Fire-fighting emergency vehicle control method and device

Abstract

The invention provides a fire-fighting emergency vehicle control method and device, and relates to the technical field of intelligent fire-fighting vehicle control, and the method comprises the steps: obtaining obstacle vehicle data and real-time fire behavior data of a fire-fighting access of a target location in response to a fire rescue request for the target location sent by a first user terminal; in response to the real-time position of the fire-fighting vehicle sent by the second user terminal, determining the current distance between the fire-fighting vehicle and the target location; based on the obstacle vehicle data, the real-time fire behavior data and the current distance, determining a vehicle moving strategy for each obstacle vehicle in the fire fighting access; and in response to the vehicle moving strategy and the environment information sent by each obstacle vehicle, controlling each obstacle vehicle to execute vehicle moving operation according to a first planned path, and obtaining the dynamic change condition of the fire in real time to relieve the situation that the fire-fighting side cannot make a decision in time and the fire-fighting vehicle cannot arrive at the fire scene in time under the shielding of the obstacle vehicles. Therefore, the technical problem of low fire scene fighting efficiency is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent firefighting vehicle control, and in particular to a firefighting emergency vehicle control method and device. Background Art

[0002] Currently, the fire monitoring subsystem obtains and analyzes infrared thermal image information of the fire monitoring area. When there is a fire, it generates fire alarm information and pushes the fire alarm information to the fire department terminal and the vehicle-mounted terminal within a preset range from the fire scene through the message push subsystem; the public safety monitoring subsystem receives the distress information from the call buttons pre-installed in the public area, and pushes the location information and video information of the area where the call button is triggered to the terminal of the relevant department through the message push subsystem; the traffic monitoring subsystem pushes the video information of the corresponding road section with traffic jams or traffic accidents to the traffic management department terminal in the corresponding jurisdiction and the vehicle-mounted terminal within a preset range through the message push subsystem.

[0003] The inventors have discovered that the fire monitoring subsystem only pushes fire alarm information, while firefighters cannot observe the fire situation in real time, and thus cannot provide better rescue decisions; when the traffic monitoring subsystem identifies that the fire emergency lane is occupied, it only contacts the car owner by phone or text message. When the car owner cannot be contacted, the vehicle cannot be moved to a safe area in time, resulting in the fire truck being unable to arrive at the fire scene in time. Summary of the invention

[0004] The purpose of the present invention is to provide a fire emergency vehicle control method and device, which can alleviate the technical problem of low fire scene fire fighting efficiency caused by the fire side's inability to make timely decisions and the fire truck's inability to reach the fire scene in time due to the obstruction of obstacle vehicles by obtaining real-time dynamic changes of the fire.

[0005] In a first aspect, the present invention provides a fire emergency vehicle control method, comprising:

[0006] In response to a fire rescue request for a target location sent by the first user terminal, obtaining obstacle vehicle data and real-time fire data of a fire passage at the target location;

[0007] In response to the real-time position of the fire truck sent by the second user terminal, determining the current distance between the fire truck and the target location;

[0008] Determine a vehicle moving strategy for each obstructing vehicle in the fire passage based on the obstructing vehicle data, the real-time fire data and the current distance;

[0009] In response to the vehicle-moving strategy and the environmental information sent by each of the obstacle vehicles, each of the obstacle vehicles is controlled to perform a vehicle-moving operation according to a first planned path.

[0010] In an optional implementation, in response to a fire rescue request for a target location sent by a first user terminal, the step of acquiring obstacle vehicle data and real-time fire data of a fire passage at the target location includes:

[0011] When receiving a fire rescue request for a target location sent by a first user terminal, image data is obtained from a video surveillance system of the target location, and real-time fire data is obtained from a sensor; wherein the real-time fire data includes road environment information, temperature information, smoke concentration, and toxic gas diffusion data of the target location;

[0012] Processing the image data based on a target detection model and edge computing to identify each vehicle target;

[0013] The position information of each vehicle target is compared with the compliant parking position information pre-stored in the database to determine the obstacle vehicle data blocking the fire passage; wherein the obstacle vehicle data includes the license plate information of the obstacle vehicle, the number of obstacle vehicles and the illegal parking position of the obstacle vehicle.

[0014] In an optional embodiment, in response to the real-time position of the fire truck sent by the second user terminal, the step of determining the current distance between the fire truck and the target location includes:

[0015] Receiving the real-time position of the fire truck obtained by the second user terminal based on GPS and Beidou positioning device;

[0016] Based on the real-time position of the fire truck and the target location, the current distance between the fire truck and the fire location is determined.

