46 results about "Similarity data" patented technology

The invention relates to the technical field of electrical digital data processing, and discloses a statistical entropy and multi-factor fused text similarity data processing method, which comprises the following steps that: a processor extracts substring sets which do not contain maximum common values of a first data sequence and a second data sequence, and calculates the quadratic sum of the lengths of substrings to generate local statistical entropy; traversing the maximum common substring set to obtain storage address indexes of the maximum common substring set in the first data sequence memory space and the second data sequence memory space, and constructing a topological mapping vector of a mapping structure displacement relationship; calculating the total number of inverted pairs of the topology mapping vector by using a merge sorting algorithm, and generating a normalized topology dissipation index; and by taking the local statistical entropy as an information carrier and taking the topological dissipation index as a structural damping factor, executing nonlinear damping modulation operation to obtain a final similarity score, and solving the technical problem that the block-level displacement cannot be identified by linear scanning logic by quantizing topological entropy increase of data distributed in a storage space.

The invention discloses a power supply control method and system based on a real-time slope adjustment algorithm, and the method comprises the steps: monitoring the load impedance characteristics of a power supply and environment noise data in real time, and obtaining an independent control time slice set; obtaining a time sequence identifier of the control time slice set, and obtaining a dynamic mode parameter packet bound with the current load characteristic; obtaining a real-time voltage / current regulation instruction set with a feed-forward compensation coefficient according to the dynamic mode parameter packet and the error tolerance data; analyzing the real-time voltage / current regulation instruction set, and combining the historical working condition similarity data to obtain an optimized PID coefficient matrix and a feed-forward gain vector; and executing the optimized PID coefficient matrix and the feed-forward gain vector, and generating and outputting a four-dimensional space trajectory signal which is completely matched with the target slope trajectory as a direct driving reference of a power supply power device. According to the embodiment of the invention, high-precision and low-jitter real-time slope tracking control can be realized, and the dynamic response speed and anti-interference robustness of a power supply system are improved.

This disclosure discloses a method for monitoring the state of a chamber using a chamber monitoring device. The method may include: measuring the state of the chamber to generate multiple state data; calculating the similarity between each state data and reference state data to obtain multiple state similarity data; obtaining multiple state similarity patterns by matching processing segment data of each processed segment with the multiple state similarity data; and using two or more state similarity patterns to determine whether the chamber is operating normally or to predict the time point of abnormal chamber operation.

This invention discloses a method and system for track fusion of ships. The method includes: first, selecting radar data and ESM data of all targets before a certain time point; performing spatial and temporal registration of the radar data and ESM data of all targets; then, calculating the similarity between data pairs based on a nearest neighbor fast discrimination method, including: distance similarity between radar data pairs, carrier frequency similarity between ESM data pairs, and azimuth similarity between radar data and ESM data; then, determining whether the data pairs correspond to the same target based on the similarity between the data pairs; if the data pairs correspond to the same target, associating the data pairs, and finally performing weighted fusion of the associating radar data and ESM data. Compared with existing technologies, this method can solve the problems of sparse data in the early stage of monitoring and the existence of detection interference and monitoring errors during the process, thereby improving the efficiency of track fusion.

The application discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG. The method first extracts business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructs a business dictionary and an ontology tree containing term similarity data. Then, the release agency weight, time effectiveness coefficient and penalty correlation degree of the regulatory provisions are quantitatively calculated to obtain an authoritative value and sort. Then, the pre-trained large language model is fine-tuned as a vector representation model using a double-constraint loss function, and the business data is vectorized and stored in a vector database. Finally, for user queries, the vector database is retrieved and combined with the regulatory provision sorting result for rearrangement, and a business response is generated after compliance verification. The method can improve the unstructured knowledge processing capability and retrieval accuracy of the bank, and realize efficient intelligent operation.

