121 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 an event alarm method, device and equipment, a medium and a product. The event alarm method comprises the following steps: receiving an alarm information set sent by each alarm source; preprocessing the alarm information set to obtain a processed alarm data set; performing similarity analysis on the alarm data items in the alarm data set to obtain a similarity data set among the alarm data items; and according to an association clustering algorithm and the similarity data set, determining clustering results of different types of alarm events, and giving an alarm. According to the method, similarity analysis is carried out on a received original alarm information set, a correlation clustering algorithm is adopted to further reduce the calculation amount and more accurately identify the similarity between the alarm information, and the similar alarm information is gathered together to form a representative clustering result, so that the accuracy of alarm information clustering is improved. And operation and maintenance personnel can carry out troubleshooting and root cause analysis more easily.

The invention provides a sea area digital twinning method and system based on buoy and station data fusion, belongs to the related technical field of sea area digital twinning, and establishes a data quality evaluation mechanism and a similarity data compression mechanism by constructing a multi-source heterogeneous ocean data acquisition system. Intelligent fusion processing of buoy data and station data is achieved, and the problem that in the prior art, the data fusion precision is low is solved; through the self-organizing growth network structure of the ocean dynamic response enhancement model, neuron connection can be automatically adjusted according to the spatial-temporal variation degree of training data, an optimal topological structure is formed, and the defect that a traditional fixed parameter model cannot adaptively process a complex ocean environment is effectively overcome; by establishing 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 ocean data sequence, and high-precision digital reproduction of the complex ocean environment is realized.

The invention discloses a computing power resource scheduling method based on artificial intelligence, and particularly relates to the technical field of resource scheduling. According to the method, semantic similarity data and real-time computing power consumption data of tasks in the training and reasoning process of a multi-task artificial intelligence model are collected, an initial data mapping model is constructed, dynamic association features between task semantic features and real-time computing power demands are analyzed, computing power demand nonlinear fluctuation features driven by task semantics are recognized, and a multi-task artificial intelligence model is constructed. Generating a force demand mutation prediction result and a computing force demand dynamic prediction result; the computing power demand mutation prediction result and the computing power demand dynamic prediction result are combined, a dynamic scheduling strategy is optimized, computing power resource allocation in the multi-task artificial intelligence model training and reasoning process is adjusted in real time, local resource redundancy and global resource scheduling imbalance are eliminated, and the overall use efficiency of artificial intelligence computing power resources is improved.

The invention discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG, and the method comprises the steps: firstly extracting business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructing a business dictionary containing term similarity data and an ontology tree; quantitatively calculating the release mechanism weight, the timeliness coefficient and the punishment association degree of the supervision clauses to obtain authority values, and sorting the authority values; then using a double-constraint loss function to finely adjust the pre-trained large language model as a vector representation model, vectorizing service data, and storing the vectorized service data into a vector database; and finally, aiming at user query, retrieving in a vector database, rearranging in combination with a supervision clause sorting result, and generating a business response after compliance verification. According to the method, the bank unstructured knowledge processing capability and retrieval precision can be improved, and efficient intelligent operation is realized.

The invention discloses a multi-view graph converter incremental learning method for identifying related diseases. The method comprises the steps of 1, heterogeneous network construction, 2, mask contrast learning, 3, graph converter feature extraction, 4, channel attention fusion, 5, depth matrix decomposition prediction and 6, incremental learning updating. According to the method, a heterogeneous network is constructed to integrate multi-source similarity data, mask contrast learning is utilized to generate multi-view embedding so as to reduce noise, node position information is coded by means of a graph converter, multi-view features are fused through a channel attention mechanism, disease association is further predicted through depth matrix decomposition, and the disease association prediction accuracy is improved. And an incremental learning strategy is designed to realize dynamic updating of new data, a new result is obtained under the condition that original input data and model retraining are not needed, and the method can adapt to the condition of explosive increase of the data.

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.

A system and method for online real-time multi-object tracking is disclosed. A particular embodiment can be configured to: receive image frame data from at least one camera associated with an autonomous vehicle; generate similarity data corresponding to a similarity between object data in a previous image frame compared with object detection results from a current image frame; use the similarity data to generate data association results corresponding to a best matching between the object data in the previous image frame and the object detection results from the current image frame; cause state transitions in finite state machines for each object according to the data association results; and provide as an output object tracking output data corresponding to the states of the finite state machines for each object.

