Equipment state monitoring method, device and system based on Internet of Things and storage medium

Abstract

The invention discloses an equipment state monitoring method, device and system based on the Internet of Things and a storage medium, and belongs to the field of equipment state monitoring method.The method comprises the following steps that cable monitoring data are collected, and the cable monitoring data comprise damage information of all cables in target equipment; temperature monitoring data are collected, and the temperature monitoring data comprise temperature information of a plurality of preset positions in the target equipment; vibration monitoring data are collected, and the vibration monitoring data comprise vibration information of a plurality of preset positions in the target equipment; importing preset data, analyzing all the collected information based on the preset data, and outputting an analysis result; and performing early warning grading according to an analysis result, and sending a corresponding notice based on an early warning grading result. According to the invention, the cable state, vibration and temperature during equipment operation can be comprehensively monitored, so that the accuracy of an equipment state analysis result is improved, errors are reduced, and the device is convenient to use.

Description

Technical Field

[0001] The present invention relates to the field of equipment status monitoring methods, and in particular to an equipment status monitoring method, device, system and storage medium based on the Internet of Things. Background Art

[0002] Condition monitoring is the process of checking and monitoring the working status parameters of the whole equipment or its components in operation, which is used to identify significant changes that can indicate potential defects in the operation of the equipment. Electrical equipment condition monitoring is to monitor the condition of electrical equipment. Online monitoring of electrical equipment lays the foundation for fault diagnosis by extracting the characteristic signals of faults in real time. Correct fault diagnosis provides a basis for maintenance. The purpose of online monitoring is to identify existing and impending defects of the equipment by measuring its condition, thereby facilitating subsequent maintenance.

[0003] The status monitoring of existing electrical equipment usually monitors the current, voltage and other parameters during its operation. The monitoring of cable status, vibration and temperature is not comprehensive enough, which leads to inaccurate analysis of the equipment status and certain errors, affecting its use. Therefore, those skilled in the art provide a device status monitoring method, device, system and storage medium based on the Internet of Things to solve the problems raised in the above background technology. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a device status monitoring method based on the Internet of Things, comprising the following steps:

[0005] Collecting cable monitoring data, where the cable monitoring data includes damage information of all cables inside the target device;

[0006] Collecting temperature monitoring data, wherein the temperature monitoring data includes temperature information of several preset locations inside the target device;

[0007] Collecting vibration monitoring data, wherein the vibration monitoring data includes vibration information of a plurality of preset positions inside the target device;

[0008] Import preset data, analyze all collected information based on the preset data, and output the analysis results;

[0009] Early warnings are graded according to the analysis results, and corresponding notifications are issued based on the early warning classification results.

[0010] As a further solution of the present invention: the specific analysis process of the cable monitoring data is as follows:

[0011] Mark all cables inside the target device as Ai, i = 1...n, where n is a positive integer;

[0012] The temperature of each part of the cable Ai is detected by an infrared thermal imager, and the part with abnormal temperature on the cable Ai is identified, and the temperature of the part is greater than the preset temperature;

[0013] If there is one abnormal temperature part in the cable Ai, the cable Ai is planned to the first sequence; if there are two abnormal temperature parts in the cable Ai, the cable Ai is planned to the second sequence; if there are more than two abnormal temperature parts in the cable Ai, the cable Ai is planned to the third sequence;

[0014] The number of cables Ai in the first sequence is marked as a1, the number of cables Ai in the second sequence is marked as a2, and the number of cables Ai in the third sequence is marked as a3;

[0015] If a1=0, a2=0, a3=0, the output analysis result is that the internal cable of the current target device is normal. If any one of a1, a2 and a3 is greater than zero, proceed to the next step;

[0016] Calculate the current cable temperature anomaly weight value V = a1 + 3 * a2 + 5 * a3;

[0017] Compare the cable temperature anomaly weight value V with the preset value v. If V < v, proceed to the next step. If V ≥ v, it indicates that there is an abnormality in the cable inside the current target device.

[0018] Construct a cable anomaly analysis model to determine whether the current cable anomaly level is within the preset temporarily controllable range.

