A method for managing IoT devices for industrial automation

Abstract

The present invention discloses an Internet of Things device management method for industrial automation, and specifically relates to the technical field of Internet of Things device management. The method comprises: step one, setting the monitoring frequency, step two, implementing scheduling measures, step three, evaluating the working effect, and step four, correcting the risk level. The present invention determines the working effect level of the target statistical period of the spraying robot in each time period by analyzing the working effect coefficient of the target statistical period of the spraying robot, and corrects the risk level of each time period of the target statistical period, thereby providing a more realistic reference value for the subsequent setting of the monitoring frequency of the spraying robot in each time period, realizing the self-learning of the spraying robot monitoring and management system, and improving the accuracy and scientificity of the industrial automation Internet of Things device management system.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things equipment management, and in particular to an Internet of Things equipment management method for industrial automation. Background Art

[0002] Industrial automation is the use of automation equipment, robots, control systems and other technologies to complete manufacturing, production and other industrial processes. With the advancement of technology, more and more industrial fields have begun to adopt automation to improve production efficiency, reduce labor costs, ensure stable quality, etc. Modern spray robots in the automotive production industry usually integrate Internet of Things technology, so that they not only have automation functions, but also can perform data communication and remote monitoring. However, in order to ensure the efficiency, stability and compliance of the coating quality during the spraying process, the spraying robot needs to be accurately monitored and managed. Therefore, it is necessary to study the equipment management technology of the spraying robot.

[0003] Prior art, such as the invention patent application with publication number: CN116852374B, discloses an intelligent robot control system based on machine vision, which ensures that the robot can complete the spraying task normally, improves the overall efficiency and spraying quality of the spraying operation, and improves the quality control and efficiency of the spraying operation. Another example is the invention patent application with publication number: CN112785134B, which discloses a production plan management system and method for docking robot spraying operations, which can monitor in real time and automatically obtain the plan source file on the remote server, parse and store it in the local database, search for production plan information through logical judgment, and forward it to the robot control system to improve production efficiency.

[0004] Combined with the above scheme, it is found that the monitoring and management of spray robots in industrial automation Internet of Things devices in the prior art is mostly applied to the monitoring and management of spraying quality. On the one hand, it ignores the different working intensity of the spray robot in time periods with different risk levels, so the frequency of monitoring of the spray robot is also different, which causes the monitoring intensity of the spray robot in time periods with different risk levels to be unable to be adjusted in real time, causing the spray robot to be prone to failures in the actual automobile production process and difficult to detect in time, resulting in reduced automobile production efficiency and difficulty in meeting actual production needs. On the other hand, the prior art ignores the correction management of the risk level of each time period in the statistical cycle of the spray robot, resulting in the automobile production workshop being unable to adjust the monitoring and management frequency of the spray robot in real time according to the predicted risk level of each time period and the corrected risk level of each time period, thereby causing the spray robot to waste energy and redundancy of monitoring data in the actual production process, resulting in the risk of inefficiency in the management of the spray robot in industrial automation Internet of Things devices. Summary of the invention

[0005] The purpose of the present invention is to provide an Internet of Things device management method for industrial automation, which solves the problems existing in the background technology.

[0006] To solve the above technical problems, the present invention provides an Internet of Things device management method for industrial automation, the method comprising: step one, setting the monitoring frequency: calculating the work risk coefficient of the spray robot in each time period of each historical statistical period, dividing the risk level of each time period of the target statistical period, and setting the monitoring frequency of each risk level time period of the target statistical period of the spray robot.

[0007] Step 2: Implement scheduling measures: Calculate the painting complexity coefficient of each type of car, implement scheduling measures for the painting robot, and obtain each car in each risk level time period of the target statistical period of the painting robot.

[0008] Step 3. Evaluate the work effect: obtain the environmental parameters of each collection point in each time period of the target statistical cycle of the spray robot, the working parameters of the spray robot and the quality parameters of each car, and evaluate the work effect level in each time period of the target statistical cycle of the spray robot.

