1347 results about "Data value" patented technology

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention belongs to the technical field of dam safety early warning, provides a dam safety monitoring method based on digital twinning, and aims at solving the problems that an existing monitoring method is weak in prediction capability, lags in early warning and the like. The method comprises the following steps: deploying multiple types of sensors to collect multi-dimensional physical state data; establishing a digital twinborn model containing a multi-physics field coupling simulation sub-model and data driving correction based on the BIM, the Internet of Things and finite elements; inputting a historical data driving model to predict an output state trend sequence and a state data value at a certain moment; performing difference analysis to construct an error vector, and calculating and correcting a deviation index; and calculating a structure health score, and if a threshold value is exceeded, early warning. The method has the advantages of improving prediction capability, enhancing adaptability, realizing comprehensive evaluation and providing systematic advanced decision basis for safety management.

The invention relates to an unmanned aerial vehicle fault traceability analysis method and device, equipment and a storage medium. The method comprises the steps of defining entity types and relationship types among entities based on a predefined fault ontology model to construct a mode layer of an unmanned aerial vehicle fault knowledge graph; based on the mode layer, extracting a fault triple from the multi-source operation data of the unmanned aerial vehicle by using a mixed extraction model, and constructing a fault knowledge graph containing instance data; endowing a dynamic weight probability representing confidence to a relation edge in the fault knowledge graph, and generating a probabilistic fault knowledge graph; and mapping to-be-analyzed fault information to the probabilistic fault knowledge graph, performing traceability analysis by using a hybrid inference engine, and outputting a fault reason and a transmission path. According to the method, structured deep fusion of domain knowledge and data value is realized, and the traceability conclusion is improved from qualitative judgment to quantitative decision support with confidence measurement.

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

The invention discloses a multi-modal data and dynamic knowledge graph fusion method, relates to the technical field of knowledge graphs, and solves the technical problems that multi-modal data weight evaluation is single in dimension and lacks a data source, time and scene collaborative quantization mechanism. In combination with data source authority and time freshness, it is ensured that data value evaluation better meets service requirements, dimension weight coefficients can be flexibly adjusted according to scenes, different from fixed coefficients in the prior art, requirements of different fields are met, data full-link metadata is stored through a directed acyclic graph, conflict sources can be rapidly traced, and the data value evaluation efficiency is improved. And automatic classification and targeted correction of conflict types are realized based on a predefined rule, and the error rate of conflict correction is reduced.

The invention relates to the technical field of gang drills, in particular to a numerical control gang drill device which comprises a machine tool body, a telescopic assembly is arranged in the machine tool body, the output end of the telescopic assembly faces downwards and is connected with an extrusion block, and a storage plate is arranged at the position, below the telescopic assembly, in the machine tool body. One side of the machine tool body is connected with a gang drill plate through an air cylinder, and the side, facing the machine tool body, of the gang drill plate is evenly connected with a plurality of through drill seats. According to the device, a second clamping block and a first laser distance measuring sensor are arranged, the first laser distance measuring sensor can detect the distance value between a drill bit and an extension block at different positions of the drill bit, and the distance value between the drill bit and the extension block can be detected in the mode that the drill bit rotates; the detected distance value is uploaded to a background control system to be compared with a standard data value, and if the detected data value exceeds a standard data value interval, it is indicated that the drill bit deforms.

The invention relates to an intelligent server fault detection method based on a BMC, and the method comprises the steps: adjusting a to-be-detected server to a debug mode of the BMC to obtain original data, judging whether the data received by the BMC is sensor fault data or not, and when the BMC receives an alarm that a sensor is abnormal; constructing a fault model and carrying out training and optimization; acquiring specified data of a to-be-detected server in real time, preprocessing the specified data, inputting the preprocessed specified data into the optimized fault model, automatically classifying the preprocessed specified data by the fault model, judging whether the to-be-detected server has a fault or a fault tendency, if so, starting an automatic repair strategy, and if not, starting an automatic repair strategy. According to the automatic decision, which repair measure is taken is determined according to the severity and the influence range of the fault, and the repair operation is executed through the automatic script or tool of the automatic operation. Through deep mining of BMC underlying data value and fusion of an intelligent model and an automatic repair mechanism, full-process optimization of server fault detection and processing is realized.

Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection. This system ensures accurate and timely anomaly prediction, enabling automated incident response to maintain the reliability and performance of infrastructure components.

A method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.

The invention provides a standard adaptive execution method and equipment based on data standard management, and belongs to the technical field of data management. The method comprises the following steps: in response to a standardization request for a service system from a user, generating a corresponding mapping relation library based on a preset root library and a source system field; wherein the mapping relation library at least comprises a mapping relation, established on the basis of a preset semantic matching model, between the source system field and the standard field. And converting each source data value of the business system according to the mapping relation library, and executing real-time verification based on a preset multi-level value domain enumeration constraint before the source data values are written into the target library, so as to intercept conversion abnormal data according to a verification result and generate an abnormal snapshot. And when it is determined that the data standard attribute change event exists or the abnormal snapshots meeting the preset conditions exist, determining a corresponding update strategy type based on the change content of the data standard version change or the root cause classification result of each abnormal snapshot, so as to perform standardized execution according to a corresponding update strategy.

The invention relates to a digital archive intelligent processing method, a storage medium and a system, which are suitable for multi-source heterogeneous archive management scenes such as colleges and universities. The method comprises the following steps of: classifying structured and unstructured data such as paper archive scanning pieces and database views, and extracting metadata and entity information by adopting a scanning and OCR (Optical Character Recognition) technology; a complex table and document content are analyzed through a model, semantic analysis (entity recognition, relation extraction and event abstract) is achieved in combination with a language model of a Transform architecture, and a structured report containing a data abstract, an entity relation graph and abnormal annotations is generated. The system is internally provided with a parameter template automatic generation module, supports cross-page content continuous restoration and sensitive data encryption desensitization, and realizes safe sharing through an API interface. The method solves the problems of low efficiency, difficulty in multi-source data fusion and the like of traditional archive processing, improves the automation level and data value mining capability of archive management, and is suitable for intelligent upgrading of complex archive scenes.

Automatic volumetric quantification can be performed for various parameters of an object by providing volumetric data, such as three-dimensional image data, to at least one neural network. A network can extract features from the data that can be used to infer a point cloud representative of the surface of the object. One or more loss functions can be used to adjust the relevant network parameters. The network can also attempt to infer a segmentation mask for the object, indicating which data values correspond to the object of interest. Since the network performs the segmentation and point cloud generation in parallel, updates to the network parameters can impact the segmentation process, effectively constraining the segmentation based on the inferred shape of the object. Ensuring that the segmentation mask corresponds closely to the surface of the object can cause the segmentation process to be more accurate than conventional segmentation processes alone.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

The invention discloses a data tracing method and device based on a runtime snapshot. Comprising the steps of generating a runtime memory snapshot file based on report traceability information; analyzing the runtime memory snapshot file, extracting all keys in the first mapping relation to construct a header character set, and querying N metadata identifiers corresponding to N header characters according to the first mapping relation; accessing a report data result storage area based on the N metadata identifiers to obtain a data value list, and rendering to generate a visual report snapshot; for each metadata identifier, extracting the metadata identifier of each metadata which directly depends on and indirectly depends on the metadata identifier from the task detail information associated with the metadata identifier, and obtaining a single metadata tree based on the hierarchical dependency relationship among the metadata; rendering and generating N metadata tree snapshots based on the N metadata trees; and displaying the metadata circulation process corresponding to the abnormal data value by skipping to display the metadata tree snapshot of the metadata identifier corresponding to the report data column where the abnormal data value is located.

