215 results about "Risk map" patented technology

Disclosed in the present invention are a low-altitude air route risk map construction method and system based on hexagonal grid cells. The method comprises: on the basis of management and control requirements for different types of airspace, forming low-altitude airspace three-dimensional hexagonal hierarchical grids by means of multi-scale partitioning of an airspace horizontal plane and multi-scale partitioning of an airspace vertical plane; constructing an unmanned aerial vehicle flight risk assessment indicator system for the low-altitude airspace three-dimensional hexagonal hierarchical grids, and after optimization, constructing an unmanned aerial vehicle operational risk assessment model based on a dynamic Bayesian network, so as to generate multi-scale grid risk values; and combining the multi-scale grid risk values with geographic location information of a region, designing a hexagonal grid code index, and generating a dynamic multi-scale low-altitude air route three-dimensional hexagonal risk map. The present invention can improve the accuracy and real-time performance of unmanned aerial vehicle flight risk assessment of grids, quickly obtain low-risk grids of an unmanned aerial vehicle in a target region, and improve the safety of air route planning.

The invention relates to the technical field of pipeline monitoring and artificial intelligence, in particular to a multi-source data fusion pipeline monitoring method and system. The method comprises the steps of performing field sorting, structure unification and risk segmentation processing by obtaining pipeline line data, historical operation archives and strategy update configuration records, and generating a session primary key configuration table; a multi-source acquisition time window is configured, an acquisition task is issued, time anchor point registration and field aperture unification are completed, and a multi-source session data packet set is generated; performing session and risk unit association, performing multi-modal feature extraction and cleaning aggregation based on artificial intelligence, and constructing a pipe network risk map structure by using a map structure data model; and calling a multi-task reasoning model and a rule component based on the atlas, and performing risk type reasoning and grade judgment to obtain a risk assessment result and a strategy updating record. According to the invention, intelligent fusion of multi-source data and closed-loop optimization based on machine learning can be realized, and intelligence and reliability of pipeline safety management are effectively enhanced.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle low-altitude complex obstacle detection and avoidance system based on multi-sensor fusion, and the system comprises a multi-mode sensing module which is used for synchronously collecting heterogeneous sensing data of the surrounding environment of an unmanned aerial vehicle; and the sensing fusion processing module is in communication connection with the multi-mode sensing module and is used for carrying out timestamp alignment, space coordinate system unification and deep fusion processing on the heterogeneous sensing data, and all-weather and high-reliability environment sensing is realized through multi-sensor fusion and hardware synchronization. The system has dynamic obstacle prediction and three-dimensional risk map construction capabilities, and realizes crossing from passive obstacle avoidance to active foresight obstacle avoidance. And a safe and smooth flight path is generated in combination with hierarchical planning and model prediction control, so that the autonomous flight safety, reliability and intelligent level of the unmanned aerial vehicle in a low-altitude complex environment are fundamentally improved.

The invention provides a coal mine personnel safety situation dynamic sensing method, device and equipment and a medium, and the method comprises the steps: carrying out the time-space reference unification of the obtained multi-source heterogeneous data of underground operation personnel, and obtaining the time-space synchronization feature data, which comprises positioning data, environment data and inertial motion data; inputting the positioning data and the inertial motion data into a pre-trained meta-learning behavior recognition model to obtain a behavior label of the operator; constructing a dynamic coupling risk map in combination with the behavior label and the environment data; and performing space-time convolution operation on the dynamic coupling risk map to obtain a prediction risk distribution map and generate an individual risk avoiding path. By adopting the method, the underground safety production level of coal mine personnel can be improved.