[0009] Step 4: Correct the risk level: Correct the risk level of each time period of the target statistical cycle of the spray robot.

[0010] Compared with the prior art, the benefits of the present invention are as follows: 1. The present invention predicts the risk level of each time period of the target statistical period according to the work risk coefficient of the spray robot in each time period of each historical statistical period, and provides a reference value for setting the monitoring frequency of the spray robot in each time period, thereby realizing energy saving and control of monitoring data in the actual production process of the spray robot, and improving the efficiency of the operation of the spray robot monitoring and management system.

[0011] 2. The present invention implements scheduling measures for automobiles in each risk level time period of the target statistical period of the spray robot according to the spraying complexity coefficient of each type of automobile and the risk level of each time period of the target statistical period, so as to improve the working efficiency of the spray robot and reduce the working complexity of the spray robot in the high risk level time period, thereby ensuring the stable operation of the spray robot and reducing the working risk of the spray robot in the actual production process.

[0012] 3. The present invention determines the work effect level of the spray robot in each time period of the target statistical period by analyzing the work effect coefficient of the spray robot in each time period of the target statistical period, and corrects the risk level of each time period of the target statistical period, thereby providing a more realistic reference value for the subsequent setting of the monitoring frequency of the spray robot in each time period, realizing the self-learning ability of the spray robot monitoring and management system, and improving the accuracy and scientificity of the industrial automation Internet of Things equipment management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0015] 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.

[0016] Reference Figure 1 As shown, the present invention provides an Internet of Things device management method for industrial automation, the method comprising: step one, setting the monitoring frequency: calculating the work risk coefficient of the spray robot in each time period of each historical statistical period, dividing the risk level of each time period of the target statistical period, and setting the monitoring frequency of the spray robot in each risk level time period of the target statistical period.

[0017] In a specific embodiment of the present invention, the working risk coefficient of the spraying robot in each time period of each historical statistical period is calculated, and the risk level of each time period of the target statistical period is divided. The specific analysis method is: based on the number of failures C of the spraying robot in each time period of each historical statistical period extracted from the database ij , the total failure time of the spraying robot N ij , where i=1,2,…,m, i represents the number of each historical statistical period, m represents the total number of historical statistical periods, j=1,2,…,n, j represents the number of each time period, and n represents the total number of time periods.

[0018] By formula: , calculate the work risk coefficient ρ of the spray robot in each time period of each historical statistical period ij , where λ1 represents the influence weight of the number of unit failures of the spraying robot extracted from the database, and λ2 represents the influence weight of the unit failure duration of the spraying robot extracted from the database, through the formula: , calculate the predicted work risk coefficient of the spray robot in each time period of the target statistical cycle ;

[0019] Predicted work risk coefficient in each time period based on the target statistical cycle of the spraying robot ,like , then the risk level of the jth time period of the target statistical period of the spraying robot is classified as high risk, where ρ' represents the judgment threshold of the risk level division of the time period extracted from the database;

[0020] like , then the risk level of the j-th time period of the target statistical period of the spray robot is classified as low risk, and the risk level of each time period of the target statistical period is obtained.

[0021] In a specific embodiment of the present invention, the monitoring frequency of each risk level time period of the target statistical period of the spraying robot is set, and the specific analysis method is as follows:

[0022] Based on the risk level of each time period of the target statistical period, the monitoring frequency of the high-risk level time period of the target statistical period of the spray robot is set to f1, and the monitoring frequency of the low-risk level time period of the target statistical period of the spray robot is set to f2, where f1 represents the preset first monitoring frequency extracted from the database, and f2 represents the preset second monitoring frequency extracted from the database.

