Rescue route planning method and system for emergency rescue vehicle

Abstract

The embodiment of the invention provides a rescue route planning method and system for an emergency rescue vehicle, and relates to the technical field of traffic control of road vehicles. The method comprises the steps of obtaining target data, obtaining first output data based on a first preset algorithm, and determining a target vehicle, a departure place and a destination based on the first output data; wherein the target data comprises a node set, a vehicle type set, fixed cost, capacity cost and time cost; the departure place is the current parking place of the target vehicle, and the destination is a to-be-rescued destination; and obtaining first output data, real-time traffic information and real-time weather information, obtaining second output data based on a second preset algorithm, and determining an optimal driving path of the target vehicle from the departure place to the destination based on the second output data. According to the invention, the problem of low rescue efficiency in the prior art is solved, and the effect of rapid rescue is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of road vehicle traffic control, and in particular, to a rescue route planning method and system for an emergency rescue vehicle. Background Art

[0002] The history of emergency rescue vehicle route planning can be traced back to early navigation and path optimization technologies, which were originally designed to improve logistics and transportation efficiency. Over time, these technologies have been applied to the fields of emergency medical services and disaster response to ensure that rescue resources can reach the scene of the accident quickly and efficiently. Early methods mainly focused on shortest path algorithms, but with the improvement of computing power and the development of algorithms, route planning began to integrate more dynamic factors, such as real-time traffic information and geographic information system (GIS) data, to improve the adaptability and accuracy of route planning.

[0003] In the latest related technologies, researchers have adopted a variety of solutions to solve the problems of vehicle scheduling and path planning. Some studies have proposed methods based on optimization algorithms, such as genetic algorithms and ant colony algorithms, which can handle complex constraints and find the optimal solution among multiple candidate paths. In addition, there are studies that use machine learning technology to predict traffic patterns and accident-prone areas by analyzing historical data, so as to plan rescue routes in advance. The development of these technologies has made the scheduling and path planning of rescue vehicles more intelligent and automated.

[0004] Although the latest technologies have made significant progress in emergency rescue vehicle route planning, existing solutions often have the problem of considering a single dimension. For example, some methods may focus on minimizing driving distance or time, while ignoring the urgency of the rescue mission and the rational allocation of resources. This single-dimensional optimization will lead to the problem of being unable to fully adapt to the changing rescue environment in practical applications, resulting in low rescue efficiency. Summary of the invention

[0005] The embodiments of the present invention provide a rescue route planning method and system for an emergency rescue vehicle, so as to at least solve the problem of low rescue efficiency in the related art.

[0006] According to one embodiment of the present invention, a rescue route planning method for an emergency rescue vehicle is provided, comprising: acquiring target data, obtaining first output data based on a first preset algorithm, and determining a target vehicle, a departure point, and a destination based on the first output data; wherein the target data comprises: a node set, a vehicle type set, a fixed cost, a capacity cost, and a time cost; the departure point is the current stay place of the target vehicle, and the destination is a destination to be rescued; acquiring the first output data, real-time traffic information, and real-time weather information, and obtaining second output data based on a second preset algorithm, and determining the best driving path of the target vehicle from the departure point to the destination based on the second output data.

[0007] According to another embodiment of the present invention, a rescue route planning system for an emergency rescue vehicle is also provided, characterized in that it includes: a scheduling model, used to obtain target data, obtain first output data based on a first preset algorithm, so as to determine the target vehicle, departure point, and destination based on the first output data; wherein the target data includes: a node set, a vehicle type set, a fixed cost, a capacity cost, and a time cost; the departure point is the current stay place of the target vehicle, and the destination is the destination to be rescued; a path selection model, used to obtain the first output data, real-time traffic information, and real-time weather information, obtain second output data based on a second preset algorithm, so as to determine the best driving path of the target vehicle from the departure point to the destination based on the second output data.

