60 results about "Composite index" patented technology

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

Embodiments of the present disclosure relate to multimodal AI-based search for digital assets via an indexing and / or search pipeline. With respect to the indexing pipeline, some embodiments obtain first data and second data associated with a first digital asset. Such data represents different data types or modalities of the same digital asset. After obtaining the first and second data, some embodiments then generate a composite index. After the composite index is built such index can then be used to execute a query via the search pipeline. To execute the query some embodiments compute a relevance score for each digital asset, of multiple digital assets, based at least in part on a measure in which each digital asset satisfies one or more parameters or conditions for two or more data types of the query. Various embodiments then rank each digital asset and present one or more associated indicators.

The invention relates to the technical field of medical data mining, and particularly discloses an elderly sepsis index model, a prediction model construction method and a prediction system.The method comprises the steps that a development queue data set and an independent verification queue data set of elderly sepsis patients at the age larger than or equal to 60 years old are determined according to an intensive care database; performing sepsis death and prognosis importance sorting on candidate inflammation-immune composite indexes of the development queue data set according to a total correlation feature selection algorithm; superposing and combining preset basic indexes, each index in the importance inflammation marker indexes and a preset importance scoring standard; comparing the scoring results of the multiple groups of joint optimization index models, and evaluating the scoring results; and determining the joint optimization index model with the optimal evaluation result as the senile sepsis index model. According to the construction method of the senile sepsis index model provided by the invention, the index model suitable for senile sepsis can be obtained based on the existing index database.

The invention provides a reservoir sediment modeling method based on multi-time-sequence multi-beam sounding point cloud data, and relates to the field of reservoir sediment monitoring and modeling, and the method comprises the steps: constructing a reservoir area space grid skeleton model, and collecting multi-time-sequence multi-beam point cloud data and corresponding hydrological auxiliary data; performing space division by adopting an adaptive tile subdivision method, generating multi-level point cloud tiles by adopting a feature point downsampling method, and establishing a three-dimensional composite index; the indexed multi-level point cloud tile set is processed through an anisotropic ICP registration method, and a point cloud tile set of multi-time-sequence registration is obtained; establishing an initial triangulated irregular network sediment model, carrying out differentiated partitioning by utilizing historical hydrological auxiliary data, and carrying out dynamic updating based on a partitioning state to obtain a dynamic reservoir sediment model; and S5, calculating to obtain deposition parameters, and mapping the deposition by adopting a three-dimensional engine to obtain an interactive three-dimensional dynamic deposition situation map. According to the invention, high-precision dynamic visualization of the reservoir sediment deposition process is realized.

The embodiment of the invention provides a rail transit 3D data defect detection method and system based on spatial indexing, and the method comprises the steps: collecting rail 3D original data, carrying out the preprocessing, and unifying the data into a unified rail coordinate system; dividing the preprocessed track 3D data into a plurality of regular grid units as space units according to spatial positions, extracting multi-dimensional features of data in each space unit, establishing a composite index for each space unit, and storing the composite index in a space-time database; after the acquired 3D data of the current track are subjected to identical processing, spatial position areas corresponding to the 3D data of the current track and historical data are quickly matched through a composite index, and a difference value of multi-dimensional features is calculated; dynamically adjusting the defect judgment threshold of each dimension according to the statistical distribution of the difference values, judging the defect type and severity of the region meeting the threshold, and generating a detection report. The problems that traditional 3D data processing is low in efficiency and long in comparison time consumption are solved.

The invention relates to the technical field of industrial data management, in particular to a supply chain data real-time integration processing method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining an external compliance data document, calling a document layout analysis model based on a multi-modal converter, extracting a text semantic vector, fusing the text semantic vector with a two-dimensional space coordinate feature to generate a composite index key, and carrying out the real-time integration processing of the supply chain data. Executing clustering analysis to recognize the text blocks and generating structured metadata; carrying out discretization processing on the business transaction data flow, and constructing an associated database with production environment and logistics data as attributes; executing cross-modal entity alignment by using the structured metadata, and establishing a bidirectional pointer link between the unstructured document index and the associated database; receiving a query vector, executing a recursive traversal algorithm based on a bidirectional pointer link, calculating a cascade influence probability as a correlation sorting score, and outputting a retrieval result; through multi-modal feature fusion and cross-modal entity alignment, deep integration and risk linkage retrieval of supply chain heterogeneous data are realized.