[0023] It should be noted that in a specific embodiment, the first monitoring frequency f1 in each time period of the high-risk level of the target statistical period of the spray robot and the second monitoring frequency f2 in each time period of the low-risk level of the target statistical period of the spray robot need to satisfy f1>f2, for example, f1=0.5, f2=0.2.

[0024] The present invention predicts the risk level of each time period of a target statistical period according to the work risk coefficient of the spray robot in each time period of each historical statistical period, and provides a reference value for setting the monitoring frequency of the spray robot in each time period, thereby achieving energy saving and control of monitoring data in the actual production process of the spray robot, and improving the efficiency of the operation of the spray robot monitoring and management system.

[0025] Step 2: Implement scheduling measures: Calculate the painting complexity coefficient of each type of car, implement scheduling measures for the painting robot, and obtain each car in each risk level time period of the target statistical period of the painting robot.

[0026] In a specific embodiment of the present invention, the calculation of the spraying complexity coefficient of each type of automobile is performed by: based on the spraying parameters of each type of automobile extracted from the database, including the spraying area S k , Spraying thickness conversion quantity H k 、Number of color series X k , surface area Z k , where k=1,2,…,p, k represents the changes in each type of car, and p represents the total number of car types.

[0027] It should be noted that, in a specific embodiment, the spray thickness conversion number is: the flat part of the car body (such as the roof, the middle part of the door, the side of the car body, etc.) is usually simpler, and the spray thickness is generally thinner. When spraying the curved part of the car body (such as the curved part of the roof, the front and rear bumpers of the car body, the wheel arches, etc.), the spray thickness is generally thicker. During the spraying process, the number of changes in the spray thickness caused by the spray robot switching from different areas is called the spray thickness conversion number.

[0028] By formula: , calculate the spraying complexity coefficient δ of each type of car k , where μ1 represents the influence weight of the unit spraying thickness conversion quantity extracted from the database, μ2 represents the influence weight of the unit color system quantity extracted from the database, and e represents a natural constant.

[0029] In a specific embodiment of the present invention, the scheduling measures for implementing the spraying robot are analyzed in the following specific method: based on the spraying complexity coefficient δ of each type of automobile k , if δ k >δ', the painting complexity level of the k-th type of car is determined to be high complexity, where δ' represents the judgment threshold of the painting complexity level of the car extracted from the database; otherwise, the painting complexity level of the k-th type of car is determined to be low complexity.

[0030] Based on the risk level of each time period of the target statistical period, various types of vehicles with high-complexity spraying complexity level are dispatched to each time period of the target statistical period with low-risk risk level for spraying treatment, and various types of vehicles with low-complexity spraying complexity level are dispatched to each time period of the target statistical period with high-risk risk level for spraying treatment.

[0031] It should be noted that, in a specific embodiment, the scheduling measures for the spray robot in each risk level time period of the target statistical period, for example, if the qth time period of the target statistical period is a high risk level, where q∈[1,n], and the spraying complexity level of the kth type of car is low complexity, then in the qth time period of the target statistical period, the kth type of car will be preferentially scheduled here for spraying processing, thereby reducing the working risk of the spray robot in the high risk level time period.

[0032] The present invention implements scheduling measures for automobiles in each risk level time period of a target statistical period of a spray robot according to the spraying complexity coefficient of each type of automobile and the risk level of each time period of a target statistical period, so as to improve the working efficiency of the spray robot and reduce the working complexity of the spray robot in high risk level time periods, thereby ensuring the stable operation of the spray robot and reducing the working risk of the spray robot in the actual production process.

[0033] Step 3. Evaluate the work effect: obtain the environmental parameters of each collection point in each time period of the target statistical cycle of the spray robot, the working parameters of the spray robot and the quality parameters of each car, and evaluate the work effect level in each time period of the target statistical cycle of the spray robot.

