75results about How to "Early warning" patented technology

The invention relates to the technical field of geological risk monitoring and early warning in tunnel construction, in particular to a karst tunnel risk intelligent early warning method, device and equipment based on deep learning, and the method comprises the steps: obtaining multi-source coupling data; performing high-dimensional fusion on the processed multi-source coupling data by using a generative adversarial network, and reconstructing a high-precision three-dimensional model of the detection area; performing geological risk grade classification on the detection area to generate a corresponding risk distribution map; generating a dynamic risk evaluation index, and triggering an early warning signal when the risk level exceeds a preset threshold value; the method comprises the steps of multi-source data acquisition, data preprocessing, high-precision three-dimensional model reconstruction, risk grade classification, dynamic risk monitoring and early warning and the like, and can solve the problems of single risk detection means, insufficient evaluation precision and poor real-time performance in existing karst tunnel construction.

The invention discloses a battery thermal runaway detection system based on an early warning function, and relates to the technical field of battery thermal runaway, and the system comprises a supervision center which is used for carrying out the real-time supervision of the data information of a plurality of batteries, carrying out the early warning of the time data of the battery thermal runaway, and facilitating the timely issuing of warning information and notification announcements, and a monitoring device which is used for monitoring the data information of a plurality of batteries. The monitoring device is used for collecting thermal runaway parameters of each detection node of a plurality of batteries, the thermal runaway parameters comprise battery voltage, temperature, current and pressure, and the protection device is used for carrying out delayed protection operation on the monitoring device. The battery thermal runaway detection system based on the early warning function establishes a thermal runaway early warning model by fusing multi-parameter data of heat, force, electricity and the like and historical data of a thermal runaway phenomenon and by utilizing various learning algorithms, monitors battery parameters and sets simple judgment standards and threshold values such as a non-working state current threshold value and the like, so that the battery thermal runaway early warning model is established. Early detection of battery thermal runaway is realized, and monitoring and early warning effects are achieved.

The invention provides a ship remote real-time early warning system based on cloud computing, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting ship operation data, environment state data and external interaction data; the data preprocessing module is used for generating standard processing data; the data grouping module is used for dividing a priority data group and a common data group; the data transmission module is used for transmitting the data to the cloud analysis module; the cloud analysis module is used for carrying out multi-source data aggregation processing on the priority data set and the common data set, carrying out real-time evaluation on the ship operation state, identifying abnormal behaviors and predicting emergencies, and generating corresponding final early warning instructions including an avoidance operation instruction and an air route adjustment suggestion; the early warning feedback module is used for sending the early warning instruction to the ship terminal; the local response module is used for executing instant triggering operation on the instruction classification data; according to the invention, autonomy and accuracy of remote real-time early warning of the ship are improved.

The invention discloses an ocean internal wave monitoring method based on deep learning, which belongs to the technical field of ocean internal wave monitoring, is used for ocean internal wave monitoring, and comprises the following steps: acquiring a flow velocity data set of an observed sea area, taking the flow velocity data set as an original data set, performing quality control on the original data set, and performing normal sequence and abnormal sequence distinguishing on the data after quality control. Dividing a training set, a verification set and a test set; constructing an ocean internal wave anomaly monitoring model, optimizing anomaly scores and reconstructing error parameters, monitoring flow velocity observation data of the sea area by using the trained ocean internal wave anomaly monitoring model, and identifying an ocean internal wave sequence; and performing internal wave mechanical characteristic analysis on the ocean internal wave sequence extracted by the ocean internal wave anomaly monitoring model, and outputting characteristics. According to the method, the behavior pattern of the normal flow velocity can be accurately captured by adopting the deep learning model, the anomaly detection of the internal wave flow is realized through the reconstruction error, the normal flow velocity and the internal wave flow velocity are effectively distinguished, and misjudgment and missed judgment caused by a traditional method are reduced.

