Emergency material route planning method, system, device and storage medium

Abstract

The application provides an emergency material route planning method, system, device and storage medium, the method comprising: determining each risk index related to a target emergency event and a weight corresponding to each risk index by using an analytic hierarchy process; determining a transportation path from a starting point to an ending point by using an improved Dijkstra algorithm according to the starting point and the ending point, wherein the improved Dijkstra algorithm considers a path risk cost and avoids a region with the highest risk cost when performing path planning, and the risk cost of the transportation path is calculated according to a risk severity in a specific region to which each coordinate in the transportation path belongs; and transporting the emergency material according to the transportation path. In the application, the improved Dijkstra algorithm mathematical calculation model finds a path with the lowest risk cost in a reasonable calculation time framework, and finds a relatively low-risk path, so that the improved Dijkstra algorithm can be used for time-sensitive emergency events.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to an emergency material route planning method, system, equipment and storage medium. Background Art

[0002] Emergency management deals with the most diverse situations daily. The primary goal of incident management is to deliver emergency services to the affected area as quickly as possible. To achieve this goal, numerous factors must be evaluated and considered within a relatively short decision-making period. In emergency response, factors such as the number of rescue personnel, resources, and equipment, as well as the routes and spatial attributes to be followed, are crucial in estimating response times. In these situations, management needs tools to assist with planning and management, and to facilitate effective assistance.

[0003] Therefore, there is an urgent need for an emergency material route planning method. Summary of the Invention

[0004] The present invention provides an emergency material route planning method, system, device and storage medium, the main purpose of which is to find the path with the lowest risk cost within a reasonable calculation time frame, effectively improving the stability and safety of emergency material transportation.

[0005] In a first aspect, an embodiment of the present invention provides a method for planning an emergency material route, comprising:

[0006] Determine each risk indicator related to a target emergency event and the corresponding weight of each risk indicator through the analytic hierarchy process, wherein the target emergency event includes a starting point, an end point, and emergency supplies;

[0007] Determining a transportation path from the starting point to the destination using an improved Dijkstra algorithm based on the starting point and the destination, wherein the improved Dijkstra algorithm considers path risk cost and avoids areas with the highest risk cost when performing path planning, wherein the risk cost of the transportation path is calculated based on the risk severity within a specific area to which each coordinate in the transportation path belongs, wherein the risk severity is calculated based on risk indicators included in the specific area and weights corresponding to the risk indicators included;

[0008] The emergency supplies are transported according to the transport route.

[0009] Furthermore, the risk severity is calculated based on the risk indicators included in the specific area and the weights corresponding to the included risk indicators, and the calculation formula is as follows:

[0010] R k =w k f k ;

[0011] wherein, R k represents the risk severity corresponding to the kth contained risk indicator, w k represents the weight corresponding to the kth contained risk indicator, f k represents the risk level corresponding to the kth contained risk indicator.

[0012] Further, the risk cost of the transportation path is calculated according to the risk severity in the specific area to which each coordinate in the transportation path belongs, and the calculation formula is as follows:

[0013]

[0014] wherein, R represents a risk cost matrix, R L represents the risk cost of the transportation path, (x, y) represents a coordinate, T L represents a coordinate set, m represents a maximum horizontal coordinate, and n represents a maximum vertical coordinate, represents the risk cost of the transportation path L between the coordinates (x, y).

[0015] Further, the risk indicators include ground personnel, vehicles, and manned aircraft.

[0016] Further, the step of determining the transportation path from the starting point to the ending point according to the starting point and the ending point by using the improved Dijkstra algorithm includes:

[0017] adding the street structure, the greening type, the greening position, and the traffic provided by the road network into the improved Dijkstra algorithm;

[0018] determining the transportation path according to the starting point and the ending point.

[0019] In a second aspect, an embodiment of the present application provides an emergency material route planning system, which includes:

[0020] an analysis module configured to determine each risk indicator related to a target emergency event and the weight corresponding to each risk indicator by using the analytic hierarchy process, wherein the target emergency event includes a starting point, an ending point, and emergency materials;

[0021] a planning module configured to determine a transportation path from the starting point to the ending point according to the starting point and the ending point by using an improved Dijkstra algorithm, wherein the improved Dijkstra algorithm considers the risk cost of the path and avoids the area with the highest risk cost when planning the path, the risk cost of the transportation path is calculated according to the risk severity in the specific area to which each coordinate in the transportation path belongs, and the risk severity is calculated according to the risk indicators contained in the specific area and the weight corresponding to the contained risk indicators;

[0022] A transport module is used to transport the emergency supplies according to the transport route.

