2321 results about "Data class" patented technology

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

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 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 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 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.

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 relates to the technical field of system monitoring, and discloses an information system full-link monitoring method based on high-frequency index acquisition optimization, which comprises the following steps: monitoring the running state of an information management system in real time, dynamically adjusting the sampling frequency by means of a customized service key identification component and a comprehensive load prediction model, and performing real-time monitoring on the sampling frequency. Transmitting the target data to the edge computing node; a lightweight monitoring agent is deployed at an edge node, and a wavelet signal decomposition algorithm is adopted to extract features and distinguish data types; constructing an information management business knowledge graph and an entity-relation-business rule base, associating abnormal features, and generating an abnormal root cause report in combination with a time sequence prediction model and a knowledge constraint large language model; based on report and information service priorities, monitoring resources are dynamically allocated in the edge-cloud collaborative architecture, and related model parameters, service association rules and constraint weights are optimized according to operation and maintenance feedback. According to the invention, targeted monitoring requirements in the business peak period and efficient utilization of system resources can be met at the same time.

The invention provides an aero-engine remaining service life prediction method based on a space-time knowledge graph and SDCNN, and belongs to the field of aero-engine health management. According to the method, a space-time knowledge graph and SDCNN neural network architecture is constructed. According to the method, a spatio-temporal knowledge graph is innovatively constructed for an aero-engine, a BERT model is adopted to carry out data type conversion, and a multi-head graph attention network and a pooling graph attention network complete feature extraction and feature fusion to obtain fusion features; and finally, inputting the fusion features into a stacked expansion convolutional neural network to carry out regression learning on feature data, and then carrying out residual life prediction on the aero-engine. According to the method, modeling and prediction are carried out on complex spatial-temporal characteristic data, the remaining service life of the aero-engine can be effectively predicted under limited data, data support is provided for formulating an aero-engine maintenance strategy, and meanwhile a new thought is provided for predicting the remaining service life of other industrial equipment.

The invention relates to the technical field of water network intelligent management and control, and discloses a water network management and control multi-source space digital intelligent fusion base construction and decision optimization method, which comprises the following steps: cooperatively obtaining multi-source space data; format conversion, coordinate system unification and systematic semantic annotation are carried out through a data preprocessing platform, and a normalized spatial data set is formed; constructing a data fusion model, quantitatively evaluating the quality of a data source by adopting a fuzzy comprehensive evaluation method so as to determine a fusion weight, and performing weighted fusion by adopting a specific algorithm aiming at different data types such as vectors, grids and three-dimensional models so as to generate a unified spatial data model; and finally, a decision optimization system performs decision scheme generation and iterative optimization based on the model in combination with a hydrodynamic model and a genetic optimization algorithm. By establishing a standardized data processing and targeted weighted fusion process, the problems that multi-source heterogeneous data is difficult to integrate and the fusion precision is not high are effectively solved, and a reliable data base is constructed.

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 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 provides a time sequence data storage method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining time sequence data and metadata information of the time sequence data, the metadata information comprising a first point location identifier, a data source and a data type corresponding to the time sequence data; according to the data source and the data type, determining a storage strategy of the time series data, including a target storage period and a target storage position; according to the storage strategy and the first point position identification, a target storage instance corresponding to the time sequence data is matched from a storage instance set, all storage instances in the storage instance set are combined and constructed according to different storage strategies and the point position identification, and all the storage instances are parallel storage instances; and storing the time sequence data to a time sequence database through the target storage instance. By determining a dynamic storage strategy driven by metadata and a parallel storage instance, the problem of performance bottleneck during high-concurrency writing of massive time series data is solved, and the real-time performance and reliability of time series data storage are ensured.

The invention belongs to the technical field of large language models, and discloses a time sequence card generation method and device based on a large language model, and the method comprises the steps: receiving a card creation instruction, and obtaining a card data type in the card creation instruction; extracting target text data in a dialog box of the large language model based on the card data type; converting the target text data into target card data corresponding to the card data type; generating a card title corresponding to the target card data according to the target text data; combining the target card data and the corresponding card titles to obtain time sequence cards; and putting the time sequence card into a tool window adjacent to the dialog box for displaying. According to the method, the manual operation of the user can be reduced, and the co-creation work efficiency and convenience of the large language model and the user are improved.

