Fire-fighting detection method and system

Abstract

The invention discloses a fire-fighting detection method and system, and aims to improve fire rescue efficiency and accuracy. The method comprises the following steps: collecting disaster scene map data, performing discretization processing, creating a space model by using a three-dimensional modeling or geographic information system, planning a route by using a path search algorithm, starting a camera to monitor and record a driving track in real time, and detecting and recording harmful gas. Through real-time monitoring and data analysis, the risk of rescue workers is effectively reduced, the rescue efficiency is improved, and the system is suitable for quick response and operation of a complex fire scene.

Description

Technical Field

[0001] The present invention relates to a method for fire detection and inspection, and also relates to a system for fire detection and inspection, belonging to the field of fire protection technology. Background Art

[0002] With the acceleration of the urbanization process, the frequency and scale of fire accidents are both increasing continuously. Especially in industrial fires and explosion accidents, due to complex situations such as high temperature, high pressure generated by chemical reactions and leakage of toxic gases, the challenges faced by fire rescue work are becoming greater and greater. Although the existing fire detection vehicle systems have the ability to detect harmful gases, they are insufficient in warning and recording the names of the detected gas types, which makes it difficult to effectively carry out the analysis and evaluation of gas types at the rescue site.

[0003] In addition, there are also problems with the existing detection vehicle systems in terms of operation. Generally, these systems need to control the movement of the detection vehicle and the operation of the robotic arm through a remote control or a wired connection, which is a rather cumbersome process and is not conducive to rapid response and operation in emergency situations. Therefore, it is necessary to improve the existing fire detection vehicle systems to improve their rescue efficiency and accuracy in complex environments.

[0004] In the Chinese patent application with the application number 202411527750.4, a gas concentration and two-dimensional map correlation detection system and method are disclosed, including a map surveying and mapping unmanned aerial vehicle, an environmental detection unmanned vehicle, a cloud platform and platform software; the platform software is used to read a two-dimensional real scene map from the cloud platform and transmit the read two-dimensional real scene map to a remote ground command center for display. This system can, in typical post-disaster outdoor scenarios, such as sites with flammable, explosive, toxic, oxygen-deficient, thick smoke, high temperature, etc., replace emergency rescue personnel to enter the hazardous chemical gas site to perform correlation detection on gas concentration and two-dimensional map, so as to formulate an emergency rescue strategy path for the site, evaluate post-disaster monitoring of toxic and harmful gases, improve efficiency, and greatly reduce the casualty risk of emergency rescue personnel. Summary of the Invention

[0005] The primary technical problem to be solved by the present invention is to provide a method for fire detection and inspection.

[0006] Another technical problem to be solved by the present invention is to provide a system for fire detection and inspection.

[0007] To achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] According to the first aspect of an embodiment of the present invention, a method for fire detection and inspection is provided, including the following steps:

[0009] S1: Collect map environment data of the disaster site, and perform cleaning, verification, integration and formatting processing on the map data;

[0010] S2: Discretize the processed map environment data to obtain the discretized map environment data;

[0011] S3: Use 3D modeling software or geographic information system to perform 3D modeling or 2D rasterization on the target area to obtain the disaster scene spatial model;

[0012] S4: Based on the disaster scene spatial model obtained in step S3, use a path search algorithm to plan a route according to the current location information and destination information;

[0013] S5: Travel to the destination according to the route plan, turn on the camera for real-time monitoring, and record and save the driving trajectory information for querying historical trajectory information;

[0014] S6: After arriving at the destination, detect and record the harmful gases through statistics, trend analysis, and correlation analysis.

[0015] Preferably, the collection of the map environment data in step S1 includes the following sub-steps:

[0016] S11: Establish a map environment data interface according to the Transmission Control Protocol;

[0017] S12: Receive the hexadecimal latitude and longitude data;

[0018] S13: Convert the hexadecimal latitude and longitude data to decimal latitude and longitude data;

[0019] S14: Mark the corresponding positions on the map using the decimal latitude and longitude data.