The invention provides a patient rehabilitation tracking system, a rehabilitation training regulation and control method based on active perception and environment self-adaption, equipment and a medium, and the method comprises the steps: determining a cooperative motion state of a nerve-muscle-bone system according to a multi-modal physiological data flow of rehabilitation training tracking of a target patient; performing intention-muscle force cooperative identification based on the cooperative motion state to obtain motion intention and muscle force contribution distribution of the target patient; determining a dynamic risk map of the target patient during rehabilitation training; variable universe fuzzy reasoning is carried out on the motion intention, the muscle force contribution distribution and the dynamic risk map, and then a rehabilitation adjustment instruction matched with the rehabilitation state of the target patient is obtained; the rehabilitation progress of the target patient is continuously tracked, and the rehabilitation training robot is controlled to execute self-adaptive power-assisted compensation according to the rehabilitation adjustment instruction. By means of the scheme, motion intention decoding and muscle force contribution analysis can be conducted on rehabilitation training of the patient, and rehabilitation training regulation and control are conducted in combination with the dynamic environment risk.

The invention relates to the field of enterprise risk assessment, and discloses an enterprise risk assessment method and system based on a large language model and high-frequency graph convolution, and the method comprises the steps: S1, building an enterprise risk association graph: generating a structured joint weight between enterprises, and building the enterprise risk association graph; fusing the semantic weight of the large language model and the structured joint weight into a final edge weight; jointly inputting the fused risk map and the multi-modal feature into two-stage attention to generate a multi-modal attention feature; and S2, enterprise risk assessment: on the enterprise risk association graph endowed with the edge weight, carrying out information propagation and aggregation by adopting high-frequency graph convolution. According to the method, edge-level gating and fusion edge weight guide risk information to be preferentially diffused along a more credible and more relevant path, high-frequency signals such as financial mutation, negative news and major events can be quickly captured, and blind diffusion of the information in a network is reduced. And the amplification effect of market systematic impact is reduced.

The invention discloses a lithium battery dynamic rapid charging track generation method based on multi-state estimation and risk criterion, belongs to the technical field of electric vehicle lithium ion battery charging, and aims to solve the problem that the charging speed, the energy efficiency and the battery life are difficult to consider in the rapid charging process of a lithium ion battery in a wide temperature range. And a relatively high lithium plating safety risk exists. The method comprises the following steps: firstly, constructing an electrochemical thermal aging coupling model, and establishing a lithium plating risk criterion and risk map based on the model; then, on-line multi-state estimation is realized based on an electrothermal coupling equivalent circuit model, and a multi-objective optimization function and a safety constraint system which comprehensively consider charging time, energy efficiency and life loss are constructed; and finally, a model prediction control framework is utilized, a state estimation result and a risk criterion are fused, and an optimal charging current track is generated in a real-time rolling manner.

The invention discloses an asset vulnerability detection method and device, electronic equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: actively sending a multi-protocol detection packet to scan a target network segment, and obtaining a first asset set; passively monitoring network traffic to extract asset feature information, and obtaining a second asset set to generate an asset list; port scanning tasks of all assets are dispatched to a plurality of scanning nodes in a distributed and parallel mode, dynamic port scanning is carried out according to a descending order of a plurality of key elements in combination with a port scanning optimization model based on risk prediction, and a full-amount port risk map is constructed; the static layer is matched with known vulnerabilities; the dynamic layer identifies suspicious behaviors deviating from a normal behavior baseline through an anomaly detection algorithm, and obtains an asset vulnerability detection result in combination with a cross validation method; according to the invention, the detection requirements of asset full coverage and early threat discovery in a complex network environment are met.

The invention discloses a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on industrial vision, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting and preprocessing an initial environment image of a current working scene of a mechanical arm, obtaining a standardized working scene image data set, carrying out dynamic object detection, and obtaining an environment perception evaluation report; identifying an unobserved area of the working scene based on the environment perception evaluation report, performing collision path calculation, and generating a visual angle adjustment action instruction; the safety path sequence is issued to a mechanical arm joint and executed, images in front of the mechanical arm are continuously collected during execution, and if an unpredicted sudden obstacle is detected, the four-dimensional risk map is dynamically updated, and path re-planning is conducted; and when the tail end of the mechanical arm successfully completes the task, obstacle avoidance path planning data of the mechanical arm are recorded and stored persistently, and an obstacle avoidance path planning record is generated. According to the method, the real-time problem of path planning of the mechanical arm is solved through construction of the four-dimensional dynamic risk map.