[0034] In a specific embodiment of the present invention, the environmental parameters of each collection point in each time period of the target statistical cycle of the spraying robot, the working parameters of the spraying robot and the quality parameters of each car are obtained by the following specific method: the environmental parameters of each collection point in each time period of the target statistical cycle of the spraying robot include: the ambient temperature T of each collection point in each time period of the target statistical cycle of the spraying robot collected by various sensors js 、Ambient humidity A js , and magnetic field strength B js , where s=1,2,…,y, s represents the number of each collection point, and y represents the total number of collection points.

[0035] It should be noted that, in a specific embodiment, the environmental parameters of each collection point in each time period of the target statistical period of the spray robot are obtained, the ambient temperature of the spray robot is collected by a temperature sensor, the ambient humidity of the spray robot is collected by a humidity sensor, and the magnetic field strength of the spray robot is collected by a fluxmeter.

[0036] The working parameters of each collection point in each time period of the target statistical cycle of the spraying robot, including the vibration frequency K of each collection point in each time period of the target statistical cycle of the spraying robot collected by various sensors js 、Pressure F js , and the device temperature L js .

[0037] It should be noted that, in a specific embodiment, the working parameters of each collection point in each time period of the target statistical cycle of the spray robot are collected by a temperature sensor to collect the equipment temperature of the spray robot, by a vibration sensor to collect the vibration frequency of the spray robot, and by a pressure sensor to collect the pressure value of the spray robot.

[0038] The quality parameters of each car in each time period of the target statistical period, including the number of bubbles U of each car collected by various instruments ja , crack number E ja , coating thickness H' in each area jar And the color difference value R of each color system jah , where a=1,2,…,b, a represents the number of each car, b represents the total number of cars, h=1,2,…,d, h represents the number of each color system of the car, d represents the total number of car colors, r=1,2,…,t, r represents the number of each area of ​​the car, and t represents the total number of car areas.

[0039] It should be noted that, in a specific embodiment, the quality parameters of each automobile in each time period of the target statistical period are collected by collecting the coating thickness of each area of ​​the automobile through an ultrasonic sensor, collecting the color difference values ​​of each color system of the automobile through a colorimeter, and obtaining pictures of each area of ​​the appearance of each automobile through a camera equipment. Through computer recognition technology, the characteristics of bubbles and cracks are extracted from the pictures of each area of ​​the appearance of each automobile, and the number of bubbles and cracks in the pictures of each area of ​​the appearance of each automobile are counted to obtain the number of bubbles and cracks of each automobile.

[0040] In a specific embodiment of the present invention, the working effect level of the target statistical period of the spraying robot is evaluated, and the specific analysis method is as follows: by analyzing the working parameters and environmental parameters of each collection point in each time period of the target statistical period of the spraying robot, the state conformity index α of each time period of the target statistical period of the spraying robot is obtained. j By analyzing the quality parameters of each car in each time period of the target statistical cycle, the spraying quality compliance index β of each time period of the target statistical cycle of the spraying robot is obtained. j .

[0041] Based on the state compliance index and spraying quality compliance index of each time period of the target statistical cycle of the spraying robot, the formula is: ω j =ln(1+α j +β j ), calculate the working effect coefficient ω in each time period of the target statistical cycle of the spraying robot j .

[0042] If jIf the working effect of the spraying robot is within the first interval, it is judged that the working effect level of the spraying robot in the jth time period of the target statistical period is highly compliant.

[0043] If j If the working effect of the spraying robot is in the second interval, it is judged that the working effect level of the spraying robot in the jth time period of the target statistical period is medium compliance.

[0044] If j If the working effect of the spraying robot is in the third interval, the working effect level of the spraying robot in the jth time period of the target statistical period is judged to be low compliance, and the working effect level of the spraying robot in each time period of the target statistical period is obtained.

[0045] It should be noted that, in a specific embodiment, each interval of the working effect of the spray robot is manually set and divided by technicians based on the working effect coefficient of each time period of each historical statistical period and the actual working conditions of the spray robot, and stored in a database.