[0020] Preferably, in step S4, the path algorithm is the Dijkstra algorithm or the Floyd-Warshall algorithm.

[0021] Preferably, the real-time monitoring in step S5 includes the following sub-steps:

[0022] S51: Establish a User Datagram Protocol connection;

[0023] S52: Establish a receiving buffer;

[0024] S53: Use the receive() method of DatagramSocket to receive video stream data packets and store them in the receiving buffer;

[0025] S54: Use the video codec library to recombine the received video stream data packets into complete video frames and decode them to obtain the decoded video stream data;

[0026] S55: Save and / or play the decoded video stream data. If saving, use the file output stream in Java to store the video stream data. If playing, use the multimedia API in Java or a third-party library for playback.

[0027] S56: Capture photos of specific disaster scenes from the decoded video stream data and send them to the mobile receiving end and the fire command center.

[0028] S57: Use the file output stream in Java to store the alarm photo data.

[0029] Preferably, the detection of harmful gases in step S6 includes the following sub-steps:

[0030] S61: Collect the original data of the gas to be detected.

[0031] S62: Clean the collected original data to remove irrelevant information, duplicate data, and outliers.

[0032] S63: Convert the sensor readings into corresponding concentration values and convert the timestamps into a unified time unit.

[0033] S64: Convert the hexadecimal format to the decimal format.

[0034] S65: Analyze the converted data to determine whether it meets the preset alarm threshold. If the analysis result is within the preset alarm threshold range, report that the gas composition is normal. If the analysis result exceeds the preset alarm threshold, initiate an alarm to the mobile receiving end and the fire command center and prompt the specific abnormal gas type.

[0035] S66: The robot continuously monitors the disaster scene and sends the monitoring data to the tablet computer and the command center in real time and / or at regular intervals for the staff to understand the dynamics of the disaster situation.

[0036] According to the second aspect of the embodiments of the present invention, there is provided a fire detection and inspection system, including a processor and a memory. Among them, the memory is coupled to the processor and is used to store a computer program. When the computer program is executed by the processor, the processor implements the above-mentioned fire detection and inspection method.

[0037] Compared with the prior art, the present invention collects and processes map environment data at the disaster scene, creates a spatial model using 3D modeling software or geographic information system, and uses a path search algorithm for route planning to achieve rapid response and precise navigation. The system can turn on the camera for real-time monitoring, record the driving trajectory, and comprehensively monitor the disaster scene through statistics, trend analysis, and correlation analysis of harmful gases. In addition, the system can send the monitoring data to the command center in real time or at regular intervals, improving the rescue efficiency and accuracy, reducing the casualty risk of emergency rescue personnel, and thus enhancing the efficiency and safety of fire rescue work in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 FIG. is a flowchart of a fire detection method provided by an embodiment of the present invention;

[0039] Figure 2 FIG. is a schematic diagram of route planning in an embodiment of the present invention;

[0040] Figure 3 FIG. is a schematic diagram of the interface for querying historical trajectories in an embodiment of the present invention;

[0041] Figure 4 FIG. is a flowchart of a method for collecting map environment data in an embodiment of the present invention;

[0042] Figure 5 FIG. is a flowchart of a method for real-time monitoring in an embodiment of the present invention;

[0043] Figure 6 FIG. is a flowchart of a method for detecting harmful gases in an embodiment of the present invention;

[0044] Figure 7 FIG. is a schematic diagram of a fire detection system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical content of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] First Embodiment

[0047] As Figure 1 shown, the first embodiment of the present invention provides a fire detection method, which at least includes the following steps:

[0048] S1: Collect map environment data at the disaster scene, and perform cleaning, verification, integration, and formatting processing on the map data.

[0049] S2: Discretize the processed map environment data to obtain discretized map environment data.

[0050] S3: Use 3D modeling software or Geographic Information System (GIS) to perform 3D modeling or 2D rasterization on the target area to obtain a spatial model of the disaster site.

[0051] S4: As Figure 2 shown, based on the spatial model of the disaster site obtained in step S3, use a path search algorithm to plan a route according to the current location information and destination information.

