171results about How to "Achieve perception" patented technology

The invention discloses an ancient building risk prediction management and control method and system based on a large model, and relates to the technical field of ancient building protection. An ancient building risk prediction management and control system based on a large model comprises a multi-source data acquisition module, a multi-modal data fusion module, a large model risk prediction module, a risk assessment and early warning module, a digital twinborn dynamic management and control module and a repair decision module. According to the method, the structure state data, the visual image data and the environment data are fused, so that the state of the historic building is comprehensively perceived; the self-supervised learning pre-trained ViT-GPT model is combined with the LSTM time sequence model, so that the time sequence analysis capability and the multi-modal data processing capability of risk prediction are remarkably improved, the future damage probability and the risk trend of the historic building can be accurately predicted, and the defects of the prior art in long-time span risk prediction are overcome.

The invention provides an oil depot tank field inspection robot task allocation method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. Constructing a multi-target collaborative scheduling model, extracting features by adopting a space-time attention mechanism, establishing a task priority evaluation and path planning sub-model, and generating an optimal task allocation strategy; and an inspection task instruction is issued, the execution condition is monitored in real time, and when abnormity is detected, the emergency module is started for optimization adjustment. According to the invention, intelligent and collaborative routing inspection task distribution is realized, and the routing inspection efficiency and safety are improved.

The invention relates to the technical field of security knowledge maps, and discloses a city security knowledge map construction method based on an AI large model and a related device, and the method comprises the steps: carrying out the vectorization feature extraction of multi-source city security event data, obtaining a multi-dimensional feature matrix, carrying out the feature analysis through a BERT encoder, and obtaining a multi-dimensional feature matrix; performing key value pair generation and feature processing on each group of query vectors in the obtained grouped query vector set; performing spatial-temporal correlation calculation on the event association feature matrix to obtain an initial security knowledge graph, and performing graph neural network learning and event classification on the initial security knowledge graph to obtain an event type division result; performing situation assessment index calculation on the event type division result to obtain security situation early warning data; and performing knowledge graph updating on the security situation early warning data to obtain an urban security knowledge graph, thereby realizing accurate classification and situation evaluation of security events and dynamic updating of the urban security knowledge graph.

The invention relates to the technical field of network security situation generation, in particular to a network security situation generation method based on multi-view monitoring. The method comprises the following steps of: constructing a multi-view data acquisition system, acquiring real-time information from a multi-dimensional data source, and comprehensively capturing potential threats by utilizing an intelligent sensing technology; cleaning, noise filtering and feature extraction are performed on the acquired data based on a dynamic threshold self-adaption multi-factor cleaning method, so that the accuracy and consistency of the data are ensured; a dynamic analysis algorithm based on deep learning is adopted, the incidence relation between the security events is mined, and the development trend of potential threats is predicted; constructing a joint learning model, and comprehensively evaluating the security state of the network environment; the security situation is dynamically displayed by using a visualization technology, and a security policy is quickly generated in combination with an automatic early warning and response mechanism to resist threats. According to the method, accurate evaluation and prediction of the network security situation are realized, and support is provided for security decision-making in a complex network environment.

The invention provides an automobile part spraying control method and system, and relates to the technical field of automobile manufacturing, and the method comprises the steps: collecting spraying environment parameters in real time, generating a comprehensive digital model containing the environment parameters and part characteristics, and carrying out the intelligent partitioning of the surface of an automobile part through a clustering partitioning algorithm, and dividing the surface into a plurality of sub-regions; on the basis of each sub-region, combining with the current environment parameters, generating an optimal spraying parameter for each sub-region by utilizing an intelligent agent trained by a reinforcement learning algorithm, and on the basis of the spatial relationship between the optimal spraying parameters and the sub-regions, optimizing a global spraying sequence by adopting an ant colony path planning algorithm, and generating an optimal spraying path; the spraying robot sprays, a real-time quality evaluation result of spraying is generated, a pre-constructed fuzzy logic controller is adopted, spraying parameters are adjusted, sprayed parts are obtained, a full-period comprehensive data set is integrated, and spraying control parameters are iteratively updated.