The invention relates to the technical field of water resource management, in particular to a water resource consumption prediction and optimization management system based on big data, and the system comprises a global data collection module which collects multi-source data; the multi-source fusion causal discovery module preprocesses data, generates a standardized data set and constructs a causal graph; the double-drive prediction anomaly recognition module calculates a prediction result; the water affair map multi-mode understanding module constructs a knowledge map; the causal decision optimization module evaluates the scheme, verifies the scheme and returns actual data to the acquisition module; according to the invention, the five-in-one sensing network is constructed to comprehensively collect multi-dimensional water resource data, the causal relationship is mined, the causal graph is constructed and the data value is improved based on the algorithm, the prediction and abnormity identification precision is improved by the dual-drive algorithm, the water affair graph module integrates data, causal and knowledge and breaks barriers, and the causal decision module generates and verifies an optimal plan. And the returned data form a closed loop, so that accurate prediction and efficient management of water resources are facilitated, and refined and intelligent development is promoted.

The invention discloses a real-time cleaning and aligning method for multi-source heterogeneous data, and particularly relates to the technical field of source heterogeneous data, a self-adaptive interface adapter is compatible with multiple types of data, and a semantic index atlas is generated through four-dimensional classification labeling; constructing a cloud-edge collaborative streaming processing framework, preprocessing edge nodes, and performing accurate cloud alignment; a dynamic rule cleaning and semantic-dimension-entity three-layer progressive alignment mechanism is adopted, and a four-dimensional quality evaluation system is combined for real-time monitoring; and through closed-loop optimization, homomorphic encryption, a block chain and other security mechanisms, data security and traceability are ensured. According to the real-time cleaning and aligning method for the multi-source heterogeneous data, the real-time performance and accuracy of data processing are effectively improved, the method is adaptive to multiple service scenes, and high-quality data support is provided for data value mining.

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

The invention discloses an agricultural insurance data acquisition and management system based on satellite remote sensing. The system comprises a data acquisition module, a preprocessing module, a verification module and a management module. The data acquisition module covers basic, universal and vector data acquisition according to data types; the preprocessing module improves the quality of collected data; the verification module verifies a cultivated land vector and insured user information, and data accuracy and reliability are enhanced; the management module builds two libraries, and constructs an agricultural insurance data large model by utilizing knowledge graph modeling. In the technical application, on the basis of satellite remote sensing and A I, large-user plot data is collected by means of an image segmentation algorithm, and the cost is reduced. A full-coverage model is constructed through abstract materialization, and business application is supported. Technical means such as satellite remote sensing, weight dispatch data superposition and AI image recognition are used for verifying data, manual operation is reduced, verification accuracy is guaranteed, data value is mined, and the agricultural insurance data management level is improved.

The invention relates to the field of industrial Internet of Things, and discloses an industrial scene-oriented multi-protocol compatible IoT acquisition and intelligent operation and maintenance system, which comprises a configuration management module used for analyzing an encrypted configuration file and dynamically loading a communication protocol driving program by using a reflection mechanism; the data acquisition module is used for establishing connection with industrial field equipment by utilizing the loaded communication protocol driving program and acquiring original data; the data processing module is used for calculating a semantic entropy value of the original data in the time window and marking a data value grade according to the semantic entropy value; the data storage module is used for executing differentiated storage operation on the data according to the data value grade; a memory management module and a data forwarding module. By constructing a data acquisition module comprising a software and hardware dual-mode interface and combining a self-adaptive driving correction mechanism based on physical layer signal feature analysis, the effect of still maintaining communication connection when standard protocol driving fails or equipment aging parameters drift is achieved.

System, method and interface for generating data visualizations are provided. The system receives a user input to specify a natural language command directed to a data source. The system also generates a prompt for generating a data visualization based on relevant data fields and data values, rules that characterize the data visualization, and a context free grammar. The system also prompts a trained large language model using the prompt to generate a structured document following a domain-specific schema based on a shorthand notation. The system also uses a parser that uses the context free grammar to map the structured document to a visual specification. The visual specification specifies the data source, visual variables, and data fields from the data source. The system also generates and displaying a data visualization based on the visual specification, including displaying visual marks representing data, retrieved from the data source, for the data fields.

Data retrieval via secure database query generation is disclosed, including: receiving, via a user interface, a user submitted request for data associated with a business context; generating a prompt to an initialized database query generation model specific to the business context based at least in part on the user submitted request; providing the prompt to the initialized database query generation model; determining a database query based at least in part on an output from the initialized database query generation model; and querying a database for matching data using the database query, wherein the matching data comprises a set of data values fetched from one or more tables of data.

