Auxiliary management system based on factory data collection and analysis

Abstract

The invention relates to the technical field of big data analysis, and discloses an auxiliary management system based on factory data collection and analysis, which comprises a factory data acquisition module, a data storage management module, a data modeling analysis module, a data risk early warning module, an equipment fault prediction module, an equipment fault early warning module and a man-machine interaction management module, product appearance data and equipment operation data on a factory production line are collected in real time, a to-be-detected target is recognized after safety verification, screw positions on the surface of the to-be-detected target are positioned after no surface defect is recognized, a mathematical model is established to analyze the quality of a produced product and judge whether the screw positions are matched, and mismatched positions are repaired and judged. A mathematical model is established based on equipment operation data, equipment faults are predicted according to temperature changes of different contacts, early warning display is carried out on the quality of produced products and the equipment faults, and the stability and consistency of the quality of the products are ensured by monitoring key parameters in the production process in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and more specifically to an auxiliary management system based on factory data collection and analysis. Background Art

[0002] With the continuous development of science and technology, especially the maturity and popularization of technologies such as industrial automation, Internet of Things, big data, cloud computing, and artificial intelligence, factory data collection and analysis have been provided with strong technical support and broad application space. Industrial automation technology has been widely used in factory production, realizing precise control and monitoring of production equipment, and Internet of Things technology has further promoted the interconnection between equipment, enabling various equipment, sensors, actuators, etc. in the factory to exchange data in real time, providing a rich data source for data collection and analysis.

[0003] Some traditional air-conditioning production lines are not yet equipped with sufficient data collection equipment and lack appearance inspection of the products produced, which makes it difficult to discover and resolve product quality problems in a timely manner. In addition, due to the lack of effective data analysis and early warning mechanisms, equipment failures and production anomalies are often not discovered until after they occur, increasing production costs and downtime. Moreover, due to the incomplete management system of production equipment, repair and maintenance work cannot be carried out in a timely manner, affecting production efficiency and product quality. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an auxiliary management system based on factory data collection and analysis to solve the problems existing in the above-mentioned background technology.

[0005] The present invention provides the following technical solution: an auxiliary management system based on factory data collection and analysis, comprising: a factory data acquisition module, a data storage management module, a data modeling and analysis module, a data risk warning module, an equipment failure prediction module, an equipment failure warning module and a human-computer interaction management module; The factory data acquisition module includes a production product data acquisition unit and a production equipment data acquisition unit, which are used to collect product appearance data and equipment operation data on the factory production line in real time and transmit the data to the data storage management module; The data storage management module receives product appearance data and equipment operation data on the factory production line collected by the factory data collection module, stores and manages the data, and transmits the data to the data modeling analysis module and the equipment failure prediction module respectively after passing the security verification; The data modeling and analysis module establishes a mathematical model to analyze the quality of production products based on the product appearance data on the factory production line transmitted by the data storage management module, and transmits the analysis results to the data risk warning module; The data risk warning module receives the production product quality analysis results transmitted by the data modeling and analysis module, issues risk warnings on the production product quality, and transmits the warning information to the human-computer interaction management module; The equipment failure prediction module establishes a mathematical model to predict equipment failure based on the equipment operation data on the factory production line transmitted by the data storage management module, and transmits the prediction result to the equipment failure warning module; The equipment failure early warning module receives the prediction result transmitted by the equipment failure prediction module, displays the early warning of the equipment failure, and transmits the early warning information to the human-computer interaction management module; The human-computer interaction management module receives the analysis results transmitted by the data risk warning module and the warning information transmitted by the equipment failure warning module, and performs risk management on abnormal situations.

[0006] Preferably, in the factory data acquisition module, the production product data acquisition unit collects the appearance data of the air-conditioning products produced on the factory production line in real time through visual inspection equipment, and the product appearance data includes: the screw positions on the surface of the target to be detected, the number of screws on the surface of the target to be detected, the normal load borne by the screw positions, and the material hardness; the production equipment data acquisition unit collects the equipment operation data on the factory production line in real time through temperature sensors, and the equipment operation data includes: the number of contact points between the factory operating equipment and the surface of the target to be detected, the initial temperature of each contact point, the ambient temperature, the current value passing through the contact point, the resistance value passing through the contact point, the surface area of ​​the target to be detected, the material density of the target to be detected, the specific heat capacity of the target to be detected, the volume of the target to be detected, and the equipment operation time.