The invention relates to an exposure sequence adaptive optimization method and device for multi-view automatic measurement, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring an original exposure time sequence of a plurality of viewpoints; the original exposure time sequence comprises a plurality of exposure segments, and each exposure segment corresponds to one viewpoint; calculating similarity data among the exposure segments, and grouping the exposure segments according to the similarity data to obtain a plurality of initial exposure groups; for each initial exposure group, re-grouping the exposure segments at the boundary position to obtain a plurality of boundary grouping results, and calculating grouping cost data for each boundary grouping result; and selecting a target grouping result from the boundary grouping results according to the grouping cost data, optimizing the target grouping result according to the grouping cost data to obtain a plurality of target exposure groups, and calculating the exposure time of each target exposure group. The method can shorten the measurement period.

The application provides a sea area digital twin method and system based on buoy and station data fusion, belongs to the related technical field of sea area digital twin, through constructing a multi-source heterogeneous marine data acquisition system, establishing a data quality evaluation mechanism and a similarity data compression mechanism, realizing intelligent fusion processing of buoy data and station data, solving the problem of low data fusion precision in traditional technology; through the self-organizing growth network structure of the marine dynamic response enhancement model, the neuron connection can be automatically adjusted according to the spatio-temporal variation degree of the training data, forming an optimal topological structure, effectively solving the defect that the traditional fixed parameter model cannot adaptively process complex marine environment; through the establishment of a dynamic parameter adjustment mechanism and a multi-algorithm adaptive switching strategy, the most suitable prediction algorithm is automatically selected according to the correlation characteristics of the marine data sequence, realizing high-precision digital reproduction of complex marine environment.

This invention relates to a method and system for detecting internal short circuits in a battery. The method includes acquiring constant current charging data of the battery under different charging cycles; the charging conditions are the same for each charging cycle; performing time alignment processing on the constant current charging data corresponding to each charging cycle to obtain a first feature curve; performing interval sampling on the first feature curve to obtain a second feature curve; calculating battery similarity data based on the second feature curve; and determining that the battery is in an internal short circuit state when the similarity data is greater than a judgment threshold. By collecting constant current charging data under different charging cycles, performing time alignment processing and interval sampling to obtain curves, and calculating battery similarity data under different cycles based on these curves, the battery is determined to be in an internal short circuit state when the similarity data is greater than a judgment threshold. This method is easy to execute online in real time in resource-constrained embedded battery management systems, does not rely on a specific battery model, and has strong stability in detecting internal short circuits in batteries.

The present application provides an evaluation method and a data generation method. The present application is to appropriately evaluate the accuracy of recognition results obtained by a recognition model. The evaluation method of work recognition accuracy includes: a result acquisition step of acquiring a recognition result obtained by a recognition model configured to recognize a work category of a work related to a work of a work person by inputting data related to the work of the work person; a correct answer acquisition step of acquiring a correct answer category corresponding to the recognition result and indicating an actual work category; and an evaluation step of evaluating the accuracy of the recognition result using similarity data, the similarity data being data in which the similarity of a plurality of work categories to each other is defined in advance and including the similarity between the work category of the recognition result and the correct answer category.

The invention discloses a multi-modal image generation system and method based on AI image analysis, and relates to the technical field of image processing, definition evaluation is performed based on pixel gray value data of each region of a to-be-processed image, and each key region of the to-be-processed image is determined according to a definition evaluation result; obtaining an image health response index based on the density data of each region of the to-be-processed image, the image recognition result accuracy data after each region of the to-be-processed image is missing, and the similarity data of each region of the to-be-processed image; and obtaining a processing demand response index based on the definition evaluation result and the image health response index of each key area of the to-be-processed image, performing corresponding operation on each key area of the to-be-processed image according to the processing demand response index, and performing corresponding definition processing operation on each key area of the to-be-processed image. The accuracy of information transmission in the network is ensured, a large number of computing resources are saved, the data storage space is reduced, and the response speed and stability of the whole system are improved.

