2067 results about "Data type" patented technology

The invention discloses a fusion method for automatic coprocessing of multi-source heterogeneous data, which comprises the following steps of: analyzing the format and semanteme of the multi-source heterogeneous data to obtain metadata features of each data source, the metadata features comprising data types, coding modes and semantic tags, and generating a standardized metadata description set; according to the metadata description set, a pre-established protocol template library is adopted, an applicable template is matched, an initial unified exchange protocol is generated, and the initial unified exchange protocol comprises a format conversion rule and a semantic mapping relation; and if the format conversion rule of the initial unified exchange protocol cannot adapt to the metadata features of the newly added data source, dynamically updating the protocol template through a machine learning algorithm to obtain an exchange protocol adaptive to the new data source.

The invention discloses an electric power field knowledge question-answering system construction method based on a large language model, and relates to the field of electric power field knowledge question-answering, and the method comprises the steps: judging the data type of electric power field knowledge, and carrying out the processing of the electric power field knowledge according to the judgment result through matching with a processing technology, and generating an entity relation triple; constructing a power field knowledge graph; optimizing the power field knowledge graph based on the attention network, outputting an answer causal path of the fault problem by using the optimized power field knowledge graph, and marking a confidence score of the answer causal path; and inputting the solution causal path and the confidence score into a language model to obtain a fault question answering result, and optimizing the question answering result according to the consistency of the fault question answering result and the power field knowledge graph. According to the method, on the premise that the fault diagnosis logic is rigorous and the result is traceable, knowledge in large-scale unstructured literatures in the power industry is activated, so that accurate question and answer services can be provided for operation and maintenance personnel in real time.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention relates to the technical field of data fusion, and discloses a multi-source heterogeneous data fusion method and system based on edge computing, and the method comprises the steps: obtaining a heterogeneous data stream, carrying out the data type recognition and data feature extraction, and obtaining an original feature set; according to the original feature set, unifying feature dimensions and adjusting a time reference to obtain time sequence vector data; according to the time sequence vector data, filling the feature value of the missing time point to obtain a multi-modal feature; performing block storage on the multi-modal features, verifying the synchronism of adjacent modals, distributing modal synchronization weight coefficients, and finally generating a fusion feature matrix; according to the fused feature matrix, identifying and filtering redundant feature dimensions, establishing a feature association map, and executing feature merging to obtain a simplified feature matrix; according to the simplified feature matrix, feature importance scores are calculated, sorting weight coefficients are arranged and distributed in a descending order according to the scores, and a multi-modal fusion semantic vector is generated. The method improves the accuracy of data analysis and decision.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a machine learning task execution and feedback method, device, equipment and medium. And performing semantic reasoning by using a pre-training language model, generating a decision result in combination with an entity relationship associated with a task type and a data type in the knowledge graph, executing a corresponding machine learning task, converting a task execution process and an execution result into natural language feedback information, and outputting the natural language feedback information to a user side. According to the method, natural language input is structured into machine learning task description, large model semantic reasoning and knowledge graph knowledge association are combined, an executable decision result is generated, automatic closed loop from task recognition, model selection to result feedback is achieved, the user operation threshold is lowered, and the model development efficiency and the interaction intelligence level are improved.

A non-transitory, processor-readable medium storing instructions that when executed by a processor, cause the processor to receive data artifacts, encode the artifacts to a standard data type, and compute, for each artifact, a hash function. The hash functions and encoded documents are stored in a first database. The processor is caused to tokenize the encoded artifacts, to produce tokens associated with natural-language identifiers extracted from the encoded artifacts. The processor is caused to transform, using an embedding model, the tokens to produce vectors that are stored in a second database and classified based on categories. The second database is configured to be queried to perform a semantic search in response to receiving a request from a user operating a user compute device. The processor is caused to retrieve, from the semantic search, a subset of vectors from the second database to be displayed on the user compute device.

Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and / or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.

