Intelligent power grid power transmission and distribution line on-line monitoring system based on artificial intelligence

Abstract

The invention relates to the technical field of intelligent power grid operation and maintenance, in particular to an intelligent power grid power transmission and distribution line on-line monitoring system based on artificial intelligence, which comprises the steps of detecting real-time environment information of each single operation area, determining an environment abnormal signal, determining an abnormal area according to the environment abnormal signal, the method comprises the following steps: acquiring a main analysis image and a single-region image of an abnormal region, determining an image sequence group, calculating an image change value in the image sequence group, calculating a risk operation value based on the image change value and acquisition time, generating a risk signal, acquiring a single-region image of an adjacent region, and generating a risk signal; the method comprises the steps of obtaining a real-time area image and a constant-potential operation image, calculating an adjacent side change difference value of the real-time area image and the constant-potential operation image, calculating an area influence value in combination with a risk operation value, determining an abnormal influence signal according to the area influence value, and further accurately judging the influence range and degree of an abnormal area. And the risk early warning capability of intelligent power grid operation is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid operation and maintenance technology, and in particular to an online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence. Background Art

[0002] Smart grid is a new type of modern power grid formed by highly integrating advanced sensor measurement technology, information and communication technology, analysis and decision-making technology, and automatic control technology with power grid infrastructure.

[0003] Prior art CN116191655A discloses an intelligent monitoring system for power grid transmission lines, including a data monitoring module installed on a tower of the transmission line to achieve real-time monitoring of the transmission line and generate monitoring data; a first forwarding module installed on the tower of the transmission line to receive the monitoring data by wired transmission; a second forwarding module, which is installed at a preset position and receives the monitoring data by wireless transmission, and sends the detection data to a cloud server after screening; a data processing module, which is arranged in the cloud server to format and classify the monitoring data after obtaining the monitoring data and to judge the detection results according to a preset template.

[0004] However, in the long-distance power distribution and transmission process of smart grids, the power grid lines are completely exposed to the open air. At this time, real-time monitoring of the environmental information around the power grid lines is required. However, when monitoring the power grid environment, abnormal judgments can only be made based on data information, and the potential threats posed by environmental changes to the power grid lines cannot be determined, thereby increasing the risk of failure in power grid operation. Summary of the invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose an online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The online monitoring system of smart grid transmission and distribution lines based on artificial intelligence includes:

[0008] The meteorological information monitoring module is used to detect the environmental information in the power grid distribution line, obtain the real-time environmental information of each single transport area, and then determine the environmental abnormality signal based on the real-time environmental information, and transmit the environmental abnormality signal to the image acquisition module;

[0009] An image acquisition module collects images of the single transport area based on the environmental abnormality signal to obtain a single-area image, and at the same time marks the single-area image of the single transport area as a main analysis image according to the single transport area generated by the environmental abnormality signal, and then transmits the single-area image and the main analysis image to the image analysis module at the same time;

[0010] An image analysis module is used to determine the abnormal area according to the environmental abnormal signal, and at the same time, obtain a single-area image and a main analysis image of the abnormal area with the time when the environmental abnormal signal is generated as a node, and then obtain an initial image from the single-area image of the abnormal area, and obtain an image sequence group based on the initial image and the main analysis image, and then calculate the image change value of two adjacent images in the image sequence group, and determine the risk operation value of the abnormal area according to the image change value and the acquisition time, and at the same time, determine the risk signal according to the risk operation value, and transmit it to the abnormal diagnosis module;

[0011] The abnormal diagnosis module is used to identify risk signals and collect single-area images of adjacent areas to obtain real-time regional images and normal operation images, calculate the difference between the adjacent edge changes of the real-time regional images and the normal operation images, and calculate the regional impact value in combination with the risk operation value. Then, according to the regional impact value, the abnormal impact signal is determined and transmitted to the terminal display module;

[0012] The terminal display module is used to receive abnormal impact signals and risk signals, display them on the terminal display device, and generate sound and light reminder information to remind relevant management personnel.