[0019] As a further solution of the present invention: the specific analysis process of the cable abnormality analysis model is:

[0020] Collect the three-dimensional coordinate point cloud data of the cables inside the target device through a three-dimensional scanning device;

[0021] Mark the locations with abnormal temperature on all identified cables Ai as Bi, where i=1···n;

[0022] The three-dimensional coordinates corresponding to the abnormal part Bi are marked as (Xi, Yi, Zi), and (Xi, Yi, Zi) is the abnormal part point;

[0023] Set a target space and move the target space so that the target space contains the largest number of abnormal points;

[0024] Count the number of abnormal points in the target space and mark them as C;

[0025] Compare the number of abnormal points C with the preset value c. If C>c, the output analysis result is that there is an abnormality in the internal cable of the target device, and the degree of abnormality is not within the preset temporary controllable range. If C≤c, the output analysis result is that there is an abnormality in the internal cable of the target device, but the degree of abnormality is within the preset temporary controllable range.

[0026] As a further solution of the present invention: the specific analysis process of the temperature monitoring data is:

[0027] The collected temperatures of several preset positions inside the target device are marked as Ti, i=1···n, where n is a positive integer;

[0028] Compare Ti with the preset temperature t. If Ti>t, it means that the temperature at the position corresponding to Ti is abnormal, and the abnormal temperature position is planned to the first cluster. If Ti≤t, the position corresponding to Ti is planned to the second cluster.

[0029] Count the number of temperature abnormality locations in the first cluster and mark them as D. If D>0, it means that the internal temperature of the target device is abnormal and proceed to the next step. If D=0, it means that the internal temperature of the target device is normal.

[0030] Construct a temperature anomaly analysis model to determine whether the current temperature anomaly is within the preset temporarily controllable range.

[0031] As a further solution of the present invention: the specific analysis process of the temperature anomaly analysis model is:

[0032] The temperature anomaly locations in the first cluster are marked as Ei, i = 1···k, where k is a positive integer;

[0033] A spatial coordinate system is established inside the target device, and the three-dimensional coordinates of the cooling fan inside the target device are marked as (x0, y0, z0);

[0034] The three-dimensional coordinates of the temperature anomaly position Ei are marked as (xi, yi, zi), and the distance between the coordinates (xi, yi, zi) and the coordinates (x0, y0, z0) is calculated and marked as Fi;

[0035] The preset distance is marked as f, and the abnormality weight value S=(f / Fi) corresponding to each temperature abnormality position Ei is calculated;

[0036] Add the abnormality degree weight values ​​S corresponding to all temperature abnormality positions Ei to obtain the total abnormality degree weight value Stotal;

[0037] The total abnormality weight value Stotal is compared with the preset value s. If Stotal>s, it means that the current temperature abnormality is not within the preset temporary controllable range. If Stotal≤s, it means that the current temperature abnormality is within the preset temporary controllable range.

[0038] As a further solution of the present invention: the specific analysis process of the vibration monitoring data is:

[0039] The collected vibration frequencies of several preset positions inside the target device are marked as Gi, where i=1···n, where n is a positive integer;

[0040] Generate a frequency radar map P based on the vibration frequencies Gi of all preset positions;

[0041] Obtain the previous vibration frequencies of all preset positions, and then generate a frequency radar graph pi based on the previous vibration frequencies, where i=1···n;

[0042] The frequency radar graph P is compared with the frequency radar graph pi for similarity. If the similarity between the frequency radar graph P and any frequency radar graph pi is greater than the preset similarity, the output analysis result is that the internal vibration of the target device is normal. If the similarity between the frequency radar graph P and all frequency radar graphs pi is less than or equal to the preset similarity, the output analysis result is that the internal vibration of the target device is abnormal.

[0043] As a further solution of the present invention: the specific process of the warning classification is:

[0044] If the analysis result shows that the target device has an internal cable abnormality, the target device has an internal temperature abnormality, or the target device has an internal vibration abnormality, the output warning level is level 1;

[0045] If the analysis results show any two of the following: abnormal cable inside the target device, abnormal temperature inside the target device, and abnormal vibration inside the target device, the output warning level is level 2;

[0046] If the analysis results show that the target device's internal cable is abnormal, the target device's internal temperature is abnormal, and the target device's internal vibration is abnormal, the output warning level is level three.

