202 results about "Multiple-scale analysis" patented technology

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention relates to the technical field of cable partial discharge detection and diagnosis, provides a high-voltage cable multi-parameter intelligent diagnosis method, system and equipment and a storage medium, and aims to solve the problems of low accuracy and poor reliability of high-voltage cable multi-parameter diagnosis caused by multiple factors. The method comprises the following steps: decomposing a partial discharge signal after interference filtering, extracting a time domain amplitude change rate and an energy distribution parameter, and generating a multi-dimensional time-frequency domain feature vector; calibrating a multi-dimensional time-frequency domain feature vector, carrying out nonlinear manifold mapping, generating a low-dimensional feature vector matched with dielectric spectrum characteristics, matching a partial discharge type with the highest similarity with an identification result, judging an insulation degradation level in combination with a final loss threshold interval, and generating a comprehensive diagnosis report. According to the invention, the ultrahigh-frequency sensor array captures the partial discharge signal matched with the dielectric spectrum, precise identification of the discharge type and dynamic evaluation of the insulation degradation level are realized through multi-scale analysis and deep learning fusion, and the reliability of high-voltage cable diagnosis is improved.

The invention relates to the technical field of smartphone surface defect detection, and discloses a smartphone defect detection method and system, and the method comprises the steps: firstly collecting defect detection image data through multi-dimensional optical imaging, and dividing the defect detection image data into a plurality of detection sub-regions; extracting surface defect features based on a texture enhancement technology, and extracting defect difference indexes in combination with pixel distribution and spatial correlation; then, the regularity of defect modes is analyzed and evaluated through multi-scale decomposition, the defect fluctuation amplitude is dynamically analyzed and evaluated through a sequential sequence, and the stability of defect difference indexes is judged by integrating the regularity and the fluctuation amplitude; and finally, comparing the stable defect difference index with reference data, and determining a detection result. According to the method, through multi-dimensional imaging, multi-scale analysis and dynamic evaluation, the recognition precision and reliability of complex surface defects are improved, the method is suitable for surface detection of smart phones of different materials and structures, and the high-quality detection requirement in large-scale production is met.

The invention relates to the technical field of data quality evaluation, in particular to a quality evaluation system and method based on river channel data. The method comprises the following steps: obtaining multi-source river channel data, and carrying out parallel data fusion to obtain river channel fusion data to be evaluated; performing error fluid propagation simulation based on the to-be-evaluated river fusion data to obtain an error statistical result set; performing spatial deviation multi-scale analysis according to the error statistical result set to obtain an elevation error distribution diagram; randomly selecting an underwater point cloud sample based on the to-be-evaluated river fusion data, and performing spatial interpolation point cloud precision calculation on the underwater point cloud sample to obtain a point cloud precision error matrix; performing quality detection and correction on the to-be-evaluated river fusion data by combining the point cloud precision error matrix and the elevation error distribution diagram to obtain quality optimization data; and based on the quality optimization data, carrying out river channel map profile visualization to generate a river channel optimization map profile. The method is helpful for improving the precision and consistency of the river surveying and mapping data.

The invention relates to an intelligent CNC (computer numerical control) machining method which comprises the following steps: dynamically monitoring a tool-workpiece interaction interface in a CNC machining process through a multi-dimensional torque sensor array to obtain a machining process feature data set, and analyzing a tool motion track in real time to obtain a tool motion state feature quantity; secondly, performing multi-scale analysis by adopting a wavelet packet decomposition technology, and determining processing quality evaluation data; and if the data does not reach the preset standard, performing microstructure reconstruction on the surface of the workpiece based on the data, and generating a workpiece processing state characteristic spectrum. Then, comprehensively analyzing the atlas by utilizing a multi-objective optimization technology to obtain a processing parameter optimization sequence; and finally, implementing closed-loop feedback control on the numerical control system according to the optimized sequence, and generating a real-time processing control instruction. The key problem of how to improve the automation level and the machining precision in the CNC machining process through an intelligent means is solved.

The invention discloses an aero-engine residual life prediction method based on multi-modal deep learning, and relates to the field of aero-engine prediction and health management. The method comprises the following steps: acquiring and preprocessing multi-sensor time sequence data; constructing a degradation sensitive feature set through multi-scale analysis of a time domain, a frequency domain and a time-frequency domain; constructing a multi-modal deep learning model comprising an original data processing module and a multi-scale feature processing module, and introducing an attention mechanism and an uncertainty quantization module into the model; the model is subjected to lightweight processing to support embedded deployment. According to the method, multi-scale features and multi-modal deep learning are fused, the uncertainty quantification capability is achieved, high-precision and interpretable residual life prediction with uncertainty quantification is achieved, and reliable support is provided for engine maintenance decision making.