The invention provides a digital economic information management method and system based on big data analysis, and the method comprises the steps: integrating the data of a consumption end, a production end and a server end through a unified platform, guaranteeing the transmission safety through the combination of differential privacy and TLS 1.3, recording the full life cycle of data through a segmented block chain, supporting the dynamic authorization and fine-grained authority control, and improving the efficiency. The method comprises the following steps: verifying data purposes by combining zero-knowledge proof, fusing NLP, image processing and time sequence analysis, constructing a knowledge graph, generating dynamic tags and composite features, simulating decision influence through anti-fact analysis, supporting generation of a Pareto optimal solution by an interactive decision tree, and providing a market trend thermodynamic diagram and a supply chain risk graph. Customized suggestions are pushed to an ERP system in real time, sensitive data are protected based on role permission and lightweight homomorphic encryption, abnormal behavior monitoring and zero-knowledge proof compliance review are combined, a service scene is simulated through an A / B sandbox, and low-code community drives demand iteration.

The invention provides a watershed flood early warning system and method based on hydrological model and mobile application coupling, and belongs to the field of watershed flood early warning. In the system, a communication and data transmission module collects data for hydrological model simulation in a watershed area; the model simulation module simulates hydrological data of rainfall runoff in a watershed area in real time by using a hydrological model based on the collected data, and transmits a simulation result to the flood risk forecasting module through the communication and data transmission module; the flood risk forecasting module generates a flood risk map based on the received simulation result and transmits the flood risk map to the communication and data transmission module; and the mobile application display module receives real-time early warning information generated by the communication and data transmission module based on the flood risk map, and displays the real-time early warning information to a user. The watershed flood is simulated in real time through the hydrological model, the flood forecasting and early warning information is sent to people in a watershed flood submerging range through the mobile application, and the accuracy and timeliness of watershed flood early warning can be improved.

The invention relates to the technical field of pilotless automobiles, in particular to a local obstacle avoidance decision-making method and system based on a dynamic risk map and a real-time game, and the method comprises the steps: obtaining the state information of an automobile and the sensing data of the surrounding environment through a multi-mode sensor; predicting a future trajectory based on the real-time state information of the dynamic obstacle; constructing a rasterized dynamic risk map which takes the vehicle as the center and is updated in real time, and distributing a comprehensive risk value for each grid; the interaction modeling of the self-vehicle and the dynamic obstacle is a non-cooperative game model, the dynamic risk map and the final prediction trajectory of the dynamic obstacle are used as input, the Nash equilibrium of the non-cooperative game model is solved in real time, and the optimal decision strategy of the self-vehicle is output; and converting the optimal decision strategy into a travelable trajectory conforming to vehicle dynamics constraints, and outputting the travelable trajectory to a vehicle control module to execute local obstacle avoidance. The method overcomes the defects of fixed parameters and poor adaptability of a traditional method, and gives consideration to the safety and high efficiency in different scenes.

The invention provides a method and a device for evaluating a space moving track, and relates to the technical field of artificial intelligence. The invention discloses a spatial movement track assessment method, which comprises the following steps of: generating track-risk image data according to a spatial movement track and a corresponding risk map; inputting the trajectory-risk image data and a pre-constructed task cue word into a pre-constructed visual-language large model to obtain an evaluation result; and transmitting the evaluation result to the target end. According to the technical scheme provided by the embodiment of the invention, the space movement track and the risk map which need to be evaluated are uniformly converted into the image representation as the input of the vision-language large model, and the image representation and the task cue word are input into the vision-language large model; the general knowledge and the cross-modal reasoning ability of the vision-language large model are utilized to finish track evaluation in real time, and the expansibility and the interpretability are good.

