1075 results about "Data conversion" patented technology

The invention relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention discloses a multi-agent collaborative reasoning system for intelligent teaching intervention, and relates to the technical field of artificial intelligence and education, and the system comprises a data collection and convergence module which collects data of learning behaviors, emotional states, academic scores and knowledge point mastering conditions of students by means of a classroom behavior analysis system, a camera, a microphone and a learning management system; according to the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the precision and individuation of teaching intervention are realized by constructing the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the system collects multi-dimensional data of students through the data acquisition and convergence module, and through the processing of the data conversion and cleaning module, the accuracy and availability of the data are ensured; the teaching strategy planning module generates a dynamic decision model by means of a large language model, a customized intervention strategy can be generated according to the learning condition and problem root of students, and the multi-agent collaborative reasoning module further improves the intelligent level of the system.

The embodiment of the invention provides a multi-source heterogeneous data integration method and device fusing a large model conversion operator. The method comprises the steps that heterogeneous data are collected from a multi-source heterogeneous data source, preprocessing operation of data cleaning is carried out, and preprocessed data features are obtained; inputting the preprocessed data features and the target format into a large language model, and generating a conversion operator including data structure analysis, field mapping and type adaptation; and based on the conversion operator, performing distributed parallel data conversion and integration in a distributed computing framework, and integrating and verifying the integrated data. According to the scheme, by introducing the intelligent reasoning ability of a large language model, the efficiency optimization of distributed calculation and the quality verification mechanism of the whole process, the core problems of the traditional data integration technology in the aspects of rule stiffness, high manual dependency and quality control deficiency are systematically solved.

The invention relates to a multi-airport luggage full-process tracking method and system, and the method comprises the following steps: data collection, tag collection of temperature, humidity and acceleration data, obtaining of a luggage tag image by a high-definition camera, synchronization of airline flight dynamic information, collection of airport real-time operation data and passenger feedback, personalized demands, data conversion, and data processing. The method comprises the following steps: coding and converting into a sequence token suitable for large model processing, a multi-head attention mechanism, focusing luggage timeliness, equipment load and other key parameters according to a specific rule, cooperating an intelligent label with a block chain, dynamically optimizing sensor parameters, recording luggage key state information and performing personalized feedback, and performing automatic identification and path planning. The system combines computer vision and a multi-head attention mechanism to identify labels in real time and optimize a transfer path, virtualizes luggage accompanying service, provides intelligent interaction and immersive experience, performs cross-airport collaborative management, monitors global airport dynamics in real time, establishes a risk early warning model, and coordinates resources to execute a transfer adjustment scheme.

The invention discloses a heterogeneous data conversion method and system based on a multi-modal large model, and aims to solve the problems of single-modal limitation, high migration cost and the like in traditional heterogeneous data processing. The method comprises the following steps: firstly, carrying out format adaptation and classification preprocessing on multi-source heterogeneous data such as images, texts, audios and videos; then, through a ViT, BERT and Transform cross attention mechanism, deep analysis and cross-modal semantic association of visual, text and audio features are realized, and visual-text-audio semantic mapping is established; and based on a dynamic prompt project, guiding the multi-modal large model to generate structured data according to a preset Schema zero sample. And then error correction and format normalization are carried out through a Drools rule engine to ensure that the data meet the business standard. And finally, supporting multi-format output of JSON / XML / database table and the like, and meanwhile, associating original data storage to realize data traceability. According to the method, the limitation of single-modal processing is broken through, and cross-modal data deep association is realized.

The invention discloses a cross-platform equipment alarm information acquisition method, and particularly relates to the field of information acquisition, the method adapts to different platform equipment through a standardized interface layer, the interface layer comprises an equipment connection protocol library and a unified data conversion unit, and an original log of the equipment is analyzed in real time based on a preset alarm rule template; extracting key information and generating an alarm event object; converting the alarm key information into an alarm event in a unified format, and adding equipment identification metadata; alarm events are asynchronously transmitted to a central processing server by utilizing a high-reliability message queue, and the transmission reliability is guaranteed by adopting various mechanisms; performing duplicate removal, priority classification and persistent storage on the alarm events in the central server, and ensuring data consistency by adopting a hierarchical storage architecture; the whole processing link is provided with perfect monitoring indexes, and automatic capacity expansion and contraction are achieved.

