3705results about How to "Increased sensitivity" patented technology

The invention discloses a coal mine dust diffusion simulation and control method based on artificial intelligence, and relates to the technical field of coal mine dust diffusion simulation and control, and the method comprises the following steps: collecting environmental data such as dust concentration, ventilation flow velocity, temperature, humidity and equipment operation state through a monitoring system in a coal mine; and cleaning and formatting processing are carried out in combination with historical working condition data. According to the method, reinforcement learning and virtual extreme scene simulation are utilized, the recognition and emergency response capability of the system to edge working conditions is improved, dust diffusion and explosion risks are effectively reduced, and mine safety is guaranteed. By dynamically adjusting the ventilation and water spraying system, efficient utilization of resources is achieved, energy consumption and operation cost are reduced, and the service life of equipment is prolonged. The system has an adaptive learning capability, can optimize a control strategy according to monitoring data and historical events, can automatically adjust even if the environment changes, improves robustness and flexibility, and reduces manual intervention.

The invention relates to the technical field of transformer harmonic monitoring, in particular to a harmonic detection and early warning system for an intelligent transformer, which obtains voltage and current signals of the transformer in real time, performs frequency domain decomposition by using Fourier transform, extracts multi-dimensional harmonic characteristics such as amplitude, phase, frequency and the like, and performs harmonic detection and early warning on the intelligent transformer. And inputting into a deep learning model to train and generate a dynamic harmonic characteristic model. On the basis, a harmonic wave anomaly classification module analyzes harmonic wave signals in real time, carries out type judgment and position association on abnormal harmonic waves, and generates a report containing anomaly types and positions. Through a dynamic early warning module, a power grid load coefficient is calculated based on power grid load state parameters collected in real time, and a harmonic detection threshold is dynamically adjusted in combination with an initially set amplitude and frequency threshold. The application of the dynamic threshold enhances the adaptability of the system to the load state change, and significantly improves the sensitivity and accuracy of harmonic detection and early warning.

The invention discloses an SCL field safety accident analysis and early warning method and system, a medium and a program product, and the method comprises the steps: carrying out the distributed cleaning and feature extraction of multi-source heterogeneous monitoring data, and obtaining a multi-dimensional time sequence feature flow; constructing a dynamic association network of a security state; identifying a key influence factor chain, and establishing a semantic analysis model; constructing a safety threshold system; evaluating the multi-dimensional time sequence characteristic flow by using a safety threshold system; when the multi-dimensional time sequence characteristic flow triggers early warning, performing tracing analysis by using a semantic analysis model to obtain a root event triggering the early warning, and simulating the development trend of the root event based on a preset evolution model; determining a predicted risk range and a risk type according to the tracing cause analysis result and the accident development trend; and the corresponding accident early warning scheme is matched in the preset database according to the risk range and the risk type, and the accident early warning scheme is sent to the monitoring terminal, so that the accuracy of judgment and classified early warning of different types of safety accidents is improved.

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

The invention relates to an optimization method for dynamic monitoring of an underground water level. Comprising the following steps: uniformly arranging a plurality of sensors in an underground water monitoring area, and monitoring multi-dimensional parameters including water pressure, temperature and humidity; the sensors form a gridding monitoring system through a wireless network; inputting multi-dimensional data acquired by the sensor array into a data fusion module, performing cooperative processing on the data by using a data fusion algorithm, and filtering noise and abnormal values; a unified water level change model is constructed, so that data of different parameters complement each other; dynamically adjusting the weight in the fusion algorithm regularly according to the historical data and the new measurement value; in the monitoring area, based on the dimensions of time and space, a space-time analysis model of underground water level changes is constructed, and the periodicity and regularity of water level fluctuation are excavated; and performing trend analysis on the historical water level data by adopting a time sequence analysis algorithm including ARIMA or LSTM to generate short-term and long-term water level prediction.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention discloses a segmentation method for rail defect detection based on improved YOLOv10 and SETR, and the method comprises the steps: S1, collecting image data, carrying out the enhancement processing of the image data, carrying out the segmentation processing, and making a data set; s2, an improved YOLOV10 algorithm model is built and trained; s3, using the improved YOLOV10 model weight to detect image data to be detected, judging whether defects exist or not, if the defects are detected, storing detection frame information of the defects, and outputting a target detection frame and confidence; and S4, transmitting the detection frame information as prompt information into the SETR model, segmenting the target by adopting the SETR model to obtain a segmentation result, and outputting a mask pattern. The improved YOLOv10 algorithm and the SETR algorithm are adopted to detect the rail defects, the calculation complexity and the efficient detection speed are kept, the detection precision is improved, and the robustness of the model is improved.