[0047] The present application also discloses an IoT-based device status monitoring system, which uses an IoT-based device status monitoring method, including:

[0048] A cable monitoring data collection module is used to collect cable monitoring data, wherein the cable monitoring data includes damage information of all cables inside the target device;

[0049] A temperature monitoring data acquisition module is used to collect temperature monitoring data, wherein the temperature monitoring data includes temperature information of several preset locations inside the target device;

[0050] A vibration monitoring data acquisition module, used to collect vibration monitoring data, wherein the vibration monitoring data includes vibration information of several preset positions inside the target device;

[0051] Data analysis module, used to analyze all collected information and output analysis results;

[0052] A preset data import module is used to import preset data into the data analysis module;

[0053] The warning notification module is used to classify warnings according to the analysis results and issue corresponding notifications based on the warning classification results.

[0054] The present application also discloses an equipment status monitoring device based on the Internet of Things, including:

[0055] Memory for storing computer programs;

[0056] The processor is used to execute the computer program to implement the device status monitoring method based on the Internet of Things.

[0057] The present application also discloses a readable storage medium for storing a computer program, wherein the computer program implements a device status monitoring method based on the Internet of Things when executed by a processor.

[0058] The beneficial effects of the present invention are embodied in:

[0059] This application can comprehensively monitor the cable status, vibration and temperature when the equipment is running, thereby improving the accuracy of the equipment status analysis results, reducing errors, and facilitating use. Among them, the monitoring and analysis of the cable status can not only quickly identify cables with temperature abnormalities, but also adopt a differentiated calculation method when calculating the abnormal weight value, so that the final cable temperature abnormality weight value is more representative. In addition, for cables with abnormalities, this application conducts a secondary analysis to determine whether the degree of abnormality is within a preset temporary controllable range, so as to save the workload of maintenance personnel. As for the location of the temperature abnormality inside the equipment, if the abnormal location is close to the cooling fan, it means that the temperature abnormality at this location is relatively serious, and it cannot be effectively alleviated even if it is close to the cooling device. Based on this, the sum of the abnormality weight values ​​of each temperature abnormal location is calculated, which can more accurately analyze the temperature abnormality inside the current equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0061] Figure 1 It is a flow chart of the device status monitoring method based on the Internet of Things;

[0062] Figure 2 A schematic diagram of a frequency radar diagram in a device status monitoring method based on the Internet of Things;

[0063] Figure 3 This is the structural block diagram of the equipment status monitoring system based on the Internet of Things. DETAILED DESCRIPTION

[0064] 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.

[0065] As mentioned in the background technology of this application, research has found that the status monitoring of existing electrical equipment usually monitors parameters such as current and voltage during its operation. The monitoring of cable status, vibration and temperature is not comprehensive enough, which leads to the analyzed equipment status being not accurate enough and having certain errors, affecting its use.

[0066] In order to solve the above-mentioned defects, the present application discloses an equipment status monitoring method, device, system and storage medium based on the Internet of Things, which can comprehensively monitor the cable status, vibration and temperature of the equipment during operation, thereby improving the accuracy of the equipment status analysis results, reducing errors and facilitating use.

[0067] The following will describe in detail how the solution of the present application solves the above technical problems in conjunction with the accompanying drawings.

[0068] See also Figure 1In an embodiment of the present invention, a device status monitoring method based on the Internet of Things includes the following steps: collecting cable monitoring data, wherein the cable monitoring data includes damage information of all cables inside the target device; collecting temperature monitoring data, wherein the temperature monitoring data includes temperature information of several preset positions inside the target device; collecting vibration monitoring data, wherein the vibration monitoring data includes vibration information of several preset positions inside the target device; importing preset data, and analyzing all collected information based on the preset data, and outputting analysis results; performing early warning classification according to the analysis results, and issuing corresponding notifications based on the early warning classification results. The present application can comprehensively monitor the cable status, vibration, and temperature of the equipment during operation, thereby improving the accuracy of the equipment status analysis results, reducing errors, and facilitating use.

