A grid-based risk analysis method, system and storage medium for transmission lines

Abstract

The present invention discloses a method and system for grid-based risk analysis of transmission lines, belonging to the technical field of power supply and distribution. The method includes identifying nodes and sub-lines in a line map; generating an attribution relationship between the nodes and the sub-lines, and generating a first influence range of each node based on the attribution relationship; dividing a topographic map into multiple basic grids, predicting a second influence range of a disaster, and obtaining the risk probability of the nodes within the second influence range based on the basic grids where each of the nodes is located. Based on the attribution relationship between the nodes and the sub-lines, the risk level of each node is calculated; transmission lines with a risk level greater than a preset level are defined as target lines, and a real-scene image covering the trend of the target lines is generated based on the topographic map and the numbers of the basic grids. The present invention solves the problem that the prior art cannot evaluate the impact of debris flow or flood disasters on transmission lines.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply and distribution, and particularly relates to a method, a system and a storage medium for grid-based risk analysis of 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 enterprises and the daily electricity consumption of the people. Through the risk analysis of transmission lines, potential safety hazards can be discovered in a timely manner, and corresponding preventive measures can be taken to ensure the stability and safety of power supply.

[0003] The following technical solutions have been proposed in the prior art for analyzing transmission lines. For example, Chinese patent document "CN116402343A" discloses a method and a system for risk analysis of transmission lines based on a statistical analysis model. This method analyzes and quantifies the impact of severe convective weather on transmission lines according to meteorological parameters in combination with the damage situation of transmission lines affected by disasters, and divides the comprehensive operation risk levels of transmission lines according to the severity of the hazards caused by line faults, formulates a risk level division table, and then associates the fault risks of transmission lines with meteorological disasters under severe convective weather to obtain the disaster distribution law of transmission lines in severe convective weather. Finally, according to the disaster distribution law, the risk levels of each section of each region of the transmission line are divided and filled into the risk level division table to prepare a risk analysis table of the transmission line, thus completing the risk analysis of the transmission line. Another example is that Chinese patent document "CN109378818B" discloses a method and a system for risk analysis of concurrent chain faults of mountain fires in a power grid. This method calculates the affected transmission lines and the mountain fire tripping probability of the transmission lines according to the real-time monitoring and early warning results of mountain fires, constructs an initial fault combination according to the tripping probability, and finally calculates the set of deterministic chain fault lines under the initial fault combination. On this basis, a probabilistic chain fault line set of the fault combination is generated; finally, for each fault combination, the grid risk index under the fault combination condition is calculated to determine the risk degree of each transmission line.

[0004] In addition to the above disasters, transmission lines are also affected by debris flows or floods, and corresponding risk assessment schemes are not recorded in the above prior art. Summary of the Invention

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

[0006] To achieve the above invention purpose, the present invention proposes a method for grid-based risk analysis of transmission lines, including:

[0007] Obtain a line map and a terrain map, where the line map includes multiple transmission lines, identify the nodes in the line map, and define the lines between adjacent nodes as sub-lines;

[0008] Generate the attribution relationship between the nodes and the sub-lines based on the functions of the nodes, and generate the first influence range of each node based on the attribution relationship;

[0009] Divide the terrain map into multiple basic grids, number each basic grid, map the line map to the terrain map, and simultaneously predict the second influence range of the disaster. Based on the basic grids where each node is located, obtain the risk probability of the nodes within the second influence range;

[0010] Calculate the risk level of each node based on the attribution relationship between the nodes and the sub-lines, and the risk probability;

[0011] Define the transmission lines with a risk level greater than the preset level as target lines, generate a real-scene image covering the trend of the target lines based on the terrain map and the numbers of the basic grids, and perform visual display.

[0012] Furthermore, obtaining the attribution relationship includes the following steps:

[0013] Divide the nodes into first function points, second function points, and third function points. The first function points unidirectionally affect the second function points, and the third function points are used to isolate the transmission lines;

[0014] Starting from each of the first function points, generate multiple connected lines with the third function points as the end points. The connected lines include at least one of the first function points and the sub-lines, and classify the second function points and the sub-lines in the connected lines as subordinate devices to the starting points therein;

[0015] Define the direction from the starting point to the end point in the connected line as the power transmission direction. Starting from each of the second function points in the connected line and with the third function point as the end point, generate multiple section lines along the power transmission direction, and classify the second function points and the sub-lines in the section lines as subordinate devices to the starting points therein.