The application discloses a feedback data generation method, a question and answer system, a question and answer device and a refrigerator. The method comprises the following steps: receiving question data, performing fuzzy search and calculation in a feedback basic database according to the question data, obtaining a standard question sequence and at least two groups of target question sequences; performing bidirectional recurrent neural network traversal prediction and window sliding convolution on the standard question sequence and the target question sequence respectively, and correspondingly obtaining a standard question feature matrix and a target question feature matrix; performing projection fusion on the standard question feature matrix and the target question feature matrix, and assigning an attention vector to the standard question feature matrix and the target question feature matrix according to the obtained question judgment matrix, and comparing and calculating to obtain question similarity data; sorting the target question sequences according to the question similarity data, searching and outputting the final answer corpus of the target question sequence corresponding to the maximum question similarity data. The application can improve the accuracy and speed of feedback and simplify the architecture.

The invention relates to a decoupling double-speed self-adaptive test method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a tested image sample and a template cue word; wherein the template cue word is set based on the category of the trained task; obtaining a pre-trained target decoupling double-speed adaptive model, and performing feature extraction on the image sample and the template cue word according to the target decoupling double-speed adaptive model to obtain image features and text features; wherein the pre-trained target decoupling double-speed self-adaptive model is obtained through a decoupling double-speed self-adaptive fine tuning step; performing similarity calculation on the image features and the text features to obtain similarity data; and testing the target decoupling double-speed adaptive model according to the similarity data to obtain a test result. By adopting the method, the plasticity and the stability of the model can be effectively balanced under the condition of data imbalance, and the test performance of the head class and the tail class is considered at the same time.

The present application relates to hyperspectral image processing technical field, disclose a kind of fast medical hyperspectral image classification method, comprising: extracting several pixels as training sample from the medical hyperspectral image to be classified;Set initial neighborhood window scale;According to initial neighborhood window scale, randomly select joint training area from the medical hyperspectral image to be classified;The similarity result between each pixel in joint training area and training sample is obtained by joint classification algorithm calculation;By k times iteration cross-validation algorithm, the initial neighborhood window scale is optimized, and the best neighborhood window scale is obtained;Using the best neighborhood window scale and joint classification algorithm, all pixels of the medical hyperspectral image to be classified are classified.Cotangent mapping cosine similarity can effectively reduce the interference of heterogeneous pixels, and local similarity data gravity can adaptively balance the contribution of minority and majority, to ensure the full use of MHSI spatial spectral information.

The application provides a method and device for determining fault type of a numerical control machine tool, wherein the method comprises: obtaining a first vector and a plurality of second vectors in a database, wherein the database is a database for storing knowledge of the numerical control machine tool in a manner of a knowledge graph; the first vector is used for representing an entity of the numerical control machine tool that has a fault, and the second vector is used for representing a fault type of the numerical control machine tool; calculating corresponding fault type similarities according to the first vector and the plurality of second vectors to obtain similarity data; and determining the fault type of the numerical control machine tool according to the similarity data. Through the application, the problem that the fault type is determined in a manner of manual intervention in the prior art, resulting in low fault maintenance efficiency, is solved.

The present application relates to a kind of adaptive empathic dialogue methods based on quantitative emotional analysis, by using pre-training language model encoder, the dialogue sentence of user input is encoded and processed as context-related hidden state sequence, the sentiment contribution of each vocabulary in the dialogue sentence to dialogue history is quantitatively calculated, obtain global sentiment representation data, global sentiment representation data is respectively mapped to different polarity sentiment space and fine-grained emotional category space, obtain sentiment polarity probability data and emotional probability data;Global sentiment representation data, sentiment polarity probability data and emotional probability data are used to quantitatively calculate the emotional clues in current speech, the dialogue target is generated using large language model and the similarity data representing the matching of emotional clues and encoded dialogue target is obtained, the response strategy is optimized based on the similarity data, and the personalized empathic dialogue matching the current dialogue sentence of user is generated, accurately realizing the personalized adaptive empathic response to user emotion.

