3934 results about "Emergency rescue" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

Disclosed is a multi-channel early warning system for real-time monitoring of meteorological data, relates to the field of data monitoring technology, and comprises a meteorological data acquisition module, a meteorological data processing module, a terrain analysis module, a UAV deployment module, a ground situation acquisition module, an emergency degree analysis module, an early warning terminal, and a database. Data acquisition of a disaster-affected site is performed using a UAV. The UAV has the characteristics of being flexible and fast, and can be quickly deployed in a disaster-affected region to monitor the ground situation in real time, comprehensively assessing the impact of meteorological disasters. Then an emergency rescue level corresponding to the meteorological disaster site is analyzed, and different warning manners are provided for different emergency rescue levels, so that people's ability to monitor and respond to meteorological disasters can be greatly improved, and the relevant departments can take corresponding rescue and protection measures, thereby effectively reducing the impact and loss of disasters.

The invention discloses a multi-sensor fusion positioning mapping and thermal imaging device and a computer readable medium. The equipment integrates positioning mapping sensors such as a thermal imaging camera, a visible light camera, a laser radar and an inertial navigation system, and is specially designed for high-risk and complex fire emergency environments. Through an end-cloud integrated cluster cooperation system and a multi-link stable transmission technology and in combination with intelligent decision support, high-precision real-time positioning and dynamic 3D temperature measurement mapping can be realized in an environment in which satellite navigation signals are weak or unavailable. The multi-mode portability and environmental adaptability design of the system ensures the high efficiency and safety of an operator / unmanned system under extreme conditions, and the advanced data processing and analysis technology of the system significantly improves the accuracy and response speed of rescue operation. Due to the innovative characteristics, the method has remarkable practical application value and market prospect in the field of fire-fighting emergency rescue.

The invention relates to the technical field of power grid fault repair, in particular to a power grid fault emergency rescue method. The method comprises the following steps: acquiring topological structure data, key equipment parameters and real-time operation state data of a power grid; constructing a power grid digital twin model according to the power grid topological structure data, the key equipment parameters and the real-time operation state data, and performing real-time state evaluation to obtain fault risk early warning data and real-time fault condition data; when the real-time fault condition data shows that a fault occurs, performing fault point positioning and fault type determination based on deep learning according to the fault risk early warning data and the real-time operation state data to obtain fault diagnosis data; and constructing a fault influence range model according to the fault diagnosis data and a preset FACTS and HVDC characteristic model. According to the method, deep learning, a graph theory and a multi-objective optimization technology are fused, and intelligent fault identification, influence range modeling and accurate emergency disposal scheme optimization are realized.

The present invention provides an elevator emergency rescue decision-making method and system based on reinforcement learning, which relates to the technical field of elevator emergency rescue, including real-time collection of elevator multimodal data through a distributed sensor network to generate an elevator state representation vector; inputting the obtained elevator state representation vector into a pre-trained graph convolutional neural network model, and simultaneously starting an edge computing unit for real-time simulation, simulating system responses under different rescue strategies, evaluating the feasibility of each rescue strategy, weightedly fusing the multi-dimensional risk score and the rescue strategy feasibility evaluation results, and generating a risk assessment report; sending a rescue instruction set to an elevator emergency rescue executive agency through a secure encrypted channel, and continuously monitoring the real-time feedback information of the elevator emergency rescue executive agency, and triggering a decision re-evaluation mechanism when an abnormal situation occurs, until a passenger safe evacuation signal is received and the elevator system restores a safe state signal, and the rescue mission is completed.

The invention relates to the technical field of wireless communication, discloses an air-sea cross-domain network reinforcement learning routing algorithm based on an SDN (Software Defined Network), and mainly aims to solve the problems of poor dynamic environment adaptability, insufficient multi-task QoS (Quality of Service) demand guarantee capability and the like caused by a static rule or local optimization of an existing air-sea cross-domain routing algorithm. Global network state perception and resource centralized scheduling are realized through an SDN controller, and a dynamic routing decision model driven by deep reinforcement learning (DRL) is constructed. A network state is sensed in real time through an SDN controller, a multi-target reward function is designed, and a cross-domain path selection strategy is dynamically optimized. According to the method, the limitation of a traditional routing protocol in a cross-domain heterogeneous network is broken through, the end-to-end transmission efficiency, the resource utilization rate and the multi-task differentiated service quality guarantee capability are remarkably improved, and the method can be widely applied to the scenes of marine environment monitoring, emergency rescue, cross-domain cooperative detection and the like.