The invention discloses an urban inland inundation prediction method and system based on space attention and depth fusion, and relates to the technical field of urban inland inundation prediction, and the method comprises the steps: laying monitoring points in a waterlogging-prone risk area, and constructing a dense perception network; constructing a dynamic space attention network, quantifying rainfall-ponding space correlation, calculating a dynamic influence weight through a learnable weight matrix, and generating an attention weight matrix to represent the influence of rainfall points on ponding points; training a long-short-term memory network model, and fusing the feature representation of the spatial correlation information and a historical ponding depth sequence to carry out ponding depth time sequence prediction; and based on topographic feature extraction, physical guide diffusion and point time sequence prediction data conversion, expanding topographic-guided point-surface data, and generating a surface region ponding risk map. Through fusion of a spatial attention mechanism and deep learning, rainfall-ponding spatial heterogeneity association is effectively captured, the accuracy and timeliness of urban inland inundation prediction are improved, and scientific decision support is provided for urban flood control and disaster reduction.

The invention relates to the technical field of intelligent traffic monitoring, and discloses a road surface accumulated water detection and identification method based on multi-view feature fusion. The method comprises the following steps: acquiring image data, depth information and environmental parameters through sensor nodes deployed at multiple positions of a road surface to form an original monitoring data set; performing multi-source feature extraction and fusion processing on the data set to obtain an accumulated water feature image set; performing spatial domain analysis by using the image set to generate a pavement partition consistency map; time dimension data is extracted based on the map, dynamic change evaluation is carried out, and a ponding evolution report is output; detecting an abnormal mode from the report, and identifying an abnormal ponding area; and calculating a risk index according to the abnormal region, and generating a final road surface ponding risk map. According to the method, through multi-source data fusion and spatio-temporal conjoint analysis, accurate detection, dynamic evolution tracking and risk assessment of pavement ponding are realized, and the accuracy and early warning capability of urban road ponding monitoring are remarkably improved.

The invention relates to the technical field of path planning control, in particular to an intelligent anti-icing snow-removing trolley control method, which comprises the following steps that a dynamic risk map is constructed, and each grid unit of the dynamic risk map comprises a comprehensive risk cost value obtained by fusing an ice and snow attachment risk value, a collision risk value and a task weight value; inputting the dynamic risk map into a collaborative game framework, and generating a plurality of path proposals in parallel through the plurality of virtual decision agents to obtain a consensus trajectory; solving an optimal control instruction set; and issuing the optimal control instruction set to an execution system of the snow removal trolley for cooperative control. According to the invention, the improved model prediction controller adjusts the prediction time domain length and the cost function weight matrix in real time, optimizes and tracks the path based on the dynamic risk map, can accurately avoid obstacles in a high-risk area, and optimizes the energy efficiency in a low-risk area.

The invention discloses a path selection method and system for AGV transportation equipment, a product and a medium. The method comprises the following steps: firstly, when the automated guided vehicle runs, calculating and updating vibration and slippage cost values for each unit of a grid map in real time by collecting vertical vibration and wheel-actual speed difference data, so as to construct a dynamic physical risk map; after a transportation task is received, obtaining exclusive vibration and slippage weights according to task characteristics (such as whether goods are fragile or not); evaluating a plurality of to-be-selected paths, and accumulating the length cost of each path and the weighted vibration cost and the weighted slip cost of all grids along the path to calculate the comprehensive passing cost; and selecting the path with the lowest comprehensive passing cost as a final execution path. By implementing the technical scheme provided by the invention, the adaptability of AGV path planning to complex physical road conditions is improved.

