558 results about "Data adaptive" patented technology

The invention relates to the technical field of intelligent manufacturing, in particular to a rolling method, system and equipment in a full-closed mode and a storage medium, and the method comprises the steps that plate thickness and temperature data are collected in real time through a thickness measuring device integrated with a temperature sensor; predicting a future thickness change trend by using a temperature-sensitive deep learning model and combining historical working condition parameters; calculating a dynamic compensation gap sequence based on the prediction result, the target thickness and the compression roller characteristics, and driving a compression roller control system to adjust the gap; and feedback data after rolling are synchronously collected, model parameters are updated in a self-adaptive mode, and closed-loop control is formed. According to the full-closed mode rolling method, high-precision rolling is achieved through closed-loop control, and the quality and efficiency are improved.

The invention relates to the field of power data management, and discloses a power big data adaptive management method and system fused with spatial-temporal feature mapping, and the method comprises the steps: obtaining a real-time operation data flow from a multi-source power terminal, and constructing an original power data set with a time sequence label and a device identifier; the method comprises the following steps: dividing an original power data set into parallel processing units based on a storage-while-computing architecture, performing dynamic index updating by adopting an event-driven index mapping rule, and constructing a multi-dimensional data index cache system with real-time responsiveness; identifying a key abnormal trajectory through a time-varying feature nesting mechanism, and performing hierarchical measurement and entropy disturbance analysis on a data fluctuation degree in the key abnormal trajectory by using a streaming feature aggregation network; identifying potential security risk nodes in combination with the structure matching degree between the historical abnormal event evolution graph and the key abnormal trajectory; and generating a multi-level response instruction chain based on the risk assessment result. The method has the advantage of improving the operation safety of the power grid.

The invention provides a frequency difference compressor based on precision perception, a gradient compression method, equipment and a medium, which are used for carrying out data compression and transmission between a server and a client so as to reduce communication overhead in personalized federated learning, and relates to the technical field of data compression. The frequency difference compressor comprises an information bottleneck rarefaction unit, a frequency domain compression unit, a dynamic quantization unit and a differential coding unit. Non-key gradient redundant components of the original gradient data are removed through an information bottleneck rarefaction unit to generate sparse gradient data; generating a metadata packet containing the first N high-energy frequency domain coefficients and the positions and the number of the first N high-energy frequency domain coefficients through a frequency domain compression unit; performing adaptive bit width mapping on the high-precision floating point gradient data into low-order integer representation through a dynamic quantization unit to generate dynamic quantization gradient data; and the differential gradient is transmitted to the client through the differential coding unit, so that data compression and transmission are completed. The method solves the problems that the communication overhead is large and gradient information cannot be reserved as far as possible in gradient transmission.

The invention relates to a steel bridge disease detection and identification method based on a large language model, and belongs to the technical field of artificial intelligence and civil engineering crossing. According to the method, a cross-modal feature alignment mechanism is constructed through a pre-trained multi-modal large language model by fusing a steel bridge image and a field customized text prompt, and a cascade detection process of'component identification-disease classification-region segmentation 'is realized. Comprising the following steps: designing a structured text prompt word bank to enhance semantic consistency, and dynamically fusing general knowledge and instance features in combination with a mixed prompt mechanism; a multi-level cross-modal alignment strategy is adopted to generate an anomaly graph, and a disease area is accurately positioned; a visual prompt enhancement module is introduced to improve the multi-scale feature discrimination ability, and the robustness in a complex environment is adjusted and optimized through data self-adaption. Under the condition of few samples or even zero samples, high-sensitivity detection and pixel-level segmentation of steel bridge cracks, corrosion and other diseases are achieved, and the problems that a traditional method is low in efficiency, poor in generalization, high in labor cost and the like are effectively solved.

The invention discloses a high-frequency power data acquisition adaptive frequency and priority processing method and device based on resource load feedback, and a storage medium, and belongs to the technical field of high-frequency power data processing. The method comprises the following steps: acquiring resource load information of a distribution network side intelligent terminal node in real time; according to the real-time resource load information, judging whether a preset sampling period of the power monitoring data needs to be adjusted, if so, dynamically correcting through a double-layer fast and slow ring adjusting mechanism to obtain a final sampling period, and otherwise, maintaining an original period; acquiring power monitoring data acquired by the node in the corresponding sampling period; and on the basis of a preset priority queue grading rule, a tube queue algorithm is utilized to adopt a differential transmission strategy for the data, so that priority processing of different levels of data is realized. The method does not need to depend on a prediction model, and realizes acquisition link elastic control and key data delay guarantee through real-time quantification of node loads, dual-time-domain closed-loop adjustment of a sampling period and hierarchical queue management messages.

