Multifunctional emergency fire rescue front-end comprehensive support data processing method and system

Abstract

The present invention provides a multifunctional emergency fire rescue front-end comprehensive support data processing method and system, including: planning the flight route of the UAV based on the fire rescue area, controlling the UAV to fly according to the flight route; processing the pictures taken by the UAV, obtaining the object parameter information of the photographed area, and pre-modeling the object parameter information based on the modeling server; displaying the pre-modeling results on the front-end comprehensive support vehicle, and at the same time, sending them to the command end for display. It is used to quickly synthesize panoramic pictures, two-dimensional pictures and three-dimensional models and push the processed data to the rear commander.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency and fire rescue, and in particular to a multifunctional emergency fire rescue front-end comprehensive support data processing method and system. Background Art

[0002] At present, there are comprehensive support vehicles that rush to the disaster site as soon as possible to establish the necessary communication system support in the event of communication paralysis, road damage, power outage, etc. The vehicles can use the public network and private network to report the arrival to the command center, and can quickly use satellite communication to report, establish audio and video communication with the command center, build an on-site command center, and interconnect with the rear command center.

[0003] The problem is that the front-line comprehensive support vehicle can only establish communication with the rear at present, and the front-line personnel also need to collect emergency mapping data, including images, videos, panoramas, two-dimensional orthophotos, and three-dimensional oblique photography data. Through emergency mapping processing, it is convenient for on-site commanders and rear commanders to make decisions. With the existing technical means, only drones or cameras can be used to take photos and videos, and the collected data cannot be processed. Due to the limitations of satellite bandwidth and network disconnection at the disaster site, the front-line personnel have no way to send the collected data to the rear for processing. At the same time, the drone screen can only be viewed through the remote control's built-in screen or a separately configured display. The screen is too small, which is not convenient for commanders to command operations. Summary of the invention

[0004] The present invention provides a multifunctional emergency fire rescue forward comprehensive support data processing method and system, which is used to enable a forward comprehensive support vehicle to obtain information of a preset area, build a model based on the information, and reduce the amount of model data to facilitate sending to a rear command end.

[0005] The present invention provides a multifunctional emergency fire rescue front-end comprehensive support data processing method, comprising:

[0006] Step S101: planning a flight route for the UAV based on the geographic information of the fire rescue area, and controlling the UAV to fly along the flight route;

[0007] Step S102: Processing the pictures taken by the drone to obtain parameter information of objects in the photographed area, and pre-modeling the parameter information of the objects based on the modeling server;

[0008] Step S103: The pre-modeling result is displayed on the display screen of the forward integrated support vehicle, and at the same time, it is also sent to the command end for display.

[0009] Preferably, the multifunctional emergency fire rescue front-end comprehensive support data processing method, the step S101: planning the flight route of the drone based on the geographical information of the fire rescue area, also includes:

[0010] According to the satellite portable station, the position of the forward integrated support vehicle is located, and a communication range covering the fire rescue area is established with the forward integrated support vehicle as the center;

[0011] Obtaining the geographical distribution of the fire rescue area, and at the same time, communicating the geographical distribution to the forward integrated support vehicle to plan the route of the drone;

[0012] Wherein, the satellite portable station is arranged on the forward integrated support vehicle.

[0013] Preferably, the multifunctional emergency fire rescue front rush comprehensive support data processing method communicates and transmits the geographical distribution to the front rush comprehensive support vehicle to plan the UAV route, and also includes:

[0014] According to the geographical distribution, obtain the actual height of the fire rescue area;

[0015] According to the actual height and the shooting route, the optimal flight speed and the optimal shooting route of the drone are analyzed, and a flight route is generated;

[0016] The flight route is the planned UAV route.

[0017] Preferably, in the multifunctional emergency fire rescue forward integrated support data processing method, the satellite portable station is a communication base station, which is used to provide communication signals for the forward integrated support vehicle corresponding to the preset area.