[0017] In an optional embodiment, the step of determining a vehicle moving strategy for each obstructing vehicle in the fire passage based on the obstructing vehicle data, the real-time fire data and the current distance includes:

[0018] Determine the scores of the multi-level processing strategy coefficients based on the obstacle vehicle data, the real-time fire data and the current distance;

[0019] Using the scores of the multi-level processing strategy coefficients to match the vehicle moving strategies corresponding to each preset threshold range;

[0020] When the score of the multi-level processing strategy coefficient matches a first preset threshold range, sending a vehicle moving notification for the obstacle vehicle to the first user terminal;

[0021] When the score of the multi-level processing strategy coefficient matches the second preset threshold range, a moving time limit and moving notification for the obstructing vehicle are sent to the first user terminal; if the obstructing vehicle is not successfully moved within the moving time limit, an automatic moving strategy is executed;

[0022] When the score of the multi-level processing strategy coefficient matches the third preset threshold range, an automatic vehicle moving strategy is executed for the obstacle vehicle.

[0023] In an optional embodiment, the step of determining the score of the multi-level processing strategy coefficient based on the obstacle vehicle data, the real-time fire data and the current distance includes:

[0024] Determining the severity of the fire based on the smoke density and fire size in the real-time fire data;

[0025] Determine the difficulty of moving the obstructing vehicle based on the road environment information in the real-time fire data;

[0026] The fire severity, the difficulty of moving the vehicle, the current distance and the number of obstacle vehicles in the obstacle vehicle data are weighted and summed based on a preset weight factor to determine the score of the multi-level processing strategy coefficient.

[0027] In an optional implementation, in response to the vehicle-moving strategy and the environmental information sent by each of the obstacle vehicles, the step of controlling each of the obstacle vehicles to perform a vehicle-moving operation according to the first planned path includes:

[0028] Under the action of the automatic vehicle moving strategy, the environmental information perceived by each of the obstacle vehicles is received, and an environmental map is generated in combination with the illegal parking positions in the obstacle vehicle data; wherein the environmental map is a two-dimensional grid map;

[0029] Based on the heuristic function, the path cost function and the illegal parking position of each of the obstacle vehicles, determine the target position in the environment map and a first planned path from the illegal parking position to the target position;

[0030] Establishing an objective function for each obstacle vehicle using a nonholonomic constraint model;

[0031] The speed and angular velocity of each of the obstacle vehicles are adjusted to update the first planned path from the illegal parking position to the target position.

[0032] In an optional embodiment, the method further comprises:

[0033] A second planned path is generated based on the image information of the fire passage after the vehicle is moved and the real-time position of the fire truck, and is sent to the second user terminal so that the fire truck drives to the target location along the second planned path.

[0034] In a second aspect, the present invention provides a fire emergency vehicle control device, comprising:

[0035] an acquisition module, in response to a fire rescue request for a target location sent by the first user terminal, acquiring obstacle vehicle data and real-time fire data of a fire passage at the target location;

[0036] A first determination module, in response to the real-time position of the fire truck sent by the second user terminal, determines the current distance between the fire truck and the target location;

[0037] A second determination module determines a vehicle moving strategy for each obstructing vehicle in the fire passage based on the obstructing vehicle data, the real-time fire data and the current distance;

[0038] The control module controls each of the obstacle vehicles to perform a vehicle moving operation according to a first planned path in response to the vehicle moving strategy and the environmental information sent by each of the obstacle vehicles.

[0039] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and capable of running on the processor, wherein the processor implements a method as described in any one of the aforementioned embodiments when executing the program.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the method described in any one of the aforementioned embodiments is implemented.

[0041] The embodiment of the present invention provides a fire emergency vehicle control method and device, in which a resident user terminal sends a target location of a fire and a fire rescue request to a cloud server, and the cloud server obtains real-time fire data of the target location and obstacle vehicle data of the fire passage of the target location based on the request; the firefighter user terminal sends the real-time position of the fire truck to the cloud server so that the cloud service determines the current distance between the target location and the fire truck; the cloud server then predicts the urgency of the current fire rescue based on the obstacle vehicle data, real-time fire data and current distance, and then determines a vehicle moving strategy for each obstacle vehicle in the fire passage; based on different vehicle moving strategies and the environmental information of each obstacle vehicle, a first planned path for each obstacle vehicle can be determined, and each obstacle vehicle can be controlled to move according to the first planned path, so that the fire truck can reach the target location in a timely and rapid manner to carry out rescue.

