597 results about "Perception model" patented technology

The invention relates to the technical field of unmanned aerial vehicle cluster control, and particularly discloses an intelligent cooperative flight path planning method and system for an unmanned aerial vehicle cluster system, and the method comprises the steps: firstly constructing a dynamic environment perception model, collecting data through all unmanned aerial vehicle sensors, and carrying out the preprocessing, attention feature extraction and federal learning fusion, and generating a global environment situation map; planning and screening a candidate track set by adopting a particle swarm-genetic hybrid optimization algorithm for adaptive weight adjustment on the basis of the image; a global optimal track consensus is achieved through an improved consensus algorithm and conflict resolution through a distributed collaborative negotiation mechanism and no human-computer interaction evaluation indexes; and finally, monitoring the environment in real time during execution, triggering dynamic re-planning when the environment is abnormal, and ensuring track adaptation through multi-level threshold and incremental planning. According to the method, the unmanned aerial vehicle cluster can quickly respond to the environment change and adjust the flight path, so that the task execution efficiency and success rate of the unmanned aerial vehicle cluster in the complex dynamic environment are improved.

The invention discloses a building robot multi-machine collaborative operation optimizing and monitoring method and system based on digital twinning, and belongs to the technical field of building construction intellectualization. The method comprises the following steps: constructing a digital twinborn body comprising an environment perception model and a kinematics / dynamics model based on a building information model and robot physical parameters; performing multi-machine task allocation and path planning containing static / dynamic obstacle avoidance in the virtual environment; synchronizing physical environment data in real time through a multi-mode sensor; dynamically optimizing operation parameters by adopting a genetic algorithm or a particle swarm algorithm and realizing closed-loop control; and generating a safety early warning and emergency scheme based on machine learning. According to the method, Markov decision path planning of reinforcement learning and multi-agent game task allocation are creatively fused, laser radar-vision-inertial navigation multi-source data fusion is adopted, the technical problems that in a traditional method, digital twinning precision is insufficient, and dynamic cooperation efficiency is low are solved, and the method is suitable for large-scale popularization and application. And the construction efficiency, the safety and the man-machine interaction experience are remarkably improved.

The invention provides an efficient remodeling production scheduling method, medium and system based on AI multi-agent dynamic negotiation, and belongs to the technical field of agents. A residual connection mechanism is used for processing time sequence correlation characteristics under a nonlinear working condition to establish a remodeling time prediction basis, a dynamic layered negotiation architecture is established to realize information interaction and decision transmission, and an equipment agent predicts remodeling time based on a multi-scale time sequence perception model and generates a bidding scheme. A coordination agent processes a bidding scheme by adopting a Pareto leading edge multi-objective optimization algorithm to balance multiple objectives, dynamically adjusts model parameters through a hybrid similarity evaluation mechanism to guarantee prediction stability, and automatically identifies an affected order subset to trigger an incremental re-negotiation process when production disturbance occurs. The technical problem that the production scheduling plan is frequently adjusted due to inaccurate remodeling time prediction is solved.

The invention relates to the technical field of micro-nano motion control, and discloses a self-adaptive control method of a micro-nano high-precision motion platform. The method comprises the following steps: acquiring real-time pose feedback data and target trajectory data of a motion platform, and extracting dynamic response features; calling the trained motion feature analysis network to perform multi-modal feature separation, and generating a platform pose feature set; based on the set, performing space-time coupling analysis on the working environment parameters through an environment disturbance perception model to obtain a fusion result containing mechanical deformation characteristics and environment disturbance characteristics; inputting a fusion result into a dynamic compensation model to calculate a track correction amount, and outputting a driving compensation instruction; and calibrating the target trajectory data in real time according to the compensation instruction, and generating an actual control signal. According to the method, through multi-modal feature analysis, space-time coupling perception and dynamic compensation, the control precision and stability of the motion platform in a complex environment are improved, and the method is suitable for a micro-nano high-precision motion control scene.

