AGV-based vehicle spontaneous combustion emergency treatment system and method

Abstract

The invention provides a vehicle spontaneous combustion emergency processing system and method based on an AGV, and belongs to the technical field of vehicle management. The system comprises an AGV robot and a control system, wherein the control system is configured to collect state data and environment sensing data of a spontaneous combustion vehicle; according to the collected environment perception data, determining obstacle distribution, and according to the collected state data, determining a fire behavior grade through a preset rule; selecting a corresponding processing strategy according to the judged fire behavior grade; and according to the judged fire behavior grade and the determined obstacle distribution, through an optimized A * algorithm, an optimal AGV robot carrying path is generated. According to the invention, rapid response, accurate isolation and intelligent cooperative processing of vehicle spontaneous combustion accidents are realized through a multi-stage fire behavior intelligent determination and dynamic path optimization technology.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle management, and particularly to an AGV-based vehicle spontaneous combustion emergency handling system and method. Background Art

[0002] With the development of intelligent transportation and warehousing logistics technologies, the demand for intelligent management of parking lots is increasing day by day. In various parking lots, especially in large-scale unmanned parking lots, logistics park parking lots and other scenarios, although the probability of vehicle spontaneous combustion accidents is low, once they occur, they will cause serious casualties and property losses. At present, the fire safety management in parking lots mainly relies on the combination of traditional fire-fighting facilities and manual inspections. By installing smoke alarms, sprinkler systems and arranging personnel for regular inspections, it is attempted to detect and control vehicle spontaneous combustion fires in a timely manner.

[0003] However, there are many deficiencies in the prior art. The traditional emergency handling system can only trigger a response when the fire develops to a certain stage and reaches the preset alarm threshold, making it difficult to achieve early and accurate identification and warning of the fire. Manual inspections have problems such as long time intervals, low efficiency, and being easily affected by human factors, and cannot monitor the fire situation in the parking lot in real time and comprehensively. In the link of fire emergency handling, there is a lack of automated and intelligent disposal means, relying on manual operation of fire-fighting equipment and evacuation of vehicles, with a slow response speed, and it is difficult to take effective measures to control the fire at the initial stage of the spread of the fire, which may lead to the expansion of the fire and cause more serious disaster consequences. Summary of the Invention

[0004] The present invention provides an AGV-based vehicle spontaneous combustion emergency handling system to solve the problems of low intelligent level and slow response speed in the prior art.

[0005] To achieve the above object, on the one hand, an embodiment of the present invention provides an AGV-based vehicle spontaneous combustion emergency handling system. The vehicle spontaneous combustion emergency handling system includes an AGV robot and a control system. The control system is configured to: collect status data of the spontaneous combustion vehicle and environmental perception data; determine the distribution of obstacles according to the collected environmental perception data, and judge the fire level according to the collected status data through a preset rule; select a corresponding processing strategy according to the judged fire level; generate an optimal AGV robot handling path through an optimized A* algorithm according to the judged fire level and the determined distribution of obstacles.

[0006] Optionally, the vehicle spontaneous combustion emergency handling system further includes a temperature sensor, a smoke detector, a camera arranged on the top of the parking lot, and a sensor group arranged on the AGV machine. The sensor group includes a lidar and an ultrasonic sensor for collecting the environmental perception data.

[0007] Optionally, the status data includes temperature data, smoke concentration data, flame intensity data, and the quantity data of surrounding combustible substances. Judging the fire level according to the collected status data includes: calculating a comprehensive evaluation value based on the status data; when the calculated comprehensive evaluation value is within the first preset interval, judging the fire level as a first-level fire; when the calculated comprehensive evaluation value is within the second preset interval, judging the fire level as a second-level fire; when the calculated comprehensive evaluation value is within the third preset interval, judging the fire level as a third-level fire; when the calculated comprehensive evaluation value is within the fourth preset interval, judging the fire level as a fourth-level fire.

[0008] Optionally, calculating the comprehensive evaluation value according to the status data includes: preprocessing and quantizing the status data to obtain a quantum state feature vector, and constructing an environmental entanglement weight matrix to describe the entanglement relationship between data; fusing the obtained quantum state feature vector and the constructed environmental entanglement weight matrix through quantum tensor product operation to obtain a fused feature; based on the game theory model, dynamically adjusting the weights of each status data according to the fire development stage by using reinforcement learning; determining the spatial topology coefficient based on the parking lot topology structure, and determining the time entropy change factor through the information entropy theory; performing spatio-temporal correction on the obtained fused feature through the determined spatial topology coefficient and time entropy change factor; generating a comprehensive evaluation value based on the corrected fused feature through a preset calculation method.