The application provides a deep rock mass multi-field coupling test data-driven intelligent grading method, relates to the technical field of deep rock mass grading, and constructs a multi-field coupling mechanism database through deep rock mass multi-field coupling test data, refines intelligent characteristic indexes representing multi-field coupling effects from the database, the intelligent characteristic indexes include traditional indexes and composite indexes, the composite indexes are determined based on geological environment parameters of the deep rock mass, the intelligent characteristic indexes are taken as inputs, corresponding deep rock mass grades are taken as outputs, a machine learning model is trained, a deep rock mass intelligent grading model under different geological environment parameters is obtained, intelligent characteristic indexes of deep rock mass to be graded in an engineering site and geological environment parameters thereof are acquired, grading is performed through the deep rock mass intelligent grading model matched with the geological environment parameters, and the grade of the deep rock mass is obtained, so that the problem of low accuracy of existing deep rock mass grading is solved, and the application is suitable for deep rock mass grading.

The invention discloses a virtual measurement method and device, equipment, a storage medium and a product, and relates to the technical field of computers, and the virtual measurement method comprises the steps that multi-time-scale abstract processing is conducted on process parameters and production data sequences obtained online, and a multi-scale time sequence group is obtained; based on a preset virtual measurement model, virtual measurement is carried out on the multi-scale time sequence group, a key parameter measurement result is obtained, and the virtual measurement model is obtained by adopting a reinforcement learning mode to carry out virtual measurement training on a preset time sequence data sample. According to the method, the multi-scale time sequence group of the online time sequence characteristics is extracted, virtual measurement is performed on the online time sequence characteristics by using the trained virtual measurement model, the key parameter measurement result of the composite index is obtained, accurate prediction of the composite index is realized, and the accuracy of the prediction result is improved. Therefore, the problem that comprehensive monitoring of the composite indexes is difficult to achieve in precise laser cutting through a traditional method is solved, and the prediction accuracy is improved.

The invention discloses a time series data analysis method and device, a storage medium and electronic equipment, and relates to the technical field of the Internet of Things. By establishing a composite index structure combining a time index and an equipment index, under the composite index structure, corresponding index entries are dynamically created for a target data flow, the framework of the composite index structure is not changed, and the time series data analysis efficiency is improved. The content is dynamically filled along with the data, and finally a complete composite index result capable of being efficiently retrieved is formed. And the time index adopts a B-Tree structure, so that data in a specific time range can be quickly positioned. Equipment indexing is realized based on a hash table, data generated by specific equipment can be efficiently retrieved, the throughput bottleneck of mass data storage is broken through, and second-level writing and aggregation query of ten millions of data are realized. And in combination with artificial intelligence AI, distributed time series data management and an Internet of Things multi-source data fusion technology, in the face of massive time series data, the analysis processing capability of the time series data is improved to meet the demand of time series data analysis.

The invention discloses a method and equipment for processing meteorological grid point data based on a database, and the method comprises the steps: analyzing original meteorological grid point data, and extracting metadata information; the metadata information is grouped and coded according to at least two dimensions, and binary bit streams corresponding to coded values obtained after information of each dimension is coded serve as input data; starting from the most significant bit of the binary bit stream, sequentially extracting one bit from each dimension according to a preset sequence for interlacing, and generating a primary key corresponding to the original meteorological grid point data; the main keys are written into a database for storage, and the database sorts the main keys according to the sizes of the main keys. According to the method provided by the invention, through a multi-dimensional joint index construction method, efficient and flexible support for multi-dimensional range query of the type is realized, complex multi-dimensional range query is converted into efficient one-dimensional continuous interval scanning of a data storage engine, and the meteorological service data retrieval performance is improved.

