318 results about "Data sorting" patented technology

A system (100) for adaptive data blending across heterogeneous BI platforms for real-time decision making, comprising: (a) a data source abstraction module configured to establish standardised and secure connections with several different Business Intelligence (BI) platforms and convert their native data structures into a unified intermediate schema; (b) a real-time data ingestion and synchronisation module operable to continuously monitor and retrieve data updates from the BI platforms using event-driven mechanisms and configurable synchronisation policies; c) an adaptive data mapping and transformation module designed to intelligently harmonize, adapt, and semantically translate disparate data formats using rule-based logic and AI-assisted schema mapping; (d) a semantic context and metadata management module configured to maintain a unified data dictionary, track data sequence and maintain semantic consistency across platforms; (e) a cross-platform query orchestration engine designed to decompose common user queries, translate them into platform-specific query languages, execute them across the relevant BI platforms, and aggregate the results into a coherent output; (f) a decision intelligence and recommendation module that analyses the mixed data to generate real-time insights, alerts and contextual recommendations for decision support; and (g) a security, governance and compliance module configured to enforce access controls, maintain audit trails and ensure compliance with data protection regulations; h) the system provides seamless, real-time and secure data blending to enable consistent analytics and decision-making across heterogeneous BI environments.

The present invention relates to the field of artificial intelligence natural language processing, and in particular to a topic clustering method driven by a large language model; the method comprises the following steps: S1) arranging document set data; S2) using the HDBSCAN clustering algorithm to perform preliminary clustering on the document set D to obtain a plurality of clusters {C k}; S3) Model the document topics using the LDA topic model to obtain the topic distribution of each document; S4) Adjust the clustering results based on the results obtained from the LDA model; S5) Further refine the topics of each cluster using LLM; S6) Establish effective and explainable associations between the generated topic list and the documents in the data set. The topic clustering system driven by the large language model of the present invention achieves a deep understanding and fine clustering of document topics by integrating HDBSCAN clustering, LDA and the large language model, providing a solid foundation for subsequent information retrieval, text mining and knowledge graph construction.

The invention relates to the technical field of financial data processing, in particular to a financial data automatic extraction and articulation processing system and method, and the system comprises a financial data obtaining module which is used for obtaining financial image data, audio data and bank API data; and the financial data processing module adopts OCR (Optical Character Recognition), Whisper voice conversion and bank API (Application Program Interface) paging query technologies to carry out standardization processing on the data and extract financial data through an anomaly detection mechanism. And the financial data sorting module classifies and stores the data to different financial databases by adopting a dynamic routing and four-way distribution strategy based on format verification and priority marks. And the financial data articulation module performs verification and difference processing on the data through a cross-library articulation rule to generate a financial analysis report. And the financial data display module monitors the financial analysis result by adopting a data snapshot and rollback mechanism, so that automatic extraction, articulation and visualization of the financial data are realized, and the accuracy and compliance of the financial data are improved.

The invention relates to the technical field of cable measurement, and discloses a cable length measurement method based on coupling inductance and TDC, and the method comprises the following steps: test pulse reset, channel sequential gating, threshold dynamic adjustment, TDC state configuration, channel stable waiting, test pulse generation, time difference precise capture, validity intelligent judgment and data sorting optimization. According to the cable length measurement method based on the coupling inductor and the TDC, the coupling inductor is adopted to replace an FPGA to generate narrow pulses, the TDC is adopted to replace a high-speed ADC, and the hardware cost is reduced by 60%-80%; by optimizing the pulse generation and echo detection circuit, the measurement blind area is reduced to 0.5 m, the short-distance cable measurement requirement is met, and the device has the advantage of being small in measurement blind area.

