251results about How to "Improve capture ability" patented technology

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

The invention provides a power cabinet working condition evolution prediction method based on a physical time sequence orthogonal decoupling architecture, and the method comprises the steps: firstly constructing a multi-dimensional non-stationary signal system based on arc disturbance and load impact characteristics, and collecting the working voltage, temperature rise upper limit, rated and withstand current and other operation parameter data of a power cabinet; in the modal decomposition and screening process, a noise suppression mechanism and a singular information retention technology are utilized, and weakening of non-Gaussian arc interference and retention of working condition abrupt change characteristics are achieved while physically independent orthogonal modal components are separated out. And finally, constructing a double-flow decoupling prediction network based on a deterministic mechanism and data-driven cooperative work. Further, static safety hyperplane constraint is introduced in a parameter updating stage, and a feasible region self-adaptive adjustment mechanism is supplemented, so that the model parameters are subjected to limited optimization on a constraint manifold defined by an electrical safety boundary; by establishing a multi-target topological potential energy function, balance between prediction precision and physical consistency is dynamically coordinated.

The invention relates to the technical field of intelligent evaluation of garment flatness, and particularly discloses an automatic evaluation method of a non-ironing suit seam flatness grade, and the method comprises the steps: obtaining image data and physical parameter data of a seam region in response to a detection trigger signal of the seam region of a non-ironing suit; and performing key scale screening based on texture contribution degree on the image data, calculating a relevance index between feature distribution and flatness grade probability distribution under different texture scales, and determining the texture scale of which the relevance index meets a preset contribution degree condition as a target key scale. According to the method, the entropy screening mechanism based on the texture contribution degree is introduced, so that the key scale which is most effective for flatness representation can be locked from the multi-source data in a self-adaptive manner, and the refinement and pertinence of feature extraction are realized.

This invention discloses a method for extracting the contours of point clouds on the surface of castings based on neural networks. The method comprises the following steps: S1: acquiring point cloud data of the casting surface; S2: preprocessing the point cloud data; S3: constructing a point cloud edge detection network based on a UNet++ neural network and training the network to obtain a trained point cloud edge detection network; the point cloud edge detection network includes an input layer, an encoder module, a feature extraction module, an attention module, a decoder module, and an output layer connected in sequence; S4: identifying the contours of various parts of the casting surface based on the trained point cloud edge detection network. This invention enhances the ability to capture detailed features of the target point cloud by constructing a point cloud edge detection network and extracting attention features of the center point features, while also exhibiting stronger robustness to sparse and noisy point clouds. Extracting the point cloud contours of parts on the casting surface using the point cloud edge detection network improves the accuracy of robots in identifying different shaped parts of the casting surface and provides better adaptability in complex industrial scenarios.

The present application relates to the field of digital image processing and information security, and discloses a document tampering detection method and system based on image data processing, comprising the following steps: first, extracting the noise residual and microscopic penetration characteristics of the document image, and constructing a physical potential energy field and a virtual viscous resistance field; then, using a Darcy law variant model for dynamic evolution, generating a virtual flow velocity vector field to simulate the sliding behavior of fluid in heterogeneous media; subsequently, constructing a heterogeneous graph based on the flow field divergence singular point and streamline trajectory, using a graph neural network to aggregate the node dynamics characteristics for deep reasoning, and finally generating a tampering positioning mask. The present application innovatively introduces fluid mechanics field theory, converts hidden static texture differences into significant dynamic flow field anomalies, solves the problem that the prior art is difficult to capture microscopic tampering traces, and significantly improves the detection accuracy and generalization ability in complex document scenarios.

This invention discloses a multi-source electromagnetic noise suppression method based on noise classification and deep learning, belonging to the field of geophysical electromagnetic exploration technology. The method includes: segmenting and preprocessing the original electromagnetic observation sequence; using an improved U-Net network with an encoder embedded in a Mamba time-series modeling module for low-frequency noise suppression; identifying strong noise types in the data segments using a ROCKET classifier; based on the classification results, calling a second improved U-Net network trained for the corresponding noise type for class-based targeted denoising; and finally, splicing the data segments to obtain complete, high-quality data. This invention, through a phased processing framework of "low-frequency pre-suppression—noise classification—class-based denoising," combined with the strong time-series modeling capabilities of the Mamba module and the efficient classification performance of ROCKET, significantly improves the suppression accuracy and signal fidelity for complex, multi-source electromagnetic noise, and is particularly suitable for processing ground and airborne electromagnetic exploration data.

