3455 results about "Information fusion" patented technology

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a decision-making method, device, equipment and medium based on multi-modal semantic alignment. Executing cross-modal alignment by taking the voice semantic map as a reference to generate associated information, fusing the voice features, the visual features, the action features and the associated information to generate a fusion feature vector, inputting a decision network to generate a decision feature vector and generate a task execution instruction, obtaining execution feedback information of the task execution instruction, and updating the decision network. According to the method, input is dominated by voice instructions, visual features, action features and semantic map depth alignment and fusion are combined, input naturalness and multi-modal data analysis and decision-making efficiency are improved, and interaction adaptability and decision-making accuracy of the model in a complex scene are improved.

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

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

The invention relates to the field of image processing, and discloses an intelligent drawing checking method and system based on a multi-modal large language model, and the method comprises the steps: obtaining a to-be-checked target engineering design drawing and a to-be-checked task description; generating a global overview drawing based on the target engineering design drawing; performing global semantic analysis according to the global overview map and the review task description through a multi-modal large language model, and generating a global semantic analysis result and to-be-reviewed local area proposal information; cutting a local image from the design drawing; performing element identification analysis on the local image through a multi-modal large language model to obtain local structured information; and performing information fusion processing on the local structured information and the global semantic analysis result, generating complete drawing information, performing compliance verification and defect positioning on the complete drawing information and the structured specification knowledge base, and generating a review report. According to the method, intelligent review of the power grid engineering design drawing can be realized, the review efficiency and accuracy are improved, and meanwhile, the resource consumption is reduced.

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

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention discloses a multi-modal fusion rumor detection method and system based on a dynamic graph convolutional neural network. According to the method, a dynamic feature graph of a language propagation path is constructed, and potential features in the language propagation process are extracted and analyzed by utilizing time sequence changes and key node relations between nodes in a propagation graph. A neural network is adopted to extract and enhance image data, text semantic features are extracted in combination with a text feature modeling network, text feature vectorization expression is achieved based on a BERT model, and rich semantic information is obtained. And a gating mechanism is introduced to dynamically adjust fusion weights of different modal features, and an information fusion strategy is optimized. A collaborative attention mechanism is further adopted for deep fusion, interactive learning of text, image and propagation path features is enhanced, and the relevance of cross-modal and time series data is improved. And finally, inputting the fused feature vectors into a classifier for accurate classification, thereby realizing accurate detection of the social media rumors. According to the method, the multi-modal features are effectively integrated, and the false information identification efficiency is remarkably improved.

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

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

According to the multi-modal automatic knowledge graph construction method based on the large language model, a multi-modal data stream is preprocessed, features are extracted, and the multi-modal data stream is mapped to a unified semantic space through a cross-modal alignment network after being processed through the large language model, a visual converter and a time sequence neural network. In the space, entities and categories are recognized based on a large language model, a triple is generated by combining a multi-modal feature judgment entity relationship, mapping fusion is performed through an ontology alignment algorithm driven by a graph neural network and a predefined domain ontology, finally knowledge is stored in a graph database, and dynamic updating is performed by means of incremental learning and online reasoning. Standardized APIs and visualization components are provided. According to the method, the construction efficiency and the automation degree of the knowledge graph are remarkably improved, the cross-modal information fusion and knowledge maintenance capability is enhanced, and the application requirements of intelligent retrieval, recommendation, decision support and the like are met.

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

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

The invention relates to the technical field of drawing recognition, and discloses an engineering drawing intelligent recognition method and system based on deep learning. The method comprises the steps of performing multi-target cooperative detection on a first processing image based on a detection model, identifying primitive information of the first processing image, and generating a second identification image; positioning a text area of the second recognition image, recognizing a character detection range, determining word tags represented by the character detection range, summarizing character information of the character detection range based on the word tags, and generating third image data; obtaining a correlation degree among the symbols, the attributes and the connecting line information, detecting whether the primitive information accords with a preset rule or not, constructing a symbol topological relation graph, and generating correction information containing a conflict position; and fusing the primitive information, the character information and the correction information to generate structured data comprising a symbol hierarchy tree, an attribute incidence matrix and a conflict label. According to the invention, the efficiency and accuracy of engineering drawing intelligent identification are improved.