[0009] Optionally, the vehicle spontaneous combustion emergency treatment system further includes a safe house and a fireproof cover arranged above each parking space. The treatment strategy includes: when the fire level is a first-level fire, controlling the AGV robot to go to the vehicle position for continuous monitoring for a preset time, and sending a warning message to the management personnel; when the fire level is a second-level fire, unfolding and lowering the fireproof cover to cover the vehicle, and starting a local ventilator to reduce the smoke concentration; when the fire level is a third-level fire, unfolding and lowering the fireproof cover, and controlling the AGV robot to move the surrounding vehicles away; when the fire level is a fourth-level fire, controlling the AGV robot to transport the spontaneously combusted vehicle to the safe house, and starting the fire extinguishing system in the safe house.

[0010] Optionally, the optimized A* algorithm is an A* algorithm that integrates dynamic obstacle avoidance and global optimization. Generating an optimal AGV robot handling path through the optimized A* algorithm according to the judged fire level and the determined obstacle distribution includes: constructing a two-dimensional grid map based on the parking lot environment data and calculating the passing cost of each node; when the fire level is a level-four fire, determining the current position of the AGV robot as the starting node and the safe house as the target node; establishing an open list and a closed list, adding the starting node to the open list, selecting the node with the minimum passing cost from the open list as the current node and moving it to the closed list; evaluating the passable nodes adjacent to the current node and updating the open list node information; repeating node selection and evaluation until the target node is added to the closed list, and backtracking to generate an initial handling path; using the Dijkstra algorithm to optimize the preliminary path, removing redundant nodes and smoothing the path inflection points to obtain the optimal AGV robot handling path.

[0011] Optionally, the vehicle spontaneous combustion emergency handling system is used to combine the selected handling strategy and the generated handling path to control the AGV robot, the fireproof cover and the safe house to perform corresponding operations.

[0012] Optionally, the control system is further used for: when a vehicle spontaneous combustion occurs, closing the parking lot entrance gate to prevent vehicles from entering; starting the warning lights and alarms in the parking lot to remind the personnel in the parking lot to evacuate.

[0013] On the other hand, a vehicle spontaneous combustion emergency handling method is also provided, which is applied to the control system of the above vehicle spontaneous combustion emergency handling system. The vehicle spontaneous combustion emergency handling method includes: acquiring the state data and environmental perception data of the spontaneously combusting vehicle; determining the fire level according to the acquired state data through a preset rule; selecting a corresponding handling strategy according to the determined fire level; generating an optimal AGV robot handling path through the optimized A* algorithm according to the acquired environmental perception data and the determined fire level; performing corresponding operations on the spontaneously combusting vehicle by combining the selected handling strategy and the generated handling path.

[0014] Optionally, the vehicle spontaneous combustion emergency handling method further includes: closing the parking lot entrance gate to prevent vehicles from entering; starting the warning lights and alarms in the parking lot to remind the personnel in the parking lot to evacuate.

[0015] An emergency handling system and method for vehicle spontaneous combustion based on AGV provided by the present invention can accurately identify a fire by collecting vehicle status and environmental data in real time, using an innovative fire level judgment method, generating an optimal handling path by combining an optimized A* algorithm, automatically executing handling strategies such as monitoring, fireproof cover covering, and vehicle handling for different fire levels, and at the same time realizing the control of the parking lot entrance and exit and the warning and evacuation of personnel, effectively improving the automation and intelligence level of vehicle spontaneous combustion emergency handling, significantly increasing the fire response speed and handling efficiency, and minimizing the casualties and property losses caused by the fire to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0017] Figure 1 is the structural diagram of the vehicle spontaneous combustion emergency handling system provided by the embodiment of the present invention;

[0018] Figure 2 is the fire judgment flow chart provided by the embodiment of the present invention;

[0019] Figure 3 is the path planning flow chart provided by the embodiment of the present invention;

[0020] Figure 4 is the emergency handling flow chart provided by the embodiment of the present invention;

[0021] Figure 5 is the flow chart of the vehicle spontaneous combustion emergency handling method provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will detail the specific embodiments of the embodiments of the present invention with reference to the drawings. It should be understood that the specific embodiments described herein are only for the purpose of illustrating and explaining the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0023] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant regulations of national laws and regulations. In the embodiments of this application, certain industry-existing solutions such as software, components, models, etc. may be mentioned. They should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0024] With the rapid growth of new energy vehicles in cities and the continuous improvement of public fire safety standards, fire emergency systems based on intelligent mobile vehicles are key facilities to prevent chain disasters caused by vehicle spontaneous combustion. The prevention and control of vehicle spontaneous combustion accidents are highly dependent on early identification of fire conditions and accurate classification and disposal. The traditional single threshold alarm mechanism is difficult to adapt to the changing conditions such as thermal radiation diffusion, smoke interference and dynamic obstacle distribution in complex parking environments, which can easily lead to delayed emergency response or improper disposal plans. Based on this, it is particularly important for the present invention to propose a more intelligent and more responsive vehicle spontaneous combustion emergency treatment system.

