Device Data Management Method and System Based on Industrial Internet of Things

Abstract

The present invention discloses a device data management method and system based on the industrial Internet of Things, belonging to the field of the industrial Internet of Things. The present invention imports the real-time obtained device operation data into a device fatigue analysis strategy to perform device operation fatigue analysis, imports the obtained product quality data produced by the device into a product production quality analysis strategy to perform device production quality analysis, imports the obtained device operation fatigue analysis result and device production quality analysis result into a production speed analysis model to perform production speed analysis, and adjusts the production of each production device according to the analyzed production speed. The present invention first comprehensively analyzes the obtained device operation data to estimate the device operation fatigue situation, and at the same time takes into account the device production quality analysis result to accurately estimate the suitable production speed of the device, improving the safety of device production.

Description

Technical Field

[0001] The present invention belongs to the field of industrial Internet of Things, and specifically relates to a device data management method and system based on the industrial Internet of Things. Background Art

[0002] Industrial Internet of Things (IIoT) refers to the application of Internet of Things technology in the industrial production field. By integrating technologies such as sensors, intelligent devices, networks, and big data analysis into the industrial production process, it realizes a new industrial form of real-time monitoring, data collection, intelligent analysis, and optimization control of devices. The industrial Internet of Things extends the concept of the Internet of Things to the industrial field, uses advanced network technologies to connect industrial devices, collects and processes data, so as to realize the intelligence, automation, and high efficiency of the production process;

[0003] In the process of device production data management, the production speed of the device is mostly set in advance, and it is impossible to comprehensively analyze the obtained device operation data during the production process to estimate the device operation fatigue situation. Furthermore, it is impossible to take into account the analysis results of the device production quality, resulting in the inability to accurately estimate the suitable production speed of the device, leading to the device being at an inappropriate production speed, thus reducing the safety of device production. Most of the existing technologies have the above problems. To solve the problems proposed in this background art, this application designs a device data management method and system based on the industrial Internet of Things. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention proposes a device data management method and system based on the industrial Internet of Things.

[0005] To achieve the above object, the present invention provides the following technical solutions: In the first aspect, a device data management method based on the industrial Internet of Things, which includes the following specific steps:

[0006] Real-time obtain device operation data and product quality data of device production through the Internet of Things gateway, and transmit them to the cloud platform in real time;

[0007] Import the real-time obtained device operation data into the device fatigue analysis strategy for device operation fatigue analysis;

[0008] Import the obtained product quality data of device production into the product production quality analysis strategy for device production quality analysis;

[0009] Import the obtained device operation fatigue analysis result and device production quality analysis result into the production speed analysis model for production speed analysis;

[0010] Adjust the production of each production device according to the production speed obtained from the analysis.

[0011] Here it should be noted that, as an optimal technical solution of the device data management method based on the industrial Internet of Things, the specific steps of obtaining the device operation data and the product quality data of the device produced in real time through the Internet of Things gateway and transmitting them to the cloud platform in real time are as follows:

[0012] S11. Collect the device operation jitter frequency and jitter amplitude data through the data acquisition terminal, and at the same time obtain the device operation temperature and operation power data, and store them in the first storage component;

[0013] S12. Collect the image data of each functional component during the device operation through the image acquisition terminal, and store them in the second storage component;

[0014] S13. Collect the product quality production data during the device operation through the product quality acquisition terminal, and store them in the third storage component;

[0015] S14. Transmit all the collected data to the cloud platform in real time through the Internet of Things.

