Multi-disaster-point emergency rescue scheduling method

Abstract

The invention belongs to the technical field of emergency rescue management, and discloses a multi-disaster-point emergency rescue scheduling method, which comprises the steps that a multi-disaster-point emergency rescue dynamic network is constructed according to real-time information of disaster-affected points, and the real-time information of the disaster-affected points comprises positions, disaster-affected time and emergency rescue decisions of the disaster-affected points; formulating an initial rescue path of each rescue vehicle in the multi-disaster-point emergency rescue dynamic network based on a preset constraint condition; and drawing a directed graph based on the formulated initial rescue path, and solving an optimal scheduling strategy of the multi-disaster-point emergency rescue dynamic network under the minimum number of vehicles in the directed graph based on a Hopcroft-Karp algorithm. According to the technical scheme of the invention, by constructing the multi-disaster-point emergency rescue'time-space-decision 'dynamic network, the problem of'minimum vehicle number' in the multi-disaster-point emergency rescue dynamic network can be solved.

Description

Technical Field

[0001] The invention belongs to the technical field of emergency rescue management, and in particular relates to a multi-disaster point emergency rescue dispatching method. Background Art

[0002] In recent years, countries around the world have been facing the growing challenges of various unconventional emergencies, which not only threaten national stability and people's safety, but also cause serious economic losses. In terms of theory, domestic and foreign scholars have conducted a lot of research on the key technologies of emergency decision-making management of unconventional events and have achieved rich research results. However, in a specific regional environment, the occurrence of an unconventional emergency will dynamically trigger a series of emergencies. The uncertainty of the evolution of such unconventional emergencies leads to the uncertainty of the number of disaster-stricken points and related rescue needs. Therefore, in terms of time and space, the emergency rescue network of unconventional emergencies is dynamically changing. At present, theoretical analysis and uncertainty decision-making research on the dynamic emergency rescue network formed on the corresponding disaster-stricken points based on the dynamic evolution of unconventional emergencies are relatively scarce, and there is no strict specification for its connotation and extension.

[0003] In summary, how to construct a multi-disaster point emergency rescue decision-making theoretical system suitable for the entire dynamic network of unconventional emergencies is an issue that the entire society urgently needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-disaster point emergency rescue dispatching method to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides a multi-disaster point emergency rescue dispatching method, comprising:

[0006] Building a multi-disaster point emergency rescue dynamic network based on real-time information of disaster-affected points, wherein the real-time information of disaster-affected points includes the location of the disaster-affected points, the time of disaster, and emergency rescue decisions;

[0007] Formulate an initial rescue path for each rescue vehicle in the multi-disaster point emergency rescue dynamic network based on preset constraints;

[0008] A directed graph is drawn based on the formulated initial rescue path, and the optimal scheduling strategy of the dynamic network of emergency rescue at multiple disaster points with the minimum number of vehicles is solved in the directed graph based on the Hopcroft-Karp algorithm.

[0009] Optionally, the process of constructing the multi-disaster point emergency rescue dynamic network includes:

[0010] The multi-disaster point emergency rescue dynamic network is constructed with the locations and disaster time of the disaster points as nodes.

[0011] Optionally, formulating an initial rescue path for each rescue vehicle in the multi-disaster point emergency rescue dynamic network based on preset constraints specifically includes:

[0012] The estimated travel time between each disaster-stricken point is estimated based on the travel time estimation model, and several paths are formulated based on the estimated travel time and a preset time upper limit parameter. Each path is judged based on preset constraints, and the path that meets the preset constraints is used as the initial rescue path.

[0013] Optionally, the preset constraint conditions specifically include:

[0014] The time taken by the rescue vehicle to reach the next disaster-stricken point from the previous disaster-stricken point is greater than or equal to the preset travel time and the connection time between adjacent disaster-stricken points is less than the preset connection time threshold.

[0015] Optionally, a directed graph is drawn based on the established initial rescue path, including:

[0016] Taking a rescue operation as a node and the action of the same rescue vehicle rescuing two disaster-stricken points consecutively as a directed edge, a directed graph is drawn based on the constructed nodes and directed edges.