The invention relates to the technical field of data processing, and discloses a heterogeneous data regularization method for multi-source information fusion, which comprises the following steps of: arranging distributed sensors in a plurality of processing stages in a sewage treatment process, and synchronously acquiring detection data of all the distributed sensors through an access protocol and a time sequence; performing semantic analysis on the detection data, performing label normalization on the detection data, and completing semantic label standardization; processing the detection data after semantic standardization to obtain metadata; constructing a data cross-layer representation model based on transfer learning, and performing feature extraction and semantic embedding on metadata to obtain uniform vectorization expression; and according to the vectorization expression, constructing an information knowledge graph oriented to the whole flow of sewage treatment. Unified vectorization expression of time sequence, semantics and structural features among different data sources is achieved, and the fusibility, interpretability and usability of data are improved.

The invention belongs to the technical field of centrifugal fan fault prediction, and provides a centrifugal fan fault trend prediction method based on multi-source information fusion, and the method comprises the following steps: in the operation process of a centrifugal fan, capturing the change trend of data distribution through multi-source data monitoring and statistical property analysis; and whether data non-stationary change of the centrifugal machine is caused by equipment aging is identified. Based on a time and space two-dimensional comparison analysis framework, physical mechanism verification is combined, suspicious aging features are locked by calculating relative deviation and aging trend index quantitative indexes, and then an average correlation coefficient and trend consistency are utilized to construct a correlation consistency index to verify feature reliability, so that the method has the capability of positioning a specific aging part, and the reliability of the aging part is improved. The problem of misjudgment caused by sensor faults or environmental interference in traditional fault diagnosis is optimized, the accuracy of aged part recognition is improved, and a clear object is provided for targeted maintenance.

The invention provides a three-dimensional human body posture estimation method and system based on multi-view visual information fusion and a storage medium, and the method comprises the steps: 1, designing the front half part of a model into Ender Layers with the same layer number as a Transform decoder at a multi-view feature fusion layer, carrying out the data enhancement of an input multi-view original image, and carrying out the reconstruction of the Ender Layers in the multi-view feature fusion layer; inputting the CNN Backbone with the shared weight to extract an initial feature map; 2, introducing a micro-reprojection optimization mechanism, deeply fusing the multi-view geometric consistency constraint into a model training process, and guiding the model to predict a three-dimensional attitude end to end; and step 3, constructing a dynamic projection compensation module. The method has the beneficial effects that the method is particularly suitable for capturing human body posture information in a multi-person interaction scene, the shielding problem and depth estimation ambiguity in a single view angle can be effectively overcome, and the robustness, precision and efficiency of three-dimensional human body posture estimation are remarkably improved.

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

The invention discloses an intelligent diagnosis method for composite faults of a permanent magnet synchronous motor based on digital twinning, relates to the technical field of fault diagnosis, and solves the technical problem of low diagnosis accuracy caused by the fact that fusion of multi-source information is not considered when a twinning model is adopted for fault diagnosis in the prior art. The method comprises the following steps: acquiring prophet data through a physical entity layer and transmitting the prophet data into a twin data layer; virtual current is generated through the virtual model layer and transmitted to the twinborn data layer; the twin data layer performs time-space synchronization on the received data to construct a virtual current signal reference library; the application layer makes a difference between predicted current output by the virtual model layer and current collected by the physical entity layer in real time, whether an electrical fault occurs or not is preliminarily judged in combination with a virtual current signal reference library, and corresponding fault features are input into a classification network according to a judgment result for fault diagnosis; the electromechanical composite fault diagnosis of the permanent magnet synchronous motor is realized by combining a multi-source information fusion technology.