The invention provides a micro-isolation and differential encryption method and system based on industrial protocol perception and a storage medium, and the method comprises the steps: firstly carrying out the static filtering of an original flow through a preset port, and obtaining a to-be-analyzed first communication message; analyzing a message behavior mode by using a time sequence feature analysis model, and extracting a semantic tag containing a function code, a data point address and a data value; if the data value exceeds the safety threshold value, real-time blocking and alarming are carried out; otherwise, generating a dynamic communication strategy based on the function code and the data point address; when the micro-isolation rule is triggered, dynamically limiting the access authority of the equipment; when the difference encryption rule is triggered, an encryption algorithm is selected according to the data sensitivity, and an algorithm acceleration module is called for execution. According to the method, micro-isolation is realized through protocol semantic analysis, and illegal function code injection is prevented; a differential encryption mechanism is adopted to reduce encryption overhead, and meanwhile, the real-time requirement of an industrial scene is met; in addition, encryption strength is dynamically adjusted in combination with channel quality, and operation reliability under extreme working conditions is guaranteed.

The invention discloses a sensor data enhancement method and system, relates to the technical field of sensor data processing, and solves the problem that an existing sensor data enhancement scheme is based on a static, single and fixed processing mode. In order to solve the problems that data distribution characteristics of different scenes cannot be adapted, environmental interference or sensor inherent errors are not considered, and the influence of abnormal values is easily caused, the technical scheme is characterized in that sensor data enhancement is performed based on a dynamic adaptive grid and a multi-compensation mechanism, and dynamic grid division, sensor error compensation and abnormal value influence processing are realized; static interpolation is upgraded into a dynamic self-adaptive data interpolation engine, data values of unknown points in a whole research area are calculated through limited sampling point numerical values, and by means of the data processing and enhancing algorithm, the data density is improved, and the problems of data sparseness and data missing are solved.

The invention relates to the technical field of data retrieval, in particular to a rapid data retrieval method and system, and the method comprises the following steps: based on a received data retrieval query request, positioning to a target data block, obtaining an access type weight and an access frequency count, and generating a data block heat value; according to the method, after a data retrieval query request is received, a target data block is quickly positioned by analyzing query content, related information of an access type and access frequency is synchronously extracted, a static access behavior is converted into a quantifiable heat value index, and the old and new degree of the data block is extracted by introducing a time difference calculation mode; therefore, the heat value is not a static counting result any more, but is a dynamic feature expression with an aging judgment basis. And a dynamic popularity evaluation value is further generated through combination of the static popularity and the aging factor, so that comprehensive judgment on the current importance and activeness of the data block is realized, and data value sorting logic is enhanced.

The embodiment of the invention provides a key value data processing method, a database platform, equipment, a medium and a product, and the method comprises the steps that in response to a disk refreshing instruction of a target storage unit in a target partition, a target key value pair in a memory buffer area of the target storage unit is obtained, the target key value pair is any key value pair in the memory buffer area, and the target storage unit in the target partition is stored in the memory buffer area; the target key value pair comprises a target index key and a corresponding target data value; if the target data value is greater than the set size threshold value, writing the target data value into a data file, and writing a target index key and a target file path of the target data value in the data file into a disk storage file of a target storage unit, the structure of the data file is the same as that of the disk storage file, the data file and the disk storage file are stored in the same path, the recovery mechanisms of the data file and the disk storage file are different, and the data file and the disk storage file are distinguished through the set file mark added in the metadata area. The invasion to the database is small, and the write amplification is reduced.

