Power transmission line gridding risk analysis method and system and storage medium

Abstract

The invention discloses a power transmission line gridding risk analysis method and system, and belongs to the technical field of power supply and distribution, and the method comprises the steps: recognizing nodes and sub-lines in a line map; generating an affiliation relationship between the nodes and the sub-lines, and generating a first influence range of each node based on the affiliation relationship; dividing a terrain map into a plurality of basic grids, predicting a second influence range of the disaster, obtaining the risk probability of the node in the second influence range based on the basic grid where each node is located, and obtaining the risk probability of the node in the second influence range based on the affiliation relationship between the node and the sub-line. Calculating to obtain a risk level of each node; and defining the power transmission line of which the risk level is greater than a preset level as a target line, and generating a live-action image covering the trend of the target line based on the terrain map and the serial number of the basic grid. The problem that the influence of debris flow or flood disasters on the power transmission line cannot be evaluated in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply and distribution, and specifically relates to a grid risk analysis method, system and storage medium for power transmission lines. Background Art

[0002] Transmission lines are an important part of the power system, and their working status directly affects the normal operation of power companies and the daily electricity consumption of the people. Through risk analysis of transmission lines, potential safety hazards can be discovered in a timely manner, so that corresponding preventive measures can be taken to ensure the stability and safety of power supply.

[0003] The following technical solutions are proposed in the prior art to analyze the transmission lines. For example, the Chinese patent document "CN116402343A" discloses a transmission line risk analysis method and system based on a statistical analysis model. This method analyzes and quantifies the impact of severe convective weather on transmission lines based on meteorological parameters and the damage to the transmission lines. It also divides the comprehensive operation risk level of the transmission lines according to the severity of the harm caused by the line failure, and formulates a risk level classification table. After that, the failure risk of the transmission line is associated with the meteorological disasters under severe convective weather, and the disaster distribution law of the transmission line in severe convective weather is obtained. Finally, according to the disaster distribution law, the risk level of each area and section of the transmission line is divided, and the risk level classification table is filled in to obtain a transmission line risk analysis table to complete the risk analysis of the transmission line. For example, the Chinese patent document "CN109378818B" discloses a risk analysis method and system for concurrent cascading failures of power grid wildfire disasters. The method calculates the affected transmission lines and the wildfire tripping probability of the transmission lines based on the real-time wildfire monitoring and early warning results, constructs the initial fault combination based on the tripping probability, and finally calculates the deterministic cascading failure line set under the initial fault combination. On this basis, a probabilistic cascading failure line set of the fault combination is generated; finally, for each fault combination, the power grid risk index under the fault combination conditions is calculated to determine the risk level of each transmission line.

[0004] In addition to the above-mentioned disasters, transmission lines may also be affected by debris flow or flood disasters, and the above-mentioned prior art does not record the corresponding risk assessment scheme. Summary of the invention

[0005] To solve the above problems, the present invention provides a grid risk analysis method, system and storage medium for power transmission lines to solve the problem in the prior art that the impact of debris flow or flood disasters on power transmission lines cannot be evaluated.

[0006] In order to achieve the above-mentioned purpose of the invention, the present invention proposes a grid-based risk analysis method for power transmission lines, comprising: Acquire a line map and a terrain map, wherein the line map includes a plurality of transmission lines, identify nodes in the line map, and define lines between adjacent nodes as sub-lines; generating an affiliation relationship between the node and the sub-circuit based on the function of the node, and generating a first influence range of each node based on the affiliation relationship; Dividing the terrain map into a plurality of basic grids, numbering each of the basic grids, mapping the route map to the terrain map, predicting a second impact range of the disaster, and obtaining the risk probability of the node within the second impact range based on the basic grids where each of the nodes is located; Based on the belonging relationship between the node and the sub-line, and the risk probability, calculate and obtain the risk level of each node; The transmission line having the risk level greater than the preset level is defined as a target line, and a real-life image covering the direction of the target line is generated based on the terrain map and the numbering of the basic grids, and is visualized.