[0023] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned emergency material route planning method when executing the computer program.

[0024] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned emergency material route planning method are implemented.

[0025] This invention proposes a method, system, device, and storage medium for emergency material route planning. Compared to existing technologies, this method excels in dynamic methods for event support, combining the robustness of the multi-criteria analytic hierarchy process (AHP) with the efficiency of the Dijkstra algorithm. One of the objectives of this embodiment is to incorporate decision makers' experience into a model that integrates their experience with the spatial dynamics of event response. To achieve this, this embodiment utilizes a multi-criteria approach to enable the conversion of empirical values ​​in complex decision-making problems. Due to the complex nature of events, the AHP seeks to address the importance of specific values ​​for a given event. This method is weighted in the decision matrix to determine the most important criteria in a given situation, providing a better approach for selecting the most appropriate path. The improved mathematical calculation model of the Dijkstra algorithm searches for the path with the lowest risk cost within a reasonable computational timeframe, identifying relatively low-risk paths. Therefore, it can be used for time-sensitive emergency events. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a method for planning an emergency material route provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of a path planning based on a risk cost graph provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of all possible access routes from starting point B to end point Destiny in an embodiment of the present invention;

[0029] Figure 4 A schematic diagram of the relationship between the total risk cost and the risk index provided by an embodiment of the present invention;

[0030] Figure 5 A comparison chart of the total risk cost of the improved Dijkstra algorithm and the original Dijkstra algorithm provided in an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of a planning path considering different combinations or risks provided by an embodiment of the present invention;

[0032] Figure 7 A schematic diagram of the structure of an emergency material route planning system provided by an embodiment of the present invention;

[0033] Figure 8 A schematic structural diagram of a computer device provided in an embodiment of the present invention.

[0034] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0035] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0036] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0037] In the embodiments of the present application, at least one refers to one or more; a plurality refers to two or more. In the description of the present application, words such as "first", "second", and "third" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0038] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, the terms "including," "comprising," "having," and their variations in this specification all mean "including but not limited to," unless otherwise specifically stated.

[0039] During emergency operations, rescue agencies must coordinate and organize personnel and supplies based on urgent needs. Embodiments of the present invention utilize a model that focuses on the dynamics of event calls, integrating various approaches with a Geographic Information System (GIS). In an emergency, managers quickly determine which emergency resource route is most appropriate to meet the event's requirements. By combining the Analytic Hierarchy Process (AHP) with the Dijkstra algorithm and leveraging the flexibility and robustness of multi-iteration methods, path planning algorithms were used and compared to generate low-risk cost paths, assessing their strength and relevance to scenarios where weak nature is unrelated.

[0040] Figure 1 A flowchart of a method for planning an emergency material route is provided in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0041] S110, determining each risk indicator related to a target emergency event and a weight corresponding to each risk indicator through an analytic hierarchy process, wherein the target emergency event includes a starting point, an end point, and emergency supplies;

[0042] First, in the embodiments of the present invention, the analytic hierarchy process (AHP) is used to determine each risk indicator associated with the target emergency event and the corresponding weight for each risk indicator. For example, one target emergency event might involve transporting chemicals, where risk indicators for chemical transport include drug leakage and road curvature. Another target emergency event might involve the transportation of disaster relief supplies, where risk indicators for disaster relief supplies include the shelf life of vegetables. Therefore, risk indicators for different target emergency events vary and need to be determined based on actual circumstances. This is not specifically limited in the embodiments of the present invention.

[0043] The target emergency event includes a starting point, an end point and emergency supplies. The starting point refers to the starting point of transportation of the emergency supplies, the end point refers to the transportation destination of the emergency materials, and the emergency supplies refer to the supplies that need emergency transportation.

[0044] Among them, the hierarchical analysis method refers to treating a complex multi-objective decision-making problem as a system, decomposing the goal into multiple goals or criteria, and then decomposing it into several levels of multiple indicators (or criteria, constraints), and calculating the hierarchical single ranking (weight) and total ranking through the fuzzy quantification method of qualitative indicators as a systematic method for optimizing decision-making with goals (multiple indicators) and multiple options.

