Internet of Things equipment supervision method and system based on code recognition

Abstract

The invention discloses an Internet of Things device supervision method and system based on code identification, and belongs to the technical field of device supervision, and the method comprises the steps: configuring an RFID code for each target device in a district, and each code comprises the position information of the target device and the related parameter information of the target device; relevant parameters of the target equipment are monitored in real time, and analysis processing is carried out according to the obtained relevant parameter information to judge whether the target equipment breaks down or not; when the target equipment does not have the fault, further analyzing according to the obtained related parameter information to judge whether the target equipment has a potential fault; and performing corresponding feedback according to a judgment result. According to the method, analysis is performed according to the acquired related parameter information, so that the potential fault risk of the target equipment is evaluated, whether the target equipment has the risk of fault can be pre-judged, and the target equipment with higher risk is maintained and managed in advance to avoid the occurrence of the fault of the target equipment.

Description

Technical Field

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

[0002] IoT devices are usually equipped with various communication technologies, such as wireless communication technologies such as Wi-Fi, Bluetooth, Zigbee, NFC, and wired communication technologies such as Ethernet and PLC, to achieve intelligent and networked applications. These devices are able to communicate with other devices and networks, and perform various tasks according to their shape and function, such as remotely accessing and operating devices, remotely accessing databases to obtain information, or accepting 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 the potential malfunction risk, which causes additional economic losses. Summary of the invention

[0004] The purpose of the present invention is to provide an Internet of Things device supervision method and system based on coding identification 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 Internet of Things 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 of the target device 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 fails in step 2 is:

[0013] S21, obtaining the voltage U, current I, temperature T, and brightness L of the target device, and comparing them with their respective preset working threshold intervals. When the monitored voltage, current, temperature, or brightness is not within the preset working threshold interval, 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 , it is determined that the target device has a fault, otherwise it goes to step S23;

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

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

[0018] Set a small monitoring period Δt, and formulate the fault value change curve Wr of each target device over time 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 fault value curve of the i-th target device over 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 It 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 The potential risk factor Gt is obtained;

[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 long test cycle, and x2 is the last test in the long 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 within 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 within the large detection period is obtained. w , the number of days with acid rain D r ;

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

[0033] Among them, P is the total number of days in the large detection cycle, 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 Internet of Things devices based on coding identification, the system is executed by the method for supervising Internet of Things devices based on coding identification, and the system comprises:

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

[0036] An environment acquisition module, which is connected to a local meteorological bureau and is used to acquire 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, so that 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 one by one, 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 the maintenance management personnel to manage and repair in the first time.

[0041] The present invention can also determine whether the target device has a potential fault based on the acquired relevant parameter information when there is no fault in the target device, so as to evaluate the potential failure risk of the target device. In this way, it can be predicted whether the target device has a risk of failure, so as to perform maintenance management in advance for the target device with higher risk, so as 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 accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

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

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

[0046] 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.

[0047] In one embodiment, a method for supervising IoT devices based on coding identification is disclosed, such as Figure 1 As shown in the figure, the regulatory method can be targeted at smart street lighting equipment, 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. The relevant parameter information includes the current size, voltage size, temperature size and brightness size 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, so that 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 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 repair it 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, so as to evaluate the potential failure risk of the target device, so that it can be predicted whether the target device has a risk of failure, so as to perform maintenance management in advance for the target device with higher risks, so as to avoid the occurrence of target device failure, thereby reducing economic losses.

[0053] As an implementation mode of the present invention, the method for determining whether the target device fails in step 2 is:

[0054] S21, obtaining the voltage U, current I, temperature T, and brightness L of the target device, and comparing them with their respective preset working threshold intervals. When the monitored voltage, current, temperature, or brightness is not within the preset working threshold interval, 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 , it is determined that the target device has a fault, otherwise it goes to step S23;

[0057] S23, set a small monitoring period Δt, and formulate 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 fault value curve of the i-th target device over 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 It is the preset deviation coefficient threshold.

[0061] Through the above technical scheme, this embodiment provides three specific methods for implementing the judgment of whether the target device has a fault. Since the main parameter changes of the target device when it fails 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, it is possible to detect in real time whether the target device has a fault, so that it can be discovered and repaired as soon as possible. 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 , the target device is judged to be faulty; if the various project data of the target device are detected to be within the normal working threshold range, but if the gap between the data of each project and the set standard comparison value is large, it means that the target device is also faulty, so the voltage, current, temperature and brightness are comprehensively analyzed to obtain the fault value, and the target device is judged to be faulty according to the size of the fault value. In this way, after comprehensive analysis of the various parameters obtained, some accidental errors can be excluded from interfering with the detection results, 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), so we can get The deviation coefficient of each target device is obtained. i >F th , it is judged that the target device is faulty; 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 time period within the small monitoring cycle, and then use the formula To determine the overall status difference between 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, the larger the deviation coefficient, the greater the risk of failure of the target device. Therefore, the deviation coefficient F i The deviation coefficient from the preset threshold F th For comparison, when F i >F thIn this way, the target device is judged to be faulty according to the historical time period in the area and the fault values ​​of a single target device and all target devices in the current time period to judge whether the target device is faulty.