The invention relates to the technical field of air target prediction, in particular to an air target trend prediction method, which comprises the following steps: S1, multi-source heterogeneous data adaptive fusion filtering processing; s2, manifold learning is constructed in the high-dimensional spatial-temporal feature space; s3, performing semantic modeling on the dynamic behavior pattern recognition intention; and S4, multi-dimensional threat situation assessment warfare area modeling is carried out. According to the method, high-precision space-time synchronization and noise suppression of radar sensor data, infrared sensor data and other sensor data are achieved through the multi-source heterogeneous data self-adaptive fusion filtering technology, target micro-Doppler features are effectively reserved, missing data are repaired, and through the combination of third-order Savitzky-Golay differential filtering and short-time Fourier transform, high-precision space-time synchronization and noise suppression of radar sensor data and infrared sensor data are achieved. An 18-dimensional compression feature space containing kinematics and electromagnetic characteristics is constructed, a nonlinear topological relation is reserved through t-SNE and an automatic encoder, the signal-to-noise ratio and feature expression capacity of original data are remarkably improved through the function, and a high-precision and low-redundancy input basis is provided for follow-up behavior recognition and prediction.

The invention discloses a multi-modal self-adaptive clamping system for assembling key components of a humanoid robot and a control method of the multi-modal self-adaptive clamping system, and the system comprises a multi-modal sensing module which is used for collecting assembling parameters; the edge calculation module is used for generating a control instruction by utilizing a multi-mode deep learning model according to the assembly parameters; the cloud service module is used for generating distillation data according to the assembly parameters; the self-adaptive clamping module is used for clamping and moving key components of the humanoid robot; and the programmable logic control module is used for controlling the self-adaptive clamping module to execute component assembling operation according to the control instruction and the distillation data. According to the invention, a multi-mode self-adaptive clamping system is realized, and the efficiency and the clamping stability are improved. The robot can be widely applied to the technical field of industrial robots.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a three-dimensional space self-adaptive unmanned aerial vehicle accurate spraying method and system and an unmanned aerial vehicle. The method relates to the technical field of unmanned aerial vehicle spraying, and comprises the following steps: scanning a spraying area on the bottom surface of a viaduct through an unmanned aerial vehicle to obtain unmanned aerial vehicle spraying quality data, and analyzing, optimizing and adjusting the unmanned aerial vehicle spraying quality data. The method comprises the following steps: scanning a spraying area on the bottom surface of a viaduct through an unmanned aerial vehicle to obtain three-dimensional point cloud data, adaptively generating a flight path and a spraying track of the unmanned aerial vehicle based on the data, flying according to a planned path and spraying, collecting spraying quality data through a sensor, analyzing the spraying quality data, and determining the spraying quality of the viaduct. The dynamic liquid level monitoring error quantized value and the unmanned aerial vehicle precise spraying method stability quantized value are obtained and analyzed with threshold values in a database, the unmanned aerial vehicle precise spraying method is optimized and adjusted, the stability of the unmanned aerial vehicle precise spraying method is improved, and the problem that in the prior art, an unmanned aerial vehicle precise spraying method is insufficient in stability is solved.

The invention discloses a high-speed camera data self-adaptive two-stage caching system and method and a storage medium, and relates to the technical field of machine vision high-speed imaging and high-speed data storage, a preprocessing unit is used for performing deserialization and parallel connection, ROI cutting and gain adjustment on collected item number data, and writing incremental FrameID and cyclic redundancy check codes into each frame; the DDR annular cache unit comprises a monitoring module, a decision module and an execution module; the decision module is used for calculating an instantaneous cache demand Cnew and a dynamic cache occupancy U; the execution module expands and shrinks an annular buffer space in a single shot through a DDR4 controller, and outputs a Hi / Lo mark in real time through an occupancy rate comparator to drive an NVMe queue manager. According to the invention, on the premise of zero extra burden in a normal working condition, millisecond-level automatic capacity expansion and high-speed flood discharge can be realized in a sudden working condition, and real-time frame-level integrity verification can be completed on a local FPGA (Field Programmable Gate Array) side. The patent is subsidized by national key research and development plans, and the project number is 2023YFF0719700.

