A method for remote maintenance of IoT devices

Abstract

The present invention discloses a method for remote maintenance of Internet of Things devices, and relates to the technical field of remote maintenance of devices. The method steps of the present invention include: device monitoring, device analysis, parameter analysis and remote maintenance; by detecting the operation of the device, when the device operates abnormally, various operating parameters in the device are analyzed to confirm whether the various operating parameters are suitable for production, and the overall situation of the device is analyzed to confirm the maintenance method of the device, and then a remote engineer is prompted to perform remote maintenance, which solves the problem of insufficient intelligence of traditional equipment maintenance monitoring, can timely adjust and update the operating parameters of the equipment as a whole, ensure the subsequent performance and overall function of the equipment, reduce the downtime of the equipment, ensure the stability and smoothness of the equipment operation, improve the equipment operation efficiency, and effectively reduce the labor cost of equipment maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of remote equipment maintenance, and in particular to a method for remote maintenance of equipment based on the Internet of Things. Background Art

[0002] With the continuous development of science and technology, IoT technology is constantly being applied in various scenarios, including network maintenance, equipment maintenance, etc. IoT devices can monitor the operating status and performance indicators of equipment in real time, and operate and adjust the equipment through remote control technology, reducing the need for maintenance personnel to work on-site, thereby reducing maintenance costs and equipment downtime, ensuring equipment production efficiency.

[0003] Traditional equipment maintenance methods usually require manual regular inspections, and maintenance personnel need to perform repairs on site. However, as equipment changes over time, its internal configuration files, operating parameters and other data need to be updated and adjusted according to specific operational requirements to ensure that the equipment can operate in the best manner and improve the efficiency and effectiveness of equipment operations. However, traditional equipment maintenance relies on manual regular inspections and is unable to promptly discover the operating status of the equipment, making it impossible to adjust and update the equipment in a timely manner, affecting the subsequent performance and overall functionality of the equipment and failing to improve the equipment's production efficiency. In addition, during equipment maintenance, maintenance personnel need to update and adjust the equipment on site based on experience, increasing the labor cost of equipment maintenance. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a method for remote maintenance of Internet of Things devices.

[0005] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a method for remote maintenance of Internet of Things devices, comprising the following steps: S1, device monitoring: monitor the devices within a specified time period to obtain the operation data and status data of the devices.

[0006] S2. Equipment analysis: judging whether the equipment is operating normally based on the equipment's operating data and status data; if the equipment is operating abnormally, executing step S3.

[0007] S3. Maintenance analysis: Obtain the operating environment data and operating data corresponding to the equipment in the specified time period, and extract various operating parameters from the equipment's operating data. At the same time, analyze the equipment's maintenance method based on the equipment's status data, where the maintenance method includes overall equipment updates and specific configuration adjustments.

[0008] S4. Parameter analysis: If the maintenance method of the equipment is specific configuration adjustment, then various parameters to be adjusted are extracted and the adjustment values ​​of various parameters to be adjusted are analyzed.

[0009] S5. Remote maintenance: The maintenance method of the equipment and the adjustment values ​​of various parameters to be adjusted are sent to the remote engineer user terminal, prompting the remote engineer to perform remote maintenance of the equipment.

[0010] Preferably, the specific analysis process for determining whether the operation of the device is normal is as follows: extracting the normal operation data interval and the normal status data interval of the device from the database, comparing the operation data of the device with the normal operation data interval, and comparing the status data of the device with the normal status data interval. If the operation data of the device is not within the normal operation data interval, or the status data of the device is not within the normal status data interval, then it is determined that the operation of the device is abnormal; if the operation data of the device is within the normal operation data interval, and the status data of the device is within the normal status data interval, then it is determined that the operation of the device is normal.

[0011] Preferably, the maintenance method of the analysis equipment, the specific analysis process is as follows: S31, extracting the operating environment data, operation data and operating status corresponding to each historical time period of the equipment from the database, and analyzing the operation similarity between the equipment in the specified time period and each historical time period based on the operating environment data and operation data corresponding to the equipment in the specified time period, comparing the operation similarity between the equipment in the specified time period and each historical time period with a preset operation similarity threshold, taking each historical time period with an operation similarity greater than the operation similarity threshold as each target time period, and taking each target time period with a normal operating status as each associated time period, and extracting the operation data and status data corresponding to each associated time period.

