A method and system for supervising Internet of Things devices based on coding identification

Abstract

The present invention discloses a method and system for Internet of Things device supervision based on coding identification, which belongs to the field of device supervision technology, including: configuring an RFID code for each target device in the area, each code containing the location information of the target device and the relevant parameter information of the target device; real-time monitoring of the relevant parameters of the target device, and analyzing and processing based on the obtained relevant parameter information to determine whether the target device has a fault; when the target device does not have a fault, further analyzing based on the obtained relevant parameter information to determine whether the target device has a potential fault; and providing corresponding feedback based on the judgment result. The present invention analyzes based on the obtained relevant parameter information, thereby evaluating the potential fault risk of the target device, so that it can be pre-judged whether the target device has a risk of failure, thereby performing early maintenance and management on the target device with higher risk, to avoid the occurrence of target device failure.

Description

Technical Field

[0001] The present invention belongs to the technical field of device supervision, and in particular relates to a method and system for supervising Internet of Things devices based on coding identification. Background Art

[0002] IoT devices are typically equipped with various communication technologies, such as Wi-Fi, Bluetooth, Zigbee, and NFC, as well as wired communication technologies like Ethernet and PLC, to enable intelligent and networked applications. These devices are able to communicate with other devices and networks and perform various tasks depending on their form factor and function, such as remotely accessing and operating devices, remotely accessing databases for information, or receiving firmware updates from manufacturers.

[0003] When some IoT devices malfunction, they are mostly discovered by patrol personnel or through reminders from the public, which is inefficient and cannot be discovered and repaired in time when the device malfunctions. In addition, when an IoT device malfunctions, maintenance personnel cannot immediately determine the specific location of the malfunction, and since the malfunction has already occurred, they cannot analyze its potential malfunction risks, which will cause additional economic losses. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for supervising IoT devices based on coding identification, so as to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for supervising IoT devices based on coding identification, the method comprising:

[0007] Step 1: Configure an RFID code for each target device in the area. Each code contains the location information and relevant parameter information of the target device;

[0008] Step 2: Monitor the relevant parameters of the target device in real time, and analyze and process the obtained relevant parameter information to determine whether the target device has a fault;

[0009] Step 3: When the target device does not have a fault, further analysis is performed based on the acquired relevant parameter information to determine whether the target device has a potential fault;

[0010] Step 4: Provide corresponding feedback based on the judgment results.

[0011] Furthermore, the relevant parameter information includes the current, voltage, temperature and brightness of the target device.

[0012] Furthermore, the method for determining whether the target device has a fault in step 2 is:

[0013] S21. Obtain the voltage U, current I, temperature T, and brightness L of the target device and compare them with their respective preset operating threshold ranges. If the monitored voltage, current, temperature, or brightness is not within the preset operating threshold range, it is determined that the target device has a fault. Otherwise, proceed to step S22.

[0014] S22, through the formula Obtain the fault value Wr of each target device and compare the obtained fault value Wr with the preset fault threshold Wr th To compare:

[0015] When Wr>Wr th If the target device fails, it is determined that the target device fails, otherwise it goes to step S23;

[0016] Among them, α1, α2, α3, and α4 are respective weight coefficients, U0 is a preset standard voltage, I0 is a preset standard current, and T0 is a preset standard temperature.

[0017] Furthermore, the working method of step S23 is:

[0018] Set a small monitoring period Δt, and draw up a curve Wr of the fault value change over time for each target device within the Δt period according to the change of the fault value. i (t);

[0019] Thus, the formula Calculate the deviation coefficient of each target device;

[0020] When F i >F th , it is determined that the target device has a fault;

[0021] Among them, Wr i (t) is the proposed curve of the fault value of the i-th target device changing with time, F i is the deviation coefficient of the i-th target device, n is the number of target devices, and i∈[1,n], t1 is the start time of the monitoring period, t2 is the end time of the monitoring period, F th is the preset deviation coefficient threshold.