The invention relates to the technical field of data processing, discloses a novel spatio-temporal stream data distributed computing load balancing method and system, a terminal and a storage medium, and aims at dynamically adjusting task distribution of distributed computing nodes through distribution prediction of spatio-temporal stream data, effectively relieving the problem of data skew in a distributed system and improving the distributed computing efficiency. The method mainly comprises the following steps: 1, space-time stream data adaptive network division and distributed node mapping; 2, carrying out deep modeling and distribution prediction on spatio-temporal flow data features; and 3, deducing distributed computing resource demands and realizing load balancing. According to the method, future laws can be effectively mined from the space-time stream data, dynamic load balancing of the computing nodes is achieved, the data skew phenomenon is effectively relieved, and the space-time stream data computing processing efficiency is improved.

The invention provides a big data artificial intelligence AI comprehensive corrosion collection computing power platform method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting data through a distributed corrosion sensor, carrying out the space-time marking, and calculating a space-time correlation matrix to achieve the adaptive fusion of heterogeneous data; constructing a corrosion environment knowledge migration framework based on a graph neural network, and establishing cross-environment knowledge migration; and inputting the fused data into the prediction model to obtain a corrosion state prediction result. According to the method, efficient fusion and accurate prediction of corrosion data are realized, and the cross-environment corrosion monitoring precision is improved.

The invention discloses a vehicle test data feature extraction method and system, and belongs to the field of vehicle test data processing, and the method comprises the steps: sampling a preset feature extraction strategy to extract time-frequency features from intermediate test data, and constructing cross features according to the time-frequency features; the preset feature extraction strategy comprises the step of extracting time domain features from the intermediate test data by using a first sliding window, and the window size of the first sliding window is adaptively adjusted according to the extracted data; after the time-frequency features and the cross features are scored through multiple preset evaluation methods, weighted summation is carried out on all scoring results, and a comprehensive score of each feature is obtained; based on the scene to which the features belong, adaptively adjusting the weight during weighted summation; and removing low-score features of which the comprehensive scores are lower than a preset score threshold to obtain high-score features, and performing dimension reduction on the high-score features to obtain optimized features. The accuracy of feature extraction is improved by dynamically adjusting the feature extraction window and the scoring standard.

The invention discloses a flexible surgical instrument-oriented multi-source sensing data adaptive fusion method and a related device. The method comprises the following steps: acquiring optical, mechanical and pose multi-mode sensing data based on a timestamp synchronization technology in a working environment; clock compensation and space coordinate transformation based on an instrument kinematics model are carried out on the multi-modal data, and space-time registration is completed; inputting the registered data into a neural radiation field model to generate continuous space implicit scene representation; inputting the implicit representation into a pre-trained RWKV fusion network to obtain a fusion feature; and superposing the fusion feature and the original low-frequency component through residual connection, and integrating to generate three-dimensional voxelization environment state data. According to the method, multi-source information high-fidelity fusion is achieved through a single path, data redundancy is remarkably reduced, and the real-time performance and precision of intra-operative environment perception are improved.

The invention discloses a traffic monitoring video rapid target extraction method for edge equipment, and relates to the technical field of intelligent traffic video processing and edge calculation target detection, and the method comprises the steps: carrying out the adaptive downsampling processing of original video frame data, and generating downsampling video frame data; extracting a foreground target candidate region, and constructing a traffic region-of-interest mask in combination with a lane line detection result; carrying out pixel AND operation on the traffic region-of-interest mask and the foreground target candidate region to generate accurate candidate target region data, and extracting a target feature vector; carrying out weighted fusion on the target feature vector through a lightweight attention mechanism, generating a fusion feature descriptor, and calculating a target confidence score; and carrying out screening and duplicate removal processing on the accurate candidate target area data, and outputting traffic target extraction result data. According to the invention, the target in the traffic video can be rapidly and accurately extracted and processed in a low-delay manner on the edge equipment with limited computing resources.

The invention belongs to the technical field of low-altitude navigation safety monitoring, and particularly relates to a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes, which is a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes such as urban air traffic, low-altitude logistics and emergency rescue. Efficient integration of multiple types of monitoring data and position output of the trusted aircraft can be achieved, and accurate data support is provided for low-altitude navigation anomaly recognition and risk deduction. The method comprises the following specific steps: constructing a low-altitude navigation scene classification system and fusion demand mapping, collecting and preprocessing multi-source heterogeneous data, quantifying data credibility and resolving conflicts, constructing a hierarchical adaptive fusion framework and outputting a fusion result. According to the method, efficient and accurate fusion of multi-source data in different scenes is realized by constructing a layered scene adaptation framework and a credibility fusion model, and finally, a high-credibility aircraft position is output, so that reliable data support is provided for low-altitude navigation anomaly recognition and risk management and control.