[0025] In response to the above problems, the present invention provides an AGV-based vehicle spontaneous combustion emergency response system. Through the deep collaboration of the fire assessment model based on fuzzy evidence fusion and the improved A* algorithm, a full-link solution covering fire suppression, hazard source isolation and emergency evacuation is constructed, effectively breaking through the dual technical bottlenecks of rapid fire disposal and secondary disaster prevention and control in complex scenarios.

[0026] Combine the following Figures 1 - 5 The present invention is described in detail.

[0027] Embodiment 1:

[0028] like Figure 1 As shown, an embodiment of the present invention provides an AGV-based vehicle spontaneous combustion emergency handling system, the vehicle spontaneous combustion emergency handling system includes an AGV robot and a control system, the control system is configured to: collect status data and environmental perception data of the spontaneous combustion vehicle; determine obstacle distribution based on the collected environmental perception data, and judge the fire level through preset rules based on the collected status data; select a corresponding processing strategy based on the judged fire level; generate an optimal AGV robot transportation path through an optimized A* algorithm based on the judged fire level and the determined obstacle distribution.

[0029] The vehicle spontaneous combustion emergency handling system described in the present invention consists of an AGV robot and a control system. The control system has a data acquisition function, collecting the status data of the spontaneously combusting vehicle and environmental perception data, providing comprehensive information support for subsequent decision-making. By determining the obstacle distribution through the environmental perception data, the AGV robot can effectively avoid obstacles during driving, ensuring operation safety; based on the status data and preset rules, the fire level is judged, enabling the system to accurately identify the severity of the fire. According to the judged fire level, corresponding handling strategies are selected, which can achieve differentiated and accurate disposal for different fires, improving the effectiveness of emergency handling. Combining the fire level and obstacle distribution, using the optimized A* algorithm to generate the optimal handling path can enable the AGV robot to quickly and efficiently reach the target position, greatly shortening the emergency response time. Generally speaking, through data-driven, accurate judgment, intelligent decision-making, and efficient path planning, the system architecture improves the intelligent level and response efficiency of vehicle spontaneous combustion emergency handling, minimizing the losses caused by the fire to the greatest extent.

[0030] Preferably, the vehicle spontaneous combustion emergency handling system further includes a temperature sensor, a smoke detector, a camera arranged on the top of the parking lot, and a sensor group arranged on the AGV machine. The sensor group includes a lidar and an ultrasonic sensor for collecting the environmental perception data.

[0031] In a preferred embodiment of the present invention, by arranging a temperature sensor, a smoke detector, and a camera on the top of the parking lot, and installing a sensor group including a lidar and an ultrasonic sensor on the AGV machine, the environmental perception data and the status data of the spontaneously combusting vehicle can be comprehensively and accurately collected. These data sources are extensive and complementary to each other, providing a rich and reliable information basis for subsequent fire judgment.

[0032] As Figure 2 shown, further preferably, the status data includes temperature data, smoke concentration data, flame intensity data, and the quantity data of surrounding combustible substances. Judging the fire level according to the collected status data through preset rules includes: calculating a comprehensive evaluation value according to the status data; when the calculated comprehensive evaluation value is in the first preset interval, judging the fire level as a first-level fire; when the calculated comprehensive evaluation value is in the second preset interval, judging the fire level as a second-level fire; when the calculated comprehensive evaluation value is in the third preset interval, judging the fire level as a third-level fire; when the calculated comprehensive evaluation value is in the fourth preset interval, judging the fire level as a fourth-level fire.

[0033] Further preferably, calculating the comprehensive evaluation value according to the state data includes: preprocessing and quantizing the state data to obtain a quantum state feature vector, and constructing an environmental entanglement weight matrix to describe the entanglement relationship between data; fusing the obtained quantum state feature vector and the constructed environmental entanglement weight matrix through quantum tensor product operation to obtain a fused feature; based on the game theory model, dynamically adjusting the weights of each state data according to the fire development stage by using reinforcement learning; determining the spatial topology coefficient based on the parking lot topology structure, and determining the time entropy change factor through the information entropy theory; performing spatio-temporal correction on the obtained fused feature through the determined spatial topology coefficient and time entropy change factor; generating a comprehensive evaluation value through a preset calculation method. The weights of each state data can be calculated by the following formula:

[0034]

[0035] where ω i represents the weight of the i-th state data, π i is the revenue value, and π j represents the value related to the j-th state data. The entropy change factor can be calculated by the following formula:

[0036]

[0037] where H(t) represents the fire information entropy at time t, and H max represents the maximum information entropy. The comprehensive evaluation value can be calculated by the following formula:

[0038]

[0039] where V represents the comprehensive evaluation value, represents the corresponding component of the corrected fused feature, λ represents the game intensity index, η represents the adjustment index, Ξ represents the spatial topology coefficient, and Ψ represents the entropy change factor.