The invention relates to the technical field of big data processing and network security, and discloses a ubiquitous network data deduplication system supporting similarity data ownership verification, which comprises an encryption module, a label generation module, a deduplication judgment module, a challenge generation module, a certification calculation module, a verification module and an integrity evidence storage module. According to the system, a plaintext data block is encrypted by deriving a high-entropy session key through two-dimensional chaotic mapping, a similarity label with a threshold value is generated by adopting # imgabs0 #, and efficient de-duplication is realized based on three-section type Hamming distance judgment; a dynamic challenge-response mechanism is utilized to provide auditable ownership proof for each downloading request, and a # imgabs1 # tree and a block chain evidence storage technology are combined to realize data integrity multi-granularity verification and rapid damaged block positioning, so that storage efficiency, security and availability are considered.

The invention discloses an object recognition method and device, equipment and a storage medium, and relates to the technical field of machine learning, and the method comprises the steps: carrying out the feature similarity analysis of a target object feature of a to-be-recognized object and a class index feature group corresponding to a target object class, and obtaining target feature similarity data; the category index feature group is a clustering center feature corresponding to the positive sample object feature; the positive sample object is a sample object which is screened based on a feature density clustering result of a plurality of original sample objects and belongs to a target object category; based on the target feature similarity data and a preset feature similarity threshold, determining object category indication information of the to-be-recognized object; the preset feature similarity threshold value is obtained by performing threshold value adaptive adjustment based on a preset identification index threshold value and the sample feature similarity data; the sample feature similar data represents the feature similarity condition between the positive sample object feature of the positive sample object and the category index feature group. By using the scheme of the invention, accurate identification of a new category can be rapidly realized.

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.

The invention discloses a self-supervision target detection method based on rid. Firstly collecting and processing data and classifying; reducing the confidence of the target detection model to obtain related information; inputting the target-containing picture into a large oracle model to obtain coordinates and description; and performing IOU matching and recording on prediction results of the two models. And performing similarity matching with a database, and processing according to a result. Carrying out matting on unmatched category data, inputting the matted data into the clip model and recording the matted data; and related unmatched data matting is input into a rid network record. And matting the low-similarity data, inputting the rid, and recording according to the similarity. Then, data sampling is carried out to ensure that specific targets and the number are consistent, a loss training model is calculated, easily-detected samples are removed, and difficult cases and background negative samples are mined until the model is stable for detection; according to the method, through multi-step cooperation, data interaction processing among models is utilized, effective training data is mined, the adaptability of the model to complex conditions is enhanced, the accuracy and stability of the target detection model are improved, and the overall detection performance is optimized.

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 invention provides a bridge structure anti-deformation monitoring method and system, and relates to the technical field of bridge deformation monitoring. The method comprises the following steps: dividing into a plurality of segmented bridge areas, and setting horizontal test points and vertical test points; determining a first distance and a first yaw angle, and a second distance and a second pitch angle; determining a horizontal anti-deformation coefficient; determining a vertical anti-deformation coefficient; obtaining segmented bridge images; determining actually measured similarity data; acquiring environment data; inputting the environment data and the segmented bridge image at the starting moment of the current monitoring period into a trained similarity prediction model to obtain predicted similarity data of a plurality of segmented bridge areas in the current monitoring period; determining a surface anti-deformation coefficient; and determining the anti-deformation capability of the bridge structure. According to the invention, non-contact monitoring can be carried out, the anti-deformation capability of the bridge structure can be comprehensively evaluated in the horizontal, vertical and environmental directions, the comprehensiveness and accuracy of evaluating the anti-deformation capability of the bridge structure are improved, and the monitoring cost is reduced.

The application discloses an industrial CT image reconstruction method and device, electronic equipment and storage equipment, comprising obtaining projection image data and preprocessing to obtain target image data; based on the pre-trained U-Net variant lightweight network, the target image data is processed to obtain fine gradient direction prediction data and structure level similarity data, the U-Net variant lightweight network is trained based on the projection image data and the true value image data, and the loss function of the U-Net variant lightweight network comprises a structure similarity loss; based on the target image data, the fine gradient direction prediction data and the structure level similarity data, the target function is iteratively updated by using an alternating direction multiplier method to obtain reconstructed image data, and the target function is a high-order regularization function constructed based on the second-order total generalized variation and the structure level non-local mean of the iterative image data. The application can inhibit the streak artifact and photon noise of sparse images, so as to meet the requirements of fast scanning and sparse image reconstruction quality.

