, and transmits them to the production data preprocessing module; S3: Based on the relevant parameters of the production auxiliary equipment during the working process, the equipment operation status parameter preprocessing model and the management control parameter preprocessing model are established through the production data preprocessing module, the production data parameters are preprocessed, and the data are transmitted to the production data analysis module; S4: Analyze the data transmitted by the production data preprocessing module through the production data analysis module, including the equipment operation status analysis unit and the management control analysis unit, calculate the equipment operation status evaluation index and the management control evaluation index, and transmit it to the production data comprehensive analysis module; S5: The equipment operation status evaluation index and the management control evaluation index are comprehensively processed and analyzed by the production data comprehensive analysis module, and the comprehensive evaluation value of the production data is calculated and transmitted to the judgment module; S6: Establish a production data comprehensive evaluation standard value through the judgment module, compare the production data comprehensive evaluation index with the production data comprehensive evaluation standard value, judge whether the production status is normal, and pass it to the human-computer interaction module; S7: Through the human-computer interaction module, according to the information transmitted by the judgment module, the corresponding alarm signal is transmitted to the relevant staff, and the corresponding optimization suggestions are generated through the AI ​​algorithm.

2. The AI-based auxiliary equipment production data management method according to claim 1, characterized in that: The equipment operation status parameters include performance efficiency parameters and process control parameters. The performance efficiency parameters include production efficiency, power consumption and time utilization rate, which are marked as η respectively. p , W E and η t The process control parameters include equipment temperature, equipment pressure, equipment speed and equipment displacement, which are marked as T, F, R and X respectively.

3. The AI-based auxiliary equipment production data management method according to claim 1, characterized in that: The management control parameters include equipment maintenance parameters and data security parameters. The equipment maintenance parameters include maintenance cycle, failure rate, equipment utilization rate and equipment maintenance cost rate, which are marked as t, η respectively. f , η c and η e ,Data security parameters include data encryption degree and data backup rate, marked as D e and η d .

4. The AI-based auxiliary equipment production data management method and system according to claim 1, characterized in that: The equipment operation status parameter preprocessing model is used to perform data preprocessing on the equipment operation status parameters, and the steps are as follows: A1: Import the equipment operation status parameters collected by the production data collection module into the equipment operation status parameter preprocessing model; A2: Calculate the performance efficiency evaluation index and process control evaluation index; The performance efficiency evaluation index is specifically expressed as: y1 represents the performance efficiency evaluation index in the production process, η p Represents the production efficiency in the production process, W E Indicates the amount of electricity consumed in the production process, η t represents the time utilization rate in the production process, α1, α2 and α3 represent the influence coefficients of production efficiency, power consumption and time utilization rate on the performance efficiency evaluation index respectively; the process evaluation index is specifically expressed as: y2 represents the process evaluation index in the production process, T represents the equipment temperature in the production process, F represents the pressure on the equipment in the production process, R represents the equipment speed in the production process, X represents the equipment displacement in the production process, β1, β2, β3 and β4 represent the influence coefficients of equipment temperature, equipment pressure, equipment speed and equipment displacement on the process evaluation index respectively, and b is a known constant.

5. The AI-based auxiliary equipment production data management method according to claim 1, characterized in that: The management control parameter preprocessing model is used to perform data preprocessing on the management control parameters, and the steps are as follows: B1: Import the management control parameters collected by the production data collection module into the management control parameter preprocessing model; B2: Calculate the equipment maintenance assessment index and data security assessment index; The equipment maintenance evaluation index is specifically expressed as: y3 represents the equipment maintenance evaluation index of production equipment, t represents the maintenance cycle of production equipment, η f represents the failure rate of production equipment, η c represents the equipment utilization rate of production equipment, η e represents the equipment maintenance cost rate of production equipment, γ1, γ2, γ3 and γ4 represent the influence coefficients of maintenance cycle, failure rate, equipment utilization rate and equipment maintenance cost rate on equipment maintenance evaluation index respectively, and c1 is a known constant; the data security evaluation index is specifically expressed as: y4 represents the data security assessment index of production data, D e Indicates the degree of data encryption of production data, η d represents the data backup rate of production data, ε1 and ε2 represent the influence coefficients of data encryption degree and data backup rate on data security assessment index respectively.