[0018] Preferably, the multifunctional emergency fire rescue front-end comprehensive support data processing method, the step S102: processing the pictures taken by the drone to obtain parameter information of objects in the photographed area, also includes:

[0019] Processing the photographed pictures to generate a panoramic picture, a two-dimensional picture and a three-dimensional model of the flight area;

[0020] According to the flight route of the drone, the flight speed of the drone in the time period of the feature video is obtained, and the flight distance of the drone is determined according to the preset time length and the flight speed of the drone;

[0021] Determine the scaling ratio between the display screen of the same sub-video and the actual scene according to the flight distance of the drone and the displacement length from the first calibration point to the second calibration point;

[0022] The current object parameters are determined according to the display image, and then the actual object parameters are obtained according to the scaling ratio.

[0023] Preferably, the multifunctional emergency fire rescue front-end comprehensive support data processing method, the step S102: pre-modeling the object parameter information based on the modeling server, also includes:

[0024] Based on the photographed pictures, images of the fire rescue area in various directions corresponding to the same height are obtained to generate a panoramic image;

[0025] Extracting orthophotos of various locations in the fire rescue area from the captured images, fusing the orthophotos to generate a complete orthophoto, and correcting the complete orthophoto according to the object parameter information to generate a two-dimensional image;

[0026] Based on the oblique photography flight method, the image data collected from the front, back, left, right and bottom views are modeled to generate a three-dimensional model. At the same time, the panoramic image, two-dimensional image and three-dimensional model are set as display information for output display.

[0027] Preferably, the multifunctional emergency fire rescue front-end comprehensive support data processing method, the step S102: processing the video shot by the drone, also includes:

[0028] According to the video transmitted by the drone, the transmitted video is processed into frames to obtain a single frame image;

[0029] Based on the single-frame image, a single-frame image gradient value is obtained, and the single-frame image gradient value is judged to determine whether the single-frame image gradient value is lower than a preset gradient value threshold;

[0030] If it is not lower than, it is determined that the clarity of the captured video is normal;

[0031] If it is lower than, it is determined that the clarity of the captured video is too low, and the single frame image is detected to obtain the frequency band range of the single frame image noise, and a corresponding noise reduction function is matched based on the frequency band range;

[0032] Inputting the corresponding denoising function into a neural network model to train the denoising model, inputting the divided single-frame images into the denoising model one by one, and outputting the denoised single-frame images;

[0033] The noise-reduced single-frame images are combined to generate a noise-reduced shot video.

[0034] Preferably, the multifunctional emergency fire rescue front rush comprehensive support data processing method, after the step S103: displaying the pre-modeling result on the front rush comprehensive support vehicle, further comprises:

[0035] Based on the information displayed in different time periods, obtain the difference between the latest displayed information and the displayed information in the previous time period, and mark the difference;

[0036] Analyze the disaster scene in the displayed information and combine it with the difference annotation to predict the subsequent development after the disaster. According to the prediction results, establish a plan and make advance deployment for the subsequent development of the disaster;

[0037] Record all displayed information and the difference between two adjacent displayed information. After the rescue is completed, review the changes at the disaster site with previous plans to check whether there were any decision-making errors in the process.

[0038] Preferably, the multifunctional emergency fire rescue front-end comprehensive support data processing method, the step S103: sending to the back-end server, also includes:

[0039] According to the information type of the displayed information, a corresponding compression key is obtained from a cloud database, and based on the compression key, corresponding included information in the displayed information is compressed to obtain a compressed file;

[0040] Sending the compressed file and the compression key to the back-end server;

[0041] The back-end server receives the compressed file and the compression key, and decompresses the corresponding compressed file based on the compression key to obtain the corresponding first information;

[0042] Determine and judge the similarity between the first information and the same type of information received last time;

[0043] If the similarity between the first information and the same type of information received last time is greater than a preset value, it is determined that the displayed information is correct;

[0044] If the similarity between the first information and the same type of information received last time is less than a preset value, the displayed information is determined to be abnormal and a prompt is given.

[0045] A multifunctional emergency fire rescue front-end comprehensive support data processing system, further comprising:

[0046] Route customization module, which plans the flight route of the drone based on the geographical information of the fire rescue area and controls the drone to fly along the flight route;

[0047] The data processing module processes the pictures taken by the drone, obtains the parameter information of the objects in the photographed area, and pre-models the parameter information of the objects based on the modeling server;

[0048] The information transmission module displays the pre-modeling results on the forward integrated support vehicle and also sends them to the command end for display.

