Gas pipe network emergency guard point site selection optimization method and device
By optimizing the location of emergency duty points in gas pipeline networks using clustering algorithms and evaluation metrics, the problem of location selection under uncertain demand conditions was solved, enabling efficient emergency response and low-cost material supply.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-29
- Publication Date
- 2026-03-10
AI Technical Summary
In existing technologies, most emergency duty point location models for gas pipeline networks are based on defined needs and fail to effectively address the location problem under uncertain demand conditions, resulting in low efficiency in emergency response.
Clustering algorithms are used to determine the coverage area of the guard points. The theoretical guard points are calculated by the distance between the initial guard points and the data points. The final guard points are selected by combining the emergency response time constraints. The site selection is optimized by using evaluation indicators.
It has improved the efficiency of emergency supplies supply and rescue and repair personnel, reduced transportation costs, and ensured the speed and coverage of emergency response.
Smart Images
Figure CN121638515A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present specification relates to the field of emergency rescue, and in particular to a gas pipeline network emergency guard point site selection optimization method and device. BACKGROUND
[0002] In recent years, the demand for gas is increasing year by year, and gas pipelines are still expanding. Town gas pipeline leakage, fire, explosion and other accidents occur frequently, which has become a major threat to public safety. Such sudden public safety events require emergency decision-making departments to carry out preliminary disposal of the accident area in a short time. The emergency guard point (hereinafter referred to as the guard point) as an indispensable part of the emergency rescue system directly determines the speed, cost, coverage rate and timeliness and efficiency of the emergency material support system during rescue.
[0003] The research on the site selection of the gas pipeline network emergency guard point is still not much, and the research on the selection, evaluation and constraint condition setting of the model has not formed a system; most of the current research on the site selection model of the gas pipeline network emergency guard point is based on the deterministic demand model, and the research on the site selection model under uncertain demand conditions is relatively less. SUMMARY
[0004] To solve the problems of the prior art, the embodiments of the present specification provide a gas pipeline network emergency guard point site selection optimization method and device.
[0005] The embodiments of the present specification provide a gas pipeline network emergency guard point site selection optimization method, which comprises: determining a guard point coverage area using a clustering algorithm according to the distribution of data points in a gas pipeline accident area; selecting at least one initial guard point from the guard point coverage area, and determining a theoretical guard point according to the distance between the initial guard point and each data point in the gas pipeline accident area; wherein the initial guard point is randomly selected from the data points in the guard point coverage area; judging whether the theoretical guard point and the corresponding data point both satisfy the emergency rescue response time constraint condition; if yes, screening the theoretical guard point according to the evaluation index to obtain the final guard point site; if not, adjusting the number of selected initial guard points, and re-determining the theoretical guard point and the final guard point site.
[0006] According to an aspect of the embodiments of the present specification, the determining the guard point coverage area according to the data point distribution in the gas pipeline accident area using a clustering algorithm comprises: extracting clustering features of the data points in the gas pipeline accident area; constructing a clustering feature tree according to the clustering features and initial parameters of the BIRCH algorithm to obtain an initial spatial clustering result of the data points; calculating the size of the contour coefficient according to the initial spatial clustering result, and determining whether the contour coefficient meets a preset condition; if not, updating the initial parameters for iteration, re-clustering and calculating the size of the contour coefficient until the contour coefficient meets the preset condition; determining the current parameters of the BIRCH algorithm and the spatial clustering result, and the spatial clustering result is the coverage range of the guard point.
[0007] According to an aspect of the embodiments of the present specification, the calculating the size of the contour coefficient according to the initial spatial clustering result comprises: calculating the contour coefficient using the following formula:
[0008] Wherein, S(i) represents the contour coefficient of the i th sample data point, a(i) represents the average distance of the i th sample data point and the sample data points in the same cluster; b(i) represents the average distance of the i th sample data point to all sample data points in other clusters C j .
[0009] According to an aspect of the embodiments of the present specification, selecting at least one initial guard point from the guard point coverage area, and determining the theoretical guard point according to the distance between the initial guard point and each data point in the gas pipeline accident area comprises: calculating the distance between the initial guard point and each data point in the guard point coverage area according to the coordinate position of the at least one initial guard point by Kmeans algorithm, and assigning the data points to the nearest initial guard point; determining whether there is an initial guard point that does not contain data points; if not, determining a new guard point according to the number of data points contained in the specific coverage area of the initial guard point; determining whether the new guard point is the same as the initial guard point; if yes, determining the initial guard point as the theoretical guard point; if not, taking the new guard point as the initial guard point and continuing to execute the foregoing steps.
[0010] According to an aspect of the embodiments of the present specification, determining whether the theoretical guard point and the corresponding data point meet the emergency rescue response time constraint condition comprises: calculating the distance between all data points in the coverage area of the theoretical guard point and the theoretical guard point; selecting the data point with the maximum distance, and determining whether the maximum response time corresponding to the maximum distance meets the emergency rescue response time constraint condition; if not, iteratively updating the number of initial guard points, re-determining whether the maximum response time between the current guard point and the data point meets the emergency rescue response time constraint condition until the number of selected initial guard points meets the rescue time constraint condition, and determining the number of guard points and the theoretical guard point.
[0011] According to an aspect of the embodiments of the present specification, the final value guard point site is obtained by screening the theoretical value guard points according to evaluation indexes, including: determining a first site selection index according to traffic conditions, office conditions, coverage range and economic conditions of the gas pipe network; determining the weight of the first site selection index and the weight of a second site selection index according to the second site selection index of each first site selection index; determining the evaluation result of the theoretical value guard point according to the weight of the first site selection index, the weight of the second site selection index and the fuzzy evaluation score; and determining the final value guard point site from the theoretical value guard points according to the evaluation result.