[0016] Furthermore, generating the first influence range based on the attribution relationship includes the following steps:

[0017] Obtain the power terminal devices managed by each of the second function points in the database, obtain the subordinate devices of each starting point, accumulate the power terminal devices including those managed by the second function points 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.

[0018] Further, calculate the risk probability based on the following steps:

[0019] If the topographic map includes mountains, set the disaster type as the first type, obtain the second influence range of the disaster, and set the risk probability of the nodes within the second influence range to a predetermined value;

[0020] If the topographic map includes rivers, set the disaster as the second type, establish a prediction model, calculate the second influence range of the disaster and the river overflow volume based on the prediction model, obtain the first height of the basic grid within the second influence range and the second height of the river, calculate the difference between the first height and the second height, establish a risk probability table, where the risk probability table includes multiple first ranges and the corresponding risk probabilities, and assign the corresponding risk probabilities to the basic grid based on the difference and the river overflow volume in combination with the first range.

[0021] Further, obtain the risk level based on the following steps:

[0022] Obtain the nodes within the second influence range, define them as influence targets, integrate the attribution relationships between the influence targets to obtain a target dataset, where the target dataset includes multiple different influence targets and the influence targets appear uniquely;

[0023] Set multiple time periods, count the historical power consumption loads of each node in the time periods, calculate the average power consumption 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 influence targets in the target dataset, is the first influence range of the nth influence target in the target dataset, is the average power consumption load of the nth influence target in the time period, is the risk probability of the nth influence target, and are the preset first weight and second weight;

[0024] For each of the risk levels, a corresponding second range is set. Based on the second range in which the risk value lies, the corresponding risk level is assigned to the node.

[0025] Further, the real-scene image is generated based on the following steps:

[0026] The line map is split into a first map, a second map, and a third map. The second map has a higher level of detail richness than the first map, and the first map has a higher level of detail richness 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 line is located in the first grid is defined as the second grid, and the remaining basic grids are defined as the third grid;

[0027] The terrain information included in the first grid, the second grid, and the third grid is obtained from the first map, the second map, and the third map respectively to generate the real-scene image of the target line.

[0028] The present invention also provides a power transmission line grid risk analysis system, which is used to implement the above-mentioned power transmission line grid risk analysis method. The system includes:

[0029] An identification module, which is used to obtain a line map and a terrain map. The line map includes multiple power transmission lines, identify the nodes in the line map, and define the lines between adjacent nodes as sub-lines;

[0030] An analysis module, which generates the attribution relationship between the nodes and the sub-lines based on the functions of the nodes, and generates the first influence range of each node based on the attribution relationship;

[0031] A first calculation module, which is used to divide the terrain map into multiple basic grids, number each basic grid, map the line map to the terrain map, and simultaneously predict the second influence range of disasters. Based on the basic grids where each node is located, obtain the risk probability of the nodes within the second influence range;

[0032] A second calculation module, which calculates the risk level of each node based on the attribution relationship between the nodes and the sub-lines, and the risk probability;

[0033] A display module, which defines the power transmission lines with the risk level greater than the preset level as target lines, generates a real-scene image covering the route of the target lines based on the terrain map and the numbers of the basic grids, and performs visual display.

[0034] The present invention also provides a computer storage medium storing program instructions, which, when the program instructions are running, control the device where the computer storage medium is located to execute the above-mentioned method for grid-based risk analysis of transmission lines.

[0035] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0036] The present invention first identifies transmission lines to obtain the attribution relationships between various nodes in the transmission lines, and based on this, generates which users' normal power consumption will be affected after an accident occurs at each node, and then analyzes the terrain where the transmission lines are located; when analyzing the terrain map, the terrain map is divided into multiple grids, and after that, by combining the prediction data of various weather conditions, the risk probability of each basic grid suffering from disasters is determined. Finally, the risk probability is combined with the first influence range of each node to obtain the risk level corresponding to each node, so that the degree of influence of different transmission lines by disasters can be determined according to the risk level, thereby realizing the risk analysis of different transmission lines.