[0008] Through one of the embodiments of the present invention, not only the dispatching cost and time cost of the rescue vehicle are taken into consideration, but also the path selection is dynamically adjusted through the integration of real-time data, so that the rescue vehicle can reach the rescue site quickly and accurately. This method overcomes the single-dimensional optimization problem existing in the background technology. Therefore, the problem of low rescue efficiency existing in the related technology is solved, thereby achieving the effect of rapid rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a flow chart of a rescue route planning method for an emergency rescue vehicle according to an embodiment of the present invention; Figure 2 is a flow chart of a method for determining basic rescue needs based on the type and severity of an accident according to an embodiment of the present invention; Figure 3 4 is a schematic diagram of the structure of a rescue route planning system for an emergency rescue vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0011] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0012] In this embodiment, a rescue route planning method for an emergency rescue vehicle is provided. Figure 1 is a flow chart of a method for planning a rescue route for an emergency rescue vehicle according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S101, obtaining target data, obtaining first output data based on a first preset algorithm, and determining a target vehicle, a departure point, and a destination based on the first output data; wherein the target data includes: a node set, a vehicle type set, a fixed cost, a capacity cost, and a time cost; the departure point is the current stopover point of the target vehicle, and the destination is the destination to be rescued; In an exemplary embodiment, the related art mainly focuses on the shortest path algorithm, without fully considering the urgency of the rescue mission and the rational allocation of resources. However, step S101 comprehensively considers fixed costs, capacity costs, and time costs, so that the present invention not only focuses on the path length, but also focuses on the rational allocation of resources and the urgency of the rescue mission, thereby providing a more comprehensive scheduling decision. Among them, the node set can include nodes of geographical locations such as landmark buildings, intersections, tunnels, construction sites, and temporary control areas in the city.

[0013] Step S102, obtaining first output data, real-time traffic information, and real-time weather information, and obtaining second output data based on a second preset algorithm, so as to determine the best driving path of the target vehicle from the departure point to the destination based on the second output data.

[0014] In an exemplary embodiment, the related art relies more on static data or prediction models and fails to respond to traffic and weather changes in real time. However, step S102 integrates real-time traffic and weather information and the first output data, so that the present invention can adapt to the current actual road conditions, select the best driving path, and improve the timeliness and accuracy of rescue.

[0015] Through step S101 to step S102, since not only the dispatching cost and time cost of the rescue vehicle are taken into consideration, but also the path selection is dynamically adjusted through the integration of real-time data, so that the rescue vehicle can reach the rescue site quickly and accurately. This method overcomes the single-dimensional optimization problem existing in the background technology. Therefore, the problem of low rescue efficiency existing in the related technology is solved, thereby achieving the effect of rapid rescue.

[0016] In one implementation, the objective function of the first preset algorithm is: ; ; in, is a node set, representing all possible nodes in the map; is a collection of vehicle types; is a fixed cost; is the capacity cost; For time cost; is the type of target vehicle; is the departure point; for the destination; For rescue needs; is the total number of vehicle types; is a decision variable, indicating whether the vehicle type Slave Node Send to Node ,exist In the case of Slave Node Send to Node .

[0017] In an exemplary embodiment, the objective function of the first preset algorithm is to minimize the total cost of emergency rescue vehicle route planning, which includes fixed cost, capacity cost and time cost. First, by minimizing the total cost, the objective function ensures the efficient use of rescue resources and avoids unnecessary waste. Second, the time cost The introduction of enables the algorithm to consider the urgency of rescue when making decisions, giving priority to rescue tasks with higher time costs, thereby improving the timeliness of rescue. It ensures the rational allocation of rescue resources, avoids excessive or insufficient concentration of resources, and improves the efficiency of resource utilization. The binary nature of the decision-making process makes it easier to implement and monitor the dispatch of rescue vehicles.

[0018] The node collection , vehicle type collection , Fixed costs , Capacity cost , time cost , decision variables As the input of the algorithm, to determine the decision variables that meet the requirements , in determining the decision variables In the case of Corresponding: The target vehicle is the vehicle that is determined to be rescued. is the starting point of the target vehicle, To reach the rescue destination, that is, to determine the target vehicle From departure point Go to the rescue destination , to implement rescue operations. The use of the first preset algorithm not only optimizes the dispatch of rescue vehicles, but also ensures that the path selection can adapt to dynamically changing traffic and weather conditions, thereby improving rescue efficiency and effectiveness.

[0019] In an exemplary embodiment, the fixed cost From the node for rescue vehicles To Node Costs that are inherent to the vehicle and do not vary with driving distance or time. This may include vehicle usage fees, fuel costs, vehicle wear and tear costs, etc. The calculation method for this fixed cost can be a pre-set standard fee or it can be calculated based on historical data and empirical formulas.

