360 results about "Command center" patented technology

The invention discloses an event early warning efficient management method and system based on park management, and relates to the technical field of safety management, and the method comprises the steps: constructing a hexagonal cellular unit layout, generating an equipment registry and a network topology structure, and forming a structured data set through data alignment and sliding window verification; the edge server loads an initial weight, calculates a dynamic weight matrix based on the LSTM network, and generates a high-order feature vector set in combination with the graph neural network; inputting the high-order feature vector set into a space-time prediction model to generate an environment evolution trend prediction value, fusing network topology and BIM model parameters to calculate a dynamic risk index, and triggering graded early warning; the command center analyzes the early warning signal to generate a fusion visual interface, historical cases are matched through federal learning, a resource allocation scheme is optimized, and an optimal disposal strategy is output through Monte Carlo simulation. By dynamically adjusting the weight of the sensor, the weight of the infrared sensor in the high-temperature-sensitive area is increased, and the abnormality capturing efficiency is improved.

The invention discloses a multi-mode city security video abnormal behavior real-time detection system and method, and belongs to the technical field of artificial intelligence and city security, and the system comprises an edge node module which is used for collecting a local video stream and sensor data, and executing local model training and real-time abnormal detection; the central coordination server module is used for federal model parameter aggregation, global model distribution and dynamic resource coordination; the multi-modal data fusion module is used for realizing cross-modal feature fusion through space-time alignment and an attention mechanism; the dynamic federation strategy module is used for calculating a load and a network state according to an edge node and adaptively adjusting a model parameter aggregation frequency; and the alarm linkage module triggers a local sound-light alarm when an abnormal behavior is detected, and encrypts and transmits event information to a command center. According to the method, the problems of privacy leakage, bandwidth pressure and multi-modal data collaboration in the prior art can be solved, and on the premise that data privacy is guaranteed, calculation overhead is minimized, and heterogeneous equipment is adapted.

The invention relates to the technical field of unmanned aerial vehicle scheduling, and discloses an emergency scene unmanned aerial vehicle task allocation method based on multi-objective optimization, and the method comprises the steps: obtaining a task parameter set of an unmanned aerial vehicle in real time through a standardized task interface, and obtaining a state data set of the unmanned aerial vehicle in real time through an unmanned aerial vehicle management platform; secondly, calculating an optimal task allocation scheme by adopting a multi-target particle swarm optimization algorithm and an improved PSO algorithm, and dynamically adjusting sudden emergency tasks and priority changes in combination with a task preemption mechanism; three task allocation plans including a time optimal scheme, a resource optimal scheme and a parameter optimal scheme are provided to ensure that tasks can be completed in the shortest time or executed at the lowest cost, and the final scheme is provided for a command center for decision making. According to the invention, the intelligence and flexibility of unmanned aerial vehicle scheduling are improved, and rapid response and global optimal resource configuration of unmanned aerial vehicle scheduling in an emergency scene are realized.

The invention provides an unmanned aerial vehicle cruise pre-warning method and monitoring system applied to high-speed traffic monitoring, and the method comprises the steps: collecting the dynamic monitoring data of a target road segment in real time through a plurality of hovering monitoring platforms disposed along a highway, and recognizing an abnormal event in the target road segment based on the dynamic monitoring data; generating a multi-level early warning signal according to the event type of the abnormal event, transmitting the multi-level early warning signal to a command center to generate an unmanned aerial vehicle dispatching instruction matched with the event type, and controlling the target unmanned aerial vehicle to move to a target position corresponding to the abnormal event based on the path planning parameter; and an early warning operation corresponding to the event type is executed through a loudspeaker module of the target unmanned aerial vehicle, the monitoring range of the hovering type monitoring platform is adjusted according to the continuous state of the abnormal event, and a flow adjusting instruction is sent to a service area control system of the vehicle in the target direction. According to the invention, the timeliness of accident early warning and the systematicness of a disposal scheme can be improved, the occurrence rate of a secondary accident is effectively reduced, and the utilization efficiency of emergency resources is optimized.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention relates to the field of unmanned aerial vehicle control, and discloses a police unmanned aerial vehicle management and control system for handling emergencies, which comprises a sensing module, a distribution module, a planning module, a control module, a tracking module and a feedback module. The sensing module is used for collecting environment data in real time and generating a three-dimensional environment model; the distribution module optimizes an unmanned aerial vehicle task distribution scheme; the planning module dynamically plans the optimal flight path of the unmanned aerial vehicle according to the task allocation result and the three-dimensional environment model; the control module dynamically adjusts the attitude and flight height of the unmanned aerial vehicle; the tracking module tracks the target position in real time and predicts the motion trail of the target; and the feedback module transmits the execution condition of the unmanned aerial vehicle, the target tracking result and the field real-time picture back to the command center. Through cooperative work of all the modules, rapid response and efficient disposal of emergencies are achieved, the system is particularly suitable for police service tasks in complex and dynamic environments, and the system has the advantages of being high in response efficiency, high in environmental adaptability and high in resource utilization rate.