6. The AI-based auxiliary equipment production data management method according to claim 1, characterized in that: The equipment operation status analysis unit is used to establish an equipment operation status analysis model, import the performance efficiency evaluation index and the process control evaluation index into the equipment operation status analysis model, and calculate the equipment operation status evaluation index, which is specifically expressed as: y 运 represents the equipment operation status evaluation index in the production process, y1 represents the performance efficiency evaluation index in the production process, y2 represents the process evaluation index in the production process, μ1 and μ2 represent the influence coefficients of the performance efficiency evaluation index and the process evaluation index on the equipment operation status evaluation index of the production equipment respectively; the management control analysis unit is used to establish a management control analysis model, import the equipment maintenance evaluation index and the data security evaluation index into the management control analysis model, and calculate the management control evaluation index, which is specifically expressed as: y 管 represents the management control evaluation index in the production process, y3 represents the equipment maintenance evaluation index of production equipment, y4 represents the data security evaluation index of production data, μ3 and μ4 represent the influence coefficients of equipment maintenance evaluation index and data security evaluation index on management control evaluation index respectively.

7. The AI-based auxiliary equipment production data management method according to claim 1, characterized in that: The calculation process of the comprehensive evaluation value of production data is as follows: C1: Establish a comprehensive analysis model for production data through the comprehensive analysis module of production data; C2: Import the equipment operation status evaluation index and management control evaluation index into the production data comprehensive analysis model; C3: Calculate the comprehensive evaluation value of production data through the comprehensive analysis model of production data, which is specifically expressed as: φ represents the comprehensive evaluation value of production data in the production process, y 运 Represents the equipment operation status evaluation index in the production process, y 管 Represents the management control evaluation index in the production process.

8. The AI-based auxiliary equipment production data management method according to claim 1, characterized in that: The production data comprehensive evaluation standard value is marked as φ 标 , when φ>φ 标 When φ<φ, it means that the comprehensive evaluation value of production data is greater than the standard value of comprehensive evaluation of production data, and production is in normal state; when φ<φ 标 , it means that the comprehensive evaluation value of production data is less than the standard value of comprehensive evaluation of production data, and production is in an abnormal state.

9. The AI-based auxiliary equipment production data management system according to claim 1, comprising an operation database, a central processing unit and a user information terminal, characterized in that: It also includes a monitoring area division module, a production data acquisition module, a production data preprocessing module, a production data analysis module, a production data comprehensive analysis module, a judgment module and a human-computer interaction module; The operation database is used to store all data of the auxiliary equipment production data management system and collect the data output by each module in real time; the central processor is used to control the instructions issued by all modules; the user information terminal is an information output device for receiving the auxiliary equipment production data management system; Monitoring area division module: divide different monitoring area types, and divide auxiliary equipment into different monitoring areas according to the actual layout and equipment distribution of the production site; Production data acquisition module: collects production data parameters in real time. Production data parameters refer to the relevant parameters of production auxiliary equipment during the working process, and transmits them to the production data preprocessing module; Production data preprocessing module: establish equipment operation status parameter preprocessing model and management control parameter preprocessing model, perform data preprocessing on production data parameters, and pass them to the production data analysis module; Production data analysis module: analyzes the data transmitted by the production data preprocessing module, including the equipment operation status analysis unit and the management control analysis unit, calculates the equipment operation status evaluation index and the management control evaluation index, and transmits it to the production data comprehensive analysis module; Production data comprehensive analysis module: Comprehensively process and analyze the equipment operation status evaluation index and management control evaluation index, calculate the comprehensive evaluation value of production data, and pass it to the judgment module; Judgment module: establishes a standard value for comprehensive evaluation of production data, compares the comprehensive evaluation index of production data with the standard value for comprehensive evaluation of production data, determines whether the production status is normal, and transmits it to the human-computer interaction module; Human-computer interaction module: Based on the information transmitted by the judgment module, the corresponding alarm signal is transmitted to the relevant staff, and the corresponding optimization suggestions are generated through the AI ​​algorithm.

10. The AI-based auxiliary equipment production data management system according to claim 1, characterized in that: The information output devices include mobile phones of production-related staff, an auxiliary equipment production data management system interface, and a large electronic screen in the production workshop.

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