The invention relates to the technical field of risk early warning, in particular to a mine safety risk early warning method and system, and the method comprises the following steps: screening abnormal behavior data related to tunnel stability monitoring based on real-time collected monitoring data, updating a behavior pattern library through employing a PrefixSpan algorithm, recognizing sequence features matched with historical accident data, and carrying out the early warning of the safety risk of a mine. And generating a sequence behavior early warning index, and performing time sequence analysis on the historical harmful gas detection data based on the sequence behavior early warning index. According to the method, a risk mode is accurately identified through real-time data flow analysis, faster early warning of potential dangers is realized, risk factors can be dynamically evaluated through combination of sequence feature identification and environmental change analysis, the real-time performance and correlation of early warning information are guaranteed, and the early warning efficiency is improved by refining a risk area and optimizing a data acquisition strategy. The operation efficiency and the response speed of the whole safety monitoring system are improved, and it is ensured that quick and accurate response can be made at the critical moment.

The invention relates to the technical field of polar region ships, in particular to an icebreaker bow strength dynamic evaluation method based on regression analysis. According to the method, corresponding simulation databases are constructed on the basis of three ice condition scenes, the structural stress response extreme value and the measuring point stress extreme value of the icebreaker bow are set as safety evaluation and early warning indexes, and on the basis of simulation data of the icebreaker bow stress response, the safety evaluation and early warning of the icebreaker bow stress response are achieved. A regression analysis method is applied to optimization of a simulation fitting model, polynomial fitting optimization is carried out on a structural stress response extreme value and a measuring point stress extreme value, the fitting precision and effectiveness are greatly improved, an optimal polynomial mapping model of the structural stress response extreme value and the measuring point stress extreme value is obtained, and the structural stress response extreme value and the measuring point stress extreme value are optimized. Safety evaluation and early warning can be carried out more quickly, and the safety and high efficiency of the icebreaker working in the ice area are ensured.

The invention discloses a fire preventing and extinguishing method for a large goaf of a coal-pillar-free island working face, and belongs to the field of working face fire preventing and extinguishing. A fire preventing and extinguishing method for a large goaf of a coal-pillar-free island working face comprises the steps that before working face stoping, the CO concentration of the goaf is detected in the modes of real-time online monitoring, regular manual detection and gas sample chromatographic analysis, and early warning is conducted on potential fire hazards; according to the concentration of CO in the air, a pressure-equalizing air control technology, a goaf guniting and leaking stoppage technology, a goaf grouting and nitrogen injection technology, a stopping agent spraying fire prevention and extinguishing technology or a combination of multiple technologies are adopted for prevention and control. Corresponding measures are taken according to the CO concentration of the goaf, on the premise that the mining safety of the working face is guaranteed, excessive prevention and control are avoided, waste of resources is reduced, the economical efficiency of coal mine enterprises is guaranteed, and the safety and the economical efficiency in the working face stoping process are both considered.

The invention discloses a coal mine fireproof monitoring and analysis method based on unmanned aerial vehicle inspection, which relates to the technical field of coal mine fireproof monitoring and analysis, and comprises the following steps: when an unmanned aerial vehicle arrives at a coal mine goaf area for inspection, acquiring environmental parameter information of the coal mine goaf area in real time, wherein the characteristic information comprises gas distribution characteristic information and airflow dynamic characteristic information. According to the method, the gas distribution characteristic information and the airflow dynamic characteristic information are acquired in real time, and the gas distribution index is generated by utilizing the machine learning model which is learned in advance, so that the goaf gas distribution is intelligently evaluated. According to gas distribution indexes, abnormal distribution is divided into low risk, medium risk and high risk, refined inspection strategies such as frequency increasing, path expanding, dynamic height adjusting and multi-machine collaborative operation are adopted for different risk levels, comprehensive monitoring and quick response are achieved, inspection resource distribution is optimized, and coal mine safety accidents are effectively prevented.

