Fire scene real-time monitoring and commanding method assisted by unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle assistance, and discloses an unmanned aerial vehicle assisted fire scene real-time monitoring and commanding method, which comprises the following steps: acquiring fire disaster situation data of a fire scene in real time through equipment such as a high-definition camera and a red thermal imager of an unmanned aerial vehicle, and outputting the fire disaster situation data to a commanding center; analyzing the fire behavior development direction, the fire source position and the smoke concentration of the fire disaster situation data; evaluating a fire scene scale and a dangerous area according to an analysis result, forming a rescue scheme according to an evaluation result, and commanding firefighters and a fire extinguishing unmanned aerial vehicle based on the rescue scheme; dynamically adjusting a rescue scheme according to the fire disaster situation data of the fire scene collected by the unmanned aerial vehicle in real time; when the fire is extinguished, the blind fire area is continuously monitored, and if abnormity occurs, an alarm is given out immediately; the unmanned aerial vehicle serves as an air base station for communication. According to the invention, instant and comprehensive fire scene information is provided for a command center, and rapid decision making is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) assisted technology, and in particular to a UAV assisted real-time fire scene monitoring and command method. Background Art

[0002] UAV-assisted real-time monitoring and command of fire scenes refers to the use of UAV technology to conduct real-time monitoring, data collection, and command and dispatch of fire scenes to improve the efficiency of fire response and the scientific nature of decision-making; UAVs are equipped with sensors such as high-definition cameras and infrared thermal imaging equipment, and can quickly fly to the fire scene, monitor the development of the fire, the location of the fire source, and the density of smoke in real time, and transmit the data to the command center to provide key support for fire command. UAVs can be used as an aerial command platform to help commanders grasp the dynamics of the fire scene, command firefighting operations, and adjust rescue strategies through real-time transmission of images and videos, so as to achieve close coordination between the air and the ground. In the case of damage to communication infrastructure, UAVs can be used as temporary communication relay stations to restore communication functions in the disaster area and ensure the timely transmission of rescue information. UAVs can carry fire-fighting bombs, rescue supplies and other equipment, and quickly reach areas that are difficult for manpower to reach to deliver supplies and provide rescue support.

[0003] Patent application number 202411620033.6 Ducted UAV and fire truck for firefighting. Among them, the ducted UAV for firefighting includes: a first support beam extending longitudinally, and at least three ducted propulsion systems are arranged on both sides of the width direction perpendicular to the length direction of the first support beam; a fire water cannon is arranged on the first support beam, and the fire water cannon can spray fire extinguishing agent. Although it can pass through narrow areas while meeting the power load, so as to improve the adaptability of the ducted UAV used for firefighting; however, the system integration and collaborative work are insufficient, resulting in insufficient data fusion and processing capabilities between the UAV and the ground, and the information transmission is not timely and accurate enough.

[0004] Patent application number 202411575482.3 discloses a park monitoring, early warning, and firefighting and rescue system based on air-ground collaboration, which relates to the field of park monitoring, early warning, and firefighting and rescue technology. In order to solve the problem of untimely discovery of park fires and uncontrollable fires, the present invention can be used in daily patrol work through drones. If a fire is found during daily patrols in the monitoring area, the fire is extinguished according to the same steps, and data is exchanged with the fire truck. The drone and the fire truck form a collaborative combat mechanism to achieve information sharing and rapid response, improve overall rescue efficiency, and intelligently select and optimize transmission channels. Although it can ensure that abnormal warning data is transmitted to the fire rescue center in the shortest time, it provides a strong guarantee for the timely discovery and rapid response of fires. Through real-time monitoring and abnormal warning mechanisms, the system can timely discover and deal with safety hazards in the park, ensuring the safe and stable operation of the park; however, the limitations of communication and data transmission are relatively low, which does not result in the inability to transmit in real time, resulting in low efficiency.

[0005] Patent application No. 202411550701.2 relates to an intelligent firefighting system based on drones. Inspired by drone plant protection systems, aircraft aerial refueling technology, aircraft carrier systems and spacecraft docking system technologies, an intelligent firefighting system based on drones is designed. The system consists of a drone and a matching fire truck, that is, a fire truck loaded with fire water or fire powder foam is used as a drone carrier. The drone flies to the fire fighting position to spray fire water or fire powder foam after the fire truck pumps fire water or fire powder foam. After the spraying is completed, it flies back to the fire truck and lands at a specific location. The system instructs the fire truck to deliver fire water or fire powder foam to the drone, and at the same time, the system instructs the fire truck to deliver the electricity or fuel required by the drone power system to the drone. Repeatedly, until the purpose of firefighting is achieved; however, the lack of system integration and collaborative work leads to insufficient data fusion and processing capabilities between the drone and the ground, resulting in insufficient timely and accurate information transmission.

[0006] At present, there are limitations in communication and data transmission, which lead to the inability to transmit in real time and insufficient data fusion and processing capabilities between UAVs and the ground. The present invention provides a UAV-assisted real-time monitoring and command method for fire scenes. Summary of the invention

[0007] The main purpose of the present invention is to provide a UAV-assisted real-time monitoring and command method for fire scenes, so as to solve the problems in the prior art of low communication and data transmission limitations resulting in the inability to transmit in real time and insufficient data fusion and processing capabilities between the UAV and the ground.

[0008] To achieve the above object, the present invention provides the following technical solutions: A method for real-time monitoring and commanding of a fire scene assisted by a drone, comprising: The fire disaster data at the fire scene is collected in real time through the equipment, and the fire disaster data is output to the command center; Analyze the fire development direction, fire source location and smoke concentration of fire disaster data; evaluate the fire scale and dangerous area based on the analysis results, formulate a rescue plan based on the evaluation results, and command firefighters and fire-fighting drones based on the rescue plan; The rescue plan is dynamically adjusted based on the fire disaster data collected by drones in real time at the fire scene; when the fire is extinguished, the dark fire area continues to be monitored and an alarm is issued if any abnormality occurs.

[0009] As a further improvement of the present invention, the process of outputting the fire disaster data to the command center includes the following steps: The fire images, temperature distribution and smoke density fire disaster data of the fire scene are collected in real time through the high-definition camera and red thermal imager of the drone, and the fire images, temperature distribution and smoke density fire disaster data are analyzed; According to the results of fire disaster analysis, the fire point, trajectory and moving speed are identified and tracked, and the image frame sequence is extracted from the video stream collected by the drone. By setting the color threshold, the potential fire point is extracted from the image frame; A subsequence containing the fire point is extracted from the image frame sequence, and the fire point, fire point location, fire point size and confidence are identified based on the subsequence of the fire point; based on the difference in the fire point position of adjacent image frames in the subsequence, the moving speed and direction of the fire point are calculated, and the calculation results are uploaded to the command center.

[0010] As a further improvement of the present invention, the process of commanding firefighters and fire-fighting drones based on the rescue plan includes the following steps: Determine the real-time environmental characteristics based on the calculation results of the fire starting point moving speed and moving direction, and divide the fire area into risk categories based on the real-time environmental characteristics and the calculation results of the fire starting point moving speed and moving direction; Determine multiple subset target areas, where multiple subset target areas are marked with risk factors; use multiple subset target areas as mapping targets and select and mark them with fire disaster data; use regional spread and regional risk as evaluation indicators; Adjustments and assessments are made based on the actual fire disaster situation updates. When the assessment results meet the retrieval threshold, a dispatch adjustment instruction is generated. As the dispatch adjustment instruction is received, the decision is corrected based on the actual disaster situation updates.

[0011] As a further improvement of the present invention, the process of risk division of the fire area includes the following steps: According to the moving speed and direction of the fire starting point, the physical laws of fire spread are captured, a dynamic model of fire spread is constructed, and the environmental characteristics of the fire area are updated in real time; According to the dynamic model of fire spread, the fire area is divided into multiple subset target areas, and each subset target area is assigned a different hazard factor according to its environmental characteristics; the hazard factor is based on the fire spread speed, direction and potential risk factors in the area; In the divided subset target area, the spread degree and risk degree of each area are calculated; the spread degree reflects the spreading speed and trend of the fire in the area, and the risk degree comprehensively evaluates the potential threat of the area to people and the environment.