[0022] Furthermore, the method for determining whether the target device has a potential fault in step 3 is:

[0023] When it is determined that the target device does not have a fault, a large detection cycle ΔT is set and m detections are performed within the large detection cycle to obtain the fault value Wr of the target device at each detection. j , and formulate the curve Wr(x) of the fault value of each target device changing with the number of detections within a large detection cycle;

[0024] By formula Obtain the potential risk factor Gt;

[0025] The obtained potential risk factor Gt is compared with the system preset risk factor threshold Gt th To compare:

[0026] When Gt>Gt th , it is determined that the target device has a potential fault;

[0027] Where σ is the fault fluctuation coefficient, E p is the environmental impact coefficient, x1 is the first test in the large test cycle, and x2 is the last test in the large test cycle.

[0028] Furthermore, the method for obtaining the fault fluctuation coefficient is:

[0029] By formula The fault fluctuation coefficient σ is obtained.

[0030] Furthermore, the method for obtaining the environmental impact coefficient is:

[0031] The average humidity value s and the average dust concentration value h during the large detection period are obtained from the local meteorological bureau through the Internet of Things technology, and the number of days with strong winds D during the large detection period is obtained. w , the number of days with acid rain D r ;

[0032] Thus, through the formula The environmental impact coefficient E p ;

[0033] Where P is the total number of days in the large detection period, a is the dust concentration coefficient, b is the humidity coefficient, c is the wind intensity coefficient, and d is the corrosion coefficient.

[0034] A system for supervising IoT devices based on coding identification, wherein the system is implemented by the method for supervising IoT devices based on coding identification, and the system comprises:

[0035] RFID encoding module, which contains the location information and related parameter information of each target device;

[0036] An environment acquisition module, which is connected to the local meteorological bureau and is used to obtain local meteorological information;

[0037] A data analysis and processing module, which processes and analyzes the acquired relevant parameter information and meteorological information to determine the target equipment failure;

[0038] The feedback module is used to provide corresponding reminder feedback according to the judgment result.

[0039] Beneficial effects of the present invention:

[0040] The present invention configures an RFID code for each target device in the area, and the code contains the location information of the target device and the relevant parameter information of the target device. In this way, management personnel can know the working status of each target device through code identification, and there is no need for management personnel to conduct inspections in turn, which can greatly save human resources and reduce cost output. Moreover, by obtaining the relevant parameter information, the working status of the target device can be understood in real time. Once the target device fails, the location of the failure can be immediately known and timely feedback reminders can be given. In this way, the target device failure and the location of the failure can be discovered in the first time, which is convenient for maintenance management personnel to manage and repair it in the first time.

[0041] The present invention can also further analyze the target device based on the obtained relevant parameter information to determine whether there is a potential fault in the target device when there is no fault in the target device, and evaluate the potential failure risk of the target device. In this way, it can be predicted whether the target device has the risk of failure, so that the target device with higher risk can be maintained and managed in advance to avoid the occurrence of target device failure, thereby reducing economic losses.

[0042] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative work.

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0047] In one embodiment, a method for supervising IoT devices based on coding identification is disclosed. Figure 1 As shown in Figure 2, this regulatory approach can be applied to smart street lighting equipment, specifically including:

[0048] Step 1: Configure an RFID code for each target device in the area. Each code contains the location information of the target device and relevant parameter information of the target device, including the current, voltage, temperature and brightness of the target device;

[0049] Step 2: Monitor the relevant parameters of the target device in real time, and analyze and process the obtained relevant parameter information to determine whether the target device has a fault;

[0050] Step 3: When the target device does not have a fault, further analysis is performed based on the acquired relevant parameter information to determine whether the target device has a potential fault;

[0051] Step 4: Provide corresponding feedback based on the judgment results.

[0052] Through the above technical solution, this embodiment configures an RFID code for each target device in the area, and the code contains the location information of the target device and the relevant parameter information of the target device. In this way, the management personnel can know the working status of each target device through code identification, and there is no need for the management personnel to inspect in turn, which can greatly save human resources and reduce cost output. In addition, through the obtained relevant parameter information, the working status of the target device can be understood in real time. Once the target device fails, the location of the failure and timely feedback reminder can be known immediately. In this way, the target device failure and the location of the failure can be discovered at the first time, which is convenient for maintenance management personnel to perform maintenance at the first time; in addition, when there is no failure in the target device, further analysis can be performed based on the obtained relevant parameter information to determine whether the target device has a potential failure, and the potential failure risk of the target device can be evaluated. In this way, it can be predicted whether the target device has the risk of failure, so that the target device with higher risk can be maintained and managed in advance to avoid the occurrence of target device failure, thereby reducing economic losses.

