459 results about "Decision fusion" patented technology

The invention discloses a wind power plant booster station multi-source data fusion anti-misoperation locking intelligent decision-making and early warning method, and relates to the technical field of intelligent misoperation prevention of a power system, and the method comprises the following steps: collecting multi-source heterogeneous data, obtaining the data through a distributed sensor network, and carrying out the edge calculation preprocessing; performing data space-time alignment and fusion, performing equipment state evaluation, and constructing a deep belief network and Bayesian network hybrid model to calculate a health index; anti-misoperation rule modeling is carried out, and operation logic verification is carried out based on a Petri network and an expert knowledge base; risk early warning decision making: fusing multi-source early warning information to divide risk levels; intelligent locking control is carried out, and a locking strategy is optimized through reinforcement learning; and performing decision support and visualization, constructing a three-dimensional digital twinborn model, and displaying operation guidance and risk early warning in combination with an AR technology. Through multi-source data fusion and intelligent decision making, the anti-misoperation locking accuracy and efficiency are improved, and the safety and the operation and maintenance level of the booster station are remarkably enhanced by equipment fault early warning three months ahead of time.

An emotional engagement detection method based on positive emotional perception is provided, which relates to a field of data analysis technologies. The emotional engagement detection method is to extract features from data of different dimensions during student classroom learning, to achieve information recognition of each dimension, and perform decision fusion and result analysis and application. The emotional engagement detection method is to extract the features from the data of different dimensions during the student classroom learning, applies a fusion strategy based on a deep learning network for subsequent supervision classification, to construct a positive emotional engagement model of a student. Different modalities information such as cognition, emotion and thinking of the student obtained from different means is integrated to a frame, to comprehensively reflect a positive emotion of the student.

The invention discloses a photoelectric multi-mode decision fusion method and system for mental disorder recognition, relates to the technical field of mental disorder recognition, and performs decision fusion based on an improved machine learning algorithm by integrating photoelectric multi-mode data and collecting and analyzing various data in real time. Through effective fusion of result data of different modal recognition models, the accuracy and stability of mental disorder recognition are improved, meanwhile, the misjudgment rate of a single modal method is reduced, and the overall diagnosis efficiency is improved. The invention not only provides an innovative method, but also designs a corresponding fusion system, and provides a comprehensive solution for early recognition and diagnosis of mental disorders.

The invention discloses a multi-beam seabed sediment classification method based on a multi-classifier decision fusion mechanism, and the method comprises the following steps: carrying out the multi-scale texture feature extraction and topographic feature extraction of an input multi-beam backscattering intensity image and depth image respectively; performing precision analysis by using K-means clustering to screen acoustic features of an optimal scale, and obtaining acoustic features of high contribution degree in combination with a geographically weighted regression analysis method; carrying out superpixel segmentation on the multi-beam backscattering intensity image to obtain a sample-superpixel block, and realizing effective expansion of seabed sample points; taking half of the expanded sample set as a training set to construct a seabed sediment classification model with multiple classifiers; and creating a multi-classifier decision fusion mechanism model to predict the type of the seabed sediment by using the classification result of each model and the producer precision, and obtaining a seabed sediment prediction result map. The method has high prediction precision and can be applied to the fields of ocean engineering construction, resource management development, surveying and mapping and the like.

The invention relates to a biped robot complex terrain self-adaptive gait planning method and a biped robot, and belongs to the technical field of robotics.The biped robot complex terrain self-adaptive gait planning method comprises the steps that a fractional order PID controller is constructed, and the fractional order PID controller comprises a multi-sensor network module, a self-adaptive module and a fractional order calculus control module; constructing a reinforcement learning model for biped robot gait planning, setting a state, an action, a decision and a reinforcement learning reward function of reinforcement learning, performing continuous iteration and optimization adjustment on the reinforcement learning model by using a strategy gradient method, updating strategy parameters, and outputting control parameters of the biped robot; and performing decision fusion on control parameters output by the fractional order PID controller and the reinforcement learning model through a comprehensive loss function to obtain globally optimal biped robot control parameters. According to the method, more accurate gait planning can be provided for the biped robot in complex terrains, and better walking flexibility and adaptability are achieved.

