14 results about "Pattern discrimination" patented technology

The invention is applied to the technical field of industrial process control, and particularly discloses a multi-view image feature fusion multi-mode process virtual sample generation method, which comprises the following steps of: S1, collecting sensor data in an industrial process through an offline detection and distributed control method, and establishing an industrial process database; s2, performing normalization processing on the collected sensor data based on a Z-Score method to obtain a normalized data set; according to the multi-mode process virtual sample generation method based on multi-view image feature fusion, mode discrimination is realized through a Gaussian mixture model, and a measurement learning method of mode preserving embedding is combined, so that the problem of data missing caused by insufficient initial data of a new process, sensor faults or working condition switching in an industrial scene is solved; feature distribution of different process modes can be accurately reflected, and after the feature distribution is combined with an original sample for training, a soft measurement model can capture more comprehensive industrial process dynamic characteristics.

The application discloses a bridge monitoring time series data similarity measurement method based on mode division, acquires environment data, structure response data and time series data of an abnormal measuring point under normal load working conditions of a bridge health monitoring system and carries out pretreatment, then carries out slope calculation of bridge monitoring time series data in each time period unit; the similarity degree of bridge monitoring time series data in each time period unit is considered through two judgment modes respectively; mode division calculation of bridge monitoring time series data is carried out based on two judgment results; the similarity degree of bridge monitoring time series data is measured based on mode division distance, and bridge monitoring time series data similarity measurement is carried out according to a high-dimensional data similarity measurement function. The application provides the possibility of direct comparison of the similarity between bridge monitoring time series data of different attributes, reduces the conversion process of the different attribute data, and fully reflects the dynamic characteristics of the time series.

This invention relates to the field of natural language processing technology, and more particularly to a method and system for language transfer recognition based on cultural semantic embedding. The method includes the following steps: acquiring source language data and target language data; performing semantic isomorphic centering on the source and target language data to obtain semantically equivalent data; decoupling pragmatic features based on the semantically equivalent data to obtain pragmatic feature data; performing contrastive cultural latent space embedding based on the pragmatic feature data to obtain bilateral cultural representation data; performing cultural modality affine transformation based on the bilateral cultural representation data to obtain cultural difference data; performing implicit distortion pattern discrimination based on the cultural difference data to obtain cultural semantic distortion data; and generating transfer results based on the cultural semantic distortion data to obtain language transfer result data. This invention decouples pragmatic features under semantic equivalence constraints and achieves language transfer recognition through cultural semantic embedding and modality affine modeling.

The invention relates to the technical field of antenna channel calibration, in particular to a global calibration method and system for the multi-channel directional diagram distinction degree of a plate-shaped array antenna, and provides the following scheme: firstly, carrying out local calibration, extracting the directional diagram characteristics of each channel to construct a local objective function, and obtaining an initial compensation parameter through an optimization method; and if the global optimization stage condition is met, introducing a particle swarm algorithm, establishing an identification subspace based on distinction degree guide particles in the double-role particle structure, and applying graph avoidance drive to the controlled cooperative particles to realize global optimization calibration of the directional diagram. According to the invention, the discrimination degree of the directional diagram and the space structure regulation and control capability are improved.

This invention relates to the field of power quality monitoring technology and provides a power quality disturbance tracing method based on a workflow platform using an intelligent diagnostic model. The method includes: collecting three-phase voltage and current waveform data from each monitoring node; when a power quality indicator exceeds a preset threshold, the workflow platform performs feature extraction to obtain a disturbance feature vector; performs disturbance pattern discrimination and selects the corresponding tracing sub-algorithm branch based on the discrimination result to obtain branch tracing results; performs hierarchical event mapping to aggregate abnormal indicators of each monitoring node into disturbance events according to spatiotemporal clustering and constructs a directed acyclic graph-like disturbance propagation chain based on the branch tracing results; inputs the disturbance feature vector and disturbance propagation chain into a trained target hybrid model; outputs the corrected disturbance propagation chain and root cause node; and finally outputs the tracing results. This invention effectively improves the tracing accuracy and efficiency in complex disturbance scenarios.

