2728 results about "Information fusion" patented technology

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

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

The invention provides an intelligent drilling speed prediction method based on physical feature guidance and multi-source information fusion, and relates to the technical field of intelligent drilling speed prediction, and the method specifically comprises the following steps: collecting multi-source heterogeneous data from a drilling real-time database, a logging system, a logging system and a geological database; constructing a dual-channel deep learning prediction model, wherein the dual-channel deep learning prediction model comprises a dual-channel convolution feature extraction module, a feature fusion module, a time sequence fusion module, a time sequence modeling module and a full connection layer which are connected in sequence; obtaining a predicted drilling speed by using a dual-channel deep learning prediction model; a joint loss function is constructed by considering a data driving error and a physical constraint error, an error is calculated according to the joint loss function, and network parameters are updated through back propagation; carrying out loop iteration training until convergence; and the trained dual-channel deep learning prediction model is used for drilling speed prediction. According to the technical scheme, the problems that in the prior art, a mechanism model is insufficient in precision, and a data driving model is poor in reliability are solved.

The invention belongs to the technical field of microwave measurement, and discloses an outer wall hollowing microwave reflection detection method based on multi-modal fusion, which comprises the following steps: carrying out multi-modal scanning on a building outer wall to be detected, obtaining visible light image data and infrared temperature distribution data of the outer wall surface, and carrying out space registration and coordinate mapping; a unified multi-modal fusion data set is formed; performing anomaly screening on the multi-modal fusion data set, and identifying a thermal anomaly region by analyzing infrared temperature distribution data; detecting a bump or crack area in combination with texture and morphology anomaly features of the visible light image data; performing information fusion on the thermal anomaly region and the bump or crack region, and extracting candidate detection regions of suspected hollowing; high-precision recognition and quantitative evaluation of the outer wall hollowing are achieved, and the precision and stability of outer wall hollowing detection are improved.

The invention discloses a multi-source information fusion equipment health diagnosis management system, and relates to the technical field of equipment management. Comprising an information acquisition module, a feature perception module, a processing fusion module, an equipment modeling module, an evolution prediction module, a root cause diagnosis module, a cross-domain inspection module, a learning sharing module, a state evaluation module, a collaborative decision-making module, a chain account book module and a visual decision-making module. According to the method, the perspectiveness and the sensitivity of anomaly detection are remarkably improved, noise interference and sampling deviation are reduced, fused data are more stable and have higher physical consistency, differential diagnosis and individual-level prediction are supported, the interpretability of diagnosis and the decision reliability are improved, false alarm and missing alarm are avoided, the early warning reliability is improved, and the method is suitable for popularization and application. And scientific, transparent and traceable health management is realized.

The invention discloses a power transmission line foreign matter detection method and system based on multi-modal image fusion, and relates to the technical field of intelligent operation and maintenance and state monitoring of a power system, a lightweight Ev-Mama architecture is introduced into a backbone network part of YOLOv13, the model keeps relatively low calculation complexity, and meanwhile, the power transmission line foreign matter detection efficiency is improved. And the modeling capability of the method on the long-range dependency relationship and the global semantic information is obviously enhanced. Besides, by using the CDIDF module, the EVCS module and the MHSAA module, on the basis of increasing a small amount of calculation, the scale sensing ability, the space structure modeling ability and the context understanding ability of the model are effectively improved, and the performance bottleneck of a traditional YOLO series network in the aspects of processing small targets, shielding targets and cross-scale information fusion is effectively relieved.