[0052] S5: As Figure 3 shown, go to the destination according to the route plan, turn on the camera for real-time monitoring, and record and save the driving trajectory information for querying historical trajectory information.

[0053] S6: After arriving at the destination, detect and record the harmful gases through statistics, trend analysis, and correlation analysis.

[0054] As Figure 4 shown, in an embodiment of the present invention, the map environment data of the disaster site mentioned in step S1 includes: topographic maps, building floor plans, and distribution map data of fire-fighting facilities of the target area. Correspondingly, collecting the map environment data includes the following sub-steps:

[0055] S11: Establish a map environment data interface according to the Transmission Control Protocol (TCP).

[0056] S12: Receive hexadecimal latitude and longitude data.

[0057] S13: Convert the hexadecimal latitude and longitude data to decimal latitude and longitude data.

[0058] S14: Mark the corresponding positions on the map using the decimal latitude and longitude data.

[0059] It should be noted that using the hexadecimal format for data transmission mainly has two advantages: First, it improves security because the hexadecimal format ensures the integrity and accuracy of the data, effectively preventing the data from being tampered with or damaged during transmission, which is crucial for the fire-fighting system as the accuracy and reliability of the data are the keys to ensuring the safe operation of the system; Second, since the hexadecimal format can be easily converted to binary, it enables the data to be compressed and decompressed more efficiently during transmission, thereby improving the real-time performance and accuracy of data transmission, which is particularly important for a fire-fighting system that requires quick response as it helps reduce data transmission latency and error rate.

[0060] The path search algorithm mentioned in step S4 is a type of algorithm used to find paths from one or more source points to one or more target points in a graph. This type of algorithm can be applied to various scenarios, such as map navigation, network routing, etc. Common path search algorithms include: Dijkstra's algorithm, Bellman-Ford algorithm, Floyd-Warshall algorithm, A* (A-star) algorithm, or Breadth-First Search (BFS) algorithm, etc. In an embodiment of the present invention, the preferred path search algorithms are Dijkstra's algorithm or Floyd-Warshall algorithm.

[0061] Among them, Dijkstra's algorithm is a graph search algorithm used to find the shortest paths from a single source point to all other vertices in a weighted graph, and is particularly suitable for graphs with non-negative edge weights.

[0062] This algorithm mainly includes the following steps:

[0063] S01: For all vertices v, set the distance array dist[v], where dist[s]=0 (s is the source point), and dist[v]=∞ (for all other vertices v). Create a priority queue (or min heap) for storing all vertices and their distances to the source point.

[0064] S02: Remove the vertex u with the minimum dist value from the priority queue (initially the source point s). For each unvisited neighbor v of u, if

[0065] dist[u]+weight(u,v)<dist[v], then update dist[v].

[0066] S03: Mark the vertex u as visited and remove it from the set of unvisited vertices.

[0067] S04: Repeat S02 to S03 until all vertices are visited or the target vertex is visited.

[0068] The Floyd-Warshall algorithm is a dynamic programming algorithm used to find the shortest paths between all pairs of vertices in a weighted graph. This algorithm achieves this goal by gradually updating a distance matrix. Its core idea is to consider each vertex in the graph as an intermediate node in turn, and use these intermediate nodes to check and update the shortest paths between other vertex pairs. Specifically, if it is found that the path through the current intermediate node is shorter than the known direct path, this path will be updated as the new shortest path, so as to ensure that when the algorithm ends, the distance matrix stores the shortest paths between all pairs of vertices.

[0069] This algorithm mainly includes the following steps:

[0070] S001: Create an n*n matrix, where n is the number of vertices. Each element in the matrix represents the distance from one vertex to another. If there is no direct path, the element is set to infinity (∞).

[0071] S002: The algorithm iteratively updates the distances in the matrix. For each pair of vertices (i, j), the algorithm checks if a shorter path can be found through an intermediate vertex k. If so, the corresponding element in the matrix is updated.