The invention discloses an operation index determination method and device for an endoscopic surgery, equipment and a storage medium, and can be applied to the technical field of surgical operation data processing. The method comprises the following steps: firstly, acquiring a surgical operation data set of a target operation object for an endoscopic surgery; the surgical operation data set comprises a plurality of pieces of surgical operation data sorted according to a time sequence. Each piece of surgical operation data comprises five-dimensional data, namely endoscope action data, endoscope type data, instrument action data, instrument type data and surgical object data. And constructing a hidden Markov model by using the surgical operation data set. In the hidden Markov model, the surgical operation described by the surgical operation data is the observed quantity, and the surgical state is the state quantity, and based on the hidden Markov model, the statistical principle is utilized to carry out quantitative evaluation on the surgical operation indexes, so that the accurate evaluation of the surgical operation when a doctor carries out the endoscopic surgery is realized, and the reference effectiveness of the operation indexes is improved.

The invention relates to a database technology, in particular to a data sorting method, a storage medium and equipment. The data sorting method comprises the steps that to-be-sorted elements and a pre-constructed data structure are obtained, and the data structure comprises a heap structure and a linked list with heap nodes in the heap structure as linked list nodes; based on the first sorting key of the to-be-sorted element, judging whether the to-be-sorted element meets the condition of adding the to-be-sorted element into the linked list or not; and if yes, adding the to-be-sorted elements into the linked list. According to the data sorting method, on the basis of the first sorting key of the to-be-sorted elements, the to-be-sorted elements meeting the conditions can be temporarily stored in the linked list with the heap nodes in the heap structure as the linked list nodes, and the problem that the execution efficiency is low due to the fact that repeated replacement operation is executed on the heap top nodes in the heap sorting process is solved; the node replacement operation in the heap sorting process is reduced, so that the overall sorting execution efficiency is improved, and the performance of a database system is improved.

The invention provides a data sorting method for a tensor processor, a tensor processor system and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring input data and the storage capacity of a single tensor processor TPU register, and distributing core id for marking an initial position for each element; when the data size of the input data is smaller than or equal to the storage capacity of the single TPU register, single register level sorting operation is executed, and a double-tone sequence of the single register is obtained; otherwise, performing double-tone sorting recursion on the input data to divide the input data into a plurality of subsequences, executing single-register-level sorting operation on each subsequence, and merging the sorted subsequences to obtain a double-tone sequence of multiple registers; wherein the core id of the element is transmitted when the single register level sorting operation is executed for each sub-sequence and the sub-sequences are combined; and adjusting the sequence of the equal elements according to the core id after the double-tone sequence is obtained. The stability of the data sorting result can be improved.

The invention discloses a calibration data selection method for training uniform quantization after a deep learning model. The method comprises four steps of clustering centroid initialization, clustering centroid updating, training set data sorting and calibration set data selection. Firstly, random data conforming to uniform distribution are sent to a network for forward reasoning, and a plurality of clustering centroids are determined; then, reasoning is performed through training set data, a knowledge distance is calculated, a clustering centroid is updated, and an activation distribution space accurately reflecting training set data distribution is constructed; then, training set data are sent to the network for reasoning again, knowledge distances are calculated, summation is carried out, and scores of all data samples are obtained and sorted; and finally, according to the size of a specified calibration set, selecting training set data with relatively high scores as the calibration set from high to low. According to the method, through reasonably selecting calibration data, the effect of training uniform quantification after the deep learning model can be improved.

A method for selecting training data for training a deep learning model is provided. [Solution] The method includes the steps of: a training data selection device generating a bipartite graph matching each of a large number of training images with an individual type; a training data selection device referring to the bipartite graph to check the number of corresponding individual types that match each training image among the individual types, selecting a specific training image with the largest number of corresponding individual types, checking the number of remaining corresponding individual types that match each training image among the remaining individual types excluding the corresponding individual type, and selecting another specific training image with the largest number of remaining corresponding individual types, thereby performing a cycle of selecting some training images that match all of the individual types, and repeating the process of performing the cycle from the remaining training images excluding the selected portion of the training images from the large number of training images until n training images are selected.

The invention relates to the technical field of engineering digitization and information, and discloses a water conveyance tunnel BIM model engineering management business application method, which comprises the following steps: carrying out management business carding, and determining an engineering unit hierarchical structure; uniformly coding the data information related to the management service and the hierarchical structure data of the engineering unit to form a service data coding result and an engineering division coding result; creating a water conveyance tunnel BIM model, and endowing model components with the codes to form BIM data carrying code information; inputting the business data and the BIM data carrying the coding information into a digital construction and management system, and associating the business data and the BIM data by using a common coding identifier; based on the association, business management digital applications of investment, progress, quality, safety and the like are carried out in a digital construction and management system. Through standardized data sorting and coding, fusion of the BIM model and engineering management business is realized, the problem of data splitting is solved, and the refinement and digitization level of water conveyance tunnel engineering management is improved.