[0046] In a specific embodiment of the present invention, the state conformity index of each time period of the target statistical cycle of the spraying robot is specifically analyzed by: based on the environmental parameters of each collection point in each time period of the target statistical cycle of the spraying robot, the formula is: , calculate the compliance judgment value (QT) of the ambient temperature of each collection point in each time period of the target statistical cycle of the spray robot js , where (T - , T + ) represents the standard ambient temperature range of the spray robot extracted from the database. According to the calculation method of the compliance judgment value of the ambient temperature of each collection point in each time period of the target statistical cycle of the spray robot, the environmental parameters of each collection point in each time period of the target statistical cycle of the spray robot are calculated to obtain the compliance judgment value (QA) of the ambient humidity of each collection point in each time period of the target statistical cycle of the spray robot. js , Compliance judgment value of magnetic field strength (QB) js .

[0047] By formula: , calculate the environmental compliance index ε of each time period of the target statistical cycle of the spray robot j .

[0048] According to the analysis method of the environmental compliance index of each time period of the target statistical cycle of the spraying robot, the working parameters of each collection point of each time period of the target statistical cycle of the spraying robot are analyzed to obtain the working compliance index η of each time period of the target statistical cycle of the spraying robot. j .

[0049] It should be noted that, in a specific embodiment, the working parameters of each collection point in each time period of the target statistical cycle of the spraying robot are calculated by the formula: , calculate the compliance judgment value (QK) of the vibration frequency of each collection point in each time period of the target statistical cycle of the spray robot js , where (K - , K + ) represents the standard vibration frequency range of the spraying robot extracted from the database.

[0050] According to the calculation method of the vibration frequency compliance judgment value of each collection point in each time period of the target statistical cycle of the spraying robot, the pressure and equipment temperature of the target statistical cycle of the spraying robot are processed to obtain the pressure compliance judgment value (QF) of each collection point in each time period of the target statistical cycle of the spraying robot. js And the device temperature meets the judgment value (QL) js .

[0051] By formula: , calculate the environmental compliance index η of each time period of the target statistical cycle of the spray robot j .

[0052] By formula: , calculate the state conformity index α of each time period of the target statistical cycle of the spraying robot j .

[0053] In a specific embodiment of the present invention, the spraying quality compliance index of each time period of the target statistical cycle of the spraying robot is analyzed by: based on the bubble number U of each car in each time period of the target statistical cycle ja , crack number E ja , coating thickness H' in each area jar And the color difference value R of each color system jah , through the formula: , calculate the spraying quality compliance index β of each time period of the target statistical cycle of the spraying robot j , where H'' ar represents the standard coating thickness of each area of ​​each car extracted from the database, R' represents the allowable color difference value of the car spraying extracted from the database, τ1 represents the influence weight of the unit number of bubbles in the car spraying extracted from the database, and τ2 represents the influence weight of the unit number of cracks in the car spraying extracted from the database.

[0054] Step 4: Correct the risk level: Correct the risk level of each time period of the target statistical cycle of the spray robot.

[0055] It should be noted that, in a specific embodiment, the database is used to store the number of failures of the spray robot and the total failure duration of the spray robot in each time period of each historical statistical period, store the influence weight of the unit number of failures of the spray robot, the influence weight of the unit failure duration and the judgment threshold of the risk level division of the time period, store the preset monitoring frequency and various spraying parameters of various types of automobiles, store the influence weight of the unit color system quantity, the influence weight of the unit spraying thickness conversion quantity, store the judgment threshold of the spraying complexity level of the automobile, and each interval of the working effect of the spray robot, store the standard coating thickness sprayed in each area of ​​each automobile, the allowable color difference value of automobile spraying, the influence weight of the unit number of bubbles sprayed by the automobile, and the influence weight of the unit number of cracks sprayed by the automobile.