The invention belongs to the technical field of emergency management, and discloses a natural disaster emergency rescue system based on multi-source sensing information fusion. The system is composed of a multi-source sensing data acquisition module, a data preprocessing and space-time registration module, a cross-modal feature extraction module, a multi-modal information fusion and conflict resolution module, a disaster type identification and grade discrimination module, a disaster influence range prediction and diffusion modeling module, and a dynamic emergency path planning and response plan generation module. A rescue scheduling and command control module; and an emergency feedback and closed loop dynamic correction module. Through multi-source sensing data fusion, cross-modal feature extraction and deep information fusion technologies, a full-space-time and full-process natural disaster emergency rescue system is constructed, comprehensive sensing, accurate recognition and dynamic plan generation of a disaster site are realized, the rescue response speed and decision scientificity are remarkably improved, and the intelligent level of emergency rescue is comprehensively improved.

The invention discloses a disaster emergency rescue command system and method, and relates to the technical field of rescue command systems. Comprising an information collection module which collects information, including disaster situations, personnel distribution and environmental conditions, of a disaster site through sensors and communication equipment; the collected information is input into the system, so that accurate disaster assessment can be conveniently obtained; the decision support module determines optimal allocation of rescue resources by using a linear programming model according to disaster assessment provided by the information collection module, and plans a rescue path by using a graph theory algorithm; and the command control module is used for converting the rescue decision of the decision support module into a specific rescue instruction. According to the system, the disaster assessment time can be shortened through real-time monitoring and rapid data processing, and the rescue decision-making speed is increased; optimal configuration of resources is realized through an algorithm, resource waste is avoided, and resource availability at a critical moment is ensured.

The invention provides an emergency rescue assisting method based on a large language model and computer equipment, and relates to the technical field of computers, and the method comprises the following steps: obtaining a current question text of a target user; performing intention recognition on the current question text to obtain a target intention type corresponding to the current question text and target intention recognition information under the target intention type; determining a target big language model matched with the target intention type from a plurality of pre-trained big language models according to the target intention type; the plurality of pre-trained large language models are pre-trained large language models of a plurality of different model frameworks in the field of emergency rescue; and by adopting the target large language model, according to the target intention identification information and a preset emergency knowledge base, generating target emergency rescue auxiliary information corresponding to the current problem text. The method does not need manual analysis, and improves the efficiency and accuracy of generating the target emergency rescue auxiliary information.

The invention relates to the technical field of radar sensing, in particular to a road traffic accident detection method and system based on roadside radar sensing, and the method comprises the following steps: S1, data collection: continuously collecting the motion data of traffic participants; s2, multi-source data fusion and preprocessing: collecting environment sensing data in real time, and fusing the collected motion data and environment sensing data into multi-source data; s3, real-time dynamic analysis: identifying abnormal behaviors of traffic participants; s4, accident identification and risk assessment: performing risk assessment on a real-time dynamic analysis result; s5, virtual accident scene reconstruction: reconstructing a virtual accident scene; and S6, alarm and information pushing: alarm information is sent to a related traffic management department and an emergency rescue unit. According to the invention, the whole process of accident occurrence can be reproduced, and reliable data support is provided for accident analysis, prediction and responsibility determination.

The invention provides an automatic task allocation method and system for an elevator emergency rescue platform, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining elevator operation state data and environment information data, constructing an emergency task model, and obtaining classified emergency tasks; inputting the classified emergency tasks into a deep reinforcement learning model to generate a task allocation scheme; the deep reinforcement learning model is formed by combining a deep Q network structure with a priority scanning algorithm, and a priority experience playback mechanism is introduced; and constructing a multi-objective optimization module comprising a time constraint, a space constraint and a resource constraint, and optimizing the task allocation scheme by the multi-objective optimization module based on an improved particle swarm algorithm to obtain an optimal task allocation scheme.