[0040] In a preferred embodiment of the present invention, the specific parameters of the state data and the specific steps for judging the fire level according to the state data are clarified, including: first, normalizing the state data such as temperature and smoke concentration, and then encoding the normalized real-time value and change rate using quantum bits, mapping the real-time value to the quantum bit amplitude and the change rate to the quantum bit phase to form the quantum state of a single state data, and then integrating the quantum states corresponding to the multi-dimensional state data to construct a quantum state feature vector containing the real-time value and change rate, providing complete and quantum-characteristic basic data support for subsequent deep data fusion and fire assessment based on quantum computing. Subsequently, the system mines the potential associations between state data according to the historical vehicle spontaneous combustion case data and real-time monitoring data, constructs an n×n matrix with the number of data types as the dimension, where the matrix element Wij It represents the entanglement weight between the i-th type of data and the j-th type of data. Higher weights are assigned to closely related data. Subsequently, using the backpropagation algorithm, with the error between the comprehensive evaluation value and the actual fire level as the loss function, multiple groups of real-time collected status data are used as inputs. According to the difference between the evaluation result and the actual situation, the matrix elements are adjusted backward, so that the weight matrix can accurately quantify the degree of mutual influence between data. Then, based on the principle of quantum tensor product operation, the quantum state eigenvector is operated with the environmental entanglement weight matrix to obtain a higher-dimensional tensor. Given that the high dimension of the operation result will increase the computational complexity, the principal component analysis method is used to reduce its dimension and extract the most representative features to obtain the fused eigenvector. This process can break through the limitations of traditional linear processing, capture the non-linear correlations between status data, and provide more comprehensive and accurate information for subsequent fire assessment and decision-making; Subsequently, a game theory model is introduced. Through reinforcement learning, the weights of each status data are dynamically adjusted according to the development stage of the fire. In this process, the status data are regarded as game participants, and each participant "plays a game" with the goal of improving the accuracy of fire assessment. The reinforcement learning algorithm uses the deviation between the actual fire assessment result and the real situation as the feedback signal. At different stages of fire development, such as the initial stage, the spreading stage, the stable stage, etc., the weights of each status data in the assessment are continuously adjusted. For example, in the initial stage of the fire, the temperature change may be emphasized, and the weight is calculated using formula (1) to highlight the key factors; Subsequently, based on the parking lot topology structure, the spatial topology coefficient is determined, and combined with the information entropy theory, the time entropy change factor is determined to perform spatio-temporal correction on the fused features. In this process, the system first calculates the quantum distance between the spontaneous ignition point and key locations such as fire-fighting facilities and evacuation channels based on the three-dimensional topology structure of the parking lot, and maps this distance to the spatial topology coefficient. The farther away from the key facilities, the larger the coefficient, to quantify the impact of spatial factors on fire development and emergency handling; At the same time, the information entropy theory is introduced. By calculating the information entropy of the fire at different time points, the complexity and uncertainty of the fire are reflected. The time entropy change factor is determined through formula (2). Finally, the spatial topology coefficient and the time entropy change factor are combined with the fused features, and the fused features are weighted and corrected through multiplication operation, so as to comprehensively consider the impact of space and time on the fire; Finally, the comprehensive evaluation value is calculated according to formula (3) to provide a scientific basis for fire level determination. It realizes a complete link from data collection to accurate assessment, greatly improving the accuracy and intelligent level of fire judgment in the vehicle spontaneous combustion emergency handling system.