The invention discloses a wind power generation abnormal data analysis method and system, and relates to the technical field of data analysis, and the method comprises the following steps: constructing a wind direction change rate enhanced perception model, analyzing the potential omen of wind direction abrupt change based on an ultra-short time scale wind direction change trend curve and a wind speed fluctuation coupling index collected at multiple measurement points, and determining the wind direction abrupt change. Generating a risk early warning label of wind wheel pointing deviation; and based on the risk early warning label, executing a high-frequency yaw disturbance prediction mechanism, and predicting an inflow angle continuous offset window caused by yaw response lag by using a nonlinear time sequence evolution trend and a short-term wind direction reversal probability curve. The wind direction sudden change early warning is realized through multi-measuring-point wind direction enhanced perception and wind speed coupling analysis, the response precision is improved and the energy consumption is reduced in combination with predictive yaw compensation and torque balance, the yaw parameters are dynamically optimized by using adaptive closed loop and reinforcement learning, the inflow angle is kept stable for a long time, and the power generation efficiency and the structural safety are improved.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention relates to the technical field of power systems, and provides a power distribution network multi-dimensional regulation and control decision method, system and device and a medium, and the method comprises the steps: inputting the preprocessed multi-source operation data into a preset state perception model, and obtaining a multi-dimensional state vector representing the operation state of a power distribution network; a multi-dimensional state vector is used as a state space, regulation and control operation is used as an action space, a composite reward function is established according to a power distribution network operation target, and modeling is carried out to obtain a Markov decision process framework; interacting with a power distribution network simulation environment by adopting a deep reinforcement learning algorithm, obtaining a current state from a state space, selecting and executing regulation and control operation in an action space according to a strategy network, updating strategy network parameters based on feedback of a composite reward function until an optimal regulation and control strategy network is obtained, and obtaining a deep reinforcement learning strategy model; and performing strategy rolling updating based on the real-time monitoring data to obtain a target regulation and control strategy. According to the invention, comprehensive optimal regulation and control of a complex operation scene can be realized.

The invention relates to an Internet of Vehicles multi-agent cooperative sensing method and system. The method comprises the following steps: constructing a collaborative sensing network, wherein the collaborative sensing network comprises a self-agent and a plurality of collaborative agents; acquiring and processing sensing data through the collaborative sensing network; performing feature extraction to obtain intermediate features; self-adaptive sparsification is carried out to obtain sparse features, and the sparse features are compressed and transmitted to a self-agent; performing time sequence feature enhancement on the features of all the agents at the self-agent end; fusing the features to obtain fused features; and constructing an Internet of Vehicles perception model, and realizing perception by the detection model according to the fused features. According to the method, the calculation complexity of traditional global attention is reduced from the square level to the linear level through an adaptive sparsification mechanism, the calculation overhead is remarkably reduced while the multi-agent feature interaction precision is kept, and the method is more suitable for real-time operation on the vehicle-mounted edge equipment with limited resources.

The invention belongs to the technical field of knowledge graph construction, and discloses a supply chain knowledge graph construction method based on time sequence dynamic perception and a large language model. Constructing a time sequence dynamic sensing model, and respectively generating a time-sensitive embedding matrix for the entity and relationship of each sub-graph in a historical time window; inputting the time-sensitive embedded matrix into an aggregator to further mine structure and semantic information of entities and relationships; establishing a dependency relationship of an autoregression model learning sub-graph in a time sequence, and generating a time sequence evolution representation; and inputting the time sequence evolution representation into a pre-trained large language model, generating candidate entities or candidate relationships to complement the fact tetrad, and updating the sub-graph sequence of the current timestamp. According to the method disclosed by the invention, the supply chain domain knowledge is adapted while the general semantic understanding capability is reserved, and the balance of dynamic evolution modeling, long-period dependency capture and efficient utilization of the domain knowledge is realized, so that the reliability and interpretability of a construction result are ensured.

The invention discloses an intelligent group area coverage control-oriented multilayer game reinforcement learning method, which comprises the following steps of: mapping each agent in an intelligent group into a game player, and constructing an income evaluation mechanism in combination with a task target of the agent to form a group game model; constructing a perception model and a communication model based on physical capability constraints of the intelligent agent, designing evaluation indexes of coverage efficiency, operation safety, energy consumption efficiency and communication collaboration, and integrating the evaluation indexes into a vector revenue function of the intelligent agent; a TD3 model is adopted as a game player role of an intelligent agent, and association between reinforcement learning and group game is established; and constructing a multi-layer semi-distributed game reinforcement learning framework, carrying out environment interaction, and solving an intelligent group area coverage control strategy meeting Pareto-Nash equilibrium. According to the method, the balance of individual and collective benefits is realized, the calculation efficiency is improved through a semi-distributed iterative algorithm, and the method is suitable for solving requirements of a large-scale complex system.