[0012] As a further improvement of the present invention, the process of updating the disaster situation and making decision corrections includes the following steps: Generate a deployment decision plan based on the evaluation results, determine at least one dispatch execution instruction and at least one dispatch command instruction information, and adjust the meaning of the instruction to correspond to the dispatch command information; locate the terminal device of the rescue personnel, and use the drone as an air base station to send the dispatch execution instruction to the matching terminal mobile device; Based on the deployment decision plan, extract the evacuation deployment information and perform high-altitude command management on the evacuation deployment information; iterate the deployment plan to determine new dispatch execution instructions, new dispatch command instruction information and new evacuation deployment information; Fire dispatch management is carried out in combination with new dispatch execution instructions, new dispatch command instruction information, new evacuation deployment information and adjustment deviation information, and high-altitude command management is performed on the adjustment deviation information.

[0013] As a further improvement of the present invention, the process of generating a deployment decision plan based on the evaluation result includes the following steps: Analyze the fire disaster assessment results and extract key indicators, including regional spread, regional risk, fire point movement speed and direction, to form structured information; generate a data table containing timestamp, regional number, risk level, spread speed and spread direction fields; According to the risk priority classification, firefighters are deployed to high-risk areas first; according to the real-time changes in the spread of fire, the deployment location of firefighters is dynamically adjusted; according to the risk priority classification, the action route of the fire-fighting drone is generated to ensure that it can cover the high-risk areas; Generate deployment decision plans for the deployment locations of firefighters and the action routes of fire-fighting drones.

[0014] As a further improvement of the present invention, the process of determining new dispatch execution instructions, new dispatch command information and new evacuation deployment information includes the following steps: Collect fire spread data in real time through drones, sensor networks and terminal devices, including key indicators such as regional spread, regional risk, and the speed and direction of fire point movement; compare the real-time collected disaster data with the initial assessment results, extract change features, and generate an updated data table containing timestamp, area number, risk level, spread speed and spread direction fields; Based on the updated disaster data, regional risk priorities are reclassified to identify new high-risk areas and potential spread paths. According to the results of the re-prioritization, the deployment positions of firefighters are adjusted to ensure that they give priority to covering new high-risk areas. At the same time, the action routes of firefighting drones are replanned. Based on the resource reallocation strategy, new dispatch execution instructions are generated to clarify the specific action tasks of firefighters and drones; based on the updated disaster data and resource reallocation strategy, new dispatch command information is generated, including action priorities, task allocation logic and real-time adjustment mechanism; based on the updated disaster data, evacuation deployment information is re-extracted to clarify the evacuation path, evacuation priority and evacuation resource allocation plan.

[0015] As a further improvement of the present invention, the process of executing high-altitude command management for adjusting deviation information includes the following steps: By monitoring the movement trajectories of firefighters and drones in real time, we can identify deviations from dispatch execution instructions and evacuation deployment information; Generate adjustment instructions based on deviation information to dynamically correct the action trajectories of firefighters and drones; The adjusted dispatch execution instructions, dispatch command information and evacuation deployment information are used as new inputs to reintegrate the real-time updated data of the disaster situation and carry out the next round of priority re-division, resource reallocation and instruction generation.

[0016] As a further improvement of the present invention, the process of dynamically adjusting the rescue plan based on the fire disaster data collected by the drone in real time at the fire scene includes the following steps: Based on the real-time collected fire disaster data, the overall situation of the fire scene is dynamically evaluated, including the speed of fire spread, temperature change trend and smoke diffusion range; combined with the fire situation assessment results, the deficiencies or deviations in the current rescue plan are identified; Based on the fire situation assessment results, the rescue priorities are re-divided, and the action deployment of firefighters and firefighting drones is adjusted according to the priorities; according to the fire situation and priority adjustment results, firefighters, firefighting drones and rescue resources are dynamically deployed; based on the adjusted rescue plan, new dispatch execution instructions and command information are generated; The adjusted rescue plan is used as a new input, and the real-time collected fire disaster data is reintegrated to form a closed-loop feedback mechanism; through closed-loop feedback, the decision-making logic and resource allocation strategy of the rescue plan are continuously optimized.

[0017] As a further improvement of the present invention, the process of identifying deficiencies or deviations in the current rescue scheme includes the following steps: Through real-time collected fire spread data, the spread trend of fire in different areas and directions can be identified; based on the real-time changes in temperature data, whether the heat distribution of the fire scene is uniform and whether there are local high-temperature areas can be evaluated; through smoke diffusion data, the impact of smoke on the visibility of rescue personnel and equipment can be evaluated; Compare the deployment of firefighters, firefighting drones and other resources in the current rescue plan with the fire situation assessment results; identify whether there are areas with insufficient or wasted resources, and determine whether the resource deployment meets the actual needs of fire spread, temperature changes and smoke diffusion; compare the action paths of firefighters and drones in the current rescue plan with the dynamic changes of the fire scene; identify whether there are situations where the action path is blocked, the action efficiency is low, or the action direction deviates from the core area of ​​the fire scene; compare the rescue goals set in the current rescue plan with the fire situation assessment results, and identify whether there are situations where the rescue goals have not been achieved or the priority setting is unreasonable; Based on the comparison results, identify whether there are situations where rescue resources fail to cover the core or key areas of the fire scene, and determine whether there are problems with delayed resource allocation or uneven resource distribution; based on the comparison results, identify whether there are situations where firefighters or drones are inefficient, and determine whether there are problems with unreasonable action path planning, untimely communication of action instructions, or insufficient action coordination; based on the comparison results, identify whether there are situations where rescue goals cannot be achieved or priority settings are unreasonable, and determine whether there is a problem of disconnection between rescue goals and the actual situation at the fire scene.

[0018] The present invention uses high-definition cameras and red thermal imagers of drones to collect fire scene images, temperature distribution, smoke density and other fire disaster data in real time, and outputs these data to the command center; it can quickly obtain key information of the fire scene; the high-definition camera and red thermal imager provide high-quality images and temperature data; cover data of multiple dimensions such as fire scene images, temperature distribution and smoke density; analyze the fire development direction, fire source location and smoke concentration of the fire disaster data; evaluate the fire scale and dangerous area according to the analysis results, form a rescue plan, and command firefighters and fire-fighting drones; through in-depth analysis of the disaster data, it can accurately judge the fire and dangerous area; quickly form a rescue plan based on the analysis results; can command firefighters and drones to carry out rescue in real time; dynamically adjust the rescue plan according to the fire disaster data collected by the drone in real time; continue to monitor the dark fire area after the fire is extinguished, and immediately issue an alarm if an abnormality occurs; the drone communicates as an aerial base station; can flexibly adjust the rescue plan according to real-time data; can still maintain monitoring of the fire scene after the fire is extinguished; the drone provides stable communication support as an aerial base station. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the steps of an embodiment of the method for real-time monitoring and commanding of a fire scene assisted by a drone of the present invention; Figure 2 A flowchart showing the steps of outputting fire disaster data to a command center in an embodiment of the method for real-time monitoring and commanding a fire scene assisted by a drone of the present invention; Figure 3 A schematic flow chart of the steps of commanding firefighters and fire-fighting drones based on a rescue plan in one embodiment of a method for real-time monitoring and commanding a fire scene assisted by a drone of the present invention; Figure 4 A flowchart showing the steps of dynamically adjusting a rescue plan based on fire disaster data collected by a drone at a fire scene in real time in one embodiment of a method for real-time monitoring and commanding a fire scene assisted by a drone of the present invention; Figure 5 It is a structural schematic diagram of an embodiment of an electronic device of the present invention; Figure 6 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0022] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] like Figure 1 As shown, this embodiment provides an embodiment of a method for real-time monitoring and commanding a fire scene assisted by a drone. In this embodiment, the method for real-time monitoring and commanding a fire scene assisted by a drone specifically includes the following steps: Step S1: using the high-definition camera and red thermal imager of the drone to collect fire scene images, temperature distribution, smoke density and other fire disaster data in real time, and output the fire disaster data to the command center; Step S2: Analyze the fire development direction, fire source location, and smoke density of the fire disaster data; evaluate the fire scale and dangerous area based on the analysis results, form a rescue plan based on the evaluation results, and command firefighters and fire-fighting drones based on the rescue plan; Step S3: dynamically adjust the rescue plan based on the fire disaster data collected by the drone in real time at the fire scene; when the fire is extinguished, continue to monitor the dark fire area and immediately issue an alarm if any abnormality occurs; the drone acts as an aerial base station for communication.

