Large UAV fire fighting system and method

Abstract

The present invention discloses a large-scale UAV fire fighting system and method, which belongs to the field of UAV fire fighting systems. The system includes an image acquisition module, a thermal imaging acquisition module, a data analysis module, a data transmission module, a UAV fire extinguishing module and a UAV power module; the image acquisition module is used to collect image information below the UAV and send the collected image information to the data analysis module; the thermal imaging acquisition module is used to collect thermal imaging information below the UAV and send the collected thermal imaging information to the data analysis module; the data analysis module is used to receive all information and analyze it, and output the analysis result; the data transmission module is used to transmit the analysis result and feedback information to the background control terminal; the UAV fire extinguishing module is used to take corresponding fire extinguishing measures according to the analysis result. The present invention can more accurately identify fires, thereby improving the overall fire fighting effect.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) fire fighting systems, and in particular to a large-scale unmanned aerial vehicle (UAV) fire fighting system and method. Background Art

[0002] The drone firefighting system is a comprehensive system designed by using drone technology and combining the actual needs of firefighting. The system uses various firefighting equipment and sensors carried by drones to achieve fire monitoring, reconnaissance, firefighting and rescue functions, providing an efficient, flexible and safe solution for firefighting.

[0003] Drones can respond to fire incidents quickly, quickly arrive at the scene for monitoring and reconnaissance, and provide timely and accurate information for firefighting decisions. In firefighting operations, drones can accurately deliver fire extinguishing agents, improve firefighting efficiency, and shorten firefighting time. When drones perform firefighting tasks, there is no need for personnel to directly enter the fire scene, reducing the safety risks of firefighters. At the same time, the high-definition cameras and infrared thermal imaging equipment carried by drones can monitor the fire scene in real time, providing safety guarantees for rescue operations.

[0004] The existing UAV firefighting system has the following problems: it cannot identify fires more accurately, and the overall firefighting effect is not good enough. Therefore, those skilled in the art provide a large-scale UAV firefighting system and method to solve the problems raised in the above background technology. Summary of the invention

[0005] The purpose of the present invention is to provide a large-scale unmanned aerial vehicle fire fighting system and method, which can more accurately identify fires, thereby improving the overall fire fighting effect, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A large-scale UAV firefighting system, comprising an image acquisition module, a thermal imaging acquisition module, a data analysis module, a data transmission module, a UAV firefighting module and a UAV power module;

[0008] The image acquisition module is used to collect image information from below the drone and send the collected image information to the data analysis module; the thermal imaging acquisition module is used to collect thermal imaging information from below the drone and send the collected thermal imaging information to the data analysis module; the data analysis module is used to receive all information and analyze it, and output the analysis results; the data transmission module is used to transmit the analysis results and feedback information to the background control terminal; the drone fire extinguishing module is used to take corresponding fire extinguishing measures according to the analysis results, and to feedback risk level information to the data analysis module in real time; the drone power module is used to provide the drone with the power required for flight according to the analysis results, and to feedback remaining power information to the data analysis module in real time.

[0009] As a further solution of the present invention: the specific process of the data analysis module analyzing the information collected by the image acquisition module is:

[0010] Perform noise reduction on the collected images and enhance the contrast and brightness of the images;

[0011] Resize the image to a size suitable for the preset model processing;

[0012] Extract color features, texture features, shape features and flickering patterns from the image, and then import the extracted features into the preset model;

[0013] The preset model performs real-time analysis on the collected images based on the imported features to determine whether there is a fire.

[0014] As a further solution of the present invention: the specific construction process of the preset model is:

[0015] The deep learning model is trained using previous fire image samples and non-fire image samples. Through training, the model learns the characteristics and patterns of fire images and can accurately classify and identify new images.

[0016] During the training process, the performance of the model is continuously evaluated, including indicators such as accuracy and recall rate; the recognition effect of the model is improved by adjusting the parameters and structure of the model;

[0017] The trained model is deployed to the data analysis module to perform real-time analysis on the images collected by the image acquisition module. When a fire image is detected, the model outputs the analysis results in a timely manner.

[0018] As a further solution of the present invention: the specific process of the data analysis module analyzing the information collected by the thermal imaging acquisition module is:

[0019] Observe the collected thermal images, where different colors represent different temperature ranges, and high temperature areas are displayed in bright colors;

[0020] Analyze temperature distribution, that is, determine whether there is an abnormally high temperature area by analyzing the temperature distribution in the thermal image;

[0021] Determine the fire situation. If there is an abnormally high temperature area in the thermal image, it means that there is a fire. Then, based on the temperature distribution and changes in the thermal image, determine the scale and spread trend of the fire.

