493 results about "Prediction residual" patented technology

The invention relates to the technical field of electric power short-term load prediction, in particular to an industrial park short-term load prediction method and system based on a Diffusion-Transform-AR model. Acquiring non-stationary historical load data and meteorological data of the industrial park, and performing missing value processing on the data; performing correlation analysis on the data by using the maximum information coefficient, selecting meteorological data with relatively high correlation, and reprocessing time data; performing time sequence decomposition on the load data by using a seasonal trend decomposition method to obtain trend term, seasonal term and residual term features; constructing a diffusion Transform prediction model, and taking the residual term, the meteorological data and the time data as input prediction residual; constructing an autoregression prediction model and taking the trend term as an input prediction trend; according to the method, the advantages of various models are combined, time sequence features are deeply mined, the prediction precision and the model generalization ability are effectively improved, and the method is suitable for complex industrial park load prediction scenes.

The invention discloses an energy storage power supply residual life prediction method and system based on dynamic weight distribution, and belongs to the technical field of energy storage power supply health management. The method comprises the following steps: acquiring voltage, current and temperature time sequence signals of energy storage power supply operation through a multi-source sensor, and constructing a degradation characteristic sequence by adopting a sliding window method; a degradation inflection point division stage is detected and identified by using a curvature extreme value, a historical degradation mode is matched based on a dynamic time warping algorithm, and an optimal model group is selected; respectively carrying out dynamic weight distribution on the time step length and the feature dimension by adopting a dual-channel attention network, and fusing a learnable coefficient with space-time attention output to generate a life prediction result; residual errors and feature drift are monitored and predicted in combination with an incremental learning mechanism, and model parameters are updated and a degradation knowledge base is expanded by adopting an elastic weight consolidation algorithm. According to the method, the key information capturing capability is enhanced through a space-time attention mechanism, the model adaptability is optimized in combination with incremental learning, and the energy storage power supply life prediction precision and the working condition generalization performance are remarkably improved.

The invention provides a dual-antenna attitude and orientation and robust self-adaption method based on integrated navigation, which comprises the following steps: combining an INS (inertial navigation system) and a dual-antenna GNSS (global navigation satellite system), and enabling the system to output continuous and stable high-precision carrier attitude information when a GNSS signal is interfered by using dual-antenna integrated navigation. According to the method, most of satellite end clock correction, ionosphere and troposphere errors and receiver end clock correction are eliminated by using double-antenna GNSS pseudo-range and carrier phase double difference, robust statistics are calculated through heading prediction residual vectors to obtain robust factors, an observation noise covariance matrix is expanded to reduce the influence of system noise and observation noise, and the system performance is improved. A Mahalanobis distance based on a prediction residual vector is introduced to detect whether a system is abnormal or not, a state prediction covariance matrix is adjusted through a self-adaptive factor, weight reduction of an abnormal INS dynamic model is achieved, and the stability and reliability of system attitude information are improved through a noise covariance self-adaptive control mechanism.

The invention relates to a multi-factor fusion Internet of Things outlier data identification method, and belongs to the technical field of Internet of Things, and the method comprises the steps: determining an identified target factor, fusing a plurality of influence factors associated with the target factor, obtaining corresponding historical data, and carrying out the preprocessing; performing correlation analysis on the specific indexes, and selecting index data having significant influence on target factors to form a final effective index combination; constructing a prediction model combining the LSTM with a feature attention mechanism, performing prediction by using the data after correlation analysis screening, outputting a prediction value in a future period of time, and obtaining an actual prediction value through reverse normalization; a prediction model is adopted, an outlier data identification method fused with multi-factor comprehensive analysis is adopted, an isolated forest algorithm is used for modeling, prediction residual errors and influence factors serve as input features, and the abnormal score of each data point is calculated; and based on the distribution of the abnormal scores, dynamically setting a threshold value by adopting a sliding window technology, and judging the data points exceeding the threshold value as outlier data.