[0045] After the AHP is constructed and the paired judgment phase is completed, expert knowledge determines the relevance of a criterion based on experience, intuition, and expertise, rather than focusing on the primary objective. A numerical scale is used to convert the language evaluation into numbers, and the values ​​from the judgment are used to construct a comparison matrix. Therefore, A can be represented as an n×n matrix:

[0046]

[0047] Where n is the number of criteria to be estimated, Ci is criterion i, and aij is the weight of criterion i to criterion j. From the judgment matrix, the importance or relative weight of the criteria can be calculated by normalization as:

[0048]

[0049] Standardization produces a single estimate of the scale on which the judgment is based, and the decision maker may be uncertain. In this case, the consistency index is introduced to measure the consistency of the judgment:

[0050]

[0051] Where CI is the consistency index, n is the number of evaluation criteria, λ max is the characteristic vector of a. The embodiment of the present invention also proposes a consistency relationship (CR) to determine whether the value of CI is appropriate.

[0052]

[0053] The acceptable limit for CR is 0.1. If CR exceeds this value, the evaluation procedure should be repeated to improve consistency. When CR is within the expected range, the method results in an approximation of the decision maker's priorities.

[0054] S120: Determine a transportation path from the starting point to the destination using an improved Dijkstra algorithm based on the starting point and the destination. The improved Dijkstra algorithm considers path risk cost during path planning and avoids areas with the highest risk cost. The risk cost of the transportation path is calculated based on the risk severity within a specific area to which each coordinate in the transportation path belongs. The risk severity is calculated based on risk indicators included in the specific area and weights corresponding to the risk indicators.

[0055] Then, based on the starting point and the end point, the Dijkstra algorithm is used to obtain the alternative paths from the starting point to the end point. When selecting the alternative paths, the risk cost and distance of the path are considered, and the areas with low risk cost and the highest risk cost are selected.

[0056] The spatial nature of emergency response is fundamental to developing effective routes. Road networks in urban areas are occasionally blocked, whether due to road obstructions, traffic jams, or other types of adverse conditions that result in traffic congestion. In these situations, GIS-provided data layers associated with Dijkstra's algorithm can be used to create new routes.

[0057] Consider a weighted graph G with n vertical points numbered from 1 to n. Let d[v] be the current distance from source vertex s to vertex v, and w(u, v) be the weight of the edge connecting u and v. The algorithm maintains a set of S vertices whose shortest distance to null is known. The GIS-integrated Dijkstra algorithm can be understood as follows:

[0058] 1. Initialization:

[0059] d[s]=0.

[0060] 2. For each vertex v in G:

[0061] s=s∪{v};

[0062] For each vertex u adjacent to v in G:

[0063] Ifd[v]+w(u,v) <d[u];

[0064] d[u]=d[v]+w(u,v).

[0065] 3. Delete v from S.

[0066] 4. Repeat steps 2 and 3 until S is empty.

[0067] By connecting it with GIS, the road network can be adapted to the origin-destination problem by appending the information provided by GIS (street structure, type of greenery, greenery and flow) to the weight attributes used in the algorithm to construct the ideal event access path.

[0068] First, the most relevant event criteria were identified using the AHP method. At this stage, past experience helped determine the importance of each criterion, generating a ranking of the most important ones. The values ​​of these criteria were used as weights in the decision matrix, which determined the routes most prepared for support. The Dijkstra algorithm was used to calculate the access routes, taking into account the coordinates of the planned routes and their presence in the GIS.

[0069] The embodiment of the present invention considers three main risks: ground personnel, vehicles and manned aircraft. The risk cost map includes all three risks.

[0070] Figure 2 A schematic diagram of a path planning based on a risk cost graph is provided in an embodiment of the present invention, such as Figure 2 As shown in Figure 1, Row represents a row and Column represents a column. The map consists of links and nodes, and each link has a risk cost value calculated based on the severity of the risk in a specific area.

[0071] The risk cost value is expressed in a color spectrum, such as Figure 2 In the rightmost chromatogram, yellow indicates high risk costs, and blue indicates low risk costs. This path avoids high-risk cost areas and has a much lower risk cost than paths with shorter distances. A key goal of the present invention is to model and generate a risk-cost map, and then use an improved algorithm to generate the most efficient path.

[0072] In order to evaluate the events after calculating each type of risk, a target safety level is introduced for each event k, defined as Ptarget k, which is no longer used as an evaluation criterion. The risk level is defined as f k , then we can get:

[0073]

[0074] Among them, P event-k represents the expected risk of transport event event-k, P target-k Indicates the risk of the actual transportation event target-k.