[0007] Preferably, in the data storage management module, security verification is performed on the data before data transmission, and the verified data is transmitted to the data modeling and analysis module and the equipment fault prediction module respectively through a secure channel.

[0008] Preferably, in the data modeling and analysis module, based on the product appearance data on the factory production line transmitted by the data storage management module, the specific contents of establishing a mathematical model to analyze the quality of the produced products are as follows: Step S01: Perform appearance quality inspection on the target to be inspected entering the factory, use visual inspection equipment to obtain the image of the target to be inspected, identify the surface of the target to be inspected without defects, and locate the position of the screws on the surface of the target to be inspected , the preset target template surface screw position is ; Step S02: Calculate the screw matching degree of the target surface to be detected, and the calculation formula is: ,in represents the matching degree of screws on the target surface to be detected, and i represents the number of screws on the target surface to be detected; Step S03: Match the screws on the target surface to be detected Matching threshold with preset For comparison, if the screws on the target surface to be detected match Greater than or equal to the preset matching threshold , then the screw position is judged to match the template. If the screw matching degree of the target surface to be detected is Less than the preset matching threshold , then it is judged that the screw position does not match the template; Step S04: The positioning result is j screw positions that do not match the template, and the wear coefficient is calculated using the following formula: ,in Indicates the wear factor of the screw position that does not match the template, represents the material wear coefficient, Indicates that the screw position that does not match the template bears the normal load. Indicates the hardness of the material; Step S05: The wear coefficient of the screw position that does not match the template With preset wear threshold For comparison, if the wear coefficient of the screw position does not match the template Less than the preset wear threshold , then the screw position can be directly repaired. If the wear coefficient of the screw position that does not match the template is Greater than or equal to the preset wear threshold , it is judged that there is a risk in repairing the screw position, and the judgment result is transmitted to the data risk warning module.

[0009] Preferably, in the data risk warning module, the production product quality analysis results transmitted by the data modeling and analysis module are received. When the judgment result is that the screw position can be directly repaired, the repair process is automatically triggered, and a repair instruction is issued to the operator through the system interface; when the judgment result is that there is a risk in repairing the screw position, a risk warning instruction is issued to the operator through the system interface.

[0010] Preferably, in the equipment failure prediction module, based on the equipment operation data on the factory production line transmitted by the data storage management module, the specific contents of establishing a mathematical model to predict equipment failure are as follows: Step S01: The contact points between the factory operating equipment and the target surface to be detected are represented by contact points, and the number of contact points is n, where n=1, 2, 3, ...N; Step S02: Calculate the temperature correction coefficient of each contact point, the calculation formula is: ,in Indicates the temperature correction coefficient of each contact, represents the initial temperature of each contact point, Indicates the ambient temperature, Indicates the current value passing through the contact. represents the resistance value passing through the contact point, a represents the average heat transfer coefficient on the contact surface, and S represents the surface area of ​​the target to be detected; Step S03: Calculate the material temperature correction coefficient, the calculation formula is: ,in represents the material temperature correction coefficient, represents the material density of the target to be detected, c represents the specific heat capacity of the target to be detected, and V represents the volume of the target to be detected; Step S04: Calculate the change in temperature of each contact point between the factory operating equipment and the target surface to be detected over time, and the calculation formula is: ,in It indicates the change of the temperature of each contact point between the factory running equipment and the surface of the target to be detected over time, and t indicates the equipment running time; Step S05: The temperature change value of each contact point The temperature change threshold is set For comparison, if the temperature change value of each contact point Less than or equal to the preset temperature change threshold , then it is judged that the equipment is running smoothly. If the temperature change value of each contact point is Greater than the preset temperature change threshold , it is judged that there is a problem with the operation of the device.

[0011] Preferably, the equipment failure warning module receives the prediction result transmitted by the equipment failure prediction module, and the temperature change value of each contact point Greater than the preset temperature change threshold When the fault occurs, the fault warning process is automatically triggered, and a fault warning instruction is issued to the operator through the system interface. The system records the time, location and related parameters of the fault.