[0041] For example, in a smart parking lot, a new energy vehicle caught fire due to a battery failure. The temperature sensor on the top of the parking lot detected that the temperature in the area where the vehicle was located rose rapidly from room temperature to 120°C in a short period of time. The smoke detector also simultaneously sensed that the smoke concentration reached 50% LEL (lower explosion limit). The camera captured an open fire at the bottom of the vehicle. The sensor group carried by the AGV robot further confirmed that there were combustible materials such as cartons piled around the vehicle. At this time, the system first normalized the state data such as temperature, smoke concentration, flame intensity, and the number of combustible materials, and converted them into components in the quantum state feature vector, while recording the temperature change rate; and constructed an environmental entanglement weight matrix W containing the coupling relationship between temperature and smoke concentration and the influence coefficient of flame intensity on surrounding combustible materials. Subsequently, the feature vector and the weight matrix were fused through quantum tensor product operations to mine nonlinear correlations between data. At this time, reinforcement learning dynamically adjusted the weight based on the characteristics of the sharp rise in temperature at the beginning of the fire. The weight of temperature data calculated by formula (1) accounted for 45%, which was significantly higher than other factors. Next, based on the three-dimensional map of the parking lot, the system calculates the quantum distance between the self-igniting vehicle and the nearest fire hydrant, determines the spatial topological system (e.g., 1.2), calculates the fire information entropy H through information entropy theory, and obtains the time entropy change factor (e.g., 0.8). Finally, the parameters are substituted into formula (2) to calculate the comprehensive evaluation value of 80. Based on the preset interval, the value is in the fourth preset interval (e.g., [76, 100]), and the system accurately determines that the fire is a level 3 fire. Based on this, the system quickly initiates the level 4 fire handling strategy.

[0042] like Figure 4 As shown, preferably, the vehicle spontaneous combustion emergency handling system also includes a safe house and a fire cover arranged above each parking space, and the handling strategy includes: when the fire level is a level one fire, controlling the AGV robot to go to the vehicle location for continuous monitoring for a preset time, and sending an early warning message to the management personnel; when the fire level is a level two fire, unfolding and lowering the fire cover to cover the vehicle, and starting the local ventilation fan to reduce the smoke concentration; when the fire level is a level three fire, unfolding and lowering the fire cover, and controlling the AGV robot to move away from the surrounding vehicles; when the fire level is a level four fire, controlling the AGV robot to move the spontaneous combustion vehicle to the safe house, and starting the fire extinguishing system in the safe house.

[0043] In a preferred embodiment of the present invention, the targeted operation plans of the vehicle spontaneous combustion emergency handling system under different fire risk levels are clarified. The system is equipped with hardware facilities such as fire shields arranged above each parking space and safe houses, providing a material basis for emergency handling. When the fire risk level is level one, the system controls the AGV robot to go to the vehicle position to continuously monitor for a preset time and send a warning message to the management personnel, achieving close attention and timely notification of early hidden dangers; in the case of a level two fire, the system automatically unfolds and lowers the fire shield to cover the vehicle, and at the same time starts the local ventilator to reduce the smoke concentration, suppressing the development of the fire from two aspects: physical isolation and air circulation; under a level three fire, on the basis of unfolding the fire shield, the system controls the AGV robot to move the surrounding vehicles away to prevent the fire from spreading by isolating flammable substances; the level four fire is the most serious, the system controls the AGV robot to carry the self-igniting vehicle to the safe house, and starts the fire extinguishing system in the safe house to strongly suppress the fire with professional fire extinguishing facilities. By formulating treatment strategies according to different fire risk levels, the precise and differentiated disposal of vehicle spontaneous combustion incidents from shallow to deep and step by step is realized, making full use of the system hardware resources, effectively improving the efficiency and effect of emergency handling, minimizing the casualties and property losses caused by the fire to the greatest extent, and ensuring the fire safety in the parking lot.

[0044] As Figure 3 shown, preferably, the optimized A* algorithm is an A* algorithm integrating dynamic obstacle avoidance and global optimization. According to the judged fire risk level and the determined obstacle distribution, the optimal AGV robot handling path is generated through the optimized A* algorithm, including: constructing a two-dimensional grid map based on the parking lot environment data and calculating the passing cost of each node; when the fire risk level is level four, determining the current position of the AGV robot as the starting node and the safe house as the target node; establishing an open list and a closed list, adding the starting node to the open list, selecting the node with the minimum passing cost from the open list as the current node and moving it to the closed list; evaluating the passable nodes adjacent to the current node and updating the open list node information; repeating node selection and evaluation until the target node is added to the closed list, and backtracking to generate the initial handling path; using the Dijkstra algorithm to optimize the preliminary path, removing redundant nodes and smoothing the path inflection points to obtain the optimal AGV robot handling path.

[0045] In a preferred embodiment of the present invention, the specific process of generating the optimal handling path is clarified. The optimized A* algorithm used in the present invention integrates dynamic obstacle avoidance and global optimization functions to adapt to the vehicle spontaneous combustion emergency handling scenario. During the generation process of the handling path, first, a two-dimensional grid map is constructed based on the parking lot environment data, and the passing cost of each node is calculated. This passing cost comprehensively considers factors such as obstacle distribution and fire impact, providing a basis for path planning. When the fire level is at the most serious level of four, it is clearly set that the current position of the AGV robot is the starting node and the safe house is the target node. Then, by establishing an open list and a closed list, the starting node is added to the open list. Each time, the node with the minimum passing cost is selected from the open list as the current node and moved to the closed list. At the same time, the adjacent passable nodes of the current node are evaluated and the information of the open list is updated. This node selection and evaluation process is continuously repeated until the target node is added to the closed list, and the initial handling path is generated by backtracking. Finally, the Dijkstra algorithm is used to optimize the initial path, removing redundant nodes and smoothing the path inflection points to obtain the optimal handling path. This path generation method comprehensively considers environmental and fire factors, can effectively guide the AGV robot to quickly and safely transport the spontaneously combusted vehicle to a safe area in a complex environment, improves the emergency handling efficiency, and reduces the risk of fire loss expansion.