The earth-rockfill dam leakage dangerous case emergency rescue method based on multi-model cooperation comprises the steps that earth-rockfill dam leakage points are obtained; collecting stress data of leakage points of the earth and rockfill dam, inputting the stress data into the early warning model, and outputting early warning values of the leakage points; obtaining leakage point water flow velocity data and impact force data of a water flow carrying object, inputting the leakage point early warning value into a leakage point danger early warning model, and outputting a leakage point danger value; performing real-time priority blocking sequencing through a leakage point priority model; the method comprises the following steps: acquiring stress data of a leakage point of a blocked earth-rockfill dam, acquiring the real-time flow velocity of the blocked leakage point and the impact force of a water flow carrying object, inputting the data into a pre-constructed blocking model, outputting a blocking stress value, and determining the separation early warning of the blocked leakage point through a preset threshold value library. According to the method, the priority sequence of emergency rescue resources is reasonably arranged according to the emergency degrees of different conditions of earth-rock dam leakage points, and efficient and rapid emergency rescue is achieved.

The invention discloses an emergency rescue strategy and system for emergencies in a tunnel, and belongs to the field of intelligent expressways and traffic control intelligence. The method comprises the following steps: acquiring historical emergency data and a historical management and control strategy; preprocessing the historical emergency data and the historical management and control strategy to obtain preprocessed data; acquiring entities, relationships and attributes from the preprocessed data; constructing a knowledge graph according to the entities, the relationships and the attributes; acquiring current emergency information; and obtaining a final emergency rescue scheme from the knowledge graph according to the current emergency information. According to the invention, by constructing the (special) long tunnel emergency rescue knowledge graph, systematic integration and efficient utilization of historical data and experience knowledge are realized, and rapid, accurate and intelligent information support and application services are provided for tunnel management departments in emergency rescue work.

The invention relates to the technical field of unmanned aerial vehicle perception, and discloses a visual navigation method and system of a six-rotor full-drive unmanned aerial vehicle, a terminal and a storage medium. A navigation framework integrating dynamic visual perception and full-drive control characteristics is constructed, and a visual encoder is adopted to extract multi-scale environment characteristics; according to the method, time sequence information is processed in combination with a long-short-term memory network, efficient feature extraction and motion trail prediction of an image sequence under high-speed rotation and violent attitude change of the unmanned aerial vehicle are achieved, the problem of feature tracking loss of a traditional algorithm in a high-dynamic scene is solved, global correlation modeling of environment features is enhanced through an attention mechanism, and the tracking accuracy of the unmanned aerial vehicle is improved. According to the six-rotor-wing full-drive unmanned aerial vehicle navigation method, the navigation precision and robustness of the unmanned aerial vehicle in a complex dynamic environment are remarkably improved by combining control instruction generation of dynamics constraints, high-precision inertial navigation compensation is not needed, and an efficient and reliable navigation solution is provided for application of the six-rotor-wing full-drive unmanned aerial vehicle in emergency rescue, high-speed inspection and other scenes.

The invention provides a coal mine enterprise and sharing center joint reserve emergency relief goods decision-making method comprising the following steps: aiming at a specific accident type, based on historical accident information, using a random forest algorithm to determine the emergency relief goods of the specific accident type; constructing a material demand model of specific types of emergency rescue materials evolved along with time and accidents in the accident emergency rescue period and the demand quantity of the emergency rescue materials; acquiring basic information of a coal mine, and determining an emergency relief material demand change curve along with time lapse and accident evolution during an accident period according to the basic information and the application demand model; and determining the storage information of the emergency rescue materials according to the demand quantity change curve requirements of the emergency rescue materials under the condition that the time and the material quantity meet the requirements. The problems of high reserve cost and low efficiency in the prior art are solved.

The invention relates to the technical field of digital simulation, and discloses an emergency rescue method and system based on unmanned aerial vehicles and an urban three-dimensional platform, and the method comprises the steps: calling an emergency rescue region model through the urban three-dimensional platform, driving an unmanned aerial vehicle formation to go to an emergency region to obtain real-time region information, and employing a pre-trained accident condition image recognition algorithm to recognize an accident condition image; accident condition characteristics such as smoke and fire, fire fighting access blockage, emergency exit blockage, urban inland inundation, dangerous slopes, dangerous building detection, illegal building of building roofs, river channel floating object recognition and high-voltage electric tower inclination monitoring are identified, and the accident condition characteristics are substituted into the regional three-dimensional model to generate an accident simulation model. Real-time accident condition feedback is provided through the accident simulation model, rescue workers are assisted to make scientific rescue decisions, through comprehensive analysis and accurate simulation of an emergency rescue area, the detection efficiency of the unmanned aerial vehicle is improved, the timeliness and accuracy of rescue are enhanced, and the problem that in the prior art, the efficiency is low through manual collection of the current accident condition is solved.

