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

Abstract

The invention provides an emergency material route planning method, system and device and a storage medium, and the method comprises the steps: determining each risk index related to a target emergency event and a weight corresponding to each risk index through an analytic hierarchy process; according to the starting point and the terminal point, a transportation path from the starting point to the terminal point is determined through an improved Dijkstra algorithm, and the improved Dijkstra algorithm considers path risk cost and avoids an area with the highest risk cost during path planning. The risk cost of the transportation path is calculated according to the risk severity degree in a specific area to which each coordinate in the transportation path belongs; and transporting the emergency materials according to the transportation path. According to the embodiment of the invention, the improved Dijkstra algorithm mathematical calculation model finds a path with the lowest risk cost in a reasonable calculation time frame and finds a path with relatively low risk, so that the method 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 every day. The main goal of incident management is to provide emergency services to the affected area as quickly as possible. In order to achieve this goal, many factors need to be evaluated and considered within a relatively short decision period. Especially in emergency response, issues such as the number of rescue personnel, resources and equipment as well as the routes to be followed and the properties of the space are crucial for estimating the response time. In these situations, management needs to be equipped with tools to help plan and manage, and facilitate better help.

[0003] Therefore, a method for emergency material route planning is urgently needed. 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 a path with the lowest risk cost within a reasonable calculation time frame, so as to effectively improve the stability and safety of emergency material transportation.

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

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

[0007] According to the starting point and the end point, a transportation path from the starting point to the end point is determined by an improved Dijkstra algorithm, wherein the improved Dijkstra algorithm considers the path risk cost and avoids the area with the highest risk cost when performing path planning, and 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 index contained in the specific area and the weight corresponding to the contained risk index;

[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] Among them, R k represents 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 kth included risk indicator.

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

[0013]

[0014] 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).

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

[0016] Further, the step of determining the transport path from the starting point to the end point by using an improved Dijkstra algorithm according to the starting point and the end point comprises:

[0017] The street structure, greening type, greening location and flow rate provided by the road network are added to the improved Dijkstra algorithm;

[0018] The transportation path is determined according to the starting point and the end point.

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

[0020] An analysis module, for determining each risk indicator related to a target emergency event and a weight corresponding to each risk indicator by using a hierarchical analysis method, wherein the target emergency event includes a starting point, an end point, and emergency supplies;

[0021] A planning module, for determining a transportation path from the starting point to the end point by using an improved Dijkstra algorithm according to the starting point and the end point, wherein the improved Dijkstra algorithm considers the path risk cost and avoids the area with the highest risk cost when performing path planning, and 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 index contained in the specific area and the weight corresponding to the contained risk index;

[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] The present invention proposes a method, system, device and storage medium for planning emergency material routes. Compared with the prior art, the method is outstanding in the dynamic method in the event support process, and proposes an arrangement that combines the robustness of the multi-criteria analytic hierarchy process and the efficiency of the Dijkstra algorithm. One of the purposes of the embodiment of the present invention is to incorporate the experience of decision makers into a model that combines the experience of decision makers with the spatial dynamics of responding to events. To achieve this, the embodiment of the present invention uses a multi-criteria method to enable the conversion of experience values ​​in complex decision problems. Due to the complex characteristics of events, the analytic hierarchy process seeks to solve the importance of specific values ​​of a given event. In the decision matrix, the weight of the method is increased to determine the most important criteria in a given situation, providing a better method to select the most suitable path. The improved mathematical calculation model of the Dijkstra algorithm searches for the path with the lowest risk cost within a reasonable calculation time frame, and searches for a relatively low-risk path, so 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 a starting point B to an 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 between 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 diagram of the structure of a computer device provided in an embodiment of the present invention.

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

[0035] The embodiments of the present application are described in detail below, and 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 cannot be understood 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, rather than 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 work are within the scope of protection of the present application.

