Emergency shelter planning method and system

Abstract

The invention provides an emergency shelter planning method and system. The method comprises the steps of obtaining an urban traffic network, marking the number of passable people in unit time of each main road based on traffic network traffic big data, obtaining a dynamic population distribution diagram of an urban region, and calculating the average number of people in the region by taking each functional region as a center; obtaining all emergency shelter candidate positions; through a genetic algorithm and an ant colony algorithm, with the average shortest passing time and the minimum planning cost as optimization targets, under the constraint of road passing loads, the optimal emergency refuge number and position distribution are planned from all emergency refuge candidate positions. By means of the scheme, global optimal planning of the emergency shelter can be achieved, the passing time of the shelter can be shortened, and the planning and design cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to an emergency shelter planning method and system. Background Art

[0002] When natural disasters such as earthquakes and typhoons occur, urban emergency shelters can provide safe shelter for the people, which can not only reduce casualties caused by disasters, but also facilitate disaster rescue. Therefore, the planning and design of emergency shelters is of great significance to urban residents.

[0003] At present, the planning of emergency shelters is mostly done by quantitatively calculating the accessibility of each emergency shelter through statistical analysis, buffer zone analysis, etc., and evaluating the value of each shelter in order to make selection plans. However, since this method is more applicable to the planning of shelters in a small area, for larger cities, it is difficult to determine factors such as the number and scale of shelters, and it is impossible to guarantee the overall optimality of shelter planning. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides an emergency shelter planning method and system, which are used to solve the problem that it is difficult to ensure global optimization in existing shelter planning.

[0005] In a first aspect of an embodiment of the present invention, a method for planning an emergency shelter is provided, comprising:

[0006] Obtain the urban traffic network, annotate the number of people that can pass through each main road per unit time based on the traffic network traffic big data, obtain the dynamic distribution map of the urban area population, and calculate the average number of people in each functional area;

[0007] Get all candidate locations for emergency shelters;

[0008] Through genetic algorithm and ant colony algorithm, taking the average shortest travel time and minimum planning cost as the optimization objectives, the optimal number and location distribution of emergency shelters are planned from all candidate locations of emergency shelters under the constraint of road traffic load.

[0009] In a second aspect of an embodiment of the present invention, there is provided an emergency shelter planning system, comprising:

[0010] The data preprocessing module is used to obtain the urban traffic network, annotate the number of people that can pass through each main road per unit time based on the traffic network traffic big data, obtain the dynamic distribution map of the urban area population, and calculate the average number of people in each functional area;

[0011] A data acquisition module, used to obtain all candidate locations of emergency shelters;

[0012] The shelter planning module is used to plan the optimal number and location distribution of emergency shelters from all candidate locations of emergency shelters under the constraints of road traffic load, using genetic algorithms and ant colony algorithms, with the average shortest travel time and minimum planning cost as optimization goals.

[0013] In a third aspect of an embodiment of the present invention, an electronic device is provided, 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 method described in the first aspect of the embodiment of the present invention when executing the computer program.

[0014] In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the embodiment of the present invention are implemented.

[0015] In the embodiment of the present invention, based on the statistical estimation of the number of passable people in the traffic network and the population distribution in each area, the number and location distribution of shelters are iterated through genetic algorithms and ant colony algorithms, and the optimal number and location distribution of emergency shelters are planned from all candidate locations of emergency shelters. This not only ensures the global optimization of shelter planning, but also achieves the shortest travel time, reduces planning and design costs, and can effectively protect the lives of the public. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 creative work.

[0017] Figure 1 A schematic flow chart of an emergency shelter planning method provided by one embodiment of the present invention;

[0018] Figure 2 A schematic diagram of the structure of an emergency shelter planning system provided by one embodiment of the present invention;

[0019] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be understood that the term "including" and other similar expressions in the specification or claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, such as a process, method, system, or device including a series of steps or units is not limited to the listed steps or units. In addition, "first" and "second" are used to distinguish different objects, not to describe a specific order.

[0022] See also Figure 1 , a flowchart of an emergency shelter planning method provided by an embodiment of the present invention includes:

[0023] S101, obtaining the urban traffic road network, marking the number of people that can pass through each main road per unit time based on the traffic road network traffic big data, obtaining the dynamic distribution map of the urban area population, and calculating the average number of people in each area with each functional area as the center;

[0024] Traffic network traffic big data can be estimated by estimating the number of people that can pass through a road within a certain period of time through population traffic data such as urban population mobile positioning, intersection monitoring cameras, and navigation map service data. The unit time can be 10 minutes, 1 hour, etc. The number of people that can pass through a road within a unit time is the number of people that can pass through the road at the peak within the unit time range in the absence of long-term congestion.