The invention discloses a multi-source intelligent analysis system and method for power plant area personnel safety management. According to the system, multiple technologies including big data analysis, a deep learning algorithm, UWB high-precision positioning, Beidou positioning and navigation, a pressure sensor, a smoke alarm and an attitude sensor are integrated into an intelligent helmet worn by a worker, and a complete safety management solution is formed. The system can collect position information, behavior states and environmental data of operators in real time, perform multi-source data fusion processing, and intelligently analyze the standardization of operation behaviors and the safety of the surrounding environment. Through multi-dimensional risk assessment, early warning of safety risks of operators is realized, early warning prompts are sent to managers, and intelligent management and control support is provided for personnel safety in a power plant. The system is mainly composed of a data acquisition module, a data fusion processing module, a behavior analysis module and an early warning and feedback module, and can significantly improve the safety management level of a power plant area and the protection effect of operators.

The invention discloses a boiler combustion state monitoring and early warning system and method, terminal equipment and a storage medium, and relates to the technical field of boiler operation. The system comprises a server, a local control subsystem, a flame television subsystem and a distributed control subsystem, the local control subsystem comprises a local control cabinet and a plurality of hearth temperature on-line monitoring devices, and the multiple hearth temperature on-line monitoring devices are located between a main combustion area and a burnout area of a boiler and arranged in a staggered mode; the server can obtain hearth temperature data transmitted by the local control subsystem, flame video data transmitted by the flame television subsystem and combustion data transmitted by the distributed control subsystem, and input the hearth temperature data, the flame video data and the combustion data into a pre-trained neural network model; and the type corresponding to the boiler combustion state is determined, and when the type is combustion deterioration, an oil feeding instruction is sent to the distributed control subsystem. By means of the system, the boiler combustion state can be automatically monitored, and the oil feeding boundary can be accurately judged.

The invention discloses a comprehensive monitoring method and system based on an optical cable sensor network, and belongs to the technical field of underground engineering monitoring, and the system comprises a wireless sensor subsystem which is used for collecting environment parameter information; the wireless transmission module is used for transmitting the environment parameter information back to a receiving node of the optical cable in a wireless signal mode; the edge computing node is used for analyzing and preprocessing the received environment parameter information in real time by adopting a self-adaptive weighted fusion algorithm; and the data processing module is used for carrying out fusion analysis on the preprocessed environment parameter information to realize monitoring and early warning on the underground tunnel space. According to the invention, various data types in the tunnel environment can be comprehensively collected, analysis is carried out through a fusion algorithm and machine learning, and the safety monitoring level of the tunnel is effectively improved.

The invention discloses a distributed photovoltaic project tracked robot inspection and cleaning method and system, and relates to the technical field of photovoltaic panel cleaning, and the method comprises the steps that a tracked cleaning robot moves and is positioned to a specified photovoltaic panel area after receiving a cleaning task sent by an operation and maintenance management platform; the stain type and the stain thickness are recognized, whether the photovoltaic panel has defects or not is recognized, a sensor detects environment parameters, and the rail cleaning robot conducts initial cleaning testing; the water pressure and the nozzle aperture are adjusted, micro-bubble water flow is released, and the photovoltaic panel is cleaned; and after cleaning is completed, the visual detection assembly scans the defect part of the photovoltaic panel again and feeds back defect information to the operation and maintenance management platform, the visual detection assembly can conduct accurate detection and defect recognition on the surface of the photovoltaic panel, and a better cleaning effect is achieved by adjusting the diameter of the microbubbles. When the photovoltaic panel has defects, the diameter and the water pressure of the microbubbles are adjusted to cope with the defects, and further damage is avoided.

