Power equipment status monitoring system based on Internet of Things technology

Abstract

The invention discloses an electric power equipment status monitoring system based on the Internet of Things technology, which relates to the technical field of equipment monitoring. The invention constructs an equipment distribution map and obtains equipment anchor points, obtains monitoring equipment information and monitoring electromagnetic data to construct an electromagnetic prediction model, obtains predicted equipment information and inputs it into the electromagnetic prediction model to obtain predicted electromagnetic data, performs visualization processing on the predicted electromagnetic data to obtain electromagnetic interference areas and superimposed interference areas, obtains deployment adjustment areas in combination with equipment anchor points, performs deployment adjustment on electric power equipment to obtain different adjustment coefficients and their corresponding interference coefficients, obtains the best deployment adjustment plan and generates a connection plan, and provides feedback on the best deployment adjustment plan and its connection plan. The technical solution of the invention can significantly reduce the negative impact of electromagnetic interference on the operating status of equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and in particular to an electric power equipment status monitoring system based on the Internet of Things technology. Background Art

[0002] Generally speaking, electric power equipment always generates electromagnetic waves during operation, which in turn generates electromagnetic interference to other electric power equipment. The electric power equipment itself is also affected by electromagnetic interference from other electric power equipment, resulting in abnormal operation and performance degradation, and also affecting the data transmission efficiency between different electric power equipment.

[0003] Therefore, how to monitor the electromagnetic interference generated and received by power equipment and adjust the power equipment accordingly according to the monitoring data, so as to reduce the impact of electromagnetic interference on power equipment, has become an urgent problem to be solved. In the existing technology, the electromagnetic interference received by power equipment is mostly monitored by Internet of Things technology;

[0004] However, in actual application scenarios, it is impossible to effectively monitor electromagnetic interference at all locations. Most of the existing technologies only stay at conventional data monitoring, and fail to effectively adjust the power equipment according to the monitoring data, let alone reduce the impact of electromagnetic interference between different power equipment. In view of the shortcomings of the existing technology, the present invention provides a power equipment status monitoring system based on the Internet of Things technology. Summary of the invention

[0005] The purpose of the present invention is to provide an electric power equipment status monitoring system based on Internet of Things technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A power equipment status monitoring system based on Internet of Things technology includes the following modules:

[0007] The data collection module is used to collect the deployment information and connection information of the power equipment, and to construct the equipment distribution map, and obtain several equipment anchor points in the equipment distribution map;

[0008] The data prediction module is used to set a number of data monitoring points, obtain the monitoring equipment information and monitoring electromagnetic data of the data monitoring points, and build an electromagnetic prediction model to obtain the data prediction points and their corresponding prediction equipment information, input the prediction equipment information into the electromagnetic prediction model, and obtain the predicted electromagnetic data of the data prediction points;

[0009] A region division module is used to perform visualization processing on the predicted electromagnetic data to obtain a visual distribution map, and to divide the electromagnetic interference region and the superimposed interference region in the visual distribution map;

[0010] A deployment adjustment module is used to obtain a deployment adjustment area in combination with a device anchor point in a visual distribution map, perform deployment adjustment on power equipment to obtain adjustment coefficients under different deployment adjustment schemes, obtain interference coefficients according to electromagnetic interference areas and superimposed interference areas under different deployment adjustment schemes, and obtain an optimal deployment adjustment scheme in combination with the adjustment coefficients;

[0011] The connection layout module is used to connect the power equipment under the optimal deployment and adjustment plan and generate a connection plan, and provide feedback on the optimal deployment and adjustment plan and its connection plan.

[0012] Furthermore, the deployment information and connection information of the power equipment are collected, and a device distribution map is constructed. The process of obtaining several device anchor points in the device distribution map includes:

[0013] The deployment information refers to the deployment location of each power device, and the connection information refers to the connection relationship between each power device. A device distribution map of the power devices is constructed based on the deployment information and the connection information. In the device distribution map, an initial device anchor point is set;

[0014] Obtain the probability values ​​of other deployment positions being selected as the next device anchor point, select the deployment position corresponding to the maximum probability value as the next device anchor point, and repeat this step until several device anchor points are selected.