[0049] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0050] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0052] Figure 1 This is a flow chart of a multifunctional emergency fire rescue front-end comprehensive support data processing method in an embodiment of the present invention;

[0053] Figure 2 This is a flow chart of a multifunctional emergency fire rescue front-end comprehensive support data processing method in another embodiment of the present invention;

[0054] Figure 3 This is a flow chart of a multifunctional emergency fire rescue front-end comprehensive support data processing method in another embodiment of the present invention;

[0055] Figure 4 This is a flow chart of a multifunctional emergency fire rescue front-end comprehensive support data processing system in an embodiment of the present invention;

[0056] Figure 5 This is a structural diagram of a multifunctional emergency fire rescue forward comprehensive support data processing vehicle in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0058] Reference below Figures 1 to 5 To describe a multifunctional emergency fire rescue front-end comprehensive support data processing method and system proposed in an embodiment of the present invention.

[0059] Embodiment 1:

[0060] like Figure 1 As shown, the present invention provides a multifunctional emergency fire rescue front-end comprehensive support data processing method, comprising:

[0061] Step S101: planning a flight route for the UAV based on the geographic information of the fire rescue area, and controlling the UAV to fly along the flight route;

[0062] Step S102: Processing the pictures taken by the drone to obtain parameter information of objects in the photographed area, and pre-modeling the parameter information of the objects based on the modeling server;

[0063] Step S103: The pre-modeling result is displayed on the display screen of the forward integrated support vehicle, and at the same time, it is also sent to the command end for display.

[0064] In this embodiment, the fire rescue area is the main rescue area on the forward integrated support vehicle; the geographic information is the general term for the number, text, image and graph of the quantity, quality, nature, distribution characteristics, connection and regularity of the substances related to the geographical environment elements of the predicted area; the object parameter information is the real data of the objects in the preset area; the modeling server is a modeling computer with high-speed CPU computing power, long-term reliable operation, strong I / O external data throughput capacity and better scalability;

[0065] The beneficial effects of the above scheme are as follows: the present invention can generate a reasonable UAV flight route through the geographic information of the fire rescue area and control the UAV to take pictures according to the flight route, and model based on the taken pictures and the modeling server. The established model can be compressed and sent to the command end, which can achieve the functions of two-dimensional marking and wall mapping, real-time image transmission, three-dimensional data processing, and three-dimensional command platform, and has the functions of advance data processing, plan system and case review, and has strong practicality.

[0066] Embodiment 2:

[0067] Based on Example 1, the step S101: planning the flight route of the drone based on the geographical information of the fire rescue area, further includes:

[0068] According to the satellite portable station, the position of the forward integrated support vehicle is located, and a communication range covering the fire rescue area is established with the forward integrated support vehicle as the center;

[0069] Obtaining the geographical distribution of the fire rescue area, and at the same time, communicating the geographical distribution to the forward integrated support vehicle to plan the route of the drone;

[0070] Wherein, the satellite portable station is arranged on the forward integrated support vehicle.

[0071] In this embodiment, the satellite portable station is a portable micro base station that can provide communication signals;

[0072] The beneficial effects of the above scheme are as follows: the present invention can locate the advance comprehensive support vehicle through a portable satellite station in the wild without signals, and then obtain the geographic information of the location of the advance comprehensive support vehicle. The flight route of the UAV can be planned according to the geographic information to ensure the quality of the pictures taken by the UAV, and it has strong practicality.

[0073] Embodiment 3:

[0074] Based on the second embodiment, the geographical distribution is communicated and transmitted to the forward integrated support vehicle to plan the route of the drone, and further includes:

[0075] According to the geographical distribution, obtain the actual height of the fire rescue area;

[0076] According to the actual height and the shooting route, the optimal flight speed and the optimal shooting route of the drone are analyzed, and a flight route is generated;

[0077] The flight route is the planned UAV route.

[0078] In this embodiment, the flight route is a flight route with the best shooting quality generated based on the optimal flight speed of the drone and the optimal shooting route;

[0079] The beneficial effects of the above scheme are as follows: the flight route can be planned according to the actual height of the preset area to prevent the UAV from colliding due to flying at too low an altitude, thereby affecting normal work.