[0053] As an embodiment of the present invention, the method for determining whether the target device has a fault in step 2 is:

[0054] S21. Obtain the voltage U, current I, temperature T, and brightness L of the target device and compare them with their respective preset operating threshold ranges. If the monitored voltage, current, temperature, or brightness is not within the preset operating threshold range, it is determined that the target device has a fault. Otherwise, proceed to step S22.

[0055] S22, through the formula Obtain the fault value Wr of each target device and compare the obtained fault value Wr with the preset fault threshold Wr th To compare:

[0056] When Wr>Wr th If the target device fails, it is determined that the target device fails, otherwise it goes to step S23;

[0057] S23. Set a small monitoring period Δt, and draw up a curve Wr of the fault value change over time for each target device within the Δt period according to the change of the fault value. i (t);

[0058] Thus, the formula Calculate the deviation coefficient of each target device;

[0059] When F i >F th , it is determined that the target device has a fault;

[0060] Among them, α1, α2, α3, and α4 are their respective weight coefficients, U0 is the preset standard voltage size, I0 is the preset standard current size, T0 is the preset standard temperature size, and Wr i (t) is the proposed curve of the fault value of the i-th target device changing with time, F i is the deviation coefficient of the i-th target device, n is the number of target devices, and i∈[1,n], t1 is the start time of the monitoring period, t2 is the end time of the monitoring period, F th is the preset deviation coefficient threshold.

[0061] Through the above technical solution, this embodiment provides three specific methods for determining whether a target device has a fault. Since the main parameter changes of a target device fault are current, voltage, temperature and brightness changes, the voltage U, current I, temperature T and brightness L of the target device are obtained and compared with their respective preset working threshold intervals. When the monitored voltage, current, temperature or brightness is not within the preset working threshold interval, it indicates that the target device has a fault. For example, assuming that the working current of a certain target device is between 0.5-0.8 amperes, when it is detected that the working current is not within this interval, it indicates that the current is too large or too small, and the target device is judged to have a fault. In this way, whether the target device has a fault can be detected in real time, so that it can be discovered and repaired in the first time. Secondly, through the formula Obtain the fault value Wr of each target device and compare the obtained fault value Wr with the preset fault threshold Wr th Compare, when Wr>Wr th When , it is judged that the target device has a fault; if it is detected that the various project data of the target device are within the normal working threshold range, but if the difference between the project data and the set standard comparison value is large, it means that the target device has also failed. Therefore, the voltage, current, temperature and brightness obtained are comprehensively analyzed to obtain the fault value, and the target device is judged to be faulty based on the size of the fault value. In this way, after a comprehensive analysis of the various parameters obtained, the interference of some accidental errors on the detection results can be eliminated, and the target device fault can be analyzed and detected more accurately. Finally, a small monitoring period Δt is set, and the fault value change curve Wr of each target device over time within the Δt period is formulated according to the change of the fault value. i (t), and thus we can get The deviation coefficient of each target device is obtained. When F i >F th When , it is determined that the target device has failed; since the target device of the entire area is a whole, the formula Determine the difference between the overall status of a single target device and all target devices in the area during the historical period within the small monitoring cycle, and then use the formula To judge the difference between the overall status of a single target device and all target devices in the area at the current end time point t2, and then conduct a comprehensive analysis to obtain the deviation coefficient. Obviously, when the deviation coefficient is larger, it means that the risk of failure of the target device is greater. Therefore, the obtained deviation coefficient F i The deviation coefficient threshold F th For comparison, when F i >F thIn this way, the target device is judged to have a fault according to the historical time period in the area and the fault values ​​of the single target device and all target devices in the current time period to determine whether the target device has a fault.

[0062] It should be noted that the respective weight coefficients α1, α2, α3, α4, the preset standard voltage U0, the preset standard current I0, the preset standard temperature T0, and the preset deviation coefficient threshold F th They can all be selected and formulated based on the historical data and experience data of the target device, and the length of the small monitoring cycle can be artificially formulated according to actual conditions, which will not be described in detail here.