[0012] S32. Extract various operating parameters and status data from the operating data and status data corresponding to each associated time period, analyze the adaptability of various operating parameters in the equipment, and evaluate the overall operating status coefficient of the equipment.

[0013] S33. Compare the fitness of various operating parameters in the equipment with the set fitness thresholds of various operating parameters, and compare the overall operating status coefficient of the equipment with the set overall operating status coefficient threshold of the equipment. If the overall operating status coefficient of the equipment is less than the overall operating status coefficient threshold of the equipment, then determine that the maintenance method of the equipment is overall equipment update; if the overall operating status coefficient of the equipment is greater than or equal to the overall operating status coefficient threshold of the equipment, and the fitness of a certain type of operating parameter is less than the fitness threshold of the operating parameter, then determine that the maintenance method of the equipment is specific configuration adjustment, and record this type of operating parameter as a parameter to be adjusted, thereby obtaining various types of parameters to be adjusted.

[0014] Preferably, the analysis specifies the similarity between the equipment in the time period and the operations in each historical time period, and the specific analysis process is as follows: according to the operating environment data and operation data corresponding to each historical time period, the operating environment data volatility and the operation data volatility are calculated, and the operating environment data volatility is divided by the sum of the operating environment data volatility and the operation data volatility to obtain the operating environment data weight coefficient, denoted as a1; the operation data volatility is divided by the sum of the operating environment data volatility and the operation data volatility to obtain the operating environment data weight coefficient, denoted as a2.

[0015] The operating environment data and operating data corresponding to each historical time period are calculated by the mean to obtain the operating environment average data and the operating average data. The operating environment data volatility is multiplied by the operating environment average data to obtain the reference operating environment data difference, recorded as ΔE; the operating data volatility is multiplied by the operating average data to obtain the reference operating data difference, recorded as ΔW.

[0016] Based on the job similarity expression: Get the job similarity κ between the equipment in the specified time period and the hth historical time period h , where E and W represent the operating environment data and operating data corresponding to the equipment in the specified time period, respectively. h , W h They respectively represent the operating environment data and operating data corresponding to the hth historical time period of the equipment, h represents the number of each historical time period, h=1,2,......,q, q is any integer greater than 2.

[0017] Preferably, the adaptability of various operating parameters in the analysis device is analyzed in the following specific process: various operating parameters and status data corresponding to each associated time period are respectively recorded as R ti and Y t , t represents the number of each associated time period, t=1,2,......,f, i represents the number of each type of operating parameter, i=1,2,......,n, f, n are all integers greater than 2, and the various operating parameters and status data of the equipment are recorded as R i and Y.

[0018] The various operating parameters and various state parameters corresponding to each associated time period are calculated by the mean, and the average values ​​of various operating parameters and state data are obtained, which are recorded as and

[0019] Through the fitness expression: Get the fitness δ of the i-th type of operating parameters in the equipment i , where e represents a natural constant and f represents the number of associated time periods.

[0020] Preferably, the overall operating status coefficient of the evaluation equipment is evaluated, and the specific evaluation process is as follows: the fitness of each type of operating parameter in the equipment is compared with the set fitness threshold of each type of operating parameter. If the fitness of a certain type of operating parameter is less than the fitness threshold of this type of operating parameter, the number of abnormal operating parameter categories is increased by 1, thereby counting the number of abnormal operating parameter categories and marking them as N.

[0021] Extract the fitness of various operating parameters in each associated time period, marked as δ ti , and calculate the stability coefficient of the overall fitness of the equipment, recorded as φ1.

[0022] Set the overall fitness stability coefficient weight factor according to the analytical formula The overall operating state coefficient φ of the equipment is obtained, where n represents the total number of operating parameter categories and τ1 is the weight factor of the stability coefficient of the overall adaptability.

[0023] Preferably, the stability coefficient of the overall fitness of the computing device is specifically calculated as follows: In the formula, φ1 represents the stability coefficient of the overall fitness of the equipment, δ (t-1)i represents the adaptability of the i-th type of operating parameters in the t-1th associated time period, represents the average difference of the i-th type of operating parameters, b is an integer,

[0024] represents the average value of the i-th type of operating parameters, and f represents the number of associated time periods.

