Regional emergency reservoir site selection method and equipment with multiple extreme disaster risks overlapped

Abstract

The invention discloses a method and equipment for site selection of a regional emergency reservoir with superposed extreme disaster risks, and the method comprises the steps: screening out typhoon paths according to a target region, clustering the typhoon paths, and obtaining the wind speeds of all path points; performing tsunami seismic source analysis on the target area to obtain tsunami wave data; carrying out emergency capability analysis on the chemical industry park in the target area to obtain a total candidate area; comprehensively evaluating risk indexes of the chemical industrial park under typhoon and tsunami situations according to the wind speed and tsunami wave data, and dividing the total candidate area into the proportion of safety candidate areas according to the risk indexes; and sorting the safety candidate areas according to the rescue distance, and recommending the safety candidate area with the top sorting as an area emergency library address. According to the method, when two disasters with relatively long research history, namely typhoon disasters and tsunami disasters, are selected for analysis, normalization is performed in the process of selecting risk factors of the typhoon disasters and the tsunami disasters, so that the phenomenon of unreasonable setting of key factors of risk analysis factor weights is avoided.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence, and more specifically, relates to a method and equipment for site selection of regional emergency storage facilities with multiple extreme disaster risks superimposed. Background Technology

[0002] When establishing chemical industrial parks, emergency supplies are typically stored within or around the park, and emergency response mechanisms are established to improve the park's emergency response capabilities and mitigate the domino effect of accidents. However, when extreme disasters strike a chemical industrial park, the probability of a major accident surges dramatically, and the damage becomes incalculable. Given the extreme nature of these disasters, the emergency response capabilities surrounding the chemical industrial park are often insufficient to handle them. For example, extreme disasters such as typhoons and tsunamis can easily cause process equipment to float and drift, pipelines connecting containers to break down, storage tank shells to deform and tip over, tank tops to break down, and power supply equipment to short-circuit, resulting in enormous property damage and loss of life.

[0003] Currently, two main technological trends have emerged: one is the application of more complex heuristic algorithms to the site selection of emergency material reserve depots; the other is the use of existing methods for assessing extreme disaster risks, combined with the functional requirements of emergency material reserve depots, to form a more targeted and directional site selection approach. However, compared to the practical needs of emergency material reserve depot site selection in chemical industrial parks, the superposition of multiple extreme disasters is a more realistic and complex issue.

[0004] Therefore, overcoming the technical defects of the existing technology is an urgent problem to be solved in this technical field. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method and equipment for constructing emergency storage facilities based on extreme disasters, the purpose of which is to solve the technical problem of how to select the site for emergency material storage facilities in areas where multiple extreme disasters overlap.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for selecting the location of regional emergency storage facilities with overlapping risks of multiple extreme disasters is provided, the method comprising:

[0007] Based on the target area, typhoon paths are selected, and the typhoon paths are clustered to obtain the wind speed at all path points.

[0008] Tsunami earthquake source analysis was performed on the target area to obtain tsunami wave data;

[0009] An emergency response capability analysis was conducted on the chemical industrial parks within the target area to obtain the overall candidate areas;

[0010] Based on the wind speed and tsunami wave data, the risk indicators of the chemical industrial park under typhoon and tsunami scenarios are comprehensively evaluated, and the proportion of the total candidate area is divided into safe candidate areas according to the risk indicators.

[0011] The candidate safe zones are then sorted according to their rescue distance, and the candidate safe zones with the highest rankings are recommended as the addresses of the regional emergency response database.

[0012] As a further improvement and supplement to the above solution, the present invention also includes the following additional technical features.

[0013] Preferably, the method for selecting typhoon paths based on the target region, clustering the typhoon paths, and obtaining the wind speeds at all path points includes:

[0014] Clustering was obtained by analyzing the typhoon path distance matrix and typhoon scenario classification for the target area;

[0015] Hourly path points are obtained by interpolation, and the maximum wind speed value of the typhoon wind speed cluster is added to the hourly path points.

[0016] Preferably, the method for performing tsunami earthquake source analysis on the target area to obtain tsunami wave data includes:

[0017] Analyze the tsunami earthquake sources along the coastline of the target area to obtain the magnitude and strong earthquake period;

[0018] Simulate tsunami scenarios along the coastline of the target area to obtain scenarios that reach the risk of coastline inundation, and select tsunami scenarios of level III or above;

[0019] The tsunami scenario is processed into a grid, and the largest tsunami wave value in each cell is obtained and filled to obtain tsunami wave data.

[0020] Preferably, the method for analyzing the emergency response capabilities of chemical industrial parks within the target area to obtain the total candidate areas includes:

[0021] The geographical information of highway entrances / exits within and around the target area that has been put into use is statistically analyzed. Chemical industrial parks within a preset distance from highway entrances / exits are considered as candidate areas, and the total candidate areas are obtained.

[0022] Preferably, the method for comprehensively assessing the risk indicators of the chemical industrial park under typhoon and tsunami scenarios based on the wind speed and tsunami wave data, and dividing the total candidate area into safe candidate areas according to the risk indicators, includes:

[0023] The risk value of the chemical industrial park affected by the typhoon scenario, the risk value affected by the tsunami scenario, and the area of ​​the chemical industrial park are multiplied as the risk value of the chemical industrial park affected by the disaster, i.e., the risk index of the chemical industrial park.

[0024] Based on the risk values ​​of each chemical industrial park, they are ranked, and then, according to the ranking results, all chemical industrial parks are divided into hazardous chemical industrial parks and safe candidate areas in proportion.

[0025] Preferably, the risk indicators of the chemical industrial park are normalized before calculation, taking into account the total risk of the chemical industrial park affected by typhoon scenarios, the maximum risk value affected by tsunami scenarios, and the area of ​​the chemical industrial park.