The invention discloses an intelligent management method for liquidation receipts based on multi-modal data fusion, and particularly relates to the field of data analysis. Comprising the steps of S1, multispectral data acquisition in a limited illumination environment, S2, cross-modal feature decoupling and recombination, S3, space-time heterograph neural network analysis, S4, multi-scale attention decision fusion, S5, resistance enhancement verification, and S6, incremental management based on knowledge distillation. According to the method, the physical anti-counterfeiting capability is remarkably improved, the paper material, the ink components and the surface structure are deeply analyzed through a multispectral sequence acquisition mechanism, and hidden tampering behaviors such as color fading and chemical altering of the thermo-sensitive paper are accurately identified. Cross-modal deep correlation analysis is achieved in a breakthrough mode, a physical-semantic decoupling technology and a space-time heterogeneous graph network are adopted for modeling, and non-dominant laws such as commodity position offset and tax rate anomaly are effectively captured.

The invention relates to a multi-source data fusion method and application system based on digital twinning, and the method comprises the steps: collecting real-time and historical data from various sensors, devices, networks and other information sources, and carrying out the cleaning and format standardization preprocessing of the collected data; the data set fusion uses a Kalman filter algorithm to perform data fusion, and the prediction step comprises: predicting a state equation at a current moment based on a state equation and a covariance matrix at a previous moment, and predicting the covariance matrix and an observation vector; the updating step comprises the steps of calculating Kalman gain and updating state estimation and a covariance matrix. The feature set fusion uses deep learning line feature data fusion, features are extracted from different data sources by using CNN, and different feature vectors are subjected to weighted fusion to form a single feature vector; decision set fusion adopts a deep learning method for decision fusion, so that the accuracy, stability and reliability of decisions are improved; the method improves the efficiency and precision of data fusion, and has a good market application prospect.

The invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention provides a gas turbine exhaust temperature sensor fault diagnosis system and method, and relates to the technical field of industrial equipment state monitoring and diagnosis. The system comprises a sensor signal acquisition module for acquiring an original temperature signal; the signal preprocessing module is used for acquiring and preprocessing original signals and working condition parameters; the feature extraction and windowing module is used for acquiring data and calculating features; the data driving diagnosis module is used for receiving the time sequence characteristics and evaluating the health state; and the decision fusion module is used for comprehensively analyzing the multi-source information and making a final fault diagnosis judgment. According to the system, the accuracy and the reliability of fault diagnosis can be remarkably improved, the false alarm rate and the missing report rate are reduced, early warning and accurate identification of early weak faults of the sensor are realized, the adaptability and the robustness of the diagnosis system to variable working conditions of the gas turbine are improved, and the safety, the economical efficiency and the operation and maintenance efficiency of operation of the gas turbine are improved.

The invention discloses a method for judging an RTK abnormal value in an automatic driving integrated navigation system, and relates to the technical field of automatic driving high-precision integrated navigation, and the method comprises the steps: collecting RTK observation data, inertial measurement unit data, a wheel speed pulse signal and LiDAR point cloud data, carrying out timestamp alignment and coordinate system unification, and generating a fusion data value; calculating a carrier-to-noise ratio weight signal quality index of the satellite through the fused data value, and generating a signal quality report; and combining the signal quality report, the current satellite geometric accuracy factor and the vehicle motion acceleration, calculating a residual threshold, constructing a coriolis force compensated double-integral prediction model by using inertial measurement unit data, and predicting the position of the vehicle at the current moment. According to the method, multi-dimensional features such as satellite signal quality, vehicle motion state and residual analysis are fused through multi-source decision, and the anomaly detection capability of complex scenes such as urban canyons is effectively improved.

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.

The invention relates to the technical field of new energy storage regulation and control, and discloses an electricity-hydrogen coupling energy storage system and method for a new energy field station. The system comprises a feature analysis unit, a topology construction unit, a regulation and control domain positioning unit and a decision optimization unit. The feature analysis unit receives operation condition information of the new energy station, and generates a condition feature vector according to real-time load fluctuation, a historical output curve and an equipment operation database by using a deep feature extraction network; the topology construction unit constructs an energy topological graph for distributed energy storage equipment and hydrogen production equipment in the electricity-hydrogen coupling system, dynamically updates a graph structure and a node state based on a real-time monitoring mechanism, and deploys the graph structure and the node state in a heterogeneous computing framework; a regulation and control domain positioning unit positions a matched regulation and control domain in a dynamic representation learning mode according to the working condition feature vector; and the decision optimization unit utilizes a multi-source decision fusion algorithm to screen regulation and control instructions in a regulation and control domain and generate a scheduling sequence, so that the adaptability and the operation efficiency of the system to working conditions are improved.