[0024] Preferably, in step S1 of this embodiment, the fire scene image, temperature distribution, smoke density and other fire disaster data are collected in real time by the high-definition camera and red thermal imager of the drone and other equipment, and the data are output to the command center; the key information of the fire scene can be quickly obtained; the high-definition camera and red thermal imager provide high-quality images and temperature data; the data of multiple dimensions such as fire scene image, temperature distribution and smoke density are covered; step S2 analyzes the fire development direction, fire source location and smoke concentration of the fire disaster data; the fire scale and dangerous area are evaluated according to the analysis results, a rescue plan is formed, and firefighters and fire-fighting drones are commanded; through in-depth analysis of the disaster data, the fire and dangerous area can be accurately judged; a rescue plan is quickly formed based on the analysis results; firefighters and drones can be commanded to perform rescue in real time; step S3 dynamically adjusts the rescue plan according to the fire disaster data collected by the drone in real time; continues to monitor the dark fire area after the fire is extinguished, and immediately issues an alarm if an abnormality occurs; the drone communicates as an aerial base station; the rescue plan can be flexibly adjusted according to real-time data; the fire scene can still be monitored after the fire is extinguished; the drone provides stable communication support as an aerial base station.

[0025] In summary, this embodiment provides the command center with real-time and comprehensive fire scene information, which helps to make quick decisions; improves the speed and accuracy of fire emergency response; optimizes the rescue process and improves rescue efficiency; reduces the risk of casualties and property losses; ensures the continued effectiveness and safety of rescue operations; and improves the overall effectiveness of fire emergency response.

[0026] Furthermore, if Figure 2 As shown, the process of outputting the fire disaster data to the command center in step S1 specifically includes the following steps: Step S11: using the high-definition camera and red thermal imager of the drone to collect fire images, temperature distribution, smoke density and other fire disaster data at the fire scene in real time, and analyzing the fire images, temperature distribution, smoke density and other fire disaster data; Step S12: Identify and track the fire point, trajectory and moving speed according to the results of the fire disaster analysis, extract image frame sequences from the video stream collected by the drone, and extract potential fire points from the image frames by setting color thresholds; Step S13: extract a subsequence containing the fire point from the image frame sequence, identify the fire point, the fire point position, the fire point size and the confidence level according to the subsequence of the fire point; calculate the moving speed and direction of the fire point based on the difference in the fire point position of adjacent image frames in the subsequence, and upload the calculation results to the command center.

[0027] Among them, the comprehensive formula for fire disaster data is:

[0028] In the formula, Represents the comprehensive value of fire disaster data, indicating the location and time The comprehensive fire status; Indicates the fire image analysis results; Indicates the temperature distribution calculation results; Indicates the smoke density calculation result; Indicates the fire point extraction result; It represents the fitting result of the fire point trajectory; Step S11: Fire image analysis formula for fire disaster data collection and analysis:

[0029] In the formula, Represents the pixel point in the fire image The intensity of the fire; Indicates i The intensity coefficient of each fire heat source; Indicates i Heat sources in The contribution weight of Indicates i The central location of the heat source; Indicates i The diffusion radius of a heat source; Temperature distribution calculation formula:

[0030] In the formula, Indicates the fire scene The temperature at Indicates the ambient temperature; Indicates j The temperature influence coefficient of each heat source; Indicates j The heat output of each heat source; Indicates j Thermal conductivity of a heat source; Indicates j The central location of the heat source; Smoke density calculation formula:

[0031] In the formula Indicates the fire scene Smoke density at Indicates the maximum value of smoke density; Indicates the center of smoke diffusion; represents the standard deviation of smoke diffusion; Step S12 Ignition point identification and tracking ignition point extraction formula:

[0032] In the formula, Indicates the binary result of the fire point pixel (1 is the fire point, 0 is the non-fire point); , , Represents pixel Red, green, and blue channel values; Indicates the color threshold; The fitting formula of the ignition point trajectory is:

[0033] In the formula, Indicates the time at which the fire started The trajectory position of Indicates k The trajectory weight of each time point; Indicates k time point; Indicates k The location of the fire at a certain time point; represents the time standard deviation; Step S13: The fire point moving speed formula for calculating the fire point moving speed and direction is:

[0034] In the formula, Indicates the moving speed of the fire point; Indicates time The location of the fire at that time; Indicates time The location of the fire at that time; Indicates a time interval; The formula for the direction of fire point movement is:

[0035] In the formula, Indicates the moving direction of the fire point (expressed in radians); Indicates time The location of the fire at that time; Indicates time The location of the fire at that time; Confidence calculation formula:

[0036] In the formula, Indicates the confidence level of the fire point; Indicates i The weight of each fire point; Indicates i The location of the fire; Indicates the average value of the fire location; represents the standard deviation of confidence calculation; the fire disaster data comprehensive formula integrates the results of fire image analysis, temperature distribution, smoke density, fire point extraction and trajectory fitting to form a unified description of the fire state; the fire image analysis formula provides basic data for fire point extraction, and the location and intensity information of the fire point comes from the fire image and temperature distribution; the fire point extraction formula relies on the results of fire image analysis and selects potential fire points through color thresholds; the fire point trajectory fitting formula calculates the movement trajectory of the fire point based on the location information of the fire point; the fire point movement speed and direction formula relies on the results of trajectory fitting to further calculate the dynamic characteristics of the fire point; the confidence calculation formula integrates the location, intensity and trajectory information of the fire point to evaluate the reliability of the fire point. These formulas together constitute the complete processing flow of fire disaster data, ensuring that the command center can grasp the dynamic information of the fire in real time and make scientific decisions.

[0037] Preferably, in step S11 of this embodiment, high-definition cameras, infrared thermal imagers and other equipment can capture key information of the fire scene in real time; the collected fire images, temperature distribution, smoke density and other data cover multiple key dimensions of the fire scene; the collected data are preliminarily analyzed; step S12 can quickly identify potential fire points from image frames by setting color thresholds; the trajectory and moving speed of the fire point can be tracked, which helps to understand the development of the fire; step S13 extracts a subsequence containing the fire point from the image frame sequence to further accurately identify the fire point and its related attributes; based on the difference in the position of the fire point of adjacent image frames, the moving speed and direction of the fire point are calculated to achieve dynamic monitoring; the calculation results are uploaded to the command center in a timely manner to ensure that the command center can grasp the latest situation of the fire scene in real time.

[0038] In summary, this embodiment provides accurate and timely fire scene data to ensure that the command center can quickly understand the fire situation; through preliminary analysis, the severity of the fire and potential development trends can be preliminarily judged to provide support for decision-making; the accuracy and efficiency of fire monitoring are improved, and the fire point can be quickly located to provide support for rapid response; by tracking the moving speed and direction of the fire point, the development trend of the fire can be predicted to provide a basis for formulating rescue plans; providing accurate fire scene data helps the command center make more accurate and rapid decisions; by dynamically analyzing the moving speed and direction of the fire point, the rescue plan can be adjusted in real time to ensure the effectiveness and safety of the rescue operation; real-time uploading and sharing of data improves the collaborative efficiency and response speed of the entire rescue system.

[0039] Furthermore, if Figure 3 As shown, the process of commanding firefighters and fire-fighting drones based on the rescue plan in step S2 specifically includes the following steps: Step S21: determining the real-time environmental characteristics based on the calculation results of the moving speed and moving direction of the fire starting point, and dividing the fire area into risk groups based on the real-time environmental characteristics and the calculation results of the moving speed and moving direction of the fire starting point; Step S22: determining a plurality of subset target areas, wherein the plurality of subset target areas are marked with risk factors; taking the plurality of subset target areas as mapping targets, and marking them with the fire disaster data; and taking the regional spread and the regional risk as evaluation indicators; Step S23: Perform adjustment evaluation according to the fire disaster update status; when the evaluation result meets the call threshold, generate a scheduling adjustment instruction, and with the receipt of the scheduling adjustment instruction, make decision corrections based on the disaster update status.