This invention provides a talent evaluation and management method and system based on artificial intelligence, relating to the field of artificial intelligence technology. The method includes: Step 1, acquiring resume data of the talent to be evaluated, and performing text recognition processing on the resume data to generate a structured text dataset; Step 2, extracting keyword feature vectors from the structured text dataset, calculating the similarity value between the keyword feature vectors and a preset talent evaluation keyword library, and generating initial similarity data; Step 3, constructing a three-dimensional point cloud dataset based on the initial similarity data, wherein each data point contains the weight coordinate information of the keyword. This invention, through structured processing, multi-dimensional feature analysis, and dynamic correction, achieves a combination of objective standardization and three-dimensional dynamics in talent evaluation, improving the comprehensiveness and accuracy of the evaluation.

The present application relates to the technical field of corn harvesting, and discloses a corn automatic harvesting system and method. The method counts a plurality of monitoring subareas of a corn planting area, deploys an image acquisition device, acquires growth characteristic data such as corn cob maturity distribution, stalk strength and leaf coverage density of each subarea, acquires real-time warehouse capacity data and conveying equipment operation state data of a warehouse scheduling center, determines initial harvesting parameters including harvesting mechanical advancing speed and corn cob stripping strength, judges whether corn cob maturity distribution data triggers a harvesting parameter adjustment condition, acquires plant image data of all subareas and extracts features to obtain plant morphological characteristic data when the condition is triggered, calculates subarea harvesting priority characteristic values according to the plant morphological characteristic data, matches the subarea harvesting priority characteristic values with a historical database, adjusts the initial harvesting parameters to obtain dynamic harvesting parameters according to a matching result, and calculates a similarity data set and corrects the dynamic harvesting parameters based on the similarity data set if there is no matching value in the historical database.

This invention relates to the field of application development technology and provides a method for identifying duplicate change orders in engineering projects. First, engineering information is correlated and integrated to obtain an intermediate dataset with a logical order of project implementation. Text extraction and data cleaning are then performed on the intermediate dataset to form change order text data. Next, the change order text data are paired based on instruction keywords to form an instruction control group. Then, a dual-thread similarity framework is used to calculate the similarity between two change order text data in the instruction control group to obtain first and second similarity data. If the first and second similarity data meet the duplication condition, the instruction control group is considered as a duplicate change order. In this way, duplicate change orders can be automatically identified, allowing for timely detection of problems and mitigation of company losses.

An embodiment for an improved method of automated version management for machine learning pipeline development is provided. The embodiment may compute a quality value of a target machine learning pipeline at a predetermined regular interval and automatically save updated versions of the target machine learning pipeline. The embodiment may extract a series of key features from detected versions of the target machine learning pipeline and cluster the detected versions of the target machine learning model pipeline by the extracted series of key features. The embodiment may identify a highest-quality version within each of a series of generated clusters. The embodiment may compute similarity scores for subsets of versions within each of the series of generated clusters. The embodiment may generate and output to a user, a visual representation of the series of generated clusters including version quality data and version similarity data.

The application provides a kind of intelligent control method of aircraft based on dynamic teaching deep reinforcement learning, comprising: constructing aircraft feedback control architecture: with deep neural network as aircraft intelligent controller, with instruction signal and real-time state as neural network input, with actuator control signal as neural network output;Constructing aircraft simulation environment containing implicit parameter random pull bias;According to implicit parameter, construct expert controller;Constructing intelligent controller neural network model: intelligent controller adopts Actor-Critic network structure when training;Constructing mixed experience pool, the data maintained by mixed experience pool includes: experience dataset, priority dataset and environment similarity dataset;Determined policy gradient is combined with dynamic expert teaching deep reinforcement learning algorithm to train deep neural network, and control strategy for maximizing the realization of flight index is obtained.The application can improve the control quality of aircraft control method facing high uncertainty of implicit dynamic parameters.