[0025] Preferably, the analysis of the adjustment values ​​of various parameters to be adjusted is specifically carried out as follows: the adaptability of various parameters to be adjusted in the device is compared with the adaptability of various operating parameters in each associated time period; if a certain type of operating parameter in a certain associated time period is of the same type as a certain type of parameter to be adjusted, and the adaptability of this type of operating parameter in the associated time period is greater than or equal to the adaptability of this type of parameter to be adjusted, then the associated time period is used as a reference associated time period for this type of parameter to be adjusted, thereby obtaining each reference associated time period for each type of parameter to be adjusted.

[0026] The equipment overall operating status coefficient corresponding to each reference associated time period of each type of parameter to be adjusted is extracted, the reference associated time period corresponding to the maximum equipment overall operating status coefficient is selected as the target time period of each type of operating parameter, and the operating parameters of the same type as the various types of parameters to be adjusted in the target time period corresponding to the various types of parameters to be adjusted are extracted as the adjustment values ​​of the various types of parameters to be adjusted.

[0027] The beneficial effects of the present invention are: 1. The present invention provides a method for remote maintenance of Internet of Things devices. By detecting the operation of the equipment, when the equipment operates abnormally, various operating parameters in the equipment are analyzed to confirm whether the various operating parameters are suitable for production, and the overall situation of the equipment is analyzed to confirm the maintenance method of the equipment, and then the remote engineer is prompted to perform remote maintenance, which solves the problem of insufficient intelligence in traditional equipment maintenance and monitoring, and can timely adjust and update the operating parameters of the equipment as a whole, ensure the subsequent performance and overall function of the equipment, reduce the downtime of the equipment, ensure the stability and smoothness of the equipment operation, improve the equipment production efficiency, and effectively reduce the labor cost of equipment maintenance.

[0028] 2. In the maintenance analysis, the present invention analyzes the adaptability of various operating parameters according to the operating environment data, operating data, operating data and status data, clearly shows whether the various operating parameters are adapted to the operating environment requirements, provides a basis for the subsequent adjustment analysis of the operating parameters, improves the accuracy of the operating parameter adjustment, and increases the adaptability of the equipment to the operating environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] 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.

[0030] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method. DETAILED DESCRIPTION

[0031] 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.

[0032] See also Figure 1 As shown, the present invention provides a method for remote maintenance of Internet of Things devices, including the following steps: S1, device monitoring: monitoring the devices within a specified time period to obtain the operating data and status data of the devices.

[0033] It should be noted that the equipment is equipped with several sensors, and the data collected by the sensors are transmitted to the equipment remote maintenance system through the Internet of Things. The equipment operation data refers to the various control parameters configured by the equipment in the operating state, and the equipment status data refers to the various performance data displayed by the equipment in the operating state; for example, when the equipment is a power generation equipment, the operation data refers to the various control parameters configured by the power generation equipment in the power generation state, and the various control parameters include the generator speed, the flow rate of the cooling medium and the fuel supply rate, etc. The status data refers to the various performance data displayed by the power generation equipment in the power generation state, and the various performance data include temperature and noise decibels, etc.

[0034] S2. Equipment analysis: judging whether the equipment is operating normally based on the equipment's operating data and status data; if the equipment is operating abnormally, executing step S3.

[0035] In a specific embodiment, the specific analysis process for determining whether the operation of the device is normal is as follows: extracting the normal operation data interval and the normal status data interval of the device from the database, comparing the operation data of the device with the normal operation data interval, and comparing the status data of the device with the normal status data interval. If the operation data of the device is not within the normal operation data interval, or the status data of the device is not within the normal status data interval, then it is determined that the operation of the device is abnormal; if the operation data of the device is within the normal operation data interval, and the status data of the device is within the normal status data interval, then it is determined that the operation of the device is normal.

[0036] It should be noted that the database of the equipment remote maintenance system stores the manual provided by the equipment manufacturer, and the normal operation data interval and the normal status data interval of the equipment are extracted from the manual.