[0075] Adjustment coefficient ω k Adjusting for all risks, the final risk cost for each category is expressed as:

[0076]

[0077] The path planning goal of the embodiment of the present invention adopts the shortest route, which finds a cost-effective path to minimize the overall risk cost in the urban environment. By defining the risk cost, the risk cost matrix is:

[0078]

[0079] Among them, R total(m,n) Represents the risk cost between coordinates (m, n).

[0080] Then the total cost of the path can be calculated as:

[0081]

[0082] S130: Transport the emergency supplies according to the transport route.

[0083] After the calculation path is determined, the calculation path is used to calculate the emergency supplies.

[0084] This invention proposes a method for emergency material route planning that, compared to existing technologies, excels in dynamic approaches for event support. It proposes an arrangement that combines the robustness of a multi-criteria analytic hierarchy process (AHP) with the efficiency of the Dijkstra algorithm. One of the objectives of this embodiment of the invention is to incorporate the experience of decision makers into a model that combines their experience with the spatial dynamics of event response. To achieve this, this embodiment utilizes a multi-criteria approach to enable the conversion of empirical values ​​in complex decision-making problems. Due to the complex nature of events, the AHP seeks to address the importance of specific values ​​for a given event. This method is weighted in the decision matrix to determine the most important criteria in a given situation, providing a better approach for selecting the most appropriate path. The improved mathematical calculation model of the Dijkstra algorithm searches for the path with the lowest risk cost within a reasonable computational timeframe, finding relatively low-risk paths. Therefore, it can be used for time-sensitive emergency events.

[0085] To demonstrate the method, an example of an emergency involving hazardous chemicals was used. In this context, the rate of incidents involving hazardous chemicals has been increasing, given the changes over the past decades, along with the associated adverse effects such as human, environmental, and economic losses. To intervene in these incidents, the services provided by the fire department are often called upon to inspect and isolate on-site points. To prepare the model, the categories of equipment, accessories, and vehicles proposed in [1] were considered. When taking into account the necessary criteria for such an incident, an example of decision modeling based on the AHP method can be constructed, as shown in Table 1, which shows the results of the process of paired comparison after the hierarchical structure, including the judgment criteria, evaluating the consistency of the judgments, and synthesizing the priorities using the AHP method.

[0086] Table 1

[0087]

[0088] To assess consistency, CR is calculated:

[0089]

[0090] If CR < 0.1, the judgments are considered consistent. According to this process, a pairwise matrix is ​​constructed for the sub-criteria. Table 2 shows the classification generated by the AHP method.

[0091] Table 2

[0092]

[0093] As shown in Table 2, vehicle weight is the highest among the evaluation criteria, followed by equipment and accessories. This ranking allows for the determination of routes using Dijkstra's algorithm. Therefore, the spatial coordination of the planned path and its occurrence constitute the origin and destination. In this embodiment of the present invention, a simulation using Dijkstra's algorithm was performed. This simulation considered a region of a graph containing 20 nodes, with the initial node containing the geographic coordinates of the planned path and the final node containing the geographic coordinates of the event. Figure 3 Table 3 shows all possible access routes from starting point B to end point Destiny in an embodiment of the present invention. The possible routes using the Dijkstra algorithm are shown in Table 3. According to Table 3, the most efficient route is {B, C, D, I, O}, which completes the task at the lowest cost.

[0094] Table 3

[0095]

[0096] Figure 4 A schematic diagram of the relationship between the total risk cost and the risk index provided by the embodiment of the present invention is shown as follows: Figure 4 As shown in the figure, w1 represents people, w2 represents vehicle, and the vertical axis represents the total risk cost. Different total risk costs have different colors. The total risk cost decreases as the risk coefficient of people increases and the risk coefficient of vehicles decreases. There is a significant linear relationship in the middle, which produces the lowest total cost value.

[0097] Figure 5 A comparison chart of the total risk cost between the improved Dijkstra algorithm and the original Dijkstra algorithm provided by the embodiment of the present invention is shown in FIG. Figure 5 As shown, the total risk cost between the shortest path and the cost-effective path is compared. Figure 5 The total risk cost of the optimal and shortest paths using the improved Dijkstra algorithm was compared across several different urban environments. Downtown area represents the city center, sparse area represents the suburbs, aerodrome area represents the airport, dentisyarea represents the area of ​​concern, and risk area represents the dangerous area. Circles represent the improved Dijkstra algorithm, and lines represent the original Dijkstra algorithm. In all environments, the cost-effective path planning method using the improved Dijkstra algorithm outperformed the paths generated by the distance-based method. The improved Dijkstra algorithm considers risk cost in path planning, avoiding high-risk cost areas, while the distance-based algorithm does not, potentially entering high-risk cost areas.