[0022] As a further solution of the present invention: the specific judgment process of the scale and spread trend of the fire is: based on the size and temperature distribution of the high-temperature area, preliminarily judge the scale of the fire, wherein the larger the high-temperature area and the higher the temperature, the larger the scale of the fire; observe the shape and boundary changes of the high-temperature area, and the direction of the temperature gradient to judge the spread direction and speed of the fire. If the high-temperature area is expanding or the temperature gradient points in a certain direction, it indicates that the fire is spreading.

[0023] As a further solution of the present invention: the specific analysis process of the abnormally high temperature area is: marking all high temperature areas in the thermal image, comparing the thermal image of the currently marked area with the historical data of the same location to identify the area with abnormally high temperature.

[0024] As a further solution of the present invention: the specific process of the UAV fire extinguishing module taking fire extinguishing measures and feeding back progress information is as follows:

[0025] If the analysis results of the image information and thermal imaging information of the data analysis module are both that there is a fire, then proceed to the next step, otherwise, the drone fire extinguishing module does not take any fire extinguishing measures;

[0026] The abnormally high temperature area in the thermal image is marked as Ai, i = 1···n, where n is a positive integer;

[0027] The area of ​​the fire area corresponding to Ai is marked as Bi, the temperature of the fire area corresponding to Ai is marked as Ci, and the fire spreading speed corresponding to Ai is marked as Di;

[0028] Sort Ai according to Di from large to small to generate a priority list;

[0029] Instruct the drone fire extinguishing module to take fire extinguishing measures for Ai according to the priority list. The higher the Ai is in the priority list, the earlier the fire will be extinguished.

[0030] Based on Bi, Ci, and Di, generate the fire radar map Pi corresponding to Ai, extract the area of ​​Pi and mark it as Qi;

[0031] Compare Qi with the preset value q. If Qi>q, plan the Ai corresponding to Qi to the first sequence. If Qi≤q, plan the Ai corresponding to Qi to the second sequence.

[0032] Count the number of Ai in the first sequence and mark it as w1, and count the number of Ai in the second sequence and mark it as w2;

[0033] Calculate the current fire severity weight value V=(3*w1+w2), and mark the preset fire severity weight value as v;

[0034] If V<v, the risk level information fed back to the data analysis module is level one risk;

[0035] If v≤V<2*v, the risk level information fed back to the data analysis module is level 2 risk;

[0036] If 2*v≤V, the risk level information fed back to the data analysis module is level 3 risk.

[0037] As a further solution of the present invention: the fire extinguishing measures taken by the drone fire extinguishing module include dropping fire extinguishing bombs and fire extinguishing agents, and extinguishing the fire through the carried fire extinguisher.

[0038] The present application also discloses a large-scale UAV firefighting method, which is applied to a large-scale UAV firefighting system, including:

[0039] The image acquisition module collects image information from below the drone and sends the collected image information to the data analysis module;

[0040] The thermal imaging acquisition module collects thermal imaging information from below the drone and sends the collected thermal imaging information to the data analysis module;

[0041] The data analysis module receives all information, analyzes it, and outputs the analysis results;

[0042] The data transmission module transmits the analysis results and feedback information to the background control terminal;

[0043] The drone fire-fighting module takes corresponding fire-fighting measures based on the analysis results and feeds back risk level information to the data analysis module in real time;

[0044] The UAV power module provides the UAV with the power required for flight based on the analysis results, and feeds back the remaining power information to the data analysis module in real time.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. This application analyzes the collected image information and thermal imaging information in real time to quickly and accurately identify the fire area, and then combines the drone fire extinguishing module to take timely and effective fire extinguishing measures to improve the overall fire fighting effect. In addition, according to the severity of the fire, this application determines the current fire risk level through risk analysis, so that the backstage staff can understand the importance of the situation. At the same time, this application gives priority to the fire area with a faster spread speed to control the expansion of the fire and improve the fire fighting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a structural diagram of a large-scale UAV firefighting system;

[0048] Figure 2 The figure is a flow chart of a large-scale UAV firefighting method. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] As mentioned in the background technology of this application, research has found that the existing drone fire-fighting system is unable to identify fires more accurately, the overall fire-fighting effect is not good enough, and there are certain defects.

[0051] In order to solve the above-mentioned defects, the present application discloses a large-scale UAV fire-fighting system and method, which can more accurately identify fires, thereby improving the overall fire-fighting effect.

[0052] The following will describe in detail how the solution of the present application solves the above technical problems in conjunction with the accompanying drawings.