[0013] As a further solution of the present invention, a method for generating an environmental abnormality signal includes:

[0014] The lines in the smart grid are divided into several areas according to the towers, and each area is marked as a single transport area. The environmental information of each single transport area is monitored in real time to obtain real-time environmental information;

[0015] Compare the real-time environmental information with the normal climate information. If the real-time environmental information of all single-transportation areas is consistent with the normal climate information, a normal operation signal is generated, and the environmental information of the power grid distribution lines is continuously monitored. If the real-time environmental information of any single-transportation area is inconsistent with the normal environmental information, an environmental abnormality signal is generated at this time.

[0016] Among them, environmental information refers to the climate information of the cable distribution area during the long-distance power transmission and distribution process of the smart grid, and normal climate information refers to the normal operating climate data of the power grid distribution lines.

[0017] As a further solution of the present invention, a method for determining a single-region image and a main analysis image includes:

[0018] When the image acquisition module detects an abnormal environmental signal, it immediately acquires images of the power distribution line, and when acquiring images of the power distribution line, it acquires images in units of single transport areas, and marks the acquired images of the single transport areas as single-area images Pi, where i represents the area numbers of different single transport areas, and i∈[1, k], where k represents the total value of single transport areas in the power distribution line;

[0019] When the image acquisition module completes the image acquisition of the power grid distribution lines, it identifies the single operation area z that generates the environmental abnormality signal based on the environmental abnormality signal, and marks the single area image corresponding to the single operation area as the main analysis image Pz, where z∈i.

[0020] As a further solution of the present invention, a method for acquiring an image sequence group includes:

[0021] S1: Identify the single transport area z that generates the environmental abnormality signal and mark it as the abnormal area, and obtain the single area image PCz and the main analysis image Pz of the abnormal area respectively, wherein the single area image PCz is the image of the single transport area z before the environmental abnormality signal is generated, and the main analysis image is the image collected after the single transport area z generates the environmental abnormality signal;

[0022] S2: first obtain n consecutive single-region images PCz, and perform mean processing on the n single-region images PCz, and then mark the image after mean processing as the initial image PCC of the abnormal area, where n is the threshold;

[0023] Continuous main analysis images are acquired, and the main analysis images and initial images are arranged in chronological order to obtain an image sequence group. At this time, in the image sequence group, the initial image is arranged in the first place, and at the same time, the main analysis image acquired in real time is directly placed in the image sequence group according to the acquisition time.

[0024] As a further solution of the present invention, a method for obtaining a risk operation value and a risk signal includes:

[0025] S3: In the image sequence group, the image label of the initial image is set to 0, and then the image labels of the remaining images are set to j in order of position, where j∈[1, J], indicating that there are J main analysis images in the image sequence group at this time;

[0026] Starting from the initial image, the back-end image is subtracted from the front-end image in turn to obtain the image change value Bj, that is, the image change value is the difference between image j+1 and image j;

[0027] S4: Based on formula The risk operation value FX is obtained, TS represents the time interval from the generation of the abnormal environmental signal to the last acquisition of the main analysis image, and α1 and α2 are both proportional coefficients;

[0028] S5: Compare the risk operation value FX with the risk threshold Fy: if the risk operation value FX is less than or equal to the risk threshold Fy, a normal signal is generated; otherwise, if the risk operation value FX is greater than the risk threshold, a risk signal is generated.

[0029] As a further solution of the present invention, subtracting the front-end image from the back-end image means subtracting the pixel values ​​in the images. If the pixel values ​​of the back-end image and the front-end image are the same, the difference here is 0. If the pixel values ​​of the back-end image and the front-end image are different, the difference here is 1. The image change value is the total value of the pixel points where the difference is 1.

[0030] As a further solution of the present invention, a method for calculating the regional impact value includes:

[0031] When a risk operation signal is identified, the single transport area adjacent to the abnormal area is obtained, and the single transport area adjacent to the abnormal area is marked as an adjacent area;

[0032] When a risk signal is detected, the single-area image of the adjacent area is immediately collected to obtain a real-time regional image. At the same time, the generation time of the environmental abnormal signal is obtained, and the generation time is used as the time node to obtain the image of the adjacent area before the time node and mark it as a normal operation image.