The present invention discloses a fire monitoring method and system based on three-dimensional laser scanning technology, which relates to the field of fire monitoring technology, including collecting a fire scene structure diagram for path planning, deploying sensors on a robot for real-time path navigation and obstacle avoidance; performing three-dimensional laser scanning and collecting data after reaching the target point, processing the collected data and performing three-dimensional modeling. The present invention combines three-dimensional laser scanning technology with a robot intelligent navigation system, utilizes high-precision three-dimensional point cloud data and sensor fusion technology, realizes comprehensive monitoring of the fire scene, and through the robot's autonomous path planning and obstacle avoidance, can dynamically collect data at the fire scene and perform three-dimensional modeling, analyze heat source distribution and smoke diffusion trajectory, and then predict the fire spread path, and generate a real-time updated three-dimensional fire scene map, which can provide efficient and accurate fire information in a complex environment, and provide an important basis for decision support and emergency rescue at the fire scene.

Disclosed herein is a vehicle-mounted hazardous chemicals safety monitoring system. The system includes a vehicle-mounted hazardous chemicals physical and chemical safety monitoring and early warning unit, a vehicle-mounted hazardous chemicals anti-theft / robbery monitoring and early warning unit, hazardous chemical transportation emergency rescue guidance unit, a vehicle-mounted gateway unit, an alarm prompt unit, and a remote management unit. This system automatically monitors physical and chemical safety information and anti-theft / robbery safety information of vehicle-mounted hazardous chemicals, and may issue an on-site voice prompt for emergency disposal or file push guidance for a vehicle driver through the hazardous chemical transportation emergency rescue guidance unit when a vehicle triggers a vehicle-mounted hazardous chemicals physical and chemical safety monitoring and early warning and a vehicle-mounted hazardous chemicals anti-theft / robbery monitoring and early warning.

The invention provides a miner safety early warning system based on UWB and AI cameras, and relates to the technical field of mine safety, the miner safety early warning system comprises a positioning module, a video monitoring and analysis module, a data fusion and processing module and an early warning and communication module, the positioning module is composed of a plurality of UWB base stations arranged in a mine and UWB positioning cards worn by miners, and the video monitoring and analysis module is connected with the early warning and communication module. The centimeter-level real-time positioning of miners is realized through ultra-wideband pulse signals; the UWB technology is applied, the positioning precision is improved to the centimeter level, the positions of miners can be accurately mastered, precious time is won for emergency rescue, intelligent recognition and analysis of personnel behaviors and environmental risks are achieved through the AI camera, the defects of traditional monitoring are overcome, potential safety hazards can be found in advance, and through multi-source data fusion and real-time processing, the safety of the miners is improved. The system can give an early warning at the first time when a danger occurs, thereby effectively avoiding the occurrence of an accident or reducing the harm degree of the accident.

The invention relates to the technical field of data processing, in particular to a tunnel fire temperature field prediction and safety area division method and system, and the method comprises the following steps: carrying out the preprocessing of the historical data of a tunnel fire, and obtaining the standardized data; establishing a combustion equation of the tunnel fire based on the standardized data; constructing a combustion model of the tunnel fire according to the combustion equation; using the standardized data to train the combustion model; and predicting a temperature field of a tunnel fire according to the trained combustion model, and imaging a prediction result to divide a safety area. According to the method, the combustion model of the tunnel fire is constructed based on the physical constraint neural network, the most matched combustion model is selected according to different tunnel fire conditions, the temperature field of the tunnel fire is predicted in real time, the prediction result is visualized, the accuracy and real-time performance of tunnel fire prediction are effectively improved, and the tunnel fire prediction efficiency is improved. And according to a visualization result, a safety area is divided, and powerful support is provided for emergency rescue.