The present invention discloses a design method for a cluster risk control model based on big data, the method comprising: step 1, selecting a monitoring location and designing video points, and deploying AI counting cameras; step 2, using a time series model to predict the total number of people in the cluster area by time period; step 3, using a police force deployment model to reasonably deploy police force according to the distribution of the number of people in each area by time period. Step 4, designing a patrol post area division model, automatically arranging and dispatching patrol robots, and timely discovering suspicious risks. Step 5, designing a risk control model, that is, establishing a cluster risk assessment system based on historical risk data, calculating the crowd gathering risk control situation index and predicting and warning in advance; achieving accurate prediction of abnormal aggregation, realizing optimal deployment of police force, and realizing adaptive patrol scheduling.

The invention relates to the technical field of tunnel construction, in particular to a tunnel excavation face surrounding rock deformation monitoring and early warning method, device and system, the system comprises a laser radar and terminal equipment, the laser radar is fixedly arranged at the position, needing safety monitoring, of a tunnel and is used for emitting laser beams to the peripheral surface of the tunnel to collect point cloud data; the terminal equipment is used for segmenting the point cloud data so as to screen out target point cloud data for the surrounding rock of the excavation face, and analyzing the target point cloud data according to a preset condition to obtain an evaluation template; when the laser radar collects real-time point cloud data of surrounding rocks of the same excavation face in the subsequent monitoring process, the real-time point cloud data and the evaluation template can be directly subjected to distance calculation one by one, if the distance values of the real-time point cloud data exceeding the threshold number are all larger than the preset threshold value, it shows that the current excavation face has the rockfall risk, and if the distance values of the real-time point cloud data exceeding the threshold number are all larger than the preset threshold value, it shows that the current excavation face has the rockfall risk. And risk early warning reminding needs to be carried out.

The invention discloses a visual monitoring method and system based on an intelligent construction site. Building site construction multi-point three-dimensional monitoring inspection data and construction process information data are obtained through a three-dimensional construction multi-point intelligent sensing layer; intelligent interactive construction control between the construction site construction equipment and the wearable constructors is carried out through the intelligent interactive construction control device; carrying out risk analysis on the plurality of monitoring points, obtaining multi-point three-dimensional monitoring inspection risk point analysis information, and judging construction process abnormal information in the construction process information data; controlling and triggering a multi-point three-dimensional monitoring inspection risk point signal and a construction process abnormal information display alarm signal; building site construction risk point location sound-light alarm and building site construction process abnormity alarm are carried out; displaying the multi-point three-dimensional monitoring inspection data and the construction process information data in blocks; the construction operation information and the construction cooperation information are displayed in an intelligent head-mounted mode; and the visual monitoring risk control and construction efficiency of the intelligent construction site is improved.

The invention discloses a new energy complete device capable of being modularly assembled and a monitoring method, and belongs to the technical field of new energy. The energy complete equipment comprises a transformer, a high-voltage cabinet, a low-voltage cabinet and a box transformer substation which can be modularly assembled; the transformer, the high-voltage cabinet and the low-voltage cabinet are arranged in a delta shape; a partition plate is arranged in the box transformer, a low-voltage chamber is arranged on one side of the partition plate, a high-voltage chamber is arranged on the other side of the partition plate, a high-voltage cabinet is arranged in the high-voltage chamber, and a low-voltage cabinet is arranged in the low-voltage chamber. The low-voltage cabinet comprises a main cabinet and an auxiliary cabinet, a frame circuit breaker is arranged in the main cabinet, the frame circuit breaker is arranged in the middle of the upper side of the main cabinet and plays a weight balancing role, a molded case circuit breaker is arranged in the auxiliary cabinet, and the molded case circuit breaker is transversely arranged in the auxiliary cabinet; different schemes can be selected according to clients and different altitude requirements, modularization is beneficial to long-distance transportation and transportation in complex terrains, and the problem that prefabricated production and selective application cannot be achieved when engineering performance is emergency is solved.