[0012] According to an aspect of the embodiments of the present specification, the second site selection index includes: vehicle access convenience, parking condition, life convenience, office condition, value guard condition, training and drilling condition, distance to the farthest failure point, time to the farthest failure point, actual guarantee radius, value guard point construction cost and operation lease cost.
[0013] The embodiments of the present specification provide a gas pipe network emergency value guard point site optimization device, the device includes: a coverage area determination unit configured to determine a value guard point coverage area using a clustering algorithm according to the distribution of data points in a gas pipeline accident area; a theoretical value guard point determination unit configured to select at least one initial value guard point from the value guard point coverage area, and determine a theoretical value guard point according to the distance between the initial value guard point and each data point in the gas pipeline accident area; wherein the initial value guard point is randomly selected from the data points in the value guard point coverage area; a judgment unit configured to determine whether the theoretical value guard point and the corresponding data point both satisfy an emergency rescue response time constraint condition; a final value guard point site selection unit configured to, if yes, obtain a final value guard point site by screening the theoretical value guard points according to evaluation indexes; and an adjustment unit configured to, if no, adjust the number of selected initial value guard points, and re-determine the theoretical value guard point and the final value guard point site.
[0014] The embodiments of the present specification also provide a computer device, which includes a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the gas pipe network emergency value guard point site optimization method when executing the computer program.
[0015] The embodiments of the present specification also provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the gas pipe network emergency value guard point site optimization method.
[0016] The present application reasonably arranges the emergency value guard points, which can not only reduce the transportation cost, but also ensure the efficiency of emergency material supply and rescue personnel controlling the danger. BRIEF DESCRIPTION OF DRAWINGS
[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present specification, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.
[0018] Figure 1 A flowchart of a gas pipeline network emergency value point site selection optimization method is shown;
[0019] Figure 2 A flowchart of a method for determining a value point coverage area is shown;
[0020] Figure 3 A flowchart of a method for determining a theoretical value point is shown;
[0021] Figure 4 A flowchart of a method for determining whether a time constraint condition is met is shown;
[0022] Figure 5 A flowchart of a method for determining a value point site selection is shown;
[0023] Figure 6 A structural schematic diagram of a gas pipeline network emergency value point site selection optimization device is shown;
[0024] Figure 7 A data point distribution schematic diagram is shown;
[0025] Figure 8 A clustering result schematic diagram is shown;
[0026] Figures 9A to 9D A schematic diagram of multiple value point coverage ranges is shown;
[0027] Figure 10 A schematic diagram of a final value point site selection is shown;
[0028] Figure 11 A structural schematic diagram of a computer device is shown.
[0029] Explanation of drawing symbols:
[0030] 601, coverage area determination unit;
[0031] 602, theoretical value point determination unit;
[0032] 603、judgment unit;
[0033] 604、final value point location unit;
[0034] 605、adjustment unit;
[0035] 1102、computer device;
[0036] 1104、processor;
[0037] 1106、memory;
[0038] 1108、drive mechanism;
[0039] 1110、input / output module;
[0040] 1112、input device;
[0041] 1114、output device;
[0042] 1116、presentation device;
[0043] 1118、graphical user interface;
[0044] 1120、network interface;
[0045] 1122、communication link;
[0046] 1124、communication bus. DETAILED DESCRIPTION
[0047] In order to make the technical personnel in the technical field better understand the technical solutions in the specification, the technical solutions in the specification will be described clearly and completely in the specification below. Obviously, the described embodiments are only part of the embodiments of the specification, not all. Based on the embodiments in the specification, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the specification.
[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.
[0049] This specification provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.
[0050] Based on the situation of pipeline natural gas companies, emergency duty points on gas pipeline networks are mainly used to store various commonly used emergency supplies for accident sites. In the event of a gas accident, gas pipeline emergency repair personnel can arrive at the scene as quickly as possible to control on-site risks and prevent secondary disasters. A well-planned layout of emergency duty points can not only reduce transportation costs but also ensure the efficient supply of emergency materials and the effective control of potential hazards by emergency repair personnel.
[0051] It should be noted that the gas pipeline emergency duty point site selection optimization method described in this specification can be used in the field of emergency rescue, but this specification does not limit the application field of the gas pipeline emergency duty point site selection optimization method and device.
[0052] Figure 1 The diagram shown is a flowchart of an embodiment of this specification regarding a method for optimizing the location of emergency duty points in a gas pipeline network. The system includes:
[0053] Step 101: Based on the distribution of data points in the gas pipeline accident area, use a clustering algorithm to determine the coverage area of the monitoring point.
[0054] In this manual, the gas pipeline accident area is typically a region of gas pipeline system failure, potential hazards, or high consequences. This area summarizes relevant gas pipeline data, including failure data, hazard data, and high-consequence area data. The data points within this area are two-dimensional spatial data points corresponding to geographical locations prone to gas pipeline hazards. Table 1 shows the pipeline data points for a specific area:
[0055] Table 1. Statistics on pipeline-related data for a certain area
[0056] Related data information Quantity statistics Failure data 197 Hidden danger data 330 High consequence area data 40 Total 567
[0057] In gas pipeline construction areas, there are multiple data points, forming one or more data point distributions within the gas pipeline accident area. Therefore, it is necessary to use clustering algorithms to classify the resulting data point distributions. Specifically, the spatial latitude and longitude coordinates of each data point are first converted into planar coordinates. Planar coordinates can be represented as x... i =(x i ,y i ), (i = 1, 2, ..., n), where i represents the i-th data point and n represents the total number of data points. Taking the data in Table 1 as an example, in order to represent all relevant data on the map and to better solve for the optimal coordinates of the data points later, duplicate data and data points that are very close to each other were removed, resulting in the final 475 data points. The latitude and longitude coordinates were then converted into planar coordinates using the Gaussian projection method, as shown in Table 2.