[0056] In a specific embodiment of the present invention, the risk level of each time period of the target statistical period of the spraying robot is corrected, and the specific analysis method is as follows: based on the work effect level in each time period of the target statistical period of the spraying robot, a correction factor ψ corresponding to the work effect level in each time period of the target statistical period of the spraying robot is extracted from the database j , based on the predicted work risk coefficient in each time period of the target statistical cycle of the spraying robot , through the formula: , calculate the corrected risk coefficient of each time period of the target statistical cycle of the spray robot .

[0057] Corrected risk coefficient for each time period based on the target statistical cycle of the spray robot ,like , then the risk level of the j-th time period of the target statistical period of the spraying robot is corrected to high risk.

[0058] like , then the risk level of the j-th time period of the target statistical period of the spraying robot is corrected to low risk, and the risk level of each time period of the target statistical period is obtained, and the corrected risk level of each time period of the target statistical period of the spraying robot is obtained.

[0059] It should be noted that, in a specific embodiment, for example, the working effect level of the spraying robot in the jth time period of the target statistical period is high compliance, and the correction factor ψ corresponding to the working effect level of the spraying robot in the jth time period of the target statistical period is extracted from the database. j =0.8, the predicted work risk coefficient of the spray robot in the jth time period of the target statistical cycle =2, then by the formula: , and obtain the corrected risk coefficient of the spray robot in the jth time period of the target statistical period =1.6.

[0060] It should be noted that, in a specific embodiment, the correction factors corresponding to the working effect levels in each time period of the target statistical period of the spray robot are preset manually and stored in a database.

[0061] The present invention determines the work effect level of the spray robot in each time period of the target statistical period by analyzing the work effect coefficient in each time period of the target statistical period of the spray robot, and corrects the risk level of each time period of the target statistical period, thereby providing a more realistic reference value for the subsequent setting of the monitoring frequency of the spray robot in each time period, realizing the self-learning ability of the spray robot monitoring and management system, and improving the accuracy and scientificity of the industrial automation Internet of Things equipment management system.

[0062] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for managing IoT devices for industrial automation, characterized in that: The method comprises: Step 1: Set the monitoring frequency: calculate the work risk coefficient of the spray robot in each time period of each historical statistical period, divide the risk level of each time period of the target statistical period, and set the monitoring frequency of the spray robot in each risk level time period of the target statistical period; The specific analysis method for calculating the working risk coefficient of the spraying robot in each time period of each historical statistical period and dividing the risk level of each time period of the target statistical period is as follows: Based on the number of failures of the spraying robot in each time period of each historical statistical period extracted from the database C ij , the total failure time of the spraying robot N ij , where i=1,2,…,m, i represents the number of each historical statistical period, m represents the total number of historical statistical periods, j=1,2,…,n, j represents the number of each time period, n represents the total number of time periods; By formula: , calculate the work risk coefficient ρ of the spray robot in each time period of each historical statistical period ij , where λ1 represents the influence weight of the number of unit failures of the spraying robot extracted from the database, and λ2 represents the influence weight of the unit failure duration of the spraying robot extracted from the database, through the formula: , calculate the predicted work risk coefficient of the spray robot in each time period of the target statistical cycle ; Predicted work risk coefficient in each time period based on the target statistical cycle of the spraying robot ,like , then the risk level of the jth time period of the target statistical period of the spraying robot is classified as high risk, where ρ' represents the judgment threshold of the risk level division of the time period extracted from the database; like , then the risk level of the j-th time period of the target statistical period of the spraying robot is classified as low risk, and the risk level of each time period of the target statistical period is obtained; Step 2: Implement scheduling measures: Calculate the painting complexity coefficient of each type of car, implement scheduling measures for the painting robot, and obtain each car in each risk level time period of the target statistical cycle of the painting robot; Step 3: Evaluate the work effect: obtain the environmental parameters of each collection point in each time period of the target statistical cycle of the spraying robot, the working parameters of the spraying robot and the quality parameters of each car, and evaluate the work effect level in each time period of the target statistical cycle of the spraying robot; Step 4: Correct the risk level: Correct the risk level of each time period of the target statistical cycle of the spray robot.