[0020] Capacity cost The costs incurred when allocating vehicles to a rescue mission. This can include the vehicle's weight, passenger capacity, or the need for special equipment. The capacity cost calculation can be related to the vehicle's capacity requirements and the size of the rescue mission. For example, if a mission requires multiple vehicles or special equipment, the capacity cost will increase accordingly. The capacity cost calculation can be determined by analyzing the rescue mission and the vehicle capacity.

[0021] Time Cost From the node for rescue vehicles To Node The cost of the time required to travel. This can include the cost of running the vehicle, the wages of the personnel, and the additional costs that may be incurred due to delayed rescue. The time cost can be calculated based on factors such as the average speed of the vehicle, road conditions, traffic congestion, etc., and can be calculated using the formula: To calculate, For Node To Node The distance It is a vehicle k The average speed.

[0022] In one embodiment, the rescue requirement satisfies the constraints: ≥ ,∀ ∈ ; in, For basic rescue needs, it means from the node To Node The minimum number of rescue vehicles required is based on the type and severity of the accident.

[0023] Figure 2 is a flow chart of a method for determining basic rescue needs based on the type and severity of an accident according to an embodiment of the present invention. In one implementation, Figure 2 As shown, basic rescue needs are determined based on the type and severity of the accident, including: Step S201, obtaining keywords based on the voice information of the rescue call; In an exemplary embodiment, for example, when a rescue call is received, the voice information on the phone describes a traffic accident. The rescue call information can be a recorded audio or a real-time call, which is authorized by both the caller and the receiver. Through speech recognition technology, the system can convert speech into text and extract keywords from it. For example, keywords may include "traffic accident", "multiple vehicles", "four injured", "fracture", "death", etc.

[0024] Step S202: Match the keywords with the stored historical accident data and historical rescue response situations to obtain matching results to determine the type and severity of the accident; In an exemplary embodiment, for example, the extracted keywords are matched with a historical accident database, which contains records of similar accidents in the past, such as accident type (such as traffic accident, fire, medical emergency, etc.), severity (such as minor, moderate, severe), and corresponding rescue response (such as number of vehicles, rescue time, rescue equipment used, etc.).

[0025] Specifically, for example, the system finds through matching that the keywords "multiple vehicles" and "injured" are associated with "serious traffic accidents" recorded in history. The rescue response to these accidents will be further analyzed, and it is found that at least 3 ambulances and 1 fire truck are usually required.

[0026] Step S203: Determine basic rescue needs based on the matching results.

[0027] In an exemplary embodiment, for example, according to the matching result in step S202, the system determines that the current accident requires at least 3 ambulances and 1 fire truck as the basic rescue demand. This demand will be used as the input of the rescue dispatch model, namely the basic rescue demand , ensuring that there are enough rescue vehicles dispatched from the current rescue vehicle's stop (departure point) to the accident site (destination).

[0028] Specifically, the system inputs this information as constraints into the upper-level scheduling model to ensure that the total number of rescue vehicles meets ≥ In this instance, the basic rescue needs At least 4, which means at least 4 vehicles are needed (3 ambulances and 1 fire truck).

[0029] Through steps S201 to S203, the emergency rescue vehicle rescue route planning method can automatically determine the basic rescue needs of the accident, thereby improving the efficiency and accuracy of the rescue response. This method reduces the need for human judgment, speeds up the dispatch process of rescue resources, and helps ensure that rescue operations can be carried out quickly and effectively.

[0030] Among them, the rescue demand satisfaction constraint ensures that from each departure point To each destination The number of rescue vehicles dispatched can meet the predetermined rescue needs .

[0031] For example, in a city area, there are 3 nodes (intersections): V={A,B,C}, and there are 2 types of rescue vehicles: K={1,2}. There are the following rescue needs: At least 2 rescue vehicles are required to travel from node A to node B.

[0032] At least one rescue vehicle is required to travel from node A to node C.

[0033] At least one rescue vehicle is required to travel from node B to node C.

[0034] These rescue needs can be expressed as: =2, , =1; For example, the calculated fixed cost, capacity cost, and time cost are as follows: =10, =15, =12, =18; =5, =8, =6, =9; =3, =4, =2, =3; It is necessary to determine whether the rescue demand constraint is met. For example, the output of the first preset algorithm is: =1, =0, =1, =0; You can check whether the constraints are satisfied: =1+1=2≥ =2 (satisfied); =0+0=0≥ =1 (not satisfied); =1+0=1≥ =1 (satisfied).