[0042] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flow chart of a fire emergency vehicle control method provided by an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of functional modules of a fire emergency vehicle control device provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of the hardware architecture of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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.

[0049] A fire emergency vehicle control method and device provided by an embodiment of the present invention can provide firefighters with real-time and accurate information about the dynamic changes of the fire when a fire occurs, thereby improving the rescue decision-making efficiency of firefighters. At the same time, the time difference of the arrival of the fire truck can be used to safely and accurately transfer illegally parked vehicles, ensuring that fire trucks and other emergency vehicles arrive at the fire scene in time for effective firefighting.

[0050] To facilitate understanding of this embodiment, a fire emergency vehicle control method disclosed in an embodiment of the present invention is first introduced in detail, and the method is applied to a cloud server.

[0051] Figure 1A flow chart of a fire emergency vehicle control method provided by an embodiment of the present invention.

[0052] See also Figure 1 As shown, the method comprises the following steps:

[0053] Step S102, in response to a fire rescue request sent by the first user terminal to a target location, obtaining obstacle vehicle data and real-time fire data of a fire passage at the target location.

[0054] The first user terminal used by residential users is connected to the cloud server. After issuing a fire rescue request, the cloud server receives the obstacle vehicle data and real-time fire data of the fire passage at the target location; the cloud server also sends the real-time fire information and fire route guidance information to the property staff, vehicle users and ordinary residents of the community using the first user terminal, so that such residents can escape; after the first user terminal receives the fire information, the cloud server sends a query to the first user terminal: whether the owner's vehicle occupies the fire passage, items occupy the corridor, and the size of the fire.

[0055] It can be understood that the cloud service realizes the functions of the fire monitoring and management system, and the first user terminal in the embodiment of the present invention can be understood as a smart terminal installed with the fire monitoring and management system subsystem (resident side).

[0056] Step S104, in response to the real-time position of the fire truck sent by the second user terminal, determining the current distance between the fire truck and the target location.

[0057] Among them, the second user terminal can be understood as an intelligent terminal installed with the fire monitoring and management system subsystem (firefighter side). Firefighters can obtain the real-time location of the fire truck and send it to the cloud server so that it can calculate the current distance between the vehicle and the target location.

[0058] Step S106, based on the obstacle vehicle data, the real-time fire data and the current distance, determine the vehicle moving strategy for each obstacle vehicle in the fire channel.

[0059] Here, based on the obstacle vehicle data obtained in the above-mentioned embodiment, the real-time fire data and the current distance, the current fire rescue urgency is predicted, and the corresponding vehicle moving strategy is selected under different urgency situations.

[0060] Step S108, in response to the vehicle-moving strategy and the environmental information sent by each obstacle vehicle, controlling each obstacle vehicle to perform a vehicle-moving operation according to the first planned path.

[0061] For the automatic vehicle moving strategy, the environmental information of each obstacle vehicle is combined to generate a first planned path for each obstacle vehicle to move the vehicle, so as to achieve the goal of barrier-free vehicles in the fire passage.

[0062] In a preferred embodiment of actual application, the resident user terminal sends the target location of the fire and the fire rescue request to the cloud server, and the cloud server obtains the real-time fire data of the target location and the obstacle vehicle data of the fire passage of the target location based on this request; the firefighter user terminal sends the real-time position of the fire truck to the cloud server so that the cloud service can determine the current distance between the target location and the fire truck; the cloud server then predicts the urgency of the current fire rescue based on the obstacle vehicle data, real-time fire data and current distance, and then determines the vehicle moving strategy for each obstacle vehicle in the fire passage; based on different vehicle moving strategies and the environmental information of each obstacle vehicle, the first planned path of each obstacle vehicle can be determined, and each obstacle vehicle can be controlled to move according to the first planned path, so that the fire truck can reach the target location in time and quickly to carry out rescue.

[0063] Based on the above embodiment, the method can also plan the route of the fire truck to further improve the fire rescue efficiency, specifically including:

[0064] Step 1.1), based on the fire passage image information after the vehicle is moved and the real-time position of the fire truck, a second planned path is generated and sent to the second user terminal, so that the fire truck drives to the target location according to the second planned path.

[0065] The video surveillance system at the target location can obtain image information of the fire passage after the vehicle is moved. At this time, the fire passage is unobstructed. According to the real-time position of the fire truck, the second planned path can be generated and sent to the fire truck to enable it to drive to the fire target location as quickly as possible.

[0066] In some embodiments, step S102 includes:

[0067] Step 1.1), when a fire rescue request for a target location is received from a first user terminal, image data is obtained from a video surveillance system of the target location, and real-time fire data is obtained from a sensor.