The invention provides a harbor district oil depot control system, belongs to the technical field of harbor district oil depot management and control, and particularly relates to a harbor district oil depot control system which comprises a multi-source data acquisition module, a data processing module, an index generation module, a production scheduling module, a safety management module, an equipment protection module and a control module. Universe data collection of the storage tank area and the loading and unloading area is achieved through the multi-source monitoring equipment, a scheduling, safety and equipment dynamic index system is formed in combination with the AI algorithm, and the data value density is improved; loading and unloading task allocation, four-level safety early warning and equipment health intervention measures are dynamically generated based on a threshold value, and automatic closed-loop management of'monitoring-analysis-execution 'is achieved; through a multi-stage early warning mechanism, full-process plan triggering from daily maintenance to emergency response is supported, and the accident prevention efficiency is improved; a three-dimensional visual billboard and a data cockpit are provided through the integrated management and control platform, cross-department collaborative decision making is achieved, and the emergency response speed is increased.

The invention discloses a model node execution sequence optimization method and electronic equipment. The method comprises the steps of obtaining a model structure file of a to-be-executed model; according to the model structure file, obtaining operation processes of the to-be-executed model, an intermediate data value generated by each operation process and a connection relation between the operation processes; constructing a directed acyclic graph by taking the operation process as a model node, taking the connection relationship as a connection sequence of each model node and taking the intermediate data value as an edge; the current actual memory is the minimum, the execution sequence of each model node is obtained based on the directed acyclic graph, and the current actual memory comprises the current occupied memory value subtracting the released intermediate data value. According to the method, the optimal model node execution sequence is determined in a software automatic analysis mode by taking the minimum current actual memory as a target, so that the execution sequence of each operation node in the to-be-executed model is optimized, and long-time occupation of the memory is avoided.

The invention discloses an intelligent data value mining method and system, a storage medium and a computer program product, and relates to the technical field of data asset management.The method comprises the steps that an enterprise internal source data document is obtained, a pre-constructed localized deployment agent is adopted to analyze the enterprise internal source data document, and a key metadata set is extracted; calling a pre-trained dynamic consanguinity analyzer to perform consanguinity analysis on the key metadata set to obtain a data value evaluation matrix; loading a pre-trained asset prediction model to perform hierarchical prediction analysis on the data value evaluation matrix to obtain a table entry feasibility prediction result, the table entry feasibility prediction result including a table entry success rate and a recommended asset type; and generating an intelligent data value disclosure report based on the entry success rate and the recommended asset type. A localized deployment agent, a data asset prediction engine and a dynamic blood relationship analyzer are mutually combined, and the data asset value and compliance risk of an enterprise are efficiently and accurately evaluated.

The invention discloses an intelligent express sorting machine system based on'end-edge-cloud 'cooperation and a sorting method, and relates to the technical field of logistics automation. According to the system, the multi-mode recognition device, the intelligent self-adaptive regulation and control device, the equipment connection and management module, the self-adaptive sorting execution mechanism and the logistics data intelligent analysis system work cooperatively. The multi-modal identification device integrates image identification and radio frequency identification technologies, so that the parcel identification accuracy is improved; the intelligent self-adaptive regulation and control device monitors and adjusts the working states of the conveyor belt and the sorting port in real time; the equipment connection and management module realizes deep connection between the equipment and the cloud, thereby facilitating remote monitoring and management; the self-adaptive sorting execution mechanism flexibly processes different types of packages; and the logistics data intelligent analysis system mines data value to optimize operation. The problems that an existing express sorting technology is low in efficiency, poor in accuracy, weak in adaptability and the like are solved, efficient, accurate and intelligent express sorting is achieved, and the requirements of the modern logistics industry are met.

The invention discloses a multi-source heterogeneous data fusion-based garment production place intelligent identification and tracing method. According to the method, deep mining of the data value is realized through multi-link collaborative linkage. In the aspect of data processing, multi-dimensional information is collected from a source, isomerism is eliminated through standardized preprocessing, and then knowledge graph mining hidden association is constructed by means of a graph neural network, so that dispersed data form an organic whole. A dynamic weighted aggregation mechanism under a federated learning architecture enables each production node to cooperatively train a model on the premise of not sharing original data, data privacy is protected, the learning ability of the model for key features is improved, and a feature importance and data volume two-factor weighting strategy further ensures the reasonability of model parameter aggregation. The production place identification result is closer to the actual production scene, the closed-loop design of the tracing system forms a complete improved loop from data input to decision output to system upgrading, and the whole method has sustainable evolution ability in actual application.