[0007] Furthermore, obtaining the attribution relationship includes the following steps: Dividing the node into a first function point, a second function point and a third function point, wherein the first function point affects the second function point unidirectionally, and the third function point is used to isolate the transmission line; Taking each of the first function points as a starting point, generating a plurality of connection lines with the third function point as an end point, wherein the connection lines include at least one of the first function point and the sub-line, and assigning the second function point and the sub-line in the connection lines as subordinate devices to the starting point; The direction from the starting point to the end point in the connecting line is defined as the transmission direction, each of the second function points in the connecting line is taken as the starting point, and the third function point is taken as the end point, and multiple section lines are generated along the transmission direction, and the second function points and the sub-lines in the section lines are assigned to the starting point as subordinate equipment.

[0008] Furthermore, generating the first influence scope based on the attribution relationship includes the following steps: Obtain each power terminal device managed by the second function point in the database, obtain the subordinate devices of each starting point, add up the power terminal devices including those managed by the second function point to obtain a total value, and set the total value as the first influence range of the starting point, where the starting point includes the first function point and the second function point.

[0009] Further, the risk probability is calculated based on the following steps: If the terrain map includes mountains, the disaster type is set to the first type, the second impact range of the disaster is obtained, and the risk probability of the node within the second impact range is set to a predetermined value; If the terrain map includes a river, the disaster is set to a second type, a prediction model is established, the second impact range of the disaster and the river overflow are calculated based on the prediction model, the first height of the basic grid within the second impact range and the second height of the river are obtained, the difference between the first height and the second height is calculated, and a risk probability table is established, the risk probability table includes multiple first ranges and the corresponding risk probabilities, and based on the difference and the river overflow, combined with the first range, the corresponding risk probability is assigned to the basic grid.

[0010] Further, the risk level is obtained based on the following steps: Acquire the nodes within the second influence range, define them as influence targets, integrate the attribution relationships between the influence targets, and obtain a target data set, wherein the target data set includes a plurality of different influence targets, and the influence target appears uniquely; Set multiple time periods, count the historical power load of each node in the time period, calculate the average power load of each time period, predict the time period when the disaster occurs, and calculate the risk value of the node based on the first formula , the first formula is: , where N is the total number of impact targets in the target data set, is the first influence range of the nth influencing target in the target data set, is the average power load of the nth time period affecting the target, is the risk probability of the nth impact target, and are the preset first weight and second weight; A second range corresponding to each risk level is set, and based on the second range in which the risk value is located, the corresponding risk level is assigned to the node.

[0011] Further, the real scene image is generated based on the following steps: The route map is split into a first map, a second map and a third map, the second map has a richer detail than the first map, the first map has a richer detail than the third map, the basic grid where the second influence range is located is defined as a first grid, the basic grid where the target route is located in the first grid is defined as a second grid, and the remaining basic grids are defined as third grids; The terrain information included in the first grid, the second grid and the third grid is acquired from the first map, the second map and the third map respectively to generate the real-view image of the target route.

[0012] The present invention also provides a transmission line grid risk analysis system, which is used to implement the above-mentioned transmission line grid risk analysis method, and the system includes: An identification module, used to obtain a line map and a terrain map, wherein the line map includes a plurality of transmission lines, identify nodes in the line map, and define lines between adjacent nodes as sub-lines; An analysis module, which generates an attribution relationship between the node and the sub-line based on the function of the node, and generates a first influence range of each node based on the attribution relationship; A first calculation module is used to divide the terrain map into a plurality of basic grids, number each of the basic grids, map the route map to the terrain map, and predict a second impact range of the disaster, and obtain the risk probability of the node within the second impact range based on the basic grids where each of the nodes is located; A second calculation module, based on the belonging relationship between the node and the sub-line, and the risk probability, calculates and obtains the risk level of each of the nodes; The display module defines the transmission line with the risk level greater than the preset level as a target line, generates a real-life image covering the direction of the target line based on the terrain map and the number of the basic grid, and performs a visual display.