[0098] To further understand the impact of risk on risk cost maps and path planning, embodiments of the present invention conducted simulations with different combinations of risk types within the same disaster-affected area. Four paths were studied: (i) Path A considers vehicles and airports, (ii) Path B considers people and the airport area, (iii) Path C considers people and vehicles, and (iv) Path D considers all three risks.

[0099] Figure 6 This is a schematic diagram of a planning path considering different combinations or risks provided by an embodiment of the present invention. Table 4 shows the total risk cost of different paths. Figure 6 Table 4 shows the simulation results for four paths, where UAV1 represents (i), UAV2 represents (ii), UAV3 represents (iii), and UAV4 represents (iv). Path D considers all three risk types and is the most cost-effective path. The total risk cost is 855.29. Path A is the worst, with a total risk cost 79.04% higher than that of Path D. Because Path A does not consider human risk in its path planning, the drone enters densely populated areas, resulting in a very high risk cost. Paths B and C have similar risk costs; however, Path B's total risk cost is 5.95% higher than that of Path C.

[0100] To further understand the differences in risk costs across these paths, the present invention examines each risk cost (personnel risk cost R1, vehicle risk cost R2, and airport risk cost R3) for each path. The results are shown in Table 5, which compares the average risk costs across different paths.

[0101] This embodiment of the present invention uses Path C as an example. This path does not consider airport-related risks. The drone path enters the airport area, resulting in an airport risk cost R3 of 98.50. In contrast, Path D, planned using a risk map that includes airport risks, avoids this high-risk cost area and results in an airport risk cost of 0. Paths A and B do not consider human and vehicle risks, respectively, resulting in high risks for both ignored risk categories. Table 4: Total Risk Costs for Different Paths

[0102] Table 4

[0103]

[0104] Table 5

[0105]

[0106] In summary, in the daily operations of emergency material supply, decision makers face the complex challenge of selecting teams and routes to respond to emergencies within a short period of time. First, the AHP method is implemented to rank the aspects most relevant to the emergency situation. Subsequently, in the proposed model, the Dijkstra algorithm is used to adjust the most appropriate aid route, taking into account the geographical coordinates of the emergency materials and the cost effects of the emergency situation. An improved path planning algorithm is used to generate optimal and efficient paths, and their performance is compared in terms of total risk cost and computation time. Finally, simulations are performed in the embodiment of the present invention to verify the feasibility and effectiveness of the proposed risk assessment model.

[0107] Figure 7 A schematic diagram of the structure of an emergency material route planning system provided by an embodiment of the present invention is shown in FIG. Figure 7 As shown, the system includes:

[0108] An analysis module 710 is configured to determine, by means of an analytic hierarchy process, each risk indicator and a corresponding weight associated with a target emergency event, wherein the target emergency event includes a starting point, an end point, and emergency supplies;

[0109] Planning module 720 is configured to determine a transportation path from the starting point to the destination using an improved Dijkstra algorithm based on the starting point and the destination. The improved Dijkstra algorithm considers path risk cost and avoids areas with the highest risk cost when performing path planning. The risk cost of the transportation path is calculated based on the risk severity within a specific area to which each coordinate in the transportation path belongs. The risk severity is calculated based on risk indicators contained in the specific area and the weights corresponding to the risk indicators contained in the specific area.

[0110] The transportation module 730 is used to transport the emergency supplies according to the transportation route.

[0111] This embodiment is a system embodiment corresponding to the above method embodiment, and its implementation process is the same as that of the above method embodiment. For details, please refer to the above method embodiment, and this system embodiment will not elaborate on it.

[0112] Each module in the above-mentioned emergency material route planning system can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in the form of software in a memory in the computer device, so that the processor can call and execute the corresponding operations of each module.

[0113] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a computer storage medium and an internal memory. The computer storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the computer storage medium. The database of the computer device is used to store data generated or obtained during the execution of a method for planning a route for emergency supplies, such as a starting point, an end point and emergency supplies. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for planning a route for emergency supplies is implemented.