[0037] 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 levels 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 conduct manual analysis based on the real-scene map, thereby quickly and reasonably helping them make the next decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of the steps for grid-based risk analysis of a transmission line according to the present invention;

[0039] Figure 2 is a schematic diagram of a line map according to the present invention;

[0040] Figure 3 is a schematic diagram after superimposing the terrain map and the grid map according to the present invention;

[0041] Figure 4 is a system structure diagram of grid-based risk analysis of a transmission line according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0043] 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 one element from another. For example, without departing from the scope of this application, the first xx script may be referred to as the second xx script, and similarly, the second xx script may be referred to as the first xx script.

[0044] As Figure 1 shown, a method for grid risk analysis of a transmission line includes:

[0045] Step S1: Obtain a line map and a terrain map. The line map includes multiple transmission lines. Identify the nodes in the line map and define the lines between adjacent nodes as sub-lines.

[0046] The line map only includes the distribution of transmission lines, specifically as Figure 2 shown; the terrain map includes the terrain distribution of the area where the transmission line is located, including terrain height, river distribution, terrain features, etc., specifically as Figure 3 shown. Figure 3 For the convenience of subsequent explanation of the solution, a simplified treatment is performed on it. Then, identify the nodes in the transmission line. The nodes are the positions of the electric poles, such as Figure 2 A1, B1, etc. in, and the line between the two is the sub-line.

[0047] Step S2: Generate the attribution relationship between the nodes and the sub-lines based on the functions of the nodes, and generate the first influence range of each node based on the attribution relationship.

[0048] In this implementation, the nodes are divided into the first function points, the second function points, and the third function points. The first function points are the starting points of the transmission lines, such as Figure 2 A1 to A3 in, the third function points are C1 to C2 among them. The role of the third function points is to isolate each line and avoid mutual influence between the lines. The remaining B1 to B6 are the second function points. Then, obtain the attribution relationship of each function point. Through the attribution relationship, it can be judged whether there is mutual influence between the function points. The method for obtaining the attribution relationship will be introduced in detail later. After obtaining the attribution relationship, the first influence range can be known. For example, when the second function point B2 is damaged, which areas of users will be affected in normal power supply.

[0049] Step S3: Divide the terrain map into multiple basic grids, number each basic grid, map the line map to the terrain map, and at the same time predict the second influence range of the disaster. Based on the basic grids where each node is located, obtain the risk probability of the nodes within the second influence range;

[0050] Divide the topographic map into multiple basic grids of the same size. In this embodiment, each basic grid is also numbered. Figure 3 For the mapped image, it can be known which node is located in which basic grid. Before or during a disaster, predict the second affected area of the disaster. Specifically, which basic grids may be affected by the disaster can be determined by future rainfall conditions and in combination with the water level sensors set in each basic grid. The method of calculating the risk probability will be introduced later.

[0051] Step S4: Based on the attribution relationship between nodes and sub-lines, and the risk probability, calculate the risk level of each node.

[0052] The risk level of a node can be calculated in combination with the attribution relationship between nodes. This embodiment includes four risk levels: 1 / 2 / 3 / 4. The specific calculation method of the risk level will be introduced later.

[0053] Step S5: Define the transmission lines with a risk level greater than the preset level as target lines, generate a real-scene image covering the route of the target lines based on the topographic map and the numbers of the basic grids, and perform visual display.

[0054] Here, the preset level is set to 3. If there is a node in a transmission line with a risk level greater than 3, then this transmission line is set as the target line, indicating that the risk level of the transmission line is relatively high. Then, generate a real-scene image of the target line so that the monitoring personnel can remotely understand the specific topographic distribution of the transmission line, which is convenient for making the next decision. The specific method of generating the real-scene image will be introduced later.

[0055] The present invention first identifies the transmission lines to obtain the attribution relationship between each node in the transmission lines, and based on this, generates which users' normal power consumption will be affected after an accident occurs at each node. Then, analyze the terrain where the transmission lines are located. When analyzing the topographic map, divide the topographic map into multiple grids. After that, by combining the prediction data of various weathers, determine the risk probability of each basic grid suffering from a disaster. Finally, combine the risk probability with the first affected area of each node to obtain the risk level corresponding to each node. In this way, the degree of influence of different transmission lines affected by the disaster can be determined according to the risk level, so as to realize the risk analysis of different transmission lines.