The invention discloses a power distribution network fault identification method and device based on artificial intelligence, and the method comprises the steps: obtaining the multi-source fault characterization information of a power distribution network, and the multi-source fault characterization information comprises telemetering data and telecommand data; performing expert database-based normalization processing on the multi-source fault representation information to generate a standardized fault data sample; matching and checking the standardized fault data sample to generate checked fault data; performing graph data conversion processing on the checked fault data according to a power distribution network graph model to generate fault graph data; and based on the fault graph data, reasoning the fault of the power distribution network through the fault reasoning model to obtain the speculated fault of the power distribution network. The multi-source information is obtained, the limitation of a single channel is broken, data differences are eliminated through expert database normalization, data accuracy is guaranteed through matching checking, graph data conversion is associated with topology, artificial intelligence reasoning gets rid of manual dependence, and efficiency and accuracy are improved.

The invention relates to a medical data dynamic management method based on multi-modal data fusion and deep reasoning, which comprises the following steps: establishing a three-layer dynamic management architecture, collecting various medical data of a patient in real time through a bottom layer, performing primary processing on the various medical data of the patient, receiving the primarily processed medical data through a middle management layer, and performing dynamic management on the medical data through the middle management layer. The double-flow Transform network architecture performs medical event prediction, the event-driven architecture triggers a data processing flow, medical data after primary processing is subjected to dynamic extraction, data conversion, data loading and multi-modal data fusion processing, a data processing result is transmitted to the application layer, the application layer performs dynamic field generation, and the event-driven architecture performs multi-modal data fusion processing. And data automatic dynamic management and real-time feedback calibration are carried out based on a data fusion analysis result. The limitation of a static field structure existing in an existing medical data management SEER database is solved, the data quality is improved, automatic management and real-time feedback calibration of follow-up visit data are achieved, and separation of a follow-up visit system and a data management platform is avoided.

The present invention discloses a distributed data integration method and system for multi-source device protocols, and relates to the field of data processing technology. The method comprises: connecting a protocol adaptation layer to perform data protocol adaptation and analysis of multi-source protocol data, and outputting conversion data; fusing distributed data in the conversion data to obtain monitoring data; obtaining abnormal warnings by detecting abnormalities in the monitoring data; performing data correction on the abnormal warnings, combining with normal data in the monitoring data, and encrypting and integrating the corrected data to obtain distributed integration results; constructing a distributed architecture with distributed data and distributed integration results, and storing and managing data. The method solves the technical problem of low data integration efficiency caused by incompatibility of multi-source device protocols in the prior art, and achieves seamless connection between different device protocols through protocol adaptation and analysis of the protocol adaptation layer, and fusion of conversion data, thereby achieving the technical effect of improving data integration efficiency.

The invention relates to the technical field of serial deserializer design, and discloses an FPGA-based direct-current balanced digital audio and video signal serial deserializer, which comprises a serializer, a deserializer, a control configuration module, a power management module, a configuration storage module and a time sequence control module, the serializer is used for converting parallel digital audio and video data into high-speed serial data streams; the deserializer is used for detecting and repairing abnormal data in the high-speed serial data stream by using a machine learning technology, and converting the repaired high-speed serial data stream back to parallel digital audio and video data; the control configuration module is used for configuring working parameters of the serializer and the deserializer through an external interface; the time sequence control module is used for managing the time sequence relation in the system and ensuring synchronization of data flow between different modules. According to the method, the defects of a traditional deserializer in the aspects of anomaly detection and repair, signal quality evaluation and parameter optimization are overcome, and the stability and quality of high-resolution digital audio and video data transmission are improved.

The invention provides an A320 model original QAR data frame structure compiling engine and method, and belongs to the field of civil aviation. The engine adopts a multi-node transverse deployment and distributed computing architecture, and comprises four modules: an original file reader, an original data standardizing device, a data converter and an engineering value data writing device, the original file reader is used for reading an original RawData file sent by external aviation software; the original data standardizing device is used for converting an original RawData file into a new standardized RawData file; the data converter decodes the new standardized RawData file according to a decoding rule to obtain plaintext engineering value data; and the engineering value data writer writes the plaintext engineering value data into different storage media. The method is suitable for large-scale, multi-source and heterogeneous flight data processing scenes, and the data processing efficiency and the system performance can be remarkably improved.

An operation accelerator that performs an operation between a floating point matrix and an integer matrix includes a first buffer storing integer matrix data; a second buffer storing floating point matrix data; a data converter to convert the floating point matrix data into an integer; and an operator to perform multiplication on the integer matrix data and integer operation target matrix data output from the data converter, wherein the data converter includes a pre-aligner to find a maximum exponent value among multiple floating point values included in the floating point matrix data, perform pre-alignment for moving a mantissa of each of floating points by a difference between the maximum exponent value and an exponent value of each of the multiple floating point values, and generate the integer operation target matrix data based on mantissas of a preset number of high-order bits extracted from among mantissas of pre-aligned floating point values.