The invention relates to the technical field of electric digital data processing, in particular to a navigation safety distance adjusting device and method for ships around an offshore platform, and the device comprises an adjusting assembly which comprises a data feedback part and a distance response part in data connection with the data feedback part; the distance response component at least comprises a first distance response module and a second distance response module, and a telescopic column is connected between the first distance response module and the second distance response module. According to the invention, the distance alarm between ship navigation and the offshore platform is realized through the data feedback component and the distance response component; furthermore, through cooperation of the rotating wheel and the rotating speed tester, the ship navigation speed and distance are judged in advance through sea wave fluctuation, distance warning is made accordingly, and finally rapid early warning and adjustment of the safe distance between the offshore platform and the surrounding navigable ship are achieved.

This invention belongs to the fields of water environment monitoring, watershed pollution simulation, and smart water management. Specifically, it provides an integrated air-space-ground small watershed water pollution monitoring system, comprising: an airborne monitoring module for collecting spatial distribution data of water quality across the entire small watershed; a ground-based monitoring module for collecting continuous temporal water quality and hydrological baseline data of the small watershed; a multi-source data management module for data processing; a model engine module for constructing and running a distributed hydrological and water quality mechanism model of the small watershed; an adaptive parameter optimization module for automatically calibrating and dynamically updating the parameters of the distributed hydrological and water quality mechanism model of the small watershed; an early warning and source tracing module for obtaining early warning and source tracing results; a long-term prediction module for obtaining water quality prediction results; and a visualization decision module for displaying early warning and source tracing results as well as water quality prediction results. This invention realizes the monitoring, early warning, source tracing, and long-term prediction of water pollution in small watersheds.

The application discloses a tire pressure anomaly early warning method and device and a storage medium, and relates to the technical field of vehicles.The application comprises the following steps: correcting a tire pressure static threshold based on the current speed, tire temperature and load state of a vehicle to obtain a tire pressure dynamic threshold; determining a tire pressure jump rate based on the current tire pressure of the vehicle; determining a tire pressure risk level based on the tire pressure dynamic threshold, the current tire pressure and the tire pressure jump rate; and outputting early warning prompt information and / or executing a vehicle control action based on the tire pressure risk level.The application dynamically adjusts the tire pressure threshold according to the current speed, tire temperature and load state, thereby improving the early warning sensitivity in high-speed, heavy-load and other scenarios; the tire pressure risk is identified by combining the tire pressure dynamic threshold and the tire pressure jump rate, so as to distinguish between slow tire pressure leakage and high-speed tire burst instantaneous jump, realize rapid identification in a high-risk scenario, and improve the accuracy of tire pressure early warning.

The present invention provides a ship remote real-time early warning system based on cloud computing, which relates to the technical field of data processing, and comprises: a data acquisition module, which is used for acquiring ship operation data, environmental status data and external interaction data; a data preprocessing module, which is used for generating standard processing data; a data grouping module, which is used for dividing into a priority data group and a common data group; a data transmission module, which is used for sending to a cloud analysis module; the cloud analysis module, which is used for performing multi-source data aggregation processing on the priority data group and the common data group respectively, performing real-time evaluation on the ship operation status, identifying abnormal behavior recognition and emergency event prediction, and generating corresponding final early warning instructions, including avoidance operation instructions and route adjustment suggestions; an early warning feedback module, which is used for sending the early warning instructions to a ship terminal; and a local response module, which is used for performing an immediate trigger operation on the instruction classification data. The present invention improves the autonomy and accuracy of the ship remote real-time early warning.

The invention relates to a portable terahertz security inspection device capable of automatic detection and a working method, and the device is characterized in that a mechanical auxiliary unit is used for driving an object to be detected to move, so that a terahertz wave source emits terahertz radiation, and the radiation penetrates through a sample to be detected, is received by a terahertz linear detector, and is converted into image information; and the automatic detection platform is used for receiving image information, carrying out preliminary integration and enhancement on the input image through an image preprocessing module, judging possible dangerous goods types through a deep convolutional neural network, calibrating each target frame and sending out an early warning. And core technologies such as a terahertz imaging technology and a deep learning algorithm are fused. Compared with the prior art, the system has the advantages of convenient carrying, comprehensive functions, automatic detection and the like, and is convenient to carry, install and use. The system can efficiently adapt to temporary security check of public activities, greatly improve security check quality, effectively prevent security risks, and meet urgent needs in the field of public security and protection.