[0016] Here it should be noted that, as an optimal technical solution of the device data management method based on the industrial Internet of Things, the device operation fatigue analysis by importing the real-time obtained device operation data into the device fatigue analysis strategy includes the following specific steps:

[0017] S21. Obtain the device operation jitter frequency and jitter amplitude data within the set evaluation period, and at the same time obtain the device operation temperature and operation power data within the set evaluation period. Import the obtained device operation jitter frequency and jitter amplitude data within the set evaluation period into the device jitter anomaly value calculation formula to calculate the device jitter anomaly value. Among them, the device jitter anomaly value calculation formula is: Among them, N is the number of device jitters within the set evaluation period, ki is the jitter amplitude of the i-th jitter of the device within the set evaluation period, and kmax is the maximum value of the safe jitter amplitude range;

[0018] Through this step: comprehensively analyze the operation jitter frequency and jitter amplitude data to understand the jitter health status of the device during operation;

[0019] S22. Import the device operation temperature, operation power data and the calculated device jitter anomaly value within the set evaluation period into the device operation fatigue value calculation formula to calculate the device operation fatigue value. Among them, the device operation fatigue value calculation formula is: Wherein, a is the temperature proportion coefficient, T is the set evaluation period duration, Tt is the device operating temperature at time t, Tm is the median of the temperature safety range, Tmax is the maximum value of the temperature safety range, Tmin is the minimum value of the temperature safety range, Pt is the device operating power at time t, Pm is the median of the device operating power safety range, Pmax is the maximum value of the device operating power safety range, Pmin is the minimum value of the device operating power safety range, dt is the time integral, and exp() is the exponential power of the natural constant e;

[0020] Through this step: comprehensively analyze the operating fatigue condition of the device by using the device operating temperature, operating power data, and the calculated device jitter anomaly value, and accurately evaluate the operating fatigue condition of the device;

[0021] S23. Obtain the image data of each functional component during the operation of the device, and import the obtained image data of each functional component into the deformation amount calculation formula to calculate the deformation amount of each functional component. Wherein, the deformation amount calculation formula is: S() is the area of the image, As is the real-time image contour data of the functional component, Bs is the starting image contour data of the functional component. Substitute the deformation amount of each obtained functional component into the functional component fatigue value calculation formula to calculate the functional component fatigue value. Wherein, the functional component fatigue value calculation formula is: Wherein, M is the number of functional components, Hj is the deformation amount of the j-th functional component, and cj is the proportion coefficient of the j-th functional component;

[0022] Through this step: comprehensively analyze the deformation condition of each functional component of the device;

[0023] S24. Obtain the calculated device operating fatigue value and functional component fatigue value, and substitute them into the device fatigue analysis value calculation formula to calculate the device fatigue analysis value. Wherein, the device fatigue analysis value calculation formula is: Y = Ysb + Yg.

[0024] It should be noted here that as an optimal technical solution of the device data management method based on the industrial Internet of Things, the step of importing the product quality data produced by the device into the product production quality analysis strategy for device production quality analysis includes the following specific steps:

[0025] S31. Obtain the product quality production data during the operation of the device. The product quality production data includes the qualified product rate data and production speed data of the produced products;

[0026] S32. Import the qualified product rate data and production speed data into the device production quality analysis value calculation formula to calculate the device production quality analysis value. Wherein, the device production quality analysis value calculation formula is: Wherein, P is the qualified product rate, and vc is the production speed data.

[0027] Here, it should be noted that, as an optimal technical solution of the device data management method based on the industrial Internet of Things, the production speed analysis by importing the obtained device operation fatigue analysis results and device production quality analysis results into the production speed analysis model includes the following specific contents:

[0028] S41. Obtain the calculated device fatigue analysis value and device production quality analysis value, substitute them into the production overall anomaly value calculation formula to calculate the production overall anomaly value, where the production overall anomaly value calculation formula is: where b is the device fatigue analysis proportion coefficient;

[0029] S42. Obtain the current device production speed and the calculated production overall anomaly value, substitute them into the required production speed calculation formula to calculate the required production speed of the device, where the required production speed calculation formula is: where Za is the set production overall anomaly standard value, and ln() is the natural logarithm with the natural constant e as the base.

[0030] Here, it should be noted that, as an optimal technical solution of the device data management method based on the industrial Internet of Things, the adjustment of the production of each production device according to the analyzed production speed includes the following specific contents:

[0031] Obtain the calculated required production speed of each device, and adjust the production speed of the device to the required production speed.