The invention provides a sorting method and device based on Paillier homomorphic encryption, electronic equipment and a medium, and the method comprises the steps: obtaining a plurality of ciphertexts issued by a data owner, enabling the data owner to encrypt a plaintext through a public key to obtain the ciphertexts, enabling the public key and a private key to be generated through a preset Paillier homomorphic encryption algorithm, and carrying out the sorting of the public key and the private key based on the length of the public key and the length of the ciphertexts, selecting a weighting coefficient of the ciphertext; obtaining a blinding coefficient based on the public key; performing weighted aggregation on the ciphertexts by adopting the blinding coefficient and the weighting coefficient to respectively obtain weighted ciphertexts corresponding to the plurality of ciphertexts; sending the plurality of weighted ciphertexts to a third-party mechanism and requesting to compare and sort the plurality of weighted ciphertexts; and obtaining a sorting result fed back by the third-party mechanism after comparing and sorting the plurality of weighted ciphertexts. The sorting algorithm is higher in decryption efficiency and more suitable for processing large-scale data sorting tasks.

The invention discloses a data sorting device and method, a chip, electronic equipment and a storage medium, and the device comprises a controller which is used for obtaining a plurality of first data groups; a parity sorting network for sorting the first data set into a second data set; the data selector is used for acquiring a target data group; the controller is used for comparing the target data set with the ith second data set at an interval of N bits to obtain an (i-1) th third data set; when the TopN is sorted, the double-adjustment merging network is used for sorting the (i-1) th third data group into the (i-1) th fourth data group; when i is equal to the number of the plurality of first data groups, taking the (i-1) th fourth data group as a TopN sorting result; and during full sorting, the double-adjustment merging network is used for carrying out iterative merging on the sorted fourth data group to obtain a full sorting result. The method can be compatible with TopN and a dual-tone full-sorting algorithm, and saves computing resources.

The invention discloses a premature infant mother psychological intervention effect assessment method and system, and relates to the technical field of psychological assessment. A premature infant mother psychological intervention effect evaluation system comprises a data arrangement module and an effect evaluation module. By analyzing the behavior analysis data set and the face analysis data set of the evaluated mother at the same time, the psychological state of the mother can be comprehensively evaluated from multiple angles; the behavior analysis model and the face analysis model respectively provide independent evaluation of behavior performance and emotional response, so that the evaluation result is more comprehensive and accurate, and the emotional fluctuation and psychological change of the mother and the response to the premature infant can be effectively identified; through multi-level and multi-dimensional behavior analysis, the mood, psychological stress and behavior mode of the mother are deeply recognized, the behavior mode of the mother is considered, the internal mechanism of the mood can be deeply discussed, and therefore powerful data support is provided for psychological intervention.

The invention discloses a performance optimization method, device and equipment for multimode query of a data vault and a medium. The method comprises the following steps: low-density screening of levels: analyzing data storage, and constructing a low-density index according to data features; the data statistics level is used for calculating and counting data source information; block scanning level: evaluating the data size, configuring block scanning parameters according to the data size, and determining the degree of parallelism of parallel scanning; a condition operator push-down level: identifying a push-down query condition, and pushing down the push-down query condition to the data source scanning operator; a cost model calculation level: planning to generate data sorting for a sorting operator through cost model calculation; a memory model calculation level: allocating a memory model according to the size of the memory; according to the cost of each layer, evaluating the performance improvement proportion or multiple brought by optimization of each layer layer by layer; and planning a data processing mode according to a layer-by-layer evaluation result. According to the method, the multi-layer optimization model is adopted, so that the performance of multi-mode query is improved.