[0040] Preferably, step S21 of this embodiment can accurately reflect the changes in the fire scene by analyzing the moving speed and direction of the fire point in combination with other real-time environmental characteristics; based on real-time environmental characteristics and fire dynamics, the fire area is divided into risk levels, which is helpful to identify high-risk areas and give priority to rescue; step S22 divides the fire area into multiple subset target areas, and assigns a hazard coefficient identifier to each area, which is helpful for refined management; the subset target areas and fire disaster data are framed and identified to facilitate rapid identification and positioning; the introduction of regional spread and regional risk as evaluation indicators can comprehensively reflect the development trend and potential threats of the fire; step S23 dynamically adjusts the evaluation results according to the real-time update of the fire disaster to ensure the timeliness of the decision-making basis; when the evaluation results meet the preset retrieval threshold, a scheduling adjustment instruction is automatically generated to achieve rapid response; as the scheduling adjustment instruction is received and executed, the decision is corrected based on the actual disaster update to ensure the continued effectiveness and accuracy of the rescue operation.

[0041] In summary, this embodiment improves the pertinence and efficiency of rescue operations, ensuring that rescue resources can be quickly invested in where they are most needed; through risk division, the routes of action of firefighters and fire-fighting drones can be more effectively guided, reducing the risks in the rescue process; it realizes refined management and evaluation of fire areas, which is helpful to formulate more accurate rescue strategies; the setting of data frame selection identifiers and evaluation indicators provides data support for decision correction and scheduling adjustment; it improves the flexibility and adaptability of rescue operations, and can quickly adjust rescue strategies according to actual conditions; through decision correction, the rescue plan can be continuously optimized to ensure the efficiency and safety of rescue operations.

[0042] Furthermore, the process of dividing the risk of the fire area in step S21 specifically includes the following steps: Step S211: According to the moving speed and moving direction of the fire starting point, the physical law of fire spread is captured, a dynamic model of fire spread is constructed, and the environmental characteristics of the fire area are updated in real time; Step S212: According to the dynamic model of fire spread, the fire area is divided into multiple subset target areas, and each subset target area is assigned a different hazard factor identification according to its environmental characteristics (such as temperature gradient, smoke concentration, combustible distribution, etc.); the hazard factor is based on the fire spread speed, direction and potential risk factors in the area (such as combustible density, terrain characteristics, etc.); Step S213: In the divided subset target areas, the spread degree and risk degree of each area are calculated; the spread degree reflects the spreading speed and trend of the fire in the area, and the risk degree comprehensively evaluates the potential threat of the area to personnel and the environment.

[0043] Among them, the dynamic model equation of fire spread is:

[0044] In the formula, Indicates the fire area at location and time Temperature distribution; It represents the thermal diffusivity, which indicates the ability of heat to be conducted in space; represents the Laplace operator, which indicates the diffusion process of temperature; represents the convection coefficient, which indicates the effect of wind on the spread of fire; represents the wind speed vector field, which indicates the wind speed and direction in the fire area; represents the temperature gradient, which indicates the rate of change of temperature in space; It represents the combustion reaction rate coefficient and the combustion characteristics of combustibles; represents the combustible density function and represents the position and time Distribution of combustibles; It represents the combustion activation energy, which indicates the energy threshold required for the combustion of combustibles; represents the universal gas constant; the first term Represents the heat conduction process; the second term Represents the convection effect of wind on the spread of fire; the third item It represents the combustion reaction of combustibles and uses the Arrhenius equation to describe the combustion rate; Spread calculation equation, Spread Reflects the fire location in the area The diffusion rate and trend are calculated by the following equation: In the formula, Indicates the location of the area and time the spread of represents the weight coefficient of temperature change rate; It represents the rate of change of temperature with time; represents the temperature gradient weight coefficient; It represents the modulus of the temperature gradient, which indicates the intensity of temperature variation in space; represents the wind speed weight coefficient; The modulus length of wind speed indicates the driving effect of wind on the spread of fire; Indicates the initial time; the spread is determined by the combined effect of temperature change rate, temperature gradient and wind speed, and the integral form reflects the cumulative effect of fire spread; Risk calculation equation, risk Comprehensive assessment area The potential threat to personnel and the environment is calculated by the following equation:

[0045] Indicates the location of the area and time the degree of risk; represents the spread weight coefficient; Indicates the degree of spread; Indicates the combustible density weight coefficient; represents the combustible density function; Represents the terrain complexity weight coefficient; represents the terrain complexity function, which indicates the steepness of the terrain and the distribution of obstacles; represents the personnel density weight coefficient; Represents the personnel density function and represents the regional location The risk level is determined by the weighted sum of the spread, combustible density, terrain complexity and population density, which comprehensively reflects the threat of fire to people and the environment. The above formulas describe the dynamic model of fire spread and the calculation method of spread and risk level; these equations combine physical laws, environmental characteristics and human factors, and can provide a scientific basis for fire risk classification.

[0046] Preferably, step S211 of this embodiment constructs a dynamic model of fire spread and updates the environmental characteristics of the fire area in real time; by analyzing the moving speed and direction of the fire starting point, a dynamic model is constructed, which can reflect the physical laws of fire spread in real time; the dynamic capture capability breaks through the limitations of traditional static models and can more accurately predict the spread trend of fire; combined with the dynamic model of fire spread, the environmental characteristics of the fire area (such as wind direction, terrain, distribution of combustibles, etc.) are updated in real time to ensure the timeliness and accuracy of risk assessment; the real-time update mechanism can adapt to the rapid changes of the fire scene and avoid misjudgment caused by data lag. Significance achieved: The dynamic model can capture the nonlinear characteristics of fire spread, significantly improve the accuracy of prediction, and provide a reliable basis for subsequent risk division and rescue decisions; by updating environmental characteristics in real time, it is ensured that risk assessment and rescue plans can quickly respond to changes in the fire scene and improve rescue efficiency. Step S212 divides the fire area and assigns a hazard coefficient mark. According to the dynamic model of fire spread, the fire area is divided into multiple subset target areas to achieve hierarchical management of risks; it can decompose complex fire areas into more manageable units to facilitate subsequent refined evaluation and scheduling; each subset target area is assigned a different hazard coefficient mark according to its environmental characteristics (such as temperature gradient, smoke concentration, combustible distribution, etc.); the dynamic identification method can more accurately reflect the actual risk level of each area and avoid the assessment bias caused by fixed weights in traditional methods. Significance achieved: Through hierarchical division and dynamic identification, the risk level of each area can be more accurately assessed, providing a scientific basis for the rational allocation of rescue resources; the dynamic identification of the hazard coefficient can help rescue personnel quickly identify high-risk areas, prioritize resource scheduling, and improve rescue efficiency. Step S213 calculates the spread and risk of each area. The spread reflects the speed and trend of fire spread in the area. Through dynamic evaluation, the local change characteristics of fire spread can be captured; the spread path of fire can be predicted more accurately, providing key information for the formulation of rescue plans; the risk comprehensively evaluates the potential threat of the area to personnel and the environment, taking into account multiple factors such as temperature gradient, smoke concentration, and distribution of combustibles; it can fully reflect the actual risk level of the area and avoid assessment bias caused by a single factor. Significance achieved: Through the comprehensive evaluation of the spread and risk, the actual risk level of each area can be more comprehensively reflected, providing multi-dimensional support for rescue decisions; the dynamic evaluation results can help rescuers formulate more scientific rescue plans and ensure the efficiency and safety of rescue operations.

[0047] In summary, the three steps of this embodiment together constitute a complete technical chain for fire area risk division; step S211 ensures the accuracy and timeliness of risk assessment through dynamic models and real-time update mechanisms; step S212 achieves refined risk assessment and optimal resource scheduling through hierarchical division and dynamic identification; step S213 improves the comprehensiveness of risk assessment and the scientific nature of rescue plans through comprehensive assessment of spread and risk. The combination of these steps not only significantly improves the efficiency and safety of fire rescue, but also provides new ideas and methods for technical innovation of fire risk management.

[0048] Furthermore, the process of updating the disaster situation and making decision corrections in step S23 specifically includes the following steps: Step S231: Generate a deployment decision plan based on the evaluation results, determine at least one dispatch execution instruction and at least one dispatch command instruction information, and adjust the meaning of the instruction to correspond to the dispatch command information; locate the terminal device of the rescuer, and use the drone as an air base station to send the dispatch execution instruction to the matching terminal mobile device; Step S232: based on the deployment decision plan, extract the evacuation deployment information, and perform high-altitude command management on the evacuation deployment information; iterate the deployment plan to determine new dispatch execution instructions, new dispatch command instruction information and new evacuation deployment information; Step S233: Combine the new dispatch execution instruction, the new dispatch command instruction information, the new evacuation deployment information and the adjustment deviation information to perform fire dispatch management, and perform high-altitude command management on the adjustment deviation information.