[0025] Among them, the maximum number of motor vehicles, non-motor vehicles and pedestrians that can pass through the road is determined based on the big data of traffic network traffic, and the maximum number of people that can pass through each section of the road is statistically estimated.

[0026] Based on the vehicle traffic data of each main road in the urban traffic road network, the average travel speed and duration of vehicles on each road can be obtained. Under the average minimum speed limit, the number of motor vehicles that can pass on the road can be obtained. Combined with the per capita vehicle ratio of urban residents, the number of motor vehicle passengers can be obtained. Combined with the peak number of non-motor vehicle passengers, the number of pedestrians, and road planning and design (such as the number of lanes, non-motor vehicle lane design, etc.), the number of people that can pass on the road can be estimated.

[0027] The functional areas refer to the population-concentrated areas in the city, including residential areas, commercial areas, industrial areas, cultural areas, etc. The population changes in these areas over a period of time (such as a week or a month) are obtained and their average values ​​are calculated.

[0028] S102, obtaining all candidate locations of emergency shelters;

[0029] The construction location of emergency shelters is limited by factors such as the locations that can be requisitioned by the city and the locations that meet the construction standards. It is necessary to plan all the candidate locations that can be constructed in advance and add all the candidate locations as candidate locations for emergency shelters to the set. Each candidate shelter in the set can include a pre-designed scale.

[0030] S103. Using genetic algorithm and ant colony algorithm, taking the average shortest travel time and minimum planning cost as optimization objectives, and under the constraints of road traffic load, the optimal number and location distribution of emergency shelters are planned from all candidate locations of emergency shelters.

[0031] Specifically, the initial number of shelters is set based on the fact that the number of people that a shelter can accommodate should be greater than the permanent population of the city and not exceed the preset population ratio;

[0032] Under the constraint of road traffic load, the optimal shelter location distribution is iteratively calculated based on the objective function through the ant colony algorithm;

[0033] Calculate the fitness of each refuge under the current genetic generation, and select the dominant individuals to enter the crossover operation through the roulette selection strategy;

[0034] In the next iteration, individuals entering the crossover operation are randomly selected and combined with the parent generation to update the population, and the ant colony algorithm continues to calculate the optimal shelter location distribution under the current genetic generation;

[0035] Return the optimal shelter location distribution under each genetic generation. When the maximum number of iterations is reached, compare the shortest shelter travel time and minimum planning cost under each number of iterations to obtain the optimal number and location distribution of shelters.

[0036] It should be noted that the scale of the shelter should be larger than the permanent population of the city and not exceed a certain proportion of the permanent population (such as 10%). For example, if the permanent population is one million, the scale of the shelter should not exceed 110. The specific proportion can be freely set according to the design standard or the number of floating population. In the iteration of the ant colony algorithm, it is necessary to plan the path from the population of each functional area to the shelter, and in order to ensure the accessibility of the shelter during the algorithm iteration, it is necessary to set the travel time constraint, that is, the maximum time for the population of each functional area to reach the adjacent shelter should not exceed the set time threshold, such as 1h.

[0037] Furthermore, emergency shelters within a certain range are searched around each functional area, and paths from the functional area to each emergency shelter are obtained through path planning, and path planning principles are set;

[0038] The path planning principles include:

[0039] When the planned path reaches the maximum load of the road, the corresponding road section will be marked as impassable, and the path will be replanned or searched for other refuge points, and the path with the shortest travel time will be selected;

[0040] When the maximum number of people has been reached at the nearest shelter point to the functional area, other shelter points within the range will be searched in turn for path planning, and the path with the shortest travel time will be selected.

[0041] Taking each functional area as the starting point and the shelter as the end point, the route planning for regional population migration is carried out based on the principle of the shortest path, and the average time required to reach the target shelter is calculated.

[0042] In this embodiment, the number and location of shelters are planned and designed based on genetic algorithms and ant colony algorithms, which can not only ensure the global optimization of shelter planning, but also achieve the minimum overall reachable time and the lowest design cost while meeting planning requirements, thereby ensuring the accessibility and rationality of shelter design.

[0043] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0044] Figure 2 A schematic diagram of the structure of an emergency shelter planning system provided by an embodiment of the present invention, the system comprising:

[0045] The data preprocessing module 210 is used to obtain the urban traffic network, annotate the number of people that can pass through each main road per unit time based on the traffic network traffic big data, obtain the dynamic distribution map of the urban area population, and calculate the average number of people in each area with each functional area as the center;

[0046] Wherein, the data preprocessing module 210 includes:

[0047] The statistical estimation unit is used to determine the maximum number of motor vehicles, non-motor vehicles and pedestrians that can pass through the road based on the traffic network traffic big data, and to statistically estimate the maximum number of people that can pass through each section of the road.