[0056] 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 where the disaster occurred on the map. In addition, after determining the risk levels of each transmission line, an alarm map covering the corresponding area can be quickly generated according to the numbers of the basic grids, enabling relevant personnel to perform manual analysis based on the real-scene map, thereby quickly and reasonably helping them make the next decision.

[0057] It should be particularly noted that through the above technical solution, the present invention solves the problem that the prior art cannot evaluate the impact of debris flow or flood disasters on transmission lines.

[0058] In this embodiment, obtaining the attribution relationship includes the following steps:

[0059] The nodes are divided into a first functional point, a second functional point, and a third functional point. The first functional point unidirectionally affects the second functional point, and the third functional point is used to isolate the transmission line.

[0060] Taking each first functional point as a starting point, multiple connected lines with the third functional point as the end point are generated. The connected lines include at least one first functional point and sub-lines. The second functional points and sub-lines in the connected lines are classified as subordinate devices and attributed to the starting point therein.

[0061] Continue to refer to Figure 2 , here, taking the determination of the attribution relationship of the first functional point A2 as an example, first, taking the first functional point A2 as the starting point, a connected line with the third functional point C1 is generated. It can be seen from the figure that there are second functional points B3 and B4 and sub-lines L1, L2, and L3 between A2 and C1. Therefore, the second functional points B3, B4, and sub-lines L1, L2, and L3 are classified as the subordinate devices of the first functional point A2, that is, attributed to the first functional point A2. Then, taking the first functional point A2 as the starting point, a connected line with the third functional point C2 is generated. It can be seen from the figure that in addition to the above functional points and sub-lines, there are also second functional points B5 and sub-lines L4 and L5. Therefore, the second functional point B5 and sub-lines L4 and L5 are classified as the subordinate devices of the first functional point A2. Here, the sub-lines are also marked and used as attribution devices, so that relevant personnel can understand which sub-lines will be affected when a problem occurs at the node.

[0062] Define the direction from the starting point to the end point in the connected line as the power transmission direction. Taking each second functional point in the connected line as the starting point and the third functional point as the end point, multiple section lines are generated along the power transmission direction. The second functional points and sub-lines in the section lines are classified as subordinate devices and attributed to the starting point therein.

[0063] Continuing with the first functional point A2 as an example, since the starting point is A2 and the ending point is C1, the power transmission direction is D. Then, taking the second functional point B4 as the starting point, along the power transmission direction, obtain the functional points and sub-lines after the second functional point B4 and before the third functional points C1 and C2. As can be seen from the figure, the subordinate devices of the second functional point B4 include the second functional points B3 and B5, and also include the sub-lines L2, L3, L4, and L5.

[0064] Through this step, the attribution relationship of each node between the power transmission lines can be obtained. From this, it can be known how large the impact range will be when each node is eroded by disasters. For example, after the first functional point A2 is damaged, because it has many subordinate devices, after it is damaged, its subordinate devices cannot supply power normally, so this will cause a very large impact range. And after the second functional point B5 is damaged, it has no subordinate devices, so the resulting impact range is small. Therefore, through this step, the importance of each node is determined, laying a foundation for determining the first impact range in the next step.

[0065] In this embodiment, generating the first impact range based on the attribution relationship includes the following steps:

[0066] Obtain the power terminal devices managed by each second functional point in the database, obtain the subordinate devices of each starting point, accumulate the power terminal devices including those managed by the second functional point to obtain a total value, and set the total value as the first impact range of the starting point. The starting points include the first functional point and the second functional point.

[0067] Specifically, the data in the database is pre-entered, which includes the power terminal devices managed by each second functional point and their quantities, 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-side devices. For example, the database records that the second functional point B4 manages 50 households' power terminal devices, and the second functional point B3 manages 110 households' power devices. It should be 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 an inclusion relationship.

[0068] After that, combining the above-mentioned attribution relationship, calculate the first impact range of each node. For example, if the second functional point B5 manages 80 households' power devices, then the first impact range of the second functional point B4 is 110 + 50 + 80 = 240 households, and the first impact range of the second functional point B3 is 50 + 80 = 130 households.