[0012] Preferably, in the human-computer interaction management module, the analysis results transmitted by the receiving data risk warning module and the warning information transmitted by the equipment fault warning module are displayed to the operator through the user interface, and the setting of the warning threshold is optimized based on the operator's feedback and historical data. When the equipment fails, the system provides maintenance suggestions based on the fault type and historical data, automatically records all operating data and fault events, and generates an analysis report.

[0013] Technical effects and advantages of the present invention: The present invention is provided with a factory data acquisition module, a data storage management module, a data modeling and analysis module, a data risk warning module, an equipment failure prediction module, an equipment failure warning module and a human-computer interaction management module, so as to collect product appearance data and equipment operation data on the factory production line in real time, identify the target to be detected after safety verification, locate the screw position on the surface of the target to be detected after identifying that the surface is defect-free, establish a mathematical model to analyze the quality of the production products to determine whether the screw positions match, and repair and determine the mismatched positions, establish a mathematical model based on the equipment operation data and predict equipment failures according to the temperature changes of different contacts, give early warnings and displays of production product quality and equipment failures, and ensure the stability and consistency of product quality through real-time monitoring of key parameters in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of an auxiliary management system based on factory data collection and analysis. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The auxiliary management system based on factory data collection and analysis involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0016] like Figure 1 As shown, the present invention provides an auxiliary management system based on factory data collection and analysis, including: a factory data acquisition module, a data storage management module, a data modeling and analysis module, a data risk warning module, an equipment failure prediction module, an equipment failure warning module and a human-computer interaction management module; The factory data collection module includes a production product data collection unit and a production equipment data collection unit. The production product data collection unit is used to collect product appearance data on the factory production line in real time. The production equipment data collection unit is used to collect factory equipment operation data in real time and transmit the data to the data storage management module; The data storage management module receives product appearance data and equipment operation data on the factory production line collected by the factory data collection module, stores and manages the data, and transmits the data to the data modeling analysis module and the equipment failure prediction module respectively after passing the security verification; The data modeling and analysis module establishes a mathematical model to analyze the quality of production products based on the product appearance data on the factory production line transmitted by the data storage management module, and transmits the analysis results to the data risk warning module; The data risk warning module receives the production product quality analysis results transmitted by the data modeling and analysis module, issues risk warnings on the production product quality, and transmits the warning information to the human-computer interaction management module; The equipment failure prediction module establishes a mathematical model to predict equipment failure based on the equipment operation data on the factory production line transmitted by the data storage management module, and transmits the prediction result to the equipment failure warning module; The equipment failure early warning module receives the prediction result transmitted by the equipment failure prediction module, displays the early warning of the equipment failure, and transmits the early warning information to the human-computer interaction management module; The human-computer interaction management module receives the analysis results transmitted by the data risk warning module and the warning information transmitted by the equipment failure warning module, and performs risk management on abnormal situations.

[0017] In this embodiment, it should be specifically explained that in the factory data acquisition module, the production product data acquisition unit collects the appearance data of the air-conditioning products produced on the factory production line in real time through visual inspection equipment, and the product appearance data includes: the screw position on the surface of the target to be detected, the number of screws on the surface of the target to be detected, the normal load borne by the screw position, and the material hardness; the production equipment data acquisition unit collects the equipment operation data on the factory production line in real time through temperature sensors, and the equipment operation data includes: the number of contact points between the factory operating equipment and the surface of the target to be detected, the initial temperature of each contact point, the ambient temperature, the current value passing through the contact point, the resistance value passing through the contact point, the surface area of ​​the target to be detected, the material density of the target to be detected, the specific heat capacity of the target to be detected, the volume of the target to be detected, and the equipment operation time.

[0018] In this embodiment, it should be specifically explained that, in the data storage management module, the data is securely verified before data transmission, and the data is encrypted using an encryption algorithm to ensure that the data cannot be easily decrypted even if it is intercepted during transmission. After receiving the encrypted data, the receiver uses the corresponding decryption algorithm to decrypt it, restore the original data, and verify the identities of the sender and receiver. The identity authentication method includes user name, password and digital certificate verification. After successful verification, the verified data is transmitted to the data modeling analysis module and the equipment fault prediction module through a secure channel.