[0017] Optionally, solving the optimal dispatching strategy of the multi-disaster point emergency rescue dynamic network with the minimum number of vehicles in the directed graph based on the Hopcroft-Karp algorithm specifically includes:

[0018] The directed graph is converted into a bipartite graph, a scheduling model is established in the bipartite graph with the goal of minimizing the number of rescue vehicles, the scheduling model is solved based on the Hopcroft-Karp algorithm, the maximum matching number of the bipartite graph is obtained, the end point of the previous rescue is connected with the starting point of the next rescue, and the optimal scheduling strategy of the dynamic network of emergency rescue at multiple disaster points with the minimum number of rescue vehicles is obtained.

[0019] Optionally, converting the directed graph into a bipartite graph specifically includes:

[0020] An undirected network is defined, and a vertex set of the undirected network is divided into two non-intersecting subsets, wherein the subsets include a starting point set of rescue vehicles and an end point set of rescue vehicles; and a bipartite graph is constructed based on the starting point set of rescue vehicles and the end point set of rescue vehicles.

[0021] Optionally, the objective function of the scheduling model is:

[0022]

[0023] Where w represents the total number of rescue vehicles on each path in the area; |V| represents the number of vertices in the original directed acyclic graph; xuv represents whether the edge (u, v) in the bipartite graph is in the match, and E is the set of directed edges.

[0024] The technical effects of the present invention are:

[0025] Based on the dynamic network of unconventional emergencies, the present invention takes the disaster-stricken points dynamically formed in the spatial dimension as nodes in the network, and the emergency rescue decisions between the disaster-stricken points form the edges in the network. Then, multiple emergency rescue decision chains are crossed together to construct a "time-space-decision" dynamic network for emergency rescue at multiple disaster points. Based on the theory of vehicle sharing networks, the problem of "minimum number of vehicles" in the dynamic network for emergency rescue at multiple disaster points is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0028] Figure 1 It is a schematic diagram of a process based on emergency rescue problems at multiple disaster sites in an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of a flow chart of a method for constructing a dynamic network for emergency rescue at multiple disaster sites based on vehicle sharing in an embodiment of the present invention;

[0030] Figure 3 A schematic diagram of a time-space-decision network model in an embodiment of the present invention;

[0031] Figure 4 FIG. 1 is a diagram of a series of rescue dispatching schemes in an embodiment of the present invention, wherein thin lines represent different possibilities of dispatching rescue vehicles;

[0032] Figure 5 is a directed graph of the rescue dispatching scheme in the embodiment of the present invention;

[0033] Figure 6 Convert different possible rescue paths into a bipartite graph in an example of an embodiment of the present invention, and find the maximum matching number of the bipartite graph;

[0034] Figure 7 This is the optimal scheduling scheme diagram in an embodiment of the present invention, and the minimum number of vehicles in the vehicle sharing network is found in different scheduling schemes. DETAILED DESCRIPTION

[0035] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0036] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0037] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art generally understood by the present invention. Although the present invention describes only preferred methods, any method similar or equivalent to that described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0038] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present application description and examples are exemplary only.

[0039] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0040] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] Embodiment 1

[0042] like Figure 1 - Figure 7As shown, this embodiment provides a multi-disaster point emergency rescue scheduling method, including: constructing a multi-disaster point emergency rescue dynamic network based on real-time information of the disaster-stricken points, the real-time information of the disaster-stricken points including the location of the disaster-stricken points, the disaster time and the emergency rescue decision; formulating an initial rescue path for each rescue vehicle in the multi-disaster point emergency rescue dynamic network based on preset constraints; drawing a directed graph based on the formulated initial rescue path, and solving the optimal scheduling strategy of the multi-disaster point emergency rescue dynamic network with the minimum number of vehicles in the directed graph based on the Hopcroft-Karp algorithm.

[0043] In view of the problems existing in the prior art, this embodiment, based on the dynamic network of unconventional emergencies, takes the disaster-stricken points dynamically formed in the spatial dimension as nodes in the network, and the emergency rescue decisions between the disaster-stricken points form the edges in the network. Then, multiple emergency rescue decision chains are crossed together to construct a "time-space-decision" dynamic network for emergency rescue at multiple disaster points. Based on the theory of vehicle sharing networks, the problem of the minimum number of vehicles in the dynamic network for emergency rescue at multiple disaster points is solved.

