13297 results about "Feature data" patented technology

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention provides a flow analysis and threat detection method and device based on machine learning, and the method comprises the steps: collecting a real-time flow data package of a target network environment, carrying out the protocol analysis and session recombination, and generating a real-time flow feature data set containing multi-dimensional flow features; loading a pre-trained multi-level threat classification model, inputting the real-time traffic feature data set into a feature extraction layer of the model, carrying out normalized coding on traffic features of corresponding dimensions through feature coding channels, generating a real-time feature vector sequence, inputting the real-time feature vector sequence into a primary classifier of the model, and classifying the real-time traffic features according to the real-time feature vector sequence; and performing abnormal probability calculation and cluster division on the real-time feature vector sequence through a mixed detection unit, outputting a primary threat tag and an abnormal confidence coefficient corresponding to each real-time feature vector, inputting the primary threat tag and the abnormal confidence coefficient into an aggregation classifier, performing dynamic weighted aggregation, and generating a comprehensive threat score so as to judge whether a threat response strategy is triggered or not. According to the invention, the accuracy and timeliness of threat detection in a complex network environment can be improved.

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention provides a micro-grid fault diagnosis and dynamic recovery method based on deep reinforcement learning, and the method focuses on the multi-modal features of key nodes through a graph attention mechanism, extracts fault features through a multi-layer graph attention layer, and captures the spatial dependence relation of micro-grid nodes to recognize a fault propagation path. Meanwhile, spatial features and historical multi-modal data are fused with the help of a gating circulation unit, space-time joint feature representation is constructed, pre-fault symptom time sequence evolution is captured, intermittency and early fault detection capacity are enhanced, the multi-modal feature data fusion problem is solved, and high-precision fault diagnosis is achieved. A knowledge distillation technology is adopted to deploy a lightweight student model at edge equipment, millisecond-level emergency response is realized, fault diffusion is prevented, and meanwhile, the accuracy of diagnosis and repair strategies is guaranteed. The optimal repair strategy is generated at the cloud through the teacher model by using the global data, the system can adapt to the topological change of the micro-grid and novel faults, and the fault processing capability is continuously improved.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The present disclosure relates to a multimodal computing-based early intelligent graded screening system for a brain disease. The system comprises: a multimodal data acquisition unit, configured to acquire multimodal data of a target patient under a specified screening grade to form a screening data set; a multimodal feature generation, completion, fusion and calculation unit, configured to generate and complete feature data of modal features in the screening data set to obtain complete modal features, and extract pathological features for calculation to obtain a first screening result; a knowledge-based intelligent screening unit, configured to encode the feature data of the modal features in the screening data set into corresponding graph structure data features, use a multimodal association graph optimized by expert knowledge to match the graph structure data features to obtain knowledge-based association features, and obtain a second screening result on the basis of the knowledge-based association features; and an intelligent graded screening unit, configured to carry out weighted calculation on the first screening result and the second screening result to obtain a final screening result. The present disclosure achieves accurate early screening of brain diseases of patients.