[0037] S3. Maintenance analysis: Obtain the operating environment data and operating data corresponding to the equipment in the specified time period, and extract various operating parameters from the equipment's operating data. At the same time, analyze the equipment's maintenance method based on the equipment's status data, where the maintenance method includes overall equipment updates and specific configuration adjustments.

[0038] It should be noted that the operating environment data are various environmental parameters of the equipment in the operating state, and the operating data are various operating parameters of the equipment in the operating state. Assume that the equipment is a power generation equipment, and the operating environment data of the power generation equipment are various environmental parameters of the power generation equipment in the power generation state. The various environmental parameters include ambient temperature and ambient humidity, etc. The operating data of the power generation equipment are various operating parameters of the power generation equipment in the power generation state. The various operating parameters include power generation rate, power generation time, power generation, etc.

[0039] In a specific embodiment, the maintenance method of the analysis equipment, the specific analysis process is as follows:

[0040] S31. Extract the operating environment data, operation data and operating status corresponding to each historical time period of the equipment from the database, and analyze the operation similarity between the equipment in the specified time period and each historical time period based on the operating environment data and operation data corresponding to the equipment in the specified time period, compare the operation similarity between the equipment in the specified time period and each historical time period with a preset operation similarity threshold, take each historical time period with an operation similarity greater than the operation similarity threshold as each target time period, take each target time period with a normal operating status as each associated time period, and extract the operation data and status data corresponding to each associated time period.

[0041] It should be noted that the database stores the normal operation data interval and normal status data interval of the device, and stores the operating environment data, operating data and operating status corresponding to each historical time period of the device.

[0042] In the above, the analysis of the similarity between the operation of the equipment in the specified time period and each historical time period is as follows: according to the operating environment data and operation data corresponding to each historical time period, the operating environment data volatility and the operation data volatility are calculated, and the operating environment data volatility is divided by the sum of the operating environment data volatility and the operation data volatility to obtain the operating environment data weight coefficient, denoted as a1; the operation data volatility is divided by the sum of the operating environment data volatility and the operation data volatility to obtain the operating environment data weight coefficient, denoted as a2.

[0043] It should be noted that the calculation process of the operating environment data volatility and the operating data volatility is as follows: the operating environment data and operating data corresponding to each historical time period are respectively recorded as E h , W h , h represents the number of each historical time period, h=1,2,......,q, q is any integer greater than 2, and the calculation formula is The operating environment data volatility ω1 and operating data volatility ω2 are obtained, where q represents the total number of historical time periods, E h-1 , W h-1 They respectively represent the operating environment data and operating data corresponding to the h-1th historical time period.

[0044] The operating environment data and operating data corresponding to each historical time period are calculated by the mean to obtain the operating environment average data and the operating average data. The operating environment data volatility is multiplied by the operating environment average data to obtain the reference operating environment data difference, recorded as ΔE; the operating data volatility is multiplied by the operating average data to obtain the reference operating data difference, recorded as ΔW.

[0045] Based on the job similarity expression: Get the job similarity κ between the equipment in the specified time period and the hth historical time period h, where E and W represent the operating environment data and operating data corresponding to the equipment in the specified time period, respectively. h , W h They respectively represent the operating environment data and operating data corresponding to the hth historical time period of the equipment.

[0046] S32. Extract various operating parameters and status data from the operating data and status data corresponding to each associated time period, analyze the adaptability of various operating parameters in the equipment, and evaluate the overall operating status coefficient of the equipment.

[0047] In the above, the adaptability of various operating parameters in the analysis device is analyzed in the following specific analysis process: the various operating parameters and status data corresponding to each associated time period are respectively recorded as R ti and Y t , t represents the number of each associated time period, t=1,2,......,f, i represents the number of each type of operating parameter, i=1,2,......,n, f, n are all integers greater than 2, and the various operating parameters and status data of the equipment are recorded as R i and Y.

[0048] The various operating parameters and various state parameters corresponding to each associated time period are calculated by the mean, and the average values ​​of various operating parameters and state data are obtained, which are respectively denoted as R i and Y.

[0049] Through the fitness expression: Get the fitness δ of the i-th type of operating parameters in the equipment i , where e represents a natural constant and f represents the number of associated time periods.