[0026] Preferably, the method for sorting the candidate safe areas according to rescue distance and recommending the top-ranked candidate safe areas as emergency storage addresses includes:

[0027] Obtain location data for safety candidate areas and hazardous chemical industrial parks;

[0028] Calculate the rescue distance from each safety candidate zone to the hazardous chemical industrial park;

[0029] The rescue distance of the candidate safe zones is analyzed and sorted to obtain the rescue distance ranking results;

[0030] Based on the ranking of rescue distances, the preferred candidate areas are determined.

[0031] Preferably, the method of obtaining hourly path points through interpolation and allocating the maximum extreme value of typhoon wind speed clustering to the hourly path points includes:

[0032] After obtaining the clustering paths, the path point data with a frequency of 1 time / 1 hour are labeled. Based on the wind speed clustering method, the maximum extreme value is inserted into four clustering path points, and the wind speed results are filled into each path point. The specific interpolation method is as follows:

[0033]

[0034] In the formula, (X1, Y1) and (X2, Y2) are the known coordinates of two points, X is the x-coordinate of the target point, and Y is the interpolated y-coordinate of the target point.

[0035] Preferably, the method for performing gridding processing on the tsunami scenario, obtaining the largest tsunami wave value in each cell and filling it to obtain tsunami wave data includes:

[0036] Convert the pixel values ​​of the obtained image to grayscale values;

[0037] Create a grayscale legend, and match each pixel on the scene image with the legend one by one. After the matching is completed, the geographic coordinates will be determined and rearranged according to the grayscale values ​​of the legend.

[0038] The maximum value of the row in the TIFF format image is resampled, and the resampled cells are divided according to the number of latitude and longitude grids of 0.05°×0.05°. The maximum tsunami wave value in the cell is extracted as the tsunami wave value of the cell.

[0039] Data synthesis is performed to obtain the average tsunami wave value for each scenario by superimposing the probabilities of the scenarios.

[0040] According to another aspect of the present invention, a regional emergency depot site selection device with multiple extreme disaster risks superimposed is provided, characterized in that it includes:

[0041] One or more processors;

[0042] A storage device for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the regional emergency storage site selection method for multiple extreme disaster risks as described in any one aspect.

[0043] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0044] In this invention, when analyzing typhoon disasters and tsunami disasters, two types of disasters with a long history of research, the risk factors for typhoon disasters and tsunami disasters are normalized during the selection process to avoid unreasonable setting of risk analysis factor weights. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0046] Figure 1 This is a schematic diagram of a method for selecting a regional emergency depot location based on multiple extreme disaster risks, as provided in Embodiment 1.

[0047] Figure 2 This is the typhoon clustering path provided in Embodiment 1;

[0048] Figure 3 This is an improved tsunami scenario effect based on the Comcot model in Embodiment 1;

[0049] Figure 4 This is a schematic diagram of a regional emergency storage site selection device with multiple extreme disaster risks superimposed, provided in Embodiment 1. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0051] Example 1

[0052] In existing technologies, a site selection model for disaster relief material reserve depots in chemical industrial parks based on typhoon disasters has been constructed. However, during the practical work of selecting emergency disaster relief material reserve depots in the target area, through expert review and field visits, three problems were found in the existing model: First, the lack of a normalization method in the selection of risk factors leads to unreasonable weighting of risk analysis factors due to inconsistencies in units such as distance and area. Second, while the model can clearly identify the impact of typhoon disasters on chemical industrial parks during the site selection process, it struggles to reflect the extreme nature of the disasters. The existing model only addresses typhoon disasters without adequately addressing their destructive potential. Third, while typhoons are the primary extreme disaster to be prevented in the site selection of material reserve depots in various chemical industrial parks within the target area, tsunamis, as another extreme disaster, also pose a significant threat to coastal provinces. How to apply tsunami disaster analysis to the site selection of disaster relief material reserve depots and integrate it with existing site selection models is a new challenge currently facing the research group.

[0053] Based on the above issues, and considering the existing site selection model for emergency material reserve warehouses in regional chemical industrial parks under typhoon scenarios, this first embodiment needs to further consider the following practical characteristics of the improved model:

[0054] Functionality: Improve the disaster risk analysis module. The improved model should be able to achieve the application of multiple disaster risk superposition.

[0055] Extremes: Improve the parts derived from the disaster risk analysis. The improved model needs to be able to reflect the impact of extreme disasters on the carrier, and ensure that the model can be effectively verified after disasters such as typhoons of level 16 or above and tsunamis of level III or above occur.

[0056] This first embodiment provides a method for selecting the location of regional emergency storage facilities with multiple extreme disaster risks overlapping, taking into account the scenarios of typhoon and tsunami extreme disasters.

[0057] Methods include, for example Figure 1 The steps shown are as follows:

[0058] S101: Select typhoon paths based on the target area, cluster the typhoon paths, select typhoon scenarios of level 16 and above, and obtain the extreme wind speeds of typhoons at all path points.

[0059] This embodiment takes Zhejiang Province as an example, taking typhoon and tsunami disasters as risk sources, and selects an ideal construction site for an emergency material reserve warehouse in a regional chemical industrial park.

[0060] S101 includes the following steps:

[0061] Step 111: Obtain the typhoon path dataset;

[0062] Step 112: Perform a preliminary check on the typhoon path dataset to identify and process missing and outlier values ​​in order to obtain a complete time series for each typhoon path.

[0063] Step 113: Use interpolation methods to fill in missing values ​​to obtain interpolated data;

[0064] Step 114: Convert the interpolated data into a time series and unify the coordinate system and time unit to obtain standardized data.