[0068] Among them, real-time fire data includes road environment information, temperature information, smoke concentration and toxic gas diffusion data of the target location; through sensors (temperature, smoke, toxic gas sensors) and video monitoring systems, real-time data of the fire scene is collected, such as fire scene images, fire severity, toxic gas diffusion, etc.

[0069] Step 1.2), process the image data based on the target detection model and edge computing to identify each vehicle target.

[0070] The target objects (vehicles) in the image data can be identified through the deep learning algorithm target detection model (YOLO) and edge computing.

[0071] Step 1.3), compare the location information of each vehicle target with the compliant parking location information pre-stored in the database to determine the obstacle vehicle data blocking the fire passage.

[0072] Among them, the obstructing vehicle data includes the license plate information of the obstructing vehicle, the number of obstructing vehicles and the illegal parking locations of the obstructing vehicles.

[0073] Specifically, the location information of each vehicle target identified by the image is compared with the compliant locations in the database. If there is no successful match, it is an illegally parked vehicle, and the specific illegally parked location and number of the obstacle vehicle are then determined.

[0074] In actual applications, obstacles may also block fire passages or residential corridors. Obstacles in fire passages and residential corridors can be identified from the image data through the above steps, and a clearing notification can be sent to the first user terminal so that residents can clear these obstacles.

[0075] As an optional embodiment, the information of illegally parked vehicles and owners can be collected: the owner's facial information can be uploaded to the cloud server through the camera installed on the driver's seat to obtain the owner's information such as the phone number bound to the facial information. The image captured by the camera is used to identify the vehicle's license plate, model and other information through algorithms.

[0076] In actual application, in step S104, the firefighter may send the real-time position of the fire truck to the cloud server so that the cloud server can calculate the current distance between the fire truck and the fire target location, including:

[0077] Step 2.1), receiving the real-time position of the fire truck obtained by the second user terminal based on GPS and Beidou positioning device;

[0078] Here, GPS, Beidou positioning and other technologies can be used to collect real-time location and road condition information of fire trucks while driving.

[0079] Step 2.2), based on the real-time position and target location of the fire truck, determine the current distance between the fire truck and the fire location.

[0080] It is understandable that the cloud server can estimate the current distance between the fire truck and the fire site when knowing the real-time position and target location of the fire truck; the current distance includes the straight-line distance and the vehicle travel distance.

[0081] In the limited time when the fire truck arrives, multi-level processing is performed under different circumstances, and the illegally parked vehicles are automatically and orderly moved, and the illegally parked vehicles are safely transferred within a limited time; specifically, step S106 can be implemented by the following steps, including:

[0082] Step 3.1), based on the obstacle vehicle data, real-time fire data and current distance, determine the scores of the multi-level processing strategy coefficients.

[0083] Multi-level processing strategy: The optimal processing strategy is generated by calculating the collected real-time data. The single fire severity is not used as the decision support for automatic vehicle moving. Four indicators are used: fire spread trend, distance and position of fire trucks, number of illegally parked vehicles in the fire lane, and difficulty of moving vehicles. Weights are configured to calculate the emergency processing strategy coefficient.

[0084] Exemplarily, the calculation of the score of the multi-level processing strategy coefficient first determines the severity of the fire based on the smoke concentration and fire size in the real-time fire data; then determines the difficulty of moving the obstructing vehicle based on the road environment information in the real-time fire data; finally, based on the preset weight factor, a weighted sum calculation is performed on the fire severity, the difficulty of moving the vehicle, the current distance, and the number of obstructing vehicles in the obstructing vehicle data to determine the score of the multi-level processing strategy coefficient.

[0085] For ease of understanding, the scores of the multi-level processing strategy coefficients can be calculated using the following formula:

[0086] Multi-level handling strategy coefficient = fire severity * weight A + distance between fire truck and target location (fire scene) * weight B + number of vehicles blocking fire passage * weight C + difficulty of moving vehicles * weight D;

[0087] Among them, A, B, C, and D are weight factors that can be preset based on experience or actual data analysis. It can be known that in theory, the multi-level processing strategy coefficient is the system automatic car moving emergency processing score, and the higher the value, the higher the priority of automatic car moving processing.