The invention discloses a mineral resource intelligent prediction method and system based on multi-source heterogeneous data fusion and deep learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and carrying out the standardization processing to form a structured data set; multi-source heterogeneous data fusion: realizing data layer space registration and feature layer weight dynamic allocation through an attention mechanism multi-modal fusion module, and outputting a high-dimensional metallogenic feature vector; constructing a CNN-LSTM mixed deep learning model and completing initialization training, and outputting an initial mineralization probability graph; and establishing a dynamic updating engine, performing model increment training based on transfer learning, correcting the mineralization probability through positive and negative sample reinforcement learning in combination with a newly added data type, and outputting a time sequence dynamic mineralization probability graph. According to the method, mineralization probability dynamic evaluation and risk quantitative updating are realized, the prediction precision and the model updating efficiency are improved, the method is adaptive to a multi-stage exploration scene, and accurate real-time support is provided for exploration decision making.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses a virtual power plant energy scheduling method and system under a local data abnormal condition, and the method comprises the steps: recognizing a data type and an incidence relation related to abnormal data through an incidence matrix after the abnormal data of a virtual power plant are collected and recognized; and calculating an abnormal data correction value based on the association relationship function and the real-time association data, and performing weighted average on the abnormal data correction value and the original abnormal data to obtain correction data. And then calculating an abnormal data time difference and a correlation deviation degree, and distributing a credibility weight for the corrected data. And finally, calculating the correlation degree between the scheduling parameter of the preset scheduling scheme and the weighted correction value, and selecting the scheme with the highest correlation degree as a virtual power plant resource scheduling scheme. By implementing the technical scheme provided by the invention, the scheduling accuracy in a data exception state is improved.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

The invention belongs to the technical field of multi-source heterogeneous data quality and analysis, and relates to a data management and intelligent analysis method oriented to power multi-source heterogeneous. According to the method, ontology model alignment, temporary ontology generation and multi-mode semantic embedding technologies are adopted, semantic unification of cross-data types is achieved, meanwhile, a spatio-temporal joint indexing mechanism is established, geographic coordinates and timestamps are bound and stored, efficient multi-dimensional query is supported, and data integration efficiency and semantic consistency are improved; the problem of poor adaptability of a fixed threshold value is solved by dynamically adjusting the operation data safety threshold value and evaluating the abnormal confidence coefficient, the detection precision and credibility of the power multi-source heterogeneous data anomaly are improved, the anomaly detection accuracy is improved, and the false alarm rate is reduced; by constructing an abnormity confirmation logic and combining equipment parameters, environment data and communication states, reasons such as equipment faults, environment interference and communication abnormity can be accurately positioned, root causes can be quickly positioned, and time can be shortened.

The invention discloses a fault diagnosis method and device, a medium and a product, and relates to the technical field of data processing. Multi-dimensional data such as text logs, time series data, topological graph structures, operation records and environmental parameters are obtained, various factors possibly involved when the fault occurs are included, different types of data can complement and verify each other, the fault features are accurately described, the limitation of single data type analysis is avoided, and the analysis efficiency is improved. And the accuracy of fault diagnosis is effectively improved. Then converting different types of multi-dimensional data into target feature representations, matching the target feature representations with data in a historical fault case library, and when a historical fault of which the matching degree is greater than a preset matching threshold value is found, determining a diagnosis conclusion and a repair scheme of the historical fault as a diagnosis conclusion and a repair scheme of the current fault. And each new fault does not need to be analyzed and reasoned, so that the fault diagnosis time is shortened, and the fault diagnosis efficiency is improved.

Predicting the salience of one or more data entities to a particular (target) data entity from among a plurality of data entities may comprise generating a graph of the plurality of data entities and a machine-learned model architecture that predicts the salience of the one or more data entities output by the machine-learned model architecture using the graph. For example, the machine-learned model architecture may comprise a first machine-learned model for generating an embedding using the content of the target data entity, a second machine-learned model for generating a vector using the data type indicated by the target data entity, and a third machine-learned model (e.g., a graph neural network or other feed-forward neural network) for generating a contextual representation of the target data entity to which other contextual representations associated with the plurality of data entities may be compared (e.g., using Euclidean distance, cosine similarity, dot product).