The invention discloses a multi-mode navigation control method and system of a high-altitude cable deicing robot, and belongs to the field of navigation control, and the multi-mode navigation control method of the high-altitude cable deicing robot comprises the following steps: carrying out data collection by using a multi-sensor fusion system carried by the robot; generating a dynamic multi-dimensional risk map based on the collected data, wherein the multi-dimensional risk map marks a moving path of the robot, a large-gradient area, a cable galloping area and a stain influence area; when it is predicted that the robot is about to enter the large-gradient area, the advancing mode of the robot is controlled to be a braking priority mode; compared with the prior art, the method has the beneficial effects that the multi-dimensional risk map is constructed, the moving path of the robot is divided into a conventional area, a large-gradient area, a cable galloping area and a stain influence area, and different control modes are adopted for different areas; therefore, the environment adaptability of the aerial cable deicing robot is remarkably improved.

The invention provides a mine multi-disaster collaborative early warning method and system based on multi-model fusion, and relates to the field of data processing. According to the method, multi-source monitoring data of a deep stope, overlying surrounding rock and a large-scale goaf are obtained, a multi-source monitoring data set is formed through space registration and time alignment, and a three-dimensional digital twinborn body is constructed and subjected to inversion correction; deducing a stable evolution behavior in the twin body to obtain a virtual precursor trajectory set, and forming a physical prediction state and a statistical prediction state respectively by combining a physical prediction model and a statistical prediction model; carrying out difference mapping on the virtual track and a real observation track to construct a multi-dimensional difference feature, and executing spatial clustering to obtain a silent abnormal cluster; and a multi-disaster risk map is further constructed in combination with a multi-disaster coupling relationship, reasoning is fused, a silent catastrophe high-risk area is identified, and a collaborative early warning result is output according to space connectivity and time continuity. By implementing the technical scheme, the accuracy of cooperative early warning for multiple disasters of the mine can be improved.

The invention discloses an animal epidemic disease port remote real-time monitoring and early warning system, and relates to the technical field of Internet of Things monitoring, and the system comprises a state evaluation module which is used for obtaining various real-time parameters in an animal port so as to determine a risk state index of the animal port; through the system, an abnormal detection area can be rapidly identified, a grading early warning mechanism is automatically triggered, quarantine personnel are assisted to preferentially dispose high-risk point locations, and an epidemic disease transmission chain is effectively blocked; in addition, through a spatial hot spot analysis technology, the system can visually display a core detection area of epidemic disease distribution, and a scientific basis is provided for accurate prevention and control; a real-time risk map and an intelligent algorithm are combined, an optimal disposal path is automatically planned, a high-risk detection area is avoided, and the epidemic situation diffusion risk is reduced to the maximum extent; meanwhile, aiming at the problem of resource shortage in an emergency scene, the system dynamically coordinates epidemic prevention resources through an elastic scheduling mechanism, can match available epidemic prevention stations nearby, and optimizes a carrier and personnel allocation scheme.

The invention relates to a project cost risk monitoring system and method. The system comprises a project cost data acquisition module for acquiring multi-dimensional project cost data including project cost, project cost, external variables and other cost data; the data change trend calculation module is used for calculating the data change trend by a time sequence analysis method; the association strength extraction module is used for comparing the trend with a preset threshold value, and extracting the association strength of the corresponding dimension data by means of a preset cost factor interaction influence model if the trend exceeds the threshold value; the risk map construction module is used for constructing an interactive risk map by using weighted directed graph modeling according to the association strength; the risk sensitivity judgment module is used for dividing project stage boundaries according to a map and judging data risk sensitivity; the monitoring framework construction module is used for selecting monitoring parameters by using a parameter optimization algorithm according to sensitivity and constructing a framework; and the evaluation result generation module is used for generating a cost risk evaluation result by using the framework verification data, so that the risk can be accurately monitored, and the hyper-branched risk is reduced.

