1595 results about "Missing data" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention discloses a cable insulation life prediction method and system based on LSTM accelerated aging mapping, and relates to the technical field of submarine cable insulation life prediction. The existing method has the defects of insufficient multi-stress nonlinear modeling, laboratory and field data separation, difficulty in small sample modeling and the like, and the insulation life of the submarine cable is difficult to accurately predict. The method comprises the following steps: data acquisition: acquiring parameters of insulation electrical performance, physical and chemical performance and mechanical performance under an accelerated aging condition; carrying out data preprocessing: carrying out homodromous processing on the inverse indexes, improving box plot denoising, filling missing data with a space-time K nearest neighbor algorithm, and carrying out normalization; constructing an LSTM model: embedding a dielectric constant differential equation as a physical constraint, and introducing an index weight; and model training and evaluation: adopting five-fold cross validation, and quantizing prediction precision through mean square errors and decision coefficients. According to the technical scheme, the prediction precision is improved, the fault risk caused by insulation aging is reduced, the maintenance cost is reduced, and the reliability of the ocean energy transmission system is improved.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to a navigation equipment health management method based on an SAITS algorithm and a digital twin platform. The method comprises the following steps: acquiring navigation equipment operation data; adopting an improved SAITS algorithm to carry out interpolation on missing data, and predicting to obtain future navigation equipment operation data; identifying current and future abnormal states of the navigation equipment by adopting a self-adaptive anomaly detection algorithm; constructing an equipment fault mode knowledge graph according to the identified equipment exception; simulating the operation of the navigation system by using a digital twin platform, verifying the confidence coefficient of equipment abnormity, and generating a health assessment report; if the confidence coefficient is not smaller than a confidence coefficient threshold value, generating an exception coping strategy in combination with an equipment fault mode knowledge graph; incremental learning or retraining is carried out on an adaptive anomaly detection algorithm to improve the detection accuracy. The method has remarkable advantages in the aspects of improving the navigation equipment management efficiency, reducing the fault risk, optimizing the maintenance cost and the like, and is particularly suitable for water transportation scenes with strict requirements on reliability and real-time performance.

The invention relates to the technical field of fault prediction, and discloses a fault prediction method for a refrigerating unit, and the method comprises the steps: employing a trend weighted interpolation method and local trend correction based on a sine function to fill missing data; a multi-scale disturbance scoring index is constructed, deviations of variables in different time windows are quantified, asymmetric risk guidance prediction distribution is constructed based on disturbance scores, and directional distinguishing and risk self-adaptive adjustment of abnormal fluctuation are achieved; a dynamic structure is introduced to drive a state updating unit, and time dependence and risk evolution are captured; a joint optimization objective function is designed, and the prediction fitting degree and the disturbance sensitivity are balanced; a structural disturbance perception output fusion module is constructed, asymmetric disturbance characteristics are extracted through symmetric disturbance variable mapping, redundant dependence is suppressed in combination with a non-mutual information suppression attention mechanism, and periodic errors are dynamically corrected by adopting a frequency offset filtering fusion device; and finally, through fusion of the loss function, precise prediction and robust early warning of the fault of the refrigerating unit are realized.

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

The invention relates to the technical field of mine industry management, in particular to an industrial management system with an industrial configuration picture presentation function, which comprises a heterogeneous data acquisition module which is composed of a plurality of sensors and realizes data fusion through a multi-protocol converter; the three-dimensional dynamic configuration engine covers the geological model construction unit, the real-time data mapping unit and the visual rendering unit; the intelligent scheduling decision module is provided with a transportation path optimization sub-module, a resource allocation sub-module and an emergency response sub-module; and a man-machine interaction terminal. An edge computing node of the heterogeneous data acquisition module adopts an improved LSTM missing data interpolation model; the transportation path optimization sub-module uses an improved ant colony algorithm, the resource allocation sub-module uses an improved multi-objective optimization model, and the emergency response sub-module uses an improved rule reasoning algorithm. The problems of data fusion, path optimization, resource allocation, emergency response, visualization and the like in mine industry management are effectively solved, and the efficiency, the safety and the intelligent level of mine management are improved.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

The invention discloses a digital twin-driven multi-source heterogeneous data fusion and intelligent analysis method for oil and gas pipeline construction. The method comprises the following steps: collecting multi-source heterogeneous data related to oil and gas pipeline construction; fusing data by using a space-time diagram neural network and constructing a dynamic offset compensation model to correct coordinate drift; constructing a data credibility dynamic evaluation chain, performing cross validation on sensor data to generate a confidence coefficient weight, and complementing missing data through a GAN complementing module to form a credibility enhanced data set; designing a layered distillation compression algorithm to compress the PINN model into a TinyLSTM engine, and developing a model increment updating protocol to realize efficient collaboration of a cloud end and an edge end; and training and optimizing a TinyLSTM engine by using the data set with enhanced credibility. According to the method, the defects in the aspects of data dynamic correction, credibility evaluation and complementation, model edge deployment real-time response and the like in the prior art are overcome, and the method has remarkable innovativeness and practicability.