[0088] The fire severity in the above formula can be calculated based on multiple indicators, such as smoke density, fire size, etc. to calculate a comprehensive score, as shown in the following formula:

[0089] Fire severity = smoke density × E + fire size × F +

[0090] Among them, E and F are weight factors pre-set based on experience or actual data analysis. For example, if the smoke density setting weight E is set to 0.4, the smoke density reaches the first smoke threshold of 0-30mg / m 3 5 points, smoke concentration reaches the second smoke threshold of 30-60mg / m 3 The fire intensity is scored as 10 points. The weight F of the fire intensity is 0.6. If the fire intensity reaches the first fire intensity threshold of level 1 to 3, it is scored as 5 points. If the fire intensity reaches the second fire intensity threshold of level 3 to 6, it is scored as 10 points. If the smoke concentration is 35mg / m 3 , the fire size is level 5, then the fire severity is 10×0.4+10×0.6=10;

[0091] The difficulty of moving a vehicle is a comprehensive score calculated by considering factors such as road width, slope, and obstacles, as shown in the following formula:

[0092] Difficulty of moving a car = road width × G + slope × H + obstacles × I

[0093] For example, the weight G of the road width can be selected as 0.5, the weight H of the slope can be selected as 0.3, and the weight I of the obstacle can be selected as 0.2. On a wide and flat road, there are no obstacles, the road width = 8 meters, the slope is 0% (i.e., a horizontal road), and the obstacle is 0 (i.e., no obstacles); at this time, the difficulty of moving the car = 8×0.5+0×0.3+0×0.2=4. For another example, on a narrow road with a very large slope and serious obstacles, the road width is 3 meters, the slope is 30% (i.e., a very steep uphill or downhill slope), and the obstacle is 2 (i.e., there are serious obstacles, such as large construction equipment, fallen trees, etc.) The difficulty of moving the car = 3×0.5+30×0.3+2×0.2=12.1.

[0094] Step 3.2), using the scores of the multi-level processing strategy coefficients to match the vehicle moving strategies corresponding to each preset threshold range.

[0095] Among them, the embodiment of the present invention matches the scores of the multi-level processing strategy coefficients with three solutions for setting threshold settings based on actual conditions and experience.

[0096] Step 3.3), when the score of the multi-level processing strategy coefficient matches the first preset threshold range, a vehicle moving notification for the obstacle vehicle is sent to the first user terminal.

[0097] At this time, the emergency level is low: notify the owner of the illegally parked car to move the car as soon as possible, and there is no need to implement the automatic car moving strategy. The fire and the progress of the fire truck can be continuously monitored. If the situation worsens, the handling strategy will be upgraded.

[0098] Step 3.4), when the score of the multi-level processing strategy coefficient matches the second preset threshold range, the moving time limit and moving notification for the obstructing vehicle are sent to the first user terminal; if the obstructing vehicle is not successfully moved within the moving time limit, the automatic moving strategy is executed.

[0099] At this time, the emergency level is medium: send an emergency car removal notice to the owner of the main illegally parked car, and set a short time limit (such as 5 minutes). If the owner does not respond, start the automatic car removal strategy and give priority to removing the vehicle that has the greatest impact on the passage.

[0100] Step 3.5), when the score of the multi-level processing strategy coefficient matches the third preset threshold range, the automatic vehicle moving strategy is executed for the obstacle vehicle.

[0101] At this time, the emergency level is high: the fully automatic vehicle moving strategy is forcibly activated to ensure that the fire escape passage is unobstructed.

[0102] It can be understood that the first preset threshold range is smaller than the second preset threshold range, and the second preset threshold range is smaller than the third preset threshold range.

[0103] On the basis of the above-mentioned embodiment, the vehicle state is adjusted and the vehicle moves according to the planned path, and necessary vehicle moving operations are performed according to environmental changes; illustratively, step S108 can also be implemented by the following steps, including:

[0104] Step 4.1), under the action of the automatic vehicle moving strategy, receive the environmental information perceived by each obstacle vehicle, and generate an environmental map based on the illegal parking locations in the obstacle vehicle data.

[0105] Among them, the vehicle to be moved first needs to sense the surrounding environment, obtain road information and the illegal parking location of the obstacle vehicle through vehicle sensors (such as lidar or camera), and generate an environmental map, which is a two-dimensional grid map.

[0106] Step 4.2), based on the heuristic function, the path cost function and the illegal parking position of each obstacle vehicle, determine the target position in the environment map and the first planned path from the illegal parking position to the target position;

[0107] Here, the environment map is represented in the form of a two-dimensional grid, each grid corresponds to a position coordinate (x, y), the current illegal parking position of the vehicle is (x0, y0), and the target position after moving the vehicle is (xp, yp).