The invention discloses a three-dimensional scene reconstruction and monitoring method based on multi-view fusion and deep learning. The method comprises the following steps: acquiring image data from different viewpoints through a plurality of cameras, and carrying out geometric calibration; denoising, correcting and enhancing the image; constructing a multi-scale convolutional neural network and a variational auto-encoder model, and extracting multi-level features; carrying out weighted fusion on the features, and carrying out three-dimensional reconstruction through sparse coding and a graph neural network; performing feature optimization by applying a dynamic graph convolutional network and a double attention mechanism, and performing model updating based on adversarial gradient descent; anomaly detection is carried out through multi-scale analysis and an adversarial variational auto-encoder, preliminary processing is carried out at a camera end by adopting an edge computing technology, and further analysis is carried out at a central server end through a heterogeneous graph neural network and sparse subspace clustering. According to the method, high-precision and intelligent three-dimensional scene reconstruction and monitoring are realized, and the method has a wide application prospect.

The invention discloses a GIS combined electric appliance operation on-line detection method, and relates to the technical field, and the method comprises the following steps: obtaining a partial discharge original time domain signal, carrying out the signal enhancement processing of the original time domain signal, extracting a weak discharge pulse signal, and generating an enhanced discharge pulse signal; constructing a holographic time-frequency matrix atlas according to the enhanced discharge pulse signals; the holographic time-frequency matrix atlas is used as an input item, and a weak discharge type is identified based on a weak discharge neural network identification model; based on the identification result and the sensor array data, acquiring space coordinates of a discharge source, and outputting a discharge type and a positioning result to the monitoring terminal; according to the method, the holographic time-frequency multi-scale analysis algorithm and the weak discharge neural network recognition mechanism are fused, so that the recognition capability, the anti-noise robustness and the positioning precision of the GIS combined electric appliance online detection system on weak discharge are remarkably improved.

The invention discloses an intelligent geophysical exploration geological exploration analysis system, and relates to the field of geological exploration, and the system comprises a collection and preprocessing module which is used for carrying out the adaptive collection of multiple types of geophysical field signals of a target exploration region, and synchronously carrying out the preliminary noise reduction and quality optimization of original signals through signal purification processing; the fusion module is used for receiving the preprocessed multi-type geophysical field signals and carrying out normalization and feature depth correlation fusion on coordinates of data of different dimensions through spatial-temporal feature correlation processing; the method can dynamically adapt to environmental noise optimization signal acquisition, extracts stratum recessive characteristics through multi-dimensional data fusion and intelligent mining, constructs an accurate three-dimensional geologic model, positions stratum attribute deviation and grades through multi-scale analysis, presents exploration results in combination with three-dimensional visualization, and generates target region optimization suggestions.

The invention discloses a composite material performance prediction method based on multi-field coupling multi-scale analysis and a knowledge graph, which integrates microstructure modeling, graph neural network representation learning, cross-scale parameter coupling modeling and robust optimization analysis. The method is suitable for prediction and design of key mechanical properties such as modulus, strength and toughness of a thermosetting / thermoplastic composite material under different working conditions. The method comprises the following steps: firstly, constructing a grain-level tissue knowledge graph based on an electron backscatter diffraction image, secondly, constructing a multi-scale input system comprising a microscopic variable (such as a fiber volume fraction), a mesoscopic variable (such as a layer thickness sequence) and a macroscopic variable (such as a load condition), and finally, performing robust optimization by utilizing a multi-objective evolutionary algorithm to obtain a multi-scale input system. And outputting a material performance prediction result and a knowledge graph associated with the ji structure-process-performance. According to the method, the accuracy and interpretability of performance prediction of the composite material can be remarkably improved, and data-knowledge dual-drive support is provided for design optimization of the high-performance composite material.

The invention discloses a thermal power generating unit load distribution optimization method and system, and relates to the technical field of load optimization distribution, and the method comprises the steps: quantifying the contribution degree of the position of each operation state monitoring device to a load prediction error based on operation influence data, and screening out a representative monitoring point set covering a unit core energy consumption node; constructing a multi-scale influence analysis model, identifying the change influence degree of the operation state data of each representative monitoring point on the load data of the thermal power generating unit, and generating a load distribution initial scheme; running state data of representative monitoring points are monitored in real time, a multi-target optimization mechanism is introduced, the weight value of each target is dynamically adjusted, and the load distribution of the thermal power generating unit is optimized in real time in combination with a load distribution initial scheme. Accurate dynamic load distribution based on multi-scale analysis and multi-objective optimization is realized, and the comprehensive benefit of a unit is improved.

