760 results about "Search and rescue" patented technology

The invention provides an IMU (inertial measurement unit)-assisted depth SLAM (simultaneous localization and mapping) method and system fusing language-vision multi-mode perception, and the system comprises functional modules such as initial calibration and semantic map initialization, pre-integration prediction and key frame judgment, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guidance loopback detection and semantic three-dimensional map incremental updating. IMU motion priori, depth geometric constraint and language model semantic factors are subjected to combined modeling through a graph optimization framework, and high-precision positioning and labeled map construction in a complex dynamic environment are achieved. Compared with the prior art which only depends on geometric or inertial information, the method has the advantages that the loop-back mismatching rate is reduced, the closed-loop convergence efficiency and the long-time relocation robustness are improved, and a semantic interface is provided for upper-layer tasks such as natural language navigation and target retrieval. The method can be widely applied to the fields of service robots, security inspection, intelligent driving, post-disaster search and rescue and the like.

The invention discloses an unmanned aerial vehicle cluster communication system based on weak signal connection. According to the invention, through the intelligent cooperation mechanism of the dynamic relay path optimization module, the communication reliability in a complex environment is significantly improved. The system analyzes cluster network topology and node states in real time, combines multi-dimensional parameters such as signal stability, energy consumption efficiency and task load, dynamically screens an optimal relay node and generates an efficient transmission path. For example, in a weak signal area, the system can automatically start an adjacent high-stability node as a relay, the problem of rigidity of a traditional fixed relay strategy is avoided, meanwhile, the signal attenuation risk is avoided in advance through predictive switching, and continuous return of key data is ensured. The capability of actively adapting to network fluctuation effectively reduces the probability of communication interruption, and can ensure the non-inductive connection of task instructions and key information especially in scenes with strict requirements on real-time performance, such as search and rescue, exploration and the like.

The invention relates to the technical field of language model and agent perception control, provides a language prompt (Prompt) construction and task planning method based on a semantic scene graph, and solves the problems that in an existing system, a graph structure cannot adapt to a language model, and task planning lacks a perception closed loop. The method comprises the following steps: acquiring environmental data through a multi-modal sensor and constructing a semantic scene graph; automatically generating a language prompt based on the semantic graph structure, wherein the language prompt is used for guiding a language model to perform natural language task planning; wherein the language prompt adopts a multi-level template generation mechanism, room-object-attribute information is embedded into a structured template, hierarchical semantic blocks of an environment section, a target section and an operation section are formed, and the expression ability and generalization of a language model are improved; and a planning result output by the language model is implemented by the execution module. According to the method, a closed-loop path from a perceptual graph to language reasoning is established, and the method is suitable for a semantic task execution system in complex scenes such as unmanned aerial vehicle inspection, search and rescue and detection.

The invention discloses a robot unknown environment autonomous exploration navigation method based on CTSAC, and the method combines the powerful sequence perception capability of Transform and a Soft Actor-Critic deep reinforcement learning framework, and introduces curriculum learning of a regular review mechanism, laser radar partition optimization processing, and refined reward setting. The state of the robot is represented by laser radar data and gyroscope data which are subjected to laser radar partition optimization processing, meanwhile, after state information is subjected to refined reward setting, corresponding reward values are obtained, corresponding experience sequences are stored in an experience pool, and data obtained through sampling of the experience pool are supplied to a neural network for training. The method is suitable for the robot to perceive the surrounding environment through the sensor in an unknown environment to perform path planning so as to reach a specified target point or establish an environment map, and is used for complex tasks such as space exploration, search and rescue, reconnaissance and the like.

The invention discloses an unmanned ship distributed cooperative dynamic target surrounding control method under multiple constraints, and relates to the field of unmanned ship cooperative control. In order to solve the problems that multiple constraint coupling is not fully considered in cooperative surrounding of multiple unmanned ships, target state estimation is insufficient in an intermittent communication environment and the like, the following technical scheme is provided: firstly, a motion model of the unmanned ships and a target is established; a distributed internal model observer is arranged for each unmanned ship, and the internal motion state of the target is estimated; then designing a distributed type progressive average surrounding guidance algorithm, and performing distributed scheduling on the expected speed and heading of the unmanned ship, so that the unmanned ship can still keep safe surrounding when the speed or direction of the target is changed; the input saturation constraint of a propeller or a steering engine is considered, so that each unmanned ship can be autonomously controlled according to an expected trajectory and speed; and finally, stable and collision-free surrounding of the dynamic target is realized under multiple constraint conditions. The method can be applied to scenes such as ocean escort and maritime search and rescue which need to perform collaborative operation around a dynamic target.