[0044] To achieve the above purpose, this embodiment provides the following solutions:

[0045] Figure 1 FIG. 1 is a flow chart of the emergency rescue problem based on multiple disaster points in this embodiment. Figure 1 Regarding the emergency rescue issues at multiple disaster sites, the following steps are used to explain them in detail:

[0046] Step 1: When an emergency occurs in a certain area and there are more than one disaster-affected point, emergency rescue is required for the disaster-affected points. There are often more than one disaster-affected point in the occurrence of an emergency, and if the rescue between multiple disaster relief points is not coordinated with each other, many problems will arise, such as untimely rescue and uneconomical rescue.

[0047] Step 2: Make corresponding rescue decisions for each disaster site, that is, the assignment and dispatch of rescue vehicles. After an emergency occurs, each disaster site should assign corresponding rescue vehicles for rescue according to its own disaster situation, including geographical location, surrounding road conditions, number of trapped people, etc.

[0048] Step 3: The data center collects the rescue decisions of each disaster site and updates the newly added rescue decisions in real time. The disaster situation in the area is always dynamic. As time changes, many new disaster sites or rescued disaster sites will appear and wait for rescue. Therefore, it is necessary to update the disaster situation in the area in real time and collect the rescue vehicle routes assigned to each disaster site.

[0049] Step 4: Construct a dynamic network for emergency rescue at multiple disaster sites to minimize the number of rescue vehicles. The multi-disaster emergency rescue problem is essentially a problem of minimizing the number of rescue vehicles for multiple disaster sites. Before minimizing, a dynamic network for emergency rescue at multiple disaster sites needs to be constructed, and then the problem of minimizing the number of vehicles needs to be solved.

[0050] Figure 2 This is a flow chart of the method for constructing a dynamic network for emergency rescue at multiple disaster sites based on vehicle sharing in this embodiment. Figure 2 The method for constructing a dynamic network for emergency rescue at multiple disaster sites based on vehicle sharing includes:

[0051] Step 101: Real-time observation of the occurrence of unconventional emergencies in the region;

[0052] This step 101 needs to determine the observation area of ​​unconventional emergency events. Due to the unpredictability of emergency events, it is necessary to observe the event area in order to make a timely response to the disaster site;

[0053] Step 102: Collect relevant information of each disaster-affected point: The relevant information of the disaster-affected point should include the time and location of the disaster and the number of rescue vehicles required by the disaster-affected point;

[0054] This step 102 collects the time and location of each disaster site and the number of rescue vehicles required; the time and location of the disaster are mainly used to analyze whether the rescue vehicles of two adjacent disaster sites can be shared. Since the severity of the disasters at each disaster site varies, it is also necessary to consider the problem of allocating multiple rescue vehicles to the same disaster site;

[0055] Step 103: Analyze the information collected from each disaster-affected point, estimate the expected travel time between each disaster-affected point through the travel time estimation model, and determine the appropriate time upper limit parameter δ to find different possible rescue paths;

[0056] According to the collected information of the disaster-affected points, the distance between each disaster-affected point is calculated, and the estimated travel time between each disaster-affected point is estimated by the travel time estimation model. Here, the travel time estimation is mainly carried out using the rescue vehicle GPS travel records that can explain the time-varying law of traffic; in order to avoid the situation where the rescue vehicle has too long idle time between two consecutive rescues in the constructed multi-disaster point emergency rescue dynamic network, a time upper limit parameter δ is introduced to balance the scale of rescue vehicles and traffic efficiency; the selection of the time upper limit parameter δ should be appropriate, not too large or too small. When δ is reduced to 0, each disaster-affected point will be assigned a special vehicle for rescue. At this time, the utilization rate of the rescue vehicle is the highest, which is an optimal solution for traffic, but it will cause the emergency management department to bear too high cost pressure. On the contrary, when δ grows too large, the scale of rescue vehicles decreases, but the idle time of rescue vehicles will increase, and traffic efficiency will decrease. Therefore, how to choose a suitable δ value needs to be further discussed.