[0069] In this embodiment, the specific analysis process of the cable monitoring data is as follows: mark all cables inside the target device as Ai, i=1···n, where n is a positive integer; detect the temperature of each part of the cable Ai by an infrared thermal imager, identify the part with abnormal temperature on the cable Ai, and the temperature of the part is greater than the preset temperature; if there is one part with abnormal temperature in the cable Ai, the cable Ai is planned to the first sequence; if there are two parts with abnormal temperature in the cable Ai, the cable Ai is planned to the second sequence; if there are more than two parts with abnormal temperature in the cable Ai, the cable Ai is planned to the third sequence; mark the number of cables Ai in the first sequence as a 1. Mark the number of cables Ai in the second sequence as a2, and mark the number of cables Ai in the third sequence as a3; if a1=0, a2=0, a3=0, then the output analysis result is that the cables inside the current target device are normal. If any one of a1, a2 and a3 is greater than zero, then proceed to the next step; calculate the current cable temperature anomaly weight value V=a1+3*a2+5*a3; compare the cable temperature anomaly weight value V with the preset value v. If V<v, proceed to the next step. If V≥v, it means that the cable inside the current target device is abnormal; construct a cable anomaly analysis model to determine whether the abnormal degree of the current cable is within the preset temporary controllable range. This setting can not only quickly identify cables with abnormal temperatures, but also use a differentiated calculation method when calculating the abnormal weight value, so that the final cable temperature anomaly weight value is more representative. It should be noted that an infrared thermal imager is used to detect the temperature of cable joints, terminals and other parts. If the temperature of a certain part is abnormally high, it indicates that the cable at that part is overheated, which is usually a sign of cable aging or damage.

[0070] In this embodiment, the specific analysis process of the cable abnormality analysis model is as follows: the three-dimensional coordinate point cloud data of the cables inside the target device is collected by a three-dimensional scanning device; the parts with abnormal temperature on all identified cables Ai are marked as Bi, i=1···n; the three-dimensional coordinates corresponding to the abnormal parts Bi are marked as (Xi, Yi, Zi), (Xi, Yi, Zi) is the abnormal part point; a target space is set, and the target space is moved so that the number of abnormal part points contained in the target space is the largest; the number of abnormal part points in the target space is counted and marked as C; the number of abnormal part points C is compared with the preset value c. If C>c, the output analysis result is that there is an abnormality in the cable inside the target device, and the degree of abnormality is not within the preset temporary controllable range. If C≤c, the output analysis result is that there is an abnormality in the cable inside the target device, but the degree of abnormality is within the preset temporary controllable range. This setting conducts a secondary analysis to determine whether the abnormality is within the preset temporary controllable range. If the abnormality is not within the preset temporary controllable range, the maintenance personnel will be arranged to go for maintenance immediately. If the abnormality is within the preset temporary controllable range, the maintenance can be carried out during the next overall equipment maintenance, thereby saving the maintenance personnel's workload. It should be noted that if the abnormal parts of the cables are concentrated in a certain area, it means that there is a large abnormality in the area, which is no longer within the controllable range and needs to be handled immediately.

[0071] In this embodiment, the specific analysis process of the temperature monitoring data is as follows: mark the collected temperatures of several preset positions inside the target device as Ti, i=1···n, where n is a positive integer; compare Ti with the preset temperature t, if Ti>t, it means that the temperature at the position corresponding to Ti is abnormal, and the abnormal temperature position is planned to the first cluster, if Ti≤t, the position corresponding to Ti is planned to the second cluster; count the number of abnormal temperature positions in the first cluster and mark them as D, if D>0, it means that the temperature inside the target device is abnormal and enter the next step, if D=0, it means that the temperature inside the target device is normal; build a temperature anomaly analysis model to determine whether the abnormal degree of the current temperature is within the preset temporarily controllable range. This setting can quickly determine whether the temperature inside the device is abnormal, and the temperature data collection at multiple positions can make the analysis results more accurate.