[0058] Table 2. Statistics on pipeline-related data for a certain area
[0059]
[0060]
[0061] Using the pipeline failure, hidden danger, and high-consequence areas in Table 2 as the basic data, BIRCH clustering was employed to determine the coverage area of the monitoring points. Specifically, through repeated numerical experiments, the clustering algorithm was used to extract the clustering features of data points in the gas pipeline accident area and construct a clustering feature tree to obtain the clustering calculation results. Simultaneously, based on the clustering effect, the parameters used to construct the clustering feature tree were repeatedly adjusted and iterated until the parameters with the best clustering result were determined. The clustering result is then the coverage area of the monitoring points in this step. In addition to the coverage area of the monitoring points calculated by the BIRCH algorithm, the coordinates of each data point within the coverage area can also be determined.
[0062] Step 102: Select at least one initial monitoring point from the monitoring point coverage area, and determine the theoretical monitoring point based on the distance between the initial monitoring point and each data point in the gas pipeline accident area.
[0063] The initial guard point is randomly selected from the data points within the coverage area of the guard point.
[0064] In this step, data points are randomly selected from the coverage area of the monitored points as initial monitored points, and then the theoretical monitored points are determined. Specifically, an enumeration method is used to determine the number of theoretical monitored points. First, assuming there is only one initial monitored point in the coverage area, the coordinates of the theoretical monitored point can be determined using k-means clustering. After determining the initial monitored points, this step also includes: calculating the distance between each data point and each initial monitored point; selecting the closest initial monitored point and assigning the data point to it. Based on the number of data points contained in the specific coverage area of each initial monitored point, the initial monitored point is determined as the theoretical monitored point.
[0065] If the theoretical guard point cannot be determined based on the initial guard point, increase the number of initial guard points and repeat the aforementioned steps. For a detailed description of this step, please refer to [link to detailed steps]. Figure 3 .
[0066] Step 103: Determine whether the theoretical monitoring points and their corresponding data points all meet the emergency response time constraint. In this step, the emergency response time constraint is the time required to reach the farthest point where an accident or disaster may occur, starting from the emergency monitoring point, following a reasonably planned route, under various anticipated conditions. This constraint directly affects the efficiency and effectiveness of the emergency response. If the time required to reach the farthest data point in the coverage area of the selected theoretical monitoring point is less than the emergency response time constraint, then the time required to reach all data points in the coverage area of the theoretical monitoring point meets the emergency response constraint. In this step, the emergency response time is calculated for each theoretical monitoring point and its farthest data point, thus determining whether all data points corresponding to that theoretical monitoring point meet the emergency response time requirement. For details of the determination process, see [link to relevant documentation]. Figure 4 describe.
[0067] Step 104: If so, select theoretical guard points based on evaluation indicators to obtain the final guard point location.
[0068] The theoretical duty stations identified in the aforementioned steps are based solely on theoretical calculations and do not take into account actual conditions, including but not limited to: traffic conditions, office conditions at the duty stations, economic costs, and the coverage area of the duty stations. Therefore, it is necessary to further screen the theoretical duty stations using specific evaluation indicators.
[0069] Step 105: If not, adjust the number of selected initial guard points and redetermine the theoretical and final guard point locations. This step is based on the result in Step 103 that the emergency response time constraint between the theoretical guard points and data points is not met, requiring adjustment of the number of selected initial guard points. The number of initial guard points is increased sequentially, and based on the new number of initial guard points, steps 102 to 103 are repeated until the theoretical guard points and final guard point locations are determined. This invention's rational arrangement of emergency guard points not only reduces transportation costs but also ensures the efficiency of emergency material supply and emergency response personnel in controlling the situation.
[0070] Figure 2 The diagram shown is a flowchart of a method for determining the coverage area of a guard point according to an embodiment of this specification, which specifically includes the following steps:
[0071] Step 201: Extract clustering features of data points in the gas pipeline accident area.
[0072] Specifically, clustering features include the number of data points n in a sub-cluster, the linear sum of n data points (denoted as LS), and the sum of squares of n data points (denoted as SS). The linear sum LS of n data points determines the center x0 of each sub-cluster, allowing for the calculation of distances between sub-clusters. The sum of squares of n data points reflects the average distance between all objects within a sub-cluster, i.e., the sub-cluster radius R.
[0073] Where n represents the number of data points in the sub-cluster; LS represents the linear sum of n data points, and SS represents the square sum of n data points;
[0074] The distance between subclusters is expressed by the following formula:
[0075] x0 = LS / n;
[0076] Where x0 represents the center of each sub-cluster, LS represents the linear sum of n data points, SS represents the square sum of n data points, reflecting the average distance between all objects within the sub-cluster, and R represents the sub-cluster radius.
[0077] Step 202: Based on the clustering features and the initial parameters of the BIRCH algorithm, construct a clustering feature tree to obtain the initial spatial clustering result of the data points.
[0078] In this step, the initial BIRCH parameters are set to the non-leaf node branching factor B, the number of subclusters in the front leaf node is greater than the leaf branching factor L and the subcluster radius threshold T, and one data point is randomly selected as the subcluster. Then, data points x are read one by one. i As an independent subcluster, it is added to the other existing subcluster that is closest to it. If x is added...i If the radius R of the subcluster is greater than the subcluster radius threshold T, then the element in the subcluster that is farthest from the center is split into a new subcluster and assigned to the corresponding leaf node. If the number of subclusters contained in the current leaf node is greater than the leaf branching factor L, then the two farthest subclusters are selected and split into two new leaf nodes, and then the other subclusters are assigned to the two newly split leaf nodes according to the principle of relative proximity.
[0079] If x is added i If the subcluster radius R does not exceed the subcluster radius threshold T, then the CF triplet of the corresponding node is updated along the path from the leaf node to the root node, and non-leaf nodes that exceed the non-leaf node branch factor B limit are split in the same way.
[0080] Step 203: Calculate the profile coefficient based on the initial spatial clustering results, and determine whether the profile coefficient meets the preset conditions.