The invention relates to an enterprise risk early warning method and device based on big data, equipment and a medium. The method comprises the following steps: respectively extracting time dynamic characteristics and space topological characteristics of nodes through a dynamic heterogeneous risk map, and generating unified space-time embedding representation through cross-modal fusion; an event logic enhanced nonlinear propagation effect of a dynamic heterogeneous graph attention network modeling risk event is adopted, and enhanced node representation and global representation are output; calculating the deviation between the node and the community mean value through topological residual detection to obtain a topological residual score, and calculating the minimum similarity between the node and the known risk prototype through similarity analysis to obtain a representation similarity score; the two scores are linearly combined and normalized to generate an unknown risk score, and a known risk probability is calculated based on a pre-training model; the comprehensive risk index is obtained through maximum value operation, comprehensive monitoring and early warning of known and unknown risks of an enterprise are achieved, and the coverage range, accuracy and timeliness of risk identification are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle distribution network inspection autonomous obstacle avoidance control method and control system. The method comprises the following steps: establishing a multi-level sensing system comprising a real-time environment sensing layer and a historical scene mapping layer; synchronously acquiring and fusing multi-sensor data in the real-time environment perception layer to form a comprehensive environment situation map, and identifying a dynamic obstacle movement track and static obstacle space distribution; inputting the obstacle information into a historical scene mapping layer, and matching the obstacle information with a typical obstacle avoidance scene library to generate an environmental risk grade map; performing real-time risk assessment on the predetermined route according to the risk map, and marking high-risk route segments; multi-dimensional search of an obstacle avoidance decision space is carried out in combination with the current state of the unmanned aerial vehicle for the high-risk leg; evaluating the execution cost and safety margin of each candidate obstacle avoidance action, and generating an optimal obstacle avoidance strategy sequence; and adjusting flight control parameters in real time according to the strategy sequence and updating the environment situation map.

The invention relates to the technical field of mine safety monitoring, and discloses a coal mine underground space digital twinning system based on numerical simulation, which comprises the following modules: a multi-source sensor assembly arranged in a roadway surrounding rock and used for collecting stress, displacement, anchor rod axial force and vibration data in real time; according to the coal mine underground space digital twinning system based on numerical simulation, dimension spanning from single index trend analysis to full-field stress and displacement continuous evolution process prediction is achieved, and more comprehensive stability cognition is provided. Model parameters are continuously updated through a dynamic self-calibration mechanism, a traditional method depending on fixed empirical values is replaced, and the long-term fidelity and prediction accuracy of the model under complex geological conditions are remarkably improved. The system provides a visual risk map based on simulation deduction and a multi-scene simulation function, the dependence on artificial experience is reduced, and a visual and scientific basis is provided for support scheme optimization and disaster emergency decision making.

The invention discloses an unmanned aerial vehicle path planning method, device and equipment and an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle flight control. Extracting risk description features from the environment image, calculating a first maximum cosine similarity between the risk description features and text features of a dynamic field semantic tag library, and outputting semantic tags and confidence; when the confidence meets a confidence constraint condition, projecting the semantic tag to a three-dimensional voxel space, and generating a three-dimensional risk map with a risk weight; constructing a target cost function associated with the risk weight; and by taking minimization of the target cost function as a target, searching an optimal flight path from the starting point to the target point in the three-dimensional risk map. The sensing generalization of the unmanned aerial vehicle in a complex low-altitude environment can be improved, and the obstacle recognition accuracy and the flight safety are improved.

The invention discloses a multi-robot process scheduling and cooperative control method based on artificial intelligence, and the method comprises the steps: carrying out the modeling of a process structure and robot resources, and generating a unified task information queue; environment perception and multi-source point cloud acquisition are carried out to generate multi-source point cloud data; carrying out Poisson curved surface reconstruction to obtain a three-dimensional reconstruction result and an occupied grid map; selecting a projection view angle and a grid resolution, and generating a multi-channel two-dimensional environment grid map; constructing an improved HarDNet network, and generating an environment semantic and risk map; performing task allocation and path planning to form a cooperative control plan; and performing cooperative execution, controlling the physical robot to execute tasks, and controlling the software robot to process businesses. Through combination of Poisson curved surface reconstruction and improved HarDNet network, unified scheduling and cooperative control of multi-robot tasks are realized.