The invention discloses a fault prediction type industrial flow instrument based on a deep learning algorithm and a diagnostic analysis system, and relates to the field of industrial automation and intelligent monitoring. The system comprises the following components: a data acquisition module, a data processing module, a dynamic adaptive learning module based on reinforcement learning, a fault prediction module and a diagnostic analysis module. Through the dynamic adaptive learning module based on reinforcement learning, unique state definition, action adjustment, reward function design and a reinforcement learning algorithm execution mechanism, fault prediction model parameters can be optimized in real time according to working condition changes, a hybrid neural network architecture of the fault prediction module is combined with an attention mechanism, and fault prediction accuracy is improved. The method further enhances the capability of capturing fault features, all the modules are in close cooperation and collaborative optimization, and compared with a traditional fault prediction system, in a complex and changeable industrial environment, the fault prediction precision is greatly improved, the prediction efficiency is remarkably improved, and potential faults can be found in advance.

The application discloses a face expression capturing model training method based on unsupervised face expression capturing model training method system and medium, obtains a group of preset prompt words for image generation, and randomly extracts a preset prompt word to generate a first two-dimensional face image; obtains a first face expression score vector and a face parameter based on the first two-dimensional face image; calculates three-dimensional face key points, and obtains a second two-dimensional face image according to the three-dimensional face key points; inputs the second two-dimensional face image into an emotion regression network to obtain a second face expression score vector; calculates a preset loss according to a preset loss calculation mode, and trains the face expression capturing model in a gradient back propagation mode. The application can train a model with a driving effect closer to a real face by generating the first two-dimensional face image corresponding to the preset prompt word, combining the face expression score vector, the face parameter and a large amount of unsupervised data, and simplifying the data acquisition process and reducing the cost.

This utility model relates to a magnetic separation device for recovering manganese-iron ore slag, belonging to the field of manganese-iron ore slag recovery technology. It mainly includes a conveying mechanism, a magnetic separation component, a lifting mechanism, and a weak magnetic enhancement component. The conveying mechanism is used for material transport; the magnetic separation component forms a magnetic field coverage area through a main magnet and an auxiliary magnet; the weak magnetic enhancement component is located below the conveyor belt and enhances the adsorption capacity for weakly magnetic substances through an electromagnetic coil group and a magnetic guide plate group; the lifting mechanism adjusts the height of the weak magnetic enhancement component. This application can significantly improve the capture capacity of weakly magnetic substances, reduce the loss of valuable metals, adapt to various material characteristics, and improve resource recovery efficiency, possessing high practicality and promotional value.

The invention relates to the technical field of index prediction in the social economic field, and discloses a social economic index set prediction method, which comprises the following steps of collecting multi-source data related to social economy, the multi-source data comprises medical business data of a medical institution, medical insurance data of a medical insurance department and social economic environment data of an external data source; and cleaning and standardizing the collected multi-source data to remove noise, missing values and abnormal values in the data, and unifying the data format and dimension. Data are obtained through multiple channels, the data basis of social and economic index analysis is enriched, multi-aspect conditions are presented, macroscopic information is reflected, understanding of phenomena and trends is enhanced, the accuracy and scientificity of index prediction are improved, and more valuable data support is provided for decision making. The problems of limited data sources and insufficient data mastering in the previous social economic index research are solved.

The application discloses a smart community police management system and method based on Internet of Things security, and belongs to the technical field of smart communities, and solves the problem that existing system data processing cannot utilize edge computing nodes for low-delay real-time analysis, and cannot meet timely early warning requirements, the method comprising preprocessing of multi-source heterogeneous community monitoring information, analysis and processing of the community monitoring information based on edge computing, and generation of a risk propagation path in combination with front-end analysis results and heterogeneity characteristics; in the application, analysis and processing of the community monitoring information based on edge computing avoids the delay of centralized processing, can quickly trigger early warning in the early stage of a risk event, thereby gaining golden disposal time for risk event disposal, and a risk analysis model realizes a leap from single-node anomaly detection to composite risk quantification through multi-dimensional feature fusion and dynamic space-time reasoning, and realizes multi-dimensional feature fusion and dynamic reasoning capability of the risk event.