The invention provides a marine equipment drawing recognition method and system based on a large model, and is applied to the field of intelligent analysis and knowledge management of ship engineering. The method comprises the following steps: receiving a marine equipment engineering drawing image, extracting global features through character and line detection after preprocessing so as to identify a drawing type, and combining symbol classification and topological graph construction to determine an equipment connection relationship, performing multi-source information reasoning on the target component by fusing a rule engine and a large language model, and outputting a high-confidence identification result; according to the scheme, the accuracy and the automation level of recognition of the equipment parts in the complex marine drawing can be remarkably improved, and the technical bottlenecks of a traditional method in the aspects of insufficient cross-modal information fusion, limited semantic understanding depth, poor heterogeneous drawing adaptability and the like are effectively solved.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention relates to a dynamic power regulation and control method and system for an electric vehicle charging pile, and the method comprises the following steps: S1, based on the real-time operation data of a charging pile cluster, employing a multi-source information fusion technology, collecting a power grid voltage fluctuation signal through a Hall sensor disposed at a charging pile end, and combining with the battery charge state data transmitted by a vehicle-mounted BMS, and eliminating noise interference by using an improved Kalman filtering algorithm, and realizing multi-dimensional data fusion by using a D-S evidence theory to generate a multi-source working condition feature set. The method has the advantages that the power grid voltage fluctuation signal and the battery charge state data are integrated through the multi-source information fusion technology, the improved Kalman filtering algorithm is adopted to eliminate noise interference, multi-dimensional data fusion is achieved in combination with the D-S evidence theory, the integrity and reliability of working condition feature extraction are remarkably improved, and the working condition feature extraction efficiency is improved. And the LSTM-ARIMA hybrid prediction model is utilized to synchronously process the nonlinear features and the periodic rules.

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention discloses a ship navigation risk dynamic early warning method and system based on multi-source information fusion, and relates to the technical field of ship navigation control, and the method comprises the steps: carrying out the time-space registration and fusion processing of multi-source data obtained in real time, and building an environment model comprising a ship, surrounding ships, static obstacles and environment elements; fusing the risk distribution of all the risk influence factors based on an environment model, and generating a dynamic risk field reflecting the risk situation of the whole water area; performing collision avoidance decision analysis based on the dynamic risk field; the environment complexity value is fed back to the generation process of the dynamic risk field, and the risk distribution form of the dynamic risk field is changed by adjusting calculation parameters of risk fusion; and risk monitoring is carried out based on the changed dynamic risk field, and when it is detected that the risk level of the area where the ship is located exceeds an early warning level threshold value, early warning information including collision avoidance suggestions is generated in combination with a recommendation strategy.

The invention relates to the technical field of artificial intelligence, in particular to a large model and multi-agent collaborative decision-making method based on dynamic knowledge flow, which comprises the following steps of: analyzing a static knowledge and dynamic information fusion relationship through joint modeling, extracting a hierarchical structure of equipment constraints and environment variables, identifying constraint conflicts and deviations in task decomposition, and obtaining a multi-agent collaborative decision-making result; screening a consistency decomposition direction, extracting a task constraint parallel optimization theory, correcting constraint conflicts, evaluating consistency changes, and outputting a collaborative task convergence robust state identifier. According to the method, by integrating static knowledge and dynamic information and optimizing understanding of task decomposition and equipment constraints, the collaborative decision-making capacity of multiple agents in a complex environment is enhanced, the conflict and deviation processing capacity in the task execution process is improved, the accuracy and consistency of tasks are enhanced, and the convergence and stability of task targets are improved; the robustness of multi-agent cooperative work is promoted, and finally more efficient resource utilization and task completion effects are achieved.

The invention relates to the technical field of micro-grid control, and discloses a micro-grid adaptive switching control method and system based on multi-source information fusion, and the method comprises the steps: obtaining the internal operation parameters of a micro-grid, and the state parameters of an external large power grid connected with the micro-grid; the internal operation parameters and the external large power grid state parameters are fused, and a real-time comprehensive operation state model of the micro-grid is constructed; based on the real-time comprehensive operation state model, adaptively determining a target operation mode of the micro-grid; and according to the target operation mode, generating and executing a smooth switching control instruction of the micro-grid. According to the method, the multi-source operation information of the large power grid inside and outside the microgrid is fused, the comprehensive operation state model is constructed in real time, the target operation mode is adaptively selected, and switching control is smoothly executed, so that efficient and stable switching of the microgrid between multiple modes such as grid connection and island can be realized, and the power supply reliability, the system stability and the electric energy quality are improved.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention relates to the technical field of fault diagnosis, in particular to a pole-mounted circuit breaker fault diagnosis method based on multi-information fusion, and the method comprises the steps: firstly obtaining electric quantity modal data such as three-phase current and coil current and mechanical quantity modal data such as mechanism vibration and voiceprint, and constructing a time-frequency domain alignment feature tensor; then, through a graph space-time attention fusion model, deeply mining physical structure association and time sequence evolution laws among heterogeneous data, and generating a dynamic space-time feature matrix; then, respectively constructing a mechanical evidence body for representing the state of the transmission chain of the operating mechanism and an electrical evidence body for representing the working condition of the vacuum arc-extinguishing chamber by adopting a feature level-decision level mixed framework; an improved D-S evidence theory is applied for fusion, when high-conflict evidences are detected, a self-adaptive arbitration mechanism is automatically triggered, conflict weights are dynamically attenuated or distributed to uncertain items, misjudgment is effectively avoided, and high-robustness collaborative diagnosis of the electromechanical state is achieved.