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

The invention relates to the field of robot attitude control, in particular to a robot attitude control method and system based on intelligent recognition. The method comprises the following steps: continuously acquiring a real-time sandbox scene video stream based on a high-definition camera; and carrying out time sequence frame segmentation on the real-time sandbox scene video stream, and carrying out global contrast enhancement processing to obtain a global contrast optimization image sequence. Carrying out dynamic object identification on the global contrast optimization image sequence, and carrying out continuous frame optical flow motion tracking to generate a dynamic object optical flow change track; optimizing the image sequence according to the global contrast to extract scene ground and scene obstacle data; performing dynamic trajectory rendering on the scene ground and scene obstacle data based on the dynamic object optical flow change trajectory so as to construct a dynamic scene twinborn model; robot position posture parameters and user operation instructions are recognized. According to the invention, timely attitude adjustment response and accurate movement position control are realized.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention discloses a vehicle trajectory prediction method, electronic equipment and a storage medium, and the method comprises the steps: obtaining vehicle trajectory data, and carrying out the classification processing and feature extraction of the vehicle trajectory data, so as to generate a standardized data set; based on the standardized data set, a vehicle trajectory prediction network model is trained, and the vehicle trajectory prediction network model comprises a time sequence feature extraction module, a space interaction feature extraction module and a decoder; the time sequence feature extraction module is realized based on an improved Mama architecture; the space interaction feature extraction module is used for processing by using a mixed framework of a Transform framework and a graph attention mechanism; the decoder processes the encoded features through a multi-head attention mechanism and considers the relevance between different time steps and different vehicles so as to generate a prediction result of the vehicle trajectory; and inputting the data of the scene to be predicted into the trained vehicle trajectory prediction network model to obtain a prediction result of the vehicle trajectory, and the method has significant advantages in the aspects of calculation efficiency, prediction accuracy and scene adaptability.

The invention discloses an information interaction method for an intelligent robot with a body, and the method comprises the steps: S1, receiving the information which is transmitted by a user and comprises a task instruction, S2, carrying out the analysis of the information based on a context understanding module, so as to recognize the specific meaning and environment conditions of the task instruction, s3, operation corresponding to the specific meaning is executed, and execution parameters are adjusted in a specific environment to ensure that the task instruction is accurately implemented; according to the information interaction method of the intelligent robot with the body, environment perception and adaptive capacity are enhanced, various perception information such as vision, hearing and touch can be integrated, comprehensive perception of the environment is achieved, instruction understanding and execution accuracy is improved, the intelligent robot with the body can analyze complex user instructions more accurately, and user experience is improved. Through real-time multi-modal data fusion and decision optimization, the robot can realize real-time integration of different data sources in a highly dynamic scene, and the task execution efficiency and the user experience are improved.

The invention discloses a robot path planning and obstacle avoidance method combining an artificial potential field and reinforcement learning. The method comprises the following steps: carrying out sensor data acquisition and fusion; environment complexity evaluation is carried out, a multi-scale planning mechanism is established, and global planning and local planning are determined based on switching conditions; carrying out artificial potential field path planning based on global planning; carrying out reinforcement learning path planning based on local planning; and judging whether the target point is reached, and returning to the determined plan until the target point is reached. According to the invention, the data of the infrared sensor and the laser radar are fused, the complementary characteristics of the two sensors are fully utilized, the comprehensive perception of static and dynamic obstacles is realized, and the adaptability of the robot in different environments is improved. According to the method, through reinforcement learning, strategy optimization and uncertainty estimation, the obstacle avoidance capability of the robot in a dynamic environment is enhanced, and the local optimum problem of a traditional artificial potential field method is solved.

The invention provides a smart park optimization method and device based on digital twinning and BIM, and the method comprises the steps: collecting the actual information of a park, and carrying out the preprocessing; based on the configured basic BIM model, integrating the basic BIM model by using actual information; constructing a three-dimensional digital twinborn model, and fusing the integrated BIM model with the three-dimensional digital twinborn model; and analyzing and processing the park operation data by using the current fused three-digit digital twinborn model so as to realize park optimization. The digital mirror image of the smart park is constructed by integrating digital twinning, BIM, Internet of Things, big data, cloud computing, GIS and other frontier technologies, and comprehensive perception, accurate analysis, intelligent decision and efficient management of the park are realized. According to the scheme, the intelligent level, the operation efficiency and the sustainable development capability of the park are improved, and convenient, efficient and intelligent service experience is provided for park managers, settled enterprises and individual users.