[0050] The present invention analyzes the adaptability of various operating parameters according to the operating environment data, operating data, operation data and status data in the maintenance analysis, clearly shows whether the various operating parameters are adapted to the operating environment requirements, provides a basis for the subsequent adjustment analysis of the operating parameters, improves the accuracy of the operating parameter adjustment, and increases the adaptability of the equipment to the operating environment.

[0051] In the above, the overall operating status coefficient of the evaluation equipment is evaluated, and the specific evaluation process is as follows: the fitness of each type of operating parameter in the equipment is compared with the set fitness threshold of each type of operating parameter. If the fitness of a certain type of operating parameter is less than the fitness threshold of this type of operating parameter, the number of abnormal operating parameter categories is increased by 1, thereby counting the number of abnormal operating parameter categories and marking it as N.

[0052] Extract the fitness of various operating parameters in each associated time period, marked as δ ti , and calculate the stability coefficient of the overall fitness of the equipment, recorded as φ1.

[0053] Furthermore, the stability coefficient of the overall adaptability of the computing device is specifically calculated as follows: In the formula, φ1 represents the stability coefficient of the overall fitness of the equipment, δ (t-1)i represents the adaptability of the i-th type of operating parameters in the t-1th associated time period, represents the average difference of the i-th type of operating parameters, b is an integer, represents the average value of the i-th type of operating parameters, and f represents the number of associated time periods.

[0054] Set the overall fitness stability coefficient weight factor according to the analytical formula The overall operating state coefficient φ of the equipment is obtained, where n represents the total number of operating parameter categories and τ1 is the weight factor of the stability coefficient of the overall adaptability.

[0055] It should be noted that the fitness stability coefficient corresponding to each historical overall update of the equipment is extracted from the equipment remote maintenance system, the number of historical overall updates in which the fitness stability coefficient is less than or equal to 0 is counted, and then divided by the total number of historical overall updates to obtain the fitness stability coefficient weight factor.

[0056] S33. Compare the fitness of various operating parameters in the equipment with the set fitness thresholds of various operating parameters, and compare the overall operating status coefficient of the equipment with the set overall operating status coefficient threshold of the equipment. If the overall operating status coefficient of the equipment is less than the overall operating status coefficient threshold of the equipment, then determine that the maintenance method of the equipment is overall equipment update; if the overall operating status coefficient of the equipment is greater than or equal to the overall operating status coefficient threshold of the equipment, and the fitness of a certain type of operating parameter is less than the fitness threshold of the operating parameter, then determine that the maintenance method of the equipment is specific configuration adjustment, and record this type of operating parameter as a parameter to be adjusted, thereby obtaining various types of parameters to be adjusted.

[0057] When it is necessary to explain, the fitness corresponding to each historical adjustment of various operating parameters of the equipment is obtained from the equipment remote maintenance system, and the minimum fitness is selected as the fitness threshold of each operating parameter; the overall operating status coefficient corresponding to each historical overall update of the equipment is obtained from the remote maintenance system, and the minimum overall operating status coefficient is selected as the overall operating status coefficient threshold of the equipment.

[0058] S4. Parameter analysis: If the maintenance method of the equipment is specific configuration adjustment, then various parameters to be adjusted are extracted and the adjustment values ​​of various parameters to be adjusted are analyzed.

[0059] In a specific embodiment, the adjustment value of each type of parameter to be adjusted is analyzed, and the specific analysis process is as follows: the adaptability of each type of parameter to be adjusted in the device is compared with the adaptability of each type of operating parameters in each associated time period. If a certain type of operating parameter in a certain associated time period is of the same type as a certain type of parameter to be adjusted, and the adaptability of this type of operating parameter in the associated time period is greater than or equal to the adaptability of this type of parameter to be adjusted, then the associated time period is used as a reference associated time period for this type of parameter to be adjusted, thereby obtaining each reference associated time period of each type of parameter to be adjusted.

[0060] The equipment overall operating status coefficient corresponding to each reference associated time period of each type of parameter to be adjusted is extracted, the reference associated time period corresponding to the maximum equipment overall operating status coefficient is selected as the target time period of each type of operating parameter, and the operating parameters of the same type as the various types of parameters to be adjusted in the target time period corresponding to the various types of parameters to be adjusted are extracted as the adjustment values ​​of the various types of parameters to be adjusted.