The invention discloses a safe operation system and a safe operation method of a space-ground integrated rescue vehicle, which are used for the rescue vehicle working in a complex disaster environment and ensure stable and reliable two-way communication connection between the rescue vehicle and a command center by establishing an intelligent switching mechanism of a low-orbit satellite main link and a ground network standby link. Real-time transmission of key instructions and field information is guaranteed, and a foundation is laid for safe operation. Advanced risk early warning is achieved through a space-ground cooperation mechanism, dynamic decision making and path planning are carried out by integrating satellite information and local sensor data, and the global adaptive capacity of the rescue vehicle to the complex disaster environment is improved. Satellite pre-sensing information is combined with a local decision, and a pre-control instruction is generated to act on a drive-by-wire chassis, so that the rescue vehicle can smoothly deal with hidden or developmental risks in advance, and the driving safety and stability are improved.

The invention relates to a multi-unmanned aerial vehicle maritime cooperative reconnaissance method based on an Archimedes algorithm and hierarchical scheduling, and the method comprises the steps: defining a patrol task region and task targets, and constructing a topological model of an unmanned aerial vehicle cluster for each task target; according to the cooperation characteristic of the unmanned aerial vehicle cluster, Archimedes patrol path planning is carried out on various unmanned aerial vehicles; based on the performance of the unmanned aerial vehicle, factors such as path length, flight time, unmanned aerial vehicle scanning range and search efficiency are comprehensively considered, and the path of the unmanned aerial vehicle is optimized, so that the unmanned aerial vehicle has relatively high execution efficiency and stability; when the leader unmanned aerial vehicle finds a target during patrol, the cluster dispatches the investigation unmanned aerial vehicle to perform a tracking task; and the investigation data is returned to the command center, and next task planning is carried out. The cooperative reconnaissance capability of the unmanned aerial vehicle cluster in complex tasks can be effectively improved.

The invention discloses a mobile application scene monitoring method and system based on audio and video, and relates to the technical field of intelligent monitoring. The method is executed by an edge computing node and comprises the following steps: S1, constructing a three-level edge computing network architecture; s2, performing multi-stage noise reduction processing on the wearable device layer, performing adaptive noise suppression based on a 200ms time window, and extracting a pure voice signal; s3, heterogeneous network bandwidth resources are allocated through a dynamic priority transmission protocol, and urgency grading is performed on the audio signal Saudio and the video frame Vframe; S4, processed audio and video data are input into a double-flow neural network for space-time alignment, and a fusion feature vector is generated; and S5, uploading the fusion feature vector and the metadata to a command center cloud platform of a center cloud layer through a secure transmission protocol. According to the invention, through a three-level edge computing network architecture, distributed processing of data is realized, the computing pressure of a central cloud layer is reduced, and the data processing efficiency is improved.

The invention discloses a fire water tanker emergency intelligent scheduling method and system based on a cloud platform, and relates to the technical field of intelligent scheduling management. According to the method, based on a multi-dimensional real-time database constructed by a cloud platform, multi-disaster data and priority data in a to-be-dispatched vehicle data dynamic acquisition and classified storage process are acquired, and preliminary dispatching distribution of to-be-dispatched vehicles is synchronously completed, so that a dispatching response initial period is shortened; after preliminary scheduling distribution, candidate scheduling schemes are generated, and meanwhile, an optimal scheduling scheme is screened out so as to accurately match emergency requirements of each target area; after the optimal scheduling scheme is obtained, based on a received scheduling instruction, a driving route and task allocation are dynamically adjusted, and scheduling information is synchronously pushed to a command center and each task scheduling site, so that rescue delay caused by information lag is avoided. Full-process optimization of data processing, scheme screening and collaborative scheduling in a multi-disaster-situation concurrent scene is realized, so that the timeliness of emergency scheduling of the fire water tanker is effectively improved.