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

The invention provides a software defect information fusion method and system based on multi-source data. The method comprises the following steps: acquiring static code characteristics, a runtime log sequence and user feedback from cross-platform software; constructing a code dependency graph based on static code features and a historical defect library, defining a log structure through code logic association, and performing space-time alignment with defect trigger nodes fed back by a user to generate a context matrix; synchronously acquiring physical parameters of hardware nodes, and dynamically coupling the physical parameters with the matrix to generate a state evolution graph; fusing the code dependency graph, the state evolution graph and user feedback, and extracting cross-modal defect features; and generating defect positioning probability distribution based on the features, and determining a repair scheme by combining physical parameter anomaly analysis. Through integration of codes, operation states and user feedback, accurate positioning and dynamic repair of defects are realized. According to the technical scheme provided by the invention, the precision and reliability of software defect information fusion can be improved.

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

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

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

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

The invention discloses a hydraulic engineering seepage intelligent monitoring system and method, and belongs to the technical field of hydraulic engineering safety monitoring. The system comprises the following modules: a multi-source data acquisition module used for acquiring multiple types of monitoring data in real time through an intelligent sensor network; the double-window time sequence analysis module is used for constructing a quick response window and a trend analysis window to realize double identification of sudden anomalies and long-term trends; the self-learning threshold optimization module is used for automatically extracting a key quantile threshold based on the distribution characteristics of the monitoring data and continuously optimizing weight configuration and early warning threshold setting of various statistical indexes; the multi-scale fusion early warning module is used for performing multi-source information fusion, generating a comprehensive change index and a multi-stage early warning state, and outputting a seepage abnormity early warning signal and a corresponding confidence coefficient; and the visual decision support module provides visual data display, emergency response guidance and intelligent decision support.

The invention relates to the technical field of intelligent fire fighting, in particular to a building electrical fire identification method and system based on multi-source information fusion, and the method comprises the steps: firstly collecting the multi-source monitoring data of an electrical system in real time, then carrying out the preprocessing of the multi-source data, including noise filtering, time alignment and feature extraction, and constructing a dynamic weight fusion model; the weight of each data source is adaptively allocated based on the feature entropy, then fusion features are input to the fire risk grading model, a fire probability index and an early warning level are output, and when the early warning level exceeds a threshold value, a linkage control instruction is triggered and an alarm is given; according to the method, through dynamic fusion of multi-source information, the electrical fire identification precision is remarkably improved, multi-dimensional parameters such as current, temperature, smoke and arc are adaptively integrated by adopting an entropy weight assignment mechanism, the problems of false alarm and missing alarm of traditional single-threshold detection are solved, the missing alarm rate and the false alarm rate are remarkably reduced, and the reliability of fire early warning is ensured.

The invention discloses a photovoltaic ultra-short-term power prediction method and system based on multi-modal information fusion, and belongs to the technical field of photovoltaic power generation prediction. The method comprises the steps: obtaining an all-sky image sequence, segmenting a cloud layer and a sky region, and extracting cloud layer boundary features; thickness features are analyzed by calculating cloud pixel point brightness indexes, overall motion features of a cloud layer are determined based on adjacent frame displacement vectors, a time sequence sub-image covering a sun area in the future is reversely captured in combination with sun position coordinates, and then time sequence features and image features are extracted by adopting a dual-channel fusion mechanism. And finally, introducing a weather-dependent decoder: identifying weather types through a lightweight classifier, dynamically weighting the fused features based on the types, and outputting a photovoltaic power prediction result. According to the invention, through a multi-scale feature cooperation and dynamic weighting mechanism, the prediction robustness under a complex meteorological condition is significantly improved.

The invention relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, and the method comprises the following steps: S1, obtaining the operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; s2, preprocessing to obtain a time-space aligned standardized data matrix; s3, performing feature extraction to obtain a multi-dimensional feature vector, and learning internal association between features by using a multi-modal deep network to obtain a joint multi-modal feature; s4, constructing a fault type identification model based on Bayesian reasoning and Monte Carlo sampling, and obtaining a fault type classification result; s5, in combination with deep learning and a physical model, obtaining a fault occurrence interval, positioning information and a fault level; and S6, based on a fault type classification result, a fault occurrence interval and positioning information, obtaining a fault level, and carrying out early warning pushing on a generated diagnosis report. According to the invention, high-precision identification, positioning and risk assessment of high-voltage cable insulation faults are realized.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.