The invention provides an intelligent unmanned cluster autonomous task planning scheme based on a large language model, the scheme mainly aims at the task planning problem of an unmanned cluster in a dynamic environment, the method adopts a Monte Carlo tree search method guided by the large language model and a global memory library, the search space is effectively constrained, the task planning efficiency is improved, and the task planning efficiency is improved. Specifically, a global memory library used for storing historical decision experience is constructed to realize reuse of historical experience so as to reduce the exploration cost of task planning, in addition, an early stop strategy and a reflection mechanism are introduced to correct the decision of a large language model according to errors generated in the search process, and the search efficiency is improved. The problem of decision errors caused by thinking circulation of a large language model is avoided, the search speed and decision robustness of an unmanned cluster task in a complex and changeable scene are further improved, and therefore an efficient and reliable technical scheme is provided for unmanned cluster cooperative combat, search and rescue monitoring and other multi-task scenes.

The invention discloses a cooperative positioning and trajectory planning method based on an air-ground robot system, and the method comprises the steps: enabling unmanned vehicles to construct a global environment map through a laser radar, achieving the positioning of the unmanned vehicles, obtaining the distances between the unmanned vehicles through the combination of an IMU (Inertial Measurement Unit) and a UWB (Ultra Wideband) distance measurement chip of the unmanned vehicles, and carrying out the real-time pose estimation. Based on the fused pose and global map information, a method of combining mixed A * search and nonlinear trajectory optimization is adopted, a collaborative trajectory planning problem is converted into a multi-constraint optimization model, the sight distance quality between air-ground robots, an unmanned vehicle team-shaped geometric structure and an unmanned aerial vehicle rapid navigation problem are comprehensively considered, and the space-ground robot cooperative trajectory planning method is established. And generating a collision-free optimized cooperative trajectory meeting multi-robot kinematics constraints in real time. According to the method, the positioning precision and the coordination degree of the air-ground coordination system in a complex dynamic environment are remarkably improved, and a safe, efficient and real-time heterogeneous multi-robot trajectory planning method is provided for various application scenes such as routing inspection, search and rescue, environment monitoring and the like.

The invention discloses an unmanned cluster search and rescue task planning method, system and device and a storage medium, and relates to the technical field of search and rescue, and the method comprises the steps: analyzing a multi-source distress signal of a region to be searched and rescued, and determining accident point information; dynamically updating the target discovery probability of each grid sub-region in the to-be-searched-and-rescued region based on the environment feature information and the target moving feature information of the to-be-searched-and-rescued region; determining a current task planning strategy based on the type number of the unmanned equipment in the unmanned cluster and the accident point information; and based on the current task planning strategy and the target discovery probability of the grid sub-region, determining a region search and rescue priority list of each unmanned device, and performing task allocation on the unmanned cluster. Through the above mode, task planning and path distribution are uniformly carried out on multiple types of unmanned equipment in combination with the environment characteristics and the target movement characteristics, accident points are rapidly covered, the search and rescue efficiency is improved, and the success rate of search and rescue tasks is improved.

The invention relates to the technical field of target detection, in particular to a search and rescue task-oriented unmanned aerial vehicle thermal imaging visual target detection method, which comprises the following steps of: acquiring multiple frames of thermal imaging images of an unmanned aerial vehicle, extracting regional thermal difference characteristics according to a window, marking a non-background region to generate a candidate set, and fitting and reconstructing a suspected thermal target contour. And analyzing the track and the thermal change rate, screening background interference, identifying jump abnormity, positioning the gravity center, and generating a target repositioning signal. According to the method, the heat value range and variance index sequence in the image area is constructed, the thermal anomaly area is judged in combination with the temperature baseline difference, background disturbance comparison is executed in combination with the direction vector of the coordinate trajectory in the multi-frame image and the thermal change parameter, the false detection probability caused by background noise is reduced, and the detection accuracy is improved. The target jump identification is carried out according to the inter-frame heat value and area change rate in linkage with the thermal isoline closure degree, the target discrimination accuracy in a shielding scene is improved, and the robustness of thermal target extraction in a complex search and rescue environment is integrally improved.