[0072] In this embodiment, the specific analysis process of the temperature anomaly analysis model is as follows: mark the temperature anomaly position in the first cluster as Ei, i=1···k, where k is a positive integer; establish a spatial coordinate system inside the target device, and mark the three-dimensional coordinates of the cooling fan inside the target device as (x0, y0, z0); mark the three-dimensional coordinates of the temperature anomaly position Ei as (xi, yi, zi), calculate the distance between the coordinates (xi, yi, zi) and the coordinates (x0, y0, z0), and mark it as Fi; mark the preset distance as f, and calculate the abnormality degree weight value S=(f / Fi) corresponding to each temperature anomaly position Ei; add the abnormality degree weight values ​​S corresponding to all temperature anomaly positions Ei to obtain the total abnormality degree weight value Stotal; compare the total abnormality degree weight value Stotal with the preset value s. If Stotal>s, it means that the abnormality degree of the current temperature is not within the preset temporary controllable range. If Stotal≤s, it means that the abnormality degree of the current temperature is within the preset temporary controllable range. For the location of abnormal temperature inside the equipment, if the abnormal location is close to the cooling fan, it means that the temperature abnormality at this location is relatively serious and cannot be effectively alleviated even if it is close to the cooling device. Based on this, the sum of the abnormality weight values ​​of each temperature abnormal location is calculated to more accurately analyze the temperature abnormality inside the current device.

[0073] In this embodiment, the specific analysis process of the vibration monitoring data is as follows: the vibration frequencies of several preset positions inside the target device collected are marked as Gi, i=1...n, where n is a positive integer; based on the vibration frequencies Gi of all preset positions, a frequency radar graph P is generated (in this embodiment, the number of preset positions is seven, and the generated frequency radar graph P is as follows: Figure 2 As shown); obtain the previous vibration frequencies of all preset positions, and then generate frequency radar graph pi based on the previous vibration frequencies, i=1···n; compare the similarity between frequency radar graph P and frequency radar graph pi, if the similarity between frequency radar graph P and any frequency radar graph pi is greater than the preset similarity, then the output analysis result is that the vibration inside the target device is normal, if the similarity between frequency radar graph P and all frequency radar graphs pi is less than or equal to the preset similarity, then the output analysis result is that the vibration inside the target device is abnormal. This setting can quickly analyze and determine whether there is abnormality in the vibration inside the current device.

[0074] In this embodiment, the specific process of early warning classification is: if any one of the following items appears in the analysis result: abnormal cable inside the target device, abnormal temperature inside the target device, and abnormal vibration inside the target device, the output early warning level is level 1; if any two of the following items appear in the analysis result: abnormal cable inside the target device, abnormal temperature inside the target device, and abnormal vibration inside the target device, the output early warning level is level 2; if all of the following items appear in the analysis result: abnormal cable inside the target device, abnormal temperature inside the target device, and abnormal vibration inside the target device, the output early warning level is level 3. This setting can facilitate the background staff to quickly know the severity of the current abnormality.

[0075] The present application also discloses a device status monitoring system based on the Internet of Things, such as Figure 3 As shown, a device status monitoring method based on the Internet of Things is adopted, including: a cable monitoring data acquisition module, used to collect cable monitoring data, wherein the cable monitoring data includes damage information of all cables inside the target device; a temperature monitoring data acquisition module, used to collect temperature monitoring data, wherein the temperature monitoring data includes temperature information of several preset positions inside the target device; a vibration monitoring data acquisition module, used to collect vibration monitoring data, wherein the vibration monitoring data includes vibration information of several preset positions inside the target device; a data analysis module, used to analyze all the collected information and output the analysis results; a preset data import module, used to import preset data into the data analysis module; an early warning notification module, used to perform early warning classification according to the analysis results, and issue corresponding notifications based on the early warning classification results.

[0076] The present application also discloses an equipment status monitoring device based on the Internet of Things, including: a memory for storing a computer program; a processor for executing the computer program to implement an equipment status monitoring method based on the Internet of Things.

[0077] The present application also discloses a readable storage medium for storing a computer program, wherein the computer program implements a device status monitoring method based on the Internet of Things when executed by a processor.