[0015] Furthermore, the process of setting a number of data monitoring points, obtaining monitoring equipment information and monitoring electromagnetic data of the data monitoring points, and constructing an electromagnetic prediction model includes:

[0016] Obtain monitoring equipment for each data monitoring point, obtain monitoring distance and monitoring quantity for each data monitoring point according to the monitoring equipment, and generate monitoring equipment information for each data monitoring point, wherein the monitoring electromagnetic data refers to the electromagnetic intensity of each data monitoring point;

[0017] The monitoring electromagnetic data and monitoring equipment information of each data monitoring point are divided into a prediction training set and a prediction test set;

[0018] Construct a recurrent neural network, use the monitoring equipment information and the monitoring electromagnetic data in the prediction training set as the input data and output data of the recurrent neural network respectively, and train the recurrent neural network to obtain an initial recurrent neural network;

[0019] The initial recurrent neural network is model verified using the prediction test set, and the initial recurrent neural network with an output value less than or equal to a preset test error threshold is used as the electromagnetic prediction model.

[0020] Further, the process of obtaining the data prediction point and its corresponding prediction device information, inputting the prediction device information into the electromagnetic prediction model, and obtaining the predicted electromagnetic data of the data prediction point includes:

[0021] The data prediction points include any points other than data monitoring points, and prediction devices of each data prediction point are obtained. The prediction distance and prediction quantity of each data prediction point are respectively obtained according to the prediction devices, and prediction device information of each data prediction point is generated;

[0022] The electromagnetic prediction model is used to generate predicted electromagnetic data of corresponding data prediction points according to the obtained prediction equipment information, and each data prediction point is bound to its predicted electromagnetic data respectively.

[0023] Furthermore, the predicted electromagnetic data is visualized to obtain a visual distribution map, and the process of dividing the electromagnetic interference area and the superimposed interference area in the visual distribution map includes:

[0024] The predicted electromagnetic data are divided into different numerical intervals according to the numerical values, a color corresponding to the wavelength is set for each numerical interval, and each data prediction point is rendered in the device distribution map with the color corresponding to the predicted electromagnetic data to obtain a visual distribution map;

[0025] An electromagnetic interference threshold is set, and an area consisting of data prediction points corresponding to all predicted electromagnetic data above the electromagnetic interference threshold is taken as an electromagnetic interference area;

[0026] Taking a single electric power equipment as a benchmark, the predicted electromagnetic data of each data prediction point are numbered according to the distance relationship with the electric power equipment, and the predicted electromagnetic data with adjacent numbers are compared to obtain the abnormal electromagnetic data of the electric power equipment;

[0027] The abnormal electromagnetic data of each power device is obtained respectively, and the area composed of the data prediction points corresponding to the abnormal electromagnetic data of all power devices is taken as the superimposed interference area.

[0028] Furthermore, the process of obtaining the deployment adjustment area by combining the device anchor point in the visual distribution map and performing deployment adjustment on the power equipment to obtain the adjustment coefficient under different deployment adjustment schemes includes:

[0029] The power equipment at the device anchor point is regarded as the anchor device, and the other power equipment is regarded as the non-anchor device. In the visual distribution diagram, the area surrounded by the three device anchor points closest to the non-anchor device is regarded as the deployment adjustment area of ​​the non-anchor device.

[0030] Using an exhaustive method to adjust the deployment of each non-anchor device to obtain different deployment adjustment schemes, wherein the deployment adjustment refers to adjusting the deployment position of each non-anchor device, and the deployment adjustment of the non-anchor device does not exceed its deployment adjustment area;

[0031] Before deployment adjustment, obtain the pre-adjustment anchoring distance Q between each non-anchored device and its device anchoring point in its deployment adjustment area. j , after deployment adjustment, the adjusted anchor distance H of each non-anchored device is obtained j , obtain the adjustment coefficient S of different deployment adjustment schemes;

[0032]

[0033] Wherein, j=1, 2, ..., m, where m represents the number of non-anchor devices.