The invention discloses a customs remote intelligent patrol system based on a 5G + unmanned aerial vehicle technology, and relates to the technical field of unmanned aerial vehicle patrol. An unmanned aerial vehicle module is responsible for performing field data acquisition and primary processing according to application scene requirements; the wireless communication terminal provides a high-speed and low-delay data transmission channel through a 5G communication network; the customs command center is responsible for gathering, analyzing, commanding and deciding the collected application scene data. The data analysis module is matched with a high-definition camera to enable collected remote sensing data to be further close to a real image, so that customs supervision work management can be more accurate and stable, an intelligent inspection route is continuously optimized by carrying a path navigation model based on an improved ant algorithm, and the intelligent inspection efficiency is improved. And the intelligent inspection efficiency is improved.

The invention discloses a collision sensing intelligent guardrail system and method based on Beidou positioning, and the method comprises the following steps: S1, collecting sensor data along a guardrail, and carrying out the preprocessing; s2, transmitting the preprocessed sensor data to an edge server, and forwarding the preprocessed sensor data to a cloud server through a cellular network; s3, constructing a gated convolutional network based on two channels, and inputting the state vector sequence into the gated convolutional network; s4, performing multi-layer nested coding and decoding operation on the collision feature vector, constructing a collision judgment function, and judging whether collision abnormity is triggered or not; s5, generating a collision early warning data packet when the collision level label is in an activated state; and S6, transmitting the collision alarm data packet to a road traffic command center for road maintenance personnel to quickly respond and deal with the collision alarm data packet. According to the invention, high-precision real-time perception and intelligent early warning of the collision state of the road guardrail are realized, and the automation level and response efficiency of traffic safety monitoring are obviously improved.

The invention discloses an intelligent water conservancy monitoring system and method based on air and underwater cooperation, and relates to the technical field of water conservancy projects, and functional modules of the system comprise an air platform, an underwater platform and a command center; comprising the following steps of: modeling a dynamic task demand based on multi-dimensional environment collaborative awareness; an air-horizontal platform task allocation mechanism based on collaborative path-load optimization; a multi-source data space-time coding and platform embedded sensing guide sampling mechanism; edge node multi-source observation collaborative fusion and local anomaly recognition; carrying out risk trend prediction and regional priority reconstruction based on a dynamic evolution network; and generating an intelligent response strategy based on risk dynamic reconstruction. According to the method, a cooperative working mechanism of an air platform and an underwater platform is constructed, and technologies of multi-source data fusion, a multi-platform linkage response strategy and the like are combined, so that systematic enhancement of a water conservancy project area in multiple dimensions of full space coverage, high time frequency, strong risk identification, response schedulability and the like is realized.

The invention discloses an unmanned aerial vehicle emergency control management method and system based on digital twinning, and belongs to the technical field of unmanned aerial vehicle control. The system comprises an unmanned aerial vehicle data acquisition module which acquires image data in an emergency area and parameter records of an unmanned aerial vehicle; the virtual scene construction module is used for constructing a virtual scene by using a digital twinning technology, and mapping and dividing a three-dimensional grid in real time; the emergency route analysis module is used for planning a feasible route for each unmanned aerial vehicle in the virtual scene and screening out key routes; the execution scheme planning module is used for analyzing each scheme according to the key route planning scheme so as to set a communication scheme; and the operation visualization module is used for supervising each unmanned aerial vehicle to fly according to the communication scheme and displaying a virtual scene in real time through a visualization page of the command center. According to the invention, through digital twin-driven multi-source sensing fusion, dynamic grid adaptation and nonlinear anti-interference path planning, the environment modeling precision and communication link reliability of the unmanned aerial vehicle group in a disaster environment are improved.