[0081] This step calculates the silhouette coefficient based on the initial spatial clustering results obtained in the preceding steps. The silhouette coefficient is used to evaluate the clustering results produced by the clustering method. The formula for calculating the silhouette coefficient is as follows:
[0082] Where S(i) represents the silhouette coefficient of the i-th sample data point, a(i) represents the average distance between the i-th sample data point and sample data points within the same cluster, and b(i) represents the distance from the i-th sample data point to other clusters C. j The average distance between all sample data points within the range.
[0083] Where a(i) is smaller, it means that the clustering effect is better and the sample should be clustered into this cluster; b(i) is larger, it means that the overall clustering effect is better and the sample i is less likely to belong to other clusters; S(i) is close to 1, which means that the clustering of sample i is reasonable; S(i) is close to -1, which means that sample i should be classified into another cluster; if S(i) is close to 0, it means that sample i is on the boundary between two clusters.
[0084] In the embodiments of this specification, the preset condition is a specific value of 1. The closer the contour coefficient is to 1, the closer the contour coefficient is to the preset condition.
[0085] Step 204: If not, update the initial parameters, re-cluster, and calculate the silhouette coefficient until the silhouette coefficient reaches the preset condition. In this step, if the difference between the silhouette coefficient calculated in step 203 and 1 is large, the initial parameters of the BIRCH algorithm need to be reset: non-leaf node branch factor B, the number of subclusters contained in the current leaf node is greater than the leaf branch factor L, and the subcluster radius threshold T. Adjust the values of parameters B, L, and T, repeat steps 202 to 203, recalculate the silhouette coefficient for the spatial clustering results after parameter adjustment, and re-determine whether the silhouette coefficient reaches the preset condition until the silhouette coefficient reaches the preset condition, then stop the iteration.
[0086] Step 205: Determine the current parameters and spatial clustering results of the BIRCH algorithm, where the spatial clustering results represent the coverage area of the monitoring points. Based on the preceding steps, after stopping iteration, determine the parameters B and L of the BIRCH algorithm and the spatial clustering results. According to the spatial clustering results, a large number of data points in the gas pipeline accident area can be accurately divided into different classes, thereby dividing the gas pipeline accident area into multiple monitoring point coverage areas. This specification will subsequently select suitable data points from these monitoring point coverage areas as monitoring points.
[0087] Figure 3 The diagram shown is a flowchart of a method for determining a theoretical guard point according to an embodiment of this specification, which specifically includes the following steps:
[0088] Step 301: Based on the coordinates of the at least one initial monitoring point, calculate the distance between the initial monitoring point and each data point in the coverage area of the monitoring point using the K-means algorithm, and assign the data points to the nearest initial monitoring point. In this step, the K-means clustering algorithm is used to... Figure 2 The selection of theoretically optimal guard points is based on the determined guard point coverage area. Figure 2 After clustering a large number of data points in the gas pipeline accident area, the gas pipeline accident area was divided into multiple monitoring point coverage areas. In this step, k data points are first randomly selected from a monitoring point coverage area as the initial monitoring points a1, a2, ..., a1 in that area. k In this step, the number of guard points within the coverage area of each guard point is enumerated sequentially. Initially, only one guard point is selected, assuming that only one guard point is needed in this area. The location of the guard point can be determined through k-means clustering.
[0089] In this step, the K-means algorithm is used to calculate the distance between each data point in the coverage area of the initial guard point and the current guard point. When there is only one initial guard point in the initial stage, all data points in the gas pipeline accident area are assigned to this one initial guard point.
[0090] When there are multiple initial guard points, to better allocate data points to their corresponding guard points, it is necessary to calculate the distance between each data point in the guard point's coverage area and the multiple initial guard points. Then, a clustering algorithm is used to assign data points closer to the initial guard points, thus distributing the data points to their respective initial guard points. This ensures that each data point is assigned to the initial guard point closest to it. Specifically, this includes:
[0091] (1) Calculate the distance between each data point and each initial guard point;
[0092] (2) Select the nearest initial guard point and assign the data point to the initial guard point.
[0093] In this step, the data point z can be calculated using the following formula. i With the initial value point a l Distance:
[0094] Among them, (x i ,y i (x) represents the coordinates of the data point after Gaussian projection. l ,y l ) represents the initial value point a. l The coordinates of a l Multiple different initial guard points can be represented separately.
[0095] Based on the distance between the data point and the initial guard point, determine the minimum / nearest distance, and assign the data point to the initial guard point corresponding to the minimum distance. The minimum / nearest distance is determined using the following formula:
[0096] Among them, a j This represents the initial guard point for the assigned data points, ||z i -a l || represents the Euclidean distance between the data point and the initial value point; 1≤l≤k.
[0097] Step 302: Determine if there are any initial guard points that do not contain data points. This step further confirms whether each initial guard point contains its own data points to avoid situations where an initial guard point is not assigned any data points. For example, suppose there are two guard points with coordinates (0,0) and (1,0), and assuming that all data points in the area covered by the guard points are located on the x-axis and to the right of coordinate (2,0). According to Euclidean distance, the initial guard point (0,0) will not be assigned any data points. Therefore, this step is necessary to confirm whether a guard point contains data points.
[0098] Step 303: If not, determine the new guard point based on the number of data points contained in the specific coverage area of the initial guard point. If all initial guard points contain data points, calculate the new guard point according to the following formula: Where: a* j N represents the j-th most recently monitored point; j L represents the specific coverage area of the j-th latest guard point. j The number of data points in the z-axis; i Indicates the specific coverage area L of the latest guard point. j The i-th data point in the middle.
[0099] In this step, data points in the coverage area can also be reassigned to new guard points based on the distance between the data points and the new guard points.
[0100] If so, randomly select a larger number of initial guard points, and redistribute data points using the Kmeans algorithm, repeating steps 301 to 302.