The invention relates to an industrial robot health state evaluation method based on multi-modal servo information mapping. The method comprises the following steps: obtaining the influence of a complex service condition on the performance of an industrial robot and a degradation mechanism; health perception features are extracted from multi-mode servo information collected in the operation process of the industrial robot; according to the collected health perception characteristics, a health perception model of the industrial robot under the complex working condition is established; an incremental learning model is established, a degradation model is obtained along with continuous addition, continuous learning and updating of new data, failure time distribution of the degradation model is predicted, decision support is provided for maintenance of industrial robot equipment, and a multi-modal servo information mapping model is constructed by deeply analyzing a performance degradation mechanism of an industrial robot under complex working conditions. And accurate sensing of the health state of the robot is realized. And a self-supervised contrast representation learning theory is utilized to solve the problem of fault sample deficiency, and the accuracy of health perception is improved. Based on an incremental learning theory of multi-modal servo information, an autonomous diagnosis model is established, and rapid diagnosis and performance degradation prediction of a new fault are realized.

The invention relates to the technical field of research and development management, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an anomaly analysis method, device, equipment and medium based on a variation test. Generating a test case through the semantic perception model; selecting a variation strategy by using a state-driven model according to the service state, and generating a variation test case based on the access control strategy; executing the variation test case in a distributed environment, and collecting system resource index data and request execution monitoring data; and inputting the collected data into the abnormal prediction model, and outputting an abnormal behavior prediction result. Through combination of semantic perception generation, state-driven selection and anomaly prediction, coverage of a test case on protocol dependence and a service state is improved, and the accuracy of anomaly detection of the object storage system is remarkably improved.

The invention relates to the technical field of battery health management and prediction, in particular to a lithium battery capacity fading trend prediction method and system, and the method comprises the steps: enabling data driving features and electrochemical features to generate deep fusion features through a dual calibration mechanism; inputting the feature sequence into an electrochemical process perception model, and encoding the feature sequence into a potential state vector representing personalized decline; and inputting the vector as an initial condition into a neural differential equation model, and finally generating a continuous health state attenuation trajectory and obtaining a prediction result by learning a decline dynamic law and performing integral solution. According to the method, deep fusion is carried out on a physical mechanism and a data driving model, the defect that a traditional black box model is weak in generalization ability is overcome, long-term, high-precision and continuous prediction of the full-life-cycle decline trend can be achieved only through early weak signals of a battery, and the reliability and the practical value are high.

The invention discloses a multi-sensor fusion-based obstacle detection and recognition method for a wharf unmanned vehicle, and relates to the technical field of intelligent driving, and the method comprises the steps: setting a maximum threshold value and a minimum threshold value, comparing a sensor coverage rate with the maximum threshold value and the minimum threshold value, executing a sensor increase and decrease strategy, and carrying out the time synchronization of sensor data; obtaining an internal parameter matrix, a distortion coefficient and an external parameter matrix, projecting three-dimensional point coordinates of the point cloud data into two-dimensional coordinates of an image plane, and converting the two-dimensional coordinates in the image data into three-dimensional coordinates in a global coordinate system; labeling the sensor data to obtain a two-dimensional bounding box and a three-dimensional bounding box, and obtaining the category and pose label of an obstacle; building a multi-modal fusion perception model, and training the multi-modal fusion perception model by taking the sensor data and the categories and pose labels of the obstacles as training data; and deploying the optimized multi-modal fusion perception model, and outputting the category and pose information of the obstacle, so that the method is suitable for a complex driving environment.

The invention discloses a radiation safety management method and system based on cloud platform data driving, and relates to the technical field of cloud platform radiation management. The method comprises the following steps: collecting radiation field data and environment state data of a target area through a deployed intelligent sensing node; uploading the radiation field data, the environment state data and the context data from the service module to a cloud platform, performing energy compensation and radiation unmixing, and constructing a multi-dimensional feature vector; and processing the multi-dimensional feature vector by using a pre-trained situational radiation perception model, identifying radiation field features and an environment situation, and generating a situational radiation safety early warning signal. The technical problem that in the prior art, radiation safety monitoring depends on single-point measurement, comprehensive judgment cannot be carried out in combination with environment and service context data, and consequently the radiation field anomaly recognition capability is insufficient is solved, and the purpose that the radiation field anomaly recognition capability is improved through cloud platform data driving and context awareness model fusion is achieved. And the technical effects of high-precision identification and situational safety early warning of the radiation field state are realized.