[0037] In the embodiments of the present application, at least one refers to one or more; multiple refers to two or more. In the description of the present application, words such as "first", "second", "third", etc. are only used to distinguish the purpose of description, 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 indicated technical features. Thus, 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 "multiple" is two or more, unless otherwise clearly and specifically defined.

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

[0039] In emergency operations, rescue agencies must coordinate and organize staff and materials according to emergency needs. The embodiments of the present invention combine with Geographic Information System (GIS) in different ways and use a model that focuses on the dynamic situation of event calls. In an emergency, managers will quickly decide to meet the requirements of the event and determine which emergency material route is the most appropriate assistance. The Analytic Hierarchy Process (AHP) is combined with the Dijkstra algorithm. The flexibility and robustness of the multi-iteration method are used and compared. Path planning algorithms are used to generate low-risk cost paths, and their strength and scenarios that are not related to weak nature are evaluated.

[0040] Figure 1 A flowchart of a method for planning an emergency material route is provided in an embodiment of the present invention, such as 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 by using a hierarchical analysis method, wherein the target emergency event includes a starting point, an end point, and emergency supplies;

[0042] First, in the embodiment of the present invention, each risk indicator related to the target emergency event and the weight corresponding to each risk indicator are determined by the hierarchical analysis method. For example, some target emergency events are the transportation of chemical drugs, and the risk indicators of the transportation of chemical drugs include drug leakage, road curvature, etc. Some target emergency events are the operation of disaster relief materials, and the risk indicators of the disaster relief materials include the shelf life of vegetables, etc. Therefore, the risk indicators of different target emergency events are different and need to be determined according to the actual situation. The embodiment of the present invention does not make specific limitations on this.

[0043] The target emergency event includes a starting point, an end point and emergency materials. The starting point refers to the transportation starting point of the emergency materials, the end point refers to the transportation destination of the emergency materials, and the emergency materials refer to the materials 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 single ranking (weight) and total ranking of the levels through the fuzzy quantification method of qualitative indicators, as a systematic method for optimizing decision-making with goals (multiple indicators) and multiple plans.

[0045] After the AHP is constructed, after the paired judgment stage, expert knowledge determines the relevance of a criterion based on experience, intuition, and expertise, rather than focusing on the main goal, using a numerical scale to convert the language evaluation into numbers, and the values ​​from the judgment are used to construct the 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 leads to an approximation of the decision maker's priorities.

[0054] S120, determining a transportation path from the starting point to the end point by using an improved Dijkstra algorithm according to the starting point and the end point, wherein the improved Dijkstra algorithm considers the path risk cost and avoids the area with the highest risk cost when performing path planning, and 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 index included in the specific area and the weight corresponding to the included risk index;

[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 building effective routes. Road networks in urban areas are occasionally blocked, whether due to road obstructions, traffic jams, or other types of adversity that cause traffic to be blocked. In such cases, the data layers provided by GIS 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 empty is known. The Dijkstra algorithm for GIS integration 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 to 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. In this phase, previous experience helped determine the importance of each criterion, generating a ranking of the most important criteria. The values ​​of these criteria were used as weights in the decision matrix, which determined the routes that were most willing to be supported. When the coordinates of the planned path and its appearance in the GIS were taken into account, the Dijkstra algorithm was used to calculate the access route.

[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 provided by an embodiment of the present invention is shown as follows: Figure 2 As shown, Row represents a row, Column represents a column, and the map consists of links and nodes. 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 represents high risk cost and blue represents low risk cost. This path avoids high risk cost areas and its risk cost is much lower than the path with short distance. The key goal of the embodiment 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 Used to adjust 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 emergency supplies are calculated using the calculation path.