[0080] Embodiment 4:

[0081] Based on Example 2, the satellite portable station is a communication base station, which is used to provide communication signals in the corresponding preset area on the forward integrated support vehicle.

[0082] In this embodiment, the communication base station is a mobile communication base station, which is an interface device for mobile devices to access the Internet, and is also a form of radio station, which refers to a radio transceiver station that transmits information to mobile phone terminals through a mobile communication exchange center in a certain radio coverage area;

[0083] The beneficial effects of the above scheme are as follows: the present invention has its own satellite portable station, which can perform positioning and information transmission under no signal conditions, and can ensure that the acquired information is sent to the command end, which has strong practicality.

[0084] Embodiment 5:

[0085] Based on Example 1, Figure 2 As shown, the step S102: processing the picture taken by the drone to obtain parameter information of the object in the photographed area, also includes:

[0086] Processing the photographed pictures to generate a panoramic picture, a two-dimensional picture and a three-dimensional model of the flight area;

[0087] According to the flight route of the drone, the flight speed of the drone in the time period of the feature video is obtained, and the flight distance of the drone is determined according to the preset time length and the flight speed of the drone;

[0088] Determine the scaling ratio between the display screen of the same sub-video and the actual scene according to the flight distance of the drone and the displacement length from the first calibration point to the second calibration point;

[0089] The current object parameters are determined according to the display image, and then the actual object parameters are obtained according to the scaling ratio.

[0090] In this embodiment, the feature video is a video of a drone flying at a constant altitude; the first feature point is a relatively obvious special point obtained in the video, such as a landmark building, which is regarded as a special point as the first feature point; the second feature point is the position of the first feature point in the feature video after a period of time, which is the second feature point.

[0091] The beneficial effects of the above scheme are as follows: the present invention can generate a feature video, and obtain the first calibration point and the second calibration point in the feature video, obtain the displacement distance required from the first calibration point to the second calibration point, obtain the proportional relationship between the actual flight distance and the changing distance in the video, obtain the scaling ratio, and calculate the parameter information of the object in the photographed area according to the scaling ratio, which can effectively obtain the parameter information of the object in the photographed area and ensure the accuracy of modeling.

[0092] Embodiment 6:

[0093] Based on Example 1, Figure 3 As shown, the step S102: pre-modeling the object parameter information based on the modeling server, further includes:

[0094] Based on the photographed pictures, images of the fire rescue area in various directions corresponding to the same height are obtained to generate a panoramic image;

[0095] Extracting orthophotos of various locations in the fire rescue area from the captured images, fusing the orthophotos to generate a complete orthophoto, and correcting the complete orthophoto according to the object parameter information to generate a two-dimensional image;

[0096] Based on the oblique photography flight method, the image data collected from the front, back, left, right and bottom views are modeled to generate a three-dimensional model. At the same time, the panoramic image, two-dimensional image and three-dimensional model are set as display information for output display.

[0097] In this embodiment, a panoramic image is an image formed by stitching together multiple images taken by a camera rotating horizontally around an axis; a two-dimensional image refers to a planar image that does not contain depth information; a three-dimensional image is a special form of information expression, characterized by data in three dimensions in the expressed space;

[0098] The beneficial effects of the above scheme are as follows: the present invention can obtain images in various directions corresponding to the same height in the fire rescue area based on the taken pictures, generate a panoramic image, and obtain a two-dimensional image based on the bird's-eye view, and then fill in the coordinate sets of each layered model based on the parameter information of the panoramic image and the two-dimensional image to generate a three-dimensional model. The panoramic image, the two-dimensional image and the three-dimensional model can be quickly obtained, the accuracy of the generated model can be ensured, and it is convenient to obtain real information and generate a solution based on the model.

[0099] Embodiment 7:

[0100] Based on Example 1, the step S102: processing the video shot by the drone, further includes:

[0101] According to the video transmitted by the drone, the transmitted video is processed into frames to obtain a single frame image;

[0102] Based on the single-frame image, a single-frame image gradient value is obtained, and the single-frame image gradient value is judged to determine whether the single-frame image gradient value is lower than a preset gradient value threshold;

[0103] If it is not lower than, it is determined that the clarity of the captured video is normal;

[0104] If it is lower than, it is determined that the clarity of the captured video is too low, and the single frame image is detected to obtain the frequency band range of the single frame image noise, and a corresponding noise reduction function is matched based on the frequency band range;

[0105] Inputting the corresponding denoising function into a neural network model to train the denoising model, inputting the divided single-frame images into the denoising model one by one, and outputting the denoised single-frame images;

[0106] The noise-reduced single-frame images are combined to generate a noise-reduced shot video.