The invention discloses a classroom real-time learning emotion knowledge and intelligent intervention system, and relates to the technical field of artificial intelligence auxiliary education. Comprising a time sequence perception modeling module, a conflict identification analysis module, an intervention priority control module, a decision fusion output module, a stability prediction and early warning module and a closed-loop vibration suppression regulation and control module, the time sequence perception modeling module establishes a unified time baseline and a phase reference field, and performs joint modeling on multi-source classroom learning condition data under the constraint of the unified time baseline and the phase reference field; time sequence semantic fusion features are extracted, and a contradictory candidate trajectory set is generated. According to the method, multi-modal time sequence semantic features are fused, a learning situation conflict source is identified, conflict intensity is quantified, an intervention priority and a punishment mechanism are constructed to guarantee rhythm stability, an evidence chain and an attention mechanism are adopted to generate a consistent decision, oscillation detection and time reversal regulation are combined, closed-loop control of the intervention process is achieved, strategy oscillation is effectively avoided, and the method is suitable for popularization and application. And the intervention continuity and stability are improved.

The invention discloses an intelligent task allocation method and related equipment. The method comprises the steps of receiving multi-format task description information, extracting multi-dimensional attributes to generate a task vector, constructing an executive vector containing multiple capability dimensions, matching an optimal main executive through a multi-layer perception model, and performing real-time monitoring and adjustment. According to the scheme, the existing defects are specifically solved: multi-format information is analyzed to extract multi-dimensional attributes, so that a system comprehensively understands the context of a task, and the understanding insufficiency of a traditional method is avoided; constructing a multi-dimensional executive capacity vector and combining a scoring model to realize accurate matching of a task and an executive, and solving the problem of unreasonable distribution caused by asymmetry of man-machine capacity; the execution state is monitored in real time, redistribution is triggered, a dynamic feedback closed loop is formed, and the defect that the dynamic performance of a labor division mechanism is insufficient is overcome, so that the task execution flexibility and reliability are improved, resource waste and decision errors are reduced, and the execution efficiency is improved.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal intelligent compliance pre-auditing method and system, and the method comprises the following steps: receiving a qualification package uploaded by a supplier, and automatically completing the decompression operation; accurately marking the decompressed page as five types of a subject license, a financial report, industry qualification, a product catalog and environmental protection and safety evaluation by using a layout perception model, and executing fingerprint hash de-duplication processing on the homologous file to generate a unique document identifier DocID; the method has the beneficial effects that the authenticity verification, financial analysis and directory matching of all qualifications of a single supplier can be completed within about 30 seconds by adopting a one-package type automatic sorting link and parallel API aggregation verification; and compared with the traditional manual item-by-item check which merges the process of several hours, the whole check period is shortened by more than 90%, and the manpower of compliance specialists is greatly released.

The invention relates to the technical field of intelligent traffic, and discloses a dynamic traffic guidance method based on traffic flow prediction under the influence of navigation information, and the method specifically comprises the steps: constructing a random dynamic traffic network model, and carrying out the quantitative description of OD demands and the time-varying characteristics of road traffic flow; establishing a path travel time perception model under the influence of navigation information, and calculating a path selection probability by adopting a Logit model; constructing a hybrid traffic distribution model based on dynamic system optimization and dynamic user balance, and performing iterative solution by adopting a continuous averaging method with a residual flow updating mechanism; forming a reinforcement learning environment by constructing a state space function, an action space function and a reward function; and training the model by using a DDQN algorithm, and optimizing a path selection strategy. According to the method, the problems of low induction precision and poor adaptability caused by neglecting node delay and lacking information fusion and utilization in the existing method are effectively solved, and the dynamic traffic induction effect is remarkably improved.

The invention relates to the field of data processing, in particular to a security agent construction system and method based on a large model. Constructing a low-temperature liquid leakage knowledge base; collecting normal operation condition data, and training the leakage sensing model in an unsupervised mode; configuring a large model with a tool calling interface as a security agent; multi-modal data are synchronously collected in real time, a preliminary abnormal alarm is generated through a perception model, and associated information is retrieved; fusing the multi-modal information by the intelligent agent to carry out leakage prediction analysis and comprehensive research and judgment; and for the confirmed leakage event, a professional tool is automatically called to carry out quantitative consequence simulation, and a scenario disposal scheme is generated. According to the invention, the dependence on scarce leakage samples is effectively overcome, and the detection reliability is improved; through knowledge enhancement and multi-modal fusion, the research and judgment accuracy is greatly improved; full-process automation from early perception, intelligent analysis to consequence severity division is realized, and the intelligent level and emergency response efficiency of industrial safety monitoring are remarkably improved.