[0065] Based on published typhoon data from 1949 to 2023, and filtered by latitude and longitude range within Zhejiang Province, 80 typhoons that passed through Zhejiang Province were ultimately obtained. These 80 typhoons were analyzed using the K-means method to obtain four clustering paths, with corresponding occurrence probabilities (qs) of 0.1625, 0.175, 0.325, and 0.3375 for typhoon scenarios 1-4, respectively. After obtaining the clustering paths, path point data with a frequency of 1 occurrence per hour were labeled. Based on the wind speed clustering method, the maximum extreme value was selected and inserted into the four clustering path points, and the wind speed results were filled into each path point.

[0066] In this first embodiment, the k-Means algorithm is used to perform cluster analysis on the typhoon paths in the preprocessed data, dividing the chemical industrial park into different typhoon scenarios, including:

[0067] Step 121: Calculate the distance matrix between different typhoon paths based on the typhoon path data;

[0068] Step 122: Based on the distance matrix, use the k-Means algorithm to cluster the typhoon paths to obtain the clustering results;

[0069] Step 123: Based on the clustering results, classify different typhoon scenarios, such as... Figure 2 As shown in Table 1, cluster paths with average wind speeds ≥ 51.0 m / s are selected as typhoon scenarios of level 16 and above. For example, cluster path 2 is selected if cluster paths are selected based on these paths.

[0070] Table 1 Clustering velocity value filling information

[0071]

[0072] In this embodiment of the invention, a distance matrix between different typhoon paths can be calculated based on preprocessed typhoon path data. This distance matrix reflects the similarity or difference between different typhoon paths. Based on the distance matrix, the k-Means algorithm is used to cluster the typhoon paths. The k-Means algorithm divides the typhoon paths into different clusters, minimizing the distance between typhoon paths within each cluster and maximizing the distance between different clusters. Specific clustering results can be obtained through the k-Means algorithm. Each cluster represents a typhoon scenario, where the typhoon paths have similar characteristics. Chemical industrial parks are divided into different typhoon scenario areas based on the clustering results. This helps to understand the impact and risks of typhoons under different scenarios, providing a basis for subsequent site selection decisions. Chemical industrial parks with similar typhoon paths can be grouped into the same scenario, and corresponding emergency plans and material reserve strategies can be formulated based on the characteristics of the scenario. This can improve the targeting and efficiency of emergency response and better address the risks brought by typhoons.

[0073] The specific solution process is as follows:

[0074] First, assume A = {a} 1 ,a 2 ,...,a p} and B = {b 1 ,b 2 Let {, ..., bq} be the set of points on two typhoon paths. Then, the formula for the partial forward Hausdorff distance between these two sets of points is:

[0075]

[0076] The formula for the partial backward Hausdorff distance between two point sets is:

[0077]

[0078] Among them, ||b j -a i || represents a i With b j The Euclidean distance between them; f F and f R ∈[0,1] are called forward fraction and backward fraction, respectively, controlling the forward distance and backward distance; th represents the order; when f F =f R When =1, the formula degenerates into the original Hausdorff distance; in short, taking formula (16) as an example, first take the point bj in set B that is closest to set A, and then calculate the distance a of each point a in set A according to a certain sorting ratio (to remove noise).i With b j The distances between points A and B are calculated, and the distances are sorted. The largest distance is then taken as the value of h(A,B). (If h(A,B) = d, it is assumed that the distance from all points in A to set B does not exceed d).

[0079] The partial bidirectional Hausdorff distance is then:

[0080]

[0081] Subsequently, based on the distance matrix D, the typhoon paths are clustered. The main steps are as follows:

[0082] Step 1: Randomly select a center u1 among the data points;

[0083] Step 2: For each data point x that has not yet been selected, calculate... That is, the distance between x and the nearest center that has already been selected;

[0084] Step 3: Randomly select a new data point as the new center using a weighted probability distribution, where the probability of the selected point x is proportional to the probability of the selected data point x. Proportional;

[0085] Step 4: Repeat Step 2 and Step 3 until k centers are selected (i.e., j = k);

[0086] Step 5: At this point, continue clustering using the selected initial centers, employing the standard k-Means algorithm.

[0087] The scenario categories are divided based on the final clustering results, and the probability of occurrence of each scenario category is determined by referring to the number of typhoon tracks in each category; simultaneously, the average distance Lc from the typhoon tracks of different clusters to the cluster center is calculated:

[0088]

[0089] Where c represents the number of cluster centers or the number of typhoon scenarios, and Li is the mean of the typhoon path to the cluster center in the i-th cluster.

[0090] The formula relating the minimum central pressure and maximum wind speed of a typhoon making landfall in China was studied and determined to be: V max =7.62(1010―P) min 0.595, V max P is the maximum wind speed at the center. min The lowest air pressure and the two are positively correlated. This proves that selecting the maximum wind speed of a typhoon as a risk factor indicator can better reflect the destructiveness of a typhoon.

[0091] To enhance the connectivity of the drawn typhoon paths and improve the accuracy of risk index calculations in the model, this embodiment uses the MATLAB interpolation function `interp1` to populate the original dataset, converting all data to a frequency of 1 time per hour. The specific interpolation method is as follows:

[0092]

[0093] In this embodiment of the invention, by acquiring a typhoon path dataset, the path and related information of the typhoon can be understood, providing a data foundation for subsequent analysis. By checking the typhoon path dataset and identifying and processing missing and outlier values, it can be ensured that the time series of each typhoon path is complete and accurate. For data with missing values, interpolation is used to fill in the original dataset, and all data is converted to data with a frequency of 1 time / 1 hour, which can obtain complete typhoon path data and avoid the impact of data incompleteness on subsequent analysis. Converting the interpolated data into a time series and unifying the coordinate system and time unit can ensure the consistency and comparability of the data, facilitating subsequent analysis and processing.