[0034] Furthermore, the interference coefficient is obtained according to the electromagnetic interference area and the superimposed interference area under different deployment adjustment schemes, and the process of obtaining the best deployment adjustment scheme in combination with the adjustment coefficient includes:

[0035] In the visual distribution diagram, the deployment position of each power device is adjusted according to different deployment adjustment schemes, and the corresponding electromagnetic interference area and superimposed interference area are obtained;

[0036] Obtain the electromagnetic interference area G in different deployment adjustment schemes a , the number of superimposed interference G s , superimposed interference area G b , obtain the interference coefficient R of different deployment adjustment schemes;

[0037]

[0038] The interference coefficient and adjustment coefficient of the same deployment adjustment scheme are bound, and the deployment adjustment scheme corresponding to the minimum interference coefficient is taken as the optimal deployment adjustment scheme. If the interference coefficients are the same, the deployment adjustment scheme corresponding to the minimum adjustment coefficient is marked as the optimal deployment adjustment scheme.

[0039] Furthermore, the process of connecting and laying out the power equipment under the optimal deployment and adjustment scheme and generating a connection scheme, and providing feedback on the optimal deployment and adjustment scheme and its connection scheme includes:

[0040] In the visual distribution diagram, the deployment position of each power device is adjusted according to the optimal deployment adjustment scheme, and the corresponding electromagnetic interference area and superimposed interference area are obtained;

[0041] According to the connection information, a connection line is re-established between the power equipment that has a connection relationship. The connection line has the least contact with the superimposed interference area and is the shortest in length. A connection plan is generated based on the established connection line, and the optimal deployment adjustment plan and its connection plan are fed back to relevant personnel.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention monitors the electromagnetic interference conditions of each power device, including monitoring electromagnetic data and monitoring equipment information, and then constructs an electromagnetic prediction model, which can predict the electromagnetic intensity at different positions, and is conducive to accurately obtaining the distribution of electromagnetic interference formed by each power device in the application scenario, and then obtaining the superimposed interference area between different power devices. In order to reduce the number and area of ​​superimposed interference areas, the power equipment is deployed and adjusted to obtain the optimal deployment adjustment plan. Under the optimal deployment adjustment plan, the connection lines between the power devices are rebuilt to generate a corresponding connection plan, which can significantly reduce the negative impact of electromagnetic interference on the operation status of the equipment;

[0044] 2. The present invention sets device anchor points and obtains deployment adjustment areas of each non-anchored device, thereby limiting the deployment adjustment of power equipment within a certain range, thereby reducing the workload of data monitoring and processing as much as possible. By obtaining the adjustment coefficients under different deployment adjustment schemes and combining the interference coefficient to obtain the optimal deployment adjustment scheme, the degree of variation of deployment adjustment of power equipment can be reduced as much as possible, and effective deployment adjustment can be achieved at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0046] like Figure 1 As shown in the figure, the power equipment status monitoring system based on the Internet of Things technology includes the following modules:

[0047] The data collection module is used to collect the deployment information and connection information of the power equipment, and to construct the equipment distribution map, and obtain several equipment anchor points in the equipment distribution map;

[0048] The data prediction module is used to set a number of data monitoring points, obtain the monitoring equipment information and monitoring electromagnetic data of the data monitoring points, and build an electromagnetic prediction model to obtain the data prediction points and their corresponding prediction equipment information, input the prediction equipment information into the electromagnetic prediction model, and obtain the predicted electromagnetic data of the data prediction points;

[0049] A region division module is used to perform visualization processing on the predicted electromagnetic data to obtain a visual distribution map, and to divide the electromagnetic interference region and the superimposed interference region in the visual distribution map;

[0050] A deployment adjustment module is used to obtain a deployment adjustment area in combination with a device anchor point in a visual distribution map, perform deployment adjustment on power equipment to obtain adjustment coefficients under different deployment adjustment schemes, obtain interference coefficients according to electromagnetic interference areas and superimposed interference areas under different deployment adjustment schemes, and obtain an optimal deployment adjustment scheme in combination with the adjustment coefficients;

[0051] The connection layout module is used to connect the power equipment under the optimal deployment adjustment plan and generate a connection plan, and provide feedback on the optimal deployment adjustment plan and its connection plan.