The invention provides a self-adaptive semantic-driven data set field matching method and system, and the system comprises a data preprocessing module which is used for carrying out the cleaning, standardization and preliminary analysis of an input data set, and extracting a field name, a data type, a field description and a data sample; the deep semantic representation modeling module is used for constructing a field-level semantic representation vector; the multi-level similarity calculation module is used for comprehensively calculating the grammatical similarity, the semantic similarity and the statistical similarity among the fields, dynamically adjusting the weight of the similarity of each level by adopting a weighted fusion algorithm, and generating a comprehensive similarity matrix; and the matching result management and application module is used for generating a field matching mapping table and a fusion suggestion according to the comprehensive similarity matrix. According to the method, high-precision automatic matching of data set fields is realized by fusing deep semantic understanding, multi-dimensional similarity calculation and incremental adaptive learning, and the efficiency and accuracy of data set fusion are remarkably improved.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention discloses a knowledge graph generation method based on multi-source data integration, and relates to the technical field of knowledge graph generation. The method comprises the following steps: collecting multi-format data, and performing cleaning, standardization and desensitization preprocessing to ensure that the data is integrated; using the fusion model to extract entities and relationships, and adapting to multi-source data types; similarity is calculated in combination with multi-dimensional features, and entity alignment disambiguation is achieved; carrying out weighted fusion on multi-source knowledge to construct a triple, and processing relation conflicts; evaluating the quality of the atlas through multiple indexes; triggering conditions are set, incremental updating and version management are adopted, and dynamic iteration of the atlas is guaranteed. According to the method, the multi-source data preprocessing quality is improved, the entity recognition and alignment precision is enhanced, relation conflicts are solved, and a high-quality time sequence knowledge graph is constructed; incremental updating is efficient and energy-saving, version management is traceable, and multi-field dynamic application requirements are met.

The invention relates to an optimization method of a data type conversion operator, computer equipment, a readable storage medium and a computer program product. The method comprises the following steps: allocating memory resources for target data; establishing a read mapping relationship between each logic index in the logic structure of the source data and the memory address of the source data, and establishing a write mapping relationship between each logic index in the logic structure and the memory address of the target data; traversing the logic structure, aiming at any traversed logic index, determining a read address corresponding to the logic index based on the read mapping relationship, performing data reading from the memory resource corresponding to the source data based on the read address, converting the read data element from the first data type to the second data type, and storing the converted data element in the memory resource. And writing the data elements subjected to data type conversion into memory resources corresponding to the target data according to the write mapping relationship. By adopting the method, the calculation capability of the data type conversion operator can be improved.

Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems to comply with system requirements frameworks that indicate specific requirements on how the computing systems should handle certain data types. In response to detecting a change to one or more digital data requirements of a system requirements framework, the disclosed systems access a configuration profile of an entity and utilize a large language model to determine if data assets or data processing operations comply with the changes to the digital data requirements. In response to determining a configuration gap of the configuration profile based on detected changes to the digital data requirements, the disclosed systems utilize a large language model to generate tasks for correcting the configuration gap. The disclosed systems generate one or more tasks by modifying data assets, data processing operations, or other digital data associated with the entity.

The invention discloses an interface dynamic implementation method, a medium and equipment. The method comprises the following steps: converting a source code interface file into a structured description file; constructing a mapping relation library of the interface components and the implementation codes; storing the structured description file as an independent interface structure data instance, storing external business data as an independent business data instance, and establishing a separated storage system of the interface structure data and the business data; and analyzing the structured description file, judging whether the attribute value of the interface component is a business data type, and if yes, replacing the business data instance with the business data to generate a final interface. According to the method, the component instance is matched based on the mapping object, the interface is dynamically generated, an exclusive analyzer does not need to be developed for a single component, and the dynamic efficiency and maintainability of the interface can be remarkably improved.

The embodiment of the invention discloses a report generation method and device, computer equipment and a readable storage medium, and the method comprises the steps: displaying a graphical user interface to prompt a report requester to input report demand information; report demand information is obtained, semantic analysis is carried out on the report demand information through a large language model LLM to obtain report generation indication information, and the report generation indication information at least comprises a data range, a data type, a data statistical parameter and a report display type; searching a database through the LLM to obtain a plurality of business data matched with the data range and the data type, and obtaining business indexes of the plurality of business data based on the data statistical parameters through the LLM; and determining report basic parameters based on the report display type through the LLM, and outputting a target report based on the report basic parameters, the multiple pieces of business data and the business indexes. By adopting the method, the technical threshold of data analysis processing can be reduced, the report generation efficiency is improved, and the method is simple, efficient and high in applicability.