The invention provides an emergency response terminal system with image data return capability, which relates to the technical field of emergency response terminals and comprises an emergency response terminal management subsystem, an emergency response terminal, a multi-mode communication network, an image data return channel and an equipment state monitoring system. The emergency call terminal management subsystem is deployed on an emergency management platform and is used for generating and auditing an early warning task, dynamically selecting a communication link, interacting with a terminal through a satellite network and receiving a field image and equipment state data returned by the terminal; through the satellite, Beidou and 4 / 5G multi-link redundancy design, it is ensured that early warning information can be reached under the three-disconnection scene of network disconnection, power failure and disconnection, the problem of communication failure of a traditional system is solved, low-delay and wide-coverage transmission of the early warning information is achieved, the encrypted image return function is adopted for providing real-time disaster situation pictures for a command center, the rescue decision accuracy is improved, and the rescue cost is reduced. And the decision support capability is improved.

The invention discloses a photovoltaic panel ground cleaning control system and method based on an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle control, and the system comprises a cleaning unmanned aerial vehicle, a ground control center system, a command center system and corresponding functions. Through modular design and multi-system cooperation, intelligentization, high efficiency and precision of photovoltaic panel cleaning operation are achieved.

Disclosed are a method, system, and apparatus of a corrections officer tactical gear using computer vision to notify of an ambient threat. In one embodiment, a personal protective equipment includes a responsive device integrated in a tactical gear and a visual sensor of the tactical gear to identify an inmate using an inmate artificial intelligence model. The responsive device notifies a wearer of the tactical gear when the visual sensor identifies an ambient threat to the wearer and / or the inmate. A geo-location device may identify a present location of the wearer when the visual sensor identifies the inmate. When the visual sensor identifies the inmate, a present location of the wearer may be transmitted from the tactical gear to a command center of a correctional facility (e.g., may also identify a distance away and / or a location of the inmate from the present location of the wearer).

The invention discloses an intelligent road system for vehicle-road cooperation safety alarm, and relates to the technical field of vehicle-road cooperation alarm, and the system comprises a communication evaluation module which inputs an optimal communication path into a communication mapping model, a space matching layer carries out the fusion of road geometry and communication topology, a state mapping layer marks the signal intensity and spike node states, and a state analysis module which carries out the analysis of the optimal communication path; generating a communication quality thermodynamic diagram; the risk decision module is used for evaluating the risk level of each region based on the communication quality thermodynamic diagram and outputting priority transmission parameters; and the information transmission module starts sound-light alarm according to the priority transmission parameters, obtains an execution state report, and transmits the execution state report back to the command center. According to the method, the communication mapping model is constructed and the priority transmission parameters are dynamically generated, so that the accuracy of communication state evaluation is improved, and efficient cooperative processing and intelligent priority distribution of multi-source alarm information are realized.

The invention relates to the technical field of communication engineering, in particular to a high-precision charge-discharge balanced intelligent positioning system integrating Beidou and ultra-wideband technologies, which integrates the Beidou and ultra-wideband technologies, introduces AI artificial intelligence, provides data support for intelligent safety management and control of engineering field personnel based on big data, and improves the safety of the engineering field personnel. The engineering site safety management level is improved; comprising a fusion type single base station which is installed and deployed in a place needing positioning and is used for receiving positioning signals and transmitting data; the solar energy complementing multimode positioning tag is worn by a constructor and is used for intelligently switching a positioning mode, complementing energy, monitoring a state and returning data; and the AI intelligent analysis system is deployed in an engineering command center or a monitoring center and is used for receiving the data and carrying out intelligent analysis, management and decision-making assistance.

The invention discloses a self-optimizing intelligent work order scheduling method and system, belongs to the technical field of work order scheduling, and solves the problems that a traditional scheduling method is poor in dynamic adaptability due to static rules, single in optimization target and dependent on artificial experience, and a model cannot be self-optimized online. According to the technical scheme, the method comprises the steps that work order features and resource state data are collected in real time and input into a pre-trained deep reinforcement learning model to generate a scheduling scheme, a scheduling deviation value is calculated based on actual feedback data to trigger model iterative optimization, and dynamic updating of a scheduling strategy is achieved; the method is mainly used for work order automatic distribution and scheduling optimization in scenes such as a grid command center, a logistics distribution platform and a customer service center.