The invention relates to the technical field of emergency management, in particular to an emergency rescue management optimization method based on an AI algorithm. The method comprises the following steps: obtaining the corresponding number and position of various rescue resources and the corresponding state and performance of various rescue devices through an emergency disaster area, carrying out rescue resource capability evaluation, and predicting the emergency rescue resource demand corresponding to each emergency rescue task; based on the emergency rescue resource demand quantity corresponding to each emergency rescue task, performing emergency priority sorting analysis on each emergency rescue task, and generating a corresponding emergency rescue priority sequence in the current area; and carrying out emergency rescue allocation management optimization on the corresponding emergency rescue priority sequence in the current area by taking the maximum rescue effect as a target and combining the corresponding emergency rescue resource capability in the current area so as to generate a corresponding emergency rescue resource allocation management decision suggestion. According to the invention, the response speed and rescue effect of emergency rescue can be improved.

The invention provides a tunnel fire behavior inversion method based on multi-modal fusion and physical constraint, and aims to solve the problem that the position and power of a fire source are difficult to invert accurately in real time in traditional fire behavior monitoring. According to the method, data, temperature distribution images and physical parameters of distributed temperature sensors in a tunnel are collected, and a spatial-temporal feature fusion network is constructed; designing an information leakage prevention training mechanism, and adopting a modal random discarding and sensor shielding technology to avoid overfitting of the model to a specific input mode; a physical constraint loss function is introduced, and the temperature gradient, the maximum temperature rise and the longitudinal attenuation law are combined to ensure the physical rationality of an inversion result; a coarse-fine two-stage position prediction framework is adopted, and meter-scale precision positioning of the position of a fire source is achieved. According to the invention, the power and position of the fire source can be inversed accurately in real time, and reliable technical support is provided for intelligent monitoring and emergency rescue of tunnel fire.

The invention discloses an unmanned ship multi-path optimization method for large-scale dynamic search and rescue tasks, and belongs to the technical field of unmanned ship path planning. According to the method, a multi-track optimization framework combined with a deep reinforcement learning algorithm is introduced, so that the efficiency and accuracy of path planning are effectively improved. Through a multi-trajectory sampling technology, the problem of trajectory data sampling deviation in a traditional method is solved, and the robustness and adaptability of an algorithm in a dynamic rescue environment are enhanced. Meanwhile, an encoder-decoder structure is designed, path exploration of the unmanned ship is optimized by utilizing reinforcement learning, and the exploration capability of the model to a complex solution space is greatly improved. According to the method, the optimal searching path can be quickly found, and the unmanned ship is prevented from falling into a local optimal solution. According to the invention, the unmanned ship can intelligently adapt to a large-scale emergency rescue scene, the search time is greatly shortened, the task success rate is improved, and key technical support is provided for the application of the unmanned ship in emergency rescue.

The invention discloses an underground space flood emergency rescue and dispatching command model construction method and device under a rainstorm situation, belongs to the technical field of construction safety management, and solves the problem that effective landing of an emergency rescue scheme cannot be ensured in the prior art. According to the method, flood simulation under multiple scenes of different rainfall capacities is carried out based on rainstorm scene data and a cellular automaton model, flood simulation results are obtained, key risk influence factors are identified, severity evaluation is carried out, and risk grade evaluation is carried out based on an underground space disaster risk evaluation index system under the rainstorm scene. The method comprises the following steps: constructing a knowledge base, matching a target underground space emergency rescue resource list with the constructed knowledge base to obtain a first emergency task list, applying the first emergency task list to a corresponding rainfall scene, performing pre-deduction by adopting a cellular automaton model, and generating a second emergency resource list according to a pre-deduction result to obtain an emergency rescue and scheduling command decision. The method is used for underground space flood emergency rescue and dispatching command under the rainstorm situation.