[0048] A data acquisition module 220 is used to acquire all candidate locations of emergency shelters;

[0049] The shelter planning module 230 is used to plan the optimal number and location distribution of emergency shelters from all candidate locations of emergency shelters under the constraints of road traffic load by using genetic algorithm and ant colony algorithm with average shortest travel time and minimum planning cost as optimization objectives.

[0050] Specifically, the initial number of shelters is set based on the fact that the number of people that a shelter can accommodate should be greater than the permanent population of the city and not exceed the preset population ratio;

[0051] Under the constraint of road traffic load, the optimal shelter location distribution is iteratively calculated based on the objective function through the ant colony algorithm;

[0052] Calculate the fitness of each refuge under the current genetic generation, and select the dominant individuals to enter the crossover operation through the roulette selection strategy;

[0053] In the next iteration, individuals entering the crossover operation are randomly selected and combined with the parent generation to update the population, and the ant colony algorithm continues to calculate the optimal shelter location distribution under the current genetic generation;

[0054] Return the optimal shelter location distribution under each genetic generation. When the maximum number of iterations is reached, compare the shortest shelter travel time and minimum planning cost under each number of iterations to obtain the optimal number and location distribution of shelters.

[0055] The shelter planning module 230 includes:

[0056] The path planning unit is used to search for emergency shelters within a certain range near each functional area, obtain the path from the functional area to each emergency shelter through path planning, and set the path planning principle;

[0057] The path planning principles include:

[0058] When the planned path reaches the maximum load of the road, the corresponding road section will be marked as impassable, and the path will be replanned or searched for other refuge points, and the path with the shortest travel time will be selected;

[0059] When the maximum number of people has been reached at the nearest shelter point to the functional area, other shelter points within the range will be searched in turn for path planning, and the path with the shortest travel time will be selected.

[0060] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described system and module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0061] Figure 3 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is used for planning and designing a shelter. Figure 3 As shown, the electronic device 3 of this embodiment includes: a memory 310, a processor 320 and a system bus 330, and the memory 310 includes an executable program 3101 stored thereon. Those skilled in the art can understand that Figure 3The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0062] Combine the following Figure 3 A detailed introduction to the various components of electronic equipment:

[0063] The memory 310 can be used to store software programs and modules, and the processor 320 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 310. The memory 310 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as cache data), etc. In addition, the memory 310 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0064] The memory 310 contains an executable program 3101 of a planning and design algorithm, and the executable program 3101 can be divided into one or more modules / units, which are stored in the memory 310 and executed by the processor 320 to perform shelter planning and design, etc. The one or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the executable program 3101 in the electronic device 3. For example, the executable program 3101 can be divided into functional modules such as a data preprocessing module, a data acquisition module, and a shelter planning module.

[0065] The processor 320 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 310, and calling data stored in the memory 310, it performs various functions of the electronic device and processes data, thereby monitoring the overall status of the electronic device. Optionally, the processor 320 may include one or more processing units; preferably, the processor 320 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 320.

[0066] The system bus 330 is used to connect the various functional components inside the computer, and can transmit data information, address information, and control information. Its types can be, for example, PCI bus, ISA bus, CAN bus, etc. The instructions of the processor 320 are transmitted to the memory 310 through the bus, and the memory 310 feeds back data to the processor 320. The system bus 330 is responsible for the data and instruction exchange between the processor 320 and the memory 310. Of course, the system bus 330 can also be connected to other devices, such as network interfaces, display devices, etc.

[0067] In the embodiment of the present invention, the executable program executed by the processor 320 included in the electronic device includes:

[0068] Obtain the urban traffic network, annotate the number of people that can pass through each main road per unit time based on the traffic network traffic big data, obtain the dynamic distribution map of the urban area population, and calculate the average number of people in each functional area;

[0069] Get all candidate locations for emergency shelters;

[0070] Through genetic algorithm and ant colony algorithm, taking the average shortest travel time and minimum planning cost as the optimization objectives, the optimal number and location distribution of emergency shelters are planned from all candidate locations of emergency shelters under the constraint of road traffic load.

[0071] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0072] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0073] As described above, the above embodiments 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 thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning an emergency shelter, characterized in that: include: Obtain the urban traffic network, annotate the number of people that can pass through each main road per unit time based on the traffic network traffic big data, obtain the dynamic distribution map of the urban area population, and calculate the average number of people in each functional area; Get all candidate locations for emergency shelters; Through genetic algorithm and ant colony algorithm, taking the average shortest travel time and minimum planning cost as the optimization objectives, the optimal number and location distribution of emergency shelters are planned from all candidate locations of emergency shelters under the constraint of road traffic load.