The invention discloses a stacking-based steel hot-rolled product mechanical property prediction method, and relates to the technical field of steel product quality prediction. The method comprises the following steps: constructing a steel hot rolling actual production data set and carrying out missing value interpolation on the data set; performing normalization processing on the complete data set, and dividing a part of data from the normalized data set as a training set; establishing a stacking ensemble learning model based on a regression chain; taking the yield strength, the tensile strength and the elongation in the training set as training labels, taking other data types as training features, and training the learning model by using the training set to obtain a steel hot-rolled product mechanical property prediction model; and the steel hot-rolled product mechanical property prediction model is practically applied to perform real-time prediction on the mechanical property of the steel hot-rolled product. According to the method, the coupling relation among the multiple target variables can be effectively processed, and the accuracy and stability of mechanical property prediction of the steel hot-rolled product can be effectively improved.

The invention discloses a data transmission method and device based on quantum encryption, which are used for improving the security and effectiveness of encrypted transmission of a monitoring data stream. According to the scheme provided by the invention, the method comprises the steps: obtaining the data type of a to-be-transmitted monitoring data stream of monitoring equipment, the transmission quantity of the monitoring data stream, and a communication capability index of a communication environment where the monitoring equipment is located; determining an encryption level corresponding to the monitoring data stream based on the data type and the transmission quantity; determining the division granularity of the monitoring data flow based on the encryption level and the communication capability index; dividing the monitoring data flow into a plurality of data packets according to the division granularity; and encrypting and transmitting each data packet by using a quantum key according to the encryption level of the monitoring data stream.

The invention provides a clinical test data security sharing method and device based on a block chain. The method is applied to the technical field of data processing, and comprises the following steps: extracting feature dimensions corresponding to various types of data, and calculating corresponding data sensitivity coefficients; determining a data sensitivity level of the corresponding data category; extracting corresponding associated metadata; selecting a corresponding encryption algorithm to encrypt the data, and generating corresponding encrypted data and a data abstract; uploading the associated metadata, the encrypted data and the data abstract to a block chain node, constructing a clinical test data sharing account book, and recording a timestamp and a node signature of data operation; extracting identity authentication information and historical access records of visitors, and calculating access credibility; judging whether the visitor is allowed to acquire the decryption key or not; after the visitor obtains the encrypted data, the integrity in the data transmission process is verified based on the non-tampering property of the block chain. Therefore, the security of clinical test data sharing based on the block chain is improved.

The invention belongs to the field of software testing and defect detection, focuses on reliability verification of a physical simulation engine, is suitable for scenes depending on physical simulation, such as robot simulation, automatic driving testing, unmanned aerial vehicle control, education and scientific research, and particularly relates to a variation-based physical simulation engine defect detection method. Potential defects of core modules of collision detection, mechanical calculation, dynamics and the like are efficiently found through a variation test method. According to the method, a feature library is constructed through static library API analysis and AST / IR code analysis, test code expansion is performed by utilizing a large model, a test driver adaptive to a specific data type is generated, a physical semantic constraint mutation seed is combined, a test is executed through libFuzzer, defect and coverage rate data are analyzed, a use case selection strategy is optimized, and the test efficiency is improved. Defects of core modules such as collision detection and dynamic solution are efficiently detected, and the test pertinence and efficiency are improved.

Methods, systems, apparatuses, and computer program products are described. A system may obtain a set of metadata associated with multiple entities of a datastore, the multiple entities including at least two different data types. The system may augment, using a generative artificial intelligence (AI) model, the set of metadata resulting in a set of augmented metadata for the multiple entities of the datastore. The system may generate, using the set of augmented metadata, a set of entity objects for the multiple entities of the datastore and in accordance with a reference schema. The system may identify, using the generative AI model and the set of entity objects as inputs into the generative AI model, first relationships between two or more first entities of the multiple entities of the datastore. Based on the identified relationships and a reference schema relationship model, the system may generate a schema relationship model.