The invention relates to the technical field of CPU card chips, and discloses a CPU card chip test method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of a CPU card chip, and obtaining an initial test data matrix; constructing a test parameter association diagram of the initial test data matrix and predicting missing data to obtain a complete test data matrix; performing principal component analysis on the complete test data matrix to obtain an effective test data set; constructing a multi-level fuzzy set according to the effective test data set to obtain fusion test data; and inputting the fusion test data into a variational auto-encoder for self-supervised pre-training, executing conditional interpolation by using a diffusion state space model to obtain a defect detection result and an uncertainty estimation value, and realizing optimal fusion of electrical characteristics and functional test data by using a multi-stage fuzzy set and an adaptive weight adjustment mechanism. Complementary advantages of dual-channel data are fully utilized, and a more comprehensive and accurate test result is provided.

The invention discloses a cargo transportation track real-time monitoring method based on the Internet of Things, and the method comprises the steps: carrying out the standardization, time sequence normalization and missing data correction of transportation parameters, abnormal event signals and state snapshots collected by all transportation nodes, extracting multi-source features, and carrying out the high-dimensional fusion; an abnormal event evidence chain is constructed by means of node state mutual identification and a chained redundancy verification mechanism, multi-node collaborative anomaly judgment is realized through a confidence weighting algorithm, and judgment robustness in a space-time asynchronous environment is improved by combining measures such as an elastic collaborative window and historical behavior prediction. The method is beneficial for realizing full-link high-confidence anomaly detection, automatic tracing and error correction and dynamic self-optimization, and improves the state perception, risk early warning and resource scheduling intelligence level between transportation nodes.

The invention discloses a data cleaning method and system based on a large language model, and the method comprises the steps: collecting and sorting a to-be-cleaned data set, formatting the to-be-cleaned data set into a text format suitable for model processing, and obtaining to-be-cleaned text data; performing text error correction and standardization processing on the to-be-cleaned text data based on the large language model, performing exception processing on the to-be-cleaned text data based on the large language model, and performing entity recognition and standardization processing on the to-be-cleaned text data based on the large language model; performing semantic consistency check on the to-be-cleaned text data based on the large language model, performing missing data generation on the to-be-cleaned text data based on the large language model, and performing repeated data processing on the to-be-cleaned text data based on the large language model; and recording all problems, implemented processing measures, generated results and various parameters in the process of cleaning the to-be-cleaned text data based on the large language model, and generating a structured report based on the record.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.

The invention discloses a traffic data interpolation method based on time-frequency feature fusion and a conditional diffusion model, and belongs to the field of intelligent traffic. The method comprises the following steps: firstly, acquiring data of each observation node and reconstructing to obtain traffic data; missing processing is carried out on the traffic data to obtain traffic data after missing processing, and missing data is used as interpolation target data; then, on the basis of the conditional diffusion model, a noise prediction network is constructed, forward noise adding is carried out on interpolation target data, reverse denoising is carried out through the noise prediction network, and the noise prediction network comprises a conditional information extraction module and a noise prediction module; and finally, training the noise prediction network, obtaining interpolation data by using the trained noise prediction network, and combining the interpolation data with the to-be-interpolated traffic data according to the observation mask of the to-be-interpolated traffic data to obtain complete traffic data. According to the method, error accumulation can be effectively avoided, meanwhile, the condition information fuses time domain and frequency domain characteristics of traffic data, and more stable interpolation is achieved.

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

The invention provides a brain tumor survival prediction method and system based on a multi-modal medical knowledge graph, and belongs to the technical field of brain tumor survival prediction. The multi-modal medical knowledge graph based on third-party knowledge base fusion is constructed; performing feature extraction on the brain tumor multi-modal data; searching an entity corresponding to the brain tumor related data in the multi-modal medical knowledge graph, and converting the entity into feature representation by using an entity representation learning method; the learned feature representation related to the brain tumor type complements the missing data mode, and finally the complemented features are input into a pre-trained survival prediction model to achieve brain tumor survival prediction. According to the multi-modal medical knowledge graph, comprehensive medical knowledge support meeting clinical requirements is provided; the multi-modal mapping knowledge domain is used for missing modal completion of brain tumor survival prediction, and a completion feature is generated by querying an associated entity through the mapping knowledge domain, so that the problem of weak modal missing processing capability in the prior art is solved.