[0032] In the second aspect, a device data management system based on the industrial Internet of Things is implemented based on the above-mentioned device data management method based on the industrial Internet of Things. It specifically includes a data acquisition module, a fatigue analysis module, a production quality analysis module, a production speed analysis module, and a production speed adjustment module. Among them, the data acquisition module is used to obtain device operation data and product quality data of device production in real time through the Internet of Things gateway and transmit them to the cloud platform in real time;

[0033] The fatigue analysis module is used to import the real-time obtained device operation data into the device operation fatigue analysis strategy for device operation fatigue analysis;

[0034] The production quality analysis module is used to import the product quality data of device production obtained into the product production quality analysis strategy for device production quality analysis;

[0035] The production speed analysis module is used to import the obtained device operation fatigue analysis results and device production quality analysis results into the production speed analysis model for production speed analysis;

[0036] The production speed adjustment module is used to adjust the production of each production device according to the analyzed production speed;

[0037] It further includes the control module, which is used for the data acquisition module, the fatigue analysis module, the production quality analysis module, the production speed analysis module, and the production speed adjustment module.

[0038] In a third aspect, an electronic device includes: a processor and a memory, where a computer program that can be called by the processor is stored in the memory;

[0039] The processor executes the above-mentioned device data management method based on the industrial Internet of Things by calling the computer program stored in the memory.

[0040] In a fourth aspect, a computer-readable storage medium stores instructions, and when the instructions run on a computer, the computer is caused to execute the device data management method based on the industrial Internet of Things as described above.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention obtains device operation data and product quality data of device production in real time through an Internet of Things gateway, and transmits them to the cloud platform in real time. The device operation data obtained in real time is imported into the device fatigue analysis strategy for device operation fatigue analysis, and the product quality data of device production obtained is imported into the product production quality analysis strategy for device production quality analysis. The device operation fatigue analysis result and the device production quality analysis result obtained are imported into the production speed analysis model for production speed analysis, and the production of each production device is adjusted according to the analyzed production speed. The present invention first comprehensively analyzes the obtained device operation data to estimate the device operation fatigue situation, and at the same time takes into account the device production quality analysis result to accurately estimate the suitable production speed of the device, improving the safety of device production. Description of the Drawings

[0042] Figure 1 It is a schematic diagram of the overall process of the device data management method based on the industrial Internet of Things of the present invention;

[0043] Figure 2 It is a schematic diagram of step S2 of the device data management method based on the industrial Internet of Things of the present invention;

[0044] Figure 3 It is a schematic diagram of the overall framework of the device data management system based on the industrial Internet of Things of the present invention. Detailed Embodiments

[0045] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0046] Embodiment 1

[0047] To solve the technical problems raised in the background art: In the process of equipment production data management, the production speed of the equipment is mostly set in advance, and it is impossible to comprehensively analyze the obtained equipment operation data during the production process to estimate the equipment operation fatigue situation, and thus it is impossible to take into account the equipment production quality analysis results, resulting in the inability to accurately estimate the suitable production speed of the equipment, leading to the equipment being at an inappropriate production speed, thereby reducing the production safety of the equipment; The present invention provides a preferred embodiment: As Figure 1 - Figure 2 shown, a method for equipment data management based on the industrial Internet of Things, which includes the following specific steps:

[0048] S1. Real-time obtain equipment operation data and product quality data of equipment production through the Internet of Things gateway, and transmit them to the cloud platform in real time;

[0049] In this embodiment, the specific steps of real-time obtaining equipment operation data and product quality data of equipment production through the Internet of Things gateway and transmitting them to the cloud platform are as follows:

[0050] S11. Collect equipment operation jitter frequency and jitter amplitude data through the data acquisition terminal, and at the same time obtain equipment operation temperature and operation power data, and store them in the first storage component;

[0051] S12. Collect image data of each functional component during the equipment operation process through the image acquisition terminal, and store them in the second storage component;