2. The method for managing IoT devices for industrial automation according to claim 1, characterized in that: The monitoring frequency of each risk level time period in the target statistical period of the spraying robot is set, and the specific analysis method is as follows: Based on the risk level of each time period of the target statistical period, the monitoring frequency of the high-risk level time period of the target statistical period of the spray robot is set to f1, and the monitoring frequency of the low-risk level time period of the target statistical period of the spray robot is set to f2, where f1 represents the preset first monitoring frequency extracted from the database, and f2 represents the preset second monitoring frequency extracted from the database.

3. The method for managing IoT devices for industrial automation according to claim 2, characterized in that: The specific analysis method for calculating the spraying complexity coefficient of each type of automobile is as follows: The spraying parameters of various types of cars extracted from the database, including the spraying area S k , Spraying thickness conversion quantity H k 、Number of colors X k , surface area Z k , where k = 1, 2, …, p, k represents the change of each type of car, and p represents the total number of car types; By formula: , calculate the spraying complexity coefficient δ of each type of car k , where μ1 represents the influence weight of the unit spraying thickness conversion quantity extracted from the database, μ2 represents the influence weight of the unit color system quantity extracted from the database, and e represents a natural constant.

4. The method for managing IoT devices for industrial automation according to claim 3, characterized in that: The specific analysis method of the scheduling measures for implementing the spraying robot is as follows: Based on the spraying complexity coefficient δ of each type of car k , if δ k >δ', the painting complexity level of the k-th type of car is determined to be high complexity, where δ' represents the judgment threshold of the painting complexity level of the car extracted from the database; otherwise, the painting complexity level of the k-th type of car is determined to be low complexity; Based on the risk level of each time period of the target statistical period, various types of vehicles with high-complexity spraying complexity level are dispatched to each time period of the target statistical period with low-risk risk level for spraying treatment, and various types of vehicles with low-complexity spraying complexity level are dispatched to each time period of the target statistical period with high-risk risk level for spraying treatment.

5. The method for managing IoT devices for industrial automation according to claim 4, characterized in that: The specific method of obtaining the environmental parameters of each collection point in each time period of the target statistical cycle of the spraying robot, the working parameters of the spraying robot and the quality parameters of each car is as follows: The environmental parameters of each collection point in each time period of the target statistical cycle of the spraying robot include: the environmental temperature T of each collection point in each time period of the target statistical cycle of the spraying robot collected by various sensors js 、Ambient humidity A js , and magnetic field strength B js , where s=1,2,…,y, s represents the number of each collection point, and y represents the total number of collection points; The working parameters of each collection point in each time period of the target statistical cycle of the spraying robot, including the vibration frequency K of each collection point in each time period of the target statistical cycle of the spraying robot collected by various sensors js 、Pressure F js , and the device temperature L js ; The quality parameters of each car in each time period of the target statistical period, including the number of bubbles U of each car collected by various instruments ja , crack number E ja , coating thickness H' in each area jar And the color difference value R of each color system jah , where a=1,2,…,b, a represents the number of each car, b represents the total number of cars, h=1,2,…,d, h represents the number of each color system of the car, d represents the total number of car colors, r=1,2,…,t, r represents the number of each area of ​​the car, and t represents the total number of car areas.