[0107] In this embodiment, the frame division process is to divide the continuous frames of the video into single-frame images; the single-frame image is the image in the video corresponding to the single frame; the gradient value of the single-frame image is the speed image of the image change;

[0108] The beneficial effects of the above scheme are as follows: the present invention can perform frame processing on the video shot by the drone to obtain a single-frame image, and perform clarity detection on the single-frame image. When the image clarity is too low, the video is subjected to noise reduction processing to enhance its clarity, which can effectively avoid the situation where some objects are not clearly observed due to the low clarity of the shot video, resulting in errors in the model data.

[0109] Embodiment 8:

[0110] Based on the embodiment 1, the step S103: after displaying the pre-modeling result on the forward integrated support vehicle, further includes:

[0111] Based on the information displayed in different time periods, obtain the difference between the latest displayed information and the information displayed in the previous time period, and mark the difference;

[0112] Analyze the disaster scene in the displayed information and combine it with the difference annotation to predict the subsequent development after the disaster. According to the prediction results, establish a plan and make advance deployment for the subsequent development of the disaster;

[0113] Record all displayed information and the difference between two adjacent displayed information. After the rescue is completed, review the changes at the disaster site with previous plans to check whether there were any decision-making errors in the process.

[0114] In this embodiment, the last time period display information is the display information generated according to the drone shooting picture in the last time period; the replay is to reproduce the entire rescue process;

[0115] The beneficial effects of the above scheme are as follows: the difference between the two displayed information can be marked, so that the user can intuitively feel the environmental changes in the preset area, and predict the subsequent development based on the marked information, establish an emergency plan, and facilitate timely rescue when the prediction is realized. The various information can be recorded for easy review, to check whether there are any decision-making errors and correct them.

[0116] Embodiment 9:

[0117] Based on Example 1, the process of step S103: sending to the backend server further includes:

[0118] According to the information type of the displayed information, a corresponding compression key is obtained from a cloud database, and based on the compression key, corresponding included information in the displayed information is compressed to obtain a compressed file;

[0119] Sending the compressed file and the compression key to the back-end server;

[0120] The back-end server receives the compressed file and the compression key, and decompresses the corresponding compressed file based on the compression key to obtain the corresponding first information;

[0121] Determine and judge the similarity between the first information and the same type of information received last time;

[0122] If the similarity between the first information and the same type of information received last time is greater than a preset value, it is determined that the displayed information is correct;

[0123] If the similarity between the first information and the same type of information received last time is less than a preset value, the displayed information is determined to be abnormal and a prompt is given.

[0124] In this embodiment, the first information is information obtained by decompressing a compressed file, and its content is a panoramic image, a two-dimensional image, and a three-dimensional model of the fire rescue area obtained based on the real-time situation of the fire rescue area photographed by the drone; the same type of information is information that is consistent with the first information category but obtained at a different time. ;

[0125] The beneficial effects of the above scheme are as follows: the present invention can obtain the first information by decompressing the compressed file according to the secret key, and judge whether there is any abnormality in the information according to the similarity between the first information and the same type of information received last time, thereby avoiding unnecessary losses caused by data abnormality due to information transmission failure.

[0126] Embodiment 10:

[0127] like Figure 4 As shown, a multifunctional emergency fire rescue front-end comprehensive support data processing system includes:

[0128] Route customization module, which plans the flight route of the drone based on the geographical information of the fire rescue area and controls the drone to fly along the flight route;

[0129] The data processing module processes the pictures taken by the drone, obtains the parameter information of the objects in the photographed area, and pre-models the parameter information of the objects based on the modeling server;

[0130] The information transmission module displays the pre-modeling results on the forward integrated support vehicle and also sends them to the command end for display.