The invention discloses a network traffic anomaly detection system based on artificial intelligence, and relates to the technical field of network security, and the system comprises the following components: a plurality of agent modules, a cooperative communication module, a decision fusion module and a model training and updating module. According to the invention, by integrating a plurality of agent modules, the multi-dimensional features of the network traffic can be comprehensively and deeply analyzed, and each agent module adopts advanced algorithms and technologies, such as a multi-dimensional feature mapping algorithm, a behavior pattern mining algorithm, a protocol semantic parsing algorithm and a periodic fluctuation decomposition algorithm. The algorithms can accurately capture the abnormal behavior of the network flow, and meanwhile, the decision fusion module adopts an adaptive dynamic weight fusion algorithm, comprehensively considers the detection result of each agent, obtains a final abnormal detection conclusion, and further improves the detection accuracy.

The invention provides an emotion recognition method and system based on evidence theory multi-modal data decision fusion. According to the method, two cluster centers of each piece of data are clustered through a PCM clustering algorithm, and the two cluster centers are used as an upper bound and a lower bound to construct an interval number model, so that the effect of the interval number model in a noise environment is improved. And a recognition framework is constructed, and the support degree of data features is calculated according to the interval length, so that the accuracy of the generated BPA during classification is improved. The BPA of each sample is calculated by using the similarity and the feature support degree of the test sample and the interval number model, and then evidence combination is carried out through a Dempster combination rule. Secondly, the conflict between the evidences is judged by using a Jousseme distance, then the similarity between the evidences is calculated to construct a similarity adjacent matrix, then the relationship strength between the evidences is defined by using the sum of the similarities, the weight of the conflict evidences is calculated and modified by integrating the direct relationship and the indirect relationship, and the original evidence body is subjected to weighted correction through the overall weight; and finally, combining through a Dempster synthesis formula to obtain a final result.

The invention discloses a pipeline leakage detection device and method based on multi-modal data fusion, and relates to the technical field of pipeline safety monitoring. According to the device, modular sensor cabins (ultrasonic waves, LiDAR, millimeter wave radar and optics) are adopted for cooperative work, and high-precision leakage detection and positioning are achieved in combination with space-time registration, a cross-modal feature alignment network and a multi-level decision fusion mechanism. Through a dynamic weight distribution model and a multi-physical field joint inversion algorithm, the problems of low detection precision and poor anti-interference capability of a traditional single sensor are effectively solved. The method is suitable for detecting defects such as corrosion and leakage of metal and nonmetal pipelines, and has the advantages of high sensitivity, strong robustness and wide applicability.

The invention relates to the technical field of learning optimization, in particular to a big data analysis-oriented deep reinforcement learning optimization algorithm, which comprises the steps of dynamic feature topological graph generation: performing time sequence correlation analysis on a big data stream input in real time to generate a dynamic feature topological graph; an enhanced state space is reconstructed, wherein the enhanced state space with space-time correlation characteristics is reconstructed through potential path mining; hierarchical decoupling training: asynchronous parameter updating is carried out by adopting a gradient isolation mechanism, and a strategy gradient flow and a value gradient flow are generated; multi-dimensional decision fusion: generating a multi-objective optimization decision through dynamic weight fusion; and feedback driving adjustment: according to the actual execution effect of the multi-objective optimization decision, reversely adjusting the construction threshold of the dynamic feature topological graph and the dimension parameter of the enhanced state space. According to the method, by constructing the dynamic feature topological graph, the time sequence dependency relationship in the big data flow can be captured in real time, the dynamic evolution process between the features is effectively reflected, and the flexibility and precision of feature modeling are remarkably improved.

The invention discloses a method for estimating the safety communication rate of a near-earth satellite by using an SDFS cooperating with a GLRT. The method comprises the following steps: constructing a communication system model between the near-earth satellite and a ground terminal; analyzing the signal and channel characteristics, and obtaining the distance between the nodes, the channel gain and the received signal distribution; under the condition that the transmitting power of each monitoring party is unknown, estimating the transmitting power by adopting generalized likelihood ratio test, and independently executing the generalized likelihood ratio test based on the observation data of each monitoring party; the superior manager performs joint judgment based on a soft decision fusion scheme in combination with the generalized likelihood ratio test result of each monitoring party and a joint threshold so as to estimate the detection error probability of the monitoring party; a joint optimization strategy of the transmitting power and the number of continuous transmission time slots is constructed, and the total throughput of the system is maximized under the condition that the cooperative concealment constraint is met; according to the invention, an innovative solution is provided for reliable communication of the near-earth satellite in a complex monitoring environment.