The present invention discloses a heating data processing system and method for a heating system, relating to the technical field of heating data processing. It solves the technical problems that it is difficult to classify and summarize the data in each node of the heating system and then perform classification processing, and it is also difficult to establish connections between data of nodes with high similarity. By using wireless communication devices to establish a data transmission channel between edge data nodes, it is convenient to achieve low-latency data transmission, enabling the system to respond to changes in real time. Establishing a data transmission channel between adjacent edge nodes can effectively disperse the network traffic load and improve the stability of the overall network. Establishing a data table at the edge data node at the heat source helps to centrally manage data from different terminals and nodes, enhancing data accessibility and management efficiency. By correlating data with high similarity, it is convenient to analyze the relationship between highly similar nodes in subsequent data mining, providing deeper information for subsequent analysis.

The present invention relates to the technical field of data table classification, and discloses a method and system for classifying and organizing data resources based on semantic recognition. The method includes: S1: Combing the theme and business distribution of data resources, and constructing a classification system in combination with the logical model of the data warehouse system; S2: Constructing category feature vectors and data table feature vectors respectively; S3: Calculating the cosine similarity between various category feature vectors and data table feature vectors, and the cosine similarity between data tables; S4: Classifying data resources according to the classification system, dividing the data classification numbers corresponding to the data tables, and storing the classification numbers to which each data table belongs and similar data tables; S5: Organizing and utilizing data resources. The method and system of the present invention can calculate the thematic semantic relationship of assets according to the metadata content of data tables, can quickly lock the semantic space and data asset scope corresponding to the demand concept, and solves the problem of cross-system, cross-business, and fragmented data asset organization and query.

The invention relates to the technical field of optical fiber sensing monitoring, and discloses a distributed optical fiber sensing monitoring method and system. The method comprises the following steps: acquiring a sensing data image through a distributed optical fiber sensor deployed in a target area; constructing an unsupervised CAE-X1 model based on a convolutional auto-encoder, wherein the model comprises an encoder and a decoder; training the YOLO model to detect low temporal resolution anomalies and high temporal resolution anomalies; the method comprises the following steps: extracting an abnormal feature coding vector by using a CAE-X1 model, and constructing an FAISS-X2-X4 similarity database; inputting a newly collected sensing data image into the CAE-X1 model to calculate a reconstruction error, and if the reconstruction error exceeds a threshold value, determining that the image is abnormal; the type of the anomaly is retrieved through an FAISS-X2-X4 similarity database; and selecting a corresponding YOLO model to perform target detection according to the anomaly type to obtain an anomaly detection result. According to the method, a large amount of label-free data can be utilized, and the defect that in an existing distributed optical fiber sensing signal processing method, abnormal event monitoring cannot be carried out through various time resolutions can be overcome.

The invention discloses a system for carrying out snoRNA and disease association prediction based on a multi-graph SAGE network, and relates to the technical field of biological information, the system comprises the following components: a data acquisition and preprocessing module, which obtains data containing snoRNA and disease association information from MNDRv3.1, carries out feature extraction on a snoRNA sequence, calculates snoRNA pairwise similarity by using a Tanimoto coefficient, and sends the snoRNA pairwise similarity to the MNDRv3.1; meanwhile, the disease pairwise similarity is obtained by adopting a DAG-based semantic similarity method; according to the method, the multi-graph heterogeneous network is constructed by integrating the paired similarity data of the snoRNA and the disease and the associated network of the snoRNA and the disease, the feature information of the snoRNA and the disease is comprehensively captured by using the GraphSAGE architecture with an attention mechanism, the complex relationship between the snoRNA and the disease is effectively captured, the prediction precision of snoRNA-disease association is remarkably improved, and the prediction efficiency of the snoRNA-disease association is improved. According to the method, higher AUC, AUPRC and F1 scores are obtained on a test set, higher classification capability and prediction reliability are displayed, and a more accurate prediction result is provided for biomedical researchers.