The application discloses a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence, which comprises an original data collection module, an original data optimization module, a time sequence double-end recovery evaluation model establishment module, a recovery evaluation model performance optimization module and a whole-cycle intelligent recovery evaluation module. The application relates to the technical field of medical data processing, in particular to a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence. The scheme innovatively proposes a time sequence double-end recovery evaluation model combining an in-hospital baseline end and a home continuous time sequence end, improves the real-time performance of patient pelvic floor muscle recovery evaluation, extracts short-term time sequence recovery features by using a bidirectional long short-term memory network, extracts long-term time sequence recovery features by combining an improved convolution residual network with a parallel activation function, significantly improves the accuracy of the recovery evaluation results, and introduces an improved optimization algorithm combined with a dynamic local search strategy, thereby significantly improving the stability and accuracy of the output results of the model.

The application discloses a kind of based on multimodal data fusion pumped storage unit fault diagnosis method, the voiceprint signal of pumped storage unit, infrared thermal image and historical operation data are collected, improved unscented Kalman filtering algorithm is used to denoising processing voiceprint set, the voiceprint signal after denoising and historical operation data are trained using COMRes+Model, future voiceprint signal is predicted, improved Deeplabv3+Model is used to train image set, and the features of infrared thermal image are extracted;Voiceprint feature, historical operation data text feature and image feature are input into improved CentralNet model for multimodal data fusion, and the fault category is output through classification identification module, so as to complete the diagnosis of pumped storage unit fault;The method can effectively improve the accuracy and efficiency of fault diagnosis by the fusion of multimodal data and the optimization of deep learning model, and provide strong guarantee for the safe operation of pumped storage unit.

This invention discloses a specular removal method based on grouped enhanced convolutional attention feature fusion, belonging to the field of image processing technology. The method constructs a specular removal model based on a CycleGAN network, which consists of a generator, a discriminator, and a loss function optimization module. The generator includes detail enhancement depth downsampling, upsampling, and grouped enhanced convolutional attention fusion modules, effectively capturing image details, reducing checkerboard artifacts, and enhancing feature fusion and capture capabilities. The discriminator uses a PatchGAN structure to improve local detail capture capabilities. The optimized loss function integrates multiple losses to improve model performance. Experimental results show that compared with various traditional and deep learning methods, the method of this invention outperforms in PSNR and SSIM metrics, exhibits strong adaptability, and can provide high-quality image data for subsequent detection of elevator door components.

The invention provides a cutterhead load multi-gait prediction method and system for assisting TBM intelligent tunneling, and the method comprises the steps: carrying out the processing of real-time monitoring data through grading preprocessing, and obtaining standardized time sequence tunneling data; tunneling control parameters strongly related to cutterhead loads are screened based on grey relational analysis to serve as key tunneling features, time synchronization and vector splicing are conducted on the key tunneling features and geological indexes, and multi-source information state vectors are constructed; establishing a multivariable fractional order autoregression fractal integral moving average model; the method comprises the following steps: constructing a BiGRU-Seqseq-TAM network; inputting the multi-source information state vector into a BiGRU-Seqseq-TAM network, extracting time sequence features, and mapping and outputting mechanism parameters to be identified; and substituting into the model to reckon the predicted value of the cutterhead load in a plurality of time steps in the future. The invention also discloses a corresponding prediction system. According to the method, the capturing capability of the model on the dynamic evolution rule of the multi-gait load of the cutterhead is enhanced, and the transparency and interpretability of the model are remarkably improved.