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention relates to the technical field of financial data security, and discloses an intelligent ERP financial system data security management and authentication method. The method comprises the following steps: acquiring an original transaction data stream in an ERP system, extracting key financial fields, and dividing the key financial fields into a sensitive data set and a common data set according to a preset rule; a dynamic encryption strategy framework is constructed based on sensitive data set attributes, the framework comprises multiple levels of encryption strength parameters, and the corresponding encryption strength can be automatically matched according to the authentication level of an access request. And monitoring a system data access behavior in real time, collecting feature data, inputting the feature data into the anomaly detection model, and triggering access blocking when an output anomaly access probability exceeds a threshold value. And generating a periodic integrity verification instruction according to the sensitive data updating frequency, performing integrity verification by using a hash chain technology, recording a result and marking a tampering risk level. And based on the association relationship between the tampering risk level and the abnormal access probability, generating an updated security policy and synchronizing the updated security policy to each data access node.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention relates to a multimedia equipment control method and system based on adaptive protocol matching. The method comprises the following steps: firstly, collecting communication protocol data and extracting features to form a protocol feature data set; secondly, performing confidence coefficient verification on a data set type identification result, and if the confidence coefficient is lower than a threshold value, extracting a depth feature through a convolutional neural network; and calling a national standard protocol adaptation conversion engine based on a classification result, converting the heterogeneous protocol into data in a unified format, and generating a secure communication data frame through national secret algorithm encryption and bidirectional identity verification. And finally, performing hash check and structured analysis on the data frame, constructing an interaction information packet in combination with user behavior characteristics, and dynamically optimizing a control strategy through real-time feedback data to generate a self-adaptive interaction control scheme. According to the method, through a technical closed loop of intelligent analysis-standard conversion-security reinforcement-closed loop optimization, the high efficiency, the intelligent level and the security performance of multimedia equipment management are remarkably improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The embodiment of the invention discloses an intelligent contract auditing method and device based on a multi-modal large model and a medium, and relates to the technical field of contract auditing, the method comprises the steps that a to-be-audited target contract document is acquired, multi-modal data in the target contract document is analyzed, and the multi-modal data comprises text data, table data and image data; performing feature extraction on the multi-modal data to obtain multi-modal feature data, mapping the multi-modal feature data to a unified dimension space so as to calculate an association weight between each modality through a cross-modal attention mechanism, establishing cross-modal bidirectional link data, and obtaining multi-modal data; the multi-modal feature data comprises any one or more of text semantic features, table relation maps and image visual features; and based on the multi-modal feature data and the cross-modal bidirectional link data, performing single-modal self-consistency verification and cross-modal contradiction detection on the target contract document to generate auditing information of the target contract document.

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The invention relates to the technical field of audio and video analysis and data processing, in particular to an AI-based multimedia equipment operation and maintenance management system, which comprises a data acquisition module, an analysis module, an equipment state modeling module, a fault prediction module, a maintenance strategy optimization module, a resource allocation module and a visual platform. The data acquisition module acquires temperature, vibration, audio and video feature data of the equipment through the sensor and the video equipment, and the data are preprocessed and then transmitted to the cloud; the equipment state modeling module constructs a dynamic baseline model based on historical data, abnormal signals are generated through real-time comparison, and the visual platform realizes multi-dimensional state monitoring through 3D topology rendering, AR auxiliary diagnosis and early warning linkage. The system solves the problems of response lag and low resource utilization rate caused by dependence on manual operation and maintenance in the prior art through a closed-loop management mechanism driven by a data stream, and improves the fault diagnosis precision and the operation and maintenance efficiency.

The invention relates to the technical field of AI interaction data processing, and discloses an AI interaction data processing system based on multi-modal perception and dynamic decision, comprising the following modules: a multi-modal perception module used for collecting environment data through a multi-source sensor; the data fusion module is used for generating fused feature data; the causal decision-making module is used for generating a decision-making action to cope with the change of the environment; the decision security module is used for identifying potential safety hazards in the high-risk scene and generating alternative decision or early warning information; the sensing calibration module is used for optimizing a sensing strategy in a changing environment; and the adaptive optimization module continuously optimizes the perception and decision strategy. According to the invention, the multi-modal sensing module is combined with a cross-modal consistency learning mechanism and a noise robustness enhancement technology, and the data fusion module introduces a context sensing attention mechanism and a multi-level feature alignment network, so that the sensing ability of the system to complex environment information and the comprehensiveness and accuracy of feature representation are effectively improved.