[0094] S102: Conduct tsunami earthquake source analysis on the target area and obtain tsunami wave data for tsunami scenarios of magnitude III and above.

[0095] S102 includes:

[0096] Step 21: Analyze the tsunami earthquake sources that may be affected by tsunamis along the regional coastline, and determine the potential maximum magnitude and strong earthquake cycle.

[0097] Step 22: Simulate the tsunami scenario along the coastline in the region to obtain the risk of coastline flooding, i.e., the risk of tsunami at level III and above.

[0098] Step 23: Grid the obtained tsunami scenarios, fill in the maximum extreme value in each cell, and overlay the number of scenarios where the coastline is threatened by a tsunami of level III or above to obtain the disaster risk value.

[0099] This embodiment uses the Comcot tsunami model to determine the impact of tsunamis on the main coastal study areas of Zhejiang Province, focusing on the South China Sea Trough, Okinawa Trough, and Ryukyu Trench.

[0100] This first embodiment conducts a simulation study on potential tsunamis. In the case that the above data cannot be accurately obtained through measurement and observation data, the longitude and latitude, strike angle, slip angle, dip angle and focal depth values ​​in the global seismic source database are referenced. In this first embodiment, three scenarios that would cause the tsunami wave in the coastal area of ​​Zhejiang Province to reach the level III or above risk are selected, that is, scenarios where the tsunami wave exceeds 1m.

[0101] The length (L), width (W), and displacement (D) of the fracture surface are calculated using the following method:

[0102]

[0103] M0 is the seismic moment, with units of dyn / cm2, equivalent to 10-7 N / m2.

[0104] M0 = μDLW

[0105] μ is the stiffness coefficient of the medium, which is 3*10. 10 N / m 2 ~5*10 10 N / m 2 In this first embodiment, 3*10 is selected. 10 N / m 2 D represents the average slip of the fault, and L and W represent the length and width of the fault plane, respectively.

[0106] Based on historical earthquake data, choosing L=2W, we derive:

[0107]

[0108] Where Δσ is the stress drop, which is mostly between 10 and 100, and 50 is selected in this first embodiment.

[0109] When applying the database, the cell energy and accurate location of the data can be adjusted based on regional tsunami and earthquake research findings and the extreme nature of the regional chemical industrial park emergency material reserve site selection model. In shallower water areas, the governing equations are rectangular linear long-wave equations, while in shallower water areas, rectangular nonlinear long-wave equations are used. The calculation process is omitted here; the parameters required in the Comcot tsunami model can be obtained using existing technologies.

[0110] Scenario 1: A 9.1 magnitude earthquake and tsunami occur in the South China Sea Trough. Comcot calculations indicate that the tsunami will reach the coastal areas of Zhejiang Province approximately 4.5-5 hours after its occurrence, initially affecting the southern regions and reaching the northern parts of Zhejiang Province approximately 7.5-8 hours later. Analysis of the data shows that tsunami waves along the Zhejiang coast will exceed 1.0 m, with most areas facing a Level III flooding risk. Locally, in the central coastal areas, the maximum tsunami wave could reach 4.0 m, posing a Level IV severe flooding risk.

[0111] Scenario 2: Following the 8.0 magnitude earthquake and tsunami in the central Okinawa Trough, Comcot calculations indicate that the tsunami will reach the coastal areas of Zhejiang Province approximately 2.5-3 hours after its occurrence, initially affecting the southern regions and reaching the northern parts of Zhejiang Province approximately 4.5-5 hours later. Analysis of the data shows that tsunami waves along the Zhejiang coast will exceed 0.5 meters, with the central and southern regions being more severely affected than the north. The maximum tsunami wave could reach 3.0 meters, posing a Level III flooding risk.

[0112] Scenario 3: Following the 8.7 magnitude earthquake and tsunami at the southern end of the Ryukyu Trench, Comcot calculations indicate that the tsunami reached the southern coastal areas of Zhejiang Province approximately 3-3.5 hours after its occurrence and the northern coastal areas approximately 5-5.5 hours later. Analysis of the data shows a clear pattern of stronger tsunami waves in the central and southern parts of Zhejiang Province compared to the northern parts. Tsunami waves in the southern and central coastal areas of Zhejiang ranged from 1 to 2 meters, with some areas exceeding 3 meters, posing a Level IV severe flooding risk.

[0113] like Figure 3 As shown, the three scenarios are superimposed for calculation, and the probabilities of the three scenarios are the same, thus obtaining the average data of the tsunami wave. Figure 3 (a) is an improved cell plot of scenario one, where a 9.1 magnitude earthquake and tsunami occur in the South China Sea Trough; Figure 3 (b) is an improved cell diagram of scenario two, depicting an 8.0 magnitude earthquake and tsunami in the central Okinawa Trough; Figure 3 (c) is an improved cell plot of the 8.7 magnitude earthquake and tsunami at the southern end of the Ryukyu Trench, Scenario 3; Figure 3 (d) is a cell chart showing the average of the three scenarios overlaid.

[0114] The method for obtaining tsunami wave data by performing gridding on the tsunami scenario, obtaining the maximum extreme value in each cell, and then filling the grid includes:

[0115] The pixel values ​​of the resulting image are converted to grayscale values ​​using the Comcot mode.

[0116] Create a grayscale legend, and match each pixel on the scene image with the legend one by one. After the matching is completed, the geographic coordinates will be determined and rearranged according to the grayscale values ​​of the legend.

[0117] The maximum value of each row in the TIFF format image is resampled. The resampled cells are divided according to the 0.05°*0.05° latitude and longitude grid. The maximum value in each cell is extracted to represent the value of that cell.