The valve condition diagnosis device shows in detail a change and a predicted change of the actuation status of the valve. In the valve condition diagnosis device, when the angle sensor detects an actuation of the valve, the processor executes: obtaining, from an angle sensor, an angle profile related to actuation of the valve; histogramming the acquired angle profile and averaging a plurality of previous and subsequent actuations to obtain a relative frequency; calculating, by using a histogram intersection method, a similarity between the histogram of the angle profile converted to the relative frequency and a histogram of an angle profile at an initial stage of operation; plotting the calculated similarity of the angle profile on a two-dimensional plane in which a horizontal axis represents a similarity of the angle profile in an opening motion and a vertical axis represents a similarity of the angle profile in a closing motion; and performing clustering processing on plotted data of the similarity of the angle profile generated by repeating the plotting for a plurality of times.

This invention discloses a multimodal data management method for computing power networks. First, a multimodal similarity-preserving hash algorithm based on latent alignment is designed, using text modalities as intermediate modalities. Through adversarial networks, the common representation of input data and corresponding text modal data is learned to generate multimodal data labels. Then, semantic relevance-preserving labels are extracted from the multimodal data using computing power network nodes to ensure the association between cross-modal similarity data. Subsequently, semantic feature extraction based on generative adversarial networks is used to extract shared feature vectors and deduplicate multimodal data similarity, significantly saving storage space. Finally, data recovery is performed based on latent diffusion technology, using the computing power network to restore the semantic similarity from shared feature vectors to the original data. This method can significantly save storage space and transmission bandwidth, effectively maintaining data availability while improving the efficiency of multimodal data management.

The invention relates to the technical field of video monitoring, and particularly provides a foreign person intrusion identification method, system and device and a storage medium, and the method comprises the steps: obtaining snapshot event data of a camera, the snapshot event data comprising snapshot image data and snapshot attribute data; performing data structure mapping on the snapshot image data and the snapshot attribute data to generate to-be-identified data; performing face region extraction, posture correction and feature extraction on the to-be-recognized data to obtain similarity data and feature vectors of faces, and obtaining recognition result data based on the similarity data, the feature vectors of the faces and the snapshot attribute data; and judging the identification result data based on a preset judgment rule to obtain identity judgment data. According to the invention, monitoring snapshot is automatically processed, identity determination is completed, identification accuracy is improved, manual dependence is reduced, and missing detection is reduced.

The application discloses an image segmentation method and device, electronic equipment and storage medium. Including: obtaining image data to be segmented, segmenting the image data to be segmented based on preset channel threshold data to obtain an initial segmentation result including at least one initial segmentation region; determining the prompt point information corresponding to each initial segmentation region, inputting the prompt point information and the image data to be segmented into a preset image segmentation model for processing to obtain a first image segmentation result of the image data to be segmented; obtaining preset text prompt information, inputting the preset text prompt information and the image data to be segmented into the preset image segmentation model for processing to obtain a second image segmentation result of the image data to be segmented; determining a target image segmentation result of the image data to be segmented based on the similarity data of the first image segmentation result and the second image segmentation result. The segmentation result can be determined without additional annotation data, improving the high precision and adaptability of image segmentation.

The invention provides a method for predicting miRNA-disease interaction based on a ternary graph hybrid architecture and a difficult sample mining framework, and solves the problems that only a miRNA-disease binary relationship is modeled, the sample is unbalanced and data leakage is easy to cause in evaluation in the existing method. The method comprises the following steps: fusing miRNA, disease and lncRNA multi-source similarity data, combining a known association structure ternary heterogeneous graph, and updating features through biological prior element path enhancement and low-rank optimization; the method comprises the following steps: extracting features by a cascade-parallel framework, modeling a ternary regulation relation by a cascade branch by using a bio-guided ternary graph Transform, learning multi-view features by a parallel branch through a dynamic heterogeneous graph convolutional network and an enhanced hypergraph convolutional network, performing node-level adaptive fusion and integration, converting the features by an attention aggregation module, and finally performing clustering and clustering. Zero-leakage evaluation is realized by matching with disease perception cross validation; a focus difficult sample mines a balance training set, and a multivariate loss optimization model outputs a prediction matrix; validity is verified through parameter analysis, module ablation experiments and cancer cases.