[0052] S13. Collect product quality production data during the equipment operation process through the product quality acquisition terminal, and store them in the third storage component;

[0053] S14. Transmit various collected data to the cloud platform in real time through the Internet of Things;

[0054] S2. Import the real-time obtained equipment operation data into the equipment fatigue analysis strategy for equipment operation fatigue analysis;

[0055] In this embodiment, importing the real-time obtained equipment operation data into the equipment fatigue analysis strategy for equipment operation fatigue analysis includes the following specific steps:

[0056] S21. Obtain the device operation jitter frequency and jitter amplitude data within the set evaluation period. At the same time, obtain the device operation temperature and operation power data within the set evaluation period. Import the obtained device operation jitter frequency and jitter amplitude data within the set evaluation period into the device jitter outlier calculation formula to calculate the device jitter outlier. The device jitter outlier calculation formula is as follows: where N is the number of device jitters within the set evaluation period, ki is the jitter amplitude of the i-th jitter of the device within the set evaluation period, and kmax is the maximum value of the safe jitter amplitude range;

[0057] Through this step: comprehensively analyze the operation jitter frequency and jitter amplitude data to understand the jitter health of the device during operation;

[0058] S22. Import the device operation temperature, operation power data, and the calculated device jitter outlier within the set evaluation period into the device operation fatigue value calculation formula to calculate the device operation fatigue value. The device operation fatigue value calculation formula is as follows: where a is the temperature proportion coefficient, T is the duration of the set evaluation period, Tt is the device operation temperature at time t, Tm is the median of the temperature safety range, Tmax is the maximum value of the temperature safety range, Tmin is the minimum value of the temperature safety range, Pt is the device operation power at time t, Pm is the median of the device operation power safety range, Pmax is the maximum value of the device operation power safety range, Pmin is the minimum value of the device operation power safety range, dt is the time integral, and exp() is the exponential power of the natural constant e;

[0059] Through this step: comprehensively analyze the device operation fatigue situation through the device operation temperature, operation power data, and the calculated device jitter outlier, and accurately evaluate the device operation fatigue situation;

[0060] At the same time, the evaluation time range can be one day, one week, or one month. Determine the evaluation frequency, that is, the time interval for data collection, which can be every minute, every hour, or every shift. An increase in jitter may indicate wear or imbalance of device components; an abnormal increase in temperature may be a sign of overheating or malfunction, and abnormal power consumption may be a sign of reduced efficiency or malfunction. Therefore, the device operation temperature, operation power data, and the calculated device jitter outlier are used here to comprehensively analyze the device operation fatigue situation;

[0061] S23. Obtain the image data of each functional component during the device operation. Import the obtained image data of each functional component into the deformation amount calculation formula to calculate the deformation amount of each functional component. The deformation amount calculation formula is as follows: Let \(S()\) be the area of the image, \(A_s\) be the real-time image contour data of the functional component, and \(B_s\) be the starting image contour data of the functional component. Substitute the deformation amounts of each obtained functional component into the functional component fatigue value calculation formula to calculate the functional component fatigue value. Among them, the functional component fatigue value calculation formula is: Among them, \(M\) is the number of functional components, \(H_j\) is the deformation amount of the \(j\)-th functional component, and \(c_j\) is the proportion coefficient of the \(j\)-th functional component;

[0062] Through this step: comprehensively analyze the deformation conditions of each functional component of the equipment;

[0063] The purpose of comprehensively analyzing the deformation conditions of each functional component of the equipment is multi-faceted. It is mainly to ensure the normal operation of the equipment, improve production efficiency, reduce maintenance costs, and enhance the safety of the equipment; by analyzing the deformation data, potential fault points such as wear, cracks, or other structural problems can be identified; the deformation data can help predict possible faults of the equipment, so as to perform maintenance in advance and avoid downtime caused by sudden faults;