[0013] The present invention also provides a computer storage medium, which stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned transmission line grid risk analysis method.

[0014] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The present invention first identifies the transmission line, thereby obtaining the attribution relationship between each node in the transmission line, and based on this, generates which users' normal electricity consumption will be affected by each node after an accident, and then analyzes the terrain where the transmission line is located; when analyzing the terrain map, the terrain map is divided into multiple grids, and then, by combining various weather forecast data, the risk probability of disasters occurring in each basic grid is determined. Finally, the risk probability is combined with the first impact range of each node to obtain the risk level corresponding to each node, so that the degree of impact of disasters on different transmission lines can be determined according to the risk level, thereby realizing risk analysis of different transmission lines.

[0015] When analyzing the terrain, the present invention divides the area into multiple basic grids, and then numbers the basic grids. By numbering the basic grids, after a disaster occurs, only the numbers of the basic grids need to be displayed, so that relevant personnel can quickly find the location of the disaster on the map. In addition, after determining the risk level of each transmission line, an alarm map covering the corresponding area can be quickly generated according to the numbers of the basic grids, so that relevant personnel can perform manual analysis based on the real-life map, thereby quickly and reasonably helping them make the next decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of the steps of grid risk analysis of power transmission lines according to the present invention; Figure 2 A schematic diagram of a route map of the present invention; Figure 3 It is a schematic diagram of the topographic map and the grid map superimposed in the present invention; Figure 4 This is a system structure diagram of a grid-based risk analysis of power transmission lines according to the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0019] like Figure 1 As shown, a grid-based risk analysis method for transmission lines includes: Step S1: Obtain a line map and a terrain map, the line map includes multiple transmission lines, identify nodes in the line map, and define lines between adjacent nodes as sub-lines.

[0020] The line map only includes the distribution of transmission lines, such as Figure 2 As shown; the topographic map includes the topographic distribution of the area where the transmission line is located, including terrain height, river distribution, topography, etc. Figure 3 As shown, Figure 3In order to facilitate the explanation of the subsequent scheme, it is simplified. Then identify the nodes in the transmission line, the nodes are the locations of the poles, such as Figure 2 A1, B1, etc., and the line between them is the sub-line.

[0021] Step S2: Generate an affiliation relationship between a node and a sub-line based on the function of the node, and generate a first influence range of each node based on the affiliation relationship.

[0022] In this implementation, the nodes are divided into the first function point, the second function point and the third function point. The first function point is the starting point of the transmission line. Figure 2 A1~A3 in the figure, the third functional point is C1~C2, the function of the third functional point is to isolate each line to avoid mutual influence between the lines, and the remaining B1~B6 are the second functional points. Then, the ownership relationship of each functional point is obtained. The ownership relationship can be used to determine whether there is mutual influence between the functional points. The method of obtaining the ownership relationship will be introduced in detail later. After obtaining the ownership relationship, the first impact range can be known. For example, when the second functional point B2 is damaged, the normal power supply of users in which areas will be affected.

[0023] Step S3: Divide the terrain map into a plurality of basic grids, number each basic grid, map the route map to the terrain map, and predict the second impact range of the disaster at the same time, and obtain the risk probability of the nodes within the second impact range based on the basic grids where each node is located; The terrain map is divided into a plurality of basic grids of the same size, and each basic grid is numbered in this embodiment. Figure 3 This is a mapped image, from which we can know which node is located in which basic grid. Before or during a disaster, the second impact range of the disaster can be predicted. The second impact range is specifically which basic grids may be affected by the disaster. It can be determined through future rainfall conditions and combined with the water level sensors set in each basic grid. The method of calculating the risk probability will be introduced later.

[0024] Step S4: Based on the relationship between nodes and sub-lines, and the risk probability, the risk level of each node is calculated.

[0025] The risk level of the node can be calculated in combination with the ownership relationship between the nodes. This embodiment includes four risk levels 1 / 2 / 3 / 4. The specific calculation method of the risk level will be introduced later.