The invention provides a data filling method and device based on a time sequence continuous deletion filling model, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a training data set; respectively inputting the forward and backward input sequences into forward and backward prediction networks of a long-term filling module to obtain network output; a linear function is introduced to carry out weighted integration on network output to obtain long-term filling module output; inputting the true value of the to-be-predicted sequence and a randomly generated continuous missing mask into a short-term filling module to obtain a short-term filling module output; and outputting and inputting the long-term and short-term filling modules into the element weighting module to obtain a filling result. The invention provides the LSTI, and relates to a time sequence filling model which can automatically combine the output of the long-term filling module and the short-term filling module aiming at the continuous missing data of the time sequence. According to the method, long-term dependence and short-term dependence are respectively modeled through two specially designed expert models, so that continuous missing data can be effectively filled.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The present application relates to the technical field of data detection, and discloses an electricity theft behavior detection method, apparatus, and device, and a storage medium. The method comprises: obtaining historical electricity consumption data of an electrical terminal within a first detection period, and assigning a data label to the historical electricity consumption data; performing data preprocessing on missing data in the historical electricity consumption data to obtain preprocessed data; training and testing a preset model on the basis of the preprocessed data and the historical electricity consumption data having the data label to obtain an electricity theft behavior detection model, wherein the preset model is constructed based on OS_CNN technology and AutoXGB technology; and by means of the electricity theft behavior detection model, detecting whether the electrical terminal has an electricity theft behavior. According to the present application, the preset model constructed based on the OS_CNN technology and the AutoXGB technology is trained and tested to obtain the electricity theft behavior detection model, so as to quickly and accurately detect, by means of the electricity theft behavior detection model, whether the electrical terminal has the electricity theft behavior.

The invention relates to a power construction deviation degree diagnosis method based on multi-modal time series data fusion, and the method comprises the steps: collecting voltage, current and frequency data through a multi-channel synchronous sampling technology, filling missing data through cubic spline interpolation, and constructing an initial data matrix; based on a hierarchical feature extraction technology, mapping the voltage frequency domain features and the current time domain statistical features to a unified feature space through canonical correlation analysis, and generating a multi-modal feature vector with a time sequence tag in combination with a sliding window; analyzing the dynamic trend of the electrical variable under multiple time scales by adopting a long short-term memory network, and capturing a key time point through an attention mechanism; a sudden change point and a stationary section are defined, an isolated forest algorithm is combined to detect an abnormal point location deviating from a trajectory, anomaly is classified as transient disturbance or continuous deviation through a multi-layer perceptron, the evolution trend of regional continuous deviation is predicted, and the key problem of the power deviation degree diagnosis capability is improved.

The invention discloses a method for predicting settlement deformation of a foundation pit pipeline based on a heterogeneous spatio-temporal data source. The method comprises the following steps: S1, preprocessing data; s2, spatial feature extraction: introducing a graph attention network, and extracting mutual influence weights among the monitoring points by using a distribution graph of the monitoring points around the foundation pit and a pipeline distribution graph to obtain the mutual influence weights among the monitoring points; s3, spatio-temporal feature fusion; s4, heterogeneous data processing; s5, time sequence prediction; by fusing the spatial features and the time sequence features of the foundation pit monitoring points, the prediction accuracy is improved in predicting the settlement deformation problem of the foundation pit pipeline; missing data is repaired by adopting a diffusion model, so that the utilization rate of the whole data set is improved; the blank of data missing is made up, the original information of the foundation pit monitoring data is reserved to the maximum extent, and the robustness and reliability of the deformation prediction model are further enhanced; the limitation of pipeline data can be effectively supplemented by utilizing multi-source heterogeneous data, and the overall prediction effect is improved.

The invention discloses a multi-source navigation data fusion method for an unmanned loader, which comprises the following steps: data acquisition: acquiring environmental perception data of satellite navigation, an inertial measurement unit IMU, a wheel type odometer and a laser radar and camera in real time; performing space-time synchronization preprocessing, realizing multi-source data time synchronization, unifying environment sensing data to a body coordinate system, eliminating abnormal data, and complementing missing data; carrying out dynamic weight calculation, establishing an error model of each sensor, and adjusting a fusion weight by using an error reciprocal exponential weighting method; layering fusion is carried out, a satellite and an IMU are fused through bottom-layer extended Kalman filtering (EKF), a laser radar and a high-precision map are fused through middle-layer ICP, a middle-layer result and a wheel type odometer are integrated through high-layer federated filtering, and high-precision fusion is achieved; and outputting and optimizing a result, outputting navigation data, performing closed-loop optimization, monitoring the health degree of the sensor, and executing redundancy switching when a fault occurs. The system comprises a corresponding processing unit, and a storage medium stores a program for executing the method.