[0072] S003: After completing the iteration, the algorithm checks if there is a negative weight cycle. If the distance from a vertex to itself is negative, then there is a negative weight cycle in the graph.

[0073] The Dijkstra algorithm and the Floyd-Warshall algorithm each have unique advantages: The Dijkstra algorithm is known for its low time complexity and can quickly determine the shortest paths from a single source vertex to all other vertices, especially suitable for graphs with non-negative edge weights; while the Floyd-Warshall algorithm focuses on solving the all-pairs shortest path problem, that is, calculating the shortest paths between all node pairs in the graph. In addition to finding the shortest paths, the Floyd-Warshall algorithm can also record the predecessor node information of each node, which enables us to easily reconstruct the specific paths from the algorithm results.

[0074] As Figure 5 shown, in an embodiment of the present invention, the real-time monitoring in step S5 includes the following sub-steps:

[0075] S51: Establish a User Datagram Protocol (UDP) connection.

[0076] S52: Establish a receive buffer.

[0077] S53: Use the receive() method of DatagramSocket to receive video stream data packets and store them in the receive buffer.

[0078] Among them, DatagramSocket is a type of socket used in network programming, mainly for sending and receiving datagrams. A datagram is a network communication protocol that allows data to be transmitted in the form of independent and self-contained messages, and each datagram contains complete destination address information. This communication method is connectionless, that is, a persistent connection does not need to be established before data transmission begins.

[0079] To go further, the receive() method of DatagramSocket is specifically used to receive datagrams sent to the socket. This method works in a blocking manner, which means that it will continue to wait until a datagram is received, the socket is closed, or an abnormal situation occurs. In short, the receive() method of DatagramSocket will continue to wait for the arrival of data until any of the above conditions are met before ending its blocking state. This design makes DatagramSocket very suitable for application scenarios that need to receive data immediately but do not need to maintain a long-term connection.

[0080] Following is the basic usage of the receive() method:

[0081]

[0082]

[0083] S54: using the video codec library, reassemble the received video stream data packets into complete video frames, and decode them to obtain decoded video stream data.

[0084] S55: The decoded video stream data is saved and / or played. If saved, the video stream data is stored using the Java file output stream. If played, the Java multimedia API or a third-party library is used to play.

[0085] S56: intercepting the alarm photo from the decoded video stream data and sending it to the mobile receiving terminal and the fire command center.

[0086] Among them, alarm photos refer to photos of specific disaster scenes taken at the disaster site.

[0087] S57: Use Java's file output stream to store the alarm photo data.

[0088] like Figure 6 As shown, in one embodiment of the present invention, the method for detecting harmful gases in step S6 includes the following sub-steps:

[0089] S61: Collecting raw data of the gas to be detected.

[0090] S62: Clean the collected raw data to remove irrelevant information, duplicate data and outliers.

[0091] S63: Convert the sensor reading into a corresponding concentration value and convert the time stamp into a uniform time unit.

[0092] S64: Convert the hexadecimal format to the decimal format.

[0093] S65: Analyze the converted data to determine whether it meets the preset alarm threshold. If the analysis result is within the preset alarm threshold range, report that the gas composition is normal. If the analysis result exceeds the preset alarm threshold, initiate an alarm to the mobile receiving end and the fire command center, and prompt the specific abnormal gas type.

[0094] S66: The robot continuously monitors the disaster scene and sends the monitoring data to the tablet computer and the command center in real time and / or at regular intervals for the staff to understand the disaster situation dynamics.

[0095] Second Embodiment

[0096] Based on the above method, the second embodiment of the present invention provides a fire detection and inspection system. As Figure 7 shown, the system includes one or more processors and a memory. Among them, the memory is coupled to the processor and is used to store a computer program. When the computer program is executed by the processor, the processor implements the method in the above embodiment.

[0097] Among them, the processor is used to control the overall operation of the system to complete all or part of the steps of the above method. The processor can be a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processing (DSP) chip, etc. The memory is used to store various types of data to support the operation of the system. These data can include, for example, instructions for any application program or method operating on the system, as well as application program related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, etc.