[0026] Step S5: define the transmission lines with risk levels greater than the preset level as target lines, generate a real-life image covering the direction of the target lines based on the terrain map and the numbering of the basic grids, and perform a visual display.

[0027] Here, the preset level is set to 3. If there is a node in the transmission line with a risk level greater than 3, this transmission line is set as the target line, indicating that the risk level of the transmission line is high. Then, a real-life image of the target line is generated so that the monitoring personnel can remotely understand the specific terrain distribution of the transmission line, which is convenient for the next decision. The specific method of generating the real-life image will be introduced later.

[0028] The present invention first identifies the transmission line, thereby obtaining the attribution relationship between each node in the transmission line, and based on this, generates which users' normal electricity consumption will be affected by each node after an accident, and then analyzes the terrain where the transmission line is located; when analyzing the terrain map, the terrain map is divided into multiple grids, and then, by combining various weather forecast data, the risk probability of disasters occurring in each basic grid is determined. Finally, the risk probability is combined with the first impact range of each node to obtain the risk level corresponding to each node, so that the degree of impact of disasters on different transmission lines can be determined according to the risk level, thereby realizing risk analysis of different transmission lines.

[0029] When analyzing the terrain, the present invention divides the area into multiple basic grids, and then numbers the basic grids. By numbering the basic grids, after a disaster occurs, only the numbers of the basic grids need to be displayed, so that relevant personnel can quickly find the location of the disaster on the map. In addition, after determining the risk level of each transmission line, an alarm map covering the corresponding area can be quickly generated according to the numbers of the basic grids, so that relevant personnel can perform manual analysis based on the real-life map, thereby quickly and reasonably helping them make the next decision.

[0030] It is particularly noteworthy that the present invention solves the problem that the prior art is unable to evaluate the impact of debris flow or flood disasters on power transmission lines through the above technical solution.

[0031] In this embodiment, obtaining the attribution relationship includes the following steps: The nodes are divided into a first function point, a second function point and a third function point. The first function point affects the second function point unidirectionally, and the third function point is used to isolate the transmission line.

[0032] Taking each first function point as a starting point, a plurality of connection lines with a third function point as an end point are generated, wherein the connection lines include at least one first function point and a sub-line, and the second function point and the sub-line in the connection lines are used as the starting point to which the subordinate devices are assigned.

[0033] Continue to refer to Figure 2, here we will explain how to determine the ownership relationship of the first function point A2. First, take the first function point A2 as the starting point to generate a connection line with the third function point C1. As can be seen from the figure, there are second function points B3 and B4 between A2 and C1, and there are sub-lines L1, L2 and L3. Therefore, the second function points B3, B4, sub-lines L1, L2 and L3 are classified as subordinate devices of the first function point A2, that is, they belong to the first function point A2. Then, take the first function point A2 as the starting point to generate a connection line with the third function point C2. As can be seen from the figure, in addition to the above-mentioned function points and sub-lines, it also includes the second function point B5 and sub-lines L4 and L5. Therefore, the second function point B5 and sub-lines L4 and L5 are classified as subordinate devices of the first function point A2. Here, the sub-lines are additionally marked and used as the belonging equipment, so that relevant personnel can understand which sub-lines will be affected when a problem occurs at the node.

[0034] The direction from the starting point to the end point in the connected line is defined as the transmission direction. The second function points in the connected line are taken as the starting points and the third function points are taken as the end points. Multiple section lines are generated along the transmission direction, and the second function points and sub-lines in the section lines are used as the starting points to which the subordinate equipment is assigned.

[0035] Continuing to use the first function point A2 as an example, since the starting point is A2 and the end point is C1, the transmission direction is D. Then, taking the second function point B4 as the starting point, along the transmission direction, obtain the function points and sub-lines after the second function point B4 and before the third function points C1 and C2. It can be seen from the figure that the subordinate equipment of the second function point B4 includes the second function points B3 and B5, and also includes sub-lines L2, L3, L4 and L5.