[0063] As an embodiment of the present invention, the method for determining whether the target device has a potential fault in step 3 is:

[0064] When it is determined that the target device does not have a fault, a large detection cycle ΔT is set and m detections are performed within the large detection cycle to obtain the fault value Wr of the target device at each detection. j , and formulate the curve Wr(x) of the fault value of each target device changing with the number of detections within a large detection cycle;

[0065] By formula Obtain the potential risk factor Gt;

[0066] The obtained potential risk factor Gt is compared with the system preset risk factor threshold Gt th To compare:

[0067] When Gt>Gt th , it is determined that the target device has a potential fault;

[0068] Where σ is the fault fluctuation coefficient, and E p is the environmental impact coefficient, x1 is the first test within the large test cycle, and x2 is the last test within the large test cycle;

[0069] The environmental impact coefficient E p The acquisition method is: obtain the average humidity value s and the average dust concentration value h during the large detection period from the local meteorological bureau through the Internet of Things technology, and obtain the number of days D with strong winds during the large detection period w , the number of days with acid rain D r ;

[0070] Thus, through the formula The environmental impact coefficient E p ;

[0071] Where P is the total number of days in the large detection period, a is the dust concentration coefficient, b is the humidity coefficient, c is the wind intensity coefficient, and d is the corrosion coefficient.

[0072] Through the above technical solution, this embodiment provides a specific method for determining whether a target device has a potential fault. First, when determining that the target device does not have a fault, a large detection cycle ΔT is set, and m detections are performed within the large detection cycle to obtain the fault value Wr of the target device at each detection. j , and formulate the curve Wr(x) of the fault value of each target device changing with the number of detections within the large detection cycle; then use the Internet of Things technology to obtain the average humidity value s and the average dust concentration value h within the large detection cycle from the local meteorological bureau, and obtain the number of days with strong winds D within the large detection cycle w , the number of days with acid rain D r , so through the formula The environmental impact coefficient E p Since high humidity and high dust content will increase the possibility of target equipment failure, and extreme weather such as strong winds and acid rain will also affect the target equipment, the formula Comprehensive analysis to obtain the environmental impact coefficient E p , it can be seen that the larger the value, the greater the potential failure risk of the target device; so finally through the formula The potential risk factor Gt is obtained, the formula It indicates the difference between the fault value change in the latter period and the fault value change in the previous period within the entire detection cycle. The larger the value, the higher the risk. The formula It indicates the fluctuation change in the entire detection cycle. The larger the value, the more unstable it is, and the higher the risk. Therefore, the potential risk coefficient Gt obtained is compared with the risk coefficient threshold Gt preset by the system. th Compare: When Gt>Gt th When the target device is detected as having a potential fault, it is determined that the target device has a potential fault. This method can be used to conduct a comprehensive analysis based on the changes and fluctuations in the fault value of the target device each time, combined with the impact of the local environment, to determine whether the target device has a potential fault risk. If the risk is determined to be high, the target device can be repaired in a timely manner to reduce the occurrence of target device failures.

[0073] It should be noted that the system preset risk factor threshold Gt th , dust concentration coefficient a, humidity coefficient b, wind intensity coefficient c and corrosion coefficient d can all be determined based on empirical data and historical data, while the maximum detection period ΔT can be determined artificially, which will not be described in detail here.

[0074] In one embodiment, a system for supervising IoT devices based on coding identification is also disclosed. Figure 2 As shown, the system includes:

[0075] RFID encoding module, which contains the location information and related parameter information of each target device;

[0076] Environmental acquisition module, which is connected to the local meteorological bureau to obtain local meteorological information;

[0077] Data analysis and processing module: The data analysis and processing module processes and analyzes the acquired relevant parameter information and meteorological information to determine the target equipment failure;

[0078] Feedback module, the feedback module is used to provide corresponding reminder feedback based on the judgment results.

[0079] The present invention configures an RFID code for each target device in the area, and the code contains the location information of the target device and the relevant parameter information of the target device. In this way, management personnel can know the working status of each target device through code identification, and there is no need for management personnel to inspect in turn, which can greatly save human resources and reduce cost output. Moreover, through the obtained relevant parameter information, the working status of the target device can be understood in real time. Once the target device fails, the location of the failure can be immediately known and timely feedback reminders can be given. In this way, the target device failure and the location of the failure can be discovered at the first time, which is convenient for maintenance management personnel to manage and repair at the first time; at the same time, the present invention can also further analyze the obtained relevant parameter information to determine whether the target device has a potential failure when there is no failure in the target device, and evaluate the potential failure risk of the target device. In this way, it can be predicted whether the target device has the risk of failure, so as to carry out maintenance and management in advance for the target device with higher risks, to avoid the occurrence of target device failure, thereby reducing economic losses.