[0131] The pre-modeling result is displayed on the front-end comprehensive support vehicle by displaying the pre-modeling result on the display screen on the left side of the front-end comprehensive support vehicle. The specific location of the display screen is as follows: Figure 5 shown.

[0132] In this embodiment, the fire rescue area is the main rescue area on the forward integrated support vehicle; the geographic information is the geographical environment elements of the predicted area; the object parameter information is the real data of the objects in the preset area; the modeling server is a modeling computer with high-speed CPU computing power, long-term reliable operation, strong I / O external data throughput capacity and better scalability;

[0133] The beneficial effects of the above scheme are as follows: the present invention can customize the flight route of the UAV according to the route customization module, control the UAV to fly and shoot along the specified route, and process the captured pictures based on the data processing module, and perform modeling, and display the established model and send the established model to the command end by the information transmission module. The rear commander can intuitively feel the real situation of the preset area based on the model, which is convenient for formulating a rescue plan.

[0134] 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.

Claims

1. A multifunctional emergency fire rescue front-end comprehensive support data processing method, characterized in that: Also includes: Step S101: planning a flight route for the UAV based on the fire rescue area, and controlling the UAV to fly along the flight route; Step S102: Processing the pictures taken by the drone to obtain parameter information of objects in the photographed area, and pre-modeling the parameter information of the objects based on the modeling server; Step S103: Display the pre-modeling result on the display screen of the forward integrated support vehicle, and at the same time, send it to the command end for display; Wherein, step S102: pre-modeling the object parameter information based on the modeling server, further comprising: Based on the photographed pictures, images of the fire rescue area in various directions corresponding to the same height are obtained to generate a panoramic image; Extracting orthophotos of various locations in the fire rescue area from the captured images, fusing the orthophotos to generate a complete orthophoto, and correcting the complete orthophoto according to the object parameter information to generate a two-dimensional image; Based on the oblique photography flight mode, the image data collected from the front, back, left, right and bottom views are modeled to generate a three-dimensional model. At the same time, the panoramic image, two-dimensional image and three-dimensional model are set as display information for output display; After the step S103: displaying the pre-modeling result on the forward integrated support vehicle, the following further comprises: Based on the information displayed in different time periods, obtain the difference between the latest displayed information and the information displayed in the previous time period, and mark the difference; Analyze the disaster scene in the displayed information and combine it with the difference annotation to predict the subsequent development after the disaster. According to the prediction results, establish a plan and make advance deployment for the subsequent development of the disaster; Record all displayed information and the difference between two adjacent displayed information. After the rescue is completed, review the changes at the disaster site and the previous plan to check whether there are any decision-making errors in the process; The process of step S103: sending to the back-end server also includes: According to the information type of the displayed information, a corresponding compression key is obtained from a cloud database, and based on the compression key, corresponding included information in the displayed information is compressed to obtain a compressed file; Sending the compressed file and the compression key to the back-end server; The back-end server receives the compressed file and the compression key, and decompresses the corresponding compressed file based on the compression key to obtain the corresponding first information; Determine and judge the similarity between the first information and the same type of information received last time; If the similarity between the first information and the same type of information received last time is greater than a preset value, it is determined that the displayed information is correct; If the similarity between the first information and the same type of information received last time is less than a preset value, the displayed information is determined to be abnormal and a prompt is given.

2. A multifunctional emergency fire rescue front-end comprehensive support data processing method according to claim 1, characterized in that: The step S101: planning the flight route of the drone based on the geographical information of the fire rescue area, further includes: According to the satellite portable station, the position of the forward integrated support vehicle is located, and a communication range covering the fire rescue area is established with the forward integrated support vehicle as the center; Obtaining the geographical distribution of the fire rescue area, and at the same time, communicating the geographical distribution to the forward integrated support vehicle to plan the route of the drone; Wherein, the satellite portable station is arranged on the forward integrated support vehicle.

3. A multifunctional emergency fire rescue front-end comprehensive support data processing method according to claim 2, characterized in that: The geographical distribution is communicated and transmitted to the forward integrated support vehicle to plan the route of the UAV, and further includes: According to the geographical distribution, obtain the actual height of the fire rescue area; According to the actual height and the shooting route, the optimal flight speed and the optimal shooting route of the drone are analyzed, and a flight route is generated; The flight route is the planned UAV route.