The invention belongs to the field of hydroelectric generating set monitoring, and discloses a set vibration signal noise reduction and feature extraction method based on IAVMD and WOA, and the method comprises the steps: constructing a comprehensive evaluation index comprising an energy difference, an average energy entropy and a center frequency variation coefficient; combining an adaptive variational mode decomposition method and a whale optimization algorithm to obtain an optimal decomposition layer number K and a penalty factor; calculating an energy entropy and a Pearson correlation coefficient of each intrinsic mode component; screening the K intrinsic mode components through a double-threshold mechanism; and calculating to obtain a denoised unit vibration signal time sequence and fault features according to the screened intrinsic mode components. According to the method, the problems of large noise interference, fuzzy characteristics, parameter dependence on experience and the like in a traditional method are solved, the precision and efficiency of unit vibration signal processing are remarkably improved, nonlinear dynamic characteristics in unit vibration signals are captured through multi-scale analysis, and the sensitivity and diagnosis accuracy of early weak faults can be improved.

The invention relates to the technical field of network security, in particular to a security monitoring method and system for network traffic. The method comprises the following steps: capturing a network flow data flow in real time, and extracting a network entity and a direct communication relationship to construct a basic communication graph; identifying and quantifying a high-order interaction mode between network entities, and taking the high-order interaction mode as an implicit feature enhanced basic communication graph to generate an enhanced security graph; processing the enhanced security map by using a multi-scale time sequence diagram neural network, and capturing a short-term burst mode and a long-term evolution mode at the same time; a dynamic anomaly score is calculated based on the network entity historical behavior baseline and the current network situation, and a security alert is generated when an adaptive threshold is exceeded. The system correspondingly comprises a flow capture module, a feature extraction module, a high-order mode analysis module, a security map construction module, a multi-scale analysis module, a dynamic risk assessment module and an intelligent alarm module, and comprehensive and accurate network threat detection is realized.

The invention relates to the technical field of design and layout of a building photovoltaic system and renewable energy source evaluation, in particular to a mountainous area building surface solar energy potential evaluation method and device and a storage medium. The building surface solar energy potential evaluation method does not depend on traditional meteorological monitoring data and is suitable for coupling influence of cloud layer dynamic change and complex terrain shielding. By combining MODIS data, a digital elevation model and a machine learning algorithm, solar energy potential evaluation of the outer surface of a mountainous area town building without meteorological data can be realized, so that the problem that a traditional method depends on basic meteorological data is solved. Through the combination of three-dimensional modeling and multi-scale analysis, the limitation of an existing method under the influence of clouds, complex terrains and building shielding is overcome, particularly, the dynamic influence of cloud change and the shielding influence of the complex terrains can be effectively considered, the calculation efficiency and precision are improved, and the method is suitable for popularization and application. And a new technical path is provided for solar energy resource evaluation in mountainous cities and towns.

The invention discloses an abnormal transaction dynamic detection method and system fusing multi-scale analysis and information entropy, and the method comprises the steps: S01, constructing a dynamic heterogeneous graph which comprises account nodes, equipment nodes and IP nodes; s02, extracting node features of each node, extracting a spectrum feature matrix of the dynamic heterogeneous graph, and forming a node feature vector of each node; step S03, clustering the dynamic heterogeneous graph to screen out candidate detection sub-graphs, inputting node feature vectors of nodes in the candidate detection sub-graphs into a pre-trained graph classification model, and identifying an abnormal account node set; and S04, calculating a corresponding dynamic risk score in real time according to the memory state vector of each abnormal account node in the time sequence diagram network and the diagram information entropy change rate so as to evaluate a real-time risk and identify a core abnormal account. According to the method, the group type abnormal transaction account can be quickly and accurately identified, and the core abnormal account can be positioned.