The invention relates to the technical field of power station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

The invention relates to the technical field of AI protection, and discloses an AI protection engine construction method and system based on Web application. The method comprises the steps of obtaining a Web application HTTP request and performing semantic analysis to obtain request feature data; performing multi-dimensional threat feature extraction to obtain a Web request threat feature set; performing mode matching on the Web request threat feature set and a preset attack mode library to obtain a threat type judgment result and attack intention information; performing time sequence behavior analysis to obtain a Web behavior abnormal index; comprehensive decision making is carried out through a multi-agent collaborative decision making system, and a defense decision making scheme is obtained; according to the defense decision scheme, a corresponding defense component is selected through a defense execution engine for safety protection, and a Web request processing result is obtained. According to the method, corresponding protection measures can be taken aiming at requests of different risk levels, so that logic vulnerability attacks which are difficult to detect by a traditional system are effectively identified and defended.

The invention discloses a health food production line control method and system based on deep management, and relates to the technical field of food production control. A raw material feature database is established, a first analysis result is output as a raw material deviation evaluation reference, data of a current batch of raw materials are collected, differences are compared, and a raw material deviation result is output; the method comprises the following steps: collecting process data, analyzing whether the process data is abnormal or not by combining with raw material deviation, generating an adjustment suggestion, executing formula and parameter adjustment, triggering a self-repairing mechanism when the process data is abnormal, identifying an intermediate product state, evaluating a quality risk level and triggering early warning. Dynamic optimization of a formula and process parameters is achieved, meanwhile, an image and spectrum fusion early warning technology is introduced, the quality control ability of intermediate products is improved, finally, optimization parameter configuration is generated for the next batch through whole-process data chain construction and causal reasoning analysis, and self-adaptive control and quality steady-state improvement in a production closed loop are achieved.

The invention relates to the technical field of unmanned aerial vehicle track control, in particular to an unmanned aerial vehicle track control method and system. The method comprises the following steps: firstly, acquiring flight braking parameters and distribution task data of an unmanned aerial vehicle, and extracting low-altitude distribution meteorological condition feature data; then, flight state simulation is carried out based on flight braking parameters and task data, simulation quantification is carried out on flight attitude instability in combination with meteorological conditions, and then trajectory avoidance behavior control learning is carried out; and finally, constructing a trajectory control model by applying a strategy gradient algorithm, and sending the trajectory control model to an unmanned aerial vehicle control center to execute trajectory control. According to the invention, the unmanned aerial vehicle trajectory control technology is optimized, so that the unmanned aerial vehicle trajectory control technology is more perfect.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system and method based on a cloud platform. The method comprises the following steps: acquiring multispectral video image data of an industrial site; analyzing the difference characteristics of visible light and infrared spectrum based on the original data of the multispectral video image, identifying the characteristic mode of electromagnetic interference, and generating spectrum difference interference fingerprint data; identifying an uninterfered area according to the spectrum difference interference fingerprint data to obtain spectrum reliability distribution data; extracting non-interfered spectrum section information based on the spectrum reliability distribution data; performing complementary fusion on the non-interfered spectrum section information and the interfered area to obtain texture enhanced video data; and analyzing the time-varying characteristic of electromagnetic interference by using the texture enhanced video data to obtain interference time sequence characteristic data. According to the invention, dynamic calibration of interference is realized by analyzing the deformation characteristic of the calibration frame, and the adaptive capacity of the system to a changing electromagnetic environment is remarkably improved.

The invention relates to the technical field of inspection monitoring, and discloses an unmanned aerial vehicle intelligent inspection monitoring method and system based on a sensor, and the method comprises the steps: obtaining an initial inspection data set; obtaining a feature data set; generating a unified target feature data set; performing anomaly detection on the target feature data set to obtain an anomaly inspection area data set; performing security level division on the abnormal inspection area to obtain a division result; performing risk degree screening on the safety risk area in the division result to obtain a plurality of high-risk point position types, and when the change threshold value of one high-risk point position reaches a preset threshold value, preliminarily determining a high-risk occurrence zone; the unmanned aerial vehicle is controlled to carry out spot hovering to carry out key monitoring and refined inspection on the high-risk occurrence zone, and a secondary inspection data set is obtained; obtaining an analysis result; and secondarily confirming that the current inspection area is in the high-risk zone, and generating a corresponding emergency response early warning strategy and a corresponding regulation and control strategy, thereby accurately monitoring the inspection area in real time.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