[0080] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments 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 claims, they should all fall within the scope of protection of the present invention.

Claims

1. A method for supervising IoT devices based on coding identification, characterized in that: The method comprises: Step 1: Configure an RFID code for each target device in the area. Each code contains the location information and relevant parameter information of the target device; Step 2: Monitor the relevant parameters of the target device in real time, and analyze and process the obtained relevant parameter information to determine whether the target device has a fault; Step 3: When the target device does not have a fault, further analysis is performed based on the acquired relevant parameter information to determine whether the target device has a potential fault; Step 4: Provide corresponding feedback based on the judgment results; The method for determining whether the target device has a fault in step 2 is as follows: S21, obtaining the voltage U, current I, temperature T, and brightness L of the target device, and comparing them with respective preset operating threshold ranges; if the monitored voltage, current, temperature, or brightness is not within the preset operating threshold range, it is determined that the target device has a fault; otherwise, the process proceeds to step S22; S22, through the formula Obtain the fault value Wr of each target device and compare the obtained fault value Wr with the preset fault threshold Wr th To compare: When Wr>Wr th If the target device fails, it is determined that the target device fails, otherwise it goes to step S23; Among them, α1, α2, α3, and α4 are their respective weight coefficients, U0 is the preset standard voltage, I0 is the preset standard current, and T0 is the preset standard temperature; The working method of step S23 is: set a small monitoring period Δt, and draw up a curve Wr of the fault value change over time of each target device within the Δt period according to the change of the fault value. i (t); Thus, through the formula Obtain the deviation coefficient of each target device; When F i >F th , it is determined that the target device has a fault; Among them, Wr i (t) is the proposed curve of the fault value of the i-th target device changing with time, F i is the deviation coefficient of the i-th target device, n is the number of target devices, and i∈[1,n], t1 is the start time of the monitoring period, t2 is the end time of the monitoring period, F th is a preset deviation coefficient threshold; The method for determining whether the target device has a potential fault in step 3 is as follows: when it is determined that the target device does not have a fault, a large detection cycle ΔT is set, and m detections are performed within the large detection cycle, thereby obtaining the fault value Wr of the target device at each detection. j , and formulate the curve Wr(x) of the fault value of each target device changing with the number of detections within a large detection cycle; By formula Obtain the potential risk factor Gt; The obtained potential risk factor Gt is compared with the system preset risk factor threshold Gt th To compare: When Gt>Gt th , it is determined that the target device has a potential fault; Where σ is the fault fluctuation coefficient, E p is the environmental impact coefficient, x1 is the first test in the large test cycle, and x2 is the last test in the large test cycle.

2. The method for supervising IoT devices based on coding identification according to claim 1, characterized in that: The relevant parameter information includes the current, voltage, temperature and brightness of the target device.

3. The method for supervising IoT devices based on coding identification according to claim 1, characterized in that: The method for obtaining the fault fluctuation coefficient is: By formula The fault fluctuation coefficient σ is obtained.

4. The method for supervising IoT devices based on coding identification according to claim 1, characterized in that: The method for obtaining the environmental impact coefficient is: The average humidity value s and the average dust concentration value h during the large detection period are obtained from the local meteorological bureau through the Internet of Things technology, and the number of days with strong winds D during the large detection period is obtained. w , the number of days with acid rain D r ; Thus, through the formula The environmental impact coefficient E p ; Where P is the total number of days in the large detection period, a is the dust concentration coefficient, b is the humidity coefficient, c is the wind intensity coefficient, and d is the corrosion coefficient.

5. A system for supervising IoT devices based on coding identification, wherein the system is implemented by the method for supervising IoT devices based on coding identification according to any one of claims 1 to 4, characterized in that: The system comprises: RFID encoding module, which contains the location information and related parameter information of each target device; An environment acquisition module, which is connected to the local meteorological bureau and is used to obtain local meteorological information; A data analysis and processing module processes and analyzes the acquired relevant parameter information and meteorological information to determine the target equipment failure; a feedback module is used to provide corresponding reminder feedback based on the judgment result.

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