[0052] It should be further explained that, in the specific implementation process, the deployment information and connection information of the power equipment are collected, and the equipment distribution map is constructed. The process of obtaining several equipment anchor points in the equipment distribution map includes:

[0053] The deployment information refers to the deployment location of each power device, which is used to reflect the specific coordinates of each power device in the application scenario; the connection information refers to the connection relationship between each power device, which is used to reflect whether there are connection lines between each power device and their corresponding layout;

[0054] Using GIS technology to construct a device distribution map of power equipment based on the collected deployment information and connection information, the device distribution map can reflect the deployment location of each power equipment in the application scenario and its corresponding connection relationship;

[0055] In the equipment distribution diagram, the deployment location of any power equipment is used as the initial equipment anchor point;

[0056] Obtain the shortest distance between each other deployment position and the current device anchor point, and record the obtained shortest distance as d;

[0057] Obtain the probability value of each other deployment position being selected as the next device anchor point, and record the obtained probability value as P;

[0058]

[0059] Select the deployment location corresponding to the maximum probability value as the next device anchor point, and repeat this step until n device anchor points are selected;

[0060] The power equipment on the device anchor point remains fixed and does not participate in the subsequent steps of adjusting the deployment position. This approach can obtain as dispersed device anchor points as possible, reducing the complexity of adjustment when the deployment position is subsequently adjusted.

[0061] It should be further explained that, in the specific implementation process, the process of setting a number of data monitoring points, obtaining the monitoring equipment information and monitoring electromagnetic data of the data monitoring points, and constructing the electromagnetic prediction model includes:

[0062] In the application scenario of power equipment, several data monitoring points are set, each of which includes an electromagnetic monitoring unit, and the electromagnetic intensity of its data monitoring point is monitored in real time by the electromagnetic monitoring unit. The monitored electromagnetic data refers to the electromagnetic intensity of each data monitoring point;

[0063] Mark all power equipment within a preset proximity distance around a data monitoring point as monitoring equipment of the data monitoring point, obtain the straight-line distance between each monitoring equipment and the data monitoring point, i.e., the monitoring distance, obtain the number of monitoring equipment at the data monitoring point, i.e., the monitoring number, and generate monitoring equipment information of the data monitoring point according to the obtained monitoring distance and monitoring number;

[0064] Obtain the monitoring electromagnetic data of each data monitoring point and its corresponding monitoring equipment information, and divide them into a prediction training set and a prediction test set;

[0065] Constructing a recurrent neural network, taking the monitoring equipment information in the prediction training set as input data of the recurrent neural network, taking the monitoring electromagnetic data in the prediction training set as output data of the recurrent neural network, and training the recurrent neural network to obtain a corresponding initial recurrent neural network;

[0066] The initial recurrent neural network is model verified using the prediction test set, and the initial recurrent neural network with an output value less than or equal to a preset test error threshold is used as the electromagnetic prediction model.

[0067] It should be further explained that, in the specific implementation process, the process of obtaining the data prediction point and its corresponding prediction device information, inputting the prediction device information into the electromagnetic prediction model, and obtaining the predicted electromagnetic data of the data prediction point includes:

[0068] The data prediction point includes any point other than the data monitoring point. Since no electromagnetic monitoring unit is set at the data prediction point, its electromagnetic intensity needs to be obtained through an electromagnetic prediction model;

[0069] Mark all power equipment within a preset proximity distance around the data prediction point as prediction equipment of the data prediction point, obtain the straight-line distance between each prediction equipment and the data prediction point, i.e., the prediction distance, obtain the number of prediction equipment at the data prediction point, i.e., the prediction number, and generate prediction equipment information of the data prediction point according to the obtained prediction distance and prediction number;

[0070] The obtained prediction device information is input into the electromagnetic prediction model, and the electromagnetic prediction model is used to output the predicted electromagnetic data of the data prediction point according to the input prediction device information. The same method is adopted to obtain the predicted electromagnetic data of each data prediction point and bind them respectively.