The invention relates to the technical field of motor control, and discloses a low-noise flux linkage adaptive feedback control system of a high-speed motor. The system comprises an acquisition module used for acquiring current, voltage and vibration data of a high-speed motor at various operation rotating speeds to obtain operation parameters and noise characteristic data of the high-speed motor; the transformation processing module is used for performing frequency domain orthogonal transformation processing on the operation parameters and the noise characteristic data of the high-speed motor to obtain flux linkage real-time observation data; the self-adaptive analysis module is used for executing multi-mode working condition self-adaptive analysis according to the flux linkage real-time observation data and generating flux linkage control reference values of different rotating speed intervals; and the output module is used for inputting the flux linkage control reference values of different rotating speed intervals into a magnetic-machine coupling control algorithm, and calculating and outputting a motor low-noise driving control signal. According to the method, the problem of magnetic flux path distortion under high-speed operation is effectively solved, and downtime and efficiency loss caused by a traditional off-line calibration method can be avoided.

The invention discloses an RFID robot tag identification optimization method and device for a dynamic scene, and the method comprises the steps: an RFID robot moves based on a preset identification path, a reader identifies tags and obtains tag information in real time, and the reader is disposed on the RFID robot and is used for reading all tags on the identification path; based on the state evaluation model, the real-time state of the RFID robot is given in combination with the label information; in combination with the real-time state of the RFID robot, a self-adaptive control strategy is adopted, and tag reading parameters of the RFID robot are adjusted; and the tag reading parameters of the RFID robot are analyzed, and the RFID robot is adjusted, so that optimization of tag identification of the RFID robot is completed. Through combination of the state evaluation model and the adaptive control strategy, the state data of the RFID system are received and analyzed in real time, and the adaptive control strategy can output an optimal parameter adjustment scheme, so that the accuracy and the real-time performance of data reading are improved while it is ensured that the RFID system always keeps efficient operation.

The invention relates to the technical field of industrial automatic monitoring, and provides a mining equipment remote monitoring method and system and a storage medium. The method comprises the following steps: dividing mining equipment into core equipment and general equipment based on equipment operation parameters; performing fault risk assessment on the core equipment by adopting a dynamic threshold value adaptively generated based on historical data, and performing deviation detection on the general equipment by adopting a fixed threshold value; fusing the fault risk assessment result of the core equipment and the deviation detection result of the general equipment to generate global maintenance decision information; optimizing a field maintenance path according to the geographic position and the emergency degree of the maintenance task; and continuously calibrating system parameters through a closed-loop feedback mechanism. According to the invention, through hierarchical monitoring and adaptive optimization, the problems of uneven distribution of monitoring resources, rigid threshold setting and isolated maintenance decision in the prior art are solved, and accurate configuration of mining equipment monitoring resources and remarkable improvement of fault early warning capability are realized.

The invention discloses a gas recognition method based on multi-source information fusion and environmental perception, and the method comprises the steps: constructing a deep feature learning framework of multi-source fusion through combining the spatial response features, time sequence features and external environmental information of gas; the method comprises the following specific steps: preprocessing collected gas data, and respectively extracting features of an image mode, a sequence mode and an environment mode; fusing the image features and the sequence features through a cross attention fusion module, and capturing the space-time correlation of the data; a cross-modal attention compensation module is introduced, so that main-modal gas data adaptively gathers key information in an auxiliary-modal environment, and effective compensation of environmental factors on gas recognition performance is realized; and finally, gas category prediction is performed through a classification decision head. According to the method, the problems that the detection is easily interfered by environmental factors, the stability is poor or the qualification is inaccurate due to the fact that modeling depends on single modal data in the existing gas identification technology are solved.