[0078] The present invention can comprehensively monitor the cable status, vibration and temperature when the equipment is running, thereby improving the accuracy of the equipment status analysis results, reducing errors, and facilitating use. Among them, the monitoring and analysis of the cable status can not only quickly identify cables with temperature abnormalities, but also adopt a differentiated calculation method when calculating the abnormal weight value, so that the final cable temperature abnormality weight value is more representative. In addition, for cables with abnormalities, the present application conducts a secondary analysis to determine whether the degree of abnormality is within a preset temporary controllable range, so as to save the workload of maintenance personnel. For the location of the abnormal temperature inside the equipment, if the abnormal location is close to the cooling fan, it means that the temperature abnormality at this location is relatively serious, and it cannot be effectively alleviated even if it is close to the cooling device. Based on this, the sum of the abnormality weight values ​​of each temperature abnormal location is calculated, which can more accurately analyze the temperature abnormality inside the current equipment.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A device status monitoring method based on the Internet of Things, characterized in that: The following steps are involved: Collecting cable monitoring data, where the cable monitoring data includes damage information of all cables inside the target device; Collecting temperature monitoring data, wherein the temperature monitoring data includes temperature information of several preset locations inside the target device; Collecting vibration monitoring data, wherein the vibration monitoring data includes vibration information of a plurality of preset positions inside the target device; Import preset data, analyze all collected information based on the preset data, and output the analysis results; Early warnings are graded according to the analysis results, and corresponding notifications are issued based on the early warning classification results.

2. The device status monitoring method based on the Internet of Things according to claim 1 is characterized in that: The specific analysis process of the cable monitoring data is as follows: Mark all cables inside the target device as Ai, i = 1...n, where n is a positive integer; The temperature of each part of the cable Ai is detected by an infrared thermal imager, and the part with abnormal temperature on the cable Ai is identified, and the temperature of the part is greater than the preset temperature; If there is one abnormal temperature part in the cable Ai, the cable Ai is planned to the first sequence; if there are two abnormal temperature parts in the cable Ai, the cable Ai is planned to the second sequence; if there are more than two abnormal temperature parts in the cable Ai, the cable Ai is planned to the third sequence; The number of cables Ai in the first sequence is marked as a1, the number of cables Ai in the second sequence is marked as a2, and the number of cables Ai in the third sequence is marked as a3; If a1=0, a2=0, a3=0, the output analysis result is that the internal cable of the current target device is normal. If any one of a1, a2 and a3 is greater than zero, proceed to the next step; Calculate the current cable temperature anomaly weight value V = a1 + 3 * a2 + 5 * a3; Compare the cable temperature anomaly weight value V with the preset value v. If V < v, proceed to the next step. If V ≥ v, it indicates that there is an abnormality in the cable inside the current target device. Construct a cable anomaly analysis model to determine whether the current cable anomaly level is within the preset temporarily controllable range.

3. The device status monitoring method based on the Internet of Things according to claim 2 is characterized in that: The specific analysis process of the cable anomaly analysis model is as follows: Collect the three-dimensional coordinate point cloud data of the cables inside the target device through a three-dimensional scanning device; Mark the locations with abnormal temperature on all identified cables Ai as Bi, where i=1···n; The three-dimensional coordinates corresponding to the abnormal part Bi are marked as (Xi, Yi, Zi), and (Xi, Yi, Zi) is the abnormal part point; Set a target space and move the target space so that the target space contains the largest number of abnormal points; Count the number of abnormal points in the target space and mark them as C; Compare the number of abnormal points C with the preset value c. If C>c, the output analysis result is that there is an abnormality in the internal cable of the target device, and the degree of abnormality is not within the preset temporary controllable range. If C≤c, the output analysis result is that there is an abnormality in the internal cable of the target device, but the degree of abnormality is within the preset temporary controllable range.