The invention relates to the technical field of intelligent operation and maintenance, in particular to an artificial intelligence operation and maintenance decision support method and system for multi-source information fusion. The method comprises the following steps: acquiring multi-source operation and maintenance data to perform multi-dimensional feature extraction to obtain multi-dimensional operation and maintenance feature data; performing continuous spatial cross-modal embedding according to the multi-dimensional operation and maintenance feature data to obtain cross-modal embedded data; performing heterogeneous feature coupling graph generation on the cross-modal embedded data to obtain coupling graph data; performing heterogeneous space fusion coding according to the coupling graph data to obtain fusion coding data; performing expert knowledge driving graph embedding on the fusion coding data to obtain operation and maintenance fusion graph data; performing root cause positioning reasoning according to the operation and maintenance fusion graph data to obtain root cause positioning data; and performing operation and maintenance decision generation according to the root cause positioning data to obtain operation and maintenance decision data. Through multi-source information fusion and intelligent reasoning, the root cause positioning accuracy and intelligent operation and maintenance decision efficiency of the system can be effectively improved.

The invention relates to the technical field of regional security and protection monitoring, in particular to a regional intrusion identification method and system under the view angle of an unmanned aerial vehicle, which adopts a multi-source information fusion framework, combines the real-time position, attitude and flight state information of the unmanned aerial vehicle through an accurate space coordinate conversion algorithm, and realizes the regional intrusion identification under the view angle of the unmanned aerial vehicle. GIS geographic information, unmanned aerial vehicle monitoring and a deep learning detection result are deeply fused, high-precision positioning of an intrusion area in an image shot by an unmanned aerial vehicle is realized, and robustness in a complex environment is improved. Targets such as personnel and vehicles in the image are efficiently recognized by applying a YOLO series algorithm, and violation intrusion behaviors are accurately judged by calculating the intersection-to-union ratio IoU of the targets and the intrusion area. The unmanned aerial vehicle is utilized to quickly cover a large-area monitoring area, and the problem that the monitoring range of a traditional fixed camera is limited is solved. In addition, by means of a space coordinate conversion algorithm, the influence caused by height and angle changes in the flight of the unmanned aerial vehicle is effectively overcome, and accurate positioning of the intrusion area in the image is achieved.

The invention relates to the technical field of agricultural equipment, in particular to a hybrid tractor whole machine dynamic property evaluation method fusing multiple working condition parameters, which comprises the following steps: acquiring an environment working condition parameter set, an operation task parameter set and an energy flow dynamic parameter set in real time; a dynamic coupling relation mathematical model is constructed based on the parameter set, the whole machine dynamic property characteristic values of the steady state working point and the transient working point are calculated respectively, and a whole machine dynamic property evaluation index set is generated; inputting the evaluation index set into a hardware-in-the-loop simulation platform, and verifying the effectiveness of the evaluation indexes through multi-modal working condition simulation; and constructing a three-dimensional dynamic property evaluation map based on the verified evaluation indexes, dividing dynamic property partitions, outputting the dynamic property partitions to the vehicle-mounted intelligent terminal in real time, and generating a self-adaptive control instruction set when entering a failure area. According to the invention, multi-source information fusion modeling, all-working-condition dynamic response evaluation and visual map feedback can be realized, and the dynamic adaptability and regulation and control intelligence of the hybrid tractor in a complex operation scene are improved.