This invention relates to the field of natural language processing technology, and more particularly to a method and system for language transfer recognition based on cultural semantic embedding. The method includes the following steps: acquiring source language data and target language data; performing semantic isomorphic centering on the source and target language data to obtain semantically equivalent data; decoupling pragmatic features based on the semantically equivalent data to obtain pragmatic feature data; performing contrastive cultural latent space embedding based on the pragmatic feature data to obtain bilateral cultural representation data; performing cultural modality affine transformation based on the bilateral cultural representation data to obtain cultural difference data; performing implicit distortion pattern discrimination based on the cultural difference data to obtain cultural semantic distortion data; and generating transfer results based on the cultural semantic distortion data to obtain language transfer result data. This invention decouples pragmatic features under semantic equivalence constraints and achieves language transfer recognition through cultural semantic embedding and modality affine modeling.

The present invention provides an information processing device that can perform processing using weight measurement data more appropriately. [Solution] The information processing device 30 comprises a measurement information acquisition unit 331, a data discrimination unit 332, a data processing execution unit 334, and a notification unit 335. The measurement information acquisition unit 331 performs an acquisition process to acquire weight measurement data from a measuring device installed in the pet toilet. The data discrimination unit 332 performs a pattern discrimination process to determine which of one or more patterns the weight measurement data group corresponds to. The data processing execution unit 334 and the notification unit 335 perform processing according to the pattern determined by the pattern discrimination process.

This invention relates to the field of power quality monitoring technology and provides a power quality disturbance tracing method based on a workflow platform using an intelligent diagnostic model. The method includes: collecting three-phase voltage and current waveform data from each monitoring node; when a power quality indicator exceeds a preset threshold, the workflow platform performs feature extraction to obtain a disturbance feature vector; performs disturbance pattern discrimination and selects the corresponding tracing sub-algorithm branch based on the discrimination result to obtain branch tracing results; performs hierarchical event mapping to aggregate abnormal indicators of each monitoring node into disturbance events according to spatiotemporal clustering and constructs a directed acyclic graph-like disturbance propagation chain based on the branch tracing results; inputs the disturbance feature vector and disturbance propagation chain into a trained target hybrid model; outputs the corrected disturbance propagation chain and root cause node; and finally outputs the tracing results. This invention effectively improves the tracing accuracy and efficiency in complex disturbance scenarios.

The application provides a differential pressure type metering method and system based on flow pattern discrimination, which first discriminates and determines a list of different flow pattern working conditions, and carries out testing according to the list by using a metering test calibration system; calculates gas phase density according to dynamic test data of the testing; and determines a two-phase Lockhart-Martinelli parameter operation model based on the gas phase density and related flow data; establishes an optimal virtual height operation model under different flow patterns according to the calculated virtual height ratio and the Lockhart-Martinelli parameter fitting; determines a gas phase metering model based on flow data after calculating the optimal virtual height ratio by using the model, and establishes a gas phase metering model in combination with the best Lockhart-Martinelli parameter; and directly uses the Lockhart-Martinelli parameter operation, the optimal virtual height operation, the gas phase metering and the gas phase metering model to realize calculation according to pipeline data on site during application. The scheme overcomes the problem that the existing technology does not comprehensively consider influencing factors, considers the influence of flow patterns on virtual height, and realizes accurate metering of gas phase and liquid phase.

The invention relates to the technical field of time sequence anomaly detection, and provides a dynamic confrontation cross-domain time sequence anomaly detection method and system. The method comprises the following steps: processing a time sequence from a plurality of source domains; extracting local continuous features by adopting a sequence division and vectorization method; performing segmentation and semantic coding on the domain knowledge text to obtain a semantic vector; splicing the domain knowledge vector and the time sequence feature to obtain a time sequence feature set containing semantic priori; constructing a time sequence reconstruction module based on a mixed attention mechanism, and obtaining reconstruction features maintaining causal consistency and global dependence modeling capability; standardizing a reconstruction error and adaptively distributing a sample weight; a double-path adversarial learning mechanism is introduced, a generator and a discriminator are constructed, cross-domain potential feature alignment is realized through alternate optimization, and the abnormal mode discrimination capability is enhanced; and finally, calculating an anomaly score for a newly input sequence and outputting potential anomaly.

