131 results about "Information redundancy" patented technology

The invention discloses a lung cancer gene mutation classification method based on frequency domain multi-scale fusion guidance, and relates to the technical field of medical image processing and gene detection. According to the MFHA mechanism provided by the invention, the pathological image is decoupled into low-frequency global and high-frequency detail sub-bands through wavelet transform, and extraction of key high-frequency features such as cell nucleus morphology and local texture is enhanced by combining multi-scale convolution and up-sampling guided by high-frequency information; the problems of insufficient feature detail mining and low feature fusion efficiency in a traditional pathological image analysis method are solved; key features are screened and focused through a channel, frequency domain-space feature deep fusion is realized through up-sampling, robust representation is constructed by combining space attention with cosine similarity and multi-dimensional statistical features, a frequency domain analysis-space focusing collaborative optimization mechanism is formed, information redundancy caused by simple feature splicing is avoided, and the robustness of the system is improved. And the classification stability of the model in a complex pathological scene is improved.

The invention discloses a reservoir multi-target optimization scheduling decision-making method based on digital twinning and AI simulation, and relates to the technical field of reservoir scheduling, the method effectively solves the problems of data fragmentation and information redundancy in traditional scheduling by constructing a multi-source data fused reservoir digital twinning body, realizes accurate virtual-real mapping of reservoir working conditions, and improves the scheduling efficiency. A high-reliability simulation basis is provided for subsequent optimization scheduling, and decision errors caused by data errors or model deviation are avoided; four core targets of flood control, power generation, ecology and water supply are considered, an engineering safety bottom line and rigidity requirements are defined through a layered constraint system, constraint satisfaction degree screening is enhanced by means of an improved multi-target optimization algorithm, and target weights can be dynamically adjusted according to a real-time scene. And after the non-dominated scheme set is optimized and output, candidate schemes are screened in combination with real-time demand quantitative scores.

The invention relates to the technical field of computer intelligent operation and maintenance and artificial intelligence, in particular to an operation and maintenance scheme generation method, system and device based on cooperation of a large language model and a knowledge graph and a medium. The method comprises the following steps: denoising and abstracting multi-source heterogeneous operation and maintenance data by using a large language model, extracting key fact dimensions, further identifying a fault entity, mapping the fault entity to an operation and maintenance knowledge graph to position an initial anchor point, executing two-stage collaborative reasoning based on the anchor point, and generating a plurality of candidate traceability paths by alternately performing relationship exploration and entity exploration; and finally, reordering the candidate paths based on path length perception and semantic correlation, dynamically controlling the termination and understanding strategy of reasoning by using a comprehensive confidence score, outputting a final solution, effectively overcoming the problems of information redundancy interference reasoning and large model illusion in a complex operation and maintenance scene through the cooperation of a large model and a knowledge graph, and improving the reliability of the system. And the accuracy of root cause positioning and the executable performance of the solution are obviously improved.

The invention provides a partial discharge mode recognition method based on multi-channel sound signal time-frequency space spectrogram fusion, and belongs to the technical field of partial discharge mode recognition. The problem that information redundancy and difference between existing multi-channel sound signals cannot be effectively fused and discriminant features cannot be extracted is solved. The method comprises the following steps: collecting and summarizing multichannel sound signal data corresponding to different partial discharge models as a data set; performing normalization processing on the original multichannel partial discharge sound signal, and mapping a time domain signal into time-frequency joint distribution to obtain a gray scale time-frequency spectrogram; performing multi-scale and multi-direction decomposition on the grayscale time-frequency spectrogram to obtain each channel sub-graph; different fusion strategies are used for fusing the channel sub-graphs obtained through decomposition; reconstructing a time-frequency space fusion spectrogram from the fused sub-graphs of each channel through inverse transformation; extracting features; identifying a partial discharge mode; the method is applied to partial discharge detection.