[0084] The present invention proposes a method for planning emergency material routes. Compared with the prior art, the method is outstanding in the dynamic method in the event support process, and proposes an arrangement that combines the robustness of the multi-criteria analytic hierarchy process and the efficiency of the Dijkstra algorithm. One of the purposes of the embodiment of the present invention is to incorporate the experience of decision makers into a model that combines the experience of decision makers with the spatial dynamics of responding to events. To achieve this, the embodiment of the present invention uses a multi-criteria method to enable the conversion of experience values ​​in complex decision problems. Due to the complex characteristics of events, the analytic hierarchy process seeks to solve the importance of specific values ​​of a given event. In the decision matrix, the weight of this method is increased to determine the most important criteria in a given situation, providing a better method to select the most suitable path. The improved mathematical calculation model of the Dijkstra algorithm searches for the path with the lowest risk cost within a reasonable calculation time frame, and searches for a relatively low-risk path, so it can be used for time-sensitive emergency events.

[0085] To demonstrate the method, an example of an emergency with hazardous chemicals was used. In this context, the rate of incidents with hazardous chemicals is increasing, given the changes over the past decades, as well as 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 the site points. To prepare the model, the categories of equipment, accessories and vehicles proposed in 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 hierarchy, 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 to be consistent. According to this process, a pairwise matrix is ​​constructed for the sub-criteria. Table 2 shows the classification produced by the AHP method.

[0091] Table 2

[0092]

[0093] As shown in Table 2, among the evaluation criteria, the weight of the vehicle is higher, followed by equipment and accessories. By sorting, the route can be determined using Dijkstra's algorithm. Therefore, the spatial coordination of the planned path shown and its occurrence constitutes the origin and destination. In the embodiment of the present invention, a simulation was performed using the algorithm based on Dijkstra. In this simulation, an area of ​​a graph containing 20 nodes was considered, the initial node contained the geographical coordination of the path planning, and the final node contained the geographical coordinates of the event. Figure 3 Table 3 is a schematic diagram of all possible access routes from the starting point B to the end point Destiny in an embodiment of the present invention, and shows possible routes using the Dijkstra algorithm. According to Table 3, the most effective 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, such as 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 personnel 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 of the improved Dijkstra algorithm and the original Dijkstra algorithm provided in 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 modified Dijkstra algorithm in several different urban environments is compared, where Downtown area represents the city center, Sparse area represents the suburbs, Aerodrome area represents the airport, dentisyarea represents the area of ​​concern, Risk area represents the dangerous area, circles represent the modified Dijkstra algorithm, and lines represent the original Dijkstra algorithm. In all environments, the paths generated by the cost-effective path planning method using the modified Dijkstra algorithm are better than the paths obtained by the distance-based method. The modified Dijkstra algorithm considers the risk cost when planning the path and avoids the high risk cost area, while the distance-based algorithm does not consider the risk cost and may enter the high risk cost area.

[0098] In order to further understand the impact of risk on risk cost map and path planning, the embodiment of the present invention simulates different risk type combinations in the same disaster area. Four paths are studied in the simulation: (i) Path A considers vehicles and airports, (ii) Path B considers people and airport areas, (iii) Path C considers people and vehicles, and (iv) Path D considers all three risks.

[0099] Figure 6 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 of the four paths, where UAV1 represents (i), UAV2 represents (ii), UAV3 represents (iii), and UAV4 represents (iv). Path D takes all three risk types into account 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. Since Path A does not consider human risk in path planning, the risk cost of the drone entering densely populated areas is very high. The risk costs of Path B and Path C are similar; the total risk cost of Path B is 5.95% higher than that of Path C.

[0100] In order to further understand the differences in risk costs of these paths, the embodiment of the present invention studies each risk cost (personnel risk cost R1, vehicle risk cost R2 and airport risk cost R3) under different paths. The results are shown in Table 5, which is a comparison of the average risk costs of different paths.