[0064] S24. Obtain the calculated equipment operation fatigue value and functional component fatigue value, and substitute them into the equipment fatigue analysis value calculation formula to calculate the equipment fatigue analysis value. Among them, the equipment fatigue analysis value calculation formula is: \(Y = Y_{sb}+Y_g\);

[0065] S3. Import the product quality data produced by the obtained equipment into the product production quality analysis strategy for equipment production quality analysis;

[0066] In this embodiment, importing the product quality data produced by the obtained equipment into the product production quality analysis strategy for equipment production quality analysis includes the following specific steps:

[0067] S31. Obtain the product quality production data during the operation of the equipment. The product quality production data includes the qualified product rate data and production speed data of the produced products;

[0068] S32. Import the qualified product rate data and production speed data into the equipment production quality analysis value calculation formula to calculate the equipment production quality analysis value. Among them, the equipment production quality analysis value calculation formula is: Among them, \(P\) is the qualified product rate, and \(v_c\) is the production speed data;

[0069] S4. Import the obtained equipment operation fatigue analysis result and equipment production quality analysis result into the production speed analysis model for production speed analysis;

[0070] In this embodiment, importing the obtained equipment operation fatigue analysis result and equipment production quality analysis result into the production speed analysis model for production speed analysis includes the following specific contents:

[0071] S41. Obtain the calculated equipment fatigue analysis value and equipment production quality analysis value, and substitute them into the overall production anomaly value calculation formula to calculate the overall production anomaly value. Among them, the overall production anomaly value calculation formula is: where b is the equipment fatigue analysis proportion coefficient;

[0072] S42. Obtain the current equipment production speed and the calculated overall production anomaly value, and substitute them into the required production speed calculation formula to calculate the required production speed of the equipment. Among them, the required production speed calculation formula is: where Za is the set overall production anomaly standard value, and ln() is the natural logarithm with the natural constant e as the base;

[0073] S5. Adjust the production of each production equipment according to the analyzed production speed;

[0074] In this embodiment, adjusting the production of each production equipment according to the analyzed production speed includes the following specific contents:

[0075] Obtain the required production speed of each equipment calculated, and adjust the production speed of the equipment to the required production speed. The operating parameters of the equipment can be automatically adjusted by programming or using an industrial control system (such as PLC);

[0076] Here, it should be noted that the equipment fatigue analysis proportion coefficient, the proportion coefficient of the jth functional component, and the temperature proportion coefficient can be obtained by the entropy weight method and the fitting method. The preferred value-taking method is: obtain 500 groups of equipment operation data and product quality data of equipment production, and at the same time obtain the ranking of the operation health duration of the equipment at each production speed. Substitute the equipment operation data and product quality data of equipment production into the production speed analysis model for production speed analysis to obtain the required production speed. Import the required production speed and the ranking of the operation health duration of the equipment at each production speed into the fitting software, and output the values of the equipment fatigue analysis proportion coefficient, the proportion coefficient of the jth functional component, and the temperature proportion coefficient that meet the highest ranking judgment accuracy rate;

[0077] It should be noted that the advantages of this embodiment over the prior art are as follows: The present invention obtains device operation data and product quality data produced by the device in real time through the Internet of Things gateway, and transmits them to the cloud platform in real time. The device operation data obtained in real time is imported into the device fatigue analysis strategy for device operation fatigue analysis, and the product quality data produced by the device obtained is imported into the product production quality analysis strategy for device production quality analysis. The device operation fatigue analysis result and device production quality analysis result obtained are imported into the production speed analysis model for production speed analysis, and the production of each production device is adjusted according to the analyzed production speed. The present invention first comprehensively analyzes the obtained device operation data to estimate the device operation fatigue situation, and at the same time takes into account the device production quality analysis result to accurately estimate the suitable production speed of the device, improving the safety of device production.