[0019] In this embodiment, it should be specifically explained that in the data modeling and analysis module, based on the product appearance data on the factory production line transmitted by the data storage management module, the specific contents of establishing a mathematical model to analyze the quality of the produced products are as follows: Step S01: Perform appearance quality inspection on the target to be inspected entering the factory, use visual inspection equipment to obtain the image of the target to be inspected, identify the surface of the target to be inspected without defects, and locate the position of the screws on the surface of the target to be inspected , the preset target template surface screw position is ; Step S02: Calculate the screw matching degree of the target surface to be detected, and the calculation formula is: ,in represents the matching degree of screws on the target surface to be detected, and i represents the number of screws on the target surface to be detected; Step S03: Match the screws on the target surface to be detected Matching threshold with preset For comparison, if the screws on the target surface to be detected match Greater than or equal to the preset matching threshold , then the screw position is judged to match the template. If the screw matching degree of the target surface to be detected is Less than the preset matching threshold , then it is judged that the screw position does not match the template; Step S04: The positioning result is j screw positions that do not match the template, and the wear coefficient is calculated using the following formula: ,in Indicates the wear factor of the screw position that does not match the template, represents the material wear coefficient, Indicates that the screw position that does not match the template bears the normal load. Indicates the hardness of the material; Step S05: The wear coefficient of the screw position that does not match the template With preset wear threshold For comparison, if the wear coefficient of the screw position does not match the template Less than the preset wear threshold , then the screw position can be directly repaired. If the wear coefficient of the screw position that does not match the template is Greater than or equal to the preset wear threshold , it is judged that there is a risk in repairing the screw position, and the judgment result is transmitted to the data risk warning module.

[0020] In the present embodiment, it should be specifically explained that, in the data risk warning module, the production product quality analysis result transmitted by the data modeling and analysis module is received, and when the judgment result is that the screw position can be directly repaired, the repair process is automatically triggered, and a repair instruction is issued to the operator through the system interface; when the judgment result is that there is a risk in repairing the screw position, a risk warning instruction is issued to the operator through the system interface.

[0021] In this embodiment, it should be specifically explained that in the equipment failure prediction module, based on the equipment operation data on the factory production line transmitted by the data storage management module, the specific content of establishing a mathematical model to predict equipment failure is as follows: Step S01: The contact points between the factory operating equipment and the target surface to be detected are represented by contact points, and the number of contact points is n, where n=1, 2, 3, ...N; Step S02: Calculate the temperature correction coefficient of each contact point, the calculation formula is: ,in Indicates the temperature correction coefficient of each contact, represents the initial temperature of each contact point, Indicates the ambient temperature, Indicates the current value passing through the contact. represents the resistance value passing through the contact point, a represents the average heat transfer coefficient on the contact surface, and S represents the surface area of ​​the target to be detected; Step S03: Calculate the material temperature correction coefficient, the calculation formula is: ,in represents the material temperature correction coefficient, represents the material density of the target to be detected, c represents the specific heat capacity of the target to be detected, and V represents the volume of the target to be detected; Step S04: Calculate the change in temperature of each contact point between the factory operating equipment and the target surface to be detected over time, and the calculation formula is: ,in It indicates the change of the temperature of each contact point between the factory running equipment and the surface of the target to be detected over time, and t indicates the equipment running time; Step S05: The temperature change value of each contact point The temperature change threshold is set For comparison, if the temperature change value of each contact point Less than or equal to the preset temperature change threshold , then it is judged that the equipment is running smoothly. If the temperature change value of each contact point is Greater than the preset temperature change threshold , it is judged that there is a problem with the operation of the device.

[0022] In this embodiment, it should be specifically explained that the equipment fault warning module receives the prediction result transmitted by the equipment fault prediction module, and the temperature change value of each contact point is Greater than the preset temperature change threshold When a fault occurs, the fault warning process is automatically triggered, and a fault warning instruction is issued to the operator through the system interface. The fault warning instruction includes the equipment fault type, fault location, fault cause and recommended maintenance measures. The operator makes a preliminary judgment based on the warning instruction and takes corresponding maintenance or adjustment measures. The system records the time, location and related parameters of the fault.