The application provides a multi-source Parkinson's disease database integration method based on LEDD-UPDRS composite index alignment, and is applied to the technical field of data processing. In view of the heterogeneous problems of multi-source Parkinson's disease databases, the application integrates heterogeneous data of PPMI, PDBP and local queues, disassembles characteristics according to four modes of heredity and clinic, introduces a standardized term library and a BERT-BiLSTM-CRF model unified term adjusted in combination with medical corpus, generates a cross-library feature table; a storage architecture is constructed based on FlashROM mode to realize data storage and retrieval; a composite observation system is built with LEDD and UPDRS-III, consistency is verified by multiple methods, data is standardized, missing data is filled in combination with multiple models to generate an integrated data set; a double prediction model is built relying on the data set, and the advantages of the data are realized by a Cohen's d value verification method; finally, cross-library joint analysis is carried out, phenotype-genotype correlation modeling and causal inference are completed, and data support is provided for Parkinson's disease research and clinical prediction.

The application discloses a foreign language teaching effect evaluation method and system based on artificial intelligence, and the content comprises data collection, speech feature extraction, multi-modal fusion evaluation modeling and teaching effect evaluation. The application relates to the technical field of intelligent evaluation of teaching effect, and particularly discloses a foreign language teaching effect evaluation method and system based on artificial intelligence. The scheme constructs a pronunciation articulation composite index through formant difference analysis, calculates acoustic semantic coupling degree, and evaluates phoneme transfer stability, so that the articulation, accuracy and coherence of speech can be accurately quantified in a multi-noise environment. The scheme introduces modal attention, realizes bidirectional interaction of multi-modal information by using full-interconnection cross-modal cross-attention, and innovatively introduces a complementary gating mechanism to adaptively adjust the modal fusion ratio. Local scores are generated by combining a lightweight network, meta-decision network dynamically allocates weights, and finally, end-to-end optimization is realized by using a differentiable weighting strategy, so that the teaching effect evaluation of fineness and accuracy is realized.

The invention relates to the field of data query, and discloses a space grid data query method based on AABB and KDTree composite index, comprising the following steps: step 1, acquiring simulation data of a part, establishing a space mapping relationship between the simulation data and a geometry of a die casting, and performing AABB space division on the geometry, the AABB space including primary AABB space division and secondary AABB space division; the primary AABB space division divides the geometry of the die casting into a plurality of topologically associated sub-regions; the sub-region is divided into a plurality of tetrahedron units by the secondary AABB space division; performing binary tree structure recombination on the tetrahedron units in the sub-regions by adopting KDTree; and 2, judging whether the target coordinate point is in the sub-region or not, traversing the tetrahedron unit of the sub-region, and remarkably reducing the calculation complexity and improving the spatial query efficiency of the large-scale grid model by introducing a spatial index structure and an algorithm optimization strategy.

The invention discloses a social media data fused urban inefficient land intelligent identification method, which comprises the following steps of: realizing multi-source data space fusion by acquiring a social media geographic marking text, an urban updating policy file, a remote sensing image and basic geographic data; a natural language processing and topic modeling method is used for extracting keywords from the city update text, a topic-feature word mapping table is constructed, a RoBERTa-l arge model and a semantic rule are combined to calculate a text emotion score, and a land use negative emotion map is generated; and constructing a multi-dimensional evaluation system including indexes such as plot ratio and road network density, calculating a composite index by adopting an AMP algorithm, dynamically allocating weights through an entropy weight method, and judging the low-efficiency land. And a white list area is introduced for verification and parameter iterative optimization, and finally, an identification result is visually displayed in WebG IS and AR platforms, so that the accuracy and decision support capability of urban low-efficiency land identification are improved.