[0057] According to the travel time calculation results and the time upper limit parameters, different possible rescue paths are found, that is, the rescue vehicles are scheduled in time and space.

[0058] Step 104: Do the adjacent disaster-affected points in the rescue path meet the following constraints? The specific constraints are:

[0059] a. The rescue vehicle arrives at the next disaster site just in time or in advance from the previous disaster site.

[0060] b. The connection time between adjacent disaster-affected points shall not exceed δ minutes.

[0061] This step 104 constrains the rescue decision of adjacent disaster-stricken points in the rescue path. The selection of the rescue path in step 103, in principle, needs to meet the constraints.

[0062] If the output result of step 104 is yes, then step 105 is executed: a directed graph is drawn, where the nodes are the disaster-affected points and the number of paths is the number of vehicles required for rescue.

[0063] The step 105 specifically includes: defining a directed network V = (N, E) according to the initially planned rescue path, where the nodes belong to the set N and correspond to a rescue T in the rescue set T. i ; The directed edges belong to the set E, corresponding to the continuous rescue of the same rescue vehicle (after rescuing the previous disaster site, it immediately rescues the next disaster site).

[0064] Based on the emergency rescue decision of each disaster site, each rescue is expressed as:

[0065]

[0066] in, Represents the expected departure time of the rescue vehicle; Represents the departure point of rescue vehicles; Represents the arrival time of the rescue vehicle; Indicates the location where the rescue vehicle arrives. Here, the departure time is the time when the rescue vehicle arrives. The earliest time for the rescue vehicle to depart is the arrival time, and the arrival time is the estimated time for the rescue vehicle to arrive. Depart the departure location at the specified time, using the travel time estimation model.

[0067] Consider two consecutive rescues T performed by the same vehicle i and T j , define the time between two rescues as the connection time, expressed as:

[0068]

[0069] If two rescue requests can be met by the same rescue vehicle, an edge is added between the two nodes. The objective function of the model can be expressed as:

[0070]

[0071] Among them, xij indicates whether two consecutive rescues Ti and Tj can be met by the same rescue vehicle, that is, whether an edge can be added between these two nodes. ij =1 means that both rescue operations can be completed with the same rescue vehicle, x ij = 0 means that the same rescue vehicle cannot be used to meet the two rescue needs. Find all possible different path scheduling solutions for the rescue vehicles.

[0072] The model has the following constraints:

[0073]

[0074] xij∈{0,1}

[0075] Among them, tij represents the estimated travel time from the departure point to the arrival point of the rescue vehicle; δ is the time upper limit parameter. A path in the network corresponds to a travel sequence served by the same rescue vehicle, that is, a dispatch. The acyclic nature of time itself ensures that any rescue vehicle in the vehicle sharing network is a directed acyclic graph, so that the minimum number of vehicles in the network can be effectively and optimally solved; the variable x ij is a binary variable that can only take the value 0 or 1, indicating two consecutive rescue T i and T j Can the same rescue vehicle be used to meet the needs?

[0076] After step 105, the following step further includes: according to the directed network, using the Hopcroft-Karp algorithm to bidirectionally match and solve the directed acyclic graph to obtain the optimal scheduling strategy, that is, the travel sequence of each vehicle under the minimum number of vehicles.

[0077] If the output result of step 104 is no, then return to step 103 to continue searching for a possible rescue path.

[0078] Figure 3 This is a schematic diagram of the time-space-decision network model based on this embodiment.

[0079] A physical path corresponds to multiple paths with different departure and arrival times and different decisions in the time-space-decision network, see Figure 3 , with time, node (space), and decision as coordinate axes, it can be seen that the physical segment (i, j) corresponds to multiple paths in the space-time network with different departure times, different arrival times, and different decisions.

[0080] Figure 4 is a diagram of a series of dispatching plans for rescue vehicles Ti in this embodiment, and thin lines represent different possibilities for dispatching rescue vehicles.

[0081] According to the construction of a dynamic network of emergency rescue at multiple disaster sites based on vehicle sharing, a rescue vehicle dispatching scheme that meets the constraints is found. The thick line represents the determined dispatching scheme, while the thin lines represent different possible dispatching schemes for rescue vehicles. The one shown here is the dispatching scheme of the rescue decision T. A To T F Several different scheduling schemes.