[0101] Step 304: Determine if the new guard point is the same as the initial guard point. In this step, it is determined whether the new guard point in step 303 is the same point as the guard point selected in step 301.
[0102] Step 305: If yes, determine the initial guard point as the theoretical guard point.
[0103] In this step, if the new guard point is the same as the initial guard point, the initial guard point will be used as the theoretical guard point.
[0104] Step 306: If not, use the new guard point as the initial guard point and continue with the aforementioned steps. Repeat steps 301 to 305 until the theoretical guard point is determined.
[0105] Figure 4 The diagram shown is a flowchart of a method for determining whether a time constraint condition is met, according to an embodiment of this specification. The method specifically includes the following steps:
[0106] Step 401: Calculate the distance between all data points in the coverage area of the theoretical guard point and the theoretical guard point. In this step, the Euclidean distance between each data point in the coverage area of the theoretical guard point and the theoretical guard point is calculated.
[0107] In this step, an enumeration method is used. The number and corresponding locations of all monitored points within their coverage area are enumerated to meet the boundary conditions for emergency response time specified by each rescue unit. In other words, only one initial monitored point is selected for calculation in this initial step. For example, one theoretical monitored point, point A, is selected. Point A has 20 data points within its coverage area; therefore, the distances between each of these 20 data points and the theoretical monitored point A are calculated.
[0108] Step 402: Select the data point with the largest distance and determine whether the maximum response time corresponding to the largest distance meets the emergency response time constraint.
[0109] From the distances between each data point and the theoretical monitoring point calculated in step 401, select the data point with the largest distance to each theoretical monitoring point. Then, calculate the response time between the monitoring point and the data point with the largest possible distance from the theoretical monitoring point using a navigation method; this is the maximum response time. For example, from the distances between 20 data points and the theoretical monitoring point A, select data point 18 corresponding to the largest distance, and determine the time required to reach data point 18 from the theoretical monitoring point; this is the maximum response time.
[0110] Compare the maximum response time with the emergency response time constraint. If the maximum response time is less than the emergency response time constraint, it indicates that the response times of other data points are also less than the emergency response time, thus satisfying the constraint. The emergency response time constraint is used to determine whether emergency personnel and supplies at the designated points can quickly arrive at the scene within the specified time limit to carry out efficient emergency repair operations, directly affecting the severity of the accident's consequences. A well-planned layout of emergency duty points can ensure the efficient supply of emergency materials and the control of the emergency situation by rescue and repair personnel.
[0111] If the maximum response time in step 402 satisfies the emergency response time constraint, it means that the currently selected theoretical guard point and all data points meet the emergency response time constraint, and the selected theoretical guard point is appropriate and correct. Therefore, the coordinates of the theoretical guard point can be determined, and the results can be combined with... Figure 5 The specific evaluation indicators are used to further screen theoretical guard points, thereby determining the final guard point location.
[0112] Step 403: If not satisfied, iteratively update the number of theoretical guard points, re-determine whether the maximum response time between the current guard point and the data point meets the emergency response time constraint, until the number of selected initial guard points meets the emergency response time constraint, and determine the number of guard points and theoretical guard points.
[0113] If the time requirement is not met, the number of theoretical monitoring points is updated iteratively. More theoretical monitoring points are selected, and the distances between all data points in the coverage area of each theoretical monitoring point and that monitoring point are determined. The maximum response time corresponding to the maximum distance is determined, and then it is checked whether the maximum response time meets the emergency response time constraint. If it does, the number of theoretical monitoring points is set to 2. If it does not, the above steps are repeated until the number of initial monitoring points meets the emergency response time constraint. The number of monitoring points and the number of theoretical monitoring points are then determined.
[0114] Figure 5 The diagram shown is a flowchart of a method for determining the location of a guard point according to an embodiment of this specification, which specifically includes the following steps:
[0115] Step 501: Determine the first site selection criteria based on the gas pipeline network's traffic conditions, office conditions, coverage area, and economic conditions.
[0116] In one aspect of the embodiments of this specification, the location of the guard post should be as convenient as possible to ensure that emergency resources can be delivered to the destination as quickly as possible; the above considerations regarding transportation conditions can be determined as the first location index related to transportation conditions.
[0117] In the embodiments described in this specification, the location of the duty station needs to fully consider the natural environment, and the location should not be in an area prone to natural disasters such as earthquakes and floods. The above considerations regarding natural conditions can be identified as the first location selection indicator related to office conditions.
[0118] In the embodiments described in this specification, the location of the duty station maximizes the coverage and coverage area of the mission, ensuring the rapid provision of emergency maintenance supplies. This is the first location selection criterion related to coverage.
[0119] In the embodiments described in this specification, the total cost of the duty station includes construction investment costs, daily operating costs, and on-duty personnel costs; among which, daily operating costs mainly include equipment maintenance costs and equipment replenishment costs; on-duty personnel costs mainly include the wages and manpower consumption of emergency repair personnel. This is the first site selection indicator related to economic conditions.
[0120] Combining the judgment matrix scaling method, the judgment matrix for the first location selection index is constructed as shown in Table 3:
[0121] Table 3. Judgment Matrix of First Site Selection Indicators
[0122]
[0123] According to the consistency test definition, when CR < 0.1, the consistency of the judgment matrix is considered acceptable, the criterion layer judgment matrix is a valid matrix, and weight calculation can be performed to obtain the weight of the first location index:
[0124] W0=[0.2622 0.1175 0.5650 0.0553]
[0125] Step 502: Determine the weight of the first location index and the weight of the second location index based on the second location index of each first location index.