Disclosed in the present application are a data processing method based on a large language model, and a large language model, an electronic device, a computer-readable storage medium and a computer program product. The method is applied to a user terminal, wherein a large language model is deployed on the user terminal, and weight parameters of linear calculation layers of the large language model are pre-quantized into format data of an integer data type. The method comprises: acquiring input data; performing vector conversion on the input data by means of an embedding layer of a large language model, so as to obtain a floating-point query vector of a floating-point data type corresponding to the input data; converting the floating-point query vector into an integer query vector of an integer data type; and performing an operation by means of weight parameters of linear calculation layers and the integer query vector, so as to obtain a query result corresponding to the input data. By means of the solution provided in the present application, a large language model can be smoothly run on a user terminal, such that the user terminal can provide services for users without needing network connectivity, and can better ensure the privacy of the users.

The invention discloses a scene and compliance fused dynamic hierarchical data encryption method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the preprocessing of the multi-source data, and obtaining to-be-encrypted data; key words in the to-be-encrypted data are extracted, and weight coefficients are distributed to the key words according to semantic roles and / or industry risk coefficients; according to the data type, determining a characteristic value corresponding to each keyword; determining a scene coefficient and a compliance coefficient; based on the weight coefficient, the feature value, the scene coefficient and the compliance coefficient, determining a data sensitivity score and a data sensitivity level corresponding to the to-be-encrypted data; and based on the data sensitivity level, encrypting the to-be-encrypted data by adopting the encryption strategy of the corresponding level. According to the method provided by the embodiment of the invention, the security of the high-sensitivity data is ensured, the resource waste caused by excessive encryption is avoided, the whole process is automatic, the labor cost and the overall cost of security protection are greatly reduced, and the comprehensive efficiency of data processing and security protection is improved.

The invention discloses a satellite signal distortion compensation and data reconstruction method, system and device and a medium, and belongs to the technical field of satellite communication, and the method comprises the steps: obtaining and processing a satellite communication time domain signal, obtaining a time-frequency graph and a local feature vector, calculating the local feature vector through an encoder, obtaining a global feature, and carrying out the calculation of the global feature; splicing the global feature and the local feature vector to generate a fusion feature vector, and recovering the resolution of the time-frequency graph based on an end-to-end compensation network; and inputting a time-frequency graph and the fusion feature vector to obtain a phase compensation factor, compensating the time-frequency graph by using the phase compensation factor, reconstructing a time-domain signal through inverse short-time Fourier transform, splicing the fusion feature vector and a combined data type label, and inputting the spliced fusion feature vector and combined data type label into a trained neural network to realize semantic reconstruction. The method solves the problem of joint compensation of signal amplitude and phase, guarantees data integrity, gets rid of dependence on complex physical parameter estimation, and can quickly adapt to ionosphere change and multipath effect environment.

The invention discloses an agent cooperation method based on multi-modal data fusion, and belongs to the technical field of multi-modal data fusion, and the method comprises the steps of dynamically collecting multi-source heterogeneous data, automatically distributing agent roles based on reinforcement learning, and optimizing task execution efficiency. The method comprises the following steps: identifying data, classifying the data according to the characteristics of the data, and establishing an efficient data flow management system for each identified data type to control the speed and sequence of the data flowing into the system from different sources, define the environment of agent operation and create a detailed capability model for each agent; historical data and a simulation scene are used to train an agent, when a new task arrives, the system determines a most suitable agent role allocation scheme in real time by using a trained reinforcement learning algorithm according to a current environment state, and feeds back an actual result to the reinforcement learning module during the task execution period of the agent.