The invention relates to the technical field of industrial defect detection, in particular to a wire harness defect detection method and system based on visual identification. The method comprises the following steps: acquiring wire harness image data, and performing image segmentation according to the wire harness image data to obtain wire harness image segmentation data; performing morphological feature extraction according to the wiring harness image segmentation data to obtain wiring harness morphological feature data, and performing three-dimensional reconstruction according to the wiring harness morphological feature data to obtain wiring harness three-dimensional reconstruction data; performing microstructure defect detection according to the wiring harness morphological characteristic data to obtain first wiring harness defect detection and first wiring harness defect detection data, and performing microstructure defect detection on the wiring harness three-dimensional reconstruction data to obtain second wiring harness defect detection data; and marking the wire harness image data according to the first wire harness defect detection data and the second wire harness defect detection data to obtain wire harness image defect marking data. According to the method, the potential defect can be identified in the fine form change of the wire harness.

The invention relates to the technical field of power systems, in particular to a power transmission and transformation network power distribution station room monitoring device based on the Internet of Things, which comprises a data acquisition module, a data preprocessing module, a heterogeneous data fusion power grid health assessment module, a fault prediction and alarm module and an intelligent maintenance module, the data acquisition module acquires real-time operation data and environmental data of power grid equipment; the data preprocessing module is used for preprocessing the acquired operation data and environment data; the heterogeneous data fusion power grid health assessment module assesses the health condition of the power grid in real time; the fault prediction and alarm module identifies potential faults of power grid equipment in advance and generates fault early warning signals. And the intelligent maintenance module automatically generates an equipment maintenance plan based on the generated fault early warning signal. According to the invention, quick response can be realized under different operation conditions, false alarms are reduced, and stable operation of a power grid is effectively guaranteed.

The invention relates to the technical field of intelligent scheduling and prediction of a power system, and discloses a power grid load prediction system and method based on machine learning, and the system comprises a multi-modal data processing and fusion module which is used for collecting multi-modal data from power grid load data, meteorological data, economic data and social events, cleaning and feature extraction processing are carried out on the multi-modal data, weighted fusion is carried out on different data features through a self-attention mechanism, and standardized feature input is provided for the hybrid prediction model module. Through a multi-modal data processing and fusion module, deep fusion is carried out on multi-modal data of power grid load data, meteorological data, economic data and social events, and a self-attention mechanism is introduced to carry out weighting processing on different data features, so that dynamic weighting is realized, key features are screened, and the accuracy of data processing is improved. The accuracy of load prediction and the comprehensive utilization capability of multi-source information are effectively improved, so that the sensitivity of the prediction model to complex environmental factors is remarkably enhanced.

The invention discloses a human body behavior recognition method and system based on a space-time fusion network, and relates to the technical field of behavior recognition, and the method comprises the steps: obtaining a human body skeleton sequence, carrying out the joint position coding of the sequence, taking the coded sequence as an input feature, inputting the input feature into the space-time fusion network based on graph convolution and Transform, and outputting a human body behavior recognition result; wherein the space-time fusion network comprises a plurality of groups of graph convolution Transform space fusion modules and multi-scale time fusion modules which are connected in sequence, after input features are input, local structure features and global space features are extracted through the parallel adaptive graph convolution module and Transform module respectively, feature fusion is carried out through the correlation fusion module, and the local structure features and the global space features are fused through the correlation fusion module. And the fused features are input into a multi-scale time fusion module, and are subjected to feature extraction through a multi-scale convolution branch and a Transform branch and then spliced, so that a more accurate human behavior recognition result is output.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

The invention discloses an emergency fire-fighting hidden danger troubleshooting method based on a multi-modal AI large model identification technology, and relates to the field of artificial intelligence. The multi-modal hidden danger knowledge base is constructed by collecting multi-modal data such as images, texts and related laws and regulations actually recorded in the hidden danger checking process, the recognition and analysis ability of hidden danger information is improved based on the deep learning and multi-modal large model technology, the multi-hop reasoning and thinking chain technology is utilized, and the hidden danger checking efficiency is improved. A complex hidden danger positioning problem can be disassembled into a series of interpretable reasoning steps, and a user is helped to understand a hidden danger judgment basis. According to the method, the hidden danger in the image is analyzed from multiple angles and levels, different hypotheses or conditions can be verified step by step, so that the judgment accuracy is improved, the information of each dimension is comprehensively synthesized for comprehensive evaluation, and the comprehensiveness and accuracy of hidden danger investigation are ensured.