[0108] The obstructing vehicle is marked as an "inaccessible area" in the grid map, represented by a binary matrix M(x, y), to detect the illegal parking position of the obstructing vehicle:

[0109]

[0110] Move from the current illegal parking position (x0, y0) to the target position (xp, yp), using a heuristic search algorithm for path planning;

[0111] Among them, the heuristic function h(x, y): usually choose Manhattan distance or Euclidean distance, the Manhattan distance formula is:

[0112] h(x,y)=∣x-xp∣+∣y-yp∣

[0113] Cost function f(x, y): The total cost is the sum of the heuristic function and the actual path cost, and the formula is:

[0114] f(x,y)=g(x,y)+h(x,y)+Penalty

[0115] Among them, g(x, y) is the actual cost from the current illegal parking location to the target location, and Penalty is the additional cost of the multi-level strategy system collected by the cloud server (if the strategy system is high, it means the situation is urgent, and the path cost can be increased to avoid passing through areas with serious fires or areas with a large number of illegally parked vehicles).

[0116] Step 4.3), use the non-holonomic constraint model to establish the objective function of each obstacle vehicle.

[0117] The obstacle vehicle drives along the planned path, and a non-holonomic constraint model is used to describe the kinematics of the vehicle to avoid obstacles.

[0118] The kinematic model is:

[0119] x˙=v·cos(θ)

[0120] y˙=v·sin(θ)

[0121] θ˙=v / L·tan(φ)

[0122] Among them, v is the vehicle speed; is the vehicle's heading angle; L is the vehicle's wheelbase; φ is the front wheel turning angle.

[0123] When the obstacle vehicle moves along the first planned path, the dynamic window method (DWA) can be used to adjust the optimized speed and angular velocity to avoid obstacles on the moving path. The objective function of this constraint model is:

[0124] G(v,ω)=α·heading(v,ω)+β·distance(v,ω)+γ·velocity(v,ω)

[0125] Among them, α, β, and γ are weight coefficients, corresponding to the optimization of direction toward the target, distance from obstacles, and speed, respectively.

[0126] Step 4.4), adjust the speed and angular velocity of each obstacle vehicle and update the first planned path from the illegal parking position to the target position.

[0127] Here, the speed and angular velocity of each obstacle vehicle are adjusted during the moving process of each obstacle vehicle, so that the objective function is optimized, and then the first planned path is updated.

[0128] In some embodiments, it is also used for emergency evacuation and personnel management; according to the fire spreading trend and building structure, it provides residents with personalized optimal evacuation routes and notifies residents to evacuate in real time through the first user terminal or broadcasting system. It can also use IoT devices to enable the cloud server to track the location of residents and rescuers in real time to ensure that no one is missed during the emergency evacuation and rescue process.

[0129] Based on the existing fire identification method, the embodiment of the present invention adds feedback to firefighters on the real-time changes in the dynamics of the fire, providing further timely and accurate rescue decision support. Taking into full consideration the uncontrollable factor of emergency road occupation during the rescue process, a multi-level processing strategy is used to calculate and obtain the optimal vehicle moving plan under different circumstances, further improving the system service effect.

[0130] In some embodiments, Figure 2 As shown, an embodiment of the present invention provides a fire emergency vehicle control device, comprising:

[0131] an acquisition module, in response to a fire rescue request for a target location sent by the first user terminal, acquiring obstacle vehicle data and real-time fire data of a fire passage at the target location;

[0132] A first determination module, in response to the real-time position of the fire truck sent by the second user terminal, determines the current distance between the fire truck and the target location;

[0133] A second determination module determines a vehicle moving strategy for each obstructing vehicle in the fire passage based on the obstructing vehicle data, the real-time fire data and the current distance;

[0134] The control module controls each of the obstacle vehicles to perform a vehicle moving operation according to a first planned path in response to the vehicle moving strategy and the environmental information sent by each of the obstacle vehicles.

[0135] Furthermore, the acquisition module is used to acquire image data from the video surveillance system of the target location and acquire real-time fire data from the sensor when a fire rescue request for the target location is received from the first user terminal; wherein the real-time fire data includes road environment information, temperature information, smoke concentration and toxic gas diffusion data of the target location; the image data is processed based on the target detection model and edge computing to identify each vehicle target; the position information of each vehicle target is compared with the compliant parking position information pre-stored in the database to determine the obstacle vehicle data blocking the fire passage; wherein the obstacle vehicle data includes the license plate information of the obstacle vehicle, the number of obstacle vehicles and the illegal parking position of the obstacle vehicle.

[0136] Furthermore, the first determination module is used to receive the real-time position of the fire truck obtained by the second user terminal based on the GPS and Beidou positioning device; based on the real-time position of the fire truck and the target location, determine the current distance between the fire truck and the fire site.