[0078] Embodiment 2

[0079] As Figure 3 shown, the device data management system based on the industrial Internet of Things is implemented based on the above-mentioned device data management method based on the industrial Internet of Things, and specifically includes a data acquisition module, a fatigue analysis module, a production quality analysis module, a production speed analysis module, and a production speed adjustment module. Among them, the data acquisition module is used to obtain device operation data and product quality data produced by the device in real time through the Internet of Things gateway and transmit them to the cloud platform in real time;

[0080] The fatigue analysis module is used to import the device operation data obtained in real time into the device fatigue analysis strategy for device operation fatigue analysis;

[0081] The production quality analysis module is used to import the product quality data produced by the device obtained into the product production quality analysis strategy for device production quality analysis;

[0082] The production speed analysis module is used to import the device operation fatigue analysis result and device production quality analysis result obtained into the production speed analysis model for production speed analysis;

[0083] The production speed adjustment module is used to adjust the production of each production device according to the analyzed production speed;

[0084] It further includes a control module for the data acquisition module, the fatigue analysis module, the production quality analysis module, the production speed analysis module, and the production speed adjustment module.

[0085] Embodiment 3

[0086] This embodiment provides an electronic device, including: a processor and a memory, where the memory stores a computer program that can be called by the processor;

[0087] The processor executes the above-mentioned device data management method based on the industrial Internet of Things by calling the computer program stored in the memory.

[0088] This electronic device can have relatively large differences due to different configurations or performances, and can include one or more processors and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the device data management method based on the industrial Internet of Things provided by the above method embodiment. This electronic device can also include other components for implementing device functions. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0089] Embodiment 4

[0090] This embodiment provides a computer-readable storage medium, on which a rewritable computer program is stored;

[0091] When the computer program runs on a computer device, it enables the computer device to execute the above-mentioned device data management method based on the industrial Internet of Things.

[0092] For example, the computer-readable storage medium can be a read-only memory, a random access memory, a compact disc read-only memory, magnetic tape, a floppy disk, and an optical data storage device, etc.

[0093] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or a data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

Claims

1. A device data management method based on the industrial Internet of Things, characterized in that It includes the following specific steps: Obtain the device operation data and the product quality data produced by the device in real time through the Internet of Things gateway, and transmit them to the cloud platform in real time; Import the obtained device operation data into the device fatigue analysis strategy to perform device operation fatigue analysis; Import the obtained product quality data produced by the device into the product production quality analysis strategy to perform device production quality analysis; Import the obtained device operation fatigue analysis results and device production quality analysis results into the production speed analysis model to perform production speed analysis; Adjust the production of each production device according to the analyzed production speed; The step of importing the obtained device operation data into the device fatigue analysis strategy to perform device operation fatigue analysis includes the following specific steps: Obtain the device operation jitter frequency and jitter amplitude data within the set evaluation period, and at the same time obtain the device operation temperature and operation power data within the set evaluation period. Import the obtained device operation jitter frequency and jitter amplitude data within the set evaluation period into the device jitter outlier calculation formula to calculate the device jitter outlier. Among them, the device jitter outlier calculation formula is: , where N is the number of device jitters within the set evaluation period, ki is the jitter amplitude of the i-th jitter of the device within the set evaluation period, and kmax is the maximum value of the safe jitter amplitude range; Obtain the device operating temperature, operating power data within the set evaluation period, and the calculated device jitter anomaly value, and import them into the device operating fatigue value calculation formula to calculate the device operating fatigue value. The device operating fatigue value calculation formula is as follows: , where a is the temperature ratio coefficient, T is the duration of the set evaluation period, Tt is the device operating temperature at time t, Tm is the median of the temperature safety range, Tmax is the maximum value of the temperature safety range, Tmin is the minimum value of the temperature safety range, Pt is the device operating power at time t, Pm is the median of the device operating power safety range, Pmax is the maximum value of the device operating power safety range, Pmin is the minimum value of the device operating power safety range, dt is the time integral, and exp() is the exponential power of the natural constant e; Obtain the image data of each functional component during the operation of the device, and import the obtained image data of each functional component into the deformation amount calculation formula to calculate the deformation amount of each functional component. Among them, the deformation amount calculation formula is: , S() is the area of the image, As is the real-time image contour data of the functional component, Bs is the starting image contour data of the functional component. Substitute the deformation amounts of the obtained functional components into the functional component fatigue value calculation formula to calculate the functional component fatigue value. Among them, the functional component fatigue value calculation formula is: , where M is the number of functional components, Hj is the deformation amount of the j-th functional component, and cj is the proportion coefficient of the j-th functional component; Obtain the calculated device operation fatigue value and functional component fatigue value, and substitute them into the device fatigue analysis value calculation formula to calculate the device fatigue analysis value. Among them, the device fatigue analysis value calculation formula is: .