The invention relates to the technical field of VR display control, in particular to an LBE enhanced multi-mode perception interaction VR display control method and system, and provides the following scheme that multi-mode perception data of a user is collected through VR head display equipment and a hand controller, position data and action data are fused by adopting a Kalman filtering algorithm, and the multi-mode perception interaction VR display control method and system are obtained. And calculating a continuous motion track of the user, dynamically adjusting the activity range based on the continuous motion track, and generating a directional dynamic range. And performing synchronization, feature extraction and weighted fusion processing on the multi-modal data to generate an interaction state vector. And analyzing real-time behaviors, action intentions and physiological feedback of the user according to the interaction state vector, judging the operation sensitivity and the emotional state of the user, and dynamically adjusting response parameters of the VR equipment. According to the method, the real-time performance, the flexibility and the immersion of large-space VR interaction are effectively improved.

The invention provides a mining intelligent watering dust fall method and system, electronic equipment and a storage medium, and relates to the field of watering dust fall. The method comprises the following steps: acquiring multi-source heterogeneous environment sensing data; preprocessing the multi-source heterogeneous environment sensing data to obtain preprocessed multi-source heterogeneous environment sensing data, and constructing a mining area holographic environment model based on the preprocessed multi-source heterogeneous environment sensing data; based on the holographic environment model of the mining area, extrapolating and estimating the dynamic change trend of the wind dust concentration by using a space-time sequence prediction algorithm to obtain a wind dust concentration distribution prediction result of each area of the mining area in a future time period; according to the technical scheme, dust suppression is taken as an optimization target, water mist parameters and a nozzle arrangement scheme are taken as decision variables, a mining area holographic environment model and a wind dust concentration distribution prediction result are taken as constraint conditions, an optimal spraying strategy meeting dust falling requirements is solved through a heuristic search algorithm, and the dust falling effect is improved and the water consumption is reduced through the technical scheme.

The invention relates to the technical field of target detection, and discloses a personnel state recognition method and system based on target recognition detection. The method comprises the steps of performing parameterization processing on collected original image data to obtain intelligent BIM data; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain target detection data; performing intelligent posture analysis processing on human body information in the target detection data to obtain optimized posture data; behavior recognition processing is carried out on the optimized attitude data to obtain behavior analysis data, and the behavior analysis data comprises violation behavior types, climbing state judgment and risk level evaluation; performing fuzzy logic evaluation processing on the behavior analysis data to obtain safety compliance data; and performing intelligent early warning processing on the safety compliance data and the real-time monitoring data to obtain full-life-cycle management data. According to the invention, the efficiency of personnel state recognition based on target recognition detection is improved.

The invention relates to the technical field of motor control, and discloses a cooperative control method and system for a high-speed motor with multiple cooling fans. The method comprises the steps that multi-dimensional data collection and standardization processing are conducted on each cooling fan unit in a target cooling system, a system state data matrix is obtained, and the target cooling system comprises a main cooling fan and a plurality of auxiliary cooling fans; performing discrete rotating speed response to obtain a continuous rotating speed control strategy; performing multi-dimensional dynamic feature extraction to obtain a system operation feature vector, and performing state feedback equation solution to obtain a mapping relation matrix; inputting a target control model for control parameter analysis to obtain an initial control sequence; inputting a dual Q network model to carry out master-slave fan control correction to obtain a control correction amount; according to the multi-cooling-fan cooperative control method and the multi-cooling-fan cooperative control system, cooperative control of the multiple cooling fans is achieved, and control conflicts are effectively avoided.