[0137] Furthermore, a second determination module is used to determine the score of the multi-level processing strategy coefficient based on the obstacle vehicle data, the real-time fire data and the current distance; use the score of the multi-level processing strategy coefficient to match the vehicle moving strategy corresponding to each preset threshold range; when the score of the multi-level processing strategy coefficient matches the first preset threshold range, send a vehicle moving notification for the obstacle vehicle to the first user terminal; when the score of the multi-level processing strategy coefficient matches the second preset threshold range, send a vehicle moving time limit and a vehicle moving notification for the obstacle vehicle to the first user terminal; if the obstacle vehicle is not successfully transferred within the vehicle moving time limit, execute an automatic vehicle moving strategy; when the score of the multi-level processing strategy coefficient matches the third preset threshold range, execute an automatic vehicle moving strategy for the obstacle vehicle.

[0138] Furthermore, the second determination module is used to determine the severity of the fire based on the smoke concentration and fire size in the real-time fire data; determine the difficulty of moving the obstacle vehicle based on the road environment information in the real-time fire data; and perform weighted sum calculation on the severity of the fire, the difficulty of moving the vehicle, the current distance and the number of obstacle vehicles in the obstacle vehicle data based on a preset weight factor to determine the score of the multi-level processing strategy coefficient.

[0139] Furthermore, the control module is used to receive the environmental information perceived by each of the obstacle vehicles under the action of the automatic vehicle moving strategy, and generate an environmental map in combination with the illegal parking positions in the obstacle vehicle data; wherein the environmental map is a two-dimensional grid map; based on the heuristic function, the path cost function and the illegal parking position of each of the obstacle vehicles, determine the target position in the environmental map and the first planned path from the illegal parking position to the target position; use the incomplete constraint model to establish the objective function of each of the obstacle vehicles; adjust the speed and angular velocity of each of the obstacle vehicles, and update the first planned path from the illegal parking position to the target position.

[0140] Furthermore, the device is also used to generate a second planned path based on the fire passage image information after the vehicle is moved and the real-time position of the fire truck, and send it to the second user terminal so that the fire truck drives to the target location along the second planned path.

[0141] An embodiment of the present invention provides an electronic device for implementing an electronic device. In this embodiment, the electronic device may be, but is not limited to, a personal computer (PC), a laptop computer, a monitoring device, a server, or other computer device with analysis and processing capabilities.

[0142] As an exemplary embodiment, see Figure 3The electronic device 110 includes a communication interface 111, a processor 112, a memory 113 and a bus 114. The processor 112, the communication interface 111 and the memory 113 are connected via the bus 114. The memory 113 is used to store a computer program that supports the processor 112 to execute the method. The processor 112 is configured to execute the program stored in the memory 113.

[0143] The machine-readable storage medium mentioned in this article can be any electronic, magnetic, optical or other physical storage device that can contain or store information, such as executable instructions, data, etc. For example, the machine-readable storage medium can be: RAM (Radom Access Memory), volatile memory, non-volatile memory, flash memory, storage drive (such as hard disk drive), any type of storage disk (such as CD, DVD, etc.), or similar storage medium, or a combination thereof.

[0144] The non-volatile medium may be a non-volatile memory, a flash memory, a storage drive (such as a hard drive), any type of storage disk (such as a CD, DVD, etc.), or a similar non-volatile storage medium, or a combination thereof.

[0145] It can be understood that the specific operation methods of each functional module in this embodiment can refer to the detailed description of the corresponding steps in the above method embodiment, and will not be repeated here.

[0146] The computer-readable storage medium provided in the embodiment of the present invention stores a computer program. When the computer program code is executed, the method described in any of the above embodiments can be implemented. For specific implementation, please refer to the method embodiment, which will not be described in detail here.

[0147] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0148] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0149] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0150] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the aforementioned embodiments, those of ordinary skill in the art should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed by the present invention, or can easily conceive of changes, or make equivalent replacements for some of the technical features therein. Such modifications, changes or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention.

Claims

1. A fire emergency vehicle control method, characterized in that: include: In response to a fire rescue request for a target location sent by the first user terminal, obtaining obstacle vehicle data and real-time fire data of a fire passage at the target location; In response to the real-time position of the fire truck sent by the second user terminal, determining the current distance between the fire truck and the target location; Determine a vehicle moving strategy for each obstructing vehicle in the fire passage based on the obstructing vehicle data, the real-time fire data and the current distance; In response to the vehicle-moving strategy and the environmental information sent by each of the obstacle vehicles, each of the obstacle vehicles is controlled to perform a vehicle-moving operation according to a first planned path.