The invention discloses a power discharge detection method and system based on artificial intelligence, and relates to the technical field of power equipment safety monitoring, and the method comprises the steps: collecting multi-modal data, and executing adaptive decomposition to extract a discharge characteristic signal; constructing a depth map joint learning model, converting the discharge feature signal into a map data structure, and generating a candidate feature set; based on the candidate feature set, training a discharge mode recognizer by adopting a comparative learning framework, establishing a mapping relationship between discharge features and discharge types and discharge positions, and completing recognition and positioning of a single discharge source; distinguishing and positioning a plurality of discharge sources with overlapped time domains by using node embedding and community detection of the graph neural network; and outputting a discharge risk assessment result in combination with the dynamic evolution of the discharge characteristics. According to the method, the accuracy and the signal-to-noise ratio of discharge feature extraction are remarkably improved through a multi-modal data adaptive decomposition technology, and the modeling capability of discharge spatial-temporal features is remarkably enhanced through a depth map combined learning framework.

The invention relates to an unsteady-state sea wave direction spectrum estimation method. The method comprises the steps of S1, signal decomposition; s2, direction distribution calculation; s3, adaptively adjusting the time domain analysis step length; s4, performing nonparametric kernel estimation on the transient wavelet distribution probability; and S5, direction spectrum time domain adaptive reconstruction is carried out. In the time domain analysis process of wavelet analysis, a two-dimensional plug-in non-uniform bandwidth algorithm is adopted to determine a data-adaptive two-dimensional non-uniform and non-parametric kernel function, the probability of different wavelets in a wave number-direction space is analyzed, the lateral propagation sea wave energy in the non-main wave direction is estimated, and the wave energy in the non-main wave direction is obtained. Therefore, the precision and the anti-noise capability of the direction spectrum estimation method are improved. In the time domain analysis process of wavelet analysis, a sea wave signal fluctuation intensity detection algorithm based on the frequency-direction-wave number three-dimensional change rate of wave energy distribution is introduced, the time duration of sea wave signal sections is divided according to the fluctuation intensity, the time domain precision is adjusted, the calculation efficiency is improved, and meanwhile, the sea wave signals which rapidly change in the time domain are accurately captured. Measurement is avoided.

The invention relates to the technical field of data sharing and access control, in particular to a block chain and reputation mechanism-based education data adaptive access control method, which comprises the following steps of: firstly, constructing a historical interaction-based credible reputation mechanism: setting default trust and reputation values for an initial visitor by an attribute mechanism; calculating an initial reputation value by combining data of other fields acquired by an oracle machine, and binding public keys and reputation information of participants, a block chain storage address and a data abstract; a data owner encrypts data by using a ciphertext policy attribute-based encryption technology, formulates a policy containing an access matrix, trust and a reputation threshold in combination with a static attribute and a reputation mechanism, and binds own public key and data information to a key list; when a visitor requests, risks are quantified, attributes and reputation are verified based on related intelligent contracts, and tokens are generated if the standards are reached, so that safe and credible sharing and dynamic control of education data are realized, and the safety and flexibility are improved.

The invention relates to the technical field of data analysis, can be applied to intelligent driving, financial science and technology, medical health and other business scenes, and discloses a multi-modal knowledge graph interpretable analysis method, device, equipment and medium, and the method comprises the steps: obtaining multi-modal original data, carrying out semantic analysis, and extracting structured feature information; constructing an entity-associated knowledge graph based on the structured feature information; performing consistency verification by using redundant information of the knowledge graph to complete data quality calibration; setting an abnormal exposure degree and user experience consistency dimension, and constructing a multi-scale aggregation model for dynamically adjusting an index weight; and generating a rating result based on the calibrated knowledge graph and the aggregation model, and performing reverse attribution analysis to generate a source mapping report. According to the method, multi-modal analysis, knowledge graph modeling and dynamic index aggregation are fused, data self-adaptive evaluation and explainable result output are achieved, and analysis accuracy and reliability are improved.

The invention discloses a Direct3D deep cutting behavior compatible method based on an atomization shader, which comprises the following steps: establishing a scene combination metadata table in a DXVK compiling stage, constructing a shader template library containing atomization components and variants, and generating an executable file of the DXVK; after the DXVK is started, deep processing API calling of the D3D application is intercepted, parameters are extracted to generate an atomization parameter set, and scene combination metadata are matched or newly added in a scene combination metadata table to obtain a recommendation strategy and shader component combination; a conversion mode is adaptively selected according to scene metadata to calculate a Vulkan depth value, and a depth comparison operation is adapted to generate a Vulkan depth conversion parameter; the DXVK maps the parameters into DRM depth control parameters irrelevant to hardware and transmits the DRM depth control parameters to a kernel, the DRM packages the DRM depth control parameters and converts the DRM depth control parameters into hardware instructions matched with the GPU, the DXVK loads a shader template and binds the parameters, original logic is spliced to generate a Vulkan shader module, a Vulkan command stream containing the hardware instructions is generated and submitted to the GPU after a pipeline is bound, and finally the GPU executes the Vulkan command stream to obtain a depth rendering result conforming to D3D native logic.