[0053] See also Figure 1In an embodiment of the present invention, a large-scale UAV firefighting system includes an image acquisition module, a thermal imaging acquisition module, a data analysis module, a data transmission module, a UAV firefighting module and a UAV power module; the image acquisition module is used to acquire image information below the UAV and send the acquired image information to the data analysis module; the thermal imaging acquisition module is used to acquire thermal imaging information below the UAV and send the acquired thermal imaging information to the data analysis module; the data analysis module is used to receive all information and analyze it, and output the analysis results; the data transmission module is used to transmit the analysis results and feedback information to the background control terminal; the UAV firefighting module is used to take corresponding firefighting measures according to the analysis results, and to feed back risk level information to the data analysis module in real time; the UAV power module is used to provide the UAV with the power required for flight according to the analysis results, and to feed back the remaining power information to the data analysis module in real time. The present application can more accurately identify fires, thereby improving the overall firefighting effect.

[0054] In this embodiment, the specific process of the data analysis module analyzing the information collected by the image acquisition module is as follows: performing noise reduction processing on the collected image (to improve image quality and reduce interference factors), and enhancing the contrast and brightness of the image (to make key features such as flames more prominent); adjusting the image to a size suitable for processing by a preset model (for subsequent feature extraction and recognition); extracting color features, texture features, shape features and flickering rules from the image, and then importing the extracted features into the preset model; the preset model performs real-time analysis on the collected image based on the imported features to determine whether there is a fire. Flames usually have a specific color range, such as red, orange and yellow. By analyzing the color distribution in the picture, it is possible to preliminarily determine whether there is a flame. The texture of the flame is dynamic and irregular, which contrasts sharply with the texture of the surrounding environment. By extracting the texture features of the picture, the existence of the flame can be further confirmed. The shape of the flame is usually irregular, but has certain similarities. The flame can be identified by calculating the characteristic parameters of the shape in the image (such as edges, corners, etc.). The flickering of the flame is one of its unique features. By detecting the frequency and regularity of the change of light intensity in the image, the flickering characteristics of the flame can be identified.

[0055] In this embodiment, the specific construction process of the preset model is as follows: the deep learning model is trained using previous fire image samples and non-fire image samples, so that the model learns the characteristics and patterns of fire images through training, and can accurately classify and identify new images; during the training process, the performance of the model is continuously evaluated, including indicators such as accuracy and recall rate; the recognition effect of the model is improved by adjusting the parameters and structure of the model; the trained model is deployed in the data analysis module, and the images collected by the image acquisition module are analyzed in real time. When a fire image is detected, the model outputs the analysis results in a timely manner. This setting can quickly and effectively train a model for identifying fires.

[0056] In this embodiment, the specific process of the data analysis module analyzing the information collected by the thermal imaging acquisition module is as follows: observing the collected thermal images, where the thermal images represent different temperature ranges in different colors (such as red, yellow, blue, etc.), and the high temperature areas are displayed in bright colors (such as red); analyzing the temperature distribution, that is, by analyzing the temperature distribution in the thermal image, determining whether there is an abnormally high temperature area; judging the fire situation, if there is an abnormally high temperature area in the thermal image, it means that there is a fire at present, and then judging the scale and spread trend of the fire based on the temperature distribution and changes in the thermal image. Fire identification through thermal imaging technology mainly depends on the infrared intensity and temperature distribution of the object. Thermal imaging instruments can capture and convert the infrared radiation energy on the surface of an object into a visual thermal image, thereby helping to identify a fire.

[0057] In this embodiment, the specific judgment process of the scale and spread trend of the fire is as follows: based on the size and temperature distribution of the high temperature area, the scale of the fire is preliminarily judged, wherein the larger the high temperature area and the higher the temperature, the larger the scale of the fire; observing the shape and boundary changes of the high temperature area, as well as the direction of the temperature gradient, the spread direction and speed of the fire are judged, and if the high temperature area is expanding or the temperature gradient points in a certain direction, it indicates that the fire is spreading. The temperature gradient is a physical quantity that describes how temperature changes with position in space. Specifically, it refers to the direction in which the temperature changes most rapidly with distance in a certain area and the rate of such change. This is a vector whose direction is usually defined as the direction of increasing temperature, and the unit is Celsius (or Fahrenheit) degrees per unit length, or Kelvin per meter (K / m) in the International System of Units.

[0058] In this embodiment, the specific analysis process of abnormally high temperature areas is as follows: all high temperature areas are marked in the thermal image, and the thermal image of the currently marked area is compared with the historical data of the same location to identify the area with abnormally high temperature. The area with abnormally high temperature is accurately identified by comparing the real-time data with the previous data.