[0082] In order to ensure that the number of rescue vehicles in the network is minimized, it is necessary to select different scheduling schemes and select the scheme with the minimum number of rescue vehicles as the optimal scheduling scheme. In this embodiment, the problem of the minimum number of vehicles in the multi-disaster point emergency rescue dynamic network is transformed into a problem of solving the minimum path coverage problem, thereby more efficiently solving the problem of the minimum number of vehicles in the network.

[0083] Figure 5 It is a directed graph of the rescue dispatching plan in this embodiment.

[0084] According to different possible vehicle rescue path decisions, they are simplified and a directed graph is drawn to find the minimum number of paths covering the entire directed graph, thereby obtaining the travel sequence of each vehicle under the minimum number of vehicles.

[0085] Based on the directed acyclic graph, it can be converted into a bipartite graph. After finding the maximum match in the bipartite graph using the Hopcroft-Karp algorithm, a node set in the bipartite graph is randomly selected, and the matching nodes are the minimum number of rescue vehicles.

[0086] Figure 6 In this embodiment, different possible rescue paths are converted into a bipartite graph, and the maximum matching number of the bipartite graph is found.

[0087] Define an undirected network G(V,E), where vertex V is divided into two non-intersecting subsets (U,V); set U represents the starting set of rescue vehicles and is located in the left half of the bipartite graph, and set V represents the destination set of rescue vehicles and is located in the right half of the bipartite graph; the two vertices u and v associated with each edge (u,v) in the graph belong to these two different vertex sets (u∈U,v∈V).

[0088] With the goal of minimizing the number of rescue vehicles, a scheduling model is established:

[0089] Objective function:

[0090]

[0091] Where w represents the total number of rescue vehicles on each path in the area; |V| represents the number of vertices in the original directed acyclic graph; x uv Indicates whether the edge (u,v) in the bipartite graph is in the matching. uv =1 means the edge (u,v) is in the matching, x uv =0 means the edge (u,v) is not in the match;

[0092] Constraints:

[0093]

[0094] xuv∈{0,1}

[0095] Among them, N(u) represents the set of vertices adjacent to vertex u, and N(v) represents the set of vertices adjacent to vertex v, which respectively ensure that the matching is legal; variable x uv It is a binary variable that can only take the value 0 or 1, indicating whether the edge is in the match.

[0096] The basic idea of ​​the Hopcroft-Karp algorithm is: when searching for augmented orbits of all unmatched vertices at the same time, all legal augmented orbits are found at the same time, and the unmatched parts of these augmented orbits are not allowed to overlap. Using the previous augmented orbit inversion method, the appropriate vertices are used to match these augmented orbits. Each match can increase the matching number by 1 until no more matching augmented orbits are found, and the maximum match is obtained. The complexity of the Hopcroft-Karp algorithm is O(V^0.5E).

[0097] Figure 7 This is the optimal scheduling scheme diagram in this embodiment, and the minimum number of vehicles in the vehicle sharing network is found in different scheduling schemes.

[0098] By using the Hopcroft-Karp algorithm to obtain the maximum matching number of the bipartite graph, the dispatching scheme for rescue vehicles can be obtained. After connecting the end point of the previous rescue with the starting point of the next rescue, the rescue path of the same rescue vehicle can be obtained, that is, the optimal dispatching strategy for the dynamic network of emergency rescue at multiple disaster sites with the minimum number of rescue vehicles. It can be seen that the number of rescue vehicles is greatly reduced under the vehicle sharing network, and at the same time, the rescue of the disaster site can be guaranteed not to be delayed, realizing the dispatching of rescue vehicles in the "time-space-decision" dynamic network of emergency rescue at multiple disaster sites.