The invention relates to the technical field of wire harness intelligent evaluation, in particular to a wire harness connection performance intelligent test method and system. The method comprises the following steps: collecting wire harness test data generated by a wire harness test device and a wire harness sensor in real time through an edge computing node; performing electrical performance detection extraction and mechanical performance detection extraction according to the wire harness test data to respectively obtain wire harness electrical performance detection data and wire harness mechanical performance detection data; performing electrical performance deep evaluation according to the wire harness electrical performance detection data to obtain wire harness electrical performance evaluation data, and performing mechanical performance deep evaluation on the wire harness mechanical performance detection data to obtain wire harness mechanical performance evaluation data; and performing environment pressure cross reference according to the wire harness electrical performance evaluation data and the wire harness mechanical performance evaluation data to obtain wire harness connection performance test evaluation data. According to the method, the accuracy of wire harness performance evaluation is improved, and reliable performance data can be obtained in different application fields.

The invention relates to an intelligent water temperature control system and method based on machine learning. The method comprises the steps that water temperature, flow, pressure and environmental parameter data of a constant water temperature control system are collected, and a standardized time sequence data set is generated; constructing a time-varying dynamic model based on the standardized time sequence data set, and performing parameter identification by using a variable forgetting factor generalized subspace tracker to obtain a time-varying pseudo-modal temperature parameter; training a multi-branch long short-term memory network according to the time-varying pseudo-modal temperature parameters and the standardized time sequence data set to obtain an initial water temperature prediction model; performing system operation anomaly detection and prediction based on the initial water temperature prediction model to obtain an anomaly detection and prediction result; solving a control strategy based on the anomaly detection and prediction result to obtain an optimal water temperature control strategy; and executing water temperature control based on the optimal water temperature control strategy, and updating the initial water temperature prediction model to obtain a target water temperature prediction model. According to the intelligent water temperature control system, accurate control over intelligent water temperature is achieved.

The invention discloses a small watershed hydrological runoff and pollution load short-term prediction refinement method, which comprises the following steps of: constructing a modeling data set, constructing a small watershed foundation base model, segmenting hydrological response units according to land utilization types, crop planting structures and reservoir pond / wetland distribution data in a refinement manner respectively, and calculating the hydrological runoff and pollution load short-term prediction of the small watershed foundation base model. A small watershed foundation base model corresponding to the optimal segmentation scheme is screened out, and a small watershed depth base model is generated; constructing a reservoir pond / wetland simulation module; coupling the small-watershed depth base model and the reservoir pond / wetland simulation module to form a small-watershed hydrology and water quality early warning model, and optimizing the small-watershed hydrology and water quality early warning model; rolling to generate a hot start file for short-term prediction; constructing a future short-term weather enhancement forecasting model; the small watershed hydrology and water quality early warning model is driven to operate, the future water volume and load output are calculated in a rolling mode, and hydrology and water quality results are output; and calibrating and updating the small watershed hydrology and water quality early warning model. The precision and timeliness of hydrology and water quality prediction of the small watershed are remarkably improved.

The invention relates to the technical field of image classification, in particular to a fetal heart monitoring image classification method and system based on a neural network. The method comprises the following steps: acquiring a fetal heart monitoring image, generating a standard monitoring image through Gaussian noise elimination and spatial domain normalization, then performing multi-channel convolution on the standard image to extract image convolution features, obtaining standard fetal heart features through compression regular optimization, performing morphological reconstruction to generate a fetal heart dynamic model, and finally obtaining the fetal heart dynamic model. Baseline features are identified based on historical data, then periodic variation quantification and safe floating range derivation are performed on fusion features, and finally a multi-scale discrimination framework is constructed to realize intelligent classification of fetal heart monitoring images. The fetal heart monitoring image classification method is more stable and more accurate.

The invention discloses a federal learning data processing method based on privacy protection, and the method comprises the following steps: S1, a client uses local data to train a generator of a generative adversarial network, and generates virtual data similar to the local data in distribution; s2, setting three privacy protection levels according to the privacy sensitivity of the local data; s3, performing model updating operation by using a homomorphic encryption technology; s4, utilizing a dynamic weight optimization mechanism based on a multi-arm bandit algorithm to obtain a priority processing data set; s5, preliminarily updating the global model, analyzing generated data through a reverse inference detection mechanism, and judging whether a privacy disclosure risk exists or not; s6, the server feeds back the updated global model to the client, and the client continues personalized model training in combination with local data; and S7, generating new virtual data, and carrying out the next round of global model training. According to the method, the generative adversarial network, differential privacy and homomorphic encryption are combined, so that efficient federated learning under privacy protection is realized.