[0036] Through this step, the ownership relationship of each node between the transmission lines can be obtained, thereby knowing the impact range of each node when it is eroded by a disaster. For example, after the first functional point A2 is damaged, since it has many subordinate devices, its subordinate devices cannot be powered normally after it is damaged, which will cause a very large range of impact. After the second functional point B5 is damaged, it has no subordinate devices, so the impact range is relatively small. Therefore, through this step, the importance of each node is determined, laying the foundation for the next step to determine the first impact range.

[0037] In this embodiment, generating the first influence range based on the ownership relationship includes the following steps: Obtain the power terminal equipment managed by each second functional point in the database, obtain the subordinate equipment of each starting point, add up the power terminal equipment managed by the second functional point to obtain a total value, and set the total value as the first influence range of the starting point, where the starting point includes the first functional point and the second functional point.

[0038] Specifically, the data in the database is pre-entered, including the power terminal devices managed by each second functional point and their quantity, and the power terminal devices not managed by the first functional point and the third functional point; the power terminal devices in this embodiment are specifically user-end devices. For example, the database records that the second functional point B4 manages the power terminal devices of 50 households, and the second functional point B3 manages the power equipment of 110 households. It is particularly noted that the power terminal devices managed by the second functional point B4 and the second functional point B3 are independent of each other and do not have a mutual inclusion relationship.

[0039] Then, combined with the above-mentioned attribution relationship, the first influence range of each node is calculated. For example, the second function point B5 manages the power equipment of 80 households, then the first influence range of the second function point B4 is 110+50+80=240 households, and the first influence range of the second function point B3 is 50+80=130 households.

[0040] In this embodiment, the risk probability is calculated based on the following steps: If the terrain map includes mountains, the disaster type is set to the first type, a second impact range of the disaster is obtained, and the risk probability of the nodes within the second impact range is set to a predetermined value.

[0041] Specifically, if there are mountains in the topographic map, it is predicted that the first type of disaster, namely, mudslide disaster, may occur. If there are rivers, it is predicted that the second type of disaster, namely, flood disaster, may occur.

[0042] In one embodiment, the predicted rainfall in the mountain area is obtained. If the predicted rainfall is greater than a preset first value, it is predicted that the first type of disaster, i.e., mudslide disaster, will occur in the basic grid involved in the mountain area. The corresponding impact range is also the basic grid involved in the mountain area, which is set as the second impact range of the disaster, and the corresponding risk probability is uniformly set to 80%. In another embodiment, a water level monitoring meter can also be set in each basic grid. When the water level of a basic grid drops significantly in a short period of time, the basic grid and the area with an altitude below the basic grid are determined as the second impact area.

[0043] The reason for setting a uniform value here is that, since debris flows involve changes in geological structures, there is a great possibility that debris flows will cause electric poles to tilt or collapse. Therefore, the risk probability of being affected by debris flows is set to a larger value here.

[0044] If the terrain map includes rivers, the disaster is set to the second type, a prediction model is established, the second impact range of the disaster and the river overflow are calculated based on the prediction model, the first height of the basic grid within the second impact range and the second height of the river are obtained, the difference between the first height and the second height is calculated, and a risk probability table is established. The risk probability table includes multiple first ranges and corresponding risk probabilities. Based on the difference and the river overflow, combined with the first range, the basic grid is assigned a corresponding risk probability.