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

[0063] As an implementation 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 The potential risk factor Gt is obtained;

[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 in the large test cycle, and x2 is the last test in the large test cycle;

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

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

[0071] Among them, P is the total number of days in the large detection cycle, 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, so as 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 obtain the average humidity value s and the average dust concentration value h within the large detection cycle from the local meteorological bureau through the Internet of Things technology, 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 that through the formula The environmental impact coefficient E p Since high humidity and high dust content will increase the possibility of failure of the target device, and extreme weather such as strong winds and acid rain will also affect the target device, 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 risk of failure of the target device; so finally through the formula The potential risk factor Gt is obtained, formula It indicates the difference between the fault value change in the latter period and the fault value change in the previous period during the entire large 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 is. Therefore, the obtained potential risk coefficient Gt is compared with the system preset risk coefficient threshold Gt th Compare: When Gt>Gt th In this way, a comprehensive analysis can be conducted based on the change and fluctuation of the target equipment's fault value each time and the local environmental impact to determine whether the target equipment has a potential failure risk. Once the risk is determined to be high, the target equipment can be repaired in advance in a timely manner to reduce the occurrence of target equipment 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] An environment 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 judge the target equipment failure;

[0078] Feedback module, the feedback module is used to provide corresponding reminder feedback according to 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, so that 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. Moreover, the working status of the target device can be understood in real time through the obtained relevant parameter information. 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 in 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, so as to 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 perform maintenance management in advance for the target device with higher risk, so as to avoid the occurrence of target device failure, thereby reducing economic losses.

[0080] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field 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 claims, they should all fall within the protection scope 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 of the target device 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.

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

3. According to claim 2, a method for supervising Internet of Things devices based on coding identification is characterized in that: The method for determining whether the target device fails in step 2 is: S21, obtaining the voltage U, current I, temperature T, and brightness L of the target device, and comparing them with their respective preset working threshold intervals. When the monitored voltage, current, temperature, or brightness is not within the preset working threshold interval, it is determined that the target device has a fault, otherwise, proceed 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 , it is determined that the target device has a fault, otherwise it goes to step S23; Among them, α1, α2, α3, and α4 are respective weight coefficients, U0 is a preset standard voltage magnitude, I0 is a preset standard current magnitude, and T0 is a preset standard temperature magnitude.

4. According to claim 3, a method for supervising IoT devices based on coding identification is characterized in that: The working method of step S23 is: Set a small monitoring period Δt, and formulate the fault value change curve Wr of each target device over time within the Δt period according to the change of the fault value. i (t); Thus, the formula Calculate 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 fault value curve of the i-th target device over 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 It is the preset deviation coefficient threshold.

5. According to claim 4, a method for supervising Internet of Things devices based on coding identification is characterized in that: The method for determining whether the target device has a potential fault in step 3 is: 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; By formula The potential risk factor Gt is obtained; 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 long test cycle, and x2 is the last test in the long test cycle.

6. The method for supervising IoT devices based on coding identification according to claim 5 is characterized in that: The method for obtaining the fault fluctuation coefficient is: By formula The fault fluctuation coefficient σ is obtained.

7. The method for supervising IoT devices based on coding identification according to claim 5 is characterized in that: The method for obtaining the environmental impact coefficient is: The average humidity value s and the average dust concentration value h within 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 within the large detection period is obtained. w , the number of days with acid rain D r ; So through the formula The environmental impact coefficient E p ; Among them, P is the total number of days in the large detection cycle, a is the dust concentration coefficient, b is the humidity coefficient, c is the wind intensity coefficient, and d is the corrosion coefficient.

8. 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 7, characterized in that: The system comprises: An RFID encoding module, wherein the RFID encoding module contains location information and related parameter information of each target device; An environment acquisition module, which is connected to a local meteorological bureau and is used to acquire local meteorological information; A data analysis and processing module, which processes and analyzes the acquired relevant parameter information and meteorological information to determine the target equipment failure; The feedback module is used to provide corresponding reminder feedback according to the judgment result.

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