The invention provides an unmanned aerial vehicle full-time perception image reconstruction method based on multi-mode cooperative reinforcement learning and degeneration decoupling. A frequency perception feature modulation model, a dual-mode dual-domain transformation module and a dynamic bidirectional guide mechanism are included. According to the system, firstly, feature information of different frequency bands is adaptively separated and modulated through a frequency sensing feature modulation model, and decoupling and compensation of composite unknown degradation are achieved; realizing cross-domain interaction and information fusion of visible light and infrared characteristics in a spatial domain and a channel domain by using a bimodal dual-domain transformation module; and finally, realizing collaborative enhancement of cross-modal degradation perception through a bidirectional dynamic guide mechanism, and generating an unmanned aerial vehicle visible light reconstruction image and an infrared super-resolution image with higher structural consistency and texture fidelity. According to the method, deep fusion and degeneration decoupling of multi-modal information can be realized in a complex degeneration environment, and the imaging quality and the environmental adaptability of an unmanned aerial vehicle full-time sensing system are remarkably improved.

The invention discloses a multi-source information fusion-based dynamic cooperative regulation and control method for a transmitting-end power grid energy storage station, and relates to the field of power grid energy storage regulation and control. The method comprises the following steps: compressing global heterogeneous data, and extracting a low-dimensional critical feature flow; constructing a dynamic coupling potential energy field, and quantifying gradient intensity to capture recessive instability; generating a layered fractal regulation strategy, resolving conflicts and outputting an instruction and a residual error; constructing a topological phase change trigger network, and triggering regulation and control through spectrum gap jump; causal tracing and model evolution are carried out, and parameters are optimized to form a closed loop. According to the invention, through fractal cooperation, topological response and evolution closed loop, regulation and control collaboration, instability response speed and system adaptability are improved, and safe and stable operation of a power grid is guaranteed.

The invention discloses an infrared image power transmission equipment target identification method and system based on YOLOv7, and relates to the technical field of electric power automation, and the method comprises the following steps: obtaining continuous frames of infrared images and visible light images of power transmission equipment, and carrying out the preprocessing; based on the processed continuous frame infrared image and visible light image, constructing a space-time multi-mode input tensor; processing the input tensor through an improved YOLOv7 model, and outputting a first target frame and a corresponding frame-level feature vector; performing cross-modal feature fusion optimization based on the frame-level feature vector to obtain a joint optimization feature; performing confidence coefficient correction on the first target frame by using the joint optimization feature to obtain a second target frame; and outputting a target recognition result based on the second target frame. The method is used for solving the defects of a traditional power transmission equipment identification method in the aspects of information fusion effect, detection result stability and result credibility.

The invention discloses a force sense feedback control method for an intelligent mechanical arm, which comprises the following steps of: 1, acquiring data through a multi-modal sensor and fusing the data to obtain a multi-dimensional perception vector; 3, calculating a force sense tracking error and a change rate and triggering an event-driven control decision mechanism; 4, designing a nonlinear compensation control rule and outputting a control torque instruction, wherein a control system comprises a multi-mode sensing module, a dynamic prediction module, an event-driven control module and a cooperative calculation module; according to the method, multi-mode sensing information is fused with the lifting force sense representation capacity, advanced adjustment is achieved in combination with a dynamic force sense prediction mechanism, event-driven control is used for reducing calculation redundancy, robustness to complex interference is enhanced through a nonlinear compensation strategy, and finally high-precision and low-delay force sense control of the mechanical arm in a dynamic interaction scene is achieved.