The invention discloses a transformer current rising test method based on deep learning, and the method comprises the following steps: collecting historical test data, and physical parameters and current operation condition data of a to-be-tested transformer; the performance of the transformer to be tested under different current and voltage conditions is predicted according to the physical parameters of the transformer to be tested by using a calculation model based on the electromagnetic theory; applying a deep learning algorithm to obtain preliminary test parameter suggestions and performance prediction based on historical data; and obtaining comprehensive test parameter suggestions and performance prediction by using a decision fusion technology. According to the method, a traditional physical model and a deep learning technology are combined, a comprehensive and accurate mode is provided to predict the performance of the transformer under specific conditions, and the method is particularly suitable for a current rising test of a high-voltage and high-power transformer. The performance of the transformer can be accurately evaluated before the transformer is put into use, the operation risk is reduced, and the reliability and safety of a power system are improved.

The invention discloses a fusion method and system based on construction site multi-source heterogeneous data, and belongs to the technical field of image processing. According to the method provided by the invention, after CNN neural network and Transform neural network feature extraction is carried out on multi-source heterogeneous data of a construction site, feature vectors of the heterogeneous source data are obtained, features extracted from the heterogeneous data are respectively input into respective MLP and Softmax layers to carry out construction specification model regression training, respective prediction results are obtained to generate local decisions, and the local decisions are subjected to construction specification model regression training. And using as input of decision fusion. And performing linear weighted summation on the prediction result to obtain a fusion result, and passing the fusion result through a Softmax layer to obtain a multi-source heterogeneous data fusion result. The fusion method is simple and has better expandability for various modes, and the most suitable analysis method can be adopted for different modes.

The invention relates to the field of forestry monitoring data processing, in particular to a forestry pest and disease damage intelligent monitoring system and method based on unmanned aerial vehicle inspection, and the system comprises a data interaction unit which is used for building a real-time communication channel between an unmanned aerial vehicle and a server through employing a WebSocket protocol; the multi-scale feature extraction unit is used for constructing a three-stage pyramid convolution structure, and respectively extracting microscopic, medium and macroscopic scale features of the blade through convolution kernels of different specifications and cavity convolution; the time sequence parameter adjusting unit is used for fusing Transform and GRU, analyzing a feature map through a multi-head self-attention mechanism, learning a tree phenological law through the GRU, performing condition normalization adjustment on convolutional layer parameters based on the tree phenological law, and distinguishing physiological and disease features; and the decision fusion unit is used for splicing and fusing the composite feature map and the time sequence feature vector, carrying out parallel processing on an SVM and a Softmax classifier, and outputting a disease and pest recognition conclusion according to a dynamic threshold decision.

The invention discloses a projectile drop point detection method, system and equipment based on dual-light decision fusion, and a medium. The method comprises the following steps: collecting visible light and thermal infrared imaging data; preprocessing the data to obtain visible light and thermal infrared images of corresponding target region time-space registration; projecting a ground target area on the obtained image after space-time registration to a corresponding reference projection target surface; respectively inputting the registered images into a typical target detection model for target detection, judging whether an explosion phenomenon is detected in the images frame by frame, obtaining positioning information and category information in respective modes, dividing detection results into two categories of matched targets and unmatched targets, and outputting an explosion bounding box after secondary decision making; calculating and correcting the position of the drop point according to the explosion bounding box, and determining the image coordinate of the drop point; and mapping the coordinate of the drop point image to the reference projection target surface to obtain the position coordinate information of the drop point in the reference projection target surface. The method improves the detection precision, and better completes the target scoring task.

The invention relates to the technical field of hydraulic valves, and discloses a digital hydraulic valve intelligent diagnosis system which comprises a data acquisition module, a data preprocessing and label generation module, a feature fusion module, a classification diagnosis and decision fusion module and a service life prediction and suggestion generation module. Data such as pressure, vibration, displacement, flow and pollution degree of the valve are comprehensively captured through deployment of a multi-source sensor array at key positions of the digital hydraulic valve and a self-adaptive acquisition strategy, early fault feature omission is avoided, then through preprocessing means such as soft-hard hybrid wavelet threshold denoising and multi-sensor time alignment, the data precision is effectively improved, and the accuracy of the data is improved. Then, through dynamic-static layered feature fusion and an attention weighting mechanism based on GRU, different feature advantages under steady-state and fault working conditions are fully combined, the fault feature distinction degree is greatly enhanced, and the problem that similar faults are likely to be confused is solved.