The invention relates to the technical field of air conditioner control, in particular to a self-adaptive energy-saving control method and system for an air conditioning unit, and the method comprises the following steps: monitoring indoor and outdoor environment temperature, humidity and illumination intensity in real time; identifying user behavior modes including rest, motion and leaving states; monitoring the operation states of the air conditioning unit in real time, wherein the operation states comprise compressor operation frequency, fan rotation speed and refrigerant pressure parameters; predicting an environment change trend and a user use demand in a period of time in the future; applying a multi-objective optimization method to generate an optimal control strategy comprehensively considering the energy-saving effect, the user comfort and the equipment stability; and according to the optimal control strategy, the operation parameters of the air conditioning unit are adjusted in real time, the environment change trend and the user use requirements in a period of time in the future can be accurately predicted, intelligent monitoring and prediction are achieved, and the energy-saving effect of the air conditioner is improved.

The invention relates to the technical field of cold-chain supply chains, and discloses an energy management and recovery method and system for a cold-chain supply chain. The method comprises the following steps: acquiring and preprocessing operating parameters and environmental parameters of the cold chain system to obtain multi-dimensional feature data; performing feature extraction and local density distribution calculation on the multi-dimensional feature data to obtain energy consumption mode clustering data; constructing an equipment operation knowledge graph according to the energy consumption mode clustering data; performing abnormal feature analysis on the equipment operation knowledge graph and the energy consumption mode clustering data through a hybrid neural network model to obtain equipment energy consumption abnormal feature data; generating an initial energy recovery scheme according to the equipment energy consumption abnormal feature data; the initial energy recovery scheme is applied to the cold chain system, the operation efficiency data and the load matching data are collected, online iterative optimization is carried out, the target energy management scheme is obtained, dynamic adjustment and continuous optimization of the energy recovery scheme are achieved, and the energy utilization efficiency of the cold chain system is improved.

The invention discloses an integrated digital intelligent cockpit domain system, and relates to the technical field of intelligent automobiles, the cockpit domain system comprises an environment sensing module, a decision planning module, a control execution module, an early warning and auxiliary driving module and a man-machine interaction and information display module; the environment sensing module is used for acquiring and analyzing the landform, curve and gradient information of the mountain road in real time, and environment data of pedestrians, vehicles, obstacles and road edges around the vehicle. The cockpit domain system has the advantages that the cockpit domain system monitors the surrounding environment of a vehicle in real time through the environment sensing module, front collision early warning and lane keeping assistance are achieved, traffic accidents are effectively prevented, the driving safety is improved, the early warning and auxiliary driving module can detect behaviors of a driver, the driving state of the driver is monitored in real time, and the driving safety is improved. Fatigue driving, distracted driving and violation operation can be found and reminded in time, and the safety, convenience and comfort of driving are improved.

The invention provides an interactive tactile glove system for astronaut training. The interactive tactile glove system comprises an upper computer virtual environment, a lower computer tactile perception glove and a tactile representation glove. The touch sensing glove is used for collecting temperature, humidity and pressure information of a hand and transmitting the information to the upper computer for processing through Bluetooth. The upper computer virtual environment is built based on a Unity 3D platform, various task scenes can be simulated, and corresponding tactile feedback signals are generated and transmitted. The tactile representation glove receives data transmitted by a virtual environment or the tactile perception glove, and real-time representation of temperature, force feedback and motion postures is achieved through an integrated semiconductor cooler, a vibration element, a bending sensor and other assemblies. According to the system, multi-modal tactile feedback can be provided in single astronaut fine operation training and cooperative operation of multiple astronauts, and the training effect and team adaptability are improved. The system not only has high integration level and stability, but also can provide multi-level and multi-mode tactile simulation according to task requirements, and is suitable for accurate training and remote operation in a complex task environment.

The invention provides an industrial robot safety monitoring method and system, and relates to the technical field of safety monitoring, and the method comprises the steps: arranging a multi-mode sensor array to collect the state information of a robot; performing empirical mode decomposition on the vibration signal to obtain an intrinsic mode function reflecting the abrasion degree of the robot transmission system; performing wavelet transform on the temperature signal to obtain a temperature feature set reflecting the heat dissipation condition of the robot; through knowledge extraction, knowledge representation and knowledge fusion technologies, semantic representation and linking are carried out on various knowledge in robot components, performance parameters, degradation modes and maintenance measures, and a management knowledge graph corresponding to the robot is constructed; and performing reasoning mining on the management knowledge graph based on a rule mining algorithm of path sorting, extracting degradation characteristics corresponding to each component of the robot in the management knowledge graph by mining a reasoning path formed by frequently co-occurring entities and relationships, and predicting a health state trend of the robot in combination with a pre-constructed time sequence prediction model.