The invention relates to the technical field of Internet of Things data processing, in particular to a multi-modal data real-time processing method for an Internet of Things gateway, and the method comprises the steps: obtaining multi-source operation data, and packaging the multi-source operation data into a standard operation data set; obtaining high-bandwidth modal data in the standard operation data set; when the maximum link delay time is greater than a preset delay abnormal threshold value, obtaining a version effective fingerprint of the target model and a local model fingerprint; when a model version abnormity diagnosis result is obtained, calculating information redundancy; obtaining correction delay time, and comparing the correction delay time with a preset normal reference delay interval; and when the correction delay time is greater than or equal to the upper limit value of the normal reference delay interval, adjusting the current decision period. By monitoring delay in real time, diagnosing consistency of model versions, eliminating redundant information and executing link adjustment, accurate processing of high-bandwidth modal data is realized, and real-time performance and reliability of Internet of Things gateway data processing are improved.

The invention provides a method and device for controlling vehicle functions, a vehicle and a storage medium, and relates to the technical field of vehicle function control. According to the method, function control information is determined, an analysis object can be clearly analyzed, and introduction of irrelevant data in the initial stage is avoided. Information redundancy needing to be processed by the preset large model is reduced from the source, and a foundation is laid for subsequent efficient analysis. And further performing semantic analysis on the function control information according to a layer-by-layer screening mode through a preset large model and a vehicle function formed based on a vertical progressive layered structure. According to the hierarchical structure, the preset large model does not need to traverse all vehicle functions, and each layer only focuses on the function of the corresponding layer, so that the number of functions needing to be processed by the preset large model can be greatly reduced, and the consumption of lexical elements is directly reduced. Meanwhile, layer-by-layer screening replaces one-by-one reasoning, the target range of each layer is rapidly narrowed, and all vehicle functions do not need to be analyzed one by one. The path for determining the function of the target vehicle can be remarkably shortened, and the determination efficiency is improved.

The invention discloses an ultra-lightweight low-resolution dark light face enhancement method and system, and belongs to the technical field of computer vision and artificial intelligence, and the method comprises the steps: obtaining a degraded image, and processing the degraded image into a shallow feature map; inputting the shallow feature map into a hierarchical network structure; setting a module group in each level of the encoder and the decoder; only the first Transform module is used for carrying out complete self-attention calculation; the other Transform modules except the first Transform module in the module group execute attention sharing operation; inputting the deep feature map into an image reconstruction module formed by a convolutional layer to generate a residual image; and carrying out pixel-level addition on the residual image and the input degraded image to obtain a recovered clear image. According to the method, the problems of high calculation complexity and information redundancy in the prior art are solved. According to the method, the calculation overhead is remarkably reduced through an attention sharing strategy, and the excellent recovery quality is ensured through wavelet enhancement.

Embodiments of the invention provide a data packet transmission method and apparatus, an electronic device and a readable storage medium. The method comprises the steps of obtaining a target data packet; extracting a target field from the target data packet; acquiring historical communication data of the network equipment, and extracting a field in the protocol frame from the historical communication data to determine information redundancy of the field; according to the importance degree corresponding to the information redundancy evaluation field, determining the importance degree corresponding to the field matched with the target field as the importance degree corresponding to the target field; splitting the target data packet into sub-data packets according to the importance degree corresponding to the target field and the network state of the network equipment, and determining a transmission strategy of the sub-data packets; the transmission strategy at least comprises a window size and / or a bandwidth proportion when the sub-data packets are transmitted; and transmitting the sub-data packets according to the transmission strategy. According to the embodiment of the invention, the target data packet is transmitted according to the importance degree of the field in the target data packet and the network state, so that the transmission efficiency and reliability are ensured.

The invention discloses a content abstract generation method based on chapter structure analysis, and belongs to the technical field of natural language processing. The method comprises the steps that firstly, an original text is preprocessed, then text structure deep analysis is carried out, the text type of the text is recognized, an explicit / implicit text relation is extracted, and special symbols are introduced through a Prompt normal form for implicit text relation extraction to strengthen logic semantics; secondly, scoring sentences by adopting a double-path scoring mechanism in combination with a deep neural network model of chapter structure features and an optimized text sorting algorithm, and fusing scores through a logistic regression model; then, screening target sentences based on a chapter relation weighted secondary modulus function and a greedy algorithm, and finally, carrying out post-processing to generate an abstract. According to the abstract generation method, the chapter structure logic is deeply utilized, so that the problems of logic unsmoothness, information redundancy or key relation missing in the existing abstract generation are solved, and the semantic coherence and information integrity of the abstract are improved.