One or more computing devices, systems, and / or methods for privacy data augmentation are provided. An augmentation pipeline is selected to process data based upon a data type of the data. The augmentation pipeline processes the data to generate information that is input into a machine learning model. The machine learning model processes the information and privacy laws to determine a subset of the data to mask. In this way, the subset of the data is masked to create augmented data that complies with the privacy laws.

Systems, methods, and non-transitory computer readable media are configured to perform operations comprising obtaining formatted account data maintained by an entity and associated with an account of a user; accessing in real time account data associated with the account from an institution after the entity has facilitated authentication of the user with the institution; and based on a machine learning model, generating synchronized data that reflects selection of a data field of a data type from the formatted account data maintained by the entity or a data field of the data type from the account data from the institution.

The invention discloses an automatic interlocking test method and system based on a RobotFrameWork framework, and belongs to the technical field of interlocking tests.The automatic interlocking test method comprises the steps that on the basis of a preset rule and a general test case, function logic decoupling is conducted on a test script in combination with the RobotFrameWork framework, and function keywords are obtained; obtaining a configuration file according to the interlocking function and the test data type, and performing feature extraction on the to-be-tested data and the interlocking table based on the configuration file to obtain associated test data; and according to the test requirements and the associated test data, performing testing in combination with the function keywords. The method comprises the following steps: performing function decoupling on a test script through a keyword driving characteristic of a RobotFrameWork framework to obtain a function keyword which only encapsulates single function logic, and extracting associated test data related to a test from to-be-tested data and an interlocking table through an interlocking function defined by a configuration file and a mapping rule of a data type. The technical problems that test efficiency is difficult to improve and script maintenance is difficult in the prior art are solved.

The invention discloses a silicon nitride ceramic thermal conductivity prediction method based on machine learning coding-free feature processing. The method comprises the following steps: acquiring a data set; declaring a chemical character string field of the sintering aid as a disordered data type, and preprocessing other numerical characteristics at the same time; keeping the field of the sintering aid as a disordered data type, and preprocessing other numerical values; selecting a gradient lifting decision tree model supporting native processing category features as a prediction model and performing optimization; a to-be-predicted formula-process parameter combination is input into the optimized prediction model, a thermal conductivity prediction value is output in real time, and the to-be-predicted formula-process parameter combination is directly input into the model through an original chemical formula character string. The invention further discloses a device for implementing the method, computer equipment and a readable storage medium. According to the invention, the original chemical formula of the sintering aid can be directly utilized, and the thermal conductivity can be predicted within millisecond response time without any code conversion.

The invention discloses an RAG knowledge base construction method and device and a medium, and the method comprises the steps: carrying out the analysis of the format, structure and semantic features of input data through a multi-level priority judgment logic, and automatically recognizing the specific data type. And calling a corresponding adaptive segmentation strategy for processing according to the identified data type, and generating data slices with semantic coherence. And asynchronously calling the large language model to generate a keyword set for each data slice, and uniformly storing the data slices, the keyword sets and associated multi-modal resource links by adopting a mixed storage mode to construct an enhanced RAG knowledge base. In the retrieval stage, query keywords are generated for user query according to the same logic, and related results are accurately retrieved from the knowledge base through a keyword matching mechanism. According to the method, the semantic segmentation problem is effectively avoided through intelligent segmentation with adaptive types, multi-modal data access is supported, and unified management and efficient retrieval of heterogeneous resources are realized.

The invention discloses a data coding method and device, electronic equipment, a processor and a storage medium, and relates to the field of large language models. The method comprises the following steps: by taking a group consisting of a plurality of continuous elements in a channel as a unit, performing grouping quantization on reasoning parameters of a neural network model in a reasoning process to obtain a plurality of groups of quantized data, mapping a first group of quantized data into first cache data based on a first adjustment coefficient, and mapping a second group of quantized data into second cache data based on a second adjustment coefficient; the data distribution can be changed due to the first adjustment coefficient, so that the method can adapt to various data types, a flexible mapping mode is provided, and the data distribution of the mapped first cache data can fit the data distribution of the first group of quantized data before quantization. And the cached first cache data is the data after the reasoning parameters are compressed, so that the memory resources required by the reasoning parameter cache, such as weight, key data and value data, are reduced, and the reasoning speed of the large language model is further improved.