[0071] It should be further explained that, in a specific implementation process, the predicted electromagnetic data is visualized to obtain a visual distribution map, and the process of dividing the electromagnetic interference area and the superimposed interference area in the visual distribution map includes:

[0072] The predicted electromagnetic data are divided into different numerical intervals according to the numerical values, and a color corresponding to the wavelength is set for each numerical interval. The larger the numerical value, the larger the wavelength. In the device distribution map, each data prediction point is rendered with the color corresponding to the predicted electromagnetic data to achieve visualization processing, and the device distribution map that has completed visualization processing is marked as a visual distribution map;

[0073] Setting an electromagnetic interference threshold, comparing the predicted electromagnetic data with the electromagnetic interference threshold, and marking an area consisting of data prediction points corresponding to all predicted electromagnetic data higher than the electromagnetic interference threshold as an electromagnetic interference area;

[0074] Taking any power equipment as an example, the predicted electromagnetic data of each data prediction point is numbered according to the distance relationship with the power equipment, and is recorded as i, where i=1, 2, ..., n, where n represents the number of data prediction points. The smaller the number, the closer it is to the power equipment. The obtained predicted electromagnetic data is marked as Y i ;

[0075] Compare the predicted electromagnetic data with adjacent numbers. If Y i ≤Y i-1 , then the predicted electromagnetic data is marked as normal electromagnetic data. If Y i >Y i-1 , where i-1 represents the previous predicted electromagnetic data of i, the predicted electromagnetic data is marked as abnormal electromagnetic data, and the abnormal electromagnetic data of each power equipment is obtained by the same method, and the area composed of the data prediction points corresponding to all abnormal electromagnetic data is marked as the superimposed interference area.

[0076] It should be further explained that, in the specific implementation process, the process of obtaining the deployment adjustment area in combination with the device anchor point in the visual distribution map and performing deployment adjustment on the power equipment to obtain the adjustment coefficient under different deployment adjustment schemes includes:

[0077] Taking any non-anchored device as an example, mark the power equipment on the device anchor point as the anchored device, and mark other power equipment as non-anchored devices. Connect the three device anchor points closest to the non-anchored device in sequence in the visual distribution diagram, mark the area surrounded by the three device anchor points as the deployment adjustment area of ​​the non-anchored device, and obtain the deployment adjustment area of ​​each non-anchored device.

[0078] The anchoring device at the device anchoring point does not participate in the deployment adjustment, and the deployment adjustment of each non-anchoring device is performed using an exhaustive method to obtain different deployment adjustment schemes, wherein the deployment adjustment refers to adjusting the deployment position of each non-anchoring device to reduce the number and area of ​​superimposed interference areas, and the deployment adjustment of each non-anchoring device does not exceed its deployment adjustment area;

[0079] Taking any deployment adjustment scheme as an example, the adjustment coefficient is obtained as follows:

[0080] Before deployment adjustment, the sum of the distances between each non-anchored device and its corresponding device anchor point in its deployment adjustment area is obtained, that is, the anchor distance before adjustment, which is recorded as Q j , where, j = 1, 2, ..., m, where m represents the number of non-anchor devices;

[0081] After deployment adjustment, the adjusted anchoring distance of each non-anchored device is obtained, denoted as H j , obtaining the adjustment coefficient S of the deployment adjustment scheme according to the anchor distance before adjustment and the anchor distance after adjustment;

[0082]

[0083] It should be further explained that, in the specific implementation process, the interference coefficient is obtained according to the electromagnetic interference area and the superimposed interference area under different deployment adjustment schemes, and the process of obtaining the best deployment adjustment scheme in combination with the adjustment coefficient includes:

[0084] Taking any deployment adjustment scheme as an example, the deployment position of each power device is adjusted according to the deployment adjustment scheme in the visual distribution map, the predicted electromagnetic intensity of each data prediction point is obtained, and its corresponding electromagnetic interference area and superimposed interference area are obtained;

[0085] The total area of ​​the electromagnetic interference region is obtained, that is, the electromagnetic interference area, which is recorded as G a , the number and total area of ​​superimposed interference regions are obtained, that is, the number of superimposed interference and the superimposed interference area, recorded as G s and G b , based on this, the interference coefficient R of the deployment adjustment scheme is obtained. If the number of superimposed interferences and the superimposed interference area are equal to 0, the interference coefficient is also 0;

[0086]

[0087] The same method is used to obtain the interference coefficients under different deployment adjustment schemes, and the interference coefficient and adjustment coefficient of the same deployment adjustment scheme are bound. The deployment adjustment scheme corresponding to the smallest interference coefficient is marked as the best deployment adjustment scheme. If the interference coefficients are the same, the deployment adjustment scheme corresponding to the smallest adjustment coefficient is marked as the best deployment adjustment scheme.