[0046] Preferably, the vehicle spontaneous combustion emergency handling system is used to combine the selected handling strategy with the generated handling path to control the AGV robot, the fireproof cover, and the safe house to perform corresponding operations.

[0047] Preferably, the control system is further used for: when a vehicle spontaneous combustion occurs, closing the parking lot entrance gate to prevent vehicles from entering; starting the warning lights and alarms in the parking lot to remind the personnel in the parking lot to evacuate.

[0048] In a preferred embodiment of the present invention, when the system detects a vehicle spontaneous combustion, the control system quickly starts two key operations: First, close the parking lot entrance gate to prevent external vehicles from entering the parking lot through physical interception, avoiding more vehicles from falling into dangerous areas, and at the same time preventing traffic jams in the parking lot caused by newly entering vehicles; Second, start the warning lights and alarms in the parking lot, with eye-catching flashing lights and high-decibel alarm sounds, to send a danger signal to the personnel in the parking lot immediately, reminding the on-site personnel to evacuate quickly and orderly, and maximizing the protection of personnel's lives. It comprehensively improves the overall safety management level and emergency handling ability of the parking lot in the face of vehicle spontaneous combustion emergencies.

[0049] As Figure 5 shown, an embodiment of the present invention further provides a vehicle spontaneous combustion emergency handling method, which is applied to the control system of the above vehicle spontaneous combustion emergency handling system. The vehicle spontaneous combustion emergency handling method includes:

[0050] S101: Obtain the status data and environmental perception data of the self-igniting vehicle;

[0051] S102: Determine the fire level according to the obtained status data through preset rules;

[0052] S103: Select the corresponding processing strategy according to the determined fire level;

[0053] S104: Generate the optimal AGV robot handling path through the optimized A* algorithm according to the obtained environmental perception data and the determined fire level;

[0054] S105: Combine the selected processing strategy with the generated handling path to perform corresponding operations on the self-igniting vehicle.

[0055] Preferably, the vehicle self-ignition emergency handling method further includes: closing the parking lot entrance gate to prevent vehicles from entering; starting the warning lights and alarms in the parking lot to remind the personnel in the parking lot to evacuate.

[0056] When the vehicle self-ignition emergency handling method provided by the embodiment of the present invention is used for emergency handling of vehicle self-ignition, first, the status data and environmental perception data of the self-igniting vehicle are obtained, the fire level is accurately determined based on preset rules, then an appropriate processing strategy is selected according to the fire level, and then the optimized A* algorithm is used to combine the environmental perception data and the fire level to generate the optimal AGV robot handling path. Finally, corresponding operations are performed on the self-igniting vehicle by combining the strategy and the path, forming a complete closed loop from data collection, fire judgment to emergency disposal. At the same time, when a self-igniting vehicle appears, the parking lot entrance gate will also be closed to prevent vehicles from entering the dangerous area, avoid traffic jams and prevent more vehicles from being threatened by the fire, and start the warning lights and alarms in the parking lot to timely remind the personnel to evacuate and ensure the safety of personnel's lives. It realizes the whole-process and systematic management of vehicle self-ignition events from monitoring, judgment, disposal to personnel protection, and significantly improves the comprehensive ability and safety of the parking lot to cope with self-ignition accidents.

[0057] Embodiment 2:

[0058] Based on the unified inventive concept, in a large-scale intelligent logistics park parking lot, a set of AGV-based vehicle self-ignition emergency handling system is deployed. The parking lot covers an area of 50,000 square meters, has 1,000 parking spaces, and the number of vehicles entering and leaving the parking lot exceeds 2,000 times per day. The vehicle types include new energy trucks, fuel transport vehicles, etc., and there is a risk of vehicle self-ignition.

[0059] At 10 o'clock in the morning on a certain day, a new energy truck loaded with plastic products suddenly caught fire at parking space No. 15, row 3, area C. The temperature sensor on the top of the parking lot quickly detected that the temperature in this area soared from 25°C to 75°C within 2 minutes. The smoke detector simultaneously reported that the smoke concentration reached 28% LEL, and the camera captured a bright fire emerging from the bottom of the vehicle. At the same time, the AGV robot located nearby, through its own lidar and ultrasonic sensors, confirmed that there were 3 vehicles loaded with flammable goods parked nearby, and there were obstacles such as fire channels.