The application provides a dangerous area recognition method and device based on machine vision, and relates to the technical field of dangerous area recognition.The application obtains monitoring image data of a danger source in a historical monitoring area, forms a training image set through preprocessing, constructs a danger recognition model, and trains the danger recognition model, performs danger source recognition on real-time monitoring image data according to the trained danger recognition model, demarcates a basic danger area according to a basic safety distance set according to the recognition result, calculates a danger coefficient of each type of static area according to historical data of each type of static area, adds an early warning distance, calculates a dynamic danger coefficient according to historical data of each type of dynamic equipment, calculates a buffer distance in front of a moving direction, obtains historical danger accident data of a to-be-recognized area, extracts an equipment feature set, and identifies a future safety state of a sub-area through a Bayesian prediction model.

This invention relates to an outdoor hydrogen flame detection system, method, storage medium, and electronic device, comprising: a multi-parameter sensing module for dynamically monitoring outdoor scene data of a hydrogen station; a data acquisition module for acquiring outdoor scene data from the multi-parameter sensing module; a core processing module for flame identification and location based on the outdoor scene data, overlaying fire information onto a color image to obtain a superimposed image, and outputting alarm signals and fire emergency response strategies based on the severity and development trend of the fire; and an application output module for visualizing the superimposed image output by the core processing module, triggering an alarm based on the alarm signal, and outputting linkage control signals based on the fire emergency response strategy. This invention solves the problems of high false alarm rate, high false alarm rate, and poor real-time performance in existing outdoor fire detection technologies for hydrogen stations, achieving rapid and accurate detection and early warning of outdoor fires at hydrogen stations, and exhibiting significant innovations in fusion algorithms and adaptability to complex environments.

This application relates to an image feature recognition experimental method and apparatus, belonging to the field of image feature recognition technology. It involves real-time acquisition of images of an experimental scene and preprocessing them to obtain clear, continuous frame images. Based on these, a tracking and locking operation is performed on a dynamic target to ensure the target remains within the effective monitoring field of view. Subsequently, precise target feature data is extracted from the locked target detection image, and hierarchical cross-matching evaluation is performed using a dual-library standard feature library, including both local and cloud-based libraries. Finally, a corresponding feedback signal is generated based on the evaluation results. This application, through a dynamic tracking and locking mechanism, can adjust the image acquisition angle in real time, ensuring the dynamic target remains continuously within the monitoring field of view. This avoids field-of-view shift and target loss under fixed-angle acquisition, effectively solving the defect of dynamic target field-of-view loss. It also addresses the problem of delayed response in security status determination caused by existing technologies relying on a single local feature library.

The invention discloses a transformer partial discharge data monitoring system and method, and relates to the technical field of power equipment monitoring, the system comprises a data acquisition unit, a data preprocessing unit, a data storage unit, a data analysis and fault diagnosis unit and a user interface; the data acquisition unit is used for acquiring various data; the data preprocessing unit is used for carrying out data cleaning, standardization and normalization processing on the data; the data storage unit is used for receiving and storing data and constructing a data set according to data content; the data analysis and fault diagnosis unit performs data analysis, fault alarm and diagnosis; and the user interface is used for providing a user interaction interface and displaying the monitoring data, the system state and the diagnosis result. The optical fiber sensor position prediction model is constructed, the optimal installation position of the optical fiber sensor can be accurately predicted, and therefore the accuracy and reliability of monitoring data are improved.