The invention relates to a coal seam distribution high-precision prediction method, system and device and a medium. The coal seam distribution high-precision prediction method comprises the following steps: acquiring original data of a target area; constructing a three-dimensional amplitude data volume based on the original data of the target area; and performing mode discrimination and full-dynamic palette display based on the maximum amplitude attribute and the waveform attribute of the three-dimensional amplitude data volume, and displaying thin-sandwiched reservoir distribution so as to realize high-precision prediction of coal seam distribution. According to the method, horizon slicing can be carried out in a seismic data visual environment at an interval of 1ms or at a selected time interval, a seismic maximum amplitude attribute plane distribution diagram is obtained, the distribution rule of the thin-sandwiched reservoir is researched by combining analysis of well data, the technical problems of large error and low resolution of the thin-sandwiched reservoir are solved, and the method is suitable for mass production. And the effect of reflecting both the longitudinal change trend characteristics and the transverse change trend characteristics is achieved, so that the thin-sandwiched reservoir can be determined more accurately.

This invention relates to a distributed target tracking method, system, and medium based on a Transformer interactive multi-model approach. The distributed target tracking method includes: acquiring observation data and constructing window features for a Transformer auxiliary model probabilistic network; establishing a multi-model IMM framework and performing parallel EKF sub-filtering; converting the local posterior estimates and covariance of nodes into information matrices and information vectors, setting initial values ​​for consistency iterations, and finally obtaining the node reliability in the 1st iteration; constructing symmetric edge scoring on edges, obtaining a double-random consistency weight matrix constructed using symmetric normalization; weighting and fusing neighborhood information according to the double-random consistency weight matrix, completing a preset number of information consistency iterations, and obtaining a fused posterior estimate. This invention improves the timeliness and stability of maneuver pattern discrimination at the node level and suppresses the spread of unreliable node information at the network level, achieving stable and robust distributed target state estimation in degraded scenarios.

The present application relates to a kind of cognitive diagnosis method and system based on heterogeneous relationship graph embedding, it aims at by the combination of graph neural network and metric learning, improve the accuracy and efficiency of cognitive diagnosis.The present application is specifically divided into four technical implementation stages of constructing multiple cognitive relationship graph and carrying out embedding processing, relationship perception coding based on graph neural network, metric learning and answer mode discrimination and joint training and model optimization.Specifically, the present application constructs multiple cognitive relationship graph and carries out embedding processing, and finely depicts the complex interaction between student, test and knowledge point.Based on graph neural network, relationship perception coding is carried out, and node embedding representation is updated, and different types of relationship information are fused.Combined with metric learning technology, the correct and wrong answer behavior of student is accurately distinguished.Finally, through joint training optimization model, the precision and robustness of cognitive diagnosis are improved, and it is suitable for personalized learning evaluation and intelligent education and other fields.

This application relates to the field of high-resolution orthophoto recognition technology, specifically to a method for identifying tobacco-crop intercropping patterns based on multi-layer semantic segmentation and row structure analysis. The method includes the following steps: acquiring high-resolution orthophotos of the target area; inputting the images into an improved HTC model for pixel-level semantic segmentation to distinguish the background, flue-cured tobacco, and other crops; identifying intercropping interference zones formed by flue-cured tobacco and its associated crops based on the semantic segmentation results and distance transformation; and extracting the row structure and identifying the intercropping pattern of the interference zones. The core of this application is to leverage the differences in spectral response, texture, and spatial geometric arrangement of crops in high-resolution orthophotos. Through a multi-layered collaborative process of "semantic segmentation – neighborhood selection – row structure analysis – pattern discrimination," it achieves accurate identification of intercropping patterns between flue-cured tobacco and other crops, realizing specialized modeling and representation capabilities for the typical intercropping pattern of alternating rows and columns of flue-cured tobacco and other crops.