[0118] Data synthesis involves obtaining the average value of each scenario by combining them based on the probability of the obtained scenarios.

[0119] The method of obtaining the maximum extremum in a 0.05°*0.05° latitude and longitude grid has two advantages: first, it can effectively reduce the amount of computation; second, it can handle relatively more extreme intensities.

[0120] S103: Conduct emergency response capability analysis on chemical industrial parks within the target area to identify candidate areas.

[0121] The geographical information of highway entrances / exits within and around the target area that has been put into use is statistically analyzed, and chemical industrial parks within a preset range of distance from highway entrances / exits are selected as candidate areas.

[0122] By using data from the Zhejiang Provincial Department of Transportation, the geographical locations of highway entrances within and around the study area that were already in use before December 2022 were statistically analyzed. Nearby exits and entrances were merged into one candidate area, resulting in a total of 254 candidate areas.

[0123] Next, we conducted kernel density analysis and DEM data analysis on emergency response capabilities in Zhejiang Province to screen and obtain candidate areas for overlapping areas, that is, to screen areas that meet the function of emergency material reserve warehouses. A total of 116 candidate areas were selected.

[0124] S104: Based on the extreme wind speed and tsunami wave data, comprehensively assess the risks of each chemical industrial park under typhoon and tsunami scenarios, and determine hazardous chemical industrial parks and safe candidate areas.

[0125] In this embodiment of the invention, typhoon and tsunami scenario data and candidate area location data are acquired, including typhoon paths, latitude and longitude of candidate areas, or other location information. Based on the typhoon and tsunami scenario and candidate area location data, a multi-objective site selection model is used to calculate the risk indicators of each candidate area under typhoon and tsunami scenarios. The calculated risk indicators of each candidate area are analyzed. Each candidate area can be evaluated and compared based on the magnitude, trend, and other factors of the indicators. Based on the risk indicator analysis results of each candidate area, the candidate areas are ranked. The ranking can be based on the magnitude of the risk indicators, from high to low or from low to high, to determine the risk level of the candidate areas. Based on the ranking results of the risk indicators of each candidate area, safe candidate areas are determined. Safe candidate areas refer to candidate areas with low risk under typhoon and tsunami scenarios, possessing high safety and reliability, and suitable for site selection as emergency material reserve depots. This helps determine which candidate areas face lower risks under typhoon and tsunami scenarios and provides a scientific basis for the site selection of emergency material reserve depots. It can improve the accuracy and reliability of the site selection method and provide a scientific basis for the site selection of emergency material reserve depots in regional chemical industrial parks.

[0126] Set risk indicators and normalize the acquired risk factors.

[0127]

[0128] X 标准化X represents the normalized index; X represents the original value of the index; Xmax represents the maximum value of the index during the statistical period; Xmin represents the minimum value of the index during the statistical period.

[0129] The normalized values ​​include one or more of the following: the area of ​​the chemical industrial park, the distance of the chemical industrial park from the nearest point in the typhoon scenario, the wind speed of the chemical industrial park from the nearest point in the typhoon scenario, the straight-line distance of the chemical industrial park from the nearest point in the tsunami scenario, and the tsunami wave when the chemical industrial park experiences a tsunami.

[0130]

[0131]

[0132] minf5 = G(6)

[0133] rt j =mintd j ×tv j ×q t ,j=1,2,…,J; t=1,2,…,T(7)

[0134] The risk value of the chemical industrial park affected by the typhoon risk point is calculated by formula 7, which is the product of the distance of chemical industrial park j from the nearest point of the typhoon scenario, the wind speed of chemical industrial park j from the nearest point of the typhoon scenario, and the probability of occurrence of typhoon scenario t.

[0135]

[0136] Formula 8 represents the total risk of chemical industrial park j being affected by typhoon scenarios.

[0137] rh j =r h ×hd j , j=1,2,…,J; h=1,2,…,H (9)

[0138] The risk level of the tsunami scenario risk point is calculated using Formula 9, and the product of the straight-line distance between the chemical industrial park j and the nearest point in the tsunami scenario is used as the risk value of the chemical industrial park affected by the tsunami risk point.

[0139] rH j =max[rh1,rh2…rh] J (10)

[0140] Formula 10 is used to select the maximum risk value of each chemical industrial park affected by tsunami risk points from the risks of each tsunami risk point.

[0141] r j =rT j ×rH j ×wj , j=1,2,…,J (11)

[0142] Formula 11 calculates the total risk of the chemical industrial park j under typhoon scenarios, the maximum risk under tsunami scenarios, and the area of ​​the chemical industrial park as the risk value affected by the disaster. In other words, the product of the risk value of the chemical industrial park under typhoon scenarios, the risk value under tsunami scenarios, and the area of ​​the chemical industrial park is used as the risk value of the chemical industrial park affected by the disaster, i.e., the risk index of the chemical industrial park.

[0143]

[0144] Z∈J(reserved)(14)

[0145] M∈I (reserved) (15)

[0146] Formulas 14 and 15 indicate that the chemical industrial parks are sorted according to their risk values, and the parks are divided into hazardous chemical industrial parks and safe candidate areas according to the sorting results.

[0147] Where: maxf1 represents the highest risk of chemical industrial parks and candidate areas being affected by tsunami disasters; maxf2 represents the highest risk of chemical industrial parks and candidate areas being affected by tsunami disasters; maxf3 represents the highest risk of the set of chemical industrial parks being affected by tsunami disasters; maxf4 represents the highest risk of the set of chemical industrial parks and candidate areas being affected by various natural disasters.