The invention belongs to the technical field of robot control, and particularly relates to a self-adaptive whole-body control method suitable for a quadruped robot in an uncertain dynamic environment, and the method integrates an extended state observer (ESO), a model prediction controller (MPC) and a whole-body controller (WBC) based on hierarchical optimization. And a closed-loop control architecture is constructed to improve the robustness and motion coordination ability of the quadruped robot in a complex environment. Firstly, external disturbance is estimated and compensated in real time through ESO; then, the MPC optimizes future system behaviors in a rolling manner under the condition of considering interference terms, and an expected foot end counter-acting force is output; and finally, the WBC is combined with the priorities of the multiple tasks to generate all joint control instructions in a constraint optimization form, and task collaboration and dynamic adaptation are achieved. The method has the advantages of being high in disturbance self-adaptive capacity, high in control precision, suitable for a multi-task environment and the like, and is particularly suitable for quadruped robot control tasks in complex dynamic scenes such as transportation and search and rescue.

The invention provides an interphone emergency rescue method and system based on ultra-low power consumption remote wakeup, and relates to the technical field of interphone emergency rescue, and the method comprises the steps: switching a normal working mode to an ultra-low power consumption mode according to a mode switching instruction generated by triggering a target emergency event; in the ultra-low power consumption mode, performing signal monitoring on a preset communication frequency band according to a preset monitoring strategy so as to receive a wake-up signal broadcasted by a first search and rescue user on the preset communication frequency band through a first search and rescue end interphone; when it is determined that the wake-up signal is received from the preset communication frequency band, performing security verification on the wake-up signal to switch the ultra-low power consumption mode to a normal working mode; and in the normal working mode, executing the target help-seeking operation. The technical problem that the emergency rescue efficiency of the interphone in the ultra-low power consumption mode is low in the prior art is solved, and the technical effect of improving the emergency rescue efficiency of the interphone in the ultra-low power consumption mode is achieved.

The invention relates to a heterogeneous unmanned ship task dynamic optimization method based on intelligent distributed CBBA, and relates to the technical field of unmanned autonomous cooperative control, the method comprises the following steps: firstly, using an intelligent distributed CBBA algorithm to realize task initial allocation, and constructing a cost function integrating distance cost, time cost and time window default penalty; and a task sequence is adjusted through a greedy strategy so as to meet strict time window constraints. And secondly, in the task execution process, a real-time monitoring and dynamic re-planning mechanism is established, and emergencies can be rapidly dealt with. A redistribution mechanism is introduced, the weight of a cost function is dynamically adjusted through a genetic algorithm, and loss and scheme optimization benefits caused by task execution subject change are balanced. Compared with the prior art, the task execution efficiency, reliability and adaptability of the heterogeneous unmanned ship cluster in a complex dynamic environment are effectively improved, and the method is suitable for complex application scenes such as maritime search and rescue, material delivery, patrol monitoring and the like.

The invention discloses a multi-unmanned aerial vehicle search and rescue task planning method based on ABC-DQN hierarchical optimization, and belongs to the technical field of multi-unmanned aerial vehicle cooperative search and rescue. According to the method, task planning is carried out on a search and rescue area applying multiple unmanned aerial vehicles based on an artificial bee colony algorithm and a deep Q network, and adaptive cooperative search and rescue of the multiple unmanned aerial vehicles is completed. According to the method, global planning of a search and rescue task and path planning of a local area are both constrained and optimized, scene analysis of a search and rescue area and selection of the number of unmanned aerial vehicles with different performances are firstly carried out, and the unmanned aerial vehicle of a central control node is determined; and then the central unmanned aerial vehicle completes static global region division by using an ABC algorithm, then unmanned aerial vehicles in a local region perform adaptive search and rescue path planning based on a DQN algorithm, and in a local search and rescue process, the local unmanned aerial vehicles and the central unmanned aerial vehicle perform information interaction. Once the target is determined to appear, the central unmanned aerial vehicle further reduces the global area and performs ABC algorithm division again until the search and rescue target is determined to be within the required range.