[0101] The embodiment of the present invention takes path C as an example. This path does not consider the risks associated with the airport. The drone path enters the airport area, resulting in an airport risk cost R3 of 98.50. In contrast, path D planning uses a risk map that includes airport risks, thereby avoiding this high risk cost area, resulting in an airport risk cost of 0. Path A and path B do not consider human risk and vehicle risk respectively, resulting in high risks for the two ignored risk categories. Table 4 Total risk cost of different paths

[0102] Table 4

[0103]

[0104] Table 5

[0105]

[0106] In summary, in the daily operation of emergency material supply, decision makers are faced with the complex challenge of selecting teams and paths to respond to emergencies in a short time. First, the AHP method is implemented to sort the aspects most relevant to the emergency. Subsequently, in the proposed model, the Dijkstra algorithm is used to adjust the most appropriate aid route, considering the cost effects such as the geographical coordinates of the emergency materials and the emergency situation, and the improved path planning algorithm is used to generate the optimal effective path, and their performance in terms of total risk cost and calculation time is compared. Finally, the embodiment of the present invention performs simulation 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 used to determine each risk indicator related to a target emergency event and a weight corresponding to each risk indicator by using a hierarchical analysis method, wherein the target emergency event includes a starting point, an end point, and emergency supplies;

[0109] A planning module 720 is used to determine a transportation path from the starting point to the end point according to the starting point and the end point by using an improved Dijkstra algorithm, wherein the improved Dijkstra algorithm considers the path risk cost and avoids the area with the highest risk cost when performing path planning, and 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 index contained in the specific area and the weight corresponding to the contained risk index;

[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 emergency material route planning system can be implemented in whole or in part by 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 a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to 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 through a system bus. Among them, 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 an emergency material route planning method, such as a starting point, an end point and emergency materials. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an emergency material route planning method is implemented.

[0114] In one embodiment, a computer device is provided, including 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 an emergency material route planning method in the above embodiment are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in this embodiment of an emergency material route planning system are implemented.

[0115] In one embodiment, a computer storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of an emergency material route planning method in the above embodiment are implemented. Alternatively, when the computer program is executed by a processor, the functions of each module / unit in the above embodiment of an emergency material route planning system are implemented.

[0116] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the 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. As an illustration and not limitation, RAM is available in many 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 can clearly understand that for the convenience and simplicity 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for planning an emergency material route, characterized in that: include: Determine each risk indicator related to the target emergency event and the weight corresponding to each risk indicator through the analytic hierarchy process, wherein the target emergency event includes the starting point, the end point and the emergency supplies; According to the starting point and the end point, a transportation path from the starting point to the end point is determined by an improved Dijkstra algorithm, wherein the improved Dijkstra algorithm considers the path risk cost and avoids the area with the highest risk cost when performing path planning, and 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 index contained in the specific area and the weight corresponding to the contained risk index; The emergency supplies are transported according to the transport route.

2. The method for planning an emergency material route according to claim 1, characterized in that: 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 represents 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 kth included risk indicator.

3. The method for planning an emergency material route according to claim 1, characterized in that: The risk cost of the transport path is calculated according to the risk severity within the specific area to which each coordinate in the transport path belongs, and 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).

4. 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.

5. The method for planning an emergency material route according to claim 1, characterized in that: The step of determining a transportation path from the starting point to the end point by using an improved Dijkstra algorithm according to 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 path is determined according to the starting point and the end point.

6. An emergency material route planning system, characterized in that: include: An analysis module, for determining each risk indicator related to a target emergency event and a weight corresponding to each risk indicator by using a hierarchical analysis method, wherein the target emergency event includes a starting point, an end point, and emergency supplies; A planning module, for determining a transportation path from the starting point to the end point by using an improved Dijkstra algorithm according to the starting point and the end point, wherein the improved Dijkstra algorithm considers the path risk cost and avoids the area with the highest risk cost when performing path planning, and 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 index contained in the specific area and the weight corresponding to the contained risk index; A transport module is used to transport the emergency supplies according to the transport route.

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

8. A computer storage medium storing a computer program, characterized in that: 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 5 are implemented.

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