[0148] H represents the set of tsunami scenario risk points; h represents a tsunami scenario risk point, h∈H;

[0149] rh indicates the risk level of the tsunami scenario risk point;

[0150] w j This represents the area of ​​the j-th chemical industrial park;

[0151] i represents a single candidate region that meets the criteria for an emergency functional region, i∈I; I represents the set of candidate regions that meet the criteria for an emergency functional region;

[0152] j represents a single chemical industrial park, j∈J; J represents the set of chemical industrial parks within a region;

[0153] Z represents the number of chemical industrial parks to be retained; z represents the number of individual retained chemical industrial parks.

[0154] T represents the set of typhoon occurrence scenarios, where each typhoon scenario represents the typhoon's path and is characterized by a certain number of typhoon cluster paths; t represents a single cluster of typhoon scenarios, t∈T;

[0155] rh j This indicates the risk of j being affected by various tsunami risk points; rhi This indicates the risk of being affected by various tsunami risk points;

[0156] rH j This represents the total risk of j being affected by each tsunami risk point; rH i This indicates the total risk affected by each tsunami risk point;

[0157] rd j The straight-line distance to the point closest to the tsunami scenario; rd i This represents the straight-line distance to the point closest to the tsunami scenario;

[0158] G represents the distance from a single candidate area to the reserved chemical industrial park by all modes of transportation; d zm Indicates the distance from a single candidate reserve area to a single reserved chemical industrial park; d Zm The distance from a single candidate area to each reserved chemical industrial park;

[0159] q t This represents the probability of typhoon scenario t occurring;

[0160] mintd j Mintd represents the distance of j from the closest point in the typhoon scenario. i This represents the distance of i from the closest point in the typhoon scenario;

[0161] tv j This represents the wind speed at the point closest to the typhoon scenario; tv i This represents the wind speed at the point i closest to the typhoon scenario (the larger value between the two points is taken);

[0162] rt j This indicates the risk of j being affected by the typhoon scenario; rt i This indicates the risk of being affected by typhoon scenarios;

[0163] rT j This indicates the total risk of j being affected by the typhoon scenario; rT i This indicates the total risk of i being affected by the typhoon scenario;

[0164] HD j hd represents the straight-line distance of j from the point closest to the tsunami scenario; i Represents the straight-line distance of i from the point closest to the tsunami scenario;

[0165] r j This represents the total risk of j being affected by various disasters; r i This represents the total risk of i being affected by various disasters;

[0166] M represents the total number of candidate regions to be retained; m represents a single candidate region to be retained.

[0167] Calculate the transportation distance from a safety candidate area to a hazardous chemical industrial park. The constraints are: after determining the transportation mode, obtain GIS data, and determine the method for calculating the distance between two points.

[0168]

[0169] F represents the set of modes of transportation; f represents the number of modes of transportation, f∈F;

[0170] Based on the risk values ​​of disaster impact, the areas are ranked and then selected for priority in conjunction with urban planning.

[0171] In practical applications, it is necessary to set the retention and exclusion index values ​​in Formulas 12 and 13 according to the actual situation of the study area. Candidate areas can be set according to the nature and function of the emergency material reserve warehouse of the regional chemical industrial park. The number of arrival methods in Formula G can be determined according to the historical analysis of disaster practice in the selected area. Then, the solution set is sorted by calculation method. Finally, the preferred area is obtained by analyzing the urban planning layout of the top-ranked selected areas. In the preferred area, the better terrain, better municipal conditions, and distance from fire sources are used as reference conditions.

[0172] The risk index is obtained from the normalization result. The risk index classifies each chemical industrial park into hazardous chemical industrial parks and safe candidate areas. In this first embodiment, the risk index is set to the top 20%.

[0173] Candidate areas are set based on the nature and function of emergency material reserve warehouses in regional chemical industrial parks. The number of arrival methods is determined based on historical analysis of disaster situations in the selected areas. The solution set is then sorted using calculation methods. Finally, the preferred areas are obtained by analyzing the urban planning layout of the top-ranked selected areas.

[0174] Based on the regional situation, the retention and exclusion index values ​​of the candidate areas are set, and the risk index of each candidate area under the superimposed typhoon and tsunami scenarios is calculated. In order to better correspond to the safety and extreme nature of the model design features, in this embodiment, the risk index is set to the top 20% of the normalized results, that is, 80% of the candidate areas are retained as safe candidate areas, and a total of 91 safe candidate areas remain.

[0175] S105: Sort the candidate safe areas according to the rescue distance, and recommend the candidate safe areas with higher rankings as emergency storage addresses.

[0176] Based on the safety candidate areas and the hazardous chemical industrial park, a multi-objective site selection model is used to calculate the rescue distance from each safety candidate area to the hazardous chemical industrial park, in order to obtain the preferred site selection areas, including:

[0177] Obtain location data for safety candidate areas and hazardous chemical industrial parks;

[0178] The rescue distance of the candidate safe zones is analyzed and sorted to obtain the rescue distance ranking results;

[0179] Based on the ranking of rescue distances, the preferred site selection area was determined.

[0180] Ultimately, the top 10 preferred areas were selected based on their distance from the rescue site. This selection was chosen to better reflect the timeliness of the model's design features. These top 10 preferred areas are mainly concentrated in Hangzhou, Shaoxing, Jiaxing, and Huzhou. In this first example, based on the city master plans of the 10 candidate safety areas, factors such as airport airspace restrictions, hydrological conditions, mineral resource distribution, and helicopter takeoff and landing were considered to influence the construction of emergency material reserve depots. Since the final site selection is for a regional chemical industrial park disaster relief material reserve depot, the factor of frequent helicopter takeoffs and landings must be considered. Therefore, candidate safety areas adjacent to airport airspace restrictions must be excluded during site selection. Furthermore, since the Qiantang River flows through the 10 candidate safety areas, the site should be located as far away from the riverbank as possible to ensure the safety of the materials. After obtaining the 10 preferred areas, decision-makers need to use factors such as favorable terrain, good municipal conditions, and distance from fire sources as reference conditions to make the final site selection.