[0033] Subtract the real-time regional image from the normal operation image to obtain the adjacent edge change difference BL, then identify the climate information that generates the environmental anomaly signal, and obtain the environmental impact factor DY corresponding to the climate information, and then use the formula YX = BL DY×b1 Get the regional impact value YX, b1 is the proportional coefficient;

[0034] The regional influence value YX is compared with the influence threshold. If the regional influence value is less than the influence threshold, a normal signal of the adjacent edge is generated. Conversely, if the regional influence value YX is greater than or equal to the influence threshold, an abnormal influence signal is generated.

[0035] As a further solution of the present invention, it also includes a power grid information storage module for collecting and storing line information of the smart grid, and then the power grid information storage module is bidirectionally communicated with the image analysis module and the abnormality diagnosis module respectively.

[0036] Compared with the prior art, the advantages of the present invention are:

[0037] The present invention divides the power grid distribution lines into several single-operation areas, detects the real-time environmental information of each single-operation area, determines the environmental abnormality signal, and then determines the abnormal area according to the environmental abnormality signal, collects the main analysis image and the single-area image of the abnormal area, determines the image sequence group, and then calculates the image change value of any adjacent image in the image sequence group, calculates the risk operation value based on the image change value and the acquisition time, and generates a risk signal, and then generates a risk signal according to the image change of the abnormal area. According to the risk signal, the management personnel timely conduct environmental inspections on the abnormal area, thereby improving the risk early warning capability of the operation of the smart grid;

[0038] The present invention also acquires single-zone images of adjacent areas to obtain real-time regional images and normal operation images, calculates the adjacent edge change difference between the real-time regional image and the normal operation image, and calculates the regional impact value in combination with the risk operation value. Then, based on the regional impact value, the abnormal impact signal is determined, and the impact range and degree of the abnormal area are accurately judged, providing timely and accurate decision support for management personnel, and then reasonably allocating resources for maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0041] Reference Figure 1 , an online monitoring system for smart grid transmission and distribution lines based on artificial intelligence, including a grid information storage module, a meteorological information monitoring module, an image acquisition module, an image analysis module, an abnormality diagnosis module and a terminal display module;

[0042] The power grid information storage module is used to collect and store the line information of the smart grid, wherein the line information includes the line distribution information, pole tower information and cable information of the smart grid, and further, the pole tower information refers to the information related to the pole tower in the transmission line of the smart grid, including the pole tower position and the pole tower distance, and the cable information refers to the information related to the cable in the transmission line, including the minimum distance between the cable and the ground, and then the power grid information storage module is bidirectionally connected to the image analysis module and the abnormal diagnosis module respectively;

[0043] The meteorological information monitoring module is used to detect the environmental information of the power distribution lines in the smart grid. When detecting the environmental information, the lines in the smart grid need to be divided into several areas according to the towers, and each area is marked as a single operation area. Further, the single operation area refers to the area between adjacent towers in the power distribution line. Then, the meteorological information monitoring module is used to monitor the environmental information of each single operation area in real time to obtain real-time environmental information;

[0044] Then the real-time environmental information is compared with the normal climate information. If the real-time environmental information of all single-transportation areas is consistent with the normal climate information, a normal operation signal is generated, and the environmental information of the power grid distribution line is continuously monitored. If the real-time environmental information of any single-transportation area is inconsistent with the normal environmental information, an environmental abnormality signal is generated and transmitted to the image acquisition module.

[0045] Among them, environmental information refers to the climate information of the cable distribution area during the long-distance power transmission and distribution process of the smart grid, such as temperature, humidity, wind force, rainfall and snowfall, etc. Normal climate information refers to the normal operating climate data of the power grid distribution line. For example, in the normal climate information, the normal range of wind speed is 0m / s to 30m / s, and the normal range of humidity is 85% to 90%. The specific normal climate information is set by technicians in this field based on big data experience;

[0046] The image acquisition module is used to identify abnormal environmental signals. When the image acquisition module detects abnormal environmental signals, it immediately acquires images of the power distribution line. Specifically, when acquiring images of the power distribution line, it acquires images in units of single transport areas, and marks the acquired images of the single transport areas as single-area images Pi, where i represents the area numbers of different single transport areas, and i∈[1, k], where k represents the total value of single transport areas in the power distribution line.