2. The device data management method based on industrial Internet of Things according to claim 1, characterized in that, The step of importing the obtained product quality data produced by the device into the product production quality analysis strategy to perform device production quality analysis includes the following specific steps: Obtain the product quality production data during the device operation, and the product quality production data includes the qualified product rate data and production speed data of the produced products; Import the yield data and production speed data into the calculation formula of the equipment production quality analysis value. Among them, the calculation formula of the equipment production quality analysis value is: , where P is the yield and vc is the production speed data.

3. The device data management method based on industrial Internet of Things according to claim 2, characterized in that, The content included in the step of importing the obtained device operation fatigue analysis results and device production quality analysis results into the production speed analysis model to perform production speed analysis is as follows: Obtain the calculated device fatigue analysis value and device production quality analysis value, substitute them into the overall production anomaly value calculation formula to calculate the overall production anomaly value. Among them, the overall production anomaly value calculation formula is: , where b is the device fatigue analysis proportion coefficient; Obtain the production speed of the current device and the calculated overall production anomaly value, and substitute them into the production speed calculation formula required by the device. The production speed calculation formula required by the device is as follows: , where Za is the set overall production anomaly standard value, and ln() is the natural logarithm with the natural constant e as the base.

4. The device data management method based on industrial Internet of Things according to claim 3, characterized in that The content included in the step of adjusting the production of each production device according to the analyzed production speed is as follows: Obtain the required production speed of each device calculated, and adjust the production speed of the device to the required production speed.

5. The device data management method based on industrial Internet of Things according to claim 4, wherein, The specific steps of obtaining the device operation data and the product quality data produced by the device in real time through the Internet of Things gateway and transmitting them to the cloud platform in real time are as follows: Collect the device operation jitter frequency and jitter amplitude data through the data acquisition terminal, and at the same time obtain the device operation temperature and operation power data, and store them in the first storage component; Collect the image data of each functional component during the device operation through the image acquisition terminal, and store them in the second storage component; Collect the product quality production data during the device operation through the product quality acquisition terminal, and store them in the third storage component; Transmit the collected various data to the cloud platform in real time through the Internet of Things.

6. A device data management system based on the industrial Internet of Things, which is implemented based on the device data management method based on the industrial Internet of Things according to any one of claims 1-5, characterized in that, It specifically includes a data acquisition module, a fatigue analysis module, a production quality analysis module, a production speed analysis module, and a production speed adjustment module. Among them, the data acquisition module is used to obtain the device operation data and the product quality data produced by the device in real time through the Internet of Things gateway and transmit them to the cloud platform in real time; The fatigue analysis module is used to import the obtained device operation data into the device fatigue analysis strategy to perform device operation fatigue analysis; The production quality analysis module is used to import the obtained product quality data produced by the device into the product production quality analysis strategy to perform device production quality analysis; The production speed analysis module is used to import the obtained device operation fatigue analysis results and device production quality analysis results into the production speed analysis model to perform production speed analysis; The production speed adjustment module is used to adjust the production of each production device according to the analyzed production speed.

7. An electronic device, comprising: A processor and a memory, where the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the device data management method based on the industrial Internet of Things according to any one of claims 1-5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that, Stored with instructions, when the instructions run on a computer, the computer is caused to execute the device data management method based on the industrial Internet of Things according to any one of claims 1-5.

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