The invention discloses a refrigerated cabinet multi-sensor heterogeneous data fusion method based on artificial intelligence, and the method comprises the following steps: collecting multi-source heterogeneous data in the operation process of a refrigerated cabinet, and constructing a unified event time axis; preprocessing the multi-modal data, and generating a physical enhanced multi-modal tensor in combination with a heat-airflow-electricity model; constructing a disturbance activation vector and a modal mask pattern, and embedding the disturbance activation vector and the modal mask pattern into a multi-modal tensor; inputting the disturbance enhanced input data into an improved Tide model to perform long sequence modeling, and outputting a state prediction sequence; performing prediction residual analysis, constructing a dynamic anomaly scoring function, and fusing a disturbance activation vector and a modal mask graph to generate an anomaly type mark and confidence fusion state representation; sparse gradient uploading, parameter aggregation and structure synchronization are carried out among multiple devices by adopting a federal training framework. According to the method, the multi-modal data fusion precision and the anomaly recognition reliability of the refrigerated cabinet can be improved, and cross-equipment collaborative intelligent optimization is realized.

The invention discloses an energy-saving early warning control method and system in a casting process, and the method comprises the steps: obtaining casting environment data, power consumption data, fuel consumption data and emission data, carrying out the data cleaning and normalization operation, and obtaining the preprocessed multivariable time series data; inputting the multivariable time series data into a pre-trained multivariable prediction model to obtain a predicted value of each variable, and performing comparison calculation on the predicted value and an actual measurement value to obtain a predicted residual error; according to the prediction residual error, sequential Bayesian analysis is carried out, the abnormal event probability of each variable is obtained, probability fusion calculation and comprehensive evaluation are carried out, and an abnormal score is obtained; and when the abnormal score is judged to be greater than a preset abnormal threshold value, generating an early warning signal, and sending the early warning signal to the casting equipment, so that the casting equipment automatically adjusts the operation parameters according to the early warning signal. According to the method, accurate environment-friendly early warning and real-time control in the casting process can be achieved.

The invention relates to the technical field of disaster prevention and reduction of a power system, and discloses an icing risk early warning method based on multi-model fusion and residual time sequence characteristic analysis, which comprises the following steps: collecting meteorological data of a line area in real time, removing abnormal values through secondary judgment of a Pauta criterion and a trend, and standardizing; adopting a TEROL algorithm to screen high-weight key features; running SWD-BP, MUL-GRNN and ELM models in parallel, constructing a dynamic weight by combining DSI, an independence weight method and an entropy weight method, and calculating a final meteorological predicted value; generating a prediction residual signal, extracting time domain features such as a mean value and a peak value, and constructing a residual feature matrix through a sliding window; and inputting an LSTM model to process a time sequence dependency relationship, and judging an icing risk level. According to the method, meteorological prediction is optimized through multi-model dynamic fusion, and deviation is analyzed and corrected in combination with residual time sequence characteristics, so that the problem of weak generalization ability of a single model is effectively solved, and the accuracy of icing risk early warning is obviously improved.

The invention relates to the field of biological medicine, and discloses a method and system for analyzing cough sound by using disease characteristics to diagnose respiratory diseases, and the method comprises the steps: deploying a six-microphone annular array to achieve the precise positioning and triggering of a sound source; self-adaptive spectral subtraction and Wiener filtering cascade are adopted to enhance the audio; segmenting a cough segment based on energy envelope; fusing the Mel-cepstrum, the linear prediction residual error, the harmonic energy ratio and the transient zero-crossing rate to construct a pathological feature matrix; extracting local, medium-range and global time sequence features through a three-branch parallel convolutional network; inputting a disease specific classifier to discriminate asthma, pneumonia and laryngitis respectively, and applying a feature decoupling regular term to improve interpretability. The system correspondingly realizes the modularized processing flow. According to the method, the cough sound collection quality and the disease subtype recognition accuracy in a complex environment are improved, meanwhile, the thermodynamic diagram is output to assist clinical decision making, and the diagnosis credibility and practicability are enhanced.