[0069] In this embodiment, calculate the risk probability based on the following steps:

[0070] If the terrain map includes mountains, set the disaster type to the first type, obtain the second influence range of the disaster, and set the risk probability of the nodes within the second influence range to a predetermined value.

[0071] Specifically, if there are mountains in the terrain map, it is predicted that the first type of disaster, i.e., debris flow disaster, may occur. If there are rivers, it is predicted that the second type of disaster, i.e., flood disaster, may occur.

[0072] In one embodiment, obtain the predicted rainfall in the mountain area. If the predicted rainfall is greater than the first preset value, it is predicted that the basic grid involved in the mountain area will have the first type of disaster, i.e., debris flow disaster. The corresponding influence range is also the basic grid involved in the mountain area, which is set as the second influence range of the disaster, and the corresponding risk probability is uniformly set to 80%. In another embodiment, a water level monitor can also be set in each basic grid. When the water level in a certain basic grid drops significantly in a short time, the basic grid and the area below the altitude of this basic grid are determined as the second influence area.

[0073] The reason for setting a unified value here is that since debris flow involves changes in the geological structure, there is a great possibility that the electric poles will tilt or collapse when debris flow occurs. Therefore, the risk probability affected by debris flow is set to a relatively large value here.

[0074] If the terrain map includes rivers, set the disaster to the second type, establish a prediction model, calculate the second influence range of the disaster and the river overflow volume based on the prediction model, obtain the first height of the basic grid within the second influence range and the second height of the river, calculate the difference between the first height and the second height, establish a risk probability table. The risk probability table includes multiple first ranges and corresponding risk probabilities, and based on the difference and the river overflow volume, combined with the first range, assign the corresponding risk probability to the basic grid.

[0075] Specifically, the prediction model is a BP neural network model. Before prediction, first obtain the river height data before rainfall, and then obtain the historical rainfall data, which includes the rainfall amount in each time period, such as the rainfall amount from 1:00 to 2:00. Then obtain how much water the river will overflow after rainfall corresponding to each time period; refer to Figure 3, in the case of relatively large rainfall, river H will overflow after rising above the river channel height. However, the specific overflow direction is affected by topographical factors. For example, the altitude of the basic grid where B1 is located is less than that of the basic grid where B2 is located. Therefore, the river will flow to the grid at B1. In addition, it is also necessary to collect which basic grids the river will flow to after overflowing during the historical time period; input the above data as a training set into the neural network model for training, and define the trained model as a prediction model; then, input the current river height and rainfall data into the neural network model, and the neural model outputs the corresponding second influence range, that is, which basic grids will be affected, and it will also output how much water the river will overflow after rainfall. The neural network model can be established through software such as MATLAB. The specific details are prior art and will not be elaborated here.

[0076] After that, obtain the corresponding altitude based on the position of the node, that is, the first altitude, and the second altitude at the river position, and obtain the difference between the first altitude and the second altitude. For example Figure 3 In it, the height of the basic grid F is higher than that of the basic grid at B1. Therefore, the water flow will flow to B1 after overflowing. Then, combined with the overflow volume of the water flow, determine the risk probability table at B1 through the risk probability table; the risk probability table includes the following data. For example, the first range is (1 - 10, 10 - 20, 20%). When the actually 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 those skilled in the art. Generally, the greater the height difference and the greater the overflow volume of the river, the greater the corresponding risk probability needs to be set.

[0077] In this embodiment, the risk level is obtained based on the following steps:

[0078] Obtain the nodes located within the second influence range and define them as influence targets. Integrate the attribution relationships between the various influence targets to obtain a target data set. The target data set includes multiple different influence targets, and the influence targets appear uniquely.

[0079] Refer to Figure 3 , assume that the second function points B4 and B3 are located within the second influence range. Then set them as influence targets, and then combine their subordinate devices into the target data set. However, since the second function point B5 is simultaneously a subordinate device of the second function points B4 and B3, it may be repeatedly reproduced in the target data set. Therefore, integrate it so that only the unique second function points B3, B4, and B5 exist in the target data set.

[0080] Set multiple time periods, count the historical power consumption loads of each node during the time periods, and calculate the average power consumption load for 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 factors affecting the target in the target dataset, is the first influence range of the nth factor affecting the target in the target dataset, is the average power consumption load during the time period of the nth factor affecting the target, is the risk probability of the nth factor affecting the target, and are the preset first weight and second weight.