[0023] In this embodiment, it should be specifically explained that in the human-computer interaction management module, the analysis results transmitted by the receiving data risk warning module and the warning information transmitted by the equipment fault warning module are displayed to the operator through the user interface, and the setting of the warning threshold is optimized based on the operator's feedback and historical data. When the equipment fails, the system provides maintenance suggestions based on the fault type and historical data, automatically records all operating data and fault events, and generates an analysis report.

[0024] In the present embodiment, it should be specifically explained that the difference between the present embodiment and the prior art lies mainly in that the present embodiment is provided with a factory data acquisition module, a data storage management module, a data modeling and analysis module, a data risk warning module, an equipment failure prediction module, an equipment failure warning module and a human-computer interaction management module, so as to collect the product appearance data and equipment operation data on the factory production line in real time, identify the target to be detected after safety verification, locate the screw position on the surface of the target to be detected after identifying that the surface is defect-free, establish a mathematical model to analyze the quality of the production product to determine whether the screw position matches, and repair and determine the mismatched position, establish a mathematical model based on the equipment operation data and predict equipment failure according to the temperature changes of different contacts, and give warnings and displays on the quality of the production product and equipment failure. In short, an auxiliary management system based on factory data collection and analysis can timely discover equipment failures and production anomalies through real-time monitoring of key parameters in the production process, ensure the stability and consistency of product quality, and improve production efficiency and product quality.

[0025] 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.

[0026] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An auxiliary management system based on factory data collection and analysis, characterized in that: include: Factory data acquisition module, data storage management module, data modeling and analysis module, data risk warning module, equipment failure prediction module, equipment failure warning module and human-computer interaction management module; The factory data acquisition module includes a production product data acquisition unit and a production equipment data acquisition unit, which are used to collect product appearance data and equipment operation data on the factory production line in real time and transmit the data to the data storage management module; The data storage management module receives product appearance data and equipment operation data on the factory production line collected by the factory data collection module, stores and manages the data, and transmits the data to the data modeling analysis module and the equipment failure prediction module respectively after passing the security verification; The data modeling and analysis module establishes a mathematical model to analyze the quality of production products based on the product appearance data on the factory production line transmitted by the data storage management module, and transmits the analysis results to the data risk warning module; The data risk warning module receives the production product quality analysis results transmitted by the data modeling and analysis module, issues risk warnings on the production product quality, and transmits the warning information to the human-computer interaction management module; The equipment failure prediction module establishes a mathematical model to predict equipment failure based on the equipment operation data on the factory production line transmitted by the data storage management module, and transmits the prediction result to the equipment failure warning module; The equipment failure early warning module receives the prediction result transmitted by the equipment failure prediction module, displays the early warning of the equipment failure, and transmits the early warning information to the human-computer interaction management module; The human-computer interaction management module receives the analysis results transmitted by the data risk warning module and the warning information transmitted by the equipment failure warning module, and performs risk management on abnormal situations.

2. The auxiliary management system based on factory data collection and analysis according to claim 1 is characterized in that: In the factory data acquisition module, the production product data acquisition unit collects the appearance data of the air-conditioning products produced on the factory production line in real time through visual inspection equipment, and the product appearance data includes: the screw position of the target surface to be detected, the number of screws on the target surface to be detected, the normal load borne by the screw position, and the material hardness; the production equipment data acquisition unit collects the equipment operation data on the factory production line in real time through temperature sensors, and the equipment operation data includes: the number of contact points between the factory operating equipment and the target surface to be detected, the initial temperature of each contact point, the ambient temperature, the current value passing through the contact point, the resistance value passing through the contact point, the surface area of ​​the target to be detected, the material density of the target to be detected, the specific heat capacity of the target to be detected, the volume of the target to be detected, and the equipment operation time.

3. The auxiliary management system based on factory data collection and analysis according to claim 1 is characterized in that: In the data storage management module, security verification is performed on the data before data transmission, and the verified data is transmitted to the data modeling and analysis module and the equipment fault prediction module respectively through a secure channel.