The invention discloses a scientific and technological achievement dynamic matching method based on RAG and a computer program product, and relates to the technical field of scientific and technological achievement supply and demand matching. The scientific and technological achievement dynamic matching method based on the RAG comprises the steps that a knowledge base is pre-constructed; obtaining user matching data, and analyzing the user matching data as demand matching data or result matching data; when the user matching data is demand matching data, searching scientific and technological achievement data related to the demand matching data in an index structure, and sorting the searched scientific and technological achievement data to form a first related queue; and in combination with the first correlation queue or the second correlation queue, generating a matching suggestion of the user matching data according to a big language model induction. By adopting the technology provided by the invention, rapid conversion and application of scientific and technological achievements can be promoted, deep integration of industry, university and research is further improved, and the marketization and commercialization efficiency of the scientific and technological achievements is improved.

A method for selecting training data for training a deep learning model is provided. [Solution] The method includes a step of a training data selection device acquiring at least one individual type corresponding to each of the multiple training data included in all training data stored in a data pool, and generating a bipartite graph matching each of the multiple training data included in all training data with the individual type; and a step of selecting n pieces of training data for training a deep learning model by referring to the bipartite graph to select n pieces of training data matching the individual types from all training data, such that the number of each of the individual types matching the n pieces of training data is within a predetermined threshold deviation.

The invention relates to the technical field of auxiliary diagnosis, and particularly discloses a genetic disease auxiliary diagnosis system based on biological characteristic information, which comprises a data arrangement module, a path analysis module, a characteristic analysis module and a path determination module, the data arrangement module extracts historical diagnosis data of the single-gene hereditary disease, constructs a diagnosis feature table and extracts a diagnosis path; the path analysis module is used for performing specific analysis on the diagnosis path and the single-gene hereditary disease and determining an independent diagnosis path; the feature analysis module performs quantitative analysis on the biological feature information of the diagnosis independent path to obtain a feature time index and construct a feature time sequence; and the path determination module performs quantitative analysis on the instability of each piece of biological characteristic information of the diagnosis independent path, and determines a stable diagnosis path. According to the method, the diagnosis process can be optimized, clinical decision and research can be assisted, the unnecessary detection cost is reduced, and the medical resource utilization efficiency is improved.

The invention discloses a feature data screening method and device for power grid fault assessment, and belongs to the field of power systems, and the method comprises the steps: firstly obtaining a local bus set of a power grid and a candidate feature set corresponding to the set; performing a fault simulation experiment on the power grid according to a preset power system simulation platform to obtain characteristic quantity data corresponding to each characteristic quantity and form a characteristic quantity data set; according to a Mahalanobis distance principle and a Spearman level correlation coefficient calculation method, sorting each characteristic quantity data in the characteristic quantity data set to obtain an intermediate candidate characteristic set; screening each intermediate candidate feature in the intermediate candidate feature set by adopting an incremental feature subset strategy to obtain a plurality of final candidate features; and finally, performing fault assessment on the power grid according to each final candidate feature and the local bus set, and outputting a fault assessment result, so that the accuracy of power grid fault assessment can be improved through implementation of the method.

The disclosure includes a digital twin system. The digital twin allows for real time classification and ranking of data received from a distributed learning knowledge acquirer. The digital twin system ranks the data while the distributed learning knowledge acquirer is performing a drift evaluation. The distributed learning knowledge acquirer uses the ranked data as training data for discriminative AI models. The digital twin system offers more flexibility and precision in ranking the data. The digital twin system is a digital twin providing contextual active dynamic learning to the distributed learning knowledge acquirer's physical system. Digital twin system causes a model driven approach to allow for superior predictive capabilities by being able to examine large state spaces.

The invention relates to the technical field of data management research and judgment, and discloses a vocational education data management research and judgment system based on historical big data. Comprising a management research and judgment module of a system for managing and analyzing data, a big data collection module for collecting related vocational education data in historical big data, and a data sorting module for collecting related information in collected mass data and sorting and classifying the same type of data, the data extraction module is used for further capturing and extracting required accurate data after obtaining related data, the database is used for storing and calling data information, and the data updating module is used for keeping the accuracy and timeliness of the data information in real time. And the changed data is updated in real time through the updating module and is stored in the database, so that the timeliness and accuracy of the information are ensured, the decision research and judgment are prevented from being influenced by the change of the information data, and the safety and the timeliness accuracy of the management research and judgment system are improved.