The present invention provides a method and system for monitoring the deformation resistance of a bridge structure, and relates to the technical field of bridge deformation monitoring. The method comprises: dividing a bridge area into multiple segmented areas, setting horizontal test points and vertical test points; determining a first distance and a first yaw angle, as well as a second distance and a second pitch angle; determining a horizontal anti-deformation coefficient; determining a vertical anti-deformation coefficient; obtaining a segmented bridge image; determining measured similarity data; obtaining environmental data; inputting the environmental data and the segmented bridge image at the beginning of the current monitoring cycle into a trained similarity prediction model to obtain predicted similarity data for multiple segmented bridge areas in the current monitoring cycle; determining a surface anti-deformation coefficient; and determining the anti-deformation capability of the bridge structure. According to the present invention, non-contact monitoring can be performed, and the anti-deformation capability of the bridge structure can be comprehensively evaluated from three directions: horizontal, vertical, and environmental, thereby improving the comprehensiveness and accuracy of evaluating the anti-deformation capability of the bridge structure and reducing the monitoring cost.

The present application discloses a method and apparatus for training a semantic vector extraction model and representing semantic vectors. The method for training the semantic vector extraction model includes: obtaining a conversation corpus from the same scenario as a downstream task corresponding to the semantic vector as a first pre-training text; performing secondary training on the trained BERT language network based on the first pre-training text to generate a BERT language network that focuses on nouns and verbs in the text; obtaining text classification data and generating a text similarity dataset based on the text classification data; and fine-tuning a multi-task classification network based on the text similarity dataset to generate a semantic vector extraction model.

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.

The application relates to the technical field of big data processing and network security, and discloses a ubiquitous network data deduplication system supporting similarity data ownership verification, which comprises an encryption module, a label generation module, a deduplication decision module, a challenge generation module, a proof calculation module, a verification module and an integrity evidence module; the system derives a high-entropy session key through two-dimensional chaotic mapping to encrypt plaintext data blocks, generates a similarity label with a threshold, and realizes efficient deduplication based on three-stage Hamming distance determination; the dynamic challenge-response mechanism is used to provide an auditable ownership proof for each download request, and the tree and block chain evidence technology are combined to realize multi-granularity checking of data integrity and rapid positioning of damaged blocks, so that the storage efficiency, security and usability are considered.

The invention provides a talent evaluation management method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, obtaining the resume data of a to-be-evaluated talent, carrying out the text recognition processing of the resume data, and generating a structured text data set; 2, extracting a keyword feature vector from the structured text data set, calculating a similarity value between the keyword feature vector and a preset talent evaluation keyword library, and generating initial similarity data; and step 3, constructing a three-dimensional point cloud data set based on the initial similarity data, wherein each data point comprises weight coordinate information of the keyword. According to the method, through structured processing, multi-dimensional feature analysis and dynamic correction, the combination of talent evaluation from objective standardization to three-dimensional dynamics is realized, and the comprehensiveness and accuracy of evaluation are improved.

The invention discloses a nasopharyngeal carcinoma radiotherapy plan generation method, device and equipment, and relates to the field of intelligent medical treatment, and the method comprises the steps: obtaining multi-modal and multi-omics data of a nasopharyngeal carcinoma patient; the multi-modal multi-omics data comprises radiotherapy plan data, radiomics data, similarity data and Hausdorff distance data; performing data cleaning and feature extraction processing on the multi-modal multi-omics data in sequence to obtain target feature data; according to the target feature data, an artificial intelligence generation method is adopted to generate a nasopharyngeal carcinoma radiotherapy plan; the nasopharyngeal carcinoma radiotherapy plan comprises an equal dose curve graph of dose distribution, a dose volume histogram of a target region and a normal organ, control point data of a linear accelerator and motion data of a multi-leaf collimator. According to the invention, the precision and generation efficiency of the nasopharyngeal carcinoma radiotherapy plan are improved.

This invention discloses a deep learning-based NLOS recognition method applicable to complex environments in multiple scenarios. First, the collected UWB channel impulse response data from multiple scenarios is transformed to obtain discrete multipath impulse response data. Multipath delay power data and multipath similarity data are then calculated to construct a new sample dataset, followed by data preprocessing. Next, a dual-input feature fusion deep learning NLOS recognition model based on multipath information is constructed to complete NLOS classification in UWB positioning systems. This deep learning-based NLOS recognition method, applicable to complex environments in multiple scenarios, can solve the problems of low recognition capability, poor generalization accuracy, and poor recognition effect in complex scenarios with limited data, thereby achieving high NLOS recognition accuracy and effectively improving UWB positioning accuracy in complex environments.

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.