The invention discloses a full-aviation life intelligent detection and emergency rescue decision-making method and system. The method comprises the following steps: dividing emergency rescue scene gridding partitions; identifying the disaster degree and the number of affected people; combining an identification result with the gridding partition map to form a red, yellow, blue and green post-disaster gridding partition map; a full-aviation transient electromagnetic method life detector, an optical personnel detection device and a microwave radar life detection device are sequentially adopted to carry out step-by-step detection on the potential affected area, and a final replacement result of red, yellow, blue and green gridding target marking is formed; determining the priority level of ground rescue entering search and rescue according to the gridding partition map and the final replacement result; searching and inputting rescue result information of ground rescue entering search and rescue, and forming box counting statistics of grid information; a rescue decision suggestion is automatically generated according to a box counting statistical result, and efficient cooperation with a ground rescue team is formed; the method can effectively overcome the limitation of a single detection method in complex terrains and extreme environments.

The buried person search and rescue device comprises a portable drilling machine, the portable drilling machine is provided with a plurality of hollow drill rod sleeves, a gas conveying channel used for conveying gas to the ground is arranged in a drill rod, and a gas outlet of the gas conveying channel is connected with a sampling gas inlet of a gas component analysis device through a gas conveying pipe. A gas purifying and filtering device and a gas extracting pump for extracting gas are connected to the gas conveying pipe in series; a plurality of air inlets communicated with the air conveying channel are formed in the side wall of each drill rod sleeve in a surrounding mode, a jet flow nozzle is arranged at each air inlet in the drill rod sleeve, each jet flow nozzle is installed at an outlet of an injection pipe, each injection pipe is connected with an electromagnetic valve in series, and an inlet of each injection pipe is connected with the jet flow pipe. The invention aims to provide the device for searching and rescuing the buried persons, which can remarkably improve the search and rescue efficiency and effect in a complex geological disaster scene, and further realize the rapid search and rescue of the trapped and buried persons in earthquake, landslide and debris flow disasters and the search and rescue of the remains of the victims.

The invention relates to the technical field of infrared thermal imaging, and particularly discloses a fire personnel intelligent search and rescue method and device based on infrared thermal imaging, and the method comprises the steps: synchronously collecting fire scene data through an infrared thermal imager and a laser radar, so as to obtain infrared thermal imaging data and fire scene three-dimensional point cloud data; further introducing a data processing technology based on deep learning to carry out feature extraction on the infrared thermal imaging data and the three-dimensional point cloud data of the fire scene so as to capture infrared thermal radiation distribution features and three-dimensional space structure features of the fire scene, and then carrying out fine-grained feature alignment joint perception on the infrared thermal radiation distribution features and the three-dimensional space structure features; therefore, the deep correlation characteristics of the fire scene heat distribution and the three-dimensional space can be captured, and the intelligent detection of the human body target can be realized on the basis. In this way, the problem that heat radiation information and space structure information are separated in a traditional method can be effectively solved, the information utilization rate is increased, and the accuracy and efficiency of human body target recognition are enhanced.

The invention discloses an under-actuated unmanned ship distributed multi-task distribution method for sea rescue, and the method comprises the following steps: carrying out the clustering and grouping through employing a nearest neighbor clustering algorithm of a greedy strategy based on the position information of a person falling into water at sea, building a task cluster package, and carrying out the clustering and grouping of the task cluster package; constructing a target function of a target allocation problem of the rescue task of the unmanned ship at sea by taking the shortest search and rescue distance as a constraint condition, and taking the optimal time satisfaction as a rescue target; and solving the target function of the target allocation problem of the rescue tasks of the maritime unmanned ships by adopting a clustering consensus consistency packet algorithm to realize task allocation of the plurality of maritime search and rescue unmanned ships carrying the plurality of search and rescue unmanned ships. Compared with an existing unmanned ship task allocation method, the allocation method designed by the invention fully considers the kinematics constraint problem caused by the underactuation characteristic of the unmanned ship, so that the allocation result is closer to the path distance from the unmanned ship to the target point, and the task performability is improved.