The invention discloses a target vector model construction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring each first vector model; identifying preset text data by utilizing the first vector models to obtain text similarity data corresponding to the first vector models; screening the first vector models based on the text similarity data corresponding to the first vector models to obtain a second vector model; wherein the second vector model is composed of at least one first vector model; and performing iterative training on the second vector model according to the text similarity data corresponding to the second vector model to obtain a target vector model. According to the technical scheme, the text similarity can be recognized based on the target vector model, and efficient calculation processing and low resource cost consumption are achieved while high matching precision is guaranteed.

The application discloses a kind of construction methods and devices of high-speed rail technology evolution graph.The construction method includes: obtaining patent data set related to high-speed rail technology and classifying according to technical field, obtain multiple technical subsets;Based on PatentsBERTa model and cosine similarity calculation formula, obtain multiple similarity data sets;Obtain the similarity threshold of each technical subset, and based on the similarity threshold, obtain multiple candidate patent groups corresponding to each technical subset respectively;Based on the candidate patent group corresponding to each technical subset, build the time evolution graph and citation network graph corresponding to each technical subset, and convert into network data using graph projection, construct multiple evolution knowledge graphs;Obtain the target technology of pre-set, and based on the evolution knowledge graph corresponding to the technical subset of target technology, construct the evolution subgraph of target technology.The construction method provided by the application can identify the evolution path of each sub-technology in high-speed rail technology.

The embodiment of the application discloses an information extraction model training method, an information extraction method, an information extraction device, an information extraction equipment and a medium. The information extraction model training method comprises the following steps: obtaining information extraction training data; inputting the information extraction training data into a label vector output submodel of an information extraction model for label identification to obtain a label representation vector of the information extraction training data; inputting the information extraction training data into a character vector output submodel of the information extraction model for character identification to obtain a character representation vector of the information extraction training data; calculating similarity data of the label representation vector and the character representation vector; and inputting the similarity data into a classifier model of the information extraction model for classification identification to obtain an information classification result of the information extraction training data. The technical scheme of the embodiment of the application can improve the training efficiency and model precision of the information extraction model, and further improve the efficiency and accuracy of information extraction.

The application provides an artificial intelligence content detection method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining a query result of a to-be-detected text in a target external data source; the target external data source is used for connecting a target external database; text feature extraction is performed on the to-be-detected text and the query result respectively to obtain a to-be-detected text vector and a query result vector; text perplexity data is obtained according to the to-be-detected text vector; feature similarity data is obtained according to the to-be-detected text vector and the query result vector; an artificial intelligence content detection result is obtained based on the feature similarity data and the text perplexity data; the artificial intelligence content detection result is used for representing whether the to-be-detected text is artificial intelligence created content. Based on the characteristics of the artificial intelligence created content, the relationship between the reverse reasoning perplexity and the artificial intelligence created content is inferred, and the feature similarity data and the perplexity data are used to determine whether the text is low-quality artificial intelligence created content.

The application discloses a time sequence SAR data filtering method suitable for broken areas of farmland, and the method comprises the following steps: acquiring time sequence SAR data to be processed, wherein the time sequence SAR data to be processed comprises a plurality of initial SAR images, the order of the plurality of SAR images is consistent with the imaging time sequence, and the plurality of initial SAR images are subjected to first time smoothing processing to obtain a plurality of preliminary filtering images; acquiring similarity data of each pixel in the preliminary filtering images, wherein the similarity data of the pixel comprises the similarity score of other pixels in the N*N neighborhood centered on the pixel, N is an odd positive number, the pixel data in the preliminary filtering images are updated according to the similarity data to obtain a spatial filtering image; and the spatial filtering image is subjected to second time smoothing processing to obtain a final filtering result. The application can effectively suppress noise and effectively preserve the edge and texture characteristics of the farmland area, thereby improving the accuracy of crop monitoring.