4. The device status monitoring method based on the Internet of Things according to claim 3 is characterized in that: The specific analysis process of the temperature monitoring data is as follows: The collected temperatures of several preset positions inside the target device are marked as Ti, i=1···n, where n is a positive integer; Compare Ti with the preset temperature t. If Ti>t, it means that the temperature at the position corresponding to Ti is abnormal, and the abnormal temperature position is planned to the first cluster. If Ti≤t, the position corresponding to Ti is planned to the second cluster. Count the number of temperature abnormality locations in the first cluster and mark them as D. If D>0, it means that the internal temperature of the target device is abnormal and proceed to the next step. If D=0, it means that the internal temperature of the target device is normal. Construct a temperature anomaly analysis model to determine whether the current temperature anomaly is within the preset temporarily controllable range.

5. The device status monitoring method based on the Internet of Things according to claim 4 is characterized in that: The specific analysis process of the temperature anomaly analysis model is as follows: The temperature anomaly locations in the first cluster are marked as Ei, i = 1···k, where k is a positive integer; A spatial coordinate system is established inside the target device, and the three-dimensional coordinates of the cooling fan inside the target device are marked as (x0, y0, z0); The three-dimensional coordinates of the temperature anomaly position Ei are marked as (xi, yi, zi), and the distance between the coordinates (xi, yi, zi) and the coordinates (x0, y0, z0) is calculated and marked as Fi; The preset distance is marked as f, and the abnormality weight value S=(f / Fi) corresponding to each temperature abnormality position Ei is calculated; Add the abnormality degree weight values ​​S corresponding to all temperature abnormality positions Ei to obtain the total abnormality degree weight value Stotal; The total abnormality weight value Stotal is compared with the preset value s. If Stotal>s, it means that the current temperature abnormality is not within the preset temporary controllable range. If Stotal≤s, it means that the current temperature abnormality is within the preset temporary controllable range.

6. The device status monitoring method based on the Internet of Things according to claim 5 is characterized in that: The specific analysis process of the vibration monitoring data is as follows: The collected vibration frequencies of several preset positions inside the target device are marked as Gi, where i=1···n, where n is a positive integer; Generate a frequency radar map P based on the vibration frequencies Gi of all preset positions; Obtain the previous vibration frequencies of all preset positions, and then generate a frequency radar graph pi based on the previous vibration frequencies, where i=1···n; The frequency radar graph P is compared with the frequency radar graph pi for similarity. If the similarity between the frequency radar graph P and any frequency radar graph pi is greater than the preset similarity, the output analysis result is that the internal vibration of the target device is normal. If the similarity between the frequency radar graph P and all frequency radar graphs pi is less than or equal to the preset similarity, the output analysis result is that the internal vibration of the target device is abnormal.

7. The device status monitoring method based on the Internet of Things according to claim 6 is characterized in that: The specific process of the warning classification is as follows: If the analysis result shows that the target device has an internal cable abnormality, the target device has an internal temperature abnormality, or the target device has an internal vibration abnormality, the output warning level is level 1; If the analysis results show any two of the following: abnormal cable inside the target device, abnormal temperature inside the target device, and abnormal vibration inside the target device, the output warning level is level 2; If the analysis results show that the target device's internal cable is abnormal, the target device's internal temperature is abnormal, and the target device's internal vibration is abnormal, the output warning level is level three.

8. An equipment status monitoring system based on the Internet of Things, characterized in that: The device status monitoring method based on the Internet of Things as claimed in any one of claims 1 to 7 comprises: A cable monitoring data collection module is used to collect cable monitoring data, wherein the cable monitoring data includes damage information of all cables inside the target device; A temperature monitoring data acquisition module is used to collect temperature monitoring data, wherein the temperature monitoring data includes temperature information of several preset locations inside the target device; A vibration monitoring data acquisition module, used to collect vibration monitoring data, wherein the vibration monitoring data includes vibration information of several preset positions inside the target device; Data analysis module, used to analyze all collected information and output analysis results; A preset data import module is used to import preset data into the data analysis module; The warning notification module is used to classify warnings according to the analysis results and issue corresponding notifications based on the warning classification results.

9. The device status monitoring device based on the Internet of Things is characterized by: include: Memory for storing computer programs; A processor is used to execute the computer program to implement the device status monitoring method based on the Internet of Things as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the device status monitoring method based on the Internet of Things is implemented as described in any one of claims 1 to 7.

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