The invention discloses a millimeter wave radar intelligent anti-interference method and system for an underground complex electromagnetic environment. The method comprises the following steps: S1, acquiring underground electromagnetic environment data by using a sensor; s2, performing calculation based on the underground electromagnetic environment data to obtain multi-dimensional state representation of the millimeter wave radar in an underground complex detection environment; s3, obtaining an anti-interference performance analysis index based on the underground electromagnetic environment data, and constructing a radar anti-interference intelligent efficiency evaluation model based on the anti-interference performance analysis index; s4, calculating an anti-interference optimal strategy by using a deep reinforcement learning algorithm based on the underground electromagnetic environment data, the multi-dimensional state representation and the radar anti-interference intelligent efficiency evaluation model; and S5, adjusting parameters of the deep reinforcement learning algorithm in real time based on an evaluation result of the radar anti-interference intelligent performance evaluation model and underground electromagnetic environment data, and obtaining a real-time updated underground complex electromagnetic environment-oriented millimeter wave radar intelligent anti-interference result.

The invention provides a calculation unloading optimization strategy based on multi-agent deep reinforcement learning, and relates to the technical field of intelligent algorithms. The core content of the calculation unloading optimization strategy based on multi-agent deep reinforcement learning is a multi-agent deep reinforcement learning algorithm designed for an MEC multi-user calculation unloading scene. According to the method, a task Q network structure for each task is constructed by utilizing a value decomposition technology, and a corresponding task Q value is generated on the basis of considering all task relationships of the intelligent agent. The task Q values can be further aggregated to obtain the Q value of the agent, so that the problem of high time and space complexity faced by the multi-task agent in a complex parameterized action space is effectively relieved. In addition, a self-attention mechanism is utilized in the hybrid network, and corresponding weights are dynamically distributed according to the characteristics of different users. Therefore, more accurate interaction representation among the intelligent agents is realized, and the flexibility and effectiveness of overall decision making are improved.

According to the intelligent hydraulic power plant operation management system and method based on digital twinning, the digital twinning model of the hydraulic power plant is constructed through the digital twinning technology, the operation data of the hydraulic power plant are collected through the sensing technology, and real-time data synchronization between the digital twinning model and the hydraulic power plant is achieved through the Internet of Things technology; meanwhile, the operation data of the hydraulic power plant are intelligently analyzed, whether the working state of the turbine is abnormal or not is intelligently analyzed based on a time sequence interaction mode between the generating capacity and multiple operation parameters influencing the generating capacity, and the analysis result is synchronously displayed in a digital twinborn model of the hydraulic power plant. In this way, comprehensive sensing, real-time monitoring and intelligent analysis of the operation state of the hydraulic power plant can be effectively achieved, and therefore the overall operation efficiency and reliability of the hydraulic power plant are improved.

The invention provides an intelligent power grid safety early warning system and a monitoring method in the technical field of intelligent power grids, which are used for solving the problem of safety early warning and monitoring function defects of the existing imperfect intelligent power grids, and comprise an information acquisition unit, a data management unit, an abnormity analysis unit and a safety early warning unit, the method comprises the steps of collecting power grid real-time data and line equipment data; constructing an adjacent matrix and a target matrix, calculating and analyzing to obtain a node weight and a target correlation degree, and analyzing by using a discrete model to obtain a dispersion index; a data evaluation value is obtained, and a data abnormal value is obtained through analysis; and generating intelligent power grid safety early warning information and a disaster recovery strategy according to the data abnormal value and the line equipment data. By constructing an intelligent power grid monitoring and early warning system, power grid equipment with potential safety problems is predicted in real time, the operation and maintenance efficiency is improved, the method can be suitable for power grid systems of different scales, the influence of faults on power grid operation is reduced, and large-area power failure events are effectively prevented.