The invention belongs to the technical field of ecological restoration evaluation, and provides a geological ecological restoration evaluation method and system for geological disaster prevention and control, electronic equipment and a storage medium. The method comprises the steps of research data acquisition, geological disaster type setting, initial evaluation, data preprocessing, model construction and training, risk determination before restoration, information acquisition after restoration, geological disaster risk prediction and geological ecological restoration evaluation. According to the method, the model is trained by using the remote sensing image, the sound wave data and the initial evaluation data, the evaluation result is predicted through the remote sensing image and the sound wave data, the risk evaluation difficulty and time are reduced, in addition, remote sensing and sound waves are combined, meanwhile, surface features and structural features are extracted, and the prediction effect of the model is optimized; through a multi-mode proximity perception model, the perception capability of the surrounding environment is improved, and the prediction precision of a single block is improved; and through the repair effect evaluation rule, a repair effect evaluation mechanism is optimized.

The invention relates to an automatic driving intelligent safety multi-task dynamic test system based on a Carla simulation platform, and belongs to the field of artificial intelligence safety and automatic driving simulation test. The system comprises an automatic driving perception model integration and training module which comprises a multi-task data collection module, a fine tuning perception model and a compilation perception model Carla API interface; the attack and defense algorithm integration and training module is used for finely adjusting an attack and defense algorithm, writing an attack and defense algorithm Carla API (Application Program Interface) and replacing Carla object textures with an adversarial patch; and the real-time attack and defense deduction and evaluation recording module comprises a sensor for acquiring Carla real-time data, replacing textures, starting a perception model and starting an evaluation record of the success rate and the accuracy rate of defense and attack. According to the method, the robustness of the automatic driving perception model in a complex attack scene can be comprehensively evaluated, security holes of a perception system can be found in time, and the reliability and the security of an automatic driving technology in a complex environment are improved.

The invention discloses a knowledge graph teaching path dynamic recommendation method and system based on reinforcement learning. The method comprises the following steps: constructing a cognitive adaptive dynamic knowledge graph; obtaining an individual multi-dimensional state vector of a student to be recommended; obtaining a trained student state perception model and a trained teaching path reinforcement learning model; inputting the individual multi-dimensional state vector of the to-be-recommended student into a trained student state perception model to obtain cognitive state information of the to-be-recommended student; and calling the trained teaching path reinforcement learning model by taking the cognitive adaptive dynamic knowledge graph as an environment and combining cognitive state information of the to-be-recommended student, so as to obtain a current personalized teaching path recommendation result of the student individual. The teaching content sequence can be dynamically adjusted according to the knowledge state and the learning behavior of the student, and the teaching integrating degree is improved; the teaching path is continuously optimized through learning, and different types of students can be adapted.

The invention discloses a large model generation content risk identification and intervention method based on deep learning, and the method comprises the steps: collecting multi-modal data outputted by a content generation system, carrying out the preprocessing of the multi-modal data, and generating the preprocessed multi-modal input data; encoding and fusing the features of the multi-modal perception model to generate global risk feature representation data; inputting global risk features into a hunting optimization algorithm, and optimizing model structure parameters, risk thresholds and intervention strategy parameters; optimizing parameter-driven risk identification and hierarchical intervention, and outputting risk levels, categories and hierarchical intervention measures; an intervention effect and user feedback are collected, and continuous optimization and self-adaptive evolution of parameters are driven. According to the method, efficient risk identification and intelligent intervention on the large model generation content are realized, and the accuracy and the automation level of content security management are remarkably improved.

The invention provides a request processing method and device, equipment and a storage medium, relates to the technical field of computers, and is used for accurately performing exception processing on a data access request. The method comprises the following steps: acquiring access behavior information corresponding to a data access request; performing quantification processing on the access behavior information based on a behavior perception model, and determining a behavior feature vector corresponding to the access behavior information; performing analysis processing on the behavior feature vector based on a risk assessment model, and determining a behavior assessment result; the behavior evaluation result is used for indicating the abnormal degree of the access behavior corresponding to the data access request; processing the data access request according to an access control strategy corresponding to the behavior evaluation result; the access control strategy is used for controlling the access authority of the data access request.