[0049] Preferably, in step S231 of this embodiment, according to the deployment decision plan, the dispatch execution instruction and the dispatch command instruction information are determined, and their meanings are guaranteed to correspond; the dispatch execution instruction is quickly and accurately sent to the matching rescue personnel terminal device by using the drone as an aerial base station; the terminal device of the rescue personnel can be accurately located to ensure that the instruction can be accurately conveyed to the relevant personnel; at the same time, the drone provides stable communication support as an aerial base station, and can maintain smooth communication even in complex environments; step S232 extracts key evacuation deployment information based on the deployment decision plan to provide guidance for evacuation operations; high-altitude command management is performed on the evacuation deployment information, and communication Global monitoring and command are carried out through aerial platforms such as drones to ensure that the evacuation operation is carried out in an orderly manner; according to the actual situation of the evacuation operation, the deployment plan is iteratively updated to determine new dispatch execution instructions, new dispatch command instruction information and new evacuation deployment information to adapt to the ever-changing fire scene; step S233 combines the new dispatch execution instructions, new dispatch command instruction information and new evacuation deployment information, as well as the adjustment deviation information, to carry out fire dispatch management; high-altitude command management is performed on the adjustment deviation information, and rescue operations are monitored and commanded and adjusted in real time through aerial platforms such as drones to ensure that the rescue operation can respond quickly and accurately to changes in the actual situation.

[0050] In summary, this embodiment improves the coordination efficiency and response speed of rescue operations, ensuring that rescue personnel can quickly execute instructions and carry out rescue work; by using drones as aerial base stations, it solves the communication problems in complex environments and provides reliable communication guarantees for rescue operations; it optimizes the organization and management of evacuation operations and improves evacuation efficiency and safety; through program iteration and updating, it ensures that rescue operations can continue to adapt to changes at the fire scene and improves the effectiveness and flexibility of rescue operations; it realizes comprehensive monitoring and management of rescue operations and ensures the efficiency and accuracy of rescue operations; through the in-depth application of high-altitude command and management, it improves the ability of rescue operations to respond to complex situations and provides strong support for the success of rescue operations.

[0051] Furthermore, the process of generating a deployment decision plan based on the evaluation result in step S231 specifically includes the following steps: Step S2311: Analyze the fire disaster assessment results, extract key indicators, including regional spread, regional risk, fire point movement speed and direction, etc., to form structured information; generate a data table containing fields such as timestamp, regional number, risk level, spread speed and spread direction; Step S2312: Based on the risk priority classification, firefighters are deployed to high-risk areas first; the deployment positions of firefighters are dynamically adjusted according to the real-time changes in the spread of the fire; based on the risk priority classification, the action routes of the firefighting drones are generated to ensure that they can cover the high-risk areas; Step S2313: Generate a deployment decision plan for the deployment location of firefighters and the action route of the fire-fighting drone.

[0052] Preferably, in step S2311 of this embodiment, the analysis of the disaster assessment results and the generation of structured information are performed. By analyzing the fire disaster assessment results, key indicators such as regional spread, regional risk, and the speed and direction of the fire point are extracted to provide data support for decision-making; the key indicators are converted into structured information (such as a data table containing fields such as timestamp, regional number, risk level, spread speed, and spread direction) to facilitate rapid processing and calling by the system. The significance achieved: By extracting and structuring key indicators, the decision-making process is ensured to be based on objective data and avoid errors caused by subjective judgment; structured information facilitates rapid identification and processing by the system, providing efficient data support for steps (such as firefighter deployment and drone route planning). Step S2312: Risk priority division and dynamic adjustment: According to the regional risk, the fire area is divided into high, medium, and low risk levels, and the priority allocation order of rescue resources is clarified; according to the real-time changes in the spread of the fire, the deployment position of the firefighters and the action route of the drone are dynamically adjusted to ensure that the rescue operation is always focused on the most dangerous area. Significance achieved: By dividing the risk priorities, it is ensured that limited rescue resources are used first in high-risk areas to minimize the losses caused by fire; the dynamic adjustment mechanism enables rescue operations to quickly respond to changes in the spread of fire and avoid rescue delays caused by information lags. Step S2313 generates a deployment decision plan. According to the risk priority division and the real-time changes in the spread of fire, the deployment position of firefighters is generated to ensure that they can quickly reach high-risk areas; according to the risk priority division and environmental characteristics, the action route of the drone is generated to ensure that it can efficiently cover high-risk areas and avoid obstacles and dangerous areas. Significance achieved: By generating the deployment position of firefighters and the action route of drones, it is ensured that rescue operations can be carried out quickly and accurately, and the efficiency of fire fighting is improved; the deployment decision plan generated based on structured information and risk priority division can minimize resource waste and action errors and improve the success rate of rescue.

[0053] In summary, this embodiment provides data support for subsequent decision-making by analyzing the disaster assessment results and generating structured information, ensuring the scientificity and efficiency of the decision-making process; through risk priority division and dynamic adjustment mechanism, it ensures that rescue resources can be used first in high-risk areas and quickly respond to changes in the spread of fire; by generating the deployment location of firefighters and the action route of drones, it ensures that rescue operations can be carried out efficiently and accurately to minimize the losses caused by fire. The three steps together constitute a complete deployment decision-making plan generation process, which is highly scientific, accurate and dynamically adjustable, and provides strong technical support for fire rescue operations.

[0054] Furthermore, the process of determining the new dispatch execution instruction, the new dispatch command instruction information and the new evacuation deployment information in step S232 specifically includes the following steps: Step S2321: Collect fire spread data in real time through drones, sensor networks and terminal devices, including key indicators such as regional spread, regional risk, and fire point movement speed and direction; compare the real-time collected disaster data with the initial assessment results, extract change features, and generate an updated data table containing fields such as timestamp, area number, risk level, spread speed and spread direction; Step S2322: Based on the updated disaster data, re-divide the regional risk priorities, identify new high-risk areas and potential spread paths; adjust the deployment positions of firefighters according to the priority redivision results to ensure that they give priority to covering new high-risk areas; at the same time, re-plan the action routes of the firefighting drones; Step S2323: Based on the resource reallocation strategy, generate new dispatch execution instructions to clarify the specific action tasks (such as fire fighting, rescue, evacuation, etc.) of firefighters and drones; based on the updated disaster data and resource reallocation strategy, generate new dispatch command information, including action priority, task allocation logic and real-time adjustment mechanism; based on the updated disaster data, re-extract evacuation deployment information, clarify evacuation path, evacuation priority and evacuation resource allocation plan; Preferably, in step S2321 of this embodiment, real-time data collection and update can collect key data of fire spread in real time through drones, sensor networks and terminal devices to ensure the timeliness of information; compare the real-time collected data with the initial assessment results, extract change characteristics, generate an updated data table, and provide an accurate basis for decision-making; generate an updated data table containing fields such as timestamp, area number, risk level, spread speed and spread direction, which is convenient for systematic processing and analysis. The significance achieved: real-time data provides an accurate basis for subsequent priority division, resource allocation and instruction generation, avoiding decision-making errors caused by information lag; it can quickly capture the dynamic changes of fire spread and improve the flexibility and efficiency of emergency response. Step S2322 regional risk priority re-division and resource adjustment, based on the updated disaster data, re-dividing regional risk priorities, identifying new high-risk areas and potential spread paths; according to the priority re-division results, adjusting the deployment positions of firefighters and drones to ensure that resources give priority to covering high-risk areas; re-planning the action routes of fire-fighting drones to improve fire-fighting efficiency. Significance achieved: By dynamically adjusting resource deployment, it is ensured that limited firefighting forces can focus on responding to the most urgent areas and avoid wasting resources; timely identification of new high-risk areas and spread paths can effectively control the spread of fires and reduce disaster losses. Step S2323 generates new dispatch execution instructions and evacuation deployment information, generates new dispatch execution instructions, clarifies the specific action tasks (such as fire fighting, rescue, evacuation, etc.) of firefighters and drones, and ensures clear action goals; based on the updated disaster data and resource reallocation strategy, generates new dispatch command information, including action priority, task allocation logic and real-time adjustment mechanism, to improve command efficiency; re-extracts evacuation deployment information, clarifies evacuation paths, evacuation priorities and evacuation resource allocation plans, and ensures the safe evacuation of personnel. Significance achieved: Through clear instructions and task allocation, the collaborative combat capabilities of firefighters and drones are improved; optimized evacuation deployment information can quickly guide personnel to evacuate and reduce casualties. Step S2324 real-time monitoring and dynamic adjustment, by real-time monitoring of the action trajectories of firefighters and drones, identify their deviations from the dispatch execution instructions and evacuation deployment information; generate adjustment instructions based on the deviation information, dynamically correct the action trajectories of firefighters and drones, and ensure that the actions are consistent with the goals; use the adjusted dispatch execution instructions, dispatch command information and evacuation deployment information as new inputs, reintegrate the real-time update data of the disaster situation, and form a closed-loop feedback mechanism. Significance achieved: Through real-time monitoring and dynamic adjustment, ensure that the actions of firefighters and drones are always consistent with the goals, avoid resource waste and action deviations; the closed-loop feedback mechanism enables the system to dynamically adjust according to real-time changes, and improve the adaptive ability of the overall emergency response.