The invention provides an automatic waybill ordering method and system based on logistics docking, and the method comprises the steps: constructing a logistics resource intelligent agent set and a logistics resource market based on logistics resource data, obtaining customer demand data, constructing a customer demand intelligent agent based on the customer demand data, and carrying out the customer demand intelligent agent set and the logistics resource market; and generating a corresponding logistics risk portrait in combination with a preset logistics risk map and a multi-factor risk convolution mechanism, putting the customer demand agent into a logistics resource market for game prediction, obtaining a corresponding game prediction result, generating a corresponding logistics risk assessment report based on the game prediction result, and performing logistics risk assessment. The method comprises the steps of performing logistics resource screening based on a logistics risk assessment report, generating corresponding logistics waybill data, generating a corresponding electronic logistics waybill in combination with a waybill adaptive conversion mechanism, obtaining an actual performance data set of the electronic logistics waybill, and performing feedback optimization on a module corresponding to an automatic waybill ordering system in combination with the actual performance data set. Therefore, the quality of service provided for merchants is improved.

The invention discloses an electromagnetic environment-friendly radiation protection assessment method and system, and the method comprises the steps: obtaining electromagnetic field data containing power frequency and high-frequency components through a data collection device, generating a multi-dimensional index data set and a radiation characteristic distribution diagram through the combination of exposure time and a crowd sensitivity index through a computational simulation method, and accurate quantitative analysis of the complex electromagnetic environment is realized. On the basis, a weight analysis method is adopted to fuse effect parameters to calculate a risk quantitative score, when the score exceeds a safety threshold value, radiation limit value data is automatically extracted to generate a visual risk map, and a processing module is linked to output a grading protection instruction. By activating protection configuration in the sensor network, decision unit parameters are dynamically adjusted according to real-time electromagnetic field data, and an optimized protection scheme including a dynamic strategy is formed. According to the invention, the conversion from passive protection to active early warning is realized, and the intelligent level and protection effect of electromagnetic radiation protection are significantly improved.

The invention discloses an intelligent robot trajectory planning method and system based on visual inspection. The method comprises the following steps: acquiring obstacle motion state data through a sensor array, performing time alignment and multi-modal fusion, and constructing an initial state vector; iteratively updating the state by adopting a particle filter algorithm, and generating a smooth trajectory sequence through continuity correction and time sequence integration; determining a potential collision area based on polynomial fitting, and performing extension to obtain a predicted collision boundary; extracting passable grids to construct a dynamic risk map, screening low-risk routes, and sequencing to determine an optimized path; performing correction according to deviation feedback by simulating a forward verification path to generate a final execution instruction; state deviation is continuously monitored in the execution process, and the control scheme is dynamically adjusted. According to the invention, real-time trajectory planning and intelligent path optimization of the robot in a dynamic obstacle environment are realized, and the movement safety of the robot is remarkably improved.

The application discloses a hydrogen-doped natural gas transportation safety risk assessment method and system, relates to the technical field of hydrogen-doped natural gas pipeline safety monitoring, and comprises the following steps: initializing a safety situation assessment engine and injecting gas components; collecting pipeline strain vibration through a distributed optical fiber sensor, identifying low-frequency noise characteristics caused by hydrogen-induced material changes; superimposing and reconstructing in combination with historical corrosion crack maps to generate a mixed risk map and divide risk sections; calculating the combustible migration rate of mixed gas and the cumulative fatigue reduction coefficient of the pipeline wall material in each risk section; and quantifying risk entropy values by comprehensively considering both, thereby generating a differentiated maintenance strategy. The method can realize early identification of hydrogen damage to pipeline materials in a hydrogen-doped environment, and realizes accurate positioning and assessment of risks by fusing historical defect information, thereby providing technical support for active safety maintenance of the pipeline.