4. The auxiliary management system based on factory data collection and analysis according to claim 1 is characterized in that: In the data modeling and analysis module, based on the product appearance data on the factory production line transmitted by the data storage management module, the specific contents of establishing a mathematical model to analyze the quality of the produced products are as follows: Step S01: Perform appearance quality inspection on the target to be inspected entering the factory, use visual inspection equipment to obtain the image of the target to be inspected, identify the surface of the target to be inspected without defects, and locate the position of the screws on the surface of the target to be inspected , the preset target template surface screw position is ; Step S02: Calculate the screw matching degree of the target surface to be detected, and the calculation formula is: ,in represents the matching degree of screws on the target surface to be detected, and i represents the number of screws on the target surface to be detected; Step S03: Match the screws on the target surface to be detected Matching threshold with preset For comparison, if the screws on the target surface to be detected match Greater than or equal to the preset matching threshold , then the screw position is judged to match the template. If the screw matching degree of the target surface to be detected is Less than the preset matching threshold , then it is judged that the screw position does not match the template; Step S04: The positioning result is j screw positions that do not match the template, and the wear coefficient is calculated using the following formula: ,in Indicates the wear factor of the screw position that does not match the template, represents the material wear coefficient, Indicates that the screw position that does not match the template bears the normal load. Indicates the hardness of the material; Step S05: The wear coefficient of the screw position that does not match the template With preset wear threshold For comparison, if the wear coefficient of the screw position does not match the template Less than the preset wear threshold , then the screw position can be directly repaired. If the wear coefficient of the screw position that does not match the template is Greater than or equal to the preset wear threshold , it is judged that there is a risk in repairing the screw position, and the judgment result is transmitted to the data risk warning module.

5. The auxiliary management system based on factory data collection and analysis according to claim 1 is characterized in that: In the data risk warning module, the production product quality analysis results transmitted by the data modeling and analysis module are received. When the judgment result is that the screw position can be directly repaired, the repair process is automatically triggered, and a repair instruction is issued to the operator through the system interface; when the judgment result is that there is a risk in repairing the screw position, a risk warning instruction is issued to the operator through the system interface.

6. The auxiliary management system based on factory data collection and analysis according to claim 1, characterized in that: In the equipment failure prediction module, based on the equipment operation data on the factory production line transmitted by the data storage management module, the specific contents of establishing a mathematical model to predict equipment failure are as follows: Step S01: The contact points between the factory operating equipment and the target surface to be detected are represented by contact points, and the number of contact points is n, where n=1, 2, 3, ...N; Step S02: Calculate the temperature correction coefficient of each contact point, the calculation formula is: ,in Indicates the temperature correction coefficient of each contact, represents the initial temperature of each contact point, Indicates the ambient temperature, Indicates the current value passing through the contact. represents the resistance value passing through the contact point, a represents the average heat transfer coefficient on the contact surface, and S represents the surface area of ​​the target to be detected; Step S03: Calculate the material temperature correction coefficient, the calculation formula is: ,in represents the material temperature correction coefficient, represents the material density of the target to be detected, c represents the specific heat capacity of the target to be detected, and V represents the volume of the target to be detected; Step S04: Calculate the change in temperature of each contact point between the factory operating equipment and the target surface to be detected over time, and the calculation formula is: ,in It indicates the change of the temperature of each contact point between the factory running equipment and the target surface to be detected over time, and t indicates the equipment running time; Step S05: The temperature change value of each contact point The temperature change threshold is set For comparison, if the temperature change value of each contact point Less than or equal to the preset temperature change threshold , then it is judged that the equipment is running smoothly. If the temperature change value of each contact point is Greater than the preset temperature change threshold , it is judged that there is a problem with the operation of the device.

7. The auxiliary management system based on factory data collection and analysis according to claim 1 is characterized in that: The equipment failure warning module receives the prediction results transmitted by the equipment failure prediction module, and the temperature change values ​​of each contact point are Greater than the preset temperature change threshold When the fault occurs, the fault warning process is automatically triggered, and a fault warning instruction is issued to the operator through the system interface. The system records the time, location and related parameters of the fault.

8. The auxiliary management system based on factory data collection and analysis according to claim 1 is characterized by: In the human-computer interaction management module, the analysis results transmitted by the receiving data risk warning module and the warning information transmitted by the equipment fault warning module are displayed to the operator through the user interface, and the setting of the warning threshold is optimized based on the operator's feedback and historical data. When the equipment fails, the system provides maintenance suggestions based on the fault type and historical data, automatically records all operating data and fault events, and generates an analysis report.

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