[0055] In summary, this embodiment builds an efficient and accurate fire emergency dispatch and command system through real-time data collection, dynamic priority division, task instruction generation and closed-loop feedback mechanism. Its technical effects include: through real-time data support and dynamic adjustment, it ensures that resources can be quickly and accurately invested in the most needed places; through risk priority division and evacuation deployment optimization, it effectively controls the spread of fire and reduces casualties and property losses; through closed-loop feedback and dynamic adjustment mechanism, it improves the system's adaptive ability and intelligence level, and provides a technical framework for future disaster emergency management.

[0056] Further, the process of performing high-altitude command management on the adjustment deviation information in step S233 specifically includes the following steps: Step S2331: monitor the movement trajectories of firefighters and drones in real time to identify deviations from dispatch execution instructions and evacuation deployment information; Step S2332: Generate adjustment instructions based on the deviation information to dynamically correct the movement trajectories of the firefighters and the drone; Step S2333: Use the adjusted dispatch execution instructions, dispatch command information and evacuation deployment information as new inputs, reintegrate the real-time updated disaster data, and conduct the next round of priority re-division, resource reallocation and instruction generation.

[0057] Preferably, step S2331 of this embodiment is real-time monitoring and deviation identification. Through drones, sensor networks and terminal devices, the action trajectories of firefighters and drones are monitored in real time to ensure the timeliness and accuracy of data; the action trajectories are compared with the dispatch execution instructions and evacuation deployment information to quickly identify deviations and provide a basis for adjustment; the deviation information is presented in a visual way to facilitate the commander to intuitively understand the current action status. The significance achieved: through real-time monitoring and deviation identification, deviations in actions can be quickly discovered to avoid decision-making errors caused by information lag; the visual deviation information enables the commander to clearly grasp the overall situation and provide intuitive support for adjustments. Step S2332 generates adjustment instructions and dynamic corrections. Based on the deviation information, adjustment instructions are automatically generated to clarify the action correction direction of firefighters and drones; the adjustment instructions are transmitted to firefighters and drones in real time to dynamically correct their action trajectories to ensure that the actions are consistent with the goals; through dynamic corrections, resources are avoided from being wasted on invalid or deviating actions, and resource utilization efficiency is improved. Significance achieved: Through dynamic correction, ensure that the actions of firefighters and drones are always consistent with the target, avoid efficiency loss due to deviation; adjust the rapid generation and transmission of instructions so that the system can respond to changes in real time and improve the flexibility of emergency response. Step S2333 Closed-loop feedback and system update, take the adjusted dispatch execution instructions, dispatch command information and evacuation deployment information as new input, reintegrate the real-time update data of the disaster situation, and form a closed-loop feedback mechanism; based on the new input data, re-prioritization, resource reallocation and instruction generation are re-performed to ensure that the system is always in the optimal state; through closed-loop feedback, it can continuously optimize its decision-making logic and resource allocation strategy to improve overall efficiency. Significance achieved: The closed-loop feedback mechanism enables the system to dynamically adjust according to real-time changes and improve the adaptive ability of the overall emergency response; by continuously integrating new data, it can continuously optimize the decision-making logic and ensure the accuracy of resource allocation and instruction generation; the closed-loop feedback and dynamic update mechanism enable the system to always be in the optimal state, improving the efficiency and effectiveness of the overall emergency response.

[0058] In summary, this embodiment constructs an efficient and accurate high-altitude command and management system through real-time monitoring, deviation identification, dynamic correction and closed-loop feedback. Its technical effects include: through real-time monitoring and dynamic correction, ensuring that the actions of firefighters and drones are always consistent with the target, avoiding waste of resources and action deviations; adjusting the rapid generation and transmission of instructions so that the system can respond to changes in real time and improve the flexibility of emergency response; through closed-loop feedback and dynamic update mechanisms, the system can continuously optimize its decision-making logic and resource allocation strategy to improve overall efficiency; through precise command and efficient resource utilization, it can effectively control the spread of fire and reduce casualties and property losses. It not only improves the technical level of fire emergency response, but also provides a technical framework for future disaster emergency management.

[0059] Furthermore, if Figure 4 As shown, the process of dynamically adjusting the rescue plan based on the fire disaster data collected by the drone at the fire scene in real time in step S3 specifically includes the following steps: Step S31: Based on the real-time collected fire disaster data, dynamically evaluate the overall situation of the fire scene, including the fire spread speed, temperature change trend and smoke diffusion range, etc.; combined with the fire situation assessment results, identify the deficiencies or deviations in the current rescue plan; Step S32: Based on the fire situation assessment results, the rescue priorities are re-divided, and the action deployment of firefighters and firefighting drones is adjusted according to the priorities; according to the fire situation and priority adjustment results, firefighters, firefighting drones and other rescue resources are dynamically deployed; based on the adjusted rescue plan, new dispatch execution instructions and command information are generated; Step S33: Use the adjusted rescue plan as a new input, reintegrate the real-time collected fire disaster data, and form a closed-loop feedback mechanism; through closed-loop feedback, continuously optimize the decision logic and resource allocation strategy of the rescue plan.

[0060] Preferably, step S31 of this embodiment dynamically evaluates the fire situation, and identifies the deficiencies or deviations in the current rescue plan in combination with the fire situation assessment results, providing data support for the adjustment. The significance achieved: the real-time perception and dynamic evaluation of the fire scene are realized, providing a scientific basis for the adjustment of the rescue plan; by identifying the deficiencies or deviations of the current plan, it is ensured that the rescue action can be corrected in time, and the waste of resources or rescue delays caused by information lag or decision-making errors are avoided. Step S32 re-divides priorities and dynamically allocates resources. Based on the fire situation assessment results, the rescue priorities are re-divide to ensure that key areas and tasks are given priority; the action deployment of firefighters and firefighting drones is adjusted according to the priorities, and firefighters, firefighting drones and other rescue resources are dynamically allocated; new dispatch execution instructions and command information are generated to ensure that the adjusted rescue plan can be quickly communicated and executed. The significance achieved: by re-dividing priorities, it is ensured that rescue resources can be concentrated on the most urgent and dangerous areas to improve rescue efficiency; dynamic allocation of resources avoids waste or deficiencies of resources, and ensures the efficiency and accuracy of rescue actions; new dispatch execution instructions and command information are generated to ensure the real-time and operability of the rescue plan adjustment. Step S33: Closed-loop feedback and continuous optimization. The adjusted rescue plan is used as a new input to reintegrate the real-time collected fire disaster data to form a closed-loop feedback mechanism. Through closed-loop feedback, the decision logic and resource allocation strategy of the rescue plan are continuously optimized to ensure the dynamic adaptability of the rescue plan. Significance achieved: The closed-loop feedback mechanism realizes the continuous optimization of the rescue plan, ensuring that the rescue action can be dynamically adjusted according to the changes in the fire situation; by continuously optimizing the decision logic and resource allocation strategy, the scientificity and accuracy of the rescue plan are improved, and the rescue risk is reduced; it provides intelligent technical support for fire emergency management, and significantly improves the efficiency and effectiveness of the overall emergency response.