The invention discloses a fire extinguishing robot control method and system based on multi-modal sensing and autonomous navigation, and the method comprises the steps: collecting environment temperature data according to a preset frequency through a thermal imager, and triggering early warning when an abnormal threshold value is reached; after early warning, the depth camera obtains a depth image, the depth image is processed through mirror reflection filtering and smoke filtering algorithms and input into a flame form recognition model to judge whether flame features exist or not, and if yes, a fire disaster is judged. And after the fire is confirmed, constructing a real-time model, planning an obstacle avoidance path and guiding the robot to a fire source according to a pre-stored three-dimensional / two-dimensional map in combination with a synchronous positioning and map construction technology. After reaching, adjusting the spraying angle of the fire extinguishing mechanism, and extinguishing fire by water mist / dry powder. During fire extinguishing, the real-time video of the fire scene and the fire source position data are uploaded to a fire-fighting command center in real time. The problems that in the prior art, autonomous fire extinguishing cannot be achieved, environment perception is poor, and early warning and fire extinguishing efficiency is low are solved.

The invention provides an unmanned aerial vehicle-based traffic accident auxiliary processing method and system, and the method comprises the steps: determining a first coordinate of an accident site on an electronic map according to alarm information, and obtaining second coordinates and electric quantity of a plurality of unmanned aerial vehicles in a standby state at the periphery; respectively calculating the distance between the unmanned aerial vehicle and the accident site, and calculating the distance score and the electric quantity score of each unmanned aerial vehicle; performing comprehensive scoring according to a preset distance weight and an electric quantity weight, and selecting the unmanned aerial vehicle with the highest comprehensive score to execute a task; the node with the minimum f (n) value is selected as the current node, and circulation is carried out in sequence until the first coordinate is reached, and a flight path is obtained; and the unmanned aerial vehicle goes to the accident site according to the flight path, shoots a field picture according to the control instruction, performs voice communication with the field, obtains picture data and voice data, and returns the picture data and the voice data to the command center. According to the invention, the most suitable unmanned aerial vehicle can be quickly and accurately selected to go to the accident scene, and the path and unmanned aerial vehicle unified scheduling are planned.

The invention relates to an unmanned system intelligent combat platform based on a large model and digital twinning, belongs to the technical field of strike evaluation, and solves the problems of low intelligent level, poor combat effectiveness evaluation and low visualization degree in the unmanned system technology in the prior art. The large combat model is used for generating combat area environment information and target information and further generating each combat scheme so as to obtain an optimal combat scheme; the digital twin virtual battlefield is used for reconstructing a virtual combat environment; the controller is further used for reconstructing a virtual target in the virtual combat environment and performing fine adjustment on the local combat environment, then striking the virtual target in the virtual combat environment according to the received optimal striking scheme, and collecting an image set after striking of the virtual target; the large combat model is also used for obtaining a simulated damage evaluation result according to the received image set after the strike; and the command center is also used for performing actual strike based on the simulated damage evaluation result. And intelligent combat of the unmanned system is realized.

The invention provides a multi-dimensional perception-driven field large-scale scene intelligent detection method and system, and relates to the technical field of scene intelligent detection. According to the method, a closed-loop system covering multi-source sensing, edge preprocessing, three-dimensional space-time fusion inference, alarm linkage response and continuous model optimization is constructed. By arranging a compound eye imaging sensor, an inertia-GNSS sensor, a millimeter wave radar sensor and an electrical parameter and environment sensor on site, synchronous acquisition and transmission of multi-modal data are realized; and de-noising, standardization and calibration processing is carried out on edge nodes, and combined identification of a target state, an event category and a risk level is completed based on a deep fusion network. When a detection result meets a preset condition, alarm information is generated, and the command center, the wearable terminal and the execution mechanism are linked to respond in time; meanwhile, feedback is periodically collected and disposed, the model is driven to be self-updated, the detection accuracy and response adaptability are continuously improved, and the method is suitable for intelligent safety monitoring of complex operation scenes such as mines, electric power and ports.

The invention relates to the technical field of anti-riot vehicles, and discloses a police anti-riot command vehicle which comprises a vehicle chassis, a compartment body arranged on the vehicle chassis and a command communication system arranged in the compartment body, and further comprises an anti-riot net laying device integrated on the top of the compartment body and used for rapidly launching and unfolding an anti-riot net towards the outside of the vehicle; the emergency supporting wheel mechanism is arranged on the vehicle chassis, corresponds to vehicle tires installed on the side face of the vehicle chassis and is used for providing emergency supporting when the vehicle tires fail. The anti-riot net laying device comprises a launching cylinder, an anti-riot net contained in the launching cylinder and a driving mechanism providing power for launching of the anti-riot net, and the launching cylinder is arranged at the top of the compartment through a mounting base. Through an integrated anti-riot net laying device and a centralized control system, a non-fatal interception means and a command center are deeply fused, and seamless connection from situation awareness to rapid disposal is achieved.