2. The method according to claim 1, characterized in that In response to a fire rescue request for a target location sent by a first user terminal, the step of acquiring obstacle vehicle data and real-time fire data of a fire passage at the target location includes: When receiving a fire rescue request for a target location sent by a first user terminal, image data is obtained from a video surveillance system of the target location, and real-time fire data is obtained from a sensor; wherein the real-time fire data includes road environment information, temperature information, smoke concentration, and toxic gas diffusion data of the target location; Processing the image data based on a target detection model and edge computing to identify each vehicle target; The position information of each vehicle target is compared with the compliant parking position information pre-stored in the database to determine the obstacle vehicle data blocking the fire passage; wherein the obstacle vehicle data includes the license plate information of the obstacle vehicle, the number of obstacle vehicles and the illegal parking position of the obstacle vehicle.

3. The method according to claim 1, characterized in that In response to the real-time position of the fire truck sent by the second user terminal, the step of determining the current distance between the fire truck and the target location comprises: Receiving the real-time position of the fire truck obtained by the second user terminal based on GPS and Beidou positioning device; Based on the real-time position of the fire truck and the target location, the current distance between the fire truck and the fire location is determined.

4. The method according to claim 2, characterized in that: The step of determining a vehicle moving strategy for each obstructing vehicle in the fire passage based on the obstructing vehicle data, the real-time fire data and the current distance comprises: Determine the scores of the multi-level processing strategy coefficients based on the obstacle vehicle data, the real-time fire data and the current distance; Using the scores of the multi-level processing strategy coefficients to match the vehicle moving strategies corresponding to each preset threshold range; When the score of the multi-level processing strategy coefficient matches a first preset threshold range, sending a vehicle moving notification for the obstacle vehicle to the first user terminal; When the score of the multi-level processing strategy coefficient matches the second preset threshold range, a moving time limit and moving notification for the obstructing vehicle are sent to the first user terminal; if the obstructing vehicle is not successfully moved within the moving time limit, an automatic moving strategy is executed; When the score of the multi-level processing strategy coefficient matches the third preset threshold range, an automatic vehicle moving strategy is executed for the obstacle vehicle.

5. The method according to claim 4, characterized in that The step of determining the scores of the multi-level processing strategy coefficients based on the obstacle vehicle data, the real-time fire data and the current distance comprises: Determining the severity of the fire based on the smoke density and fire size in the real-time fire data; Determine the difficulty of moving the obstructing vehicle based on the road environment information in the real-time fire data; The fire severity, the difficulty of moving the vehicle, the current distance and the number of obstacle vehicles in the obstacle vehicle data are weighted and summed based on a preset weight factor to determine the score of the multi-level processing strategy coefficient.

6. The method according to claim 2, characterized in that In response to the vehicle-moving strategy and the environmental information sent by each of the obstacle vehicles, the step of controlling each of the obstacle vehicles to perform a vehicle-moving operation according to a first planned path includes: Under the action of the automatic vehicle moving strategy, the environmental information perceived by each of the obstacle vehicles is received, and an environmental map is generated in combination with the illegal parking positions in the obstacle vehicle data; wherein the environmental map is a two-dimensional grid map; Based on the heuristic function, the path cost function and the illegal parking position of each of the obstacle vehicles, determine the target position in the environment map and a first planned path from the illegal parking position to the target position; Establishing an objective function for each obstacle vehicle using a nonholonomic constraint model; The speed and angular velocity of each of the obstacle vehicles are adjusted to update the first planned path from the illegal parking position to the target position.

7. The method according to claim 1, characterized in that The method further comprises: A second planned path is generated based on the image information of the fire passage after the vehicle is moved and the real-time position of the fire truck, and is sent to the second user terminal so that the fire truck drives to the target location along the second planned path.

8. A fire emergency vehicle control device, characterized in that: include: an acquisition module, in response to a fire rescue request for a target location sent by the first user terminal, acquiring obstacle vehicle data and real-time fire data of a fire passage at the target location; A first determination module, in response to the real-time position of the fire truck sent by the second user terminal, determines the current distance between the fire truck and the target location; A second determination module determines a vehicle moving strategy for each obstructing vehicle in the fire passage based on the obstructing vehicle data, the real-time fire data and the current distance; The control module controls each of the obstacle vehicles to perform a vehicle moving operation according to a first planned path in response to the vehicle moving strategy and the environmental information sent by each of the obstacle vehicles.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a program stored in the memory and capable of being run on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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