[0035] In the above example, the rescue demand from node A to node C was not met because only 0 vehicles were dispatched. This indicates that the decision variables need to be rescaled. , to ensure that all rescue demand constraints are met.

[0036] In the above manner, the rescue demand meets the constraints, ensuring that the dispatch of rescue vehicles can meet the rescue needs of each specific route, thereby improving the rescue efficiency and effect.

[0037] In one embodiment, the total number of vehicle types satisfies the constraint: Vehicle Type The total number of .

[0038] In an exemplary embodiment, for example, there are 3 nodes (intersections) in a city area: V={A, B, C}, and there are 2 types of rescue vehicles: K={1, 2}. The following total number of vehicles is retrieved from the database: The total number of vehicle type 1 is 3.

[0039] The total number of vehicle type 2 is 2.

[0040] Now, determine whether the total number constraint of vehicle types is satisfied. For example, the output of the first preset algorithm is: =1, =0, =1, =0; =0, =1, =1, =0; Check whether the constraints are satisfied: (satisfy); (satisfy); In the above example, both types of vehicle total constraints are satisfied. This ensures that the total number of rescue vehicles of each type does not exceed the number of available vehicles, thus avoiding over-dispatching and waste of resources.

[0041] In the above way, the total number of vehicle types meets the constraints, ensuring that the dispatch of rescue vehicles not only meets the rescue needs but also takes into account the limitations of available resources, thereby improving the rescue efficiency and resource utilization efficiency.

[0042] In one implementation, the objective function of the second preset algorithm is: ; , , , ; in, is the weight of path P; is a set of paths; Select for the path.

[0043] In an exemplary embodiment, the objective function of the second preset algorithm is an optimization problem for determining the best driving path for the emergency rescue vehicle from the departure point to the destination. This objective function considers all possible paths and attempts to find the path with the minimum total weight, where the weight can be the length of the path, the travel time, or any other relevant cost indicator. The objective function is explained below with examples: For example, there is a simple rescue scenario with two nodes (departure point A and destination B), and two types of rescue vehicles (type 1 and type 2). There are two possible paths P1 and P2 from A to B.

[0044] The weight of path P1 is =5 (for example, this might represent the length or travel time of the route).

[0045] The weight of path P2 is =7.

[0046] For example, the following decision variables are calculated: , indicating that the type 1 vehicle chooses path P1 from node A to node B.

[0047] , indicating that the type 2 vehicle chooses path P2 from node A to node B.

[0048] According to the objective function, we want to minimize the total weight, so the algorithm will choose the path with the smallest weight. In this example, for type 1 vehicles, path P1 is chosen because its weight is less than the weight of path P2. For type 2 vehicles, path P2 is chosen because it is the only choice for this type of vehicle (e.g. ).

[0049] Therefore, the objective function of the second preset algorithm selects the best path from the departure point to the destination for each type of rescue vehicle by considering all possible paths and their weights. This approach ensures that the rescue vehicle can choose the optimal driving path according to the current traffic and weather conditions, thereby improving the rescue efficiency.

[0050] In one embodiment, the path selection satisfies the constraints: , , .

[0051] In an exemplary embodiment, the path selection constraint is to ensure that in the emergency rescue vehicle route planning method, the path selection from each departure point to each destination for each type of vehicle is consistent with the scheduling decision. This constraint ensures that once a decision is made to dispatch a certain type of vehicle from a node To Node , then select one or more paths P for the vehicle.

[0052] For example, in a simple rescue scenario, there are 3 nodes: V = {A, B, C}, and there are 2 types of rescue vehicles: K = {1, 2}. For example, there are 2 possible paths from node A to node B: ={P1,P2}.

[0053] For example, the output of the first preset algorithm is: =1, indicating that it is decided to dispatch a vehicle of type 1 from node A to node B.

[0054] =0, indicating that vehicles of type 2 are not dispatched from node A to node B.

[0055] Now, we need to ensure that the path selection satisfies the constraints. For type 1 vehicles, the path selection from node A to node B is: , indicating that path P1 is selected.

[0056] , indicating that path P2 is not selected.

[0057] Based on the constraints, check: ; This equation holds, indicating that the path selection for type 1 vehicles from node A to node B is consistent with the dispatch decision. That is, since the decision is made to dispatch this type of vehicle, a path is selected for it (path P1 in this example).