[0060] The control system immediately collected the above state data and environmental perception data, and processed the data of temperature, smoke concentration, flame intensity and the number of surrounding combustible substances according to the process set by the system. Finally, the comprehensive evaluation value was calculated to be 60, and the fire level was determined to be a level-three fire according to the preset interval.

[0061] According to the level-three fire, the control system quickly activated the corresponding treatment strategy: immediately deploy and lower the fireproof cover above parking space No. 15, row 3, area C to cover the self-igniting vehicle; at the same time, control the AGV robot numbered AGV-003 to perform the task of moving the surrounding vehicles. In the path planning stage, the system first constructs a two-dimensional grid map based on the parking lot environment data, raises the passing cost of the nodes in the area of the self-igniting vehicle and the surrounding obstacles, determines the current position of AGV-003 as the starting node, and the open area where the surrounding vehicles are to be transferred as the target node. Through the optimized A* algorithm, an open list and a closed list are established, nodes are continuously evaluated and information is updated to generate an initial handling path, and then the Dijkstra algorithm is used to optimize the path to remove redundant inflection points. AGV-003 safely transfers the 3 surrounding vehicles to the designated area along the optimal path in turn.

[0062] While performing the above operations, the control system closes the entrance gate of the parking lot to prevent subsequent vehicles from entering; activates the warning lights and alarms throughout the venue, and the flashing red lights and sharp alarm sounds quickly remind the staff and drivers in the park to evacuate in an orderly manner.

[0063] In summary, the vehicle spontaneous combustion emergency handling system and method provided by the present invention can quickly and accurately collect vehicle spontaneous combustion state data and environmental perception data through various types of sensors mounted on the top of the parking lot and AGV robots. It creatively integrates quantum computing and game theory deeply into the comprehensive evaluation value calculation process to achieve accurate determination of the fire level. For different fire levels, the system automatically matches differential handling strategies such as fireproof cover covering, vehicle handling, and starting the fire extinguishing system, and generates the optimal handling path for AGV robots by combining the optimized A* algorithm with the obstacle distribution to ensure the efficient execution of emergency operations. At the same time, the system can also synchronously close the parking lot entrance gate, start warning lights and alarms to achieve comprehensive safety control of vehicles and personnel, forming a complete closed-loop from fire monitoring and early warning, precise disposal to personnel and vehicle protection, significantly improving the automation and intelligence level of vehicle spontaneous combustion emergency handling in the parking lot, greatly shortening the emergency response time, effectively reducing the risk of casualties and property losses caused by fires, and providing strong technical support for the fire safety management of the parking lot.

[0064] It should be understood that in various embodiments of the present invention, the magnitude of the serial numbers of the above processes does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0065] In addition, the terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0066] It should be understood that in the embodiments of the present invention, "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0067] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0068] 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 units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0069] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings, direct couplings, or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be electrical, mechanical, or other forms of connection.

[0070] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0071] In addition, the functional units in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0072] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by firmware, or by a combination thereof. When implemented in software, the above functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. The computer-readable medium includes computer storage media and communication media, where the communication media includes any medium that facilitates the transfer of a computer program from one place to another. The storage media can be any available medium that can be accessed by a computer. By way of example but not limitation: the computer-readable medium can include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer. In addition, any connection can suitably be a computer-readable medium. For example, if the software is transmitted using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwave from a website, server or other remote source, then the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, wireless and microwave are included in the definition of the medium. As used in the present invention, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disk generally magnetically replicates data, while disc optically replicates data with a laser. The above combinations should also be included within the scope of protection of the computer-readable medium.

[0073] In summary, the above description is only a preferred embodiment of the technical solution of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An AGV-based vehicle spontaneous combustion emergency handling system, characterized in that, The vehicle spontaneous combustion emergency handling system includes an AGV robot and a control system, and the control system is configured to: Collect the status data of the spontaneously combusting vehicle and the environmental perception data; Determine the obstacle distribution based on the collected environmental perception data, and judge the fire level according to the collected status data through a preset rule; Select a corresponding processing strategy according to the judged fire level; Generate an optimal AGV robot handling path through an optimized A* algorithm according to the judged fire level and the determined obstacle distribution.

2. The vehicle spontaneous combustion emergency handling system according to claim 1, wherein, The vehicle spontaneous combustion emergency handling system further includes a temperature sensor, a smoke detector, a camera arranged on the top of the parking lot, and a sensor group arranged on the AGV machine. The sensor group includes a lidar and an ultrasonic sensor for collecting the environmental perception data.