[0061] S5. Remote maintenance: The maintenance method of the equipment and the adjustment values ​​of various parameters to be adjusted are sent to the remote engineer user terminal, prompting the remote engineer to perform remote maintenance of the equipment.

[0062] It should be noted that remote engineers can log in to the equipment remote maintenance system through the user end to update the equipment as a whole and adjust the parameters to be adjusted, where the user end includes computers, tablets, etc.

[0063] The embodiment of the present invention detects the operation of the equipment. When the equipment operates abnormally, it analyzes various operating parameters in the equipment to confirm whether the various operating parameters are suitable for production, analyzes the overall situation of the equipment, confirms the maintenance method of the equipment, and then prompts the remote engineer to perform remote maintenance, thereby solving the problem of insufficient intelligence in traditional equipment maintenance and monitoring. It can timely adjust and update the operating parameters of the equipment as a whole, ensure the subsequent performance and overall function of the equipment, reduce the downtime of the equipment, ensure the stability and smoothness of the equipment operation, improve the production efficiency of the equipment, and effectively reduce the labor cost of equipment maintenance.

[0064] 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 in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A method for remote maintenance of IoT devices, characterized in that: The steps include: S1. Equipment monitoring: monitor the equipment within a specified time period and obtain the equipment's operating data and status data; S2, equipment analysis: judging whether the equipment is operating normally according to the equipment operation data and status data; if the equipment is operating abnormally, executing step S3; S3, maintenance analysis: obtain the operating environment data and operation data corresponding to the equipment in the specified time period, and extract various operating parameters from the equipment's operating data. At the same time, analyze the equipment's maintenance method based on the equipment's status data, where the maintenance method includes overall equipment update and specific configuration adjustment; The maintenance method of the analytical equipment and the specific analysis process are as follows: S31, extracting the operating environment data, operation data and operating status corresponding to each historical time period of the equipment from the database, and analyzing the operation similarity between the equipment in the specified time period and each historical time period according to the operating environment data and operation data corresponding to the equipment in the specified time period, comparing the operation similarity between the equipment in the specified time period and each historical time period with a preset operation similarity threshold, taking each historical time period with an operation similarity greater than the operation similarity threshold as each target time period, taking each target time period with a normal operating status as each associated time period, and extracting the operation data and status data corresponding to each associated time period; S32, extracting various operating parameters and status data from the operating data and status data corresponding to each associated time period, analyzing the adaptability of various operating parameters in the equipment, and evaluating the overall operating status coefficient of the equipment; S33, comparing the fitness of various operating parameters in the device with the set fitness threshold of various operating parameters, and comparing the overall operating state coefficient of the device with the set overall operating state coefficient threshold of the device. If the overall operating state coefficient of the device is less than the overall operating state coefficient threshold of the device, it is determined that the maintenance mode of the device is overall equipment update; if the overall operating state coefficient of the device is greater than or equal to the overall operating state coefficient threshold of the device, and the fitness of a certain type of operating parameter is less than the fitness threshold of the operating parameter, it is determined that the maintenance mode of the device is specific configuration adjustment, and the operating parameter of this type is recorded as a parameter to be adjusted, thereby obtaining various parameters to be adjusted; The adaptability of various operating parameters in the analysis device is analyzed in detail as follows: The various operating parameters and status data corresponding to each associated time period are respectively denoted as R ti and Y t , t represents the number of each associated time period, t=1,2,......,f, i represents the number of each type of operating parameter, i=1,2,......,n, f, n are all integers greater than 2, and the various operating parameters and status data of the equipment are recorded as R i and Y; The various operating parameters and various state parameters corresponding to each associated time period are calculated by the mean, and the average values ​​of various operating parameters and state data are obtained, which are recorded as and Through the fitness expression: Get the fitness δ of the i-th type of operating parameters in the equipment i , where e represents a natural constant and f represents the number of associated time periods; The overall operating status coefficient of the evaluation equipment is evaluated as follows: Compare the fitness of various operating parameters in the device with the set fitness threshold of various operating parameters. If the fitness of a certain type of operating parameter is less than the fitness threshold of the operating parameter, the number of abnormal operating parameter types is increased by 1, and the number of abnormal operating parameter types is counted and marked as N. Extract the fitness of various operating parameters in each associated time period, marked as δ ti , and calculate the stability coefficient of the overall fitness of the equipment, recorded as φ1; Set the overall fitness stability coefficient weight factor according to the analytical formula The overall operating state coefficient φ of the equipment is obtained, where n represents the total number of operating parameter categories, and τ1 is the weight factor of the stability coefficient of the overall adaptability; The stability coefficient of the overall fitness of the computing device is specifically calculated as follows: In the formula, φ1 represents the stability coefficient of the overall fitness of the equipment, δ (t-1)i represents the adaptability of the i-th type of operating parameters in the t-1th associated time period, represents the average difference of the i-th type of operating parameters, b is an integer, represents the average value of the i-th type of operating parameters, and f represents the number of associated time periods; S4. Parameter analysis: If the maintenance method of the equipment is specific configuration adjustment, then various parameters to be adjusted are extracted and the adjustment values ​​of various parameters to be adjusted are analyzed; The adjustment values ​​of various parameters to be adjusted are analyzed, and the specific analysis process is as follows: Compare the adaptability of various parameters to be adjusted in the device with the adaptability of various operating parameters in each associated time period. If a certain type of operating parameter in a certain associated time period is of the same type as a certain type of parameter to be adjusted, and the adaptability of the operating parameter in the associated time period is greater than or equal to the adaptability of the parameter to be adjusted, then the associated time period is used as a reference associated time period for the parameter to be adjusted, thereby obtaining each reference associated time period for each type of parameter to be adjusted; Extract the equipment overall operation state coefficient corresponding to each reference associated time period of each type of parameter to be adjusted, select the reference associated time period corresponding to the maximum equipment overall operation state coefficient as the target time period of each type of operation parameter, and extract the operation parameters of the same type as the parameters to be adjusted in the target time period corresponding to each type of parameter to be adjusted as the adjustment values ​​of each type of parameter to be adjusted; S5. Remote maintenance: The maintenance method of the equipment and the adjustment values ​​of various parameters to be adjusted are sent to the remote engineer user terminal, prompting the remote engineer to perform remote maintenance of the equipment.