The invention discloses a data quantitative query method and system based on spatial grids. According to the method, data of various types and formats are obtained in various modes, and the data are read into formats which can be recognized by a system. And then, analyzing and reconstructing the data, and completing cleaning and screening operations, including data translation and analysis, spatial data processing, JSON data sorting and the like. And performing precision unification and quantitative reconstruction on the processed data, and finally storing the processed data into a database and generating a query index. The method has efficient data processing and query capabilities, and can efficiently store and quickly retrieve large-scale spatial data.

The present invention proposes a large-screen data statistics method based on multi-dimensional configuration, which includes: configuring database query statements and determining the configurations corresponding to the input database and the output database; aligning the source data business fact table using a high-frequency value inheritance conflict resolution algorithm; configuring different field accumulation strategies for local organizational statistical data with policy tags, and configuring an accumulation extraction strategy; sorting the data bound to the extraction strategy and the local organizational statistical data with policy tags; generating a path animation sequence based on a secondary pop-up data set and a statistical optimization path scheme, and integrating it into the display large screen to obtain the final large-screen display result. The present invention automatically standardizes heterogeneous fields of different databases through a database-driven semantic alignment algorithm, solves the problem of multi-source data integration, and uses high-frequency value inheritance conflict resolution technology to significantly improve the accuracy of filling empty fields and eliminate statistical deviations.

The invention belongs to the technical field of positioning technologies, and particularly relates to a mine underground personnel positioning method and system based on environmental characteristics, which are suitable for personnel accurate positioning and dynamic correction in a complex structure and strong interference environment. The method comprises the steps of data classification and standardization processing, mine structure modeling, sensitive area identification and labeling, real-time positioning data acquisition and correction, and dynamic correction of an original positioning result in combination with environmental characteristics such as electromagnetic signal fluctuation, so that the accuracy and stability of underground positioning are improved. The system comprises a data arrangement module, a structure modeling module, a sensitive area identification module, an area fusion module and a positioning correction module, and all the modules cooperate to realize high-precision tracking of personnel positions and sensitive area early warning. The method can effectively overcome the error accumulation problem of a traditional positioning method under the underground strong interference condition, is suitable for underground operation scenes such as coal mines and metal and non-metal mines, and has wide application prospects.

The invention relates to an enterprise data analysis method and device, and relates to the technical field of data governance, and the method comprises the steps: carrying out the Text2SQL task fine tuning of a large language model through a low-rank adaptation technology, constructing a structured knowledge base containing an enterprise data dictionary, a field calling relation and a national standard, converting the unstructured industry specification into a structured data standard knowledge base, and realizing knowledge vectorization storage by using an M3E-base vector model; sampling representative samples based on to-be-sorted data fields and retrieving related rules, integrating the representative samples into thinking chain cues, inputting the thinking chain cues into a fine tuning model to generate a labeling classification result, and outputting a data sorting result after multi-model evaluation and verification; generating a regular expression through a retrieval data standard to screen non-standard data, sampling to generate an SQL conversion code, iteratively cleaning until the standard is reached, and outputting standardized data; and finally, executing index optimization and anomaly detection based on the fine tuning model, and receiving natural language query to generate an SQL and a visual report.

The invention relates to the field of information retrieval, and discloses an efficient document search method and device, equipment and a storage medium. The method comprises the steps that a document set is preprocessed, and an index structure is constructed; analyzing and optimizing a query word input by a user to generate a target retrieval request; retrieving an initial document set containing the target retrieval request based on the index structure; and carrying out correlation scoring on the initial document set by adopting a preset multi-dimensional scoring strategy, sorting according to a scoring result, and screening out a document set most related to the target retrieval request. According to the efficient document searching method provided by the invention, the document set most relevant to the user search query is screened out by analyzing and optimizing the user search query and combining the relevance score and the behavior data sorting result, so that the problems of low retrieval efficiency and poor result relevance of a traditional document searching method are effectively solved; and the document searching efficiency and the result quality are improved.