[0045] Specifically, the prediction model is a BP neural network model. Before making a prediction, the river height data before rainfall is first obtained, and then the historical rainfall data is obtained. The historical rainfall data includes the rainfall in each time period, such as the rainfall from 1:00 to 2:00, and then the amount of water overflow in the river after rainfall in each time period is obtained; refer to Figure 3 , in the case of heavy rainfall, river H will overflow after it is higher than the river channel height, but the specific overflow direction is affected by terrain factors, such as the altitude of the basic grid where B1 is located is lower than the basic grid where B2 is located, so the river will flow to the grid at B1. In addition, the basic grids that the river will overflow to in the historical time period are collected; the above data are input into the neural network model as a training set for training, and the trained model is defined as a prediction model; then, the river height and rainfall data of the current time period are input into the neural network model, and the neural model outputs the corresponding second impact range, that is, which basic grids will be affected, and how much water will overflow from the river after rainfall. The neural network model can be established through software such as MATLAB, and the specific details are existing technologies and will not be repeated here.

[0046] Then, based on the location of the node, the corresponding altitude, i.e., the first altitude, and the second altitude of the river location are obtained, and the difference between the first altitude and the second altitude is obtained, such as Figure 3 In the example, the height of the basic grid F is higher than the height of the basic grid at B1, so the water will flow to B1 after overflowing. Then, the risk probability table at B1 is determined by combining the overflow amount of the water flow; the risk probability table includes the following data, such as the first range is (1-10, 10-20, 20%), when the actual calculated difference is 5 and the predicted overflow water volume is 15, the corresponding risk probability is 20%. The risk probability table can be set by technicians in this field. Generally, the greater the height difference and the greater the overflow amount of the river, the greater the corresponding risk probability needs to be set.

[0047] In this embodiment, the risk level is obtained based on the following steps: Nodes within the second influence range are obtained and defined as influence targets. The attribution relationships between the influence targets are integrated to obtain a target data set. The target data set includes multiple different influence targets, and the influence target appears uniquely.

[0048] Reference Figure 3 Assuming that the second function points B4 and B3 are located in the second influence range, both are set as influence targets, and then their subordinate devices are combined into the target data set. However, since the second function point B5 is a subordinate device of both the second function points B4 and B3, it may be repeated in the target data set. Therefore, it is integrated so that only the unique second function points B3, B4 and B5 exist in the target data set.

[0049] Set multiple time periods, count the historical power load of each node in the time period, calculate the average power load of each time period, predict the time period when the disaster occurs, and calculate the risk value of the node based on the first formula , the first formula is: , where N is the total number of impact targets in the target data set, is the first influence range of the nth influencing target in the target data set, is the average power load in the nth time period affecting the target, is the risk probability of the nth impact target, and are the preset first weight and second weight.

[0050] A corresponding second range is set for each risk level, and a corresponding risk level is assigned to the node based on the second range where the risk value is located.

[0051] For example, if the time is split into 1 hour, each day is divided into 24 time periods, and then the historical power load of each time period is counted. Here, the power load of the same time period in the same month is added and the average value is obtained. For example, the power load of the second function point B3 from 14:00 to 15:00 every day in January is accumulated, and then divided by 31, the average power load from 14:00 to 15:00 can be obtained. After that, if the time period when the disaster occurs is 14:00 to 15:00, the average power load of the time period is obtained, and then the corresponding risk value is calculated by the first formula; since the target data set includes the second function points B3, B4 and B5, N=3, and since B3=110 in this embodiment, A1=110 in the corresponding formula, corresponding to Q1 as the average power load of the second function point B3, and U1 as the risk probability of the second function point B3.

[0052] The second range can be set to (100, 200, 1). Assuming the calculated risk value is 120, which is between 100 and 200, its risk level is set to 1.

[0053] In this embodiment, the real scene image is generated based on the following steps: The route map is split into a first map, a second map and a third map. The second map has more details than the first map, and the first map has more details than the third map. The basic grid where the second influence range is located is defined as the first grid, the basic grid where the target route in the first grid is located is defined as the second grid, and the remaining basic grids are defined as the third grid.

[0054] The terrain information included in the first grid, the second grid and the third grid is acquired from the first map, the second map and the third map respectively to generate a real-life image of the target route.