[0059] In this embodiment, the specific process of the drone fire extinguishing module taking fire extinguishing measures and feeding back progress information is as follows: if the analysis results of the image information and the thermal imaging information of the data analysis module are both that there is a fire, then proceed to the next step; otherwise, the drone fire extinguishing module does not take fire extinguishing measures; mark the abnormally high temperature area in the thermal image as Ai, i=1···n, where n is a positive integer; mark the area of ​​the fire area corresponding to Ai as Bi, mark the temperature of the fire area corresponding to Ai as Ci, and the fire spread speed corresponding to Ai as Di; sort Ai from large to small according to Di to generate a priority list; let the drone fire extinguishing module take fire extinguishing measures for Ai according to the priority list, and the earlier Ai is in the priority list, the earlier the fire will be extinguished; based on Bi, Ci, Di , generate the fire radar map Pi corresponding to Ai, extract the area of ​​Pi and mark it as Qi; compare Qi with the preset value q, if Qi>q, then plan the Ai corresponding to Qi to the first sequence, if Qi≤q, then plan the Ai corresponding to Qi to the second sequence; count the number of Ai in the first sequence and mark it as w1, count the number of Ai in the second sequence and mark it as w2; calculate the current fire degree weight value V=(3*w1+w2), and mark the preset fire degree weight value as v; if V<v, the risk level information fed back to the data analysis module is the first level risk; if v≤V<2*v, the risk level information fed back to the data analysis module is the second level risk; if 2*v≤V, the risk level information fed back to the data analysis module is the third level risk. In terms of the severity of the fire, this application determines the risk level of the current fire through risk analysis, so as to facilitate the background staff to understand the importance of the situation. At the same time, this application gives priority to the fire area with a faster spread speed to control the expansion of the fire and improve the fire fighting effect.

[0060] In this embodiment, the fire extinguishing measures taken by the drone fire extinguishing module include dropping fire extinguishing bombs and fire extinguishing agents, and extinguishing the fire through the carried fire extinguisher.

[0061] like Figure 2 As shown, the present application also discloses a large-scale UAV fire-fighting method, which is applied to a large-scale UAV fire-fighting system, including: an image acquisition module acquires image information from below the UAV, and sends the acquired image information to a data analysis module; a thermal imaging acquisition module acquires thermal imaging information from below the UAV, and sends the acquired thermal imaging information to a data analysis module; the data analysis module receives all information and analyzes it, and outputs the analysis results; the data transmission module transmits the analysis results and feedback information to a background control terminal; the UAV fire-fighting module takes corresponding fire-fighting measures according to the analysis results, and feeds back risk level information to the data analysis module in real time; the UAV power module provides the UAV with the power required for flight according to the analysis results, and feeds back remaining power information to the data analysis module in real time.