The invention discloses a feature point detection method based on edge saliency and scale sensitivity. The method comprises the following steps of performing Gaussian smooth denoising on an input image; calculating the edge saliency of the pixel points through a Laplace operator; calculating a texture change degree based on the local gradient magnitude; generating a scale sensitivity weighted value through multi-scale analysis; candidate points are screened in combination with edge saliency, texture weighting and scale weighted values, and mismatching points are removed through non-maximum suppression; and finally, key points are enhanced and marked. According to the method, the edge structure, the texture information and the multi-scale features are fused, the robustness and accuracy of feature point detection are improved, and the method is particularly suitable for image matching and target recognition tasks in complex scenes.

The invention relates to the technical field of battery manufacturing, and discloses an environment-friendly manufacturing method of a high-capacity battery. The method comprises the following steps: firstly, collecting relevant data of sustainable material attributes, green process monitoring and environment footprints, and extracting an initial feature time sequence from the relevant data; a fusion feature set of material-process-environment interaction is extracted, a multi-scale model structure is constructed in combination with data acquisition precision of different making partitions, and multi-resolution feature mapping is generated; performing trend analysis on the time evolution data to generate a battery performance degradation spectrogram; stress response distribution of the core production stage is determined through simulation calculation, and a manufacturing state response data set is obtained; and identifying the number of performance degradation cycles, calculating a residual capacity value by fusing a material degradation model, and evaluating the total life of the battery produced in an environment-friendly manner after integration. According to the method, environmental protection and green consideration of the whole production process is realized through multi-dimensional data fusion and multi-scale analysis.

The invention discloses a lightweight real-time mapping system based on cloud edge collaboration and a VLA-HDMap high-frequency interaction method, and aims to solve the problems of slow updating, high cost and low safety of HDMap. The model has the capabilities of real-time environment understanding, semantic analysis and end-to-end decision instruction output. The vehicle end can download the cloud HMap in real time, and also participates in crowdsourcing data acquisition and uploading. According to the local lightweight mapping method provided by the invention, a real-time circulation mechanism of'acquisition-application-correction 'is used, so that the HDMap is highly consistent with the actual environment. A cloud edge collaborative hierarchical parallel incremental update global HDMap is also provided, the essence is a time-space intersection multi-scale analysis model of minute-level traffic flow prediction and day / week-level map differentiation detection, a high-risk region of traffic flow peak + map change can be efficiently identified, and a convex hull algorithm is used to obtain a region, which needs to be locally updated, of the whole HDMap. According to the invention, a new-generation intelligent traffic system can be constructed, and the safety application of group intelligence, symbiotic economy and large-scale automatic driving can be promoted.

The invention relates to the technical field of geographic information systems, and discloses a geographic information data processing method and system.The geographic information data processing method comprises the steps that geographic data are packaged into self-activation data nodes; monitoring a network state in real time through a network environment perception function; constructing an activation threshold function based on a neuron activation principle to determine data transmission; carrying out multi-scale analysis on the data stream by applying wavelet transform to realize self-adaptive compression; predicting hotspot distribution dynamic scheduling resources through space-time analysis; an edge-fog-cloud three-level cooperative processing architecture is constructed to realize distributed processing; according to the method, the technical problem of geographic information data transmission and processing in an unstable network environment is solved, the overall performance and the adaptive capacity of the system are improved through a multi-level adaptive mechanism, and the method has wide application value.

The invention relates to the technical field of water body pollution monitoring, in particular to a multispectral image fused water body pollutant accurate recognition system, which comprises a data acquisition module, a cloud processing module, a boundary processing module, a pollution recognition module, a diffusion prediction module and a visualization module, the system constructs sub-pixel representation of a water pollutant boundary by using a differential geometry manifold theory, and realizes high-precision pollutant boundary description through a multi-scale analysis and curvature flow optimization technology; enhancing pollutant characteristic expression by adopting multispectral image fusion and an optimal wave band selection technology; using a support vector machine model to accurately identify various pollutant types such as oil films, oil spots, algae blooms and the like; based on a boundary fine description result and a pollutant type identification result, the diffusion trend of pollutants is accurately predicted in combination with historical flow, wind direction and wind speed data, and the system improves the water pollutant boundary identification precision and enhances the identification capability of complex boundary forms and low-contrast regions.