[0126] In the embodiments of this specification, the first site selection index related to traffic conditions includes the following second site selection index: vehicle accessibility index, parking conditions index, and living convenience index. The vehicle accessibility index indicates the ease with which emergency rescue vehicles can quickly and safely enter and exit the duty station in an emergency. For example, near roads or highways, vehicle accessibility is good, allowing vehicles to arrive and leave quickly. The parking conditions index indicates the quality and suitability of the parking and lodging conditions provided by the duty station for emergency rescue vehicles. The duty station should have sufficient parking space to accommodate all emergency rescue vehicles that need to park, and the ground should be firm, flat, and able to withstand the weight of heavy vehicles, preventing problems such as vehicles sinking or tilting. The living convenience index indicates the convenience and comfort of the living conditions provided by the duty station for emergency rescue personnel, facilitating staff commuting and material transportation, and being in a safe environment, avoiding threats to staff's lives due to factors such as security or natural disasters.
[0127] In the embodiments of this specification, the first location selection indicators related to office conditions include the following second location selection indicators: office conditions indicators, duty conditions indicators, and training and drill conditions indicators.
[0128] The office conditions indicator refers to the quality of the working environment and facilities provided by the duty station for emergency rescue personnel. The duty station should have stable communication facilities, such as telephone, internet, and radio, to ensure the receipt of emergency rescue instructions and alarms. The duty station conditions indicator refers to the ability to provide safe and convenient material storage facilities to ensure the timely replenishment and management of emergency supplies and equipment. The training and drill indicator refers to the quality and suitability of the training and drill facilities and environment provided by the duty station for emergency rescue personnel. The duty station should have sufficient space and facilities for daily training and drills, including indoor training rooms and outdoor drill areas.
[0129] In the embodiments of this specification, the first location index related to coverage includes the following second location index: distance to the farthest failure point index, time to reach the farthest failure point index, and actual protection radius index.
[0130] The indicators are as follows: Distance to the farthest failure point: The distance from the emergency response duty station to the farthest possible location where an accident or disaster may occur; used to assess the coverage of the duty station. Time to reach the farthest failure point: The time required to reach the farthest possible location where an accident or disaster may occur, starting from the emergency response duty station and following a reasonably planned route, under various anticipated conditions; this time directly affects the efficiency and effectiveness of the emergency response. Actual coverage radius: The maximum area that the emergency response duty station can effectively cover and provide rescue services within a certain timeframe; this radius directly affects the effectiveness and coverage of the emergency response.
[0131] In the embodiments of this specification, the first site selection indicator related to economic conditions includes the following second site selection indicators: construction cost indicator and annual operating / leasing cost indicator. The construction cost indicator represents the total cost required to establish an emergency response and rescue post, and is an important economic indicator for assessing the rationality of the site selection. The annual operating / leasing cost indicator represents the total operating and leasing costs required for the emergency response and rescue post in each year, and this indicator is directly related to the project's continued operation and economic feasibility.
[0132] In this step, there are 11 second location selection indicators. The analytic hierarchy process (AHP) can be used to construct judgment matrices for these indicators, calculate their eigenvectors and eigenvalues, and determine their weights through hierarchical ranking. The judgment matrices for four of the second location selection indicators are shown in Tables 4 to 7 below.
[0133] Table 4. Judgment Matrix for Second Site Selection Criterion B1
[0134]
[0135] Table 5. Judgment Matrix for Second Site Selection Criterion B2
[0136]
[0137] Table 6. Judgment Matrix for Second Site Selection Criterion B3
[0138]
[0139]
[0140] Table 7. Judgment Matrix for Second Site Selection Criterion B4
[0141]
[0142] Using the above matrix and the definition of consistency test, the largest eigenvalue λ of matrix B1 is obtained. max Given a B2 matrix with 2, CI = 0, CR = 0; the largest eigenvalue λ of the B2 matrix. maxThe value is 4.0533, CI = 0.0178, CR = 0.02 < 0.1; the largest eigenvalue λ of matrix B3. max The value is 3, CI = CR = 0; the largest eigenvalue λ of matrix B4 is... max Given a value of 2, CI = CR = 0, and as shown in Tables 5-8, the relative consistency index CR of each second location criterion is less than 0.1. Therefore, the determination of each second location criterion is reasonable. The weight vector of each second location criterion is as follows:
[0143] W1 = [0.3333 0.6667]
[0144] W2=[0.0665 0.3077 0.5288 0.1030]
[0145] W3 = [0.2500 0.2500 0.5000]
[0146] W4 = [0.3333 0.6667]
[0147] Based on the weights of the first and second site selection indicators, a comprehensive weight table is obtained as shown in Table 8:
[0148] Table 8. Comprehensive Weighting Table
[0149]
[0150] Step 503: Determine the evaluation result of the theoretical guard point based on the weight of the first site selection index, the weight of the second site selection index, and the fuzzy evaluation score.
[0151] First, a set of evaluation comments was set up, corresponding to four levels: completely satisfied, satisfied, neutral, and dissatisfied. Fuzzy comprehensive evaluation method was used to comprehensively evaluate the site selection project for the guard post. Based on the four levels of completely satisfied, satisfied, neutral, and dissatisfied comments, the second site selection index layer in the evaluation index system for the guard post site selection scheme was evaluated. (See Table 9 below.)
[0152] Table 9 Evaluation Results of Evaluation Indicators
[0153] Evaluation index Evaluation result Evaluation index Evaluation result C1 Vehicle access convenience (0.75 0.25 0 0) C7 Distance to the farthest failure point (0.5 0.5 0 0) C2 Parking condition (0 0 0.5 0.5) C8 Time to reach the farthest failure point (0.25 0.75 0 0) C3 Life convenience (0.25 0.75 0 0) C9 Actual security radius (0.25 0.5 0 0.25) C4 Office condition (0.5 0.25 0.25 0) C10 Guard point construction cost (0 0.75 0 0.25) C5 Guard condition (0.5 0.25 0 0.25) C11 Annual operation / rental fee (0 0.5 0.25 0.25) C6 Training condition (0.25 0.25 0.5 0)
[0154] The evaluation index matrix R can be obtained from the above evaluation results and index weights, and then the evaluation result V = W × R of the evaluated object can be obtained.