1. Name of the product in this design: Electronic device with a graphical user interface featuring interactive cards for predicting the Shanghai Stock Exchange Composite Index. 2. Intended use of this design: for use in electronic devices. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: To display the Shanghai Composite Index and its rise and fall, allowing users to quickly understand market conditions; to display the proportion of each party, allowing users to see the general public's sentiment. The information presented above provides a convenient entry point for participating in the event, enhancing the user's interactive experience with the intelligent agent. 6. Description of the changing states of the graphical user interface: The main view displays the date predicted for the current rise or fall; it also displays the current Shanghai Composite Index and its rise or fall, displayed in red for rise and green for fall; clicking "Bullish" in the "Main View" will take you to the interface change state graph, which displays detailed information about the bullish outlook.

The invention discloses an intelligent cache processing method for real-time annotation data streams, and belongs to the technical field of cross-domain data processing, and the method comprises the steps: receiving a multi-source annotation data stream, mapping fields through a time sequence diagram neural network, calculating the similarity, generating a mapping rule, executing incremental cleaning, and outputting a structured annotation data stream; extracting data entities, attributes and relationships, constructing a heterogeneous cache atlas, and establishing a quaternary composite index; then constructing a heat-temperature-cold hierarchical cache architecture, dynamically distributing data entities based on real-time heat entropy, relation strength and the like, and synchronously updating the atlas and the index; low-confidence data is identified through entity confidence evaluation, correction suggestion feedback cleaning is generated, and data source subgraph copy parameters and hotspot entity states are synchronized by means of a security aggregation algorithm; evaluating the network based on a deep strategy to realize fine-grained dynamic access control; according to the method, the caching efficiency, the data credibility and the access security of the multi-source annotation data are improved, and the method is suitable for a real-time data stream processing scene.

The application relates to the technical field of electric digital data processing, and discloses a big data fast indexing and querying system based on general surgery diagnosis and treatment characteristics, which comprises a data stream acquisition module, a topological sequence reorganization module, a physical space mapping module, an index key synthesis module and a preloading execution engine; the topological sequence reorganization module analyzes heterogeneous diagnosis and treatment data streams and reorganizes the data streams into logically continuous patient whole-course data aggregates; the physical space mapping module sequentially maps the aggregates to a continuous physical address space of a bottom-layer storage; the index key synthesis module generates composite index items according to physical address offsets among data blocks; and the preloading execution engine drives the bottom-layer storage to burst read, the application converts cross-modal diagnosis and treatment correlation queries into one-way sequential reading of the storage, reduces the track delay and access overhead in the searching process, and guarantees the searching response delay certainty under the condition that the data volume continuously grows.

The application provides a knowledge base construction method and system based on retrieval enhancement and knowledge graph. First, the preprocessing stage converts the text data into structured data set containing entities, attributes and relationships, realizing the conversion from unstructured or semi-structured text data to machine interpretable data. Then, through block encapsulation, an atomic knowledge unit is formed, which becomes the smallest knowledge carrier that can be associated and operated. Then, on the one hand, by constructing a composite index based on vectorization technology, efficient semantic retrieval association between atomic knowledge units is established; on the other hand, by constructing a knowledge graph, a deep logical relationship association between atomic knowledge units is established, forming a reasoning semantic network. Finally, through system integration, an enhanced knowledge base with efficient semantic retrieval and deep relationship reasoning capability is formed, thereby solving the technical problems of low data structuring degree and insufficient data association in the prior art.

A support scheme intelligent recommendation method and system based on slope engineering knowledge base belong to the technical field of slope support design. The method comprises the following steps: constructing a slope engineering special knowledge base, and grading / quantifying a multi-dimensional engineering index of the support scheme; building a three-dimensional visualization environment of slope engineering, and completing slope excavation contour design through a bias algorithm+ground model clipping; adopting a face-to-face topology intersection algorithm to realize geological zoning of slope contour surface unit level, and obtaining lithological characteristics; dividing support zoning; searching for a candidate support scheme through a composite index; and selecting a recommended scheme based on multi-dimensional engineering index comparison. The system is a special system adapted to the actual scene of slope engineering, and supports functions such as cross-platform deployment, collaborative work, internal network security, offline access, etc. The method and system can significantly improve the accuracy, compliance and design efficiency of slope support scheme selection, and are suitable for complex three-dimensional slope and multi-team collaborative engineering scenes.