The invention discloses a ViT model lightweight method for video action recognition, which comprises the following steps of: performing motion estimation in a data processing stage, calculating to obtain motion intensity and time redundancy scores of tokens, and performing window division on video frames according to the scores. Afterwards, a window-global token merging strategy is adopted to effectively merge redundant tokens, the first three layers of the model process local information through adaptive window merging, and from the fourth layer, the model repeatedly executes global merging according to the depth of the layers so as to realize gradually enhanced feature representation; therefore, the number of tokens is reduced, the complexity of the ViT model in space-time self-attention calculation is reduced, and the problem of high calculation resource consumption caused by space-time information redundancy of the current ViT model in a video task is solved. Calculation overhead and energy consumption can be remarkably reduced in tasks such as video action recognition, action detection and time sequence event understanding, and meanwhile the characterization capacity of a key dynamic area is kept.

The invention relates to the technical field of product traceability information management, in particular to a product traceability authentication full-process management system, which comprises the following steps of: determining equipment feature tags of different user types by analyzing equipment communication information in historical account login information, and matching current user equipment scanning information to identify the type of the current user equipment; historical browsing information is called based on the user type, the occurrence frequency of page browsing content and a node time coefficient are analyzed, a node content expression coefficient is calculated to generate information arrangement data, and finally the data are displayed on terminal equipment, so that a product full-process traceability data closed loop is realized, the authentication credibility is improved, and the user experience is improved. And meanwhile, the user type is accurately identified and the service is adapted, so that information redundancy is avoided, the terminal experience is optimized, the user acceptability is enhanced, and the problems of insufficient whole-process coverage and extensive service of a traditional system are further solved.

The invention relates to the technical field of power equipment detection, and discloses a GIS foreign matter recognition and positioning method and system based on a multi-sensing double-order fusion network, and the method comprises the steps: synchronously collecting ultrahigh frequency, ultrasonic wave and pulse current signals of GIS partial discharge through a multi-mode sensor, and constructing a discharge pulse correlation octree after clock synchronization and adaptive noise reduction; foreign matter recognition is completed by means of feature-level and decision-level dual-order fusion, and accurate positioning of a discharge source is achieved by combining initial positioning of a time difference method and bisecting face iteration. And furthermore, the foreign matter motion trail is restored, the discharge risk is evaluated, and a graded early warning instruction is output. According to the method, multi-modal features can be effectively associated, information redundancy is eliminated, GIS foreign matter identification accuracy and positioning precision are improved, and dynamic assessment and graded early warning of discharge risks are realized.

The invention discloses a robust intelligent fault diagnosis method and system based on dynamic hypergraph semantic fusion and adaptive decoupling distillation. Comprising the steps of multi-source heterogeneous signal synchronous sample generation, cross-modal space-time hypergraph semantic map construction, self-adaptive decoupling distillation model construction, multi-target game optimization and self-adaptive fusion, and uncertainty perception decision and early warning. The incidence relation between different modals and scale features is modeled through high-order hyperedge, advantage complementation and efficient fusion of the features are achieved, and the ability of capturing tiny fault abnormal details and the robustness of the model are remarkably enhanced. Dynamic weighted fusion and interaction among multi-modal and multi-granularity features are realized through a cross-granularity dynamic adaptive fusion layer, the problems of information redundancy and low fusion efficiency in a traditional feature splicing method are solved, and continuous prediction and early fault early warning of the equipment health state are realized. And the decision reliability in a complex industrial scene is improved.