The invention provides a sound classification target model construction method and system, and relates to the technical field of sound signal processing, and the method comprises the steps: obtaining original sound data and auxiliary modal data, carrying out the sound data conversion processing of the original sound data, and obtaining corresponding multi-resolution sound image data; processing the auxiliary modal data to obtain a corresponding auxiliary modal space-time matrix; performing local feature processing on the multi-resolution sound image data and the auxiliary modal space-time matrix to obtain local sound image features and auxiliary modal features; constructing a hierarchical feature sequence space according to the local sound image features and the auxiliary modal features, and obtaining a corresponding fusion feature sequence; and constructing a sound classification target model according to the fusion feature sequence, and generating a dynamic sound index according to the sound classification target model, thereby obtaining a sound classification result, and improving the accuracy of sound classification.

A display device may include a display panel which includes pixels, a data driver which provides data voltages corresponding to output image data to the pixels, and a data converter which generates a compensation grayscale from reference compensation values for compensating at least one selected from a color shift and a luminance shift corresponding to a temperature based on an input temperature, an input grayscale included in input image data, and an input luminance, and converts the input image data into the output image data based on the compensation grayscale.

The invention provides a structured data representation method, a structured data conversion method, a structured data scheduling method, a structured data conversion device, a structured data scheduling device, a structured data conversion system, a medium and electronic equipment. The representation method comprises the following steps: responding to an operation of dragging a module from a module library to a visual editing canvas by a user to form a node corresponding to the dragged module; responding to the operation that a user establishes visual connection between input ports and output ports of different nodes on the canvas to form edges, wherein the edges define a data flow and calculation dependency relationship between the nodes; a structured data representation of a machine-readable AI model structure is generated based on all the nodes on the canvas and user-configured parameters thereof, and a set of edges defining the edge interconnection relationship. According to the representation method, conversion from graphical representation to structured data of the artificial intelligence model is completed.

The invention provides a multi-database automatic synchronization method under an AI platform, and belongs to the technical field of database automatic synchronization. The captured change records are converted into synchronous messages in a unified format, the synchronous messages are input into an intelligent scheduling optimization model for dependency analysis and transaction reordering based on dynamic programming, data conversion rules are dynamically generated according to mode version differences, and data consistency is verified through a Merkle tree hierarchical structure; the multi-stage self-adaptive compression transmission and data writing are completed after the target node executes conflict detection and circular dependency processing, and finally the final consistency among the multiple databases is guaranteed through a periodic full-amount account checking task, so that the technical problem of synchronization performance bottleneck caused by low transaction scheduling efficiency in the automatic synchronization process of the multiple databases is solved.

A modulation signal processing apparatus and method is provided. The apparatus is applied to a microcontroller unit. The apparatus includes a first signal processing module and a second signal processing module. The first signal processing module is connected to an analog-to-digital converter of the microcontroller unit, and used for receiving a completion mark output by the converter, collecting a conversion value output by the converter, and generating corresponding bit data according to a total number of sampling times and a preset half-cycle value. The second signal processing module is connected to a PWM output timer of the microcontroller unit, and used for converting the bit data into a modulation data packet that complies with a Qi protocol, and respectively calculating a reload value of the PWM output timer and a corresponding frequency modulation parameter according to the modulation data packet and a preset modulation configuration parameter.

The invention relates to a construction method of a coefficient prediction model, a coefficient prediction method and a storage medium, and the construction method comprises the steps: carrying out the modeling of a bridge prestressed beam through finite element numerical software, simulating a tensioning process, constructing a bridge parameter coefficient array, dividing the bridge parameter coefficient array into a training data set and a verification data set, performing data standardization and tensor data conversion, and constructing a data loader; an AND-based full-connection multi-layer multi-task neural network is constructed, and a standardizing device, a forward propagation function, a loss function, a learning rate scheduler and an optimizer are arranged; and carrying out model training, packaging a prediction function, and integrating a standardizing device and the trained prediction model. The prediction method comprises the step of predicting the friction resistance loss coefficient and the pipeline deviation coefficient according to the prediction model. According to the method and the device, coefficient prediction can be quickly, efficiently and accurately realized by only needing several on-site easy-to-measure parameters.