The invention provides an intelligent marine emergency equipment automatic positioning method and system. Wherein multiple distress signals are received and fused, and optimal geographical location information and timestamps are generated; determining a distress area on a preset sea area electronic map, correcting a distress position, evaluating probability distribution of each potential distress area, and generating a primary search target with the highest probability; a thermal imaging and optical sensing system on the rescue unmanned aerial vehicle is started for scanning, and a high-accuracy potential survivor position is generated; the search and rescue path is dynamically adjusted and optimized, the search strategy is adjusted, the optimal flight path is updated in real time, and an economical and efficient search and rescue path plan is generated; and timely feeding back the accurate coordinates of the position of the potential survivor to the nearest rescue coordination center, and guiding first-aid equipment to go along the optimal path to implement rescue. According to the technical scheme provided by the invention, the search and rescue efficiency and accuracy are improved, and the position of a potential survivor can be quickly and accurately positioned.

The invention relates to the technical field of data processing, and provides an earthquake narrow space accurate rescue method using a micro intelligent robot. The method comprises the following steps: acquiring millimeter wave radar point cloud data and thermal imaging data of an infrared thermal imager based on a target area; a ruin environment three-dimensional model is prepared according to the data, and a passage path map is generated; self-adaptive networking is deployed for the micro robot cluster, and structural stability evaluation and residual vibration risk prediction are carried out in combination with the atlas; distributing detection partitions and rescue tasks of the micro robot cluster in real time based on an evaluation result; vital sign information is introduced, detection partition and task balanced distribution are optimized, and a parallel scheduling strategy is output. The technical problem that the rescue efficiency and accuracy in the narrow space are low due to the complex environment and incomplete sensing data is solved, and the technical effects that life search and rescue and task allocation are conducted in the narrow space through the micro robot cluster, and the accuracy and efficiency of earthquake narrow space rescue are improved are achieved.

The intelligent fire rescue robot capable of climbing the stairs comprises a tracked robot body, the tracked robot body comprises a body driving wheel, a shell and a bottom plate, a driving mechanism of the tracked robot body is installed on the rear portion of the robot, and two swing mechanisms are symmetrically installed on the front portion of the robot. A steering engine of each swinging mechanism is mounted below the adjacent bottom plate; each swinging mechanism comprises a steering engine, a swinging driving shaft, a swinging plate, a swinging driven wheel, a swinging crawler belt and a core-missing wheel; one end of the swinging driving shaft is connected with a driving steering engine shaft at the output end of the steering engine through a coupler, and the other end of the swinging driving shaft sequentially penetrates through a connecting plate connected with the bottom plate and the center-missing wheel and is connected with the swinging plate through a flange coupler; the upper space of the bottom plate is divided into two parts, one part is used for installing the stretching platform, and the other part is used for installing the detection execution module and the water storage tank. The system has better adaptability to complex terrains, and can realize the integration of discovery, fire extinguishment and search and rescue.

The invention discloses an unmanned ship intelligent search and rescue method and system, and the method comprises the steps: deploying an unmanned ship cluster driven by a multi-agent algorithm, collecting environment parameters, generating an initial cooperative path, starting an improved PV-RCNN model to detect a target in real time, and transmitting a result to an algorithm state space to adjust motion parameters. And repeatedly detecting and adjusting until a surrounding track is generated. The system comprises a distributed dynamic environment parameter acquisition unit, a multi-agent decision control unit and the like. According to the method and the system, the cooperative path is dynamically adjusted through the multi-agent algorithm, the target features are accurately extracted in combination with the improved target detection model, closed-loop linkage is formed, the defects that the adaptability to the dynamic environment is insufficient and target detection and control are disjointed in the traditional technology are overcome, and the search and rescue efficiency and accuracy are improved.

The invention discloses a multi-source cooperative rocker arm-tracked robot composite mechanical structure for earthquake search and rescue, and belongs to the technical field of intelligent rescue equipment. A rocker arm-crawler belt-wheel three-dimensional motion structure system is innovatively designed, a multi-degree-of-freedom rocker arm structure driven by a steering engine is adopted in the middle of the robot, and a double-crawler belt differential driving module driven by a motor is arranged at the bottom of the robot; a swing arm balance wheel set is arranged on the front portion to assist climbing operation. According to the composite structure, a posture stability control algorithm is established through a mass center dynamic model, and intelligent switching of three motion modes of wheel type high-speed maneuvering, crawler belt obstacle crossing and rocker arm climbing can be achieved. Tests show that the structure can enable the robot to stably cross a 100 mm vertical obstacle, good posture stability is kept in unstructured terrains such as gravel and rubble, and meanwhile the environment is sensed through various sensors. Compared with a traditional single motion structure, the multi-dimensional maneuvering ability in the complex post-disaster environment is achieved through mechanical structure innovation, and the terrain adaptability of rescue equipment is effectively improved.