The invention provides double-granularity alignment efficient partial correlation video retrieval based on implicit fragment modeling and semantic decomposition, and aims to solve the problems of information redundancy and low efficiency in an existing video modeling method, the problem of granularity mismatching between sentence representation and video frame features and the problem that alignment between a text and a video is not refined enough. According to the method, the expression and modeling of video data are optimized by introducing a structure combining a Gaussian mixture model and Transform, and multi-scale local details with short time span are adaptively integrated by introducing a window attention mechanism and a cross attention mechanism, so that finer features are obtained, and cross-modal similarity score calculation of texts and videos is facilitated. The method comprises the following steps: data preprocessing and frame segmentation: preprocessing and segmenting an input video into frames, and preparing for feature extraction after each frame of image is subjected to standardization processing; implicit modeling is carried out on the fragment-level features, uniformly sampled frame-level visual features are input into a Gaussian mixture modeling module, adjacent frame focusing modeling is carried out through a multi-scale Gaussian attention mechanism, and fragment-level representation of different receptive fields is implicitly formed. Enhancing local details of the frame-level features, performing convolution operation on each frame by adopting windows with different scales, and calculating a feature relationship in each local window; local features of all scales are fused through a cross attention mechanism, semantic expression of frame features is enhanced, and it is ensured that each video frame can capture important local details.

The invention provides a video description information generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target video of to-be-generated video description information; segmenting the target video to obtain a plurality of video clips; extracting a key frame set corresponding to each video clip from each video clip; performing character recognition on the key frame set to obtain text information corresponding to each video clip; inputting the key frame set corresponding to each video clip and the text information into a preset target model to obtain clip description information corresponding to each video clip; and generating video description information of the target video based on all the fragment description information. By means of the information processing method and device, the technical problems of key information loss and irrelevant information redundancy in the related technology are solved.

The invention discloses a motor imagery electroencephalogram signal classification method and device, a terminal and a storage medium, and relates to the field of biomedical signal processing.The method comprises the steps that electroencephalogram signals based on user motor imagery are obtained and preprocessed, and electroencephalogram features are determined; performing multi-scale spatio-temporal feature extraction and space and channel decoupling reconstruction on the electroencephalogram features, and determining target spatio-temporal enhancement features; and classifying the target space-time enhancement features through a classification output layer, and determining a classification result. Due to the fact that space and channel decoupling reconstruction is carried out on the features, redundant correlation of the cross-electrode electroencephalogram signals is systematically eliminated, and the problems that in the prior art, space and channel information are jointly processed, information redundancy is caused, and calculation burden is increased can be effectively solved.

The application relates to the technical field of agricultural early warning, in particular to a multi-stage linkage agricultural flood early warning information automatic broadcasting system, and the following scheme is provided: a sensing terminal collects farmland images, combines environmental data, analyzes waterlogging features by using a graph neural network, and thus precise flood disaster risk assessment is realized. After disaster identification, the system dynamically adjusts the broadcasting range and the communication protocol according to the risk level, so as to ensure that the early warning information is rapidly transmitted to the receiving terminal. In addition, an iterative operation mechanism is adopted, a cluster head terminal is selected according to the consistency of the waterlogging features, information redundancy transmission is avoided, and network resource use is optimized. The application improves the real-time performance, accuracy and resource utilization efficiency of the flood disaster early warning system, and has obvious innovation and application value.

The application discloses a kind of based on multi-modal feature fusion sentiment analysis method and system, comprising: through Bi-GRU capture context relationship between text modal, speech modal and image modal each other, while based on cross-modal attention mechanism, text modal, speech modal and image modal are combined two by two, obtain the interactive sentiment representation between text-image, text-speech and image-speech modal, through the multi-head attention mechanism of regular term, text modal, speech modal and image modal are carried out joint sentiment representation, obtain the interactive sentiment representation of three kinds of modal, finally single modal, double modal and three modal emotion feature cascade are classified finally emotion.The application solves the problem that feature information is not enough rich due to the modeling of context information in the existing multi-modal sentiment analysis algorithm, also solves the information limited problem when using single-head attention mechanism for feature learning and the feature information redundancy problem existing in multi-head attention mechanism.

The present disclosure relates to an information sending method and device, equipment and a storage medium, and relates to the technical field of Internet. In the present disclosure, for different replacement scenarios of the same object, a corresponding relationship is established based on exclusive sub-information in each replacement scenario and stored in a relationship database. Thus, the terminal subsequently obtains exclusive sub-information in each replacement scenario from the relationship database to display the object, thereby realizing differentiated display of the object information in different replacement scenarios of the same object. For each replacement scenario, the exclusive information is displayed specifically, the demand of the user in each replacement scenario is accurately hit, information redundancy is reduced, the display effect of the object is improved, and the conversion rate of the object is improved.