The invention provides a flight path prediction method and system based on predictive coding. The method comprises the following steps: collecting historical flight path data, and performing flight path prediction on the historical flight path data by using a reference prediction module to obtain a reference prediction point; calculating a high-order motion track residual error between the reference prediction point and the real track point by using a residual error encoder, and carrying out one-hot coding on the high-order motion track residual error to establish a residual error coding index; a high-order probability distribution mapping module is used for learning a deep mapping relation between historical flight path data and residual error coding indexes so as to carry out training; inputting a to-be-predicted historical flight track into the reference prediction module and the trained high-order probability distribution mapping module in parallel to respectively obtain a predicted track point and a predicted residual error coding index; decoding the prediction residual error coding index through a residual error decoder to obtain a residual error at a prediction moment; and adjusting the predicted track point based on the residual error at the prediction moment to obtain a final prediction result.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

The present invention provides a point cloud intra prediction method and device based on weights optimization of neighbors. The invention relates to intra prediction for point cloud attribute compression, by optimizing the weights of the neighboring points on the basis of the density of the point cloud in three directions, i.e. x, y and z directions, and specifically, calculating the optimized weight of each neighboring point by optimizing corresponding coefficients of three coordinate components, i.e. x, y and z coordinate components of distances. The invention can improve the accuracy of intra prediction by means of enhancing the utilization of the overall geometric information of the point cloud, and then transformation, quantification and entropy are carried out on the prediction residuals, such that a better point cloud attribute compression performance is achieved.

To provide a method and apparatus for video processing.SOLUTION: A video processing method comprises: determining skipping of a transform process for a prediction residual based on one of width and height of the Cb component of a prediction block; signaling the maximum transform size for use in a transform skip in a sequence parameter set (SPS); and bypass-coding a parameter that specifies whether a transform-skip mode is selected.SELECTED DRAWING: Figure 21

The invention discloses an energy storage frequency modulation control method and system for a super capacitor coupled lithium battery, and belongs to the technical field of energy storage and frequency modulation control, and the method comprises the steps: collecting data from an original frequency modulation instruction sequence, generating a substitution sequence through a search method, and generating a regular optimal substitution sequence through positive, negative and comprehensive optimization; training a GRU model by using the optimal substitution sequence, and optimizing hyper-parameters of a GRU neural network through an improved whale optimization algorithm; and inputting the optimal substitution sequence into the optimized GRU model to generate a preliminary prediction result, and correcting the prediction result in combination with the deviation between the prediction residual error and the original data to obtain a final prediction result. According to the method, prediction errors are remarkably reduced, the prediction precision of the frequency modulation control system is improved, the local optimum problem is effectively avoided, the globality of the optimization process is improved, and it is ensured that the generated optimal sequence has higher quality; the whale optimization algorithm is improved, the algorithm convergence efficiency is remarkably improved, and falling into a local optimal solution is avoided.

The invention discloses a tobacco primary processing technology regulation and control method based on a large model, and the method comprises the steps: carrying out the feature extraction at an edge layer according to technological parameters, raw material detection data and environmental parameters collected in a historical time interval, and obtaining a plurality of feature vectors; according to the multiple feature vectors, a quality index is obtained through prediction in an edge layer through a preset prediction model, and according to the quality index, process parameters are adjusted for execution; and according to the quality index acquired after execution and the quality index obtained by prediction, obtaining a prediction residual moving average at a decision-making layer, and when the prediction residual moving average is continuously greater than an error threshold for multiple times, triggering model re-calibration and synchronizing to an edge layer. According to the method, the multi-modal features are fused in the edge layer, closed-loop control is realized through the real-time prediction model, and the prediction accuracy and the response speed are improved. In combination with a prediction residual moving average dynamic calibration mechanism, the problem of model failure caused by parameter mutation is effectively solved, and production continuity is guaranteed.