The utility model provides an intelligent scheduling command platform with integrated scheduling, and effectively solves the problem that the scheduling work is influenced because an interphone and a camera in the prior art are lack of effective means of effective electric quantity. The dispatching command platform comprises an electric quantity monitoring module, the electric quantity monitoring module comprises a primary diagnosis circuit and a secondary diagnosis circuit, the primary diagnosis circuit detects a current signal of a battery in dispatching equipment during use by using a current sensor, obtains a reminding signal and an early warning signal based on the current signal, and sends the reminding signal and the early warning signal to the secondary diagnosis circuit; the reminding signal and the early warning signal are output to a command center; the primary diagnosis circuit is connected with one end of the secondary diagnosis circuit, and the other end of the secondary diagnosis circuit is connected with the command center.

The invention discloses a forest fire unmanned aerial vehicle video monitoring system and method. The system comprises a data acquisition unit arranged on an unmanned aerial vehicle and responsible for acquiring multi-source monitoring data and spatial attitude data of a target area; the data processing unit is arranged on a ground station and is used for receiving and processing the data and generating fused situation data containing the position and situation of the fire behavior through a fire behavior recognition algorithm; the dynamic path planning unit is used for automatically generating a cruising or path tracking instruction according to the fused situation data, the geographic information and the state of the unmanned aerial vehicle; and the command decision-making unit is arranged in a ground command center and is responsible for visualized display, spatial analysis and emergency command of the fire behavior and issuing a path instruction to the unmanned aerial vehicle for execution so as to realize a monitoring and disposal closed loop. According to the invention, multi-source data efficient processing, fire accurate identification, dynamic path planning and emergency command efficient linkage can be realized, the monitoring coverage range and the response speed are improved, the labor cost is reduced, and the fire loss is reduced.

Embodiments of the disclosed is a system comprising a wearable command center. One such embodiment comprises a case clip top portion; a chest mount bottom portion; a hinge member assembly; a connector cartridge assembly; a case; and at least one structural guide rail.

The invention discloses a heterogeneous communication network queuing time delay violation probability prediction method based on a yoke theory. The method is suitable for an emergency scene emergency communication system cooperatively constructed by an unmanned aerial vehicle and a high-altitude platform. The method comprises the following steps: modeling links from an unmanned aerial vehicle to a high-altitude platform and from the high-altitude platform to a ground command center in a communication network into a two-hop heterogeneous queuing service system; secondly, modeling a dynamic evolution process of data arrival and channel service by adopting a Markov Monte Carlo method; and finally, constructing a multi-hop heterogeneous network joint arrival-service yoke process based on a yoke theory, and deducing a violation probability boundary expression of end-to-end queuing time delay. The invention provides a time delay violation probability prediction method in cooperative emergency rescue communication of an unmanned aerial vehicle and a high-altitude platform. The service reliability of a system under given time delay constraint is accurately quantified.

The invention discloses a fire-fighting indoor rescue positioning method and system based on national secret trusted communication, and relates to the technical field of rescue worker indoor positioning, and the method comprises the following steps: step 100, forming a hybrid positioning network through a UWB positioning base station, a UWB positioning card and a visual positioning node; step 200, acquiring an initial position and a motion trail of the trapped person, encrypting the initial position and the motion trail by adopting a national secret algorithm, and uploading the encrypted initial position and the motion trail to a command center digital twin platform; step 300, a rescue worker terminal collects multi-source sensor data and receives a UWB base station signal, and real-time position information and sign information of a rescue worker are generated in combination with a pre-stored indoor micro map; step 400, encrypting real-time position information and sign information of the rescue workers through a national secret algorithm, selecting a communication frequency band by adopting a dynamic spectrum allocation mechanism, and transmitting the communication frequency band to a digital twin platform of a command center; and step 500, the command center digital twin platform fuses the real-time position of the rescue worker and the information of the trapped person, generates a rescue obstacle avoidance path and transmits the rescue obstacle avoidance path to the rescue worker terminal.