The invention discloses a multi-device cooperative global multi-mode long-endurance emergency system, and belongs to the technical field of emergency safety. The system focuses on a dual-device collaborative architecture, multi-mode communication, multi-stage energy guarantee and intelligent risk assessment technology. The master device integrates a master control module, a multi-mode communication module, an environment sensor group module, an energy management module, an interaction module and the like, and supports a one-dragging-six mode. The system integrates 5G full-band communication and a Beidou independent positioning technology, double-link redundancy of the main device and the wearable device is constructed, and the communication coverage rate is increased from less than 60% to 100%. The beacon module achieves RSSI positioning with the precision smaller than or equal to 5 meters, the near-field search and rescue time is shortened by 40%, the complex terrain positioning efficiency is improved by 50%, the rescue response time is shortened by more than 50%, and the three-break scene requirements are met. The rescue efficiency of remote scenes such as earthquakes, fire disasters, deserts, forests, seaborne and polar regions is remarkably improved, and meanwhile the single-equipment light-weight emergency requirements of scenes such as child monitoring and outdoor exploration are met. The application prospects in the fields of medical treatment, old-age care, camping, chemical engineering and the like are wide.

The invention discloses a search and rescue robot environment perception method based on a multi-task semantic recognition network, and relates to the field of search and rescue robots, and the method comprises the following steps: obtaining an RGB image and a depth image of a search and rescue environment, and constructing a multi-modal search and rescue data set; multi-task semantic recognition network training is carried out, and transfer learning and model lightweight strategy optimization are combined; the multi-task semantic recognition network comprises an encoder and a decoder; wherein the encoder comprises a double-flow parallel backbone network and a multi-stage feature fusion module, and the neck network decoder comprises a target detection branch, an obstacle semantic segmentation branch and a drivable channel segmentation branch; and deploying the optimized multi-task semantic recognition network to a search and rescue robot, and outputting an environment perception result in real time. According to the method, the precision of environment perception of the search and rescue robot is improved, the real-time performance and the computing resource requirement are effectively balanced, and the adaptability and robustness of the search and rescue robot in a complex and changeable post-disaster scene with scarce samples are enhanced.

The present invention discloses a convoy search and rescue calculation method based on DogLeg cooperation. The first step: Obtain the information of the rescue aircraft of the search and rescue aircraft. The second step: Obtain the information of the auxiliary aircraft, and set the value of the DogLeg corner θ in the accompanying flight path of the auxiliary aircraft. The third step: Set the cooperation constraints between the search and rescue aircraft and the auxiliary aircraft. The fourth step: Set the turning speed V1 of the auxiliary aircraft flying at a constant speed within the arc corner, and the average speed V2 of the auxiliary aircraft flying along a straight line. The fifth step: Construct the time cooperation relational expression f(t)=0 between the search and rescue aircraft and the auxiliary aircraft. The sixth step: Construct the route cooperation relational expression f(x)=0 between the search and rescue aircraft and the auxiliary aircraft. The seventh step: Obtain the relative position relationship between the search and rescue aircraft and the auxiliary aircraft. The purpose of the present invention is to provide a convoy search and rescue calculation method and system based on DogLeg cooperation that can timely and accurately plan the flight routes of helicopters and auxiliary aircraft.

The invention provides a detection method and system for earthquake search and rescue, and relates to the technical field of search and rescue robots, and the system comprises an air detection module which is used for obtaining life body information through a composite life detector; the life body information is transmitted to the ground search and rescue module through the communication device; receiving path planning information by using a communication device, and controlling the unmanned aerial vehicle to detect according to a planned path; the ground search and rescue module is used for performing optimal path planning on the unmanned aerial vehicle and the search and rescue trolley based on a depth-first search algorithm by utilizing the processing sub-module to obtain path planning information; based on the path planning information, controlling the search and rescue trolley to perform a search and rescue task; and the base station sub-module is used for converting the life body information into life body information digital signals, sending the life body information digital signals to search and rescue personnel, and sending path planning information to a communication device of the air detection module to complete earthquake search and rescue. The problem that trapped persons are difficult to effectively rescue in a complex post-disaster environment is solved.