The present invention relates to the field of three-dimensional data processing technology, and in particular to a method and system for three-dimensional visualization of surveying and mapping data based on the Internet of Things. The method comprises the following steps: collecting original surveying and mapping data within a surveying and mapping area through Internet of Things sensors, wherein the original surveying and mapping data includes terrain elevation data and environmental monitoring data; preprocessing the original surveying and mapping data to obtain standard surveying and mapping data; performing multi-scale analysis on the terrain elevation data of the standard surveying and mapping data to obtain multi-scale terrain features; and performing multi-scale terrain detection on the multi-scale terrain features to obtain multi-scale terrain data. The present invention utilizes data processing technology, Internet of Things technology, and three-dimensional mapping technology to more accurately reflect the spatiotemporal relationship between terrain and environment, and construct a three-dimensional surveying and mapping visualization model, thereby improving the accuracy and effect of three-dimensional visualization of surveying and mapping data.

The invention discloses a fire behavior detection method based on multi-modal large model multi-scale analysis and depth reasoning, and relates to the technical field of artificial intelligence safety monitoring. The method comprises the following steps: acquiring visible light video data, infrared image data and audio data in the same fire scene; respectively preprocessing the data to obtain processed data; inputting the processed data into a corresponding modal encoder in a pre-trained ImageBind multi-modal model for processing, and respectively outputting a feature corresponding to each modal; a gating fusion module is adopted to perform fusion calculation on the features corresponding to the modals, and unified scene representation is output; and carrying out multi-dimensional depth reasoning by adopting a depth reasoning module to obtain a comprehensive result of the multi-dimensional reasoning, and outputting a fire confidence coefficient score and an interpretability basis based on the comprehensive result of the multi-dimensional reasoning. According to the invention, the accuracy of fire detection can be improved.

The invention relates to the field of image processing, discloses a marine environment change monitoring method and device based on a remote sensing image and a medium, and provides a powerful tool for decision support by visually displaying risk distribution in a pseudo-color image form. Comprising the following steps: acquiring an original sea surface temperature image, performing fractional order differential enhancement processing, generating an enhanced temperature gradient map, extracting a frontal surface topological feature matrix, extracting a chlorophyll concentration map by using a multispectral remote sensing image, constructing a multiband phase coherent field, generating an ecological feature tensor field, and generating a multi-scale fusion image. And extracting a feature contour line based on the image, calculating a curvature gradient, generating a thermodynamic diagram, and outputting a marine environment dynamic risk map. According to the method, multi-source remote sensing data are fused, comprehensive, accurate and real-time monitoring of marine environment changes is realized through multi-scale analysis and feature enhancement, and powerful technical support is provided for marine environment management, disaster early warning and ecological protection.

The invention discloses a distribution box operation state monitoring method and device, and relates to the field of intelligent operation and maintenance of a power system. The method comprises the following steps: firstly, processing collected environment, load and vibration acceleration data of a distribution box to obtain standardized multi-dimensional time series data; monitoring characteristic parameters are extracted from the data, and short-term and long-term multi-scale characteristic vectors are obtained through sliding window statistics; and finally, inputting the data into an attention mechanism feature fusion model to obtain a risk anomaly score, setting a dynamic threshold value by combining statistical distribution of historical normal operation scores of the distribution box, and judging a fault trend type according to the dynamic threshold value. The scheme has the advantages that the limitation of a single data source is avoided through multi-source data fusion, and the anomaly recognition accuracy is improved; single window information and long-term trend can be captured through multi-scale analysis; the dynamic threshold value adapts to different equipment and environments and is more accurate than a fixed threshold value; and hierarchical evaluation can provide clear maintenance suggestions and assist operation and maintenance decisions.

According to the software supply chain dependency robustness evaluation method and device, the medium and the equipment provided by the invention, when robustness evaluation is performed on the open source software supply chain, the initial dependency graph is constructed according to the software dependency data from the plurality of data sources in the open source software supply chain, so that the space-time complexity of the dependency chain can be comprehensively restored; noise reduction is carried out on the initial dependency graph to form a noise reduction dependency graph by using a noise reduction auto-encoder and a graph neural network, and then multi-scale analysis is carried out to form an analysis result; the loss function of the noise reduction auto-encoder is formed by combining reconstruction loss and structure maintenance loss, and the graph neural network is trained by adopting a weighted aggregation function and a multi-task loss function and is evaluated through a structure disturbance experiment. Therefore, the robustness evaluation result obtained by performing multi-dimensional index evaluation on the analysis result can fully consider the influence of data noise, dynamic structure change and attack scenes, has relatively high accuracy, and provides powerful support for software supply chain security management.