To provide a method and apparatus for video processing.SOLUTION: A video processing method comprises: determining skipping of a transform process for a prediction residual based on one of width and height of the Cr component of a prediction block; signaling the maximum block size for use in a transform skip in a sequence parameter set (SPS); and bypass-coding a parameter that specifies whether a transform-skip mode is selected.SELECTED DRAWING: Figure 21

The invention discloses a force control joint dynamic error compensation method based on Kalman filtering, and the method comprises the steps: collecting and preprocessing multi-source signal data, and forming a force control joint input data set; constructing an energy hierarchical Kalman filtering model, executing energy constraint correction and outputting state estimation; calculating a prediction residual error, carrying out time-frequency analysis, adjusting a compensation gain, and generating residual error information; torque and angular velocity signals are extracted, a semantic state is recognized, and semantic gating parameters are output; fusing the state estimation, the residual information and the semantic parameters to generate a dynamic compensation instruction signal; energy layer and residual information changes are monitored, self-calibration is triggered, and a feedback closed loop is formed. According to the invention, by introducing energy hierarchical Kalman filtering, time-frequency modulation compensation and a semantic gating feedback mechanism, dynamic error self-adaptive accurate compensation of the force control joint in a complex multi-disturbance environment is realized.

A three-dimensional data encoding method includes: calculating a prediction residual that is a difference between information of a three-dimensional point included in point cloud data and a predicted value; quantizing the prediction residual using a quantization parameter; determining, using the quantization parameter, a second bit count of second information indicating a first bit count of first information on the prediction residual quantized; and generating a bitstream including the first information, the second information, and third information indicating the second bit count.

The invention discloses an air conditioning system load prediction method and device, a medium and equipment, and relates to the technical field of load prediction. The method comprises the following steps: firstly, acquiring historical load data of the air conditioning system and load influence factors of the historical load data in the same period, then constructing a preliminary load prediction model based on a seasonal difference autoregression moving average model according to the historical load data, and determining a prediction residual error of the preliminary load prediction model to the historical load data; then training the generalized regression neural network to obtain a residual error prediction model according to the prediction residual error of the load influence factors and the initial load prediction model on the historical load data, and finally inputting the load data to be predicted into the initial load prediction model to carry out initial load prediction, and correcting the load data through the residual error prediction model to obtain an initial load prediction result. And determining the predicted load of the air conditioning system at the target moment according to the initial load and the correction. According to the invention, the convergence speed, the prediction efficiency and the prediction accuracy are improved.

An apparatus configured to select a predetermined intra prediction mode out of a plurality of intra-prediction modes which includes a first set of intra-prediction modes and a second set of matrix-based intra-prediction modes. The apparatus is configured to select a subset of one or more secondary transforms dependent on the predetermined intra prediction mode so that the subset is nonempty in case of the predetermined intra prediction mode being contained in the first set of intra-prediction modes or in the second set of matrix-based intra-prediction modes. The apparatus is configured to derive a transformed version of a prediction residual for a predetermined block, which is related to a spatial domain version of the prediction residual of the predetermined block via a transform defined by a concatenation of a primary transform and a predetermined secondary transform out of the subset of secondary transforms.

The invention provides a voice reconstruction method and system based on entropy coding residual quantization and frequency spectrum restoration, and relates to the technical field of artificial intelligence voice signal processing, and the method comprises the steps: obtaining an original voice waveform, inputting the voice waveform into a neural voice coding and decoding model, firstly entering a coder to map the input voice waveform into acoustic potential representation, and then entering a frequency spectrum restoration model; performing residual quantization on the acoustic potential characterization layer by layer through a residual vector quantization module, introducing a gating-based dynamic layer number selection mechanism and entropy regularization constraint, enabling bits to be adaptively distributed among different voice segments, reconstructing reconstructed acoustic features of the potential characterization, inputting the reconstructed acoustic features into a decoder, restoring the reconstructed acoustic features into a time domain waveform, and outputting the time domain waveform. And mapping to a logarithmic magnitude spectrum domain through a spectrum repairing module, predicting a residual error in the logarithmic magnitude spectrum domain and performing confidence gating fusion to obtain a complex spectrum, and outputting after time domain synthesis to obtain reconstructed speech. According to the invention, high fidelity, intelligibility and transmission reliability of the voice can be considered at an extremely low bit rate.