[0058] In this way, the path selection constraints ensure that the dispatch decision of the rescue vehicle matches the actual path selection, so that the rescue vehicle can effectively perform the rescue mission according to the predetermined plan and path. This method improves the accuracy and reliability of rescue route planning, ensures the rational use of rescue resources and the timely completion of rescue missions.

[0059] In one embodiment, the method further comprises: Obtain real-time traffic information and real-time weather information, and adjust the weight of path P based on the third preset algorithm ; Among them, real-time traffic information is obtained based on the real-time traffic monitoring system, and real-time weather information is obtained based on the geographic information system.

[0060] In an exemplary embodiment, this step involves obtaining real-time traffic information and real-time weather information, and adjusting the weight of the path based on the information using a third preset algorithm. The purpose of this process is to dynamically adjust routing to respond to real-time traffic and weather changes, thereby optimizing the driving paths of rescue vehicles.

[0061] For example, an emergency rescue vehicle needs to go from node A to node B for a rescue mission, and there are two paths P1 and P2 to choose from.

[0062] First, get real-time traffic information: For example, through the real-time traffic monitoring system, it is obtained that the path P1 is currently experiencing traffic congestion, while the path P2 has smooth traffic.

[0063] Among them, the data provided by the traffic monitoring system includes traffic flow, vehicle speed, accident reports, etc. on each road section.

[0064] At the same time, get real-time weather information: For example, using a geographic information system (GIS), it is obtained that there is currently heavy rain in the path P1 area, while the weather in the path P2 area is clear.

[0065] Among them, the weather information may include rainfall, visibility, road wetness, etc.

[0066] Then adjust the path weight : According to real-time traffic and weather information, the weights of paths P1 and P2 will be adjusted based on a third preset algorithm.

[0067] For example, if path P1 is slow due to traffic congestion and bad weather, its weight will be incremented to reflect its lower priority.

[0068] On the contrary, the weight of path P2 Will remain low as it is the superior option.

[0069] By dynamically adjusting the path weights, the rescue vehicle will be guided to choose path P2 from node A to node B, thus avoiding the impact of traffic congestion and bad weather.

[0070] This method can significantly improve rescue efficiency and ensure that rescue vehicles reach their destination quickly and safely.

[0071] Additionally, this approach improves the adaptability and accuracy of route planning because it can respond to changing traffic and weather conditions in real time.

[0072] In this way, not only the driving path of the rescue vehicle is optimized, but also the flexibility and effectiveness of the emergency rescue vehicle route planning method are enhanced, enabling it to fully adapt to the changing rescue environment.

[0073] In one implementation, the third preset algorithm is: ; in, is the basic weight of path P; T is the traffic congestion coefficient; W is the weather impact coefficient; , To adjust the coefficient, control the weight of traffic and weather on the path respectively degree of impact.

[0074] In an exemplary embodiment, the third preset algorithm is to dynamically adjust the path weight , so that changes in real-time traffic and weather conditions can be taken into account in emergency rescue vehicle route planning. The algorithm optimizes the driving path of rescue vehicles by adjusting the path weights to reflect current traffic congestion and weather effects.

[0075] For example, there are two paths P1 and P2 from node A to node B, and their basic weights are and .

[0076] The real-time traffic monitoring system provides information indicating that route P1 currently has moderate traffic congestion and the traffic congestion coefficient ; Path P2 has smooth traffic and traffic congestion coefficient .

[0077] The GIS provides information indicating that route P1 will pass through a rainstorm area, and the weather influence coefficient ; The weather condition of path P2 is good, and the weather influence coefficient .

[0078] For example, the adjustment factor and .

[0079] According to the third preset algorithm, the adjusted weights of the two paths can be calculated: =11.9; =12; In the above example, the weight of path P1 is adjusted to 11.9, while the weight of path P2 remains at 12. Although the basic weight of path P1 is low, its adjusted weight is high due to the adverse effects of traffic and weather, which will cause the path selection model to be more inclined to select path P2 as the driving path for the rescue vehicle.

[0080] In this way, the third preset algorithm can dynamically adjust the path weight to adapt to real-time traffic and weather changes, thereby improving the adaptability and accuracy of rescue route planning. This method can ensure that the rescue vehicle chooses the optimal path and reaches the destination quickly and safely, improving rescue efficiency.