The invention provides an intelligent question and answer method and device based on large model multi-source information fusion and electronic equipment, relates to the technical field of computers, in particular to the fields of big data, intelligent search and the like, and can be used for application scenes such as intelligent question and answer. According to the specific implementation scheme, pre-retrieval is conducted from a data table structure information base and a data table field information base according to a user problem, and a pre-retrieval result is obtained; retrieving from a business knowledge base, a business rule base and a question number template base according to the user question to obtain a knowledge enhancement result; according to the pre-retrieval result and the knowledge enhancement result, utilizing a query instruction generation model to obtain a query statement; and querying from the data table set according to the query instruction to generate an answer corresponding to the query word. The scheme can improve the professionality of intelligent questioning and answering, and is suitable for application scenes of various industries.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention belongs to the field of medical image processing and radiotherapy quality control, particularly relates to a gamma distribution diagram calculation method based on a diffusion model, and aims to solve the problem that the traditional radiotherapy quality guarantee efficiency is low. The method includes deriving a field flux profile image from a radiotherapy planning system. A reverse process noise estimation network is improved based on a conditional diffusion model, a field flux distribution diagram image is taken as conditional input, field flux distribution diagram information is extracted layer by layer at an encoder part, information fusion with noise path coding is carried out at different coding scales, diffusion noise is predicted by adopting multi-scale fusion Unet, and the prediction precision is improved. And constructing to obtain a gamma distribution diagram generation model. A dual-path residual block is adopted as a basic module of an encoder and a decoder of the multi-scale fusion Unet, a moving window jump connection module is adopted to integrate global and local features of different stages, and the moving window jump connection module introduces spatial offset through a moving window mechanism.

The invention discloses a ship anti-collision early warning method based on multi-source heterogeneous information fusion, and the method comprises the steps: S1, obtaining the multi-source target information of a ship navigation radar, an AIS, and an infrared camera, and unifying the targets of all sensors to a same coordinate system; s2, preprocessing the radar and the AIS target; s3, performing information fusion on the preprocessed radar and AIS target; s4, performing information fusion on the target after radar and AIS fusion and the infrared image recognition target; and S5, based on the dynamic information of the final fusion target and the state of the ship, calculating the relative distance and orientation with the ship, and when the target is located in a preset fan-shaped area right in front of the ship and the relative distance is smaller than a dynamic danger threshold value, triggering a multi-stage acousto-optic character alarm. According to the invention, various sensor information can be integrated to detect and track the water surface target, and the robustness and the detection rate are improved, so that a more accurate early warning effect can be achieved on the water surface obstacle when the ship sails.

The invention relates to an unmanned aerial vehicle dynamic environment sensing method based on a visual large model, electronic equipment and a medium, and the method comprises the steps: carrying out the instance segmentation and optical flow estimation of multiple frames of original images based on the visual large model and an optical flow estimation network, and obtaining a dynamic mask and inter-frame optical flow information; performing feature point classification on the depth information, the dynamic masks and the inter-frame optical flow information corresponding to the multiple frames of images to obtain a classification result; constructing a re-projection error model based on the classification result to obtain an estimated pose; evaluating the quality of the feature points based on the estimated pose to obtain an optimized pose; and constructing a feature point global map based on the world coordinates of the continuous frame road sign points and the optimized poses. Through a multi-information fusion and step-by-step optimization mechanism, the method has relatively high robustness, can adapt to various dynamic environments, and improves environment perception performance and operation reliability in different scenes.

The invention discloses a hydrogen leakage accident holographic perception and disaster situation dynamic prediction system in a tunnel scene, and belongs to the technical field of hydrogen energy traffic safety and intelligent risk management. According to the system, multi-modal information such as environmental parameters, hydrogen concentration, heat source temperature, leakage acoustic characteristics and vehicle states is collected in real time through a multi-modal sensing unit; abnormal detection, time synchronization and space registration are carried out through the information fusion module to generate fusion data in a unified format; the hazard source analysis module locates a leakage source, estimates a leakage rate, locates an ignition source and extracts disaster characteristics based on the fused data; the disaster dynamic prediction module predicts spatio-temporal evolution of diffusion, combustion and explosion by using an agent model and calculates disaster levels; the feedback early warning unit synchronously outputs the prediction information to the tunnel monitoring platform and issues early warning information; and the data storage and self-optimization module uniformly stores historical data and optimizes the prediction model through incremental learning. The system realizes real-time monitoring, rapid evaluation and intelligent disposal of tunnel hydrogen leakage accidents.