[0099] This embodiment discloses a dynamic network scheduling method for emergency rescue at multiple disaster sites based on vehicle sharing, the method comprising obtaining disaster site information of disaster sites affected by unconventional emergencies in a region; wherein the information of the disaster site includes the geographical location of the disaster site, the time of the disaster, and the emergency rescue decision of the disaster site; constructing a time-space-decision network model based on the information of the disaster site; defining a directed network based on the emergency rescue decision of each disaster site, which must meet the constraints; based on the directed network, using the Hopcroft-Karp algorithm to bidirectionally match and solve the directed acyclic graph to obtain the optimal scheduling strategy, that is, the travel sequence of each vehicle under the minimum number of vehicles. This embodiment can realize the construction of a dynamic network for emergency rescue at multiple disaster sites, and reduce the number of rescue vehicles by using a vehicle sharing network, greatly shortening the rescue time and rescue costs, and meeting the needs of the future development of emergency decision-making management technology for unconventional events.

[0100] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-disaster point emergency rescue dispatching method, characterized in that: include: Building a multi-disaster point emergency rescue dynamic network based on real-time information of disaster-affected points, wherein the real-time information of disaster-affected points includes the location of the disaster-affected points, the time of disaster, and emergency rescue decisions; Formulate an initial rescue path for each rescue vehicle in the multi-disaster point emergency rescue dynamic network based on preset constraints; A directed graph is drawn based on the formulated initial rescue path, and the optimal scheduling strategy of the dynamic network of emergency rescue at multiple disaster points with the minimum number of vehicles is solved in the directed graph based on the Hopcroft-Karp algorithm.

2. A multi-disaster point emergency rescue dispatching method according to claim 1, characterized in that: The construction process of the multi-disaster point emergency rescue dynamic network includes: The multi-disaster point emergency rescue dynamic network is constructed with the locations and disaster time of the disaster points as nodes.

3. The multi-disaster point emergency rescue dispatching method according to claim 1, characterized in that: Based on the preset constraints, the initial rescue path of each rescue vehicle is formulated in the multi-disaster point emergency rescue dynamic network, specifically including: The estimated travel time between each disaster-stricken point is estimated based on the travel time estimation model, and several paths are formulated based on the estimated travel time and a preset time upper limit parameter. Each path is judged based on preset constraints, and the path that meets the preset constraints is used as the initial rescue path.

4. The multi-disaster point emergency rescue dispatching method according to claim 1, characterized in that: The preset constraints specifically include: The time taken by the rescue vehicle to reach the next disaster-stricken point from the previous disaster-stricken point is greater than or equal to the preset travel time and the connection time between adjacent disaster-stricken points is less than the preset connection time threshold.

5. The multi-disaster point emergency rescue dispatching method according to claim 1, characterized in that: Draw a directed graph based on the initial rescue path, including: Taking a rescue operation as a node and the action of the same rescue vehicle rescuing two disaster-stricken points consecutively as a directed edge, a directed graph is drawn based on the constructed nodes and directed edges.

6. The multi-disaster point emergency rescue dispatching method according to claim 1, characterized in that: Based on the Hopcroft-Karp algorithm, the optimal scheduling strategy of the multi-disaster point emergency rescue dynamic network with the minimum number of vehicles is solved in the directed graph, specifically including: The directed graph is converted into a bipartite graph, a scheduling model is established in the bipartite graph with the goal of minimizing the number of rescue vehicles, the scheduling model is solved based on the Hopcroft-Karp algorithm, the maximum matching number of the bipartite graph is obtained, the end point of the previous rescue is connected with the starting point of the next rescue, and the optimal scheduling strategy of the dynamic network of emergency rescue at multiple disaster points with the minimum number of rescue vehicles is obtained.

7. A multi-disaster point emergency rescue dispatching method according to claim 6, characterized in that: Converting the directed graph into a bipartite graph specifically includes: An undirected network is defined, and a vertex set of the undirected network is divided into two non-intersecting subsets, wherein the subsets include a starting point set of rescue vehicles and an end point set of rescue vehicles; and a bipartite graph is constructed based on the starting point set of rescue vehicles and the end point set of rescue vehicles.

8. The multi-disaster point emergency rescue dispatching method according to claim 6, characterized in that: The objective function of the scheduling model is: Where w represents the total number of rescue vehicles on each path in the region; |V| represents the number of vertices in the original directed acyclic graph; x uv Indicates whether the edge (u, v) in the bipartite graph is in the matching, and E is a set of directed edges.

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