4. A multifunctional emergency fire rescue front-end comprehensive support data processing method according to claim 2, characterized in that: The satellite portable station is a communication base station, which is used to provide communication signals for the preset area corresponding to the forward integrated support vehicle.

5. A multifunctional emergency fire rescue front-end comprehensive support data processing method according to claim 1, characterized in that: The step S102: processing the image taken by the drone to obtain parameter information of objects in the photographed area, further includes: Processing the captured images to generate a panoramic image, a two-dimensional image, and a three-dimensional model of the flight area; According to the flight route of the drone, the flight speed of the drone in the time period of the feature video is obtained, and the flight distance of the drone is determined according to the preset time length and the flight speed of the drone; Determine the scaling ratio between the display screen of the same sub-video and the actual scene according to the flight distance of the drone and the displacement length from the first calibration point to the second calibration point; The current object parameters are determined according to the display image, and then the actual object parameters are obtained according to the scaling ratio.

6. A multifunctional emergency fire rescue front-end comprehensive support data processing method according to claim 1, characterized in that: The step S102: processing the pictures taken by the drone, further includes: According to the video transmitted by the drone, the transmitted video is processed into frames to obtain a single frame image; Based on the single-frame image, a single-frame image gradient value is obtained, and the single-frame image gradient value is judged to determine whether the single-frame image gradient value is lower than a preset gradient value threshold; If it is not lower than, it is determined that the clarity of the captured video is normal; If it is lower than, it is determined that the clarity of the captured video is too low, and the single frame image is detected to obtain the frequency band range of the single frame image noise, and a corresponding noise reduction function is matched based on the frequency band range; Inputting the corresponding denoising function into a neural network model to train the denoising model, inputting the divided single-frame images into the denoising model one by one, and outputting the denoised single-frame images; The noise-reduced single-frame images are combined to generate a noise-reduced shot video.

7. A multifunctional emergency fire rescue front-end comprehensive support data processing system, characterized in that: Also includes: Route customization module, which plans the flight route of the drone based on the geographical information of the fire rescue area and controls the drone to fly along the flight route; The data processing module processes the pictures taken by the drone, obtains the parameter information of the objects in the photographed area, and pre-models the parameter information of the objects based on the modeling server; The information transmission module displays the pre-modeling results on the forward integrated support vehicle and sends them to the command end for display; The method of pre-modeling the object parameter information by the data processing module based on the modeling server includes: Based on the photographed pictures, images of the fire rescue area in various directions corresponding to the same height are obtained to generate a panoramic image; Extracting orthophotos of various locations in the fire rescue area from the captured images, fusing the orthophotos to generate a complete orthophoto, and correcting the complete orthophoto according to the object parameter information to generate a two-dimensional image; Based on the oblique photography flight mode, the image data collected from the front, back, left, right and bottom views are modeled to generate a three-dimensional model. At the same time, the panoramic image, two-dimensional image and three-dimensional model are set as display information for output display; After the pre-modeling results are displayed on the forward integrated support vehicle through the information transmission module, it includes: Based on the information displayed in different time periods, obtain the difference between the latest displayed information and the information displayed in the previous time period, and mark the difference; Analyze the disaster scene in the displayed information and combine it with the difference annotation to predict the subsequent development after the disaster. According to the prediction results, establish a plan and make advance deployment for the subsequent development of the disaster; Record all displayed information and the difference between two adjacent displayed information. After the rescue is completed, review the changes at the disaster site and the previous plan to check whether there are any decision-making errors in the process; The process of sending to the backend server through the information transmission module includes: According to the information type of the displayed information, a corresponding compression key is obtained from a cloud database, and based on the compression key, corresponding included information in the displayed information is compressed to obtain a compressed file; Sending the compressed file and the compression key to the back-end server; The back-end server receives the compressed file and the compression key, and decompresses the corresponding compressed file based on the compression key to obtain the corresponding first information; Determine and judge the similarity between the first information and the same type of information received last time; If the similarity between the first information and the same type of information received last time is greater than a preset value, it is determined that the displayed information is correct; If the similarity between the first information and the same type of information received last time is less than a preset value, the displayed information is determined to be abnormal and a prompt is given.

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