[0155] First location selection indicator layer B1:
[0156]
[0157]
[0158] Evaluation result V1 of theoretical guard point 1:
[0159]
[0160] Given the actual geographical conditions, if the theoretical guard point does not meet the construction conditions, distance relaxation can be performed within a radius of 500m to 1000m of the calculated theoretical guard point. Other suitable guard point sites can be selected within this range, and the evaluation results can be determined.
[0161] Step 504: Based on the evaluation results, determine the final guard point locations from the theoretical guard points. This step may involve calculating the satisfaction levels of multiple theoretical guard points within the guard point coverage area. Select the points with the highest satisfaction levels as the final guard points.
[0162] Based on the final evaluation results, the rationality assessment of this theoretical guard point location scheme is as follows: the proportion of those completely satisfied is 0.2959, the proportion of those satisfied is 0.4052, the proportion of those in a neutral position is 0.1117, and the proportion of those dissatisfied is 0.1872. According to the principle of maximum membership, this theoretical guard point location scheme can be considered to have good rationality.
[0163] In this manual, based on the evaluation results of each indicator in the first site selection indicator layer, the final theoretical duty point evaluation result V = W0 × R is obtained as shown in Table 10, and the duty point information is shown in Table 11, which meets the requirement of 45 minutes emergency response time stipulated by the emergency rescue unit.
[0164] Table 10 Evaluation Results of Theoretical Guard Points
[0165]
[0166] Table 11 Results of Guard Point Location Selection
[0167] Guard point Coordinate Response time 1 (103.840,30.058) 43 2 (103.759,29.982) 42 3 (103.917,30.051) 44 4 (103.844,29.839) 45
[0168] Optimizing the location of monitoring points is directly related to the natural gas company's response speed and ultimate effectiveness in the event of a gas pipeline accident.
[0169] like Figure 6 The diagram shown is a structural schematic of a gas pipeline emergency duty point location optimization device according to an embodiment of this specification. The diagram illustrates the basic structure of the gas pipeline emergency duty point location optimization device. The functional units and modules can be implemented using software, or they can be implemented using general-purpose chips or specific chips to optimize the location of gas pipeline emergency duty points. The device specifically includes:
[0170] The coverage area determination unit 601 is used to determine the coverage area of the guard point based on the distribution of data points in the gas pipeline accident area using a clustering algorithm;
[0171] The theoretical monitoring point determination unit 602 is used to select at least one initial monitoring point and determine the theoretical monitoring point based on the distance between the initial monitoring point and each data point in the gas pipeline accident area; wherein, the initial monitoring point is randomly selected from the data points in the coverage area of the monitoring point;
[0172] Judgment unit 603 is used to determine whether the theoretical guard point and the corresponding data point both meet the emergency rescue response time constraint conditions;
[0173] The final guard point location unit 604 is used to select theoretical guard points based on evaluation indicators to obtain the final guard point location if necessary.
[0174] The adjustment unit 605 is used to adjust the number of selected initial guard points if no, and to redetermine the theoretical guard points and the final guard point locations.
[0175] Figure 7 The diagram shown is a schematic representation of data point distribution according to an embodiment of this specification. The diagram illustrates spatial data points represented by latitude and longitude coordinates. This diagram can be obtained through analysis of a gas pipeline network topology diagram.
[0176] Figure 8 The diagram shown illustrates a clustering result from an embodiment of this specification. Based on pipeline failures, potential hazards, and high-consequence areas, clustering features of data points in gas pipeline accident areas are extracted, and BIRCH clustering is used to determine the coverage area of monitoring points. Through repeated numerical experiments, the data points are divided into four clusters. The center point of each cluster is further obtained through clustering and is shown as a black square. This demonstrates how data points can be divided into four sub-clusters.
[0177] Based on the distribution of gas pipelines, one monitoring point is set up within the coverage area of the monitoring point for calculation. The theoretical monitoring point location is solved using K-means. The theoretical monitoring point location is shown in Table 12.
[0178] Table 12 Information on Theoretical Guard Points
[0179] Theoretical guard point Latitude and longitude coordinates 1 (103.840,30.058) 2 (103.759,29.982) 3 (103.917,30.051) 4 (103.844,29.839)
[0180] Figures 9A to 9D The diagram shown illustrates the coverage area of multiple monitoring points in an embodiment of this specification. It shows the locations and surrounding environment of monitoring points 1, 2, 3, and 4 on the satellite map.
[0181] Figure 10 This diagram illustrates a final location selection for a monitoring point according to an embodiment of this specification. The diagram shows the locations of monitoring points 1, 2, 3, and 4 on a map, as well as the districts where the monitoring points are located and the nearby transportation network.
[0182] like Figure 11 The diagram illustrates a computer device provided in an embodiment of this specification. The gas pipeline emergency duty point location optimization method described in this application can be applied to the computer device. The computer device 1102 may include one or more processors 1104, such as one or more central processing units (CPUs), each of which can implement one or more hardware threads. The computer device 1102 may also include any memory 1106 for storing any kind of information such as code, settings, data, etc. Non-limitingly, for example, the memory 1106 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 1102. In one case, when the processor 1104 executes associated instructions stored in any memory or combination of memories, the computer device 1102 can perform any operation of the associated instructions. The computer device 1102 also includes one or more drive mechanisms 1108 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.
[0183] Computer device 1102 may also include an input / output module 1110 (I / O) for receiving various inputs (via input device 1112) and providing various outputs (via output device 1114). A specific output mechanism may include a presentation device 1116 and an associated graphical user interface (GUI) 1118. In other embodiments, the input / output module 1110 (I / O), input device 1112, and output device 1114 may be omitted, and the device may function solely as a computer device within a network. Computer device 1102 may also include one or more network interfaces 1120 for exchanging data with other devices via one or more communication links 1122. One or more communication buses 1124 couple the components described above together.