The invention discloses multi-adsorption-site nitrogen-doped porous carbon, a preparation method and application in iodine adsorption, and the preparation method comprises the following steps: carrying out primary calcination on a zeolite imidazate framework material, mixing with potassium hydroxide, and then carrying out secondary calcination to obtain the multi-adsorption-site nitrogen-doped porous carbon. According to the invention, a zeolite imidazate framework material is used as a precursor, potassium hydroxide is introduced for high-temperature calcination activation treatment, the obtained product inherits a rhombic dodecahedron framework of ZIF-8, a continuous through pore system and a nitrogen-doped carbon material with high pyrrole nitrogen content are formed, and the static adsorption capacity of the nitrogen-doped carbon material to iodine can reach 7.99 g / g.

The invention relates to the technical field of ultra-precision machining, in particular to an ultra-precision cutting machining surface roughness online measuring method and system. First, a machining process signal is read. Secondly, calculating the weight of the processing process signal; the processing process signals are weighted according to the weights, and the weighted processing process signals are fused to serve as first input data; the machining process signal is used as additional input data. Then, obtaining a first input result and an additional input result according to the first input data and the additional input data, and obtaining a first fusion result according to the first input result and the additional input result; thirdly, inputting a high-dimensional feature fusion branch according to the first fusion result and additionally extracted first high-dimensional feature information and additional high-dimensional feature information to obtain a second fusion result; and finally, obtaining a surface roughness measurement result. According to the method, surface roughness prediction of ultra-precision cutting machining is more accurate.

The invention provides a cross-domain transfer learning laser ultrasonic layering defect detection method suitable for a composite material and application thereof.The method comprises the steps that firstly, multi-channel input containing a frequency domain, a time-frequency domain and Koopman operator characteristics is constructed for the problem of data distribution deviation caused by the batch difference of the composite material; secondly, extracting and fusing high-dimensional spatial-temporal features through a multi-channel attention mechanism and a graph convolutional network; and finally, migrating the defect features of the source domain to the target domain in combination with dynamic distribution self-adaption and domain adversarial training strategies. The method can effectively overcome model performance reduction caused by material attribute change, realizes high-precision layering defect identification and imaging under the condition of no target domain label, and is suitable for composite material detection in a complex industrial scene.

The present application relates to the field of computer vision, and specifically discloses a DHA algal oil soft capsule filling amount insufficient detection method based on a deep learning neural network, comprising: accurately separating each capsule instance from a production line transmission image through a lightweight instance segmentation model; synchronously analyzing the global contour and local density features of a single capsule region using a multi-scale feature extraction network, which innovatively introduces an auxiliary density estimation module to enhance the perception of the content filling state; and fusing the above features to complete the accurate classification of the filling state. The present application effectively solves the practical problems of feature confusion, insensitivity to minor changes, poor dense detection effect, and the difficulty in balancing model efficiency and accuracy, and realizes high-precision and high-efficiency automatic detection of DHA algal oil soft capsule filling amount insufficient.

The invention discloses a refined behavior recognition method and system for a virtual teaching scene, and solves the technical problem that the precision of a final behavior recognition result is poor due to an existing refined behavior recognition method for the virtual teaching scene. The method comprises the steps that a virtual teaching scene behavior image is acquired, the virtual teaching scene behavior image is input into a behavior measurement model based on multilayer perception self-adaption, and the behavior measurement model based on multilayer perception self-adaption comprises a feature extraction network, a feature enhancement network and a posture decoding network; performing multi-scale feature extraction on the virtual teaching scene behavior image through a feature extraction network, and outputting multi-scale fusion features; performing feature enhancement on the multi-scale fusion features by adopting a feature enhancement network, and outputting target enhancement features; and inputting the target enhanced feature into a posture decoding network for posture decoding, and generating a behavior recognition result.

The invention relates to the technical field of equipment monitoring and control, and discloses a power take-off wheel real-time energy consumption monitoring method and system and a storage medium. The method comprises the following steps: synchronously acquiring a torque value and a rotating speed value to construct a sampling data record; torque fluctuation gradient setting sampling frequency is calculated, and whether the sampling frequency is switched or not is judged based on the fluctuation gradient change rate and the hysteresis band; calculating instantaneous power and accumulated energy consumption, establishing a power baseline, and generating an energy consumption abnormal event when the deviation degree exceeds a preset number of times; and according to the deviation degree and the duration, determining an alarm level and executing corresponding alarm. According to the invention, the real-time performance of energy consumption monitoring and the utilization efficiency of edge computing resources are improved.