[0047] When the image acquisition module completes the image acquisition of the power grid distribution line, it identifies the single transport area z that generates the environmental abnormality signal based on the environmental abnormality signal, and marks the single area image corresponding to the single transport area as the main analysis image Pz, where z∈i;

[0048] It should be further explained that before the abnormal environmental signal is identified, the power grid distribution line is monitored in real time by the monitoring device. When the abnormal environmental signal is detected, the image acquisition module collects the image of the single transport area in real time in the video monitoring screen to obtain a single area image. In this embodiment, the method for performing video monitoring on the power grid distribution line is set to satellite communication technology;

[0049] Then the image acquisition module transmits the main analysis image Pz and the single zone image Pi in the power grid distribution line to the image analysis module simultaneously;

[0050] The image analysis module is used to analyze the single-zone image Pi and the main analysis image Pz and generate a risk operation signal. The specific method for generating the risk operation signal includes:

[0051] S1: Identify the single transport area z that generates the environmental abnormality signal and mark it as the abnormal area, and obtain the single area image PCz and the main analysis image Pz of the abnormal area respectively, wherein the single area image PCz is the image of the single transport area z before the environmental abnormality signal is generated, and the main analysis image is the image collected after the single transport area z generates the environmental abnormality signal;

[0052] S2: first obtain n consecutive single-region images PCz, and perform mean processing on the n single-region images PCz, and then mark the image after mean processing as the initial image PCC of the abnormal area, where n is a threshold value, and in this embodiment, n is 100;

[0053] Then, continuous main analysis images are acquired, and the main analysis images and initial images are arranged in time order to obtain an image sequence group. At this time, in the image sequence group, the initial image is arranged at the first position, and the main analysis images acquired in real time are directly placed in the image sequence group according to the acquisition time;

[0054] S3: In the image sequence group, the image label of the initial image is set to 0, and then the image labels of the remaining images are set to j in order of position, where j∈[1, J], indicating that there are J main analysis images in the image sequence group at this time;

[0055] Starting from the initial image, the back-end image is subtracted from the front-end image in turn to obtain the image change value Bj, that is, the image change value is the difference between image j+1 and image j;

[0056] It should be further explained that, in this embodiment, subtracting the front-end image from the rear-end image refers to subtracting the pixel values ​​in the images. If the pixel values ​​of the rear-end image and the front-end image are the same, the difference here is 0. If the pixel values ​​of the rear-end image and the front-end image are different, the difference here is 1. The image change value is the total value of the pixel point where the difference is 1.

[0057] S4: Based on formula The risk operation value FX is obtained, TS represents the time interval from the generation of the abnormal environment signal to the last main analysis image acquisition, α1 and α2 are both proportional coefficients, and the specific values ​​of α1 and α2 are obtained by technicians in this field after big data calculation;

[0058] It should be further explained that the risk operation value indicates the environmental change state of the abnormal area. The larger the risk operation value FX is, the greater the impact of the environment in the abnormal area on the power grid is, and the greater the probability that the power grid operation is at risk. The smaller the risk operation value FX is, the smaller the impact of the environment in the abnormal area on the power grid is, and the smaller the impact of the environment on the power grid operation is.

[0059] S5: Compare the risk operation value FX with the risk threshold Fy: if the risk operation value FX is less than or equal to the risk threshold Fy, a normal signal is generated, indicating that the environmental state of the abnormal area is within the normal operation state range of the power grid; conversely, if the risk operation value FX is greater than the risk threshold, a risk signal is generated, indicating that the environmental state of the abnormal area is not within the normal operation state range of the power grid. The specific value of the risk threshold Fy is obtained by technicians in this field after big data calculation;

[0060] Afterwards, the image analysis module is connected to the terminal display module and the abnormality diagnosis module in a one-way communication manner;

[0061] The abnormal diagnosis module is used to identify risk signals and receive risk operation values. When a risk operation signal is identified, the single transport area adjacent to the abnormal area is obtained, and the single transport area adjacent to the abnormal area is marked as an adjacent area. Then, the regional impact value of the adjacent area is calculated, and an abnormal impact signal is generated. The specific method for generating the abnormal impact signal includes:

[0062] When a risk signal is detected, the single-area image of the adjacent area is immediately collected to obtain a real-time regional image. At the same time, the generation time of the environmental abnormal signal is obtained, and the generation time is used as the time node to obtain the image of the adjacent area before the time node and mark it as a normal operation image.