[0184] Communication link 1122 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 1122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.
[0185] Corresponding to Figures 1 to 5 In addition to the methods described above, embodiments of this specification also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the methods described above.
[0186] This specification also provides computer-readable instructions, wherein when a processor executes the instructions, the program therein causes the processor to perform the following... Figures 1 to 5 The method shown.
[0187] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.
[0188] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this specification generally indicates that the preceding and following related objects have an "or" relationship.
[0189] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.
[0190] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0191] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.
[0192] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described in this specification, depending on actual needs.
[0193] Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0194] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0195] This specification uses specific embodiments to illustrate the principles and implementation methods of this specification. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this specification. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this specification. Therefore, the content of this specification should not be construed as a limitation of this specification.
Claims
1. A gas pipeline network emergency value point site selection optimization method, characterized in that, The method comprises: According to the distribution of data points in the gas pipeline accident area, the cluster algorithm is used to determine the guard point coverage area; Select at least one initial guard point from the guard point coverage area, and determine the theoretical guard point according to the distance between the initial guard point and each data point in the gas pipeline accident area; wherein the initial guard point is randomly selected from the data points in the guard point coverage area; Determine whether the theoretical guard point and the corresponding data point meet the emergency rescue response time constraint condition; If yes, select the theoretical guard point according to the evaluation index to obtain the final guard point site; If not, adjust the number of selected initial guard points, and re-determine the theoretical guard point and the final guard point site.
2. The method of claim 1, wherein, According to the distribution of data points in the gas pipeline accident area, the cluster algorithm is used to determine the guard point coverage area, which comprises: Extract the clustering characteristics of the data points in the gas pipeline accident area; According to the clustering characteristics and the initial parameters of the BIRCH algorithm, a clustering characteristic tree is constructed to obtain the initial spatial clustering result of the data points; Calculate the contour coefficient size according to the initial spatial clustering result, and determine whether the contour coefficient meets the preset condition; If not, update the initial parameters for iteration, re-cluster and calculate the contour coefficient size until the contour coefficient meets the preset condition; Determine the current parameters of the BIRCH algorithm and the spatial clustering result, and the spatial clustering result is the guard point coverage range.
3. The method of claim 2, wherein, According to the initial spatial clustering result, the contour coefficient size is calculated, which comprises: The contour coefficient is calculated using the following formula: where S(i) represents the silhouette coefficient of the i-th sample data point, a(i) represents the average distance of the i-th sample data point to the sample data points within the same cluster; and b(i) represents the average distance of the i-th sample data point to all sample data points in other clusters C j where S(i) represents the silhouette coefficient of the i-th sample data point, a(i) represents the average distance of the i-th sample data point to the sample data points within the same cluster; and b(i) represents the average distance of the i-th sample data point to all sample data points in other clusters C 4. The method of claim 1, wherein, Select at least one initial guard point from the guard point coverage area, and determine the theoretical guard point according to the distance between the initial guard point and each data point in the gas pipeline accident area, which comprises: According to the coordinate position of the at least one initial guard point, the distance between the initial guard point and each data point in the guard point coverage area is calculated by Kmeans algorithm, and the data points are assigned to the nearest initial guard point; Determine whether there is an initial guard point that does not contain data points; If not, determine the new guard point according to the number of data points contained in the specific coverage area of the initial guard point; Determine whether the new guard point is the same as the initial guard point; If yes, the initial guard point is determined as the theoretical guard point; If not, the new guard point is used as the initial guard point, and the previous steps are continued.
5. The method of claim 4, wherein, Determine whether the theoretical guard point and the corresponding data point meet the emergency rescue response time constraint condition, which comprises: Calculate the distance between all data points in the coverage area of the theoretical guard point and the theoretical guard point; Select the data point with the maximum distance, and determine whether the maximum response time corresponding to the maximum distance meets the emergency rescue response time constraint condition; If not, update the number of initial guard points for iteration, and re-determine whether the maximum response time between the current guard point and the data point meets the emergency rescue response time constraint condition until the number of selected initial guard points meets the rescue time constraint condition, and the number of guard points and the theoretical guard point are determined.
6. The method of claim 5, wherein, Select the theoretical guard point according to the evaluation index to obtain the final guard point site, which comprises: The first site selection index is determined according to traffic conditions, office conditions, coverage range and economic conditions of the gas pipeline network; The weight of the first site selection index and the weight of the second site selection index are determined according to the second site selection index of each first site selection index; The evaluation result of the theoretical value guard point is determined according to the weight of the first site selection index, the weight of the second site selection index and the fuzzy evaluation score; The final value guard point site is determined from the theoretical value guard point according to the evaluation result.
7. The method of claim 6, wherein, The second site selection index includes vehicle access convenience, parking condition, life convenience, office condition, value guard condition, training and drilling condition, distance to the farthest failure point, time to the farthest failure point, actual support radius, value guard point construction cost and operation lease cost.
8. A device for optimizing the location of an emergency point of a gas network, characterized in that it comprises: The device includes: A coverage area determination unit configured to determine a value guard point coverage area using a clustering algorithm according to data point distribution in a gas pipeline accident area; A theoretical value guard point determination unit configured to select at least one initial value guard point from the value guard point coverage area, and determine a theoretical value guard point according to distance between the initial value guard point and each data point in the gas pipeline accident area; wherein the initial value guard point is randomly selected from data points in the value guard point coverage area; A judgment unit configured to judge whether the theoretical value guard point and the corresponding data point both satisfy an emergency rescue response time constraint condition; A final value guard point site selection unit configured to, if yes, screen the theoretical value guard point according to evaluation indexes to obtain a final value guard point site; An adjustment unit configured to, if no, adjust the number of selected initial value guard points, and re-determine the theoretical value guard point and the final value guard point site.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium characterized by, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method of any one of claims 1 to 7.