This invention discloses an AI-based physical education exam scoring method, belonging to the field of exam scoring methods, including the following steps: (1) activating the cameras deployed in the exam room to collect motion images and facial expression images of the examinee during pull-ups; (2) analyzing the collected facial expression images using AI image recognition technology to divide the exam process into three stages; (3) for each stage, analyzing the examinee's motion images using AI technology to determine whether each pull-up motion is a qualified motion; (4) counting the number of qualified motions in each stage and scoring them separately, adding the scores of the three stages to obtain the examinee's final exam score. This invention can divide the exam into three stages based on facial expression recognition, perform differentiated analysis on the motion characteristics of each stage, better fit the examinee's physical fitness change patterns, and make the evaluation results more targeted.

This invention relates to the field of data analysis technology, specifically to an intelligent analysis method and system for pile foundation vibration monitoring data. The method includes: acquiring a current vibration subsequence and a sample vibration subsequence; acquiring the energy intensity characteristic value and main frequency characteristic value of the vibration subsequence; obtaining a reference subsequence set corresponding to the current vibration subsequence based on the difference in influence data between the current vibration subsequence and the sample vibration subsequence; obtaining the relative amplitude anomaly index value and the relative frequency anomaly index value of the current vibration subsequence based on the characteristic value difference, time distance, and difference in influence data between the current vibration subsequence and the reference subsequences in the reference subsequence set; and performing pile foundation vibration anomaly early warning based on the relative amplitude anomaly index value and the relative frequency anomaly index value of the current vibration subsequence. Furthermore, this invention can improve the accuracy and reliability of pile foundation vibration anomaly early warning.

The invention discloses regenerated activated carbon for VOCs treatment and a preparation method of the regenerated activated carbon, and belongs to the technical field of activated carbon regeneration. The method comprises the following steps: (1) pretreatment: sequentially performing solvent desorption, activating treatment and plasma treatment on activated carbon saturated by adsorption to obtain a regenerated activated carbon matrix; and (2) MOF derived rare earth doping surface modification: immersing the regenerated activated carbon matrix into an alcoholic solution containing zinc salt and rare earth salt, adding a 2-methylimidazole ligand solution, enabling the ZIF-8 type metal organic framework to grow on the surface of the activated carbon in situ, and then performing carbonization treatment at 600-800 DEG C in an inert atmosphere. According to the invention, a defect site formed by plasma pretreatment is used as a chemical anchor point for MOF growth, so that a firm chemical bonding structure is formed by a modified layer and a carbon skeleton; the rare earth oxide nanoparticles formed in situ provide additional adsorption active sites, so that broad-spectrum and efficient adsorption of various types of VOCs is realized.

The invention relates to a river flow prediction method, which comprises the following steps of: 1, dividing a daily runoff data set, and performing multi-stage decomposition on a training set by using Haar wavelets to obtain de-noising parameters; 2, multiplexing the verification set and the test set to obtain data; step 3, carrying out first-order differential transformation, and zooming to an interval of [0, 1] by using MinMaxScaler to obtain a normalized value of a differential sequence; 4, mapping the data to a high-dimensional space through a linear embedding layer, and then carrying out position coding to obtain data; step 5, inputting the data obtained in the step 4 into a DTransformer encoder, so that a catastrophe point obtains a higher attention weight so as to strengthen difference characteristics of adjacent time steps and obtain encoder output; 6, inputting the data obtained in the step 5 into an LSTM decoder, and performing feature recombination to obtain encoder output; 7, mapping the data obtained in the step 6 to 7-dimensional output through a full connection layer; and 8, performing inverse normalization and inverse difference transformation on the data obtained in the step 7 to obtain final output.