To provide a method and apparatus for video processing.SOLUTION: A video processing method comprises: determining skipping of a transform process for a prediction residual based on one of width and height of the dimension of a luma sample of a prediction block; signaling a parameter indicating the maximum transform block size for use in a transform skip in a sequence parameter set (SPS); and bypass-coding a parameter that specifies whether a transform-skip mode is selected.SELECTED DRAWING: Figure 21

Systems and techniques are provided for coding video data based on optical flow correction and residual correction. For example, a decoding device may obtain an encoded video data frame associated with an input frame, the encoded video data frame including optical flow correction and residual correction. A predicted optical stream may be generated based on the one or more reference frames and the reference optical stream. A corrected predicted frame may be generated based on the predicted optical flow and the optical flow correction. A prediction residual may be generated based at least on the corrected prediction frame and a first reference frame included in the one or more reference frames. The decoding device may generate a reconstructed input frame based on the corrected prediction frame, the prediction residual, and the residual correction.

The invention discloses a significant wave height two-stage space-time prediction method and system based on diffusion residual correction, and relates to the technical field of sea wave prediction, historical significant wave height data and corresponding historical wind field data are input into a significant wave height space-time prediction model for processing, and a preliminary significant wave height prediction result is obtained; and inputting the initial prediction error, historical significant wave height data and synchronous forecast wind field data into a residual error correction module based on a diffusion model, and compensating the initial prediction error to obtain a final significant wave height prediction result. A diffusion model based on an EDM framework is innovatively introduced to carry out refined reconstruction on a prediction residual error, and a basic prediction field, a synchronous wind field dynamic factor and a prediction aging code are used as multi-dimensional physical strong conditions to be injected into a diffusion generation process. According to the method, high-frequency texture details and non-linear fluctuation components missed by the first-stage model can be certainly recovered, long-term deviation accumulation is relieved to a certain extent, the high-wave region prediction quality is improved, and the prediction stability is improved.

Disclosed in the embodiments of the present application are a motion search processing method, system and device for video coding, and a storage medium. The technical solution provided in the embodiments of the present application comprises: during a motion search process of video coding, determining an optimal start point on the basis of an integer pixel search, and performing a sub-pixel search on reference blocks one by one starting from a reference block corresponding to the optimal start point (S110); during the sub-pixel search, calculating gradient information in a direction corresponding to the current reference block, comparing the gradient information with a set gradient threshold value, according to a comparison result, performing skip determination on a sub-pixel search flow in the direction corresponding to the current reference block, and under a first set situation, skipping the sub-pixel search flow in the direction corresponding to the current reference block (S120); and acquiring a real-time minimum distortion cost of the current reference block and the sum of absolute values of prediction residuals of an optimal mode, performing skip determination on sub-pixel search flows in all directions of the current reference block according to the gradient information, the minimum distortion cost and the sum of the absolute values of the prediction residuals, and skipping the sub-pixel search flows in all directions of the current reference block under a second set situation (S130). By means of the method, during a pixel search, parts having relatively small gradients are screened out and sub-pixel search flows thereof are skipped, thereby reducing the time consumption of a motion search, and thus improving the video coding efficiency.

Provided are an encoding and decoding method, a code stream, an encoder, a decoder and a storage medium, applied to the decoder, the method comprising: determining vertex information of a three-dimensional grid (S701); performing connectivity prediction according to the vertex information of the three-dimensional grid, and determining a predicted weight value of an edge of the three-dimensional grid (S702); analyzing the code stream, and determining a predicted residual value of an edge of the three-dimensional grid (S703); and determining a reconstruction weight value of the edge of the three-dimensional grid according to the predicted weight value and the predicted residual value (S704). According to the encoding and decoding method, the geometric encoding efficiency of the three-dimensional grid can be improved, and the compression performance of the three-dimensional grid is improved.