This invention relates to the field of equipment insulation defect detection technology, and in particular to a method for constructing a rotationally invariant partial discharge (PDD) map for insulation defect diagnosis, as well as an insulation defect diagnosis method. The method for constructing the PDD map includes: acquiring PDD signals, determining the origin of the polar coordinate system, and constructing a polar coordinate system; determining the optimal number of display points for map plotting based on clustering quality evaluation; and performing a final resampling of the original PDD signals based on the optimal number of display points, mapping the sampling results to the polar coordinate system to generate a rotationally invariant PDD map for insulation defect diagnosis. This method adaptively determines the optimal number of display points, avoiding both insufficient points leading to blurred feature information and excessive points introducing information redundancy. This improves the comparability of the maps and the consistency of feature extraction, thereby significantly enhancing the accuracy and robustness of subsequent deep learning models in identifying insulation defects.

The invention discloses an equipment defect identification method based on multi-modal information fusion and self-adaptive information optimization, and aims to solve the problems that a traditional single-modal defect identification method is difficult to effectively fuse multi-source heterogeneous information in a substation inspection log, and the model efficiency is low due to information redundancy. The method comprises the following steps: firstly, constructing a gating level attention multi-modal feature fusion network, and realizing deep integration and cross-type interaction modeling of multi-modal information; secondly, designing an information attribute importance analysis mechanism, and quantifying the contribution degree and redundancy of each information attribute to defect identification; then, constructing a self-adaptive information attribute selection framework, and realizing accurate identification through joint optimization of information attribute screening and model training; and finally, an intelligent identification system covering data processing, feature fusion, attribute optimization and defect prediction is established.

The invention relates to a retrieval enhancement generation method, system and device based on information optimization and a storage medium. Aiming at the technical problems of high calculation cost and damaged generation quality caused by context information redundancy and noise accumulation due to simple Top-K retrieval in the existing retrieval enhancement generation technology, the invention creatively introduces the concept of ''in engineering thermodynamics, establishes isomorphic mapping of information retrieval and thermodynamics, and improves the information retrieval efficiency. A high-value information unit is preferably selected from a retrieval result by calculating an'information 'value fusing semantic correlation (information temperature) and redundancy (information invalid performance) and adopting a self-adaptive dynamic screening algorithm based on marginal maximization and dead-state temperature threshold truncation. According to the scheme, the length of the input context and the calculation cost can be remarkably reduced while the generation quality is ensured, the robustness of the system in a noise environment is improved, and high-efficiency and high-quality retrieval enhancement generation is realized.

The invention provides an oil and gas pipeline leakage detection method and system fusing multi-modal characteristics, and belongs to the field of pipeline leakage detection. The invention aims to solve the problems that in a signal conversion link during pipeline leakage detection, a traditional Grubrum angle field does not consider local time sequence correlation sufficiently to restrict feature representation, single-mode processing has information one-sided, multi-mode fusion lacks deep interaction, and information redundancy is difficult to effectively reduce. The method comprises the following steps of: performing conversion from a one-dimensional time sequence signal to a two-dimensional image on a pipeline signal by adopting a wavelet threshold filtering and angle calculation weighting improvement-based Gramb angle difference field method; extracting the spatial characteristics of the two-dimensional GADF image through a Swin Transform, and obtaining the time sequence characteristics of the one-dimensional time sequence signal through a gating circulation unit; and finally, carrying out weighted fusion on the spatial features and the time sequence features by utilizing a weighted cross attention mechanism module, capturing cross-modal relevance, carrying out classification by utilizing fused features, and identifying different working conditions of the pipeline.