[0061] In summary, this embodiment ensures the real-time and accuracy of the rescue action by real-time collection of fire disaster data and dynamic adjustment of the rescue plan; avoids resource waste or rescue delays caused by information lag or decision-making errors; realizes the intelligence and efficiency of the rescue plan by dynamically evaluating the fire situation, re-dividing priorities, dynamically allocating resources and closed-loop feedback mechanism; significantly improves the efficiency and effect of fire emergency response, and reduces rescue risks and costs; ensures the scientificity and adaptability of the rescue plan through closed-loop feedback mechanism and continuous optimization strategy; provides new technical ideas and methods for fire emergency management, and promotes the innovation and development of emergency management technology. Through mutual cooperation, each step realizes the precision, intelligence and efficiency of fire emergency response. Its technical effect is to perceive the fire situation in real time, dynamically adjust the rescue plan, and continuously optimize the decision logic and resource allocation strategy, which is significant in significantly improving the efficiency and effect of fire emergency response. It not only meets the needs of modern emergency management, but also lays a technical foundation for the development of future intelligent emergency response systems.

[0062] Furthermore, the process of identifying deficiencies or deviations in the current rescue plan in step S31 specifically includes the following steps: Step S311: Identify the spread trend of the fire in different areas and directions through the real-time collected fire spread data; evaluate whether the heat distribution of the fire scene is uniform and whether there are local high-temperature areas based on the real-time changes in temperature data; evaluate the impact of smoke on the visibility of rescue personnel and equipment through smoke diffusion data; Step S312: Compare the deployment of firefighters, firefighting drones and other resources in the current rescue plan with the fire situation assessment results; identify whether there are areas with insufficient resources or waste of resources, and judge whether the resource deployment meets the actual needs of fire spread, temperature change and smoke diffusion; compare the action paths of firefighters and drones in the current rescue plan with the dynamic changes of the fire scene; identify whether there are situations where the action path is blocked, the action efficiency is low, or the action direction deviates from the core area of ​​the fire scene; compare the rescue goals set in the current rescue plan (such as controlling the spread of fire, protecting key areas, evacuating personnel, etc.) with the fire situation assessment results, and identify whether there are situations where the rescue goals have not been achieved or the priority setting is unreasonable; Step S313: Based on the comparison results, identify whether there is a situation where rescue resources fail to cover the core area or key area of ​​the fire scene, and determine whether there is a problem of delayed resource allocation or uneven resource distribution; based on the comparison results, identify whether there is a situation where firefighters or drones are inefficient, and determine whether there are problems with unreasonable action path planning, untimely communication of action instructions, or insufficient action coordination; based on the comparison results, identify whether there is a situation where rescue goals cannot be achieved or priority settings are unreasonable, and determine whether there is a problem of disconnection between rescue goals and the actual situation of the fire scene.

[0063] Preferably, the multi-dimensional perception of the fire situation in step S311 of this embodiment can accurately identify the spread trend of the fire in different areas and directions through the real-time collected fire spread data; provide data support for the dynamic division and resource allocation of the fire scene, and ensure that the rescue plan can respond to the changes in the fire in a timely manner; based on the real-time changes in temperature data, evaluate whether the heat distribution of the fire scene is uniform and whether there are local high-temperature areas, provide a basis for the adjustment of the fire-fighting strategy, and ensure that the fire-fighting resources can accurately cover the high-temperature areas; through the smoke diffusion data, evaluate the impact of smoke on the visibility of rescue personnel and equipment, provide a reference for rescue route planning and personnel evacuation, and ensure the safety of the rescue operation. The significance achieved: through multi-dimensional perception of the fire situation, comprehensively grasp the dynamic changes of the fire scene, and provide a scientific basis for the adjustment of the rescue plan; ensure that the formulation and adjustment of the rescue plan can be based on real-time data to avoid decision-making errors caused by delayed or insufficient information. Step S312: Multi-level comparison of rescue plans, comparing the deployment of firefighters, firefighting drones and other resources in the current rescue plan with the fire situation assessment results; identifying areas with insufficient or wasted resources to ensure that resource deployment can meet the actual needs of the fire scene; comparing the action paths of firefighters and drones in the current rescue plan with the dynamic changes of the fire scene to identify situations where the action paths are blocked, the action efficiency is low, or the action direction deviates from the core area of ​​the fire scene to ensure the efficiency of the rescue action; comparing the rescue goals set in the current rescue plan with the fire situation assessment results to identify situations where the rescue goals have not been achieved or the priority setting is unreasonable to ensure the scientific nature and feasibility of the rescue goals. Significance achieved: Through multi-level comparison, comprehensively evaluate the execution effect of the current rescue plan and identify its deficiencies or deviations; ensure that the adjustment of the rescue plan can be based on the actual situation of the fire scene to avoid waste of resources or rescue delays caused by the disconnection between the plan and the actual situation. Step S313 accurately identifies deficiencies or deviations. Based on the comparison results, it is identified whether there are situations where rescue resources fail to cover the core area or key area of ​​the fire scene, providing a basis for the dynamic adjustment of resource allocation to ensure that resources can accurately cover the core area of ​​the fire scene; based on the comparison results, it is identified whether there are situations where firefighters or drones are inefficient, providing a basis for the optimization of action path planning and command transmission, ensuring the efficiency of rescue operations; based on the comparison results, it is identified whether there are situations where rescue goals have not been achieved or the priority setting is unreasonable, providing a basis for the adjustment and priority setting of rescue goals, ensuring the scientific nature and feasibility of rescue goals. Significance achieved: By accurately identifying deficiencies or deviations in the current rescue plan, a clear direction is provided for the subsequent adjustment of the rescue plan; ensuring that the optimization of the rescue plan can be based on the actual situation of the fire scene, avoiding poor rescue results due to unrecognized deviations.

[0064] In summary, this embodiment provides a scientific basis for the formulation and adjustment of rescue plans through multi-dimensional perception of the fire situation, ensuring that the rescue plan can be based on real-time data; through multi-level comparison, comprehensively evaluate the execution effect of the current rescue plan, identify the deficiencies or deviations therein, and ensure that the adjustment of the rescue plan can be based on the actual situation of the fire; by accurately identifying the deficiencies or deviations in the current rescue plan, provide a clear direction for the subsequent adjustment of the rescue plan, and ensure that the optimization of the rescue plan can be based on the actual situation of the fire. These steps together constitute a hierarchical dynamic analysis process, which not only improves the scientificity and accuracy of the rescue plan, but also provides a new technical idea and method for the dynamic adjustment of the fire emergency response. Through this process, the efficiency and effectiveness of fire rescue can be significantly improved, and the losses caused by fire can be minimized.

[0065] like Figure 5 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 1 includes a processor 11 and a memory 12 coupled to the processor 11.

[0066] The memory 12 stores program instructions for implementing the drone-assisted real-time fire scene monitoring and command method of any of the above-mentioned embodiments.

[0067] The processor 11 is used to execute program instructions stored in the memory 12 to perform real-time monitoring and command of the fire scene assisted by a drone.

[0068] The processor 11 may also be referred to as a CPU (Central Processing Unit). The processor 11 may be an integrated circuit chip having the ability to process signals. The processor 11 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0069] Further, Figure 6The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 2 of the embodiment of the present application stores program instructions 21 capable of implementing all the above methods, wherein the program instructions 21 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0070] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0071] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0072] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A method for real-time monitoring and commanding of a fire scene assisted by an unmanned aerial vehicle, characterized in that: include: The fire disaster data at the fire scene is collected in real time through the equipment, and the fire disaster data is output to the command center; Analyze the fire development direction, fire source location and smoke concentration of fire disaster data; evaluate the fire scale and dangerous area based on the analysis results, formulate a rescue plan based on the evaluation results, and command firefighters and fire-fighting drones based on the rescue plan; The rescue plan is dynamically adjusted based on the fire disaster data collected by drones in real time at the fire scene; when the fire is extinguished, the dark fire area continues to be monitored and an alarm is issued if any abnormality occurs.