The invention discloses a power grid multi-scale safety monitoring method and system based on a knowledge graph, and belongs to the technical field of power system operation and control. The method comprises the following steps: acquiring multi-source heterogeneous data of a power grid to construct a heterogeneous time sequence knowledge graph; calculating a real-time risk propagation index (RPI) of a node according to the current operation state of the power grid, and dynamically generating a self-adaptive multi-scale graph hierarchical structure based on the index; performing state-aware spatio-temporal feature learning on each level to generate hierarchical state embedding; performing cross-scale causal inference on the embedded input prediction model, and predicting a future security event sequence and a propagation path thereof; and finally generating a security situation assessment report and a preventive control strategy. According to the method, the problems of single analysis scale and hierarchical structure solidification in the prior art are solved, active and accurate prediction of the power grid security situation is realized through a risk-driven dynamic multi-scale analysis framework, and the capacity of predicting and defending cascading failures is remarkably improved.

The invention belongs to the technical field of image processing, and particularly relates to an intelligent detection method for a distribution line of a power system, and the method comprises the steps: carrying out the multi-scale analysis of a tower infrared image, and obtaining the gradient amplitude and direction of a pixel point; based on the local fluctuation of the gradient amplitude and the aggregation stability degree of the gradient direction, calculating the degree of the pixel points belonging to the temperature mutation type edge, and dividing the pixel points into temperature mutation type, slow deformation type and non-edge pixel points; calculating a significance coefficient of each edge pixel point in combination with the pixel point temperature value, the temperature change trend and the edge type; and finally, weighting the area proportions of different types of abnormal regions based on the significance coefficient, evaluating the overall abnormal temperature rise degree of the tower and even the distribution line, and dynamically correcting the maintenance cycle interval. According to the invention, different types of temperature edges can be accurately distinguished, key fault points are effectively identified, and the accuracy and reliability of detection are improved.

The invention discloses a flotation froth image multi-scale analysis method and system, and the method comprises the steps: carrying out the standardization processing of an original flotation froth image, obtaining a standardized image, inputting the standardized image into a multi-scale MAE encoder, outputting a multi-scale feature map, carrying out the element addition and fusion of the multi-scale feature map, and obtaining a fusion feature map, inputting the fusion feature map into a bubble segmentation branch, outputting a segmentation probability map, inputting the segmentation probability map and the fusion feature map into a bubble size distribution statistics branch, outputting a bubble size distribution proportion vector, inputting two continuous frames of fusion feature maps into a foam flow velocity estimation branch, and outputting a flow velocity vector; and performing format regularization on the segmentation probability graph, the bubble size distribution proportion vector and the flow velocity vector, and then outputting. Therefore, the problem of wrong segmentation of adhesion bubbles can be solved, region misjudgment caused by illumination interference in traditional connected domain analysis is avoided, dependence on image definition is reduced, and more comprehensive data support is provided for flotation process adjustment.

The invention discloses a power supply quality analysis method and system for a power distribution area, and relates to the field of power systems, and the method comprises the steps: carrying out the preprocessing of a power load collected in real time and external data, and building a dynamic load prediction model to obtain a load change prediction result; calculating an unbalance degree by combining three-phase current and voltage data, identifying a three-phase unbalance risk, and adjusting a correction result of load distribution by using an online correction algorithm; performing multi-scale analysis on the voltage data, and identifying an abnormal fluctuation event; and combining the results to carry out power supply quality comprehensive evaluation and formulating an optimization strategy. According to the method, data missing and abnormity can be processed, and the data credibility is improved; load change is accurately predicted, and power supply efficiency is improved; three-phase imbalance and voltage abnormal fluctuation are identified in time, and equipment loss and fault risk are reduced; loads are dynamically adjusted, and stable power supply is guaranteed; a strategy is made through multi-index evaluation, and continuous improvement and optimization of power supply quality are promoted.

The invention discloses a multi-source state trend integrated data fusion method for a weak support large-scale new energy delivery system. According to the invention, a data source identification and hierarchical access mechanism, a multi-source synchronization and calibration method based on a unified time reference, a topology-oriented space mapping method, a trend feature extraction method based on a sliding window and multi-scale analysis, and a state-trend integrated weak support risk characterization index system are constructed; unified modeling and online sensing of key quantities such as voltage supporting capacity, power flow margin, standby level, renewable output fluctuation and the like are realized. Compared with an existing monitoring mode depending on a single system, a single time scale or a single index, the method has the advantages that on the basis of ensuring data quality and time-space consistency, a unified data base and trend sensing capability can be provided for safety evaluation, cooperative regulation and market linkage control of the weak-support large-scale new energy delivery system; and the method has relatively high engineering applicability and popularization value.