[0081] In summary, the present invention effectively solves the problem of low rescue efficiency in the related art through an innovative rescue route planning method for emergency rescue vehicles, and significantly improves the effect of rapid rescue. The following are the key features of the present invention and its contribution to improving rescue efficiency: 1. Comprehensive cost considerations: The objective function of the first preset algorithm comprehensively considers fixed cost, capacity cost and time cost, focusing not only on path length but also on the rational allocation of resources and the urgency of rescue missions, providing a more comprehensive scheduling decision.

[0082] 2. Real-time data integration: The second preset algorithm integrates real-time traffic and weather information and dynamically adjusts the path weight so that path planning can adapt to the current actual road conditions, select the best driving path, and improve the timeliness and accuracy of rescue.

[0083] 3. Multi-dimensional decision-making and constraints: The rescue demand satisfaction constraint ensures that the number of rescue vehicles dispatched from each departure point to each destination can meet the predetermined rescue demand, while the total number of vehicle types satisfies the constraint to avoid over-dispatching and waste of resources.

[0084] 4. Intelligent scheduling: By automatically acquiring keywords from the voice information of the rescue call and matching them with historical accident data, the present invention can quickly determine the type and severity of the accident, thereby determining basic rescue needs, reducing human judgment, and speeding up the dispatch of rescue resources.

[0085] 5. Dynamic path adjustment: The third preset algorithm dynamically adjusts the path weight by considering real-time traffic and weather conditions, optimizes the driving path of the rescue vehicle, and improves rescue efficiency.

[0086] 6. Comprehensive optimization: The embodiments of the present invention not only optimize the dispatching of rescue vehicles, but also dynamically adjust the path selection through the integration of real-time data, thereby ensuring that the rescue vehicles can reach the rescue site quickly and accurately.

[0087] Through the above features, the solution of the present invention overcomes the single-dimensional optimization problem existing in the background technology and provides a more comprehensive and flexible emergency rescue vehicle route planning solution. This method not only improves the rescue efficiency, but also ensures the timeliness and effectiveness of the rescue mission, so that the rescue vehicle can reach the destination quickly and safely, and improves the utilization efficiency of rescue resources and the overall effect of the rescue operation.

[0088] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by adding the necessary general hardware platform with the help of software, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0089] In the embodiments of the present invention, a rescue route planning system for an emergency rescue vehicle is also provided, and the system is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0090] Figure 3 FIG. 1 is a schematic diagram of a rescue route planning system for an emergency rescue vehicle according to an embodiment of the present invention. Figure 3 As shown, the system includes: The scheduling model 31 is used to obtain target data, obtain first output data based on a first preset algorithm, and determine the target vehicle, the departure point, and the destination based on the first output data; wherein the target data includes: a node set, a vehicle type set, a fixed cost, a capacity cost, and a time cost; the departure point is the current stopover point of the target vehicle, and the destination is the destination to be rescued; The path selection model 32 is used to obtain the first output data, real-time traffic information, and real-time weather information, and obtain the second output data based on the second preset algorithm to determine the best driving path for the target vehicle from the departure point to the destination based on the second output data.

[0091] The above technical solution not only considers the dispatching cost and time cost of rescue vehicles through the two-layer planning model (combination of scheduling model and path selection model), but also dynamically adjusts the path selection through the integration of real-time data, so that rescue vehicles can reach the rescue site quickly and accurately. This method overcomes the single-dimensional optimization problem existing in the background technology, provides a more comprehensive and flexible emergency rescue vehicle route planning solution, effectively improves the rescue efficiency, and ensures the timeliness and effectiveness of the rescue mission.

[0092] In one implementation, the objective function of the first preset algorithm is: ; ; in, is a node set, representing all possible nodes in the map; is a collection of vehicle types; is a fixed cost; is the capacity cost; For time cost; is the type of target vehicle; is the departure point; for the destination; For rescue needs; is the total number of vehicle types; is a decision variable, indicating whether the vehicle type Slave Node Send to Node ,exist In the case of Slave Node Send to Node .

[0093] In one embodiment, the rescue requirement satisfies the constraints: ≥ ,∀ ∈ ; in, For basic rescue needs, it means from the node To Node The minimum number of rescue vehicles required is based on the type and severity of the accident.