The invention discloses an empty box recognition method, device and equipment and a storage medium, and relates to the technical field of logistics and cargo detection, the empty box recognition method comprises the steps that a real-time video stream of a to-be-recognized vehicle is acquired, and the vehicle comprises a box body capable of loading cargos; scanning the box body by using a laser radar to obtain three-dimensional point cloud data of the box body; on the basis of a pre-trained empty box recognition model, whether the box body is an empty box or not is determined according to the real-time video stream and the three-dimensional point cloud data of the box body, and the pre-trained visual empty box recognition model is obtained according to the images of the empty box body and the non-empty box body in the box door opening state and the three-dimensional point cloud data. And training the visual empty box identification model. According to the scheme, accurate recognition of the empty box is achieved through image and radar data association and decision fusion, the vehicle empty box recognition speed can be effectively increased, the workload of people is relieved, and the empty box recognition efficiency is improved.

The invention relates to the field of electric power monitoring, in particular to an electric power monitoring system intrusion detection method and system based on flow analysis. The method comprises the following steps: collecting network traffic, analyzing and recombining to obtain structured session data; time sequence behavior features and function code distribution features are extracted to construct a multi-dimensional feature set; inputting the feature set into a compliance rule base and a behavior baseline model in parallel, and respectively outputting a rule matching result and an abnormal deviation degree score; generating a comprehensive threat index by adopting a weighted decision fusion strategy; and when the index exceeds a dynamic threshold value, intrusion is determined and an alarm is given. According to the invention, the problem of insufficient precision and adaptability caused by single feature dimension and isolated detection mechanism is solved.

The invention relates to a human body behavior recognition method, device and system based on WiFi and vision multi-mode fusion. The human body behavior recognition method processes WiFi channel state information and video data through two independent network models. The method comprises the following steps: firstly, carrying out feature extraction and recognition on WiFi channel state information by utilizing a WiFi deep learning model; then, performing feature extraction and recognition on the video data by using a visual deep learning model; and finally, splicing the identification results of the two and carrying out decision fusion. According to the method, multi-modal data fusion in a feature layer is avoided, and the influence of modal information change on a prediction result is reduced, so that the robustness of the human body behavior recognition method under the conditions of data noise, modal failure and the like and the accuracy of human body behavior recognition are improved.

The invention discloses a help-calling emergency response system based on an intelligent communication network, particularly relates to the field of intelligent communication, and comprises a multi-source data acquisition layer, a preprocessing and feature extraction layer, a progressive analysis engine, a decision fusion center and an execution control layer. According to the method, the data integrity and the anti-interference capability are remarkably improved, the time-space reference unification module adopts a PTP protocol and a coordinate system conversion technology, accurate alignment of multi-source data is achieved, a multi-dimensional feature matrix is generated through time-frequency conjoint analysis, and the problem that the feature extraction dimension of a traditional system is single is solved; the progressive analysis engine triggers a hierarchical response strategy based on dynamic fusion of a transmission robustness index, a space confidence coefficient and an environmental risk value, combines terrain refraction compensation and a timeliness attenuation mechanism to optimize decision logic, breaks through the limitation of a fixed threshold response mode, adapts to communication quality fluctuation and environmental sudden change, reduces rescue delay risk, and improves rescue efficiency. And the resource scheduling accuracy and the system intelligence level are improved.

A framework for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. The present disclosure melds different types of similarity scores (whole vehicle and wheels / hubcaps) between target and test vehicles to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. The present disclosure provides improved accuracy for side-view vehicle matching even under different illumination conditions and elevation angles.

The invention relates to the technical field of 5G infrastructure intelligent detection, in particular to a multi-sensor fusion intelligent detection method and system for a 5G infrastructure, which realizes real-time monitoring of the appearance and environmental parameters of 5G infrastructure equipment by integrating an image sensor, a temperature and humidity sensor and a vibration sensor, establishes a data interaction protocol, and improves the detection accuracy of the 5G infrastructure equipment. Processing and analyzing the collected data, generating a joint certificate, checking and verifying the integrity and availability of the data, then carrying out fault detection and health state evaluation by using a visual fault detection model and a state variable prediction model, and carrying out intelligent decision making according to an evaluation result and a decision fusion rule base. Comprising self-diagnosis operation, fault information recording, emergency alarm sending and emergency plan triggering, collected data, fault levels and intelligent scheduling decisions are displayed in real time through a graphical interface, and a visual monitoring interface is provided for maintenance personnel. According to the invention, the operation efficiency and safety of the 5G infrastructure are improved.