3. The vehicle spontaneous combustion emergency handling system according to claim 1, characterized in that, The status data includes temperature data, smoke concentration data, flame intensity data, and the quantity data of surrounding combustible substances. Judging the fire level according to the collected status data through a preset rule includes: Calculate a comprehensive evaluation value according to the status data; When the calculated comprehensive evaluation value is within the first preset interval, judge the fire level as a first-level fire; When the calculated comprehensive evaluation value is within the second preset interval, judge the fire level as a second-level fire; When the calculated comprehensive evaluation value is within the third preset interval, judge the fire level as a third-level fire; When the calculated comprehensive evaluation value is within the fourth preset interval, judge the fire level as a fourth-level fire.

4. The vehicle spontaneous combustion emergency handling system according to claim 3, characterized in that, Calculating the comprehensive evaluation value according to the status data includes: Preprocess and quantize the status data to obtain a quantum state feature vector, and construct an environmental entanglement weight matrix to describe the entanglement relationship between data; Fuse the obtained quantum state feature vector and the constructed environmental entanglement weight matrix through quantum tensor product operation to obtain a fusion feature; Based on the game theory model, dynamically adjust the weights of each status data according to the fire development stage by using reinforcement learning; Determine the spatial topology coefficient based on the parking lot topology structure, and determine the time entropy change factor through the information entropy theory; Perform spatio-temporal correction on the obtained fusion feature through the determined spatial topology coefficient and time entropy change factor; Generate a comprehensive evaluation value through a preset calculation method based on the corrected fusion feature.

5. The vehicle spontaneous combustion emergency handling system according to claim 1, characterized in that, The vehicle spontaneous combustion emergency handling system further includes a safe house and a fireproof cover arranged above each parking space. The processing strategy includes: When the fire level is a first-level fire, control the AGV robot to go to the vehicle position for continuous monitoring for a preset time, and send a warning message to the management personnel; When the fire level is a second-level fire, unfold and lower the fireproof cover to cover the vehicle, and start a local ventilator to reduce the smoke concentration; When the fire level is a third-level fire, unfold and lower the fireproof cover, and control the AGV robot to move the surrounding vehicles away; When the fire level is a fourth-level fire, control the AGV robot to transport the spontaneously combusting vehicle to the safe house, and start the fire extinguishing system in the safe house.

6. The vehicle spontaneous combustion emergency handling system according to claim 5, characterized in that, The optimized A* algorithm is an A* algorithm that integrates dynamic obstacle avoidance and global optimization. According to the judged fire level and the determined obstacle distribution, an optimal AGV robot handling path is generated through the optimized A* algorithm, including: Construct a two-dimensional grid map based on the parking lot environment data and calculate the passing cost of each node; When the fire level is level four fire, determine the current position of the AGV robot as the starting node and the safe house as the target node; Establish an open list and a closed list, add the starting node to the open list, select the node with the minimum passing cost from the open list as the current node and move it to the closed list; Evaluate the passable nodes adjacent to the current node and update the node information in the open list; Repeat node selection and evaluation until the target node is added to the closed list, and backtrack to generate an initial handling path; Optimize the preliminary path using the Dijkstra algorithm, remove redundant nodes and smooth the path inflection points to obtain the optimal AGV robot handling path.

7. The vehicle spontaneous combustion emergency handling system according to claim 5, characterized in that, The vehicle spontaneous combustion emergency handling system is used to combine the selected handling strategy and the generated handling path to control the AGV robot, the fireproof cover and the safe house to perform corresponding operations.

8. The vehicle spontaneous combustion emergency handling system according to claim 1, characterized in that, The control system is also used for: When a vehicle spontaneous combustion occurs, close the parking lot entrance gate to prevent vehicles from entering; Activate the warning lights and alarms in the parking lot to remind the people in the parking lot to evacuate.

9. A method for emergency handling of vehicle spontaneous combustion, characterized in that, For the control system applied to the vehicle spontaneous combustion emergency handling system described in claims 1-8, the vehicle spontaneous combustion emergency handling method includes: Obtain the status data of the spontaneous combustion vehicle and the environmental perception data; Determine the fire level according to the obtained status data through preset rules; Select a corresponding handling strategy according to the determined fire level; Generate an optimal AGV robot handling path through the optimized A* algorithm according to the obtained environmental perception data and the determined fire level; Combine the selected handling strategy and the generated handling path to perform corresponding operations on the spontaneous combustion vehicle.

10. The vehicle spontaneous combustion emergency handling method according to claim 9, characterized in that, The vehicle spontaneous combustion emergency handling method further includes: Close the parking lot entrance gate to prevent vehicles from entering; Activate the warning lights and alarms in the parking lot to remind the people in the parking lot to evacuate.

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