[0181] In this embodiment of the invention, location data of safety candidate areas and hazardous chemical industrial parks are acquired, including latitude and longitude or other location information; based on the location data of safety candidate areas and hazardous chemical industrial parks, the rescue distance from each safety candidate area to the hazardous chemical industrial park is calculated; the calculated rescue distances from each safety candidate area to the hazardous chemical industrial park are analyzed and ranked; the rescue distances can be evaluated and compared according to the magnitude, trend and other factors; based on the ranking results of the rescue distances, a preferred site selection area is determined; the preferred area refers to a safety candidate area that is far from the hazardous chemical industrial park, has a short path or good transportation conditions, and is suitable as a site selection for an emergency material reserve warehouse; this helps to determine a safety candidate area that is far from the hazardous chemical industrial park and has a short path as a site selection for an emergency material reserve warehouse; it can improve the efficiency and accuracy of emergency response, reduce potential risks and losses, and ensure that emergency materials can reach the hazardous chemical industrial park in a timely manner;

[0182] The rescue distance includes the rescue distance for helicopter-transported supplies and the rescue distance for truck-transported supplies.

[0183] In this first embodiment, by acquiring and processing typhoon path datasets and analyzing tsunami and earthquake sources, accurate and reliable data can be obtained, providing a foundation for subsequent analysis and decision-making. Cluster analysis of the preprocessed typhoon path data and tsunami and earthquake source analysis of the target area can classify the chemical industrial park into different typhoon and tsunami scenarios, allowing for a better understanding of the impact and risks under different scenarios. Based on the risk indicators of each chemical industrial park under typhoon and tsunami scenarios, the degree of danger of the chemical industrial park can be assessed, helping to identify hazardous chemical industrial parks and providing a basis for subsequent site selection. Analysis of the chemical industrial parks can assess the emergency response capabilities of each region and determine candidate areas. Based on the risk indicators of each candidate area under typhoon and tsunami scenarios, the safety level of the candidate areas can be assessed, helping to determine safe candidate areas and providing a basis for subsequent site selection. Based on the safe candidate areas and hazardous chemical industrial parks, the rescue distance from each safe candidate area to the hazardous chemical industrial park is calculated. Based on distance and other factors, the optimal site selection area is selected, thereby improving the efficiency and accuracy of emergency response and reducing potential risks and losses.

[0184] In this first embodiment, typhoon and tsunami disasters, two types of disasters with a long history of research, are selected for study. First, an improved k-Means machine learning clustering algorithm is used to cluster typhoon paths and obtain the extreme wind speeds at typhoon path points to assess the risk of chemical industrial parks within the region. Then, an improved Comcot model is used to simulate tsunami scenarios, and extreme tsunami wave data along the coast is obtained through gridding. Candidate areas with emergency response capabilities are selected based on the functional characteristics of emergency material reserve depots in regional chemical industrial parks. Hazardous chemical industrial parks and safe candidate areas are determined by setting risk indicators. Finally, safe candidate areas are ranked by calculating rescue distances, and the top-ranked areas are analyzed for planning to obtain the preferred site selection areas. This first embodiment and its results provide some insights into emergency facility site selection decisions under extreme disasters.

[0185] Example 2

[0186] A site selection device for regional emergency storage facilities with overlapping risks of multiple extreme disasters, such as Figure 4 As shown, the device includes:

[0187] One or more processors;

[0188] A storage device for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the regional emergency storage site selection method for multiple extreme disaster risks as described in any one embodiment.

[0189] Figure 4 This is a schematic diagram of the structure of the regional emergency depot site selection equipment provided in this embodiment three, which involves multiple extreme disaster risks. Figure 4A block diagram of an exemplary regional emergency depot site selection device suitable for implementing embodiments of the present invention with multiple extreme disaster risks superimposed is shown. Figure 4 The regional emergency depot site selection equipment shown, which demonstrates multiple extreme disaster risks overlapping, is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0190] like Figure 4 As shown, the location equipment for regional emergency depots with overlapping risks of multiple extreme disasters is presented in the form of general-purpose equipment. The components of the location equipment for regional emergency depots with overlapping risks of multiple extreme disasters may include, but are not limited to: one or more processors or processing units, memory, and buses connecting different system components (including memory and processing units).

[0191] A bus refers to one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0192] Location-based facilities for regional emergency repositories facing multiple extreme disaster risks typically include various computer-readable media. These media can be any available media accessible to equipment that can be corrected by intelligent logging interpretation models, including volatile and non-volatile media, and portable and immovable media.

[0193] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory. Regional emergency depot site selection equipment with overlapping risks of multiple extreme disasters may further include other portable / non-portable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-portable, non-volatile magnetic media (…). Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4 Not shown, disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to a bus via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0194] A program / utility having a set (at least one) of program modules can be stored, for example, in memory. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this invention.

[0195] The regional emergency depot site selection device, which addresses the overlapping risks of multiple extreme disasters, can also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable users to interact with the device, and / or any device that allows it to communicate with one or more other devices (e.g., network cards, modems, etc.). This communication can be achieved through input / output (I / O) interfaces. Furthermore, the device for correcting intelligent well logging interpretation models can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. Figure 4 As shown, the network adapter communicates with other modules of the regional emergency depot location equipment, which is designed to handle multiple extreme disaster risks, via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the regional emergency depot location equipment, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0196] The processing unit executes various functional applications and data processing by running programs stored in memory, such as implementing the regional emergency repository site selection method for multiple extreme disaster risks provided in any embodiment of the present invention. Specifically: Typhoon paths are selected based on the target area; typhoon paths are clustered to obtain the extreme wind speeds of typhoons at all path points; typhoon scenarios of level 16 and above are selected; tsunami and earthquake source analysis is performed on the target area, obtaining tsunami wave data for level III and above tsunami scenarios; emergency response capabilities of chemical industrial parks within the target area are analyzed to obtain candidate areas; the risks of each chemical industrial park under typhoon and tsunami scenarios are comprehensively assessed based on the extreme wind speeds and tsunami wave data to determine hazardous chemical industrial parks and safe candidate areas; the safe candidate areas are ranked according to rescue distance, and the top-ranked safe candidate areas are recommended as emergency repository locations.