The invention relates to the technical field of financial services, in particular to a testing method and system based on financial environment stability. The method comprises the following steps: carrying out real-time state monitoring on a financial transaction system, and generating system state basic data; constructing a virtual test environment according to the system state basic data, and performing test scene adaptive configuration to generate test environment parameter data; performing multi-dimensional pressure test analysis on the test environment parameter data, calculating a system stability index, and generating stability reference data; performing dynamic load gradient processing according to the stability reference data to obtain system pressure gradient data; and performing risk level mapping on the system pressure gradient data to generate risk assessment mapping data. According to the invention, the virtual test environment is constructed and self-adaptive configuration is carried out, so that a real transaction scene can be simulated, the correlation and effectiveness of the test are improved, and the stable operation of the system under different pressure conditions is ensured.

The invention relates to a polyp image segmentation method based on pyramid vision Transform, and belongs to the technical field of digital medical treatment. The method comprises the following steps: capturing global context information through a pyramid vision Transform module, and obtaining feature maps with different resolutions from a plurality of extraction stages; detail information is extracted from the shallow feature map of the first stage through a cyclic cross attention module, and shallow features are obtained; a multi-scale feature fusion sensing module is used for carrying out fusion processing on the feature maps extracted in multiple stages, deep features are extracted, and the deep features and shallow features are fused and then used for image segmentation; and designing weighted binary cross entropy loss and weighted IoU loss to train the above process, and segmenting the polyp image after training is completed. According to the invention, through the pyramid vision Transform module, the cyclic cross attention module and the multi-scale feature fusion perception module, the global information learning ability, the detail information capturing ability and the segmentation performance are significantly improved.

The invention discloses a personalized hypertensive nephropathy risk assessment method and system based on a graph convolutional network, and the method comprises the steps: collecting modal data related to hypertensive nephropathy, and carrying out the preprocessing; constructing a patient similarity graph; performing multi-source information fusion and domain knowledge integration; sparse processing is carried out on the graph structure, and standardization processing is carried out on an adjacent matrix; modeling through a time sequence diagram convolutional network and a long-short term memory network, and capturing the evolution trend of patient characteristics along with time; through a self-attention mechanism and a graph regularization item, the transmission of important features is enhanced; a multi-head attention mechanism is adopted, and different types of relation modes are captured; optimizing model parameters by using an Adam optimization algorithm and a regularization method; and a risk assessment result is optimized by adopting weighted stacking and a self-adaptive attention mechanism. According to the risk assessment method, accurate assessment of the risk of hypertensive nephropathy is realized by constructing the multi-dimensional and dynamically changing patient similarity graph.

The invention discloses a postoperative physical sign monitoring system based on medical information processing. The postoperative physical sign monitoring system comprises a data acquisition module, a physical sign feature extraction module, a postoperative physical sign evaluation model establishment module and a postoperative physical sign monitoring module. The invention belongs to the field of medical information processing, and particularly relates to a postoperative physical sign monitoring system based on medical information processing. Quantifying the total change trend of the heart rate fluctuation in the monitoring time; the quantitative basis is provided for the postoperative recovery process by integrating the blood pressure and heart rate changes; the sign monitoring effect is improved; focusing on dynamic adjustment through a time weight function, a recovery stage weight and an environment correction factor in a postoperative key time period; deviation weights are introduced for fluctuations of heart rates and blood pressures, loss coefficients are dynamically adjusted, segmented loss functions are designed according to physical sign values, conditions of different risk intervals are processed respectively, and the method adapts to uncertainty of individual differences and physical sign fluctuations of postoperative patients; and thus, the accuracy of post-operation sign monitoring is improved.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention relates to the technical field of computer storage, in particular to an encryption method and related equipment. The encryption method comprises the following steps: generating a unique device identifier through a polymorphic ring structure, wherein the unique device identifier is used for generating a unique identifier and a key of the solid-state storage device; based on the generated device identifier, a multi-dimensional dynamic key is generated in combination with a multi-dimensional mixed function and is used for data encryption; and encrypting the segmented data by using a multi-dimensional dynamic key, and embedding a progressive vector check code into each data segment to enhance the security of the data segment level. According to the method, the defects of the existing solid-state storage equipment encryption technology in the aspects of key management, encryption mechanism, key irreversibility, real-time monitoring and the like are overcome, and all-around guarantee is provided for data storage security.