The invention relates to the technical field of data processing, in particular to a metal wire data analysis method and system. The method comprises the following steps: acquiring wire interface data of composite metal; acquiring a wire rod interface characteristic matrix based on the wire rod interface data; constructing a wire rod interface characteristic model according to the wire rod interface characteristic matrix; differential signal conversion processing is carried out on the wire rod interface characteristic model, and wire rod interface change characteristics are extracted based on a differential signal conversion result; constructing a wire rod interface defect initial indication diagram according to the wire rod interface change characteristics; performing multi-scale decomposition processing on the basis of the wire rod interface defect initial indication graph to obtain interface defect feature data; and identifying wire interface defect type features according to the interface defect feature data. According to the method, a closed-loop optimization mechanism of defect characteristics and production process parameters is established, and the interface quality and reliability of the high-performance composite metal wire are remarkably improved.

The invention provides a self-adaptive error compensation method and system for focused ion beam equipment, and the method comprises the steps: obtaining a real-time error signal set generated by the focused ion beam equipment in a machining process, training a self-adaptive error compensation model according to the real-time error signal set, and generating an error compensation parameter set; inputting the error compensation parameter set into a control unit of the focused ion beam equipment, and generating a real-time compensation control instruction set; adjusting an ion source parameter and a deflection electrode voltage parameter of the focused ion beam equipment according to the real-time compensation control instruction set, and outputting an adjusted ion beam path parameter set; and performing ion beam processing on the target processing area according to the adjusted ion beam path parameter set, and monitoring morphology feature data of the processed area in real time to verify an error compensation effect. According to the invention, harmful effects caused by thermal effects, electric fields or magnetic fields can be eliminated, and the self-adaptability and long-term reliability of a compensation strategy are ensured.

The invention relates to the technical field of tooth modeling, and discloses a three-dimensional tooth modeling system based on computer vision, computer equipment and a readable storage medium. According to the method, mirror reflection, diffuse reflection and subsurface scattering components in an original image are separated, mirror reflection intensity is normalized in combination with a dynamic truncation algorithm, pixel saturation is eliminated, groove and nest textures are reserved, a complete point cloud is obtained based on a two-dimensional texture image and cubic spline repair, and a multi-exposure point cloud sequence is obtained through bimodal calibration. The method comprises the following steps: solving the problem of data dislocation, carrying out weight assignment and data fusion on three-dimensional points in a plurality of exposure point cloud sequences to obtain three-dimensional fusion feature data, combining layered optical modeling and photon tracking compensation deviation, fusing clinical constraints, finally dynamically adjusting parameters, feeding back and optimizing, and outputting a micron-sized precision model. The modeling defect caused by difficulty in effectively coordinating feature contribution degrees under different exposure conditions is overcome, and high-precision modeling is realized.

The invention relates to the technical field of data fusion, in particular to a power distribution equipment whole life cycle management system and method based on data fusion. The method comprises the following steps: acquiring multi-modal real-time sensing data and an equipment management log, and performing pre-semantic extraction and coding compression to obtain an edge semantic fragment; analyzing the topological structure of the power distribution equipment by using the equipment management log, and performing federal feature distributed modeling on the edge semantic fragment by using the topological structure of the power distribution equipment to obtain federal feature data of the power distribution equipment; state evolution causal inference is carried out based on the federation characteristic data of the power distribution equipment to obtain an equipment state evolution path; performing operation state dynamic simulation and prediction evaluation according to the equipment state evolution path to obtain a dynamic twin evolution body; and carrying out equipment operation parameter iterative optimization on the equipment management log according to the dynamic twin evolution body to obtain an optimized equipment operation parameter group. According to the invention, the use efficiency and the operation reliability of equipment are improved, and the maintenance cost is reduced.