2. The method according to claim 1, characterized in that The number of people that can pass through each main road per unit time based on the traffic network traffic big data includes: Based on the traffic network traffic big data, the maximum number of motor vehicles, non-motor vehicles and pedestrians that can pass through the road is determined, and the maximum number of people that can pass through each section of the road is statistically estimated.

3. The method according to claim 1, characterized in that The genetic algorithm and ant colony algorithm are used to optimize the average shortest travel time and the minimum planning cost, and the optimal number and location distribution of emergency shelters are planned from all candidate locations of emergency shelters under the constraints of road traffic load, including: The initial number of shelters is set based on the fact that the number of people that a shelter can accommodate should be greater than the permanent population of the city and should not exceed the preset population ratio; Under the constraint of road traffic load, the optimal shelter location distribution is iteratively calculated based on the objective function through the ant colony algorithm; Calculate the fitness of each refuge under the current genetic generation, and select the dominant individuals to enter the crossover operation through the roulette selection strategy; In the next iteration, individuals entering the crossover operation are randomly selected and combined with the parent generation to update the population, and the ant colony algorithm continues to calculate the optimal shelter location distribution under the current genetic generation; Return the optimal shelter location distribution under each genetic generation. When the maximum number of iterations is reached, compare the shortest shelter travel time and minimum planning cost under each number of iterations to obtain the optimal number and location distribution of shelters.

4. The method according to claim 1, characterized in that: The method of using genetic algorithm and ant colony algorithm to optimize the average shortest travel time and the minimum planning cost and to plan the optimal number and location distribution of emergency shelters from all candidate locations of emergency shelters under the constraint of road traffic load also includes: Search for emergency shelters within a certain range near each functional area, obtain the path from the functional area to each emergency shelter through path planning, and set the path planning principle; The path planning principles include: When the planned path reaches the maximum load of the road, the corresponding road section will be marked as impassable, and the path will be replanned or searched for other refuge points, and the path with the shortest travel time will be selected; When the maximum number of people has been reached at the nearest shelter point to the functional area, other shelter points within the range will be searched in turn for path planning, and the path with the shortest travel time will be selected.

5. An emergency shelter planning system, characterized in that: include: The data preprocessing module is used to obtain the urban traffic network, annotate the number of people that can pass through each main road per unit time based on the traffic network traffic big data, obtain the dynamic distribution map of the urban area population, and calculate the average number of people in each functional area; A data acquisition module, used to obtain all candidate locations of emergency shelters; The shelter planning module is used to plan the optimal number and location distribution of emergency shelters from all candidate locations of emergency shelters under the constraints of road traffic load, using genetic algorithms and ant colony algorithms, with the average shortest travel time and minimum planning cost as optimization goals.

6. The system according to claim 5, characterized in that The data preprocessing module comprises: The statistical estimation unit is used to determine the maximum number of motor vehicles, non-motor vehicles and pedestrians that can pass through the road based on the traffic network traffic big data, and to statistically estimate the maximum number of people that can pass through each section of the road.

7. The system according to claim 5, characterized in that The genetic algorithm and ant colony algorithm are used to optimize the average shortest travel time and the minimum planning cost, and the optimal number and location distribution of emergency shelters are planned from all candidate locations of emergency shelters under the constraints of road traffic load, including: The initial number of shelters is set based on the fact that the number of people that a shelter can accommodate should be greater than the permanent population of the city and should not exceed the preset population ratio; Under the constraint of road traffic load, the optimal shelter location distribution is iteratively calculated based on the objective function through the ant colony algorithm; Calculate the fitness of each refuge under the current genetic generation, and select the dominant individuals to enter the crossover operation through the roulette selection strategy; In the next iteration, individuals entering the crossover operation are randomly selected and combined with the parent generation to update the population, and the ant colony algorithm continues to calculate the optimal shelter location distribution under the current genetic generation; Return the optimal shelter location distribution under each genetic generation. When the maximum number of iterations is reached, compare the shortest shelter travel time and minimum planning cost under each number of iterations to obtain the optimal number and location distribution of shelters.

8. The system according to claim 5, characterized in that The shelter planning module includes: The path planning unit is used to search for emergency shelters within a certain range near each functional area, obtain the path from the functional area to each emergency shelter through path planning, and set the path planning principle; The path planning principles include: When the planned path reaches the maximum load of the road, the corresponding road section will be marked as impassable, and the path will be replanned or searched for other refuge points, and the path with the shortest travel time will be selected; When the maximum number of people has been reached at the nearest shelter point to the functional area, other shelter points within the range will be searched in turn for path planning, and the path with the shortest travel time will be selected.

9. An electronic 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 shelter planning method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the steps of an emergency shelter planning method as described in any one of claims 1 to 4 are implemented.

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