The invention discloses a desertification monitoring method and system based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the weighted scoring of multi-source satellite remote sensing data according to the spectrum, cloud coverage and time resolution, and automatically switching to a data source with a higher priority when the score is lower than a threshold value. Thirdly, performing initial segmentation on the image by using a multi-scale segmentation algorithm in combination with spectrum and texture features, optimizing boundaries through a region growing algorithm, and merging regions with small spectrum differences into desertification plaques; and extracting construction land expansion and agricultural activity intensity indexes from the time series data, and establishing a quantitative relation model with desertification plaque change. And adopting a Kriging space-time interpolation algorithm for missing data to generate a continuous curved surface. And finally, fusing a multi-period segmentation result and human factor data, and outputting a desertification boundary dynamic change diagram and a human influence weight distribution diagram. According to the invention, accurate monitoring of the desertification process and quantitative evaluation of human influence are realized, and a scientific basis is provided for desertification control.

The invention provides an OCR (Optical Character Recognition) method and system for high-precision table data structuring. The method comprises the following steps: converting an original image into a grayscale image and preprocessing the grayscale image; extracting a table edge structure line and filling a fracture part; detecting longitudinal and transverse straight lines in the preprocessed image, calculating intersection points, and determining a table row-column structure; dividing a cell region and positioning to generate a cell coordinate matrix; pixels in the cells are divided into a frame influence area and an effective data area, and the frame influence area executes neighborhood mean filtering and weighted fusion operation; performing end-to-end detection on characters and symbols in the effective data area, and outputting an OCR recognition result with coordinates; and dynamically generating a table structure template based on the cell coordinate matrix and an OCR recognition result, speculating a strategy matching field type through a rule, processing and merging cell missing data based on adjacent cell information, and outputting structured data. The reliability and accuracy of the OCR technology are improved, and the requirement of automatic information processing for high-precision data extraction is met.

The invention relates to the technical field of electric power topology identification, and discloses an electric power topology rapid identification method based on depth map matching, and the method comprises the following steps: S1, carrying out the multi-source heterogeneous data fusion and adaptive noise reduction, and employing an adaptive threshold noise reduction algorithm; s2, modeling a dynamic space-time diagram; s3, attention-driven graph matching is carried out, and a two-channel graph neural network is used; and S4, updating the incremental topology library. For the condition that the real-time data of the low-voltage power distribution network generates significant noise due to equipment aging and acquisition errors, a self-adaptive threshold noise reduction algorithm is adopted, high-frequency noise components are accurately filtered out according to a threshold value, missing data are intelligently repaired in combination with a space-time Kriging interpolation method, the purity and integrity of the data are ensured, and a data basis is provided for subsequent topology recognition.

The embodiment of the invention provides a report customization method and device based on medical data. A report customization method based on medical data belongs to the technical field of data processing, and obtains standardized data after redundant data, missing data and inaccurate data are removed through data cleaning and standardization processing, so as to improve the data source quality of a generated report. The method comprises the following steps: standardizing data of a user demand report, generating structured data by performing multi-dimensional analysis and feature extraction on the standardized data to highlight the correlation between the data so as to facilitate the generation of a high-quality report, and finally, mapping the structured data to a user demand report template and combining compliance check and risk assessment rules embedded in the user demand report template so as to improve the user demand report quality. And the function that the report meets the customized demand of a customer is further improved.

Method, network device and a computer program in a communication network (100) for the assisting of an application server (130) using the Constrained Application Protocol, CoAP, in the communication of a data object (600) to client devices (110), whereby the data object comprises data blocks (601-609). The network device is operative to inform of a multicast repair functionality. The network device is operative to receive at least one indication of at least one missing data block from a client device that did not successfully receive a data block within a transmission window. The network device is operative to decide to transmit and initiate transmission of the one or missing data block via unicast, multicast or both unicast and multicast. A computer program product is also disclosed.

The invention discloses an unsupervised time sequence missing data filling method based on a dynamic cause and effect graph structure, which comprises the following steps: S1, collecting a data sample, and processing to obtain an initial filling complete data table; s2, clustering all the initial filling complete data tables, and selecting representative data tables; s3, constructing a sub-causal graph of each representative data table, and further constructing a total causal graph; s4, on the basis of a causal relationship in the total causal graph, constructing and training a corresponding weighted expandable depth time sequence convolutional network for the to-be-filled target feature column to obtain a missing value filling model, and further performing coarse filling on missing values; and S5, performing refined secondary filling on the target feature column by using the obtained missing value filling model to obtain a complete data table. The invention provides a filling method without real missing labels, optimization is carried out through a self-consistency or structure maintenance principle, the dynamic dependence and real causal relationship between the time series data can still be captured in a dynamic complex scene, and a better data filling effect is achieved.