The invention relates to the technical field of voice emotion recognition, in particular to a man-machine interaction voice sensing method and system based on a gradient intelligent calling subnet pool. The method comprises the steps of obtaining an emotion data set; constructing a man-machine interaction voice perception model based on a gradient intelligent dispatching sub-network pool; the system comprises an acoustic clue sensing purification module, a layered acoustic essential coding module, a gradient harmony subnet pool module, a task specific feature extraction module, a focus and confidence joint calibration module, a self-adaptive optimization strategy module and a real-time reasoning and decision fusion module. Carrying out emotion decision making by utilizing the constructed human-computer interaction voice perception model; and outputting a decision result. According to the invention, through the acoustic clue sensing purification module and the layered acoustic essential coding, the problems of emotional information distortion and identity feature confusion caused by real environmental noise are fundamentally solved.

The invention provides an information enhancement retrieval method and system based on a large model and a vector knowledge base, which are applied to the field of medical supply chain management and trigger a medical SPD service scene perception model by receiving a retrieval request initiated by a user through a medical SPD service terminal. The medical SPD service scene perception model is internally provided with a service process and a retrieval intention mapping rule, and scene adaptation parameters matched with a retrieval request can be output. And calling a medical SPD vector knowledge base for storing medical SPD service full-process structured and unstructured knowledge units, and constructing a dynamic association network of the retrieval intention and the knowledge units by using the scene adaptation parameters. And inputting the dynamic association network into a medical SPD service enhancement large model which is jointly trained by medical SPD service data and scene cases, generating a knowledge enhancement set, further generating a medical SPD service execution scheme comprising operation steps, association knowledge bases and data support sources, and pushing the medical SPD service execution scheme to a service terminal. The accuracy and comprehensiveness of medical SPD service information retrieval are improved.

The invention belongs to the technical field of robot perception and decision making, and discloses a multi-modal fused intelligent target perception method and system, and the method comprises the steps: obtaining multi-modal data in a distribution network operation scene, carrying out the preprocessing, fusing the knowledge of the distribution network operation field, and generating knowledge-enhanced multi-modal features; carrying out cross-modal alignment processing on the knowledge-enhanced multi-modal features, carrying out graph structure-based joint semantic and space alignment on isomorphic modals, and carrying out feature projection-based binding alignment on heterogeneous modals to obtain consistent aligned multi-modal features in a shared semantic space; based on a dynamic adaptive strategy, screening and fusing the aligned multi-modal features to generate unified fusion features; and inputting the fusion features into a target perception model, and outputting an image segmentation result and a point cloud semantic segmentation result of the distribution network operation target. According to the method, high-precision segmentation and positioning of the target in a complex distribution network scene are realized, and safe and efficient operation of the robot is effectively supported.

The invention discloses a network topic hot spot extraction method based on bullet screen semantic recognition, and aims to solve the problems of bullet screen data semantic sparsity, semantic offset, noise interference and the like. The method is characterized by comprising the following steps: carrying out semantic coding on a bullet screen by utilizing a Transform structure; a dynamic semantic evolution perception model is constructed, semantic offset is measured through KL divergence, and topological correlation analysis is carried out through GCN; constructing a space-time density field in combination with a video time axis to realize space-time coupling feature fusion; automatically extracting a hot spot cluster by adopting an improved density peak clustering algorithm; and predicting a hotspot evolution trend by using an LSTM model. By means of the technical scheme, topic hotspots can be accurately captured, semantic evolution logic can be recognized, and the purity and predictability of hotspot extraction are improved.

The invention provides a target detection credibility estimation method fusing data and model uncertainty, and belongs to the field of data processing, and the method comprises the steps: obtaining an image quality score through an image quality evaluation model; outputting a target frame by using a two-dimensional detection model, and performing Bayesian correction of confidence in combination with an image quality score and a weather perception model result; meanwhile, outputting a three-dimensional target frame by using a laser radar point cloud detection model, and correcting three-dimensional detection confidence in combination with a target distance and weather conditions; according to the statistical relationship between the number of point clouds in the detection frame and the detection accuracy, constructing the point number confidence coefficient based on Bayesian reasoning; and performing structural consistency analysis according to the spatial size of the detection frame and the prior size distribution of the target category, and outputting structural matching confidence. And finally, the five indexes are fused, and the overall credibility of the sensing result is calculated. According to the method, the credible judgment capability of a sensing system in a complex environment can be improved, and the method has good practicability and engineering value.