2. The method for real-time fire scene monitoring and command assisted by a drone according to claim 1 is characterized in that: The process of exporting fire disaster data to the command center includes the following steps: The fire images, temperature distribution and smoke density fire disaster data of the fire scene are collected in real time through the high-definition camera and red thermal imager of the drone, and the fire images, temperature distribution and smoke density fire disaster data are analyzed; According to the results of fire disaster analysis, the fire point, trajectory and moving speed are identified and tracked, and the image frame sequence is extracted from the video stream collected by the drone. By setting the color threshold, the potential fire point is extracted from the image frame; A subsequence containing the fire point is extracted from the image frame sequence, and the fire point, fire point location, fire point size and confidence are identified based on the subsequence of the fire point; based on the difference in the fire point position of adjacent image frames in the subsequence, the moving speed and direction of the fire point are calculated, and the calculation results are uploaded to the command center.

3. The method for real-time monitoring and commanding of a fire scene assisted by a drone according to claim 1, characterized in that: The process of commanding firefighters and firefighting drones based on the rescue plan includes the following steps: Determine the real-time environmental characteristics based on the calculation results of the fire starting point moving speed and moving direction, and divide the fire area into risk categories based on the real-time environmental characteristics and the calculation results of the fire starting point moving speed and moving direction; Determine multiple subset target areas, where multiple subset target areas are marked with risk factors; use multiple subset target areas as mapping targets and select and mark them with fire disaster data; use regional spread and regional risk as evaluation indicators; Adjustments and assessments are made based on the actual fire disaster situation updates. When the assessment results meet the retrieval threshold, a dispatch adjustment instruction is generated. As the dispatch adjustment instruction is received, the decision is corrected based on the actual disaster situation updates.

4. The method for real-time monitoring and commanding of a fire scene assisted by a drone according to claim 3 is characterized in that: The process of risk stratification of fire areas includes the following steps: According to the moving speed and direction of the fire starting point, the physical laws of fire spread are captured, a dynamic model of fire spread is constructed, and the environmental characteristics of the fire area are updated in real time; According to the dynamic model of fire spread, the fire area is divided into multiple subset target areas, and each subset target area is assigned a different hazard factor according to its environmental characteristics; the hazard factor is based on the fire spread speed, direction and potential risk factors in the area; In the divided subset target area, the spread degree and risk degree of each area are calculated; the spread degree reflects the spreading speed and trend of the fire in the area, and the risk degree comprehensively evaluates the potential threat of the area to people and the environment.

5. The method for real-time fire scene monitoring and command assisted by a drone according to claim 3 is characterized in that: The process of updating the disaster situation and revising the decision includes the following steps: Generate a deployment decision plan based on the evaluation results, determine at least one dispatch execution instruction and at least one dispatch command instruction information, and adjust the meaning of the instruction and the dispatch command information; locate the terminal device of the rescue personnel, and use the drone as an air base station to send the dispatch execution instruction to the matching terminal mobile device; Based on the deployment decision plan, extract the evacuation deployment information and perform high-altitude command management on the evacuation deployment information; iterate the deployment plan to determine new dispatch execution instructions, new dispatch command instruction information and new evacuation deployment information; Fire dispatch management is carried out in combination with new dispatch execution instructions, new dispatch command instruction information, new evacuation deployment information and adjustment deviation information, and high-altitude command management is performed on the adjustment deviation information.

6. The method for real-time fire scene monitoring and command assisted by a drone according to claim 5, characterized in that: The process of generating a deployment decision plan based on the evaluation results includes the following steps: Analyze the fire disaster assessment results and extract key indicators, including regional spread, regional risk, fire point movement speed and direction, to form structured information; generate a data table containing timestamp, regional number, risk level, spread speed and spread direction fields; According to the risk priority classification, firefighters are deployed to high-risk areas first; according to the real-time changes in the spread of fire, the deployment location of firefighters is dynamically adjusted; according to the risk priority classification, the action route of the fire-fighting drone is generated to ensure that it can cover the high-risk areas; Generate deployment decision plans for the deployment locations of firefighters and the action routes of fire-fighting drones.

7. The method for real-time fire scene monitoring and command assisted by a drone according to claim 5, characterized in that: The process of determining new dispatch execution instructions, new dispatch command information and new evacuation deployment information includes the following steps: Collect fire spread data in real time through drones, sensor networks and terminal devices, including key indicators of regional spread, regional risk, speed and direction of movement of the fire point; compare the real-time collected disaster data with the initial assessment results, extract change features, and generate an updated data table containing timestamp, area number, risk level, spread speed and spread direction fields; Based on the updated disaster data, regional risk priorities are reclassified to identify new high-risk areas and potential spread paths. According to the results of the re-prioritization, the deployment positions of firefighters are adjusted to ensure that they give priority to covering new high-risk areas. At the same time, the action routes of firefighting drones are replanned. Based on the resource reallocation strategy, new dispatch execution instructions are generated to clarify the specific action tasks of firefighters and drones; based on the updated disaster data and resource reallocation strategy, new dispatch command information is generated, including action priorities, task allocation logic and real-time adjustment mechanism; based on the updated disaster data, evacuation deployment information is re-extracted to clarify the evacuation path, evacuation priority and evacuation resource allocation plan.

8. The method for real-time monitoring and commanding of a fire scene assisted by a drone according to claim 5, characterized in that: The process of implementing high-altitude command and management for adjusting deviation information includes the following steps: By monitoring the movement trajectories of firefighters and drones in real time, we can identify deviations from dispatch execution instructions and evacuation deployment information; Generate adjustment instructions based on deviation information to dynamically correct the action trajectories of firefighters and drones; The adjusted dispatch execution instructions, dispatch command information and evacuation deployment information are used as new inputs to reintegrate the real-time updated data of the disaster situation and carry out the next round of priority re-division, resource reallocation and instruction generation.

9. The method for real-time monitoring and commanding of a fire scene assisted by a drone according to claim 1, characterized in that: The process of dynamically adjusting the rescue plan based on the fire disaster data collected by the drone in real time at the fire scene includes the following steps: Based on the real-time collected fire disaster data, the overall situation of the fire scene is dynamically evaluated, including the speed of fire spread, temperature change trend and smoke diffusion range; combined with the fire situation assessment results, the deficiencies or deviations in the current rescue plan are identified; Based on the fire situation assessment results, the rescue priorities are re-divided, and the action deployment of firefighters and firefighting drones is adjusted according to the priorities; according to the fire situation and priority adjustment results, firefighters, firefighting drones and rescue resources are dynamically deployed; based on the adjusted rescue plan, new dispatch execution instructions and command information are generated; The adjusted rescue plan is used as a new input, and the real-time collected fire disaster data is reintegrated to form a closed-loop feedback mechanism; through closed-loop feedback, the decision-making logic and resource allocation strategy of the rescue plan are continuously optimized.

10. The method for real-time fire scene monitoring and command assisted by a drone according to claim 9, characterized in that: The process of identifying gaps or deviations in current rescue programs includes the following steps: Through real-time collected fire spread data, the spread trend of fire in different areas and directions can be identified; based on the real-time changes in temperature data, whether the heat distribution of the fire scene is uniform and whether there are local high-temperature areas can be evaluated; through smoke diffusion data, the impact of smoke on the visibility of rescue personnel and equipment can be evaluated; Compare the deployment of firefighters, firefighting drones and resources in the current rescue plan with the fire situation assessment results; identify whether there are areas with insufficient or wasted resources, and determine whether the resource deployment meets the actual needs of fire spread, temperature changes and smoke diffusion; compare the action paths of firefighters and drones in the current rescue plan with the dynamic changes of the fire scene; identify whether there are situations where the action path is blocked, the action efficiency is low, or the action direction deviates from the core area of ​​the fire scene; compare the rescue goals set in the current rescue plan with the fire situation assessment results, and identify whether there are situations where the rescue goals have not been achieved or the priority setting is unreasonable; Based on the comparison results, identify whether there are situations where rescue resources fail to cover the core or key areas of the fire scene, and determine whether there are problems with delayed resource allocation or uneven resource distribution; based on the comparison results, identify whether there are situations where firefighters or drones are inefficient, and determine whether there are problems with unreasonable action path planning, untimely communication of action instructions, or insufficient action coordination; based on the comparison results, identify whether there are situations where rescue goals cannot be achieved or priority settings are unreasonable, and determine whether there is a problem of disconnection between rescue goals and the actual situation at the fire scene.

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