[0062] The present invention analyzes the collected image information and thermal imaging information in real time, quickly and accurately identifies the fire area, and then combines the drone fire extinguishing module to take timely and effective fire extinguishing measures to improve the overall fire fighting effect. In addition, according to the severity of the fire, the present application determines the risk level of the current fire through risk analysis, so that the backstage staff can understand the urgency of the situation. At the same time, the present application gives priority to the fire area with a faster spread speed to control the expansion of the fire and improve the fire fighting effect.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0064] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A large-scale UAV fire-fighting system, characterized in that: It includes image acquisition module, thermal imaging acquisition module, data analysis module, data transmission module, UAV fire extinguishing module and UAV power module; The image acquisition module is used to acquire image information below the drone and send the acquired image information to the data analysis module; the thermal imaging acquisition module is used to acquire thermal imaging information below the drone and send the acquired thermal imaging information to the data analysis module; The data analysis module is used to receive and analyze all information and output the analysis results; the data transmission module is used to transmit the analysis results and feedback information to the background control terminal; the drone fire extinguishing module is used to take corresponding fire extinguishing measures according to the analysis results and feedback the risk level information to the data analysis module in real time; The UAV power module is used to provide the UAV with the power required for flight according to the analysis results, and to feed back the remaining power information to the data analysis module in real time; The specific process of the data analysis module analyzing the information collected by the image acquisition module is as follows: Perform noise reduction on the collected images and enhance the contrast and brightness of the images; Resize the image to a size suitable for the preset model processing; Extract color features, texture features, shape features and flickering patterns from the image, and then import the extracted features into the preset model; The preset model performs real-time analysis on the collected images based on the imported features to determine whether there is a fire; The specific construction process of the preset model is as follows: The deep learning model is trained using previous fire image samples and non-fire image samples. Through training, the model learns the characteristics and patterns of fire images and can accurately classify and identify new images. During the training process, the performance of the model is continuously evaluated, including the accuracy and recall rate indicators; the recognition effect of the model is improved by adjusting the parameters and structure of the model; The trained model is deployed to the data analysis module to perform real-time analysis on the images collected by the image acquisition module. When a fire image is detected, the model outputs the analysis results in a timely manner. The specific process of the data analysis module analyzing the information collected by the thermal imaging acquisition module is as follows: Observe the collected thermal images, where different colors represent different temperature ranges, and high temperature areas are displayed in bright colors; Analyze temperature distribution, that is, determine whether there is an abnormally high temperature area by analyzing the temperature distribution in the thermal image; Determine the fire situation. If there is an abnormally high temperature area in the thermal image, it means that there is a fire. Then, based on the temperature distribution and changes in the thermal image, determine the scale and spread trend of the fire. The specific judgment process of the scale and spread trend of the fire is as follows: preliminarily judge the scale of the fire based on the size and temperature distribution of the high temperature area, wherein the larger the high temperature area and the higher the temperature, the larger the scale of the fire; observe the shape and boundary changes of the high temperature area, as well as the direction of the temperature gradient, to judge the spread direction and speed of the fire, if the high temperature area is expanding or the temperature gradient points in a certain direction, it indicates that the fire is spreading; The specific analysis process of the abnormally high temperature area is as follows: marking all high temperature areas in the thermal image, comparing the thermal image of the currently marked area with the historical data of the same location to identify the area with abnormally high temperature; The specific process of the UAV fire extinguishing module taking fire extinguishing measures and feeding back progress information is as follows: If the analysis results of the image information and thermal imaging information of the data analysis module are both that there is a fire, then proceed to the next step, otherwise, the drone fire extinguishing module does not take any fire extinguishing measures; The abnormally high temperature area in the thermal image is marked as Ai, i = 1···n, where n is a positive integer; The area of ​​the fire region corresponding to the abnormally high temperature region Ai is marked as Bi, the temperature of the fire region corresponding to the abnormally high temperature region Ai is marked as Ci, and the fire spreading speed corresponding to the abnormally high temperature region Ai is marked as Di; Sort the abnormally high temperature areas Ai according to the fire spreading speed Di from large to small to generate a priority list; Instruct the drone fire extinguishing module to take fire extinguishing measures for the abnormally high temperature area Ai according to the priority list. The higher the abnormally high temperature area Ai is in the priority list, the earlier the fire will be extinguished. Based on the fire area Bi, the fire area temperature Ci, and the fire spread speed Di, a fire radar map Pi corresponding to the abnormally high temperature area Ai is generated, and the area of ​​the fire radar map Pi is extracted and marked as Qi; Compare Qi with the preset value q. If Qi>q, plan the abnormally high temperature area Ai corresponding to Qi to the first sequence. If Qi≤q, plan the abnormally high temperature area Ai corresponding to Qi to the second sequence. The number of abnormally high temperature regions Ai in the first sequence is counted and marked as w1, and the number of abnormally high temperature regions Ai in the second sequence is counted and marked as w2; Calculate the current fire severity weight value V=(3*w1+w2), and mark the preset fire severity weight value as v; If V<v, the risk level information fed back to the data analysis module is level one risk; If v≤V<2*v, the risk level information fed back to the data analysis module is level 2 risk; If 2*v≤V, the risk level information fed back to the data analysis module is level 3 risk.

2. A large-scale UAV fire fighting system according to claim 1, characterized in that: The fire-fighting measures taken by the drone fire-fighting module include dropping fire-fighting bombs and fire-fighting agents, as well as extinguishing fires through the onboard fire extinguishers.

3. A large-scale UAV fire-fighting method, characterized in that: A large-scale unmanned aerial vehicle fire fighting system as claimed in any one of claims 1 to 2, comprising: The image acquisition module collects image information from below the drone and sends the collected image information to the data analysis module; The thermal imaging acquisition module collects thermal imaging information from below the drone and sends the collected thermal imaging information to the data analysis module; The data analysis module receives all information, analyzes it, and outputs the analysis results; The data transmission module transmits the analysis results and feedback information to the background control terminal; The drone fire-fighting module takes corresponding fire-fighting measures based on the analysis results and feeds back risk level information to the data analysis module in real time; The UAV power module provides the UAV with the power required for flight based on the analysis results, and feeds back the remaining power information to the data analysis module in real time.

Images