The invention discloses a fusion diagnosis method for potential multiple faults of an aero-engine gas path in a transition state, and belongs to the field of aero-engine fault diagnosis. The fusion diagnosis method for the potential multiple faults of the aero-engine gas path in the transition state comprises the steps that collaborative noise reduction and multi-dimensional representation of aero-engine gas path transition state data are studied to obtain multi-dimensional gas path transition state sample data; constructing an aero-engine gas path transition state potential multi-fault fusion diagnosis model, training the diagnosis model by using the multi-dimensional gas path transition state sample data, and outputting a fault diagnosis result by using the trained aero-engine gas path transition state potential multi-fault fusion diagnosis model; the aero-engine gas path transition state potential multi-fault fusion diagnosis model is explained, and a fault mechanism is revealed; accurate mapping and mechanism revealing from limited gas path monitoring information to potential multiple faults are achieved, and theoretical and application support is provided for interpretable intelligent operation and maintenance of an aero-engine.

The invention relates to the technical field of power transmission line detection, in particular to the technical field of active power distribution network power quality monitoring and management, and particularly relates to an active power distribution network power quality panoramic sensing and abnormity positioning management system. Comprising a monitoring terminal adaptation module, a monitoring point optimization configuration module, an electric energy quality sensing early warning module, a composite electric energy quality traceability module, an electric energy quality comprehensive treatment module and a power distribution network dispatching sensing module. According to the invention, full-process management and control from optimal configuration of the electric energy quality monitoring terminal, accurate perception, positioning and tracing of problems, comprehensive treatment and intelligent support of power distribution network dispatching are realized; various electric energy quality problems in the active power distribution network are effectively solved, safe and stable operation of the power grid is guaranteed, the power utilization quality and satisfaction degree of users are improved, and efficient and reliable development of the active power distribution network is promoted.

The invention discloses an aromatherapy intelligent margin calculation method based on vehicle body data dynamic perception. The method comprises the following steps: S1, generating an initial data set; s2, inputting the initial data set into a multi-channel variational encoder, and outputting hierarchical feature representation; s3, inputting the hierarchical feature representation into a spatio-temporal context sensing module to generate spatio-temporal feature representation; s4, inputting the spatial-temporal feature representation into a heterogeneous feature mapping layer to generate a unified data representation; s5, inputting the unified data representation into a dynamic prediction regulation and control module, and generating an optimized feature representation; s6, inputting the optimized feature representation into an aromatherapy residual amount prediction model, and performing residual amount prediction by using a Gaussian mixture process; s7, inputting an aromatherapy residue prediction result into a feedback integrator, and carrying out self-supervised learning; and S8, integrating to a vehicle-mounted system, displaying an aromatherapy allowance state and sending a personalized notice. According to the method, accurate prediction and calculation of the vehicle-mounted aromatherapy allowance are realized by utilizing multi-channel variational coding, spatio-temporal context perception, a Gaussian process and the like.

The invention provides a scene management and intelligent recommendation method and system based on a data space, and the method comprises the steps: selecting a system function in a preset database, and carrying out the matching of a corresponding data type according to the selected system function, making a corresponding data acquisition plan according to system functions and data types; collecting data based on the formulated data collection plan and selecting a data processing model matched with the collected data according to the collected data; processing the collected data to form a feature fusion layer, and introducing an attention mechanism and an inter-channel information transmission mechanism into the selected data processing model to optimize the selected data processing model; and training the optimized data processing model to obtain an attention mechanism weight matrix, and adjusting the obtained attention weight matrix to realize personalized recommendation. The technical problem that the heterogeneous data processing and recommendation ability is insufficient in the related technology is solved.

The invention is suitable for the technical field of vehicles, and provides a camera sharing method and device, a vehicle and a storage medium, and the method comprises the steps: detecting at least one vehicle whose distance to a current vehicle is within a preset range; wherein the at least one vehicle is a vehicle of which the camera networking sharing authority is started; if a target vehicle selection instruction is obtained, and the target vehicle selection instruction carries an identifier of a target vehicle, obtaining image data shot by a camera of the target vehicle according to the identifier of the target vehicle; wherein the target vehicle is a vehicle in the at least one vehicle; and controlling the current vehicle to display the image data of the target vehicle. The system can assist a driver in safe driving and parking.