2. A method for remote maintenance based on Internet of Things devices according to claim 1, characterized in that: The specific analysis process of judging whether the operation of the equipment is normal is as follows: The normal operation data interval and normal status data interval of the equipment are extracted from the database, the operation data of the equipment are compared with the normal operation data interval, and the status data of the equipment are compared with the normal status data interval. If the operation data of the equipment is not within the normal operation data interval, or the status data of the equipment is not within the normal status data interval, it is determined that the operation of the equipment is abnormal; if the operation data of the equipment is within the normal operation data interval, and the status data of the equipment is within the normal status data interval, it is determined that the operation of the equipment is normal.

3. A method for remote maintenance based on Internet of Things devices according to claim 1, characterized in that: The analysis is performed to determine the similarity between the operation of the equipment in the specified time period and the operation of each historical time period. The specific analysis process is as follows: According to the operating environment data and operating data corresponding to each historical time period, the operating environment data volatility and the operating data volatility are calculated, and the operating environment data volatility is divided by the sum of the operating environment data volatility and the operating data volatility to obtain the operating environment data weight coefficient, which is recorded as a1; the operating data volatility is divided by the sum of the operating environment data volatility and the operating data volatility to obtain the operating environment data weight coefficient, which is recorded as a2; The operating environment data and operating data corresponding to each historical time period are calculated by the mean to obtain the operating environment average data and the operating average data. The operating environment data volatility is multiplied by the operating environment average data to obtain the reference operating environment data difference, which is recorded as ΔE; the operating data volatility is multiplied by the operating average data to obtain the reference operating data difference, which is recorded as ΔW; Based on the job similarity expression: Get the job similarity κ between the equipment in the specified time period and the hth historical time period h , where E and W represent the operating environment data and operating data corresponding to the equipment in the specified time period, respectively. h , W h They respectively represent the operating environment data and operating data corresponding to the hth historical time period of the equipment, h represents the number of each historical time period, h=1,2,......,q, q is any integer greater than 2.

Images