The present invention belongs to the technical field of power line monitoring and early warning, and discloses a method and system for early warning of severe convection of power lines based on meteorological radar data. The method obtains reflectivity grid data; judges the severe convection area according to the reflectivity grid data, and designs complexity parameters to measure the structural complexity and spatial connectivity of the severe convection area; judges whether a squall line is formed according to the spatial and temporal correlation between adjacent severe convection areas; judges the movement direction, movement speed and intensity change of the squall line, and evaluates the impact of the squall line on the power line, and issues an early warning. The present invention greatly improves the timeliness and accuracy of the early warning and reduces the false alarm rate and missed alarm rate by designing complexity parameters and conducting judgment and analysis of the squall line.

The invention belongs to the technical field of electric power circuit monitoring and early warning, and discloses an electric power circuit severe convection early warning method and system based on meteorological radar data. Judging a severe convection area according to the reflectivity raster data, and designing complexity parameters for measuring the structural complexity and the space connectivity of the severe convection area; judging whether squall lines are formed or not according to space and time relevance between the adjacent severe convection regions; and studying and judging the movement direction, movement speed and intensity change of the squall line, evaluating the influence of the squall line on the power line, and issuing an early warning. According to the method, complexity parameters are designed, and the squall line is researched, judged and analyzed, so that the timeliness and accuracy of early warning are greatly improved, and the false alarm rate and the missing report rate are reduced.

The present application relates to the technical field of data calculation and scheduling arrangement in the power industrial control production scene, and discloses a power industrial control calculation workflow DAG deadlock avoidance method based on incremental topology, comprising: modeling the dependency relationship between workflow input and running state information as a multidimensional dependency graph; locating the affected node set according to the multidimensional dependency graph and the change event, and adjusting the node sequence in the affected node set to obtain an incremental topology sequence, and completing the construction of the affected subgraph; performing explicit loop and implicit loop risk detection processing on the affected subgraph, classifying the risk detection results, combining the classification results with the business target to generate a conflict resolution strategy, so as to realize the directed acyclic graph deadlock avoidance; converting the conflict resolution strategy into executable operations to perform change processing on the multidimensional dependency graph, and performing structure and semantic verification on the change result to verify the deadlock avoidance effect of the workflow directed acyclic graph. The present application can improve the continuous operation capability of the workflow.

The present application relates to the technical field of multi-modal data fusion and spatial distance calculation, in particular to a flexible direct current station illegal operation identification system and method based on multi-modal data, wherein a multi-modal sensing unit synchronously collects behavior video stream and three-dimensional point cloud data, a tool extension analysis unit identifies tools by improving a single-stage target detection model, generates a calibrated tool extension length compensation vector in combination with a human body posture estimation algorithm, a ranging path evaluation unit constructs a grid map, fits a cylindrical model and reconstructs a shortest conflict-free path when the occlusion ratio exceeds one-third, and outputs an effective ranging distance, a spatial compensation calculation unit projects the compensation vector to the ranging direction to obtain a compensation amount, and a dynamic safety distance fusion unit superimposes and calculates an actual operation distance, compares with a dynamic determination threshold, triggers an illegal signal, and synchronously starts an audible and visual alarm and a monitoring platform push, thereby ensuring operation safety.

The present invention relates to a navigation rescue method and system based on Beidou short message rapid positioning, comprising the following steps: step S1, decoding the data of the Beidou navigation system received by the reference base station to obtain the Beidou short message; step S2, the rapid positioning platform performs position calculation on the decoded Beidou short message to obtain positioning information; step S3, judging the calculated positioning information and issuing an early warning. The differential platform in the present invention can perform fine tracking processing based on multiple reference message messages of the reference base station to obtain baseband observations for differential calculation that eliminate the transmission error between the base station and the differential platform, and multiple reference channels are finely tracked to obtain a carrier phase error with a smaller error, which can achieve accurate positioning in a rescue environment.