The invention relates to the field of offshore platform monitoring and early warning, and discloses a remote video inspection system and method for an offshore unmanned platform, and the method comprises the steps: a multi-modal data fusion network implementation module obtains multi-modal sensing data; an edge computing node of the edge-cloud co-processing implementation module is responsible for receiving and caching multi-modal sensing data; the self-adaptive task scheduling mechanism module is used for monitoring load states of edge nodes and cloud resources in real time; judging that the task execution position is an edge end, a cloud end or a collaborative mode of edge coarse screening and cloud end fine judgment; when the network bandwidth is limited, the transmission of key alarm data and model updating data is guaranteed preferentially; the intelligent analysis module adopts an improved YOLOv8 target detection model and a multi-modal data fusion network to realize accurate identification and dynamic tracking of equipment abnormity and security risks; and edge end lightweight reasoning and cloud deep reinforcement learning are fused to form a closed-loop inspection flow integrating data acquisition, intelligent analysis and decision feedback.

The invention relates to the technical field of power distribution fault positioning, in particular to a power distribution single-phase earth fault positioning method and device based on multi-source information fusion. The device comprises a data acquisition module, a fault line selection module, a grading emergency module, a fault positioning module and a parameter optimization module. According to the method, the zero-sequence current is monitored in real time, the dynamic change curve is generated, the fault line is accurately identified in combination with amplitude and phase characteristics, electrical parameters, environmental factors and load importance are fused to carry out comprehensive risk assessment and start a hierarchical response mechanism, and the fault point of the fault line is accurately positioned by adopting a voltage drop method. And the monitoring frequency and the decision threshold value are adaptively adjusted according to the fault characteristics and historical data, so that response lag and insufficient precision of a traditional fault positioning method are avoided, full-flow intelligent management from fault detection, accurate positioning to parameter self-optimization is realized, the accuracy and efficiency of fault processing are improved, and safe and stable operation of a power grid is ensured.

The invention discloses a GIS-based traffic project land expropriation and demolition digital management system and method, and particularly relates to the technical field of geographic information intelligent monitoring. NDVI vegetation features of satellite images and a building contour feature map of unmanned aerial vehicle data are extracted respectively, unified geographic projection conversion is carried out in combination with a sensor coordinate flow, and the data of the NDVI vegetation features and the building contour feature map are acquired; setting a cross-modal feature alignment mechanism driven by an adversarial network, extracting multi-scale space correlation features through wavelet transform, dynamically generating a space-time consistency compensation coefficient in combination with a sensor displacement vector, and realizing centimeter-level precision multi-source data adaptive fusion by using a dynamic weight fusion model; the spatial distance between a boundary post and a demolition red line is calculated in real time through a ray tracing algorithm, a dynamic safety threshold is generated in combination with a historical dispute rate, the defect of delay of traditional static threshold early warning is broken through, and the bottleneck problems of precision and timeliness in land acquisition and demolition management are solved by constructing multi-source geographic data fusion and dynamic early warning.

The invention relates to the technical field of computer-aided engineering, in particular to a pure cotton dark-color fabric dye-resistant area dynamic prediction method based on a dye diffusion model. The method comprises the following steps: acquiring a parameter data set representing a fabric microstructure, dye properties and a printing process; on the basis, a multi-scale coupling mathematical model is constructed, the dye diffusion range is predicted, the diffusion process is decomposed into seepage diffusion between macroscopic yarns and diffusion in microcosmic fibers through the model, and the diffusion rate difference of the dye in the fabric direction is represented through anisotropic diffusion tensors; through finite element analysis of structure perception, running a model to calculate dye space-time concentration distribution, and generating geometric data for predicting a bleeding boundary according to a preset concentration threshold value; according to the data, printing process parameters and model parameters are adjusted in a self-adaptive mode, and dynamic prediction of the dye-resistant area is achieved; according to the method, computer simulation and finite element analysis technologies are utilized, and the accuracy of dynamic prediction of the dyeing-resistant area of the pure cotton dark-color fabric is improved.