The invention relates to the technical field of thermal signal early warning, and discloses a thermal signal analysis early warning method and system based on a knowledge graph, and the method comprises the following steps: S101, collecting thermal signal data; s102, acquiring an enthalpy value and a mass flow rate, and generating an energy conservation residual error; s103, evaluating a temperature change trend, generating a thermal inertia prediction residual error, evaluating a channel coupling degree to obtain a cross-channel coefficient, and determining a cross-channel coefficient residual error; s104, normalizing the energy conservation residual error, the thermal inertia prediction residual error and the cross-channel coefficient residual error respectively, and fusing to generate an abnormal score; and S105, mounting the energy conservation residual error, the thermal inertia prediction residual error, the cross-channel coefficient residual error and the abnormal score to a preset knowledge graph to generate a fault early warning triple. According to the invention, high-precision signal processing, multi-dimensional fault detection, intelligent reasoning and accurate early warning of the thermal system are realized.

A moving picture encoding method for increasing coding efficiency includes: determining whether or not to apply orthogonal transformation, to calculate a value of an orthogonal transform skip flag; performing the orthogonal transformation on a prediction residual according to the value of the orthogonal transform skip flag, to calculate at least one orthogonal transform coefficient; performing quantization on at least the one orthogonal transform coefficient, to calculate at least one quantized coefficient; performing variable-length encoding on the orthogonal transform skip flag; and changing a scan order for at least the one quantized coefficient according to the value of the orthogonal transform skip flag, and performing variable-length encoding on at least the one quantized coefficient in the scan order after the change.

A decoder and an encoder for coding a digital time-varying signal from or into a data stream are presented. The decoder is configured to decode the digital time-varying signal from the data stream in temporal blocks by decoding each temporally residual-predicted temporal block of the digital time-varying signal by predicting the respective temporally residual-predicted temporal block using a selected prediction mode out of a set of prediction modes to obtain a prediction signal, determining a prediction residual signal of the respective temporally residual-predicted temporal block, and correcting the prediction signal using the prediction residual signal, wherein, in the determining the prediction residual signal, sequentially decode second-stage-predicted residual samples of residual samples of the respective temporally residual-predicted temporal block along a sample order by deriving a second-stage residual sample prediction value for a currently decoded second-stage-predicted residual sample based on already decoded residual samples within a template of a predetermined number of sample positions preceding the currently decoded second-stage-predicted residual sample in sample order, decoding a second-stage-correction value for the currently decoded second-stage-predicted residual sample from the data stream, and correcting the second-stage residual sample prediction value using the second-stage-correction value.

A method for decoding a predetermined block of a picture using intra-prediction by reading, for each of predetermined intra-prediction blocks, from a data stream, a mode index identifying a matrix-based intra-prediction mode is disclosed. Samples of each predetermined intra-prediction block are predicted by computing a matrix-vector product between an input vector derived from reference samples and a prediction matrix associated with the identified matrix-based intra-prediction mode (k)—and associating components of an output vector obtained by the matrix-vector product onto sample positions of the predetermined block. Further predetermined intra-predicted blocks of the picture are predicted to obtain a prediction signal. A transformation flag is decoded from the data stream using context adaptive binary arithmetic coding, and a prediction residual is decoded and re-transformed using a reverse transformation based on the transformation flag to obtain a prediction residual signal. The prediction residual signal is used to correct the prediction signal.

The invention belongs to the technical field of point cloud, and relates to a point cloud attribute coding method based on a graph attention network, which comprises the following steps: acquiring point cloud data, and carrying out block division on the point cloud data; performing detail level generation on the point cloud data subjected to block division, predicting point cloud attributes according to a detail level generation sequence, and inputting a neighbor point set with attribute information into a multi-head image attention layer for feature extraction to obtain a prediction residual error of a current point; encoding the prediction residual error of the current point to obtain an attribute code stream; inputting the attribute code stream into a decoding module to obtain a prediction residual error; searching a neighbor point set of unpredicted points in the prediction point set, and inputting the neighbor point set into the attention layer of the multi-head image to predict the current point; combining the predicted value with the decoded predicted residual error to obtain a reconstruction attribute; according to the method, efficient compression and accurate reconstruction of the point cloud data are realized in a layer-by-layer recursive predictive coding mode, and the method can be applied to a three-dimensional scene needing efficient data transmission and storage.