The invention relates to a hierarchical intention-based intelligent decision-making method and device for cooperative search and rescue of multiple unmanned aerial vehicles. The method comprises the steps of obtaining an environment simulation diagram; constructing a multi-unmanned aerial vehicle cooperative search and rescue model; performing feature extraction on a search and rescue target and an obstacle through a multi-unmanned aerial vehicle cooperative search and rescue model to obtain a target position feature and an obstacle position feature; performing information gain grid division based on the environment simulation graph, and determining a local information gain field of the proxy unmanned aerial vehicle; according to the local information gain field of the agent unmanned aerial vehicle, information of a neighborhood unmanned aerial vehicle is fused for calculation, and local gain information of the agent unmanned aerial vehicle is obtained; performing feature extraction on the local gain information to obtain local gain features; performing attention focusing on the target position feature, the obstacle position feature and the local gain feature, and outputting a multi-source aggregation feature; and processing the multi-source aggregation features through a multi-layer perceptron, and outputting normal distribution of actions of the unmanned aerial vehicle. The search and rescue efficiency of the unmanned aerial vehicle in the search and rescue scene can be improved.

The invention relates to an unmanned aerial vehicle scheduling method and system for emergency rescue, a terminal and a storage medium, and belongs to the technical field of emergency rescue, the unmanned aerial vehicle scheduling method comprises the following steps: sending an inspection instruction to a front-end unmanned aerial vehicle cluster, and the front-end unmanned aerial vehicle cluster performing inspection according to a preset inspection path; after the front-end unmanned aerial vehicle cluster finds the disaster site, receiving a preliminary assessment report of the disaster site sent by the front-end unmanned aerial vehicle cluster; the front-end unmanned aerial vehicle cluster generates and uploads a preliminary evaluation report after discovering a disaster site; inputting the preliminary evaluation report into a pre-constructed scoring model to obtain a score value of the preliminary evaluation report; calling a current dispatching scheme matched with the score value from a historical dispatching database; and sending a rescue instruction to the standby unmanned aerial vehicle cluster and the rescue terminal according to the current dispatch scheme. The method has the beneficial effects that the response time of the rescue unmanned aerial vehicle is shortened, so that the search and rescue efficiency is improved.

The invention relates to the technical field of agent path planning, in particular to a land unmanned vehicle cooperative search and rescue simulation method, which comprises the following steps of: establishing a search and rescue simulation environment containing a plurality of unmanned vehicles based on a search and rescue scene; each unmanned vehicle obtains the states of all unmanned vehicles in real time; each unmanned vehicle transmits the obtained state to a neural network model and other unmanned vehicles, the neural network model generates the action probability distribution of each unmanned vehicle according to the input state information, and each unmanned vehicle selects the optimal action based on the current strategy and the corresponding action probability distribution; after each unmanned vehicle executes the optimal action and cooperates with other unmanned vehicles, the search and rescue simulation environment generates an award and a new state; training and updating the neural network model; according to the method, a near-end strategy optimization algorithm based on the reward center is utilized, the search strategy is optimized, and the task completion efficiency is improved.

The invention provides a personnel detection method and system applied to a fire scene, and the method comprises the steps: firstly obtaining a multi-source signal data set of the fire scene, enabling the multi-source signal data set to comprise communication signal units which are collected at different positions and are provided with time stamps, carrying out the signal feature extraction processing of the multi-source signal data set, and carrying out the detection of the multi-source signal data set; the method comprises the following steps: obtaining spatial distribution characteristics and time sequence change characteristics of a communication signal unit, then calling a pre-trained personnel positioning model to carry out joint positioning analysis on the spatial distribution characteristics and the time sequence change characteristics, and generating a potential personnel position candidate set corresponding to the communication signal unit; target position information of to-be-rescued personnel in the fire scene is determined according to the potential personnel position candidate set, finally, a rescue guide instruction containing position coordinates is generated based on the target position information, the rescue guide instruction is sent to a rescue terminal to trigger personnel search and rescue operation, and therefore indirect positioning is achieved through communication signals, and rescue efficiency is improved. Limitations of a traditional method are overcome, and rescue efficiency is improved.