The present invention relates to the technical field of power systems. Disclosed is a relay protection external operation information intelligent management system, comprising: an input module, a data snapshot module, a data acquisition module, a data conversion module, a data storage module, a fault analysis module, a report generation module, a signaling alarm module, a data analysis module 0, and a real-time data calling module. The relay protection external operation information intelligent management system provided by the present invention provides multiple reliable data interaction modes, including wired transmission, wireless transmission and calling of other systems, for relay protection external operation information, and collects and analyzes the relay protection external operation information before a fault so as to provide powerful support for fault analysis of dispatchers and secondary personnel, thereby avoiding erroneous analysis and determination on the fault. The present invention achieves better effects in the aspects of applicability, network occupation and system cooperation.

The invention relates to a firmware updating method, system and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a dynamic code and to-be-burnt data; the dynamic code is updated when the system-on-chip is changed; downloading the dynamic code to an SRAM (Static Random Access Memory) of a system-on-chip based on a debugging interface board; the debugging interface board is used for connecting a debugging interface of the system-on-chip to an upper computer and performing data conversion according to a debugging protocol of the debugging interface; based on the debugging interface board, sending a burning instruction to the system-on-chip and downloading the to-be-burnt data to an SRAM (Static Random Access Memory) of the system-on-chip; the dynamic code is used for writing the to-be-burnt data into a FLASH of the system-on-chip in response to the burning instruction. By adopting the method, the firmware updating efficiency and flexibility of the system-on-chip can be improved.

The invention discloses an electric power multi-protocol data conversion method and system based on object model dynamic mapping, and the method comprises the steps: receiving an original protocol message, recognizing a protocol type, extracting a logic name as a unique identifier of equipment, extracting an object identifier as a measurement point identifier, and extracting an electric energy array as a measurement point value; according to the protocol type and the equipment unique identifier, matching a corresponding object model template from a unified object model library; mapping the equipment unique identifier, the measuring point identifier, the measuring point numerical value and the acquisition timestamp obtained by analysis to corresponding fields in a template; and generating an object model data object conforming to a standard format, packaging the object into a message, executing an encryption authentication mechanism of a network layer and a transmission layer through the power Internet of Things security access gateway, and sending the message to an Internet of Things management platform. According to the invention, standardized, automatic and secure access of different protocol terminals is realized through protocol analysis and the unified object model library.

The invention provides a multi-platform informatization construction system and method based on a computer, and solves the problems that existing multi-platform data synchronization is not timely, formats are not compatible and the like. The system comprises a real-time data capture module, and a data change event is generated in real time through database transaction log monitoring; the intelligent routing decision module optimizes a transmission path in combination with rules and machine learning; the data conversion adaptation module realizes automatic adaptation of a format and an interface by utilizing dynamic template matching; the conflict detection and resolution module guarantees data consistency based on version control and priority judgment; the data synchronization execution module transmits data according to an optimized path, and the monitoring management module monitors the whole process in real time. The method comprises the steps of data capture, routing decision, format conversion, synchronous execution and monitoring feedback. According to the scheme, the data synchronization efficiency and accuracy are improved, the multi-platform integration operation and maintenance cost is reduced, and the method is suitable for multi-scene informatization construction of enterprises, government affairs and the like.

The invention belongs to the technical field of cloud computing security, and discloses a multi-cloud security management center system and method based on AI. Aiming at the problems of difficulty in asset management, low safety operation efficiency, high labor cost and the like in a multi-cloud environment, the system lightens AI Agent through a data acquisition layer, acquires core resource data in real time by a differentiation strategy, and constructs a three-dimensional dynamic knowledge graph after standardization of a cross-cloud data conversion engine; the intelligent analysis layer integrates five-dimensional data such as logs and traffic by using a multi-modal fusion engine, and realizes attack link dynamic deduction (the accuracy rate reaches 98%) and intelligent alarm noise reduction (the false alarm rate is reduced to below 10%) based on a graph neural network; the strategy management layer generates a cross-cloud unified strategy in combination with domestic and overseas large-scale boards and a natural language processing technology; and the automatic execution layer realizes automatic vulnerability repair (the success rate is not lower than 90%) and AKSK authority dynamic management. The method comprises the whole process of data acquisition, knowledge graph construction, risk analysis, strategy generation and automatic disposal. According to the method, the asset management time can be shortened to 1 minute, the vulnerability repair period is shortened to 30 minutes from 48 hours, the safety operation labor cost is reduced by 70%, and an efficient, intelligent and low-cost safety management scheme is provided