The application provides a coding and decoding method, system, storage medium and equipment based on entropy coding, the method comprising: in response to part of the symbols in the to-be-coded symbols having been coded, taking the sum of the probabilities corresponding to the remaining symbols which have not been coded as a remaining probability; in response to the current symbol being prepared to be coded, calculating the code length corresponding to the current probability of the current symbol, and calculating a threshold based on the code length and the remaining probability; modifying the code length based on the threshold to obtain a new code length; obtaining a current code word based on the current probability and the threshold, and decoding the current code word. The application solves the problem that the equal-length coding method in the prior art causes information redundancy in entropy coding, reduces the bit width required for coding, and in turn reduces the data quantity, which is beneficial to saving storage space.

The application discloses a method for extracting keywords information of bidding and opening bidding based on regular expression and scrapy, comprising the following steps: S1, determining the analysis field of the bidding and opening bidding project; S2, collecting and sorting the keyword list of the field, wherein the keyword list is created as an ordered keyword list or an unordered keyword list according to the field requirement; and S3, selecting the subsequent processing method according to the specific field condition. By combining the regular expression and the scrapy, the application can extract multiple keywords information of the bidding and opening bidding project, so as to avoid the problem of missing search. The application respectively extracts the keywords information of the bidding, the winning bidding and the opening bidding project, can carry out targeted query extraction according to the different keyword extraction modes of the opening bidding project, realizes the quick extraction of important information in the text, removes the useless data, solves the problems of information redundancy, reading difficulty and the like.

The present application relates to an unsupervised speech recognition modeling method based on phoneme segment level representation discretization, belonging to the field of speech recognition. The present application uses an IFMF model to extract phoneme features from the original audio through a speech feature discretization module, then trains a K-Means clustering algorithm to cluster the audio representation, and obtains the clustering index as a discrete label; through an adversarial learning module, a generator network and a discriminator are trained; through a speech discrete representation decoder module, a language model is used to decode the phoneme discretization-based model output obtained by unsupervised training. The present application fuses multiple speech features, adopts a phoneme segment level representation discretization method, considers the influence of tone information on the Lao language, and as much as possible reduces the influence of information redundancy caused by fine-grained features on cross-modal modeling. The method of the present application achieves competitive results compared with traditional speech recognition methods.

A video retrieval method based on a three-dimensional convolutional neural network of a fusion feature gate, which is composed of the steps of dataset preprocessing, dataset division, three-dimensional convolutional neural network construction, three-dimensional convolutional neural network training and three-dimensional convolutional neural network testing. The three-dimensional convolutional neural network is optimized and improved, the technical problem of low retrieval accuracy in the prior art is solved, and a different conception scheme is provided for solving similar problems. Three feature gates are adopted, the feature gate is composed of a reset gate and an update gate, the technical problem of video information redundancy in the prior art is solved, the video feature information can be more accurately extracted, and the foundation for further retrieval is laid. The present application has the advantages of high retrieval accuracy, fast retrieval speed and good retrieval effect, and can be used for video image retrieval.

The invention discloses a root tracing method and device for knowledge content, equipment and a readable storage medium. The method comprises the following steps: positioning at least one piece of difference information from target answer content uploaded by a target user; the root cause classification of each piece of difference information is determined, the root cause classification is the error type to which the information corresponding to the point losing point belongs, and the error type comprises information known and information unknown; and / or the error type comprises information missing and information redundancy; the root cause tracing result of the difference information under the error type is determined through the large language model, and the root cause tracing result of the difference information can be displayed to the user, so that the user can perform improvement based on the root cause tracing result of the difference information, and the analysis ability of the user is helped to be improved.

The application provides a trackless navigation texture information redundancy analysis method, comprising: processing original image data of a current wear area by using an edge detection algorithm to obtain wear boundary contour coordinates and gray distribution data; extracting boundary coordinate point sets from a reliable anchor point area and determining an optimized update frequency value according to the position and shape matching degree of the boundary coordinate point sets and historical path map data; incrementally updating the path map by using the optimized update frequency value, integrating the gray distribution data of the reliable anchor point area and the texture detail loss degree value, and generating an enhanced positioning reference data set; updating the enhanced positioning reference data set according to supplementary data and performing periodic matching degree evaluation according to the optimized update frequency value to maintain long-term positioning consistency.