A lane line detection method based on a neural network model includes the following four steps: step 1, a data sorting and collection module, which integrates multiple data sets, re-labels and cleans and screens, and constructs a new lane line detection training data set to ensure label consistency and data quality; step 2, an image data preprocessing module, in which the data preprocessing process uses cropping, scaling, flipping, deformation, tone adjustment, noise addition, median filtering and other methods to enhance the training image; step 3, a model training module, which adopts multi-model and multi-step training, and adjusts the data sampling distribution for different training stages; step 4, a detection result generation and fusion module, in which the lane line detection result is generated and fused, and two methods are used to enhance the output result. The method improves the accuracy and robustness of the lane line detection method by integrating multiple models, dynamically adjusting the data distribution and post-processing steps.

The invention is suitable for the technical field of data processing, and provides a data sorting method and device, electronic equipment and a storage medium, and the data sorting method comprises the steps: converting a to-be-sorted one-dimensional array into a matrix; dividing the matrix into a plurality of sub-matrixes, and determining reference elements in each sub-matrix; performing partition operation on each sub-matrix based on a single instruction multiple data stream (SIMD) instruction set to obtain a first partition and a second partition of each sub-matrix; sorting the data elements in the first partition and the second partition of each sub-matrix to obtain a target sub-matrix after sorting of each sub-matrix is completed; and recombining the target sub-matrixes of the plurality of sub-matrixes, and converting the recombined target matrix into an ordered target one-dimensional array. The large-scale data sets can be efficiently sorted, and the processing efficiency of the large-scale data sets is improved.

The invention provides an artificial intelligence-based paper archive automatic inputting and archiving method and system. The method comprises the following steps of: 1, digitally converting a paper archive into a digital archive; 2, comparing the digital archive with an in-library template; 3, when the digital archives have templates in the library, extracting, inputting and indexing, and classifying the archives; 4, when the digital archives do not have templates in the library, determining the position content of the to-be-input elements of the archives according to the archives of the same category; 5, if the same class of archives do not exist in the step 4, related archive elements are input and added into a to-be-learned template library, and the like. Template generation, template recognition, annual archive catalogue rule learning and the like are combined, full-process automation of paper archive data recognition, data input and classified catalogue is achieved under the condition of no manual intervention, and the efficiency is improved. Therefore, the labor loss and the error rate of data sorting are reduced, and the working efficiency is improved.

The invention discloses an improved two-dimensional ordered statistical constant false alarm rate detector implementation method based on a CPU. The implementation steps are as follows: inputting a two-dimensional data matrix; performing initialization setting; for each detection unit, judging whether the sample value of the detection unit is the maximum value in a nine-block grid taking the detection unit as the center or not before detection; if yes, judging whether the sample value of the detection unit is greater than k arbitrary values of all reference unit sample values multiplied by a threshold factor or not through parallel processing; if yes, the detection unit is judged as a target, and the adjacent undetected unit does not need to be detected subsequently. According to the method, the number of detection units is greatly reduced, the processing time is shortened, meanwhile, in the detection process, a CPU does not need to open up an extra memory space to store all reference units to be sorted, data sorting work which is complex in traditional two-dimensional ordered statistics constant false alarm rate and long in consumed time is ingeniously avoided, the time complexity and the space complexity of an algorithm are reduced, and the detection efficiency is improved. And the real-time performance of target detection is ensured.

The invention provides an intelligent report generation method, device and system and an electronic medium, a front-end and rear-end separated architecture is adopted, a front end obtains a user instruction through an AI window module and sends the user instruction to a server, the server preprocesses the instruction and analyzes the instruction by means of an AI large model, and a report is generated. And after keyword extraction and weight determination, matching with semantic description information of functions in the comprehensive function library. If matching succeeds, report business logics such as data retrieval, report generation and chart drawing are executed, and a result is returned to the front end; if matching fails, error detection and correction are carried out, and information needing to be supplemented / modified is fed back. According to the method, the defects of traditional report data processing are overcome, efficient and accurate data arrangement and report generation are achieved through the automatic process and the AI technology, and the intelligent report analysis and statistics service efficiency and quality are improved.