[0088] It should be further explained that, in the specific implementation process, the process of connecting and laying out the power equipment under the optimal deployment and adjustment plan and generating a connection plan, and providing feedback on the optimal deployment and adjustment plan and its connection plan includes:

[0089] In the visual distribution diagram, the deployment position of each power equipment is adjusted according to the optimal deployment adjustment plan, the predicted electromagnetic intensity of each data prediction point is obtained, and its corresponding electromagnetic interference area and superimposed interference area are obtained;

[0090] According to the connection information, the corresponding connection lines are re-established between the power equipment that have a connection relationship. The connection lines have the least contact with the superimposed interference area and the shortest length. According to the connection lines between the various devices, the corresponding connection plan is generated, and the obtained optimal deployment adjustment plan and its corresponding connection plan are fed back to the relevant personnel.

[0091] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The power equipment status monitoring system based on Internet of Things technology is characterized by: Includes the following modules: The data collection module is used to collect the deployment information and connection information of the power equipment, and to construct the equipment distribution map, and obtain several equipment anchor points in the equipment distribution map; The data prediction module is used to set a number of data monitoring points, obtain monitoring equipment information and monitoring electromagnetic data of the data monitoring points, and build an electromagnetic prediction model to obtain data prediction points and prediction equipment information, input the prediction equipment information into the electromagnetic prediction model, and obtain the predicted electromagnetic data of the data prediction points; A region division module is used to perform visualization processing on the predicted electromagnetic data to obtain a visual distribution map, and to divide the electromagnetic interference region and the superimposed interference region in the visual distribution map; A deployment adjustment module is used to obtain a deployment adjustment area in combination with a device anchor point in a visual distribution map, perform deployment adjustment on power equipment to obtain adjustment coefficients under different deployment adjustment schemes, obtain interference coefficients according to electromagnetic interference areas and superimposed interference areas under different deployment adjustment schemes, and obtain an optimal deployment adjustment scheme in combination with the adjustment coefficients; A connection layout module is used to connect and layout the power equipment under the optimal deployment and adjustment scheme and generate a connection plan, and provide feedback on the optimal deployment and adjustment scheme and its connection plan; The process of obtaining the deployment adjustment area and obtaining the adjustment coefficients under different deployment adjustment schemes includes: The power equipment at the device anchor point is regarded as the anchor device, and the other power equipment is regarded as the non-anchor device. In the visual distribution diagram, the area surrounded by the three device anchor points closest to the non-anchor device is regarded as the deployment adjustment area of ​​the non-anchor device. Using an exhaustive method to adjust the deployment of each non-anchor device to obtain different deployment adjustment schemes, wherein the deployment adjustment refers to adjusting the deployment position of each non-anchor device, and the deployment adjustment of the non-anchor device does not exceed its deployment adjustment area; Before deployment adjustment, obtain the pre-adjustment anchoring distance Q between each non-anchored device and its device anchoring point in its deployment adjustment area. j , after deployment adjustment, the adjusted anchor distance H of each non-anchored device is obtained j , obtain the adjustment coefficient S of different deployment adjustment schemes; Where, j = 1, 2, ..., m, m represents the number of non-anchor devices; The process of obtaining the interference coefficient and obtaining the optimal deployment adjustment solution includes: In the visual distribution diagram, the deployment position of each power device is adjusted according to different deployment adjustment schemes, and the corresponding electromagnetic interference area and superimposed interference area are obtained; Obtain the electromagnetic interference area G in different deployment adjustment schemes a , the number of superimposed interference G s , superimposed interference area G b , obtain the interference coefficient R of different deployment adjustment schemes; The interference coefficient and adjustment coefficient of the same deployment adjustment scheme are bound, and the deployment adjustment scheme corresponding to the minimum interference coefficient is taken as the optimal deployment adjustment scheme. If the interference coefficients are the same, the deployment adjustment scheme corresponding to the minimum adjustment coefficient is marked as the optimal deployment adjustment scheme.