[0063] Subtract the real-time regional image from the normal operation image to obtain the adjacent edge change difference BL, then identify the climate information that generates the environmental anomaly signal, and obtain the environmental impact factor DY corresponding to the climate information, and then use the formula YX = BL DY×b1 The regional impact value YX is obtained, b1 is the proportionality coefficient, and the specific value of b1 is obtained by technicians in this field after big data calculation;

[0064] It should be further explained that the environmental impact factor DY is a threshold value, and different environmental information corresponds to different environmental impact factors, and the environmental impact factors corresponding to different environmental information are obtained by technicians in this field after big data calculation;

[0065] Then, the regional impact value YX is compared with the impact threshold. If the regional impact value is less than the impact threshold, a normal signal of the adjacent area is generated, indicating that the operating environment of the adjacent area is in a normal operating state. On the contrary, if the regional impact value YX is greater than or equal to the impact threshold, an abnormal impact signal is generated, indicating that the environment of the abnormal area has an impact on the power grid operation of the adjacent area. The impact threshold is obtained by technicians in this field after big data calculation.

[0066] After that, there is a one-way communication connection between the abnormal diagnosis module and the terminal display module;

[0067] The terminal display module is used to receive the transmission signals from the image analysis module and the abnormal diagnosis module, and display the transmission signals on the terminal display device. At the same time, if there are risk signals or abnormal impact signals in the transmission signals, the terminal display module will generate sound and light reminder information and remind relevant managers.

[0068] It should be further explained that the image analysis module and the abnormality diagnosis module in this embodiment are both processed based on artificial intelligence technology, thereby improving the reliability of the operation of the smart grid.

[0069] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An online monitoring system for smart grid transmission and distribution lines based on artificial intelligence, characterized in that: include: The meteorological information monitoring module is used to detect the environmental information in the power grid distribution line, obtain the real-time environmental information of each single transport area, and then determine the environmental abnormality signal based on the real-time environmental information, and transmit the environmental abnormality signal to the image acquisition module; An image acquisition module collects images of the single transport area based on the environmental abnormality signal to obtain a single-area image, and at the same time marks the single-area image of the single transport area as a main analysis image according to the single transport area generated by the environmental abnormality signal, and then transmits the single-area image and the main analysis image to the image analysis module at the same time; An image analysis module is used to determine the abnormal area according to the environmental abnormal signal, and at the same time, obtain a single-area image and a main analysis image of the abnormal area with the time when the environmental abnormal signal is generated as a node, and then obtain an initial image from the single-area image of the abnormal area, and obtain an image sequence group based on the initial image and the main analysis image, and then calculate the image change value of two adjacent images in the image sequence group, and determine the risk operation value of the abnormal area according to the image change value and the acquisition time, and at the same time, determine the risk signal according to the risk operation value, and transmit it to the abnormal diagnosis module; The abnormal diagnosis module is used to identify risk signals and collect single-area images of adjacent areas to obtain real-time regional images and normal operation images, calculate the difference between the adjacent edge changes of the real-time regional images and the normal operation images, and calculate the regional impact value in combination with the risk operation value. Then, according to the regional impact value, the abnormal impact signal is determined and transmitted to the terminal display module; The terminal display module is used to receive abnormal impact signals and risk signals, display them on the terminal display device, and generate sound and light reminder information to remind relevant management personnel.

2. The online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence according to claim 1 is characterized in that: The method for generating an environmental abnormality signal includes: The lines in the smart grid are divided into several areas according to the towers, and each area is marked as a single transport area. The environmental information of each single transport area is monitored in real time to obtain real-time environmental information; Compare the real-time environmental information with the normal climate information. If the real-time environmental information of all single-transportation areas is consistent with the normal climate information, a normal operation signal is generated, and the environmental information of the power grid distribution lines is continuously monitored. If the real-time environmental information of any single-transportation area is inconsistent with the normal environmental information, an environmental abnormality signal is generated at this time. Among them, environmental information refers to the climate information of the cable distribution area during the long-distance power transmission and distribution process of the smart grid, and normal climate information refers to the normal operating climate data of the power grid distribution lines.