[0055] The first map, the second map and the third map all include the same grid division method, but the detail richness of each basic grid in the second map is the highest, followed by that in the first map, and the smallest in the third map; when generating the real-life image of the target new map, the basic grids outside the target line are defined as the third grids, and the information of these grids is not very important, so the real-life image is obtained from the third map, the area covered by the second influence range is more important, so the grids therein are obtained from the first map, and the grids in the second influence range and which are the target lines are the most important, so the real-life image is obtained from the second map.

[0056] As is well known, the greater the richness of details, the larger the space occupied by a single basic grid, and general maps need to be obtained from a third party, so the larger the space occupied, the longer the transmission time. In addition, after being transmitted to the local, it needs to be rendered, which also takes time; if the real-life image with the highest precision of all grids is obtained, although the richness of details is very high, it is generated slowly, which is not conducive to timely analysis of disasters; if the real-life image with the lowest precision of all grids is obtained, although the generation speed is fast, it will cause blurred images, which is not conducive to viewing map details, and therefore may lead to the inability to formulate a suitable rescue route; therefore, this embodiment ensures both the speed of map generation and the richness of details of important parts through the above method.

[0057] The present invention also provides a transmission line grid risk analysis system, which is used to implement the above-mentioned transmission line grid risk analysis method, and the system includes: An identification module is used to obtain a line map and a terrain map, the line map includes multiple transmission lines, identify nodes in the line map, and define lines between adjacent nodes as sub-lines; An analysis module, generating an attribution relationship between nodes and sub-lines based on the functions of the nodes, and generating a first influence range of each node based on the attribution relationship; The first calculation module is used to divide the terrain map into a plurality of basic grids, number each basic grid, map the route map to the terrain map, and predict the second impact range of the disaster, and obtain the risk probability of the nodes within the second impact range based on the basic grids where each node is located; The second calculation module calculates the risk level of each node based on the relationship between the nodes and the sub-lines and the risk probability; The display module defines the transmission lines with risk levels greater than the preset level as target lines, generates a real-life image covering the direction of the target lines based on the terrain map and the numbering of the basic grid, and displays it visually.

[0058] The present invention also provides a computer storage medium, which stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned transmission line grid risk analysis method.

[0059] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0060] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0061] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0063] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A grid-based risk analysis method for power transmission lines, characterized in that: include: Acquire a line map and a terrain map, wherein the line map includes a plurality of transmission lines, identify nodes in the line map, and define lines between adjacent nodes as sub-lines; generating an affiliation relationship between the node and the sub-circuit based on the function of the node, and generating a first influence range of each node based on the affiliation relationship; Dividing the terrain map into a plurality of basic grids, numbering each of the basic grids, mapping the route map to the terrain map, predicting a second impact range of the disaster, and obtaining the risk probability of the node within the second impact range based on the basic grids where each of the nodes is located; Based on the belonging relationship between the node and the sub-line, and the risk probability, calculate and obtain the risk level of each node; The transmission line having the risk level greater than the preset level is defined as a target line, and a real-life image covering the direction of the target line is generated based on the topographic map and the numbers of the basic grids, and a visual display is performed; When acquiring the attribution relationship, the node is divided into a first function point, a second function point and a third function point, the first function point unidirectionally affects the second function point, and the third function point is used to isolate the transmission line; Taking each of the first function points as a starting point, generating a plurality of connection lines with the third function point as an end point, wherein the connection lines include at least one of the first function point and the sub-line, and assigning the second function point and the sub-line in the connection lines as subordinate devices to the starting point; The direction from the starting point to the end point in the connecting line is defined as the transmission direction, each of the second function points in the connecting line is taken as the starting point, and the third function point is taken as the end point, and multiple section lines are generated along the transmission direction, and the second function points and the sub-lines in the section lines are assigned to the starting point as subordinate equipment.

2. A grid-based risk analysis method for power transmission lines according to claim 1, characterized in that: Generating the first influence scope based on the attribution relationship comprises the following steps: Obtain each power terminal device managed by the second function point in the database, obtain the subordinate devices of each starting point, add up the power terminal devices including those managed by the second function point to obtain a total value, and set the total value as the first influence range of the starting point, where the starting point includes the first function point and the second function point.