[0114] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for planning a route for emergency supplies are implemented in the aforementioned embodiment. Alternatively, when the processor executes the computer program, the functions of the modules / units in the embodiment of a system for planning a route for emergency supplies are implemented.

[0115] In one embodiment, a computer storage medium is provided, which stores a computer program. When executed by a processor, the computer program implements the steps of the emergency supply route planning method described in the above embodiment. Alternatively, when executed by a processor, the computer program implements the functions of the modules / units of the above embodiment of the emergency supply route planning system.

[0116] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0117] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0118] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for planning an emergency material route, characterized in that: include: Determine each risk indicator related to a target emergency event and the corresponding weight of each risk indicator through the analytic hierarchy process, wherein the target emergency event includes a starting point, an end point, and emergency supplies; Determining a transportation path from the starting point to the destination using an improved Dijkstra algorithm based on the starting point and the destination, wherein the improved Dijkstra algorithm considers path risk cost and avoids areas with the highest risk cost when performing path planning, wherein the risk cost of the transportation path is calculated based on the risk severity within a specific area to which each coordinate in the transportation path belongs, wherein the risk severity is calculated based on risk indicators included in the specific area and weights corresponding to the risk indicators included; transporting the emergency supplies according to the transport route; The risk severity is calculated based on the risk indicators included in the specific area and the weights corresponding to the included risk indicators. The calculation formula is as follows: R k =w k f k ; Among them, R k Indicates the risk severity corresponding to the k-th included risk indicator, w k represents the weight corresponding to the k-th included risk indicator, f k Indicates the risk level corresponding to the k-th included risk indicator, P event-k P represents the expected risk of transport event event-k. target-k It represents the risk of the actual transportation event target-k; The risk cost of the transport path is calculated based on the risk severity within the specific area to which each coordinate in the transport path belongs. The calculation formula is as follows: Among them, R represents the risk cost matrix, R L represents the risk cost of the transportation path, (x, y) represents the coordinates, T L Represents a coordinate set, m represents the maximum value of the horizontal coordinate, n represents the maximum value of the vertical coordinate, Represents the risk cost of the transportation path L between coordinates (x, y).

2. The method for planning an emergency material route according to claim 1, characterized in that: The risk indicators include ground personnel, vehicles and manned aircraft.

3. The method for planning an emergency material route according to claim 1, characterized in that: The step of determining a transport path from the starting point to the end point by using an improved Dijkstra algorithm based on the starting point and the end point comprises: The street structure, greening type, greening location and flow rate provided by the road network are added to the improved Dijkstra algorithm; The transportation route is determined according to the starting point and the end point.

4. An emergency material route planning system, characterized in that: include: An analysis module, configured to determine, by means of an analytic hierarchy process, each risk indicator related to a target emergency event and a weight corresponding to each risk indicator, wherein the target emergency event includes a starting point, an end point, and emergency supplies; a planning module for determining, based on the starting point and the end point, a transportation path from the starting point to the end point using an improved Dijkstra algorithm, wherein the improved Dijkstra algorithm considers path risk cost and avoids areas with the highest risk cost when performing path planning, wherein the risk cost of the transportation path is calculated based on the risk severity within a specific area to which each coordinate in the transportation path belongs, wherein the risk severity is calculated based on risk indicators contained in the specific area and weights corresponding to the risk indicators contained; A transport module, configured to transport the emergency supplies according to the transport route; The risk severity is calculated based on the risk indicators included in the specific area and the weights corresponding to the included risk indicators. The calculation formula is as follows: R k =w k f k ; Among them, R k Indicates the risk severity corresponding to the k-th included risk indicator, w k represents the weight corresponding to the k-th included risk indicator, f k Indicates the risk level corresponding to the k-th included risk indicator, P event-k P represents the expected risk of transport event event-k. target-k It represents the risk of the actual transportation event target-k; The risk cost of the transport path is calculated based on the risk severity within the specific area to which each coordinate in the transport path belongs. The calculation formula is as follows: Among them, R represents the risk cost matrix, R L represents the risk cost of the transportation path, (x, y) represents the coordinates, T L Represents a coordinate set, m represents the maximum value of the horizontal coordinate, n represents the maximum value of the vertical coordinate, Represents the risk cost of the transportation path L between coordinates (x, y).

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the emergency material route planning method according to any one of claims 1 to 3 are implemented.

6. A computer storage medium storing a computer program, wherein: When the computer program is executed by a processor, the steps of the emergency material route planning method according to any one of claims 1 to 3 are implemented.

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