[0081] A corresponding second range is set for each risk level, and based on the second range where the risk value is located, the corresponding risk level is assigned to the node.

[0082] For example, if it is split by 1 hour, it means splitting each day into 24 time periods, and then counting the historical power consumption loads of each time period. Here, the power consumption loads of the same time period in the same month are added together and the average value is calculated. For example, the power consumption loads of the second functional point B3 from 14:00 to 15:00 every day in January are accumulated and then divided by 31 to obtain the average power consumption load from 14:00 to 15:00. After that, if the time period when the disaster occurs is from 14:00 to 15:00, the average power consumption load of this time period is obtained, and then the corresponding risk value is calculated through the first formula; since the target dataset includes the second functional points B3, B4, and B5, so N = 3, and since B3 = 110 in this embodiment, so A1 = 110 in the corresponding formula, and correspondingly Q1 is the average power consumption load of the second functional point B3, and U1 is the risk probability of the second functional point B3.

[0083] The second range can be set to (100, 200, 1). Assuming the calculated risk value is 120, which is between 100 and 200, so its risk level is set to 1.

[0084] In this embodiment, the real-scene image is generated based on the following steps:

[0085] The line map is split into a first map, a second map, and a third map. The detail richness of the second map is greater than that of the first map, and the detail richness of the first map is greater than that of 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 line is located in the first grid is defined as the second grid, and the remaining basic grids are defined as the third grid.

[0086] The terrain information included in the first grid, the second grid, and the third grid is obtained from the first map, the second map, and the third map respectively to generate the real-scene image of the target line.

[0087] The first map, the second map, and the third map all include the same grid division method. However, the detail richness of each basic grid in the second map is the highest, followed by the first map, and the smallest in the third map. When generating the real-scene image of the target new map, the basic grids outside the target line are defined as the third grids. The information of these grids is less important, so the real-scene image is obtained from the third map. The area covered by the second influence range is relatively important, so the grids therein are obtained from the first map. And the grids that are both in the second influence range and the target line are the most important, so the real-scene image is obtained from the second map.

[0088] As is well known, the greater the detail richness, the larger the space occupied by a single basic grid. And since general maps need to be obtained from a third party, the larger the space occupied, the longer the transmission time. Additionally, after being transmitted to the local area, it also needs to be rendered, which also takes time. If real-scene images with the highest precision of all grids are obtained, although the detail richness is very high, the generation is slow, which is not conducive to timely analysis of disasters. If real-scene images with the lowest precision of all grids are obtained, although the generation speed is fast, it will cause the picture to be blurred, which is not conducive to viewing the map details, and thus may lead to the inability to formulate a suitable emergency rescue route. Therefore, through the above method in this embodiment, both the generation speed of the map and the detail richness of the important parts are ensured.

[0089] The present invention also provides a power transmission line grid-based risk analysis system, which is used to implement the above-mentioned power transmission line grid-based risk analysis method. The system includes:

[0090] An identification module, which is used to obtain a line map and a terrain map. The line map includes multiple power transmission lines, identify the nodes in the line map, and define the lines between adjacent nodes as sub-lines;

[0091] An analysis module, which generates the attribution relationship between nodes and sub-lines based on the functions of the nodes, and generates the first influence range of each node based on the attribution relationship;

[0092] A first calculation module, which is used to divide the terrain map into multiple basic grids, number each basic grid, map the line map to the terrain map, and at the same time predict the second influence range of disasters. Based on the basic grids where each node is located, obtain the risk probability of the nodes within the second influence range;

[0093] A second calculation module, which calculates and obtains the risk level of each node based on the attribution relationship between nodes and sub-lines, and the risk probability;

[0094] A display module, which defines the power transmission lines with a risk level greater than the preset level as target lines, generates a real-scene image covering the trend of the target lines based on the terrain map and the numbers of the basic grids, and performs visual display.

[0095] The present invention also provides a computer storage medium storing program instructions, wherein when the program instructions run, they control the device where the computer storage medium is located to execute the above-mentioned method for grid risk analysis of a transmission line.

[0096] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0097] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The above 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 methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of 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 (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0098] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0099] The above embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

[0100] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within 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 grid where each node 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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