3. A grid-based risk analysis method for power transmission lines according to claim 2, characterized in that: The risk probability is calculated based on the following steps: If the terrain map includes mountains, the disaster type is set to the first type, the second impact range of the disaster is obtained, and the risk probability of the node within the second impact range is set to a predetermined value; If the terrain map includes a river, the disaster is set to a second type, a prediction model is established, the second impact range of the disaster and the river overflow are calculated based on the prediction model, the first height of the basic grid within the second impact range and the second height of the river are obtained, the difference between the first height and the second height is calculated, and a risk probability table is established, the risk probability table includes multiple first ranges and the corresponding risk probabilities, and based on the difference and the river overflow, combined with the first range, the corresponding risk probability is assigned to the basic grid.

4. A grid-based risk analysis method for power transmission lines according to claim 2, characterized in that: The risk level is obtained based on the following steps: Acquire the nodes within the second influence range, define them as influence targets, integrate the attribution relationships between the influence targets, and obtain a target data set, wherein the target data set includes a plurality of different influence targets, and the influence target appears uniquely; Set multiple time periods, count the historical power load of each node in the time period, calculate the average power load of each time period, predict the time period when the disaster occurs, and calculate the risk value of the node based on the first formula , the first formula is: , where N is the total number of impact targets in the target data set, is the first influence range of the nth influencing target in the target data set, is the average power load of the nth time period affecting the target, is the risk probability of the nth impact target, and are the preset first weight and second weight; A second range corresponding to each risk level is set, and based on the second range in which the risk value is located, the corresponding risk level is assigned to the node.

5. A grid-based risk analysis method for power transmission lines according to claim 1, characterized in that: The real scene image is generated based on the following steps: The route map is split into a first map, a second map and a third map, the second map has a richer detail than the first map, the first map has a richer detail than the third map, the basic grid where the second influence range is located is defined as a first grid, the basic grid where the target route is located in the first grid is defined as a second grid, and the remaining basic grids are defined as third grids; The terrain information included in the first grid, the second grid and the third grid is acquired from the first map, the second map and the third map respectively to generate the real-view image of the target route.

6. A transmission line grid risk analysis system, used to implement a transmission line grid risk analysis method as claimed in any one of claims 1 to 5, characterized in that: include: An identification module, used to obtain a line map and a terrain map, wherein the line map includes a plurality of transmission lines, identify nodes in the line map, and define lines between adjacent nodes as sub-lines; An analysis module, which generates an attribution relationship between the node and the sub-line based on the function of the node, and generates a first influence range of each node based on the attribution relationship; A first calculation module is used to divide the terrain map into a plurality of basic grids, number each of the basic grids, map the route map to the terrain map, and predict a second impact range of the disaster, and obtain the risk probability of the node within the second impact range based on the basic grids where each of the nodes is located; a second calculation module, based on the attribution relationship between the node and the sub-line, and the risk probability, calculating and obtaining the risk level of each of the nodes; when obtaining the attribution relationship, dividing the node into a first function point, a second function point and a third function point, the first function point unidirectionally affects the second function point, the third function point is used to isolate the transmission line, taking each of the first function points as a starting point, generating a plurality of connected lines with the third function point as an end point, the connected line including at least one of the first function point and the sub-line, the second function point and the sub-line in the connected line as subordinate equipment belonging to the starting point, defining the direction from the starting point to the end point in the connected line as the transmission direction, taking each of the second function points in the connected line as the starting point and the third function point as the end point, generating a plurality of section lines along the transmission direction, and the second function point and the sub-line in the section line as subordinate equipment belonging to the starting point; The display module defines the transmission line with the risk level greater than the preset level as a target line, generates a real-life image covering the direction of the target line based on the terrain map and the number of the basic grid, and performs a visual display.

7. A computer storage medium, characterized in that: The computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute a grid risk analysis method for power transmission lines as described in any one of claims 1-5.

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