Case management systems and techniques are disclosed. In various embodiments, searching case instances is facilitated. An indication to create a composite index across hierarchical case nodes comprising a case model is received. Case instance data associated with the case model is used to generate the composite index. The composite index is made available to be used to optimize searches of a plurality of case instances with which the case instance data is associated.

The embodiment of the invention provides a fan regulation and control method and system based on multi-source environmental parameter self-adaption, and the method comprises the steps: firstly processing multi-source data, and carrying out macroscopic scene recognition to understand the context of a current environment; afterwards, the contextual information is utilized to guide an attention mechanism to generate dynamic weights for various environmental parameters, so that a composite environmental pressure index capable of accurately reflecting the current core regulation and control requirement is calculated, the system can dynamically adjust the control focus according to a real-time scene, and finally, the control focus of the system is dynamically adjusted according to the real-time scene. By inputting the composite index and the target value into the PID controller for accurate adjustment, the optimization of the operation strategy of the fan can be realized, and the intelligent level, the adaptive capacity and the comprehensive energy efficiency of the regulation and control system are remarkably improved.

The invention discloses a DNA sequence local alignment method based on k-bounded matching statistics, and aims to solve the problems of slow alignment, limitation on downstream application and more resource occupation caused by internal contradictions and limitations in speed, sensitivity, coordinate information retention and resource consumption when large-scale genome data is processed by the conventional DNA sequence alignment method. The method comprises the following steps of: constructing a composite index for a reference DNA sequence set by utilizing spectrogram Burrows-Wheeler conversion, scanning and querying basic groups of DNA sequences one by one by utilizing the composite index, generating a k-bounded matching statistical array, generating a de-randomized matching statistical array by utilizing a random matching model, defining a grammar rule and a translation rule, and carrying out translation on the de-randomized matching statistical array. And processing the de-randomized matching statistical array by using a grammar rule and a translation rule to obtain a character type comparison event of the query DNA sequence, and finding a comparison result corresponding to the query DNA sequence in the reference DNA sequence set based on the character type comparison event.

Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and / or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments. Probable root causes of incidents and pathways for anomaly propagation through the CPS are identified using causal interference and the incident timeline graph to detect underlying vulnerabilities and predict future system weaknesses. Recommended actions are generated and executed for incident resolution and system optimization.

The application discloses a reusable spatial index template chip, a replicable template chip, a spatial transcriptome chip and a preparation method and application thereof. A composite index library compatible with a general sequencing platform is designed, spatial barcode mapping is completed on a commercial chip to obtain a spatial index template chip. Through molecular hybridization and enzymatic extension technology, a known coordinate array is copied to a receptor chip, and multiple generations of copying are supported, so that a batch spatial transcriptome chip preparation system sharing the same spatial coordinate atlas is constructed. The application realizes deep compatibility with a standard sequencing platform through composite index library design, decouples a spatial capture carrier from an original sequencing chip, significantly reduces experimental starting cost, and provides a large-scale solution for high-throughput and low-cost spatial omics large-scale application.

The application discloses a real-time intelligent cache processing method for labeled data stream, and belongs to the technical field of cross-domain data processing, comprising the following steps: receiving a multi-source labeled data stream, mapping fields and calculating similarity through a time sequence graph neural network, generating a mapping rule, performing incremental cleaning, and outputting a structured labeled data stream; extracting data entities, attributes and relationships, constructing a heterogeneous cache graph and establishing a four-element composite index; then constructing a hot-warm-cold layered cache architecture, dynamically distributing data entities based on real-time heat entropy, relationship strength and the like, synchronously updating the graph and the index; identifying low-confidence data through entity confidence evaluation, generating a correction suggestion, feeding back cleaning, and synchronously updating the parameters of each data source subgraph copy and the state of hot entities with the aid of a security aggregation algorithm; and realizing fine-grained dynamic access control based on a deep policy evaluation network; the method improves the cache efficiency, data reliability and access security of multi-source labeled data, and is suitable for real-time data stream processing scenarios.