The invention discloses a disaster emergency rescue nursing skill training system based on virtual reality, and relates to the technical field of virtual reality. A high-simulation virtual disaster scene is constructed based on real data, diversified injury condition virtual objects matched with the scene are generated, a secondary disaster risk diffusion effect and emergency prompt information are overlaid in real time, physiological state changes such as blood pressure and respiration generated by nursing operation differences in the rescue process of wounded personnel are dynamically simulated, and the rescue effect of the wounded personnel is improved. The problems of single scene and fixed injury condition in traditional training are solved, real-time and multi-modal feedback of student operation is realized through accurate comparison and hierarchical identification of multi-dimensional operation characteristics, the defects of guidance lagging and general are overcome, a personalized and adaptive training scheme is generated according to a comprehensive evaluation result, the progress is dynamically adjusted, and the training efficiency is improved. And scene simulation, operation practice, accurate feedback and personalized improvement are integrated.

The invention provides a major accident-oriented rescue resource demand quantity prediction and dynamic supply method, which relates to the technical field of emergency rescue, and comprises the following steps: acquiring accident site multi-source data information for situation awareness, performing long-term prediction on rescue demand quantity based on a symplectic geometric structure and Hamiltonian dynamic mapping, and determining the rescue demand quantity; and the supply network is dynamically optimized by using the Riemannian curvature flow to generate the rescue resource supply scheduling scheme, so that the resource demand can be accurately predicted, the supply path can be optimized, the rescue efficiency can be improved, the resource waste can be reduced, and the accurate delivery of the rescue resources can be realized.

The invention relates to the technical field of rescue path planning, and discloses a low-altitude traffic emergency rescue path planning method and system based on geographic information space-time analysis, and the method comprises the steps: obtaining geographic information data collected by different data sources, and carrying out the fusion of the same kind of data of the different data sources; based on the fused geographic information data, adding timestamps and time interval attributes to construct four-dimensional spatio-temporal data; determining a preliminary rescue path based on the four-dimensional spatio-temporal data through an improved A * algorithm; on the basis of the preliminary rescue path, local or global path optimization is carried out according to the real-time change of geographic information in the flight process, and the accuracy, timeliness and safety of low-altitude emergency rescue path planning are improved.

The invention provides a shelter rapid power supply recovery method and system suitable for an emergency rescue task. The method comprises the following steps: collecting a power interruption signal, analyzing the power interruption signal, and generating an emergency response instruction; fault points are automatically identified and accurately positioned, the importance level of each electric device in the shelter is evaluated, and a dynamic priority list and a device demand report are generated; intelligently selecting a stand-by power supply combination scheme, starting a stand-by power supply switching process, and generating a stand-by power supply activation instruction; after a standby power supply activation instruction is received, the power supply sequence and power distribution of all devices are dynamically adjusted, normal operation of key facilities is preferentially guaranteed, and a device power supply plan is generated; and continuously monitoring the working state of the standby power supply and the influence of the standby power supply on the overall power system of the cabin, dynamically optimizing the power supply strategy, and generating an optimized power supply scheme. According to the technical scheme provided by the invention, the response speed and the power supply reliability of the shelter hospital or the temporary emergency facility during power interruption are improved.

The invention is applicable to the technical field of emergency rescue, and relates to an optimal path planning method based on a fire rescue road network directed graph, which comprises the following steps: reconstructing the road network directed graph according to a road priority principle by using multi-source data such as an open street map road network, road conditions, survey data, real-time road conditions and the like and fusing remote sensing image data; on the basis, based on a dynamic weight model, an optimal path planning algorithm based on a fire rescue road network directed graph is further constructed. The application of the method can effectively make up for the deficiency of online map path planning used in the existing research, so that the path planning better fits the path planning requirement in a real fire rescue scene, and the driving path of fire rescue can be accurately calculated, so that the path is more reasonably planned, and the efficiency of fire rescue is improved.

The invention relates to a flood prevention block stone reserve high-precision measuring and calculating method, which comprises the following steps: aerial photography and three-dimensional modeling are carried out on the overground part of a flood prevention block stone by using two modes of unmanned aerial vehicle oblique photography and unmanned aerial vehicle laser radar, and the overground volume of the flood prevention block stone is measured and calculated; a ground penetrating radar is adopted to detect the settlement depth of the flood prevention block stone, the volume of the underground part of the block stone is calculated, the volume of the whole block stone can be obtained by adding the volume of the overground part of the flood prevention block stone and the volume of the underground part of the block stone, and the weight of the flood prevention block stone can be calculated. According to the invention, the reserve of the overground and underground parts of the flood prevention block stone can be accurately measured, and technical support is provided for flood prevention and emergency rescue.