2. The power equipment status monitoring system based on Internet of Things technology according to claim 1 is characterized in that: The process of building a device distribution map and obtaining several device anchor points includes: The deployment information refers to the deployment location of each power device, and the connection information refers to the connection relationship between each power device. A device distribution map of the power devices is constructed based on the deployment information and the connection information. In the device distribution map, an initial device anchor point is set; Obtain the probability values ​​of other deployment positions being selected as the next device anchor point, select the deployment position corresponding to the maximum probability value as the next device anchor point, and repeat this step until several device anchor points are selected.

3. The power equipment status monitoring system based on Internet of Things technology according to claim 2 is characterized in that: The process of obtaining monitoring equipment information and monitoring electromagnetic data and building an electromagnetic prediction model includes: Obtain monitoring equipment for each data monitoring point, obtain monitoring distance and monitoring quantity for each data monitoring point according to the monitoring equipment, and generate monitoring equipment information for each data monitoring point, wherein the monitoring electromagnetic data refers to the electromagnetic intensity of each data monitoring point; The monitoring electromagnetic data and monitoring equipment information of each data monitoring point are divided into a prediction training set and a prediction test set; Construct a recurrent neural network, use the monitoring equipment information and the monitoring electromagnetic data in the prediction training set as the input data and output data of the recurrent neural network respectively, and train the recurrent neural network to obtain an initial recurrent neural network; The initial recurrent neural network is model verified using the prediction test set, and the initial recurrent neural network with an output value less than or equal to a preset test error threshold is used as the electromagnetic prediction model.

4. The power equipment status monitoring system based on Internet of Things technology according to claim 3 is characterized in that: The process of obtaining data prediction points and prediction equipment information and obtaining prediction electromagnetic data includes: The data prediction points include any points other than data monitoring points, and prediction devices of each data prediction point are obtained. The prediction distance and prediction quantity of each data prediction point are respectively obtained according to the prediction devices, and prediction device information of each data prediction point is generated; The electromagnetic prediction model is used to generate predicted electromagnetic data of corresponding data prediction points according to the obtained prediction equipment information, and each data prediction point is bound to its predicted electromagnetic data respectively.

5. The power equipment status monitoring system based on Internet of Things technology according to claim 4 is characterized in that: The process of obtaining a visual distribution map and dividing the electromagnetic interference area and the superimposed interference area includes: The predicted electromagnetic data are divided into different numerical intervals according to the numerical values, a color corresponding to the wavelength is set for each numerical interval, and each data prediction point is rendered in the device distribution map with the color corresponding to the predicted electromagnetic data to obtain a visual distribution map; An electromagnetic interference threshold is set, and an area consisting of data prediction points corresponding to all predicted electromagnetic data above the electromagnetic interference threshold is taken as an electromagnetic interference area; Taking a single electric power equipment as a benchmark, the predicted electromagnetic data of each data prediction point are numbered according to the distance relationship with the electric power equipment, and the predicted electromagnetic data with adjacent numbers are compared to obtain the abnormal electromagnetic data of the electric power equipment; The abnormal electromagnetic data of each power device is obtained respectively, and the area composed of the data prediction points corresponding to the abnormal electromagnetic data of all power devices is taken as the superimposed interference area.

6. The power equipment status monitoring system based on Internet of Things technology according to claim 5 is characterized in that: The process of generating a connection plan and providing feedback on the optimal deployment adjustment plan and its connection plan includes: In the visual distribution diagram, the deployment position of each power device is adjusted according to the optimal deployment adjustment scheme, and the corresponding electromagnetic interference area and superimposed interference area are obtained; According to the connection information, a connection line is re-established between the power equipment that has a connection relationship. The connection line has the least contact with the superimposed interference area and is the shortest in length. A connection plan is generated based on the established connection line, and the optimal deployment adjustment plan and its connection plan are fed back to relevant personnel.

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