The invention relates to a bioelectrochemical sensor used for detecting hydrogen peroxide and a manufacturing method of the bioelectrochemical sensor. The novel bioelectrochemical sensor is a sensor with a three-electrode system, wherein the counter electrode is a platinum electrode, the reference electrode is saturated calomel electrode and the working electrode is gold electrode. The sensor is based on orientation combination of a polypeptide horse radish peroxidase with characteristic sequence, the promotion effect of the polypeptide horse radish peroxidase on the activity of the enzyme and the characteristic of high sensitivity of electrochemical detection methods. The sensitivity of hydrogen peroxide sensors is effectively improved and satisfactory results are obtained.

The invention discloses a deep learning-based deep forged compressed face image identification method, which belongs to the field of artificial intelligence image identification, and comprises the following steps of: 1, carrying out frequency domain modal extraction on a deep forged compressed face image by adopting dynamic adaptive discrete cosine transform; 2, a multi-head self-attention mechanism and a multi-dimensional entropy state load mutual feedback mechanism are adopted to extract frequency domain high-dimensional load features; step 3, extracting spatial domain features based on the pre-trained deep residual network; 4, performing feature fusion on the frequency domain high-dimensional load features and the spatial domain features based on an attention mechanism; and 5, discriminating and classifying the fusion features by using a classifier comprising a full connection layer. According to the method, high-accuracy identification can be carried out on the compressed face image manufactured by adopting a deep counterfeiting means.

The invention discloses a building curtain wall BIM forward design method, and relates to the technical field of building curtain wall design, and the method specifically comprises the following steps: obtaining environment dynamic information of each region in real time, and respectively generating an air flow equilibrium coefficient and a thermal effect index of each region; and constructing a heat effect risk model for the generated air flow equilibrium coefficient and heat effect index of each region, generating a heat effect risk coefficient of each region, and predicting a risk level of a heat effect caused by unbalanced air flow in each region of the air layer in the double-layer curtain wall. Dividing each area of the air layer in the double-layer curtain wall into a high-risk area, a medium-risk area and a low-risk area according to the prediction result; and taking corresponding air flow optimization and heat effect control measures according to the division result of each area of the air layer in the double-layer curtain wall. The heat effect problem caused by unbalanced air flow of the double-layer curtain wall is solved, and the effects of optimizing the heat preservation performance and reducing energy consumption are achieved.

The invention discloses a visual detection system for an automobile door panel assembly, and relates to the technical field of automatic detection, and the visual detection system comprises the steps: installing a high-precision industrial camera, and carrying out the synchronous collection of the multi-view image data of an automobile door panel; preprocessing the acquired multi-view image data of the automobile door panel; carrying out detail enhancement on the preprocessed multi-view image data of the automobile door panel by adopting a super-resolution algorithm; integrating the enhanced multi-view image data of the automobile door panel into an automobile door panel panorama through binocular stereoscopic vision matching, and constructing an automobile door panel three-dimensional model; the automobile door panel three-dimensional model is compared with a standard automobile door panel three-dimensional model, and the buckle defect in the automobile door panel is recognized; and carrying out position marking and defect type analysis on the identified defect points, and generating an automobile door panel detection report according to the defect type analysis. According to the invention, details are enhanced by using a super-resolution algorithm, defects are accurately positioned by using a three-dimensional model, and the detection efficiency and accuracy are improved.

The invention relates to the technical field of neural networks, and further relates to a power grid topology fault positioning system based on a graph neural network. The system comprises a power grid topology feature extraction unit used for regarding a power grid as a graph network and carrying out real-time data acquisition on each node of the power grid, and each element in a feature matrix represents the electrical characteristics of a certain node of the power grid at a certain time; the multi-layer graph neural network model unit is used for taking the feature matrix as the input of a first layer as a feature extraction result; the fault identification and positioning unit is used for calculating fault probability distribution based on the feature extraction result; and calculating the credibility of the obtained fault position. According to the method, the modeling capability of the system on the complex power grid topology is improved, the real-time performance and robustness of fault detection are enhanced, and accurate positioning of the fault area and position is realized.

The invention relates to a depth forgery detection method, device and equipment based on cross-modal mask modeling. The method comprises the following steps: constructing a deep forgery detection model comprising a multi-mode mask modeling module, a trans-attention fusion module and a contrast learning module; performing training optimization on the deep forgery detection model by integrating the audio and video reconstruction loss and the audio and video comparison learning loss; and inputting a to-be-detected audio and video sequence into the trained deep forgery detection model, outputting to-be-detected audio and video trans-attention coding features according to the model, carrying out audio and video matching correlation detection by inputting the to-be-detected audio and video trans-attention coding features into the classifier, and outputting a forgery detection result of the audio and video sequence. By adopting the method, the characterization of a real video can be learned by utilizing a mask modeling technology, and the characteristics of an audio-visual mode are subjected to cross fusion and comparative learning, so that the forgery detection performance is improved.