3. The online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence according to claim 1 is characterized in that: The method for determining the single-area image and the main analysis image includes: When the image acquisition module detects an abnormal environmental signal, it immediately acquires images of the power distribution line, and when acquiring images of the power distribution line, it acquires images in units of single transport areas, and marks the acquired images of the single transport areas as single-area images Pi, where i represents the area numbers of different single transport areas, and i∈[1, k], where k represents the total value of single transport areas in the power distribution line; When the image acquisition module completes the image acquisition of the power grid distribution lines, it identifies the single operation area z that generates the environmental abnormality signal based on the environmental abnormality signal, and marks the single area image corresponding to the single operation area as the main analysis image Pz, where z∈i.

4. The online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence according to claim 1 is characterized in that: The method for obtaining the image sequence group includes: S1: Identify the single transport area z that generates the environmental abnormality signal and mark it as the abnormal area, and obtain the single area image PCz and the main analysis image Pz of the abnormal area respectively, wherein the single area image PCz is the image of the single transport area z before the environmental abnormality signal is generated, and the main analysis image is the image collected after the single transport area z generates the environmental abnormality signal; S2: first obtain n consecutive single-region images PCz, and perform mean processing on the n single-region images PCz, and then mark the image after mean processing as the initial image PCC of the abnormal area, where n is the threshold; Continuous main analysis images are acquired, and the main analysis images and initial images are arranged in chronological order to obtain an image sequence group. At this time, in the image sequence group, the initial image is arranged in the first place, and at the same time, the main analysis image acquired in real time is directly placed in the image sequence group according to the acquisition time.

5. The online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence according to claim 4 is characterized in that: The methods for obtaining risk operation values ​​and risk signals include: S3: In the image sequence group, the image label of the initial image is set to 0, and then the image labels of the remaining images are set to j in order of position, where j∈[1, J], indicating that there are J main analysis images in the image sequence group at this time; Starting from the initial image, the back-end image is subtracted from the front-end image in turn to obtain the image change value Bj, that is, the image change value is the difference between image j+1 and image j; S4: Based on formula The risk operation value FX is obtained, TS represents the time interval from the generation of the abnormal environmental signal to the last acquisition of the main analysis image, and α1 and α2 are both proportional coefficients; S5: Compare the risk operation value FX with the risk threshold Fy: if the risk operation value FX is less than or equal to the risk threshold Fy, a normal signal is generated; otherwise, if the risk operation value FX is greater than the risk threshold, a risk signal is generated.

6. The online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence according to claim 5 is characterized in that: Subtracting the front-end image from the back-end image means subtracting the pixel values ​​in the images. If the pixel values ​​of the back-end image and the front-end image are the same, the difference here is 0. If the pixel values ​​of the back-end image and the front-end image are different, the difference here is 1. The image change value is the total value of the pixel point where the difference is 1.

7. The online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence according to claim 1 is characterized in that: The calculation method of regional impact value includes: When a risk operation signal is identified, the single transport area adjacent to the abnormal area is obtained, and the single transport area adjacent to the abnormal area is marked as an adjacent area; When a risk signal is detected, the single-area image of the adjacent area is immediately collected to obtain a real-time regional image. At the same time, the generation time of the environmental abnormal signal is obtained, and the generation time is used as the time node to obtain the image of the adjacent area before the time node and mark it as a normal operation image. Subtract the real-time regional image from the normal operation image to obtain the adjacent edge change difference BL, then identify the climate information that generates the environmental anomaly signal, and obtain the environmental impact factor DY corresponding to the climate information, and then use the formula YX = BL DY×b1 Get the regional impact value YX, b1 is the proportional coefficient; The regional influence value YX is compared with the influence threshold. If the regional influence value is less than the influence threshold, a normal signal of the adjacent edge is generated. Conversely, if the regional influence value YX is greater than or equal to the influence threshold, an abnormal influence signal is generated.

8. The online monitoring system for smart grid power transmission and distribution lines based on artificial intelligence according to claim 1 is characterized in that: It also includes a power grid information storage module for collecting and storing line information of the smart grid, and then the power grid information storage module is bidirectionally communicated with the image analysis module and the abnormality diagnosis module respectively.

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