The invention discloses a bridge engineering knowledge base construction method and system, and the method comprises the steps: classifying the multi-modal data of each project in the bridge engineering field according to the data type, carrying out the structural conversion, forming a structural data document corresponding to each project, and constructing a modular parallel workflow; the data types comprise texts, images and voices; splitting each structured data document into document segments according to semantic features, and generating document segment vectors by using an embedded model; forming double indexes by using all document fragment vectors and an entity-relationship-attribute triple of the bridge knowledge graph, and constructing a structured data document library-document fragment vector library-knowledge graph three-layer knowledge base; and training a bridge large model based on implicit rule coding, explicit engineering constraint and a rule activation mechanism, and embedding the trained bridge large model into the three-layer knowledge base to complete construction of the bridge engineering knowledge base. Data are fully utilized, the coverage range of the knowledge base is expanded, a multi-dimensional evidence source is provided, and question and answer credibility is improved.

The invention relates to the technical field of artificial intelligence, and particularly provides a heterogeneous data preprocessing method, system and device and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous original data, and analyzing the multi-source heterogeneous original data into intermediate format sample data; calling a large language model to generate a field semantic tag; converting the original field into a standard field in a standard field library through a field mapping rule; cleaning abnormal values by using a machine learning model to obtain a primary sample data set; performing semantic analysis on the unstructured text fields to generate structured feature vectors, and adding the structured feature vectors into a data set; and finally, performing feature derivation on the structured field according to the data type and the semantic tag, and outputting a final data set. According to the method, automatic processing from the original data to the training ready data set is realized, and the data preprocessing efficiency, the automation degree and the reliability are remarkably improved.

A method includes obtaining defect data and context data in association with a substrate, and providing the defect data and the context data to a first trained machine learning model as input. The method further includes obtaining output from the first trained machine learning model based on the defect data and the context data. The output is indicative of a predicted root cause in association with the defect data. The method further includes performing a corrective action in view of the output.

The invention relates to the technical field of data processing, in particular to a metadata-based dynamic link mapping generation method, which comprises the following steps of: acquiring distributed node metadata and historical track data; analyzing the static dependency relationship to generate a basic topology, predicting risk characteristics through a long short-term memory network, and merging to generate an enhanced topology model; concurrently executing conventional scene A * search, abnormal scene Monte Carlo tree search and a high-concurrency scene genetic algorithm based on the enhanced topology model, and outputting a path effectiveness index; dynamic weight factors are generated for data types of numerical value abnormity, time sequence fluctuation and protocol change through a streaming computation engine; constructing three-layer link mapping of a main service path, a degradation path and a pressure measurement path; and verifying node coverage and dependency transitivity, dynamically generating a score feedback optimization algorithm, injecting a flow test, and comparing performance data to position a service bottleneck node. The problems of insufficient topology adaptation, multi-protocol dependence inference lag and concurrent pressure measurement distortion caused by metadata staticization are solved.

A method for analyzing datasets for vulnerabilities to adversarial attacks includes storing, by a processing server, a dataset comprising multiple data types in a database; preprocessing the dataset using data preprocessing algorithms based on the data types; performing data featurization to identify dataset features; generating, for each data type, a victim model based on the dataset; determining performance metrics of the victim models; generating attack datasets corresponding to each data type and including the performance metrics and dataset features; executing adversarial attacks, including membership inference, property inference and reconstruction attacks, on the victim models; determining adversarial attack success metrics, including accuracy, precision and F1 score; updating the attack datasets to include the adversarial attack success metrics; and generating, for each updated attack dataset, a vulnerability metric comprising a library of algorithms and models specific to the combination of adversarial attack and data type.