[0098] In an exemplary embodiment, the system can be specifically implemented by a computer chip or an entity, or by a product with a certain function, for executing the above method and achieving the same technical effect as the above method. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0099] In another exemplary embodiment, the present invention further provides a computer-readable storage medium including program instructions, and when the program instructions are executed by a processor, the steps of the method in any of the above embodiments are implemented. For example, the computer-readable storage medium may be the above-mentioned memory including program instructions, and the above program instructions may be executed by the processor to complete the above method and achieve the same technical effect as the above method.

[0100] It should be noted that the above-mentioned multiple embodiments are only examples, and the technical solutions of each embodiment can be combined, all within the protection scope of the present invention.

[0101] The above provides a detailed description of the method and system for fire detection and inspection provided by the present invention. For those of ordinary skill in the art, any obvious changes made without departing from the essence of the present invention will constitute an infringement of the patent right of the present invention and will bear corresponding legal responsibilities.

Claims

1. A method for fire detection and inspection, characterized in that The steps include: S1: Collect map environment data of the disaster site, and clean, verify, integrate and format the map data; S2: discretizing the processed map environment data to obtain discretized map environment data; S3: Use 3D modeling software or geographic information system to perform 3D modeling or 2D rasterization processing on the target area to obtain a spatial model of the disaster site; S4: Based on the disaster site spatial model obtained in step S3, a path search algorithm is used to perform route planning according to the current location information and destination information; S5: Go to the destination according to the route planning, turn on the camera for real-time monitoring, and record and save the driving trajectory information for querying historical trajectory information; S6: After arriving at the destination, harmful gases are detected and recorded through statistics, trend analysis and correlation analysis.

2. The method according to claim 1, wherein In step S1, collecting map environment data includes the following sub-steps: S11: Establishing a map environment data interface according to the transmission control protocol; S12: receiving hexadecimal longitude and latitude data; S13: Convert the hexadecimal longitude and latitude data into decimal longitude and latitude data; S14: Mark the corresponding location on the map using the decimal latitude and longitude data.

3. The method according to claim 1, characterized in that In step S4, the path algorithm is Dijkstra algorithm or Floyd-Warshall algorithm.

4. The method according to claim 1, characterized in that In step S5, real-time monitoring includes the following sub-steps: S51: Establishing a User Datagram Protocol connection; S52: Establish a receiving buffer; S53: Use the receive() method of DatagramSocket to receive the video stream data packet and store it in the receiving buffer; S54: using the video codec library, reassembling the received video stream data packets into complete video frames, and decoding them to obtain decoded video stream data; S55: Save and / or play the decoded video stream data; if saving, use Java's file output stream to store the video stream data; if playing, use Java's multimedia API or a third-party library to play; S56: capturing a specific disaster scene photo from the decoded video stream data, and sending the photo to the mobile receiving terminal and the fire command center; S57: Use Java's file output stream to store the alarm photo data.

5. The method according to claim 1, wherein In step S6, the method for detecting harmful gases includes the following sub-steps: S61: Collecting raw data of the gas to be detected; S62: Clean the collected raw data to remove irrelevant information, duplicate data and outliers; S63: converting the sensor reading into a corresponding concentration value and converting the timestamp into a unified time unit; S64: Convert the hexadecimal format to the decimal format; S65: Analyze the converted data to determine whether it meets the preset alarm threshold; If the analysis result is within the preset alarm threshold, the gas composition is reported as normal; If the analysis result exceeds the preset alarm threshold, an alarm will be sent to the mobile receiving terminal and the fire command center, and the specific abnormal gas type will be prompted; S66: The robot continuously monitors the disaster site and sends the monitoring data to the tablet computer and the command center in real time and / or at regular intervals for the staff to understand the dynamics of the disaster situation.

6. A fire detection and inspection system, characterized in that It includes a processor and a memory; wherein, the memory is coupled to the processor for storing a computer program, and when the computer program is executed by the processor, the processor implements the method described in any one of claims 1 to 5.

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