The invention relates to the technical field of engineering cost, and discloses a data entry method for engineering cost software, which comprises the following steps of: S1, importing an engineering quantity list file in an Excel format through an interface of the engineering cost software, and automatically analyzing a data structure of the engineering quantity list file; s2, based on a preset intelligent matching algorithm, performing dynamic mapping on the Excel data field and a data field of a project cost software function module; s3, adopting a data type self-adaptive rule to automatically classify and convert numerical values, texts and compound data in the Excel; s4, based on a data association generation algorithm, automatically establishing a hierarchical relationship and a dependency relationship between the engineering quantity list items; s5, executing one-button data import, and writing the processed structured data into a project cost software database in batches; according to the method, high-precision automatic mapping of the Excel field and the software module is realized through an intelligent matching algorithm and a dynamic weight adjustment mechanism based on the feature vector similarity, the matching accuracy is greatly improved, and the manual correction frequency is greatly reduced.

The invention discloses a matrix acceleration and conversion method and system supporting variable blocks, and relates to the technical field of big data processing and GPU parallel computing. The method comprises the following steps: acquiring multi-source heterogeneous data, and generating a numerical matrix with uniform row and column dimensions through data type identification, field semantic analysis and cross-format standardized conversion; dynamically calculating an optimal partitioning threshold value based on matrix sparseness, row and column sizes and GPU video memory bandwidth parameters, adopting a full partitioning strategy for the dense region according to a row and column ratio and entropy analysis, and implementing a compression storage partitioning strategy for the sparse region; a GPU multi-level cache system is used for distributing heterogeneous blocks, a CUDA kernel function is automatically selected in combination with block density, and parallel computing is achieved through a thread bundle dynamic scheduling mechanism. According to the method, the problems of low heterogeneous data fusion efficiency, video memory bandwidth waste and unbalanced calculation load are solved, and the matrix operation speed is remarkably improved.

The invention discloses a system and method for ensuring data consistency of a low-voltage power distribution network, and the method comprises the following steps: obtaining multi-source operation data uploaded by all collection devices in the low-voltage power distribution network, and converging the multi-source operation data to a unified processing channel; a timestamp dynamic alignment and mapping mechanism is adopted, sampling frequency and time offset are detected and corrected, and a unified time axis is generated; executing unit conversion and field unification operation according to the data type label, and constructing a standard field structure; carrying out prediction compensation on missing data by adopting a time sequence sliding window interpolation model; a sliding window anomaly rate detection strategy is applied to identify numerical value abrupt change and execute anomaly replacement; and performing structured output on the data set subjected to the consistency processing. According to the method, fine alignment, standardized processing and high-reliability correction of multi-source heterogeneous data can be realized, and the data consistency level and decision support capability of the low-voltage power distribution network in operation monitoring, analysis and control scenes are improved.

The invention belongs to the technical field of electric power automation, and particularly relates to an automatic modeling method of power grid dispatching knowledge based on a large model and a related system.The electric power knowledge is decomposed into an explainable intermediate reasoning path through the thinking chain technology, and adaptive small models are selected according to the path to form a modeling link; a modeling link is represented as a triple form, edges and nodes are complemented, a complete knowledge graph is obtained, problems in the knowledge graph are further decomposed to construct a reasoning link, meanwhile, input data are monitored in real time, newly added entities are associated with the reasoning link, and unified representation and processing of heterogeneous power system data are achieved. Due to the fact that data from different sources are different in format, precision and semantics, according to the method, through thinking chain decomposition and small model link processing, sub-module optimization and dynamic adaptation aiming at different data types are achieved, and precision loss and adaptation difficulty caused by data heterogeneity are effectively solved.

The invention discloses an analysis report generation method and device, equipment, a medium and a product. The method comprises the steps of determining a target report template in response to an analysis report generation request, and analyzing the target report template to determine a to-be-filled variable in the target report template; determining a corresponding data acquisition strategy according to the data type corresponding to the to-be-filled variable, and acquiring target data from at least two target systems based on a preset robot process automation technology and the data acquisition strategy; and according to a preset configuration file and the target data, determining a variable type and target filling information corresponding to the to-be-filled variable so as to execute a corresponding filling operation on the target report template, and generating a target analysis report. According to the technical scheme, the preset report template and the configuration file can be utilized to automatically perform data acquisition, processing and filling, so that the analysis report is quickly and accurately generated.