The application relates to the technical field of data analysis, and particularly discloses a social media data analysis report generation method and system, which comprises the following steps: constructing a three-level index system comprising atomic indexes, derivative indexes and composite indexes for social media data characteristics; defining and calculating logic standardization annotation of each index in the three-level index system based on metadata management specifications to form a social media standardized index dataset; determining the hierarchical correlation between indexes based on the social media standardized index dataset, and constructing an index tree with the composite indexes as root nodes, the derivative indexes as intermediate nodes and the atomic indexes as leaf nodes; when an index data anomaly is detected, locating the abnormally associated social media dimensions based on the index tree and grey correlation analysis; generating an abnormal reason deduction link according to the abnormally associated social media dimensions, and generating a social media data analysis report; and providing strong and comprehensive decision basis for social media operation.

This application discloses a method, apparatus, and device for generating intelligent question-answering based on a large model. The method includes: standardizing multi-source heterogeneous spatiotemporal data obtained from the power grid to obtain spatiotemporal standard data; constructing a power grid knowledge graph based on the spatiotemporal standard data and configuring a spatiotemporal composite index for knowledge query; constructing spatiotemporal question-answer pairs based on the power grid knowledge graph, and fine-tuning and optimizing the initial question-answering model based on the spatiotemporal question-answers to obtain an optimized question-answering model; and generating intelligent question-answer pairs based on a pre-set user cognitive profile and dynamic spatiotemporal perception prompts through the optimized question-answering model. Therefore, this application can solve the technical problem that the existing technology has poor understanding of the fusion of time and space dimensions, resulting in low efficiency and accuracy of current large-model intelligent question-answering systems for the power grid.

The invention relates to the technical field of user portrait construction management, and discloses an insurance buying behavior data user portrait construction management system, which comprises a data standardization module used for collecting multi-source insurance buying behavior data and equipment fingerprint data and generating a standardized user event sequence; the payment discipline extraction module is used for extracting payment discipline sets such as on-time fulfillment rate from the event sequence; the journey stable extraction module is used for extracting journey stable sets such as equipment consistency rate; the index synthesis module is used for respectively generating a payment discipline composite index and a journey stability index; the consistency evaluation module is used for calculating a coupling consistency score and generating a quadrant label and a consistency warning mark; the index updating module is used for updating various indexes based on the newly added records; and the portrait solidification storage module is used for structurally storing the index result and generating a user portrait record. According to the method, the structured quantitative expression of the user insurance behavior characteristics and the dynamic construction and management of the user portraits are realized.

The invention provides an intelligent grading method driven by deep rock mass multi-field coupling test data, and relates to the technical field of deep rock mass grading, a multi-field coupling mechanism database is constructed through the deep rock mass multi-field coupling test data, and intelligent characteristic indexes representing the multi-field coupling effect are condensed from the multi-field coupling mechanism database; the intelligent characteristic indexes comprise traditional indexes and composite indexes, the composite indexes are determined based on geological environment parameters where the deep rock mass is located, the intelligent characteristic indexes serve as input, corresponding deep rock mass grades serve as output, a machine learning model is trained, and intelligent deep rock mass grading models under different geological environment parameters are obtained. The intelligent characteristic indexes and the geological environment parameters of the to-be-graded deep rock mass on the engineering site are obtained, grading is conducted through the deep rock mass intelligent grading model matched with the geological environment parameters, the grade of the deep rock mass is obtained, the problem that existing deep rock mass grading is low in accuracy is solved, and the deep rock mass grading method is suitable for deep rock mass grading.