6. The method for managing IoT devices for industrial automation according to claim 5, characterized in that: The specific analysis method for evaluating the working effect level of the spraying robot in each time period of the target statistical cycle is as follows: By analyzing the working parameters and environmental parameters of each collection point in each time period of the target statistical cycle of the spraying robot, the state conformity index α of each time period of the target statistical cycle of the spraying robot is obtained. j By analyzing the quality parameters of each car in each time period of the target statistical cycle, the spraying quality compliance index β of each time period of the target statistical cycle of the spraying robot is obtained. j ; Based on the state compliance index and spraying quality compliance index of each time period of the target statistical cycle of the spraying robot, the formula is: ω j =ln(1+α j +β j ), calculate the working effect coefficient ω in each time period of the target statistical cycle of the spraying robot j ; If j If the robot is in the first interval of the working effect of the spraying robot, the working effect level in the jth time period of the target statistical period of the spraying robot is judged to be highly compliant; If j If the working effect of the spraying robot is in the second interval, the working effect level of the spraying robot in the jth time period of the target statistical period is judged to be medium compliance; If j If the working effect of the spraying robot is in the third interval, the working effect level of the spraying robot in the jth time period of the target statistical period is judged to be low compliance, and the working effect level of the spraying robot in each time period of the target statistical period is obtained.

7. The method for managing IoT devices for industrial automation according to claim 6, characterized in that: The state conformity index of each time period of the target statistical cycle of the spraying robot is analyzed in the following specific method: Based on the environmental parameters of each collection point in each time period of the target statistical cycle of the spray robot, the formula is: , calculate the compliance judgment value (QT) of the ambient temperature of each collection point in each time period of the target statistical cycle of the spray robot js , where (T - , T + ) represents the standard ambient temperature range of the spray robot extracted from the database. According to the calculation method of the compliance judgment value of the ambient temperature of each collection point in each time period of the target statistical cycle of the spray robot, the environmental parameters of each collection point in each time period of the target statistical cycle of the spray robot are calculated to obtain the compliance judgment value (QA) of the ambient humidity of each collection point in each time period of the target statistical cycle of the spray robot. js , Compliance judgment value of magnetic field strength (QB) js ; By formula: , calculate the environmental compliance index ε of each time period of the target statistical cycle of the spray robot j ; According to the analysis method of the environmental compliance index of each time period of the target statistical cycle of the spraying robot, the working parameters of each collection point of each time period of the target statistical cycle of the spraying robot are analyzed to obtain the working compliance index η of each time period of the target statistical cycle of the spraying robot. j ; By formula: , calculate the state conformity index α of each time period of the target statistical cycle of the spraying robot j .

8. The method for managing IoT devices for industrial automation according to claim 6, characterized in that: The spraying quality compliance index of each time period of the target statistical cycle of the spraying robot is analyzed in the following specific method: The number of bubbles U of each car in each time period based on the target statistical cycle ja , crack number E ja , coating thickness H' in each area jar And the color difference value R of each color system jah , through the formula: , calculate the spraying quality compliance index β of each time period of the target statistical cycle of the spraying robot j , where H'' ar represents the standard coating thickness of each area of ​​each car extracted from the database, R' represents the allowable color difference value of the car spraying extracted from the database, τ1 represents the influence weight of the unit number of bubbles in the car spraying extracted from the database, and τ2 represents the influence weight of the unit number of cracks in the car spraying extracted from the database.

9. The method for managing IoT devices for industrial automation according to claim 6, characterized in that: The specific analysis method of correcting the risk level of each time period of the target statistical cycle of the spraying robot is as follows: Based on the working effect level in each time period of the target statistical cycle of the spraying robot, the correction factor ψ corresponding to the working effect level in each time period of the target statistical cycle of the spraying robot is extracted from the database j , based on the predicted work risk coefficient in each time period of the target statistical cycle of the spraying robot , through the formula: , calculate the corrected risk coefficient of each time period of the target statistical cycle of the spray robot ; Corrected risk coefficient for each time period based on the target statistical cycle of the spray robot ,like , then the risk level of the jth time period of the target statistical period of the spraying robot is corrected to high risk; like , then the risk level of the j-th time period of the target statistical period of the spraying robot is corrected to low risk, and the risk level of each time period of the target statistical period is obtained, and the corrected risk level of each time period of the target statistical period of the spraying robot is obtained.

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