This invention discloses a method, system, and storage medium for multivariate time-series feature extraction and grade prediction in flotation processes. The method includes the following steps: Step S1: Raw data input and encoding; encoding and structured input of time-series data composed of various process variables; Step S2: Extracting dynamic features; Step S3: Condition-guided encoding modeling; Step S4: Enhancing the saliency of key variables and important time segments; employing a multi-head attention mechanism to match key-value pairs generated from target-guided query vectors and multi-scale features; Step S5: Outputting a prediction module; performing feature fusion and nonlinear mapping on the attention mechanism output to output the predicted concentrate grade and recovery rate for future times. The system and storage medium are both based on the above method. This invention has advantages such as higher intelligence, better controllability, and improved prediction accuracy and model adaptability for key indicators in the flotation process.

The invention relates to the technical field of electric power system operation and maintenance, and particularly discloses an intelligent agent construction method and system for electric power inspection, which can automatically trigger an inspection task, schedule edge equipment to execute inspection and identify equipment defects based on a visual large model by collecting multi-source operation data of electric power equipment in real time and performing intelligent analysis. And generating a structured abnormal point location and a disposal suggestion, and finally forming a complete inspection report and pushing the inspection report to the operation and maintenance management platform. By automatically judging the risk and generating the targeted inspection task, manual intervention is not needed, and compared with a traditional mode depending on a fixed period or threshold alarm, task response is more timely, coverage is more accurate, the capacity of capturing sudden hidden dangers is effectively improved, intelligentization and automation of the whole inspection process are achieved, manual intervention is reduced, and the inspection efficiency is improved. The inspection efficiency and accuracy are improved; equipment abnormity and potential risks can be identified in time, and rapid disposal is supported.

The application discloses a multi-complexity behavior recognition system and method based on adaptive feature extraction, and relates to the technical field of artificial intelligence, comprising a human body behavior data acquisition module, a human body behavior data transmission module, a human body behavior data storage module, a human body behavior data preprocessing module, an MFAEF simple human body behavior recognition network module, an LSGRA complex behavior recognition network module and a human body behavior information application module; the MFAEF simple behavior recognition network module comprises a feature pre-extraction unit, a parallel multi-dimensional space-time feature extraction and reuse unit and an adaptive multi-dimensional space-time feature extraction unit, a feature fusion unit and a simple behavior discrimination output unit which are sequentially connected; and the LSGRA complex behavior recognition network module comprises a single-window simple behavior feature acquisition unit, a cyclic multi-window attention unit and a complex behavior discrimination output unit which are sequentially connected. The application adopts the above structure to make up for the deficiencies of high cost, easy interference and poor privacy of the behavior recognition based on vision.

The application relates to the technical field of medical safety, in particular to a patient violence behavior detection method and system based on an intelligent bracelet, which comprises a data acquisition module, a behavior analysis module, an abnormality judgment module and a prewarning execution module. The physiological and motion signals of a patient are collected in real time through multidimensional sensors, a behavior characteristic matrix is generated, a matching degree index is calculated, a violence tendency is analyzed and judged in combination with a threshold interval and time continuity, and graded prewarning and multi-channel notification are realized. The application can significantly improve the recognition accuracy of complex behavior patterns, reduce misjudgment and false positives, ensure that medical staff can respond in time, improve the safety management efficiency, simplify the medical management process, and provide a reliable solution for the whole-process automatic detection and prewarning of patient violence behavior.

The application discloses a coal mine user power supply risk analysis method based on digital twinning, comprising the following steps: collecting coal mine power supply node current, voltage and switch state data, obtaining digital twin simulation value, calculating deviation sequence along time sequence; reconstructing the deviation curve, calculating the segmented derivative to generate the deviation evolution trajectory and the change rate sequence; extracting the switch state event to form the event sequence, generating the equidistant time grid data, normalizing and aligning the deviation trajectory; constructing the node connection relationship matrix to map the risk intensity vector, expanding the risk propagation chain along the topological path, accumulating calculation, applying the line impedance and load disturbance attenuation; weighting and summarizing the risk value at the end of the topological link to generate the whole network risk curve; arranging the node risk index sequence to form the structured risk table, and outputting the whole network risk curve and the node risk index. The application realizes dynamic quantification of the whole network power supply risk along the node deviation evolution, and improves the risk analysis precision and the visual decision value.