[0094] In one embodiment, determining basic rescue needs based on the type and severity of the accident includes: Acquire keywords based on the voice information of the rescue call; Match the keywords with the stored historical accident data and historical rescue response situations to obtain matching results to determine the type and severity of the accident; Determine basic rescue needs based on the matching results.

[0095] In one embodiment, the total number of vehicle types satisfies the constraint: Vehicle Type The total number of .

[0096] In one implementation, the objective function of the second preset algorithm is: ; , , , ; in, is the weight of path P; is a set of paths; Select for the path.

[0097] In one embodiment, the path selection satisfies the constraints: , , .

[0098] In one embodiment, the system is further configured to include: Obtain real-time traffic information and real-time weather information, and adjust the weight of path P based on the third preset algorithm ; Among them, real-time traffic information is obtained based on the real-time traffic monitoring system, and real-time weather information is obtained based on the geographic information system.

[0099] In one implementation, the third preset algorithm is: ; in, is the basic weight of path P; T is the traffic congestion coefficient; W is the weather impact coefficient; , To adjust the coefficient, control the weight of traffic and weather on the path respectively degree of impact.

[0100] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0101] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0102] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0103] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0104] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0105] An embodiment of the present invention further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method described in each embodiment of the present invention.

[0106] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0107] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A rescue route planning method for an emergency rescue vehicle, characterized in that: include: Obtain target data, obtain first output data based on a first preset algorithm, and determine a target vehicle, a departure location, and a destination based on the first output data; wherein the target data includes: a node set, a vehicle type set, a fixed cost, a capacity cost, and a time cost; the departure location is the current stop location of the target vehicle, and the destination is the destination to be rescued; The first output data, real-time traffic information, and real-time weather information are obtained, and second output data is obtained based on a second preset algorithm, so as to determine the best driving path of the target vehicle from the departure point to the destination based on the second output data.

2. The method according to claim 1, characterized in that The objective function of the first preset algorithm is: ； ; in, is a node set, representing all possible nodes in the map; is a collection of vehicle types; is a fixed cost; is the capacity cost; For time cost; is the type of target vehicle; is the departure point; for the destination; For rescue needs; is the total number of vehicle types; is a decision variable, indicating whether the vehicle type Slave Node Send to Node ,exist In the case of Slave Node Send to Node .

3. The method according to claim 2, characterized in that The rescue requirement satisfies the constraints: ≥ ,∀ ∈ ； in, For basic rescue needs, it means from the node To Node The minimum number of rescue vehicles required is based on the type and severity of the accident.

4. The method according to claim 2, characterized in that: The total number of vehicle types satisfies the constraint: Vehicle Type The total number of .

5. The method according to claim 2, characterized in that: The objective function of the second preset algorithm is: ； ， ， ， ； in, is the weight of path P; is a set of paths; Select for the path.

6. The method according to claim 5, characterized in that The path selection satisfies the constraints: ， ， 。 7. The method according to claim 5, characterized in that Also includes: Acquire real-time traffic information and real-time weather information, and adjust the weight of the path P based on a third preset algorithm ; The real-time traffic information is obtained based on a real-time traffic monitoring system, and the real-time weather information is obtained based on a geographic information system.

8. The method according to claim 7, characterized in that The third preset algorithm is: ； in, is the basic weight of path P; T is the traffic congestion coefficient; W is the weather impact coefficient; , To adjust the coefficient, control the weight of traffic and weather on the path respectively degree of impact.

9. The method according to claim 3, characterized in that: Determine basic rescue needs based on the type and severity of the incident, including: Acquire keywords based on the voice information of the rescue call; Matching the keywords with stored historical accident data and historical rescue response situations to obtain matching results to determine the type and severity of the accident; The basic rescue requirement is determined based on the matching result.

10. A rescue route planning system for an emergency rescue vehicle, characterized in that: include: A scheduling model is used to obtain target data, obtain first output data based on a first preset algorithm, and determine a target vehicle, a departure point, and a destination based on the first output data; wherein the target data includes: a node set, a vehicle type set, a fixed cost, a capacity cost, and a time cost; the departure point is the current stopover point of the target vehicle, and the destination is a destination to be rescued; A path selection model is used to obtain the first output data, real-time traffic information, and real-time weather information, and obtain second output data based on a second preset algorithm to determine the best driving path for the target vehicle from the departure point to the destination based on the second output data.

Images