[0197] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters, characterized in that, The method includes: Based on the target area, typhoon paths are selected, and the typhoon paths are clustered to obtain the wind speed at all path points. Tsunami earthquake source analysis was performed on the target area to obtain tsunami wave data; An emergency response capability analysis was conducted on the chemical industrial parks within the target area to obtain the overall candidate areas; Based on the wind speed and tsunami wave data, the risk indicators of the chemical industrial park under typhoon and tsunami scenarios are comprehensively evaluated, and the proportion of the total candidate area is divided into safe candidate areas according to the risk indicators. The candidate safe zones are then sorted according to their rescue distance, and the candidate safe zones with the highest rankings are recommended as the addresses of the regional emergency response database.

2. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters as described in claim 1, characterized in that, The method for selecting typhoon paths based on the target region, clustering typhoon paths, and obtaining the wind speed at all path points includes: Clustering was obtained by analyzing the typhoon path distance matrix and typhoon scenario classification for the target area; Hourly path points are obtained by interpolation, and the maximum wind speed value of the typhoon wind speed cluster is added to the hourly path points.

3. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters as described in claim 1, characterized in that, The method for analyzing the tsunami earthquake source in the target area and obtaining tsunami wave data includes: Analyze the tsunami earthquake sources along the coastline of the target area to obtain the magnitude and strong earthquake period; Simulate tsunami scenarios along the coastline of the target area to obtain scenarios that reach the risk of coastline inundation, and select tsunami scenarios of level III or above; The tsunami scenario is processed into a grid, and the largest tsunami wave value in each cell is obtained and filled to obtain tsunami wave data.

4. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters as described in claim 1, characterized in that, The method for analyzing the emergency response capabilities of chemical industrial parks within the target area to obtain the total candidate areas includes: The geographical information of highway entrances / exits within and around the target area that has been put into use is statistically analyzed. Chemical industrial parks within a preset distance from highway entrances / exits are considered as candidate areas, and the total candidate areas are obtained.

5. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters according to any one of claims 1 to 4, characterized in that, The method for comprehensively assessing the risk indicators of chemical industrial parks under typhoon and tsunami scenarios based on the wind speed and tsunami wave data, and dividing the total candidate area into safe candidate areas according to the risk indicators, includes: The risk value of the chemical industrial park affected by the typhoon scenario, the risk value affected by the tsunami scenario, and the area of ​​the chemical industrial park are multiplied as the risk value of the chemical industrial park affected by the disaster, i.e., the risk index of the chemical industrial park. Based on the risk values ​​of each chemical industrial park, they are ranked, and then, according to the ranking results, all chemical industrial parks are divided into hazardous chemical industrial parks and safe candidate areas in proportion.

6. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters according to claim 5, characterized in that, Before calculating the risk indicators for the chemical industrial park, the total risk of the chemical industrial park affected by typhoon scenarios, the maximum risk value affected by tsunami scenarios, and the area of ​​the chemical industrial park were normalized respectively.

7. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters according to claim 5, characterized in that, The method for sorting the candidate safe zones according to rescue distance and recommending the top-ranked candidate safe zones as emergency repository addresses includes: Obtain location data for safety candidate areas and hazardous chemical industrial parks; Calculate the rescue distance from each safety candidate zone to the hazardous chemical industrial park; The rescue distance of the candidate safe zones is analyzed and sorted to obtain the rescue distance ranking results; Based on the ranking of rescue distances, the preferred candidate areas are determined.

8. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters according to claim 2, characterized in that, The method of obtaining hourly path points through interpolation and then assigning the maximum extreme value of typhoon wind speed clustering to the hourly path points includes: After obtaining the clustering paths, the path point data with a frequency of 1 time / 1 hour are labeled. Based on the wind speed clustering method, the maximum extreme value is inserted into four clustering path points, and the wind speed results are filled into each path point. The specific interpolation method is as follows: In the formula, (X1, Y1) and (X2, Y2) are the known coordinates of two points, X is the x-coordinate of the target point, and Y is the interpolated y-coordinate of the target point.

9. The method for selecting the location of regional emergency depots with overlapping risks of multiple extreme disasters according to claim 3, characterized in that, The method for performing gridded processing on the tsunami scenario and filling in the largest tsunami wave value in each cell to obtain tsunami wave data includes: Convert the pixel values ​​of the obtained image to grayscale values; Create a grayscale legend, and match each pixel on the scene image with the legend one by one. After the matching is completed, the geographic coordinates will be determined and rearranged according to the grayscale values ​​of the legend. The maximum value of the row in the TIFF format image is resampled, and the resampled cells are divided according to the number of latitude and longitude grids of 0.05°×0.05°. The maximum tsunami wave value in the cell is extracted as the tsunami wave value of the cell. Data synthesis is performed to obtain the average tsunami wave value for each scenario by superimposing the probabilities of the scenarios.

10. A site selection device for regional emergency depots with overlapping risks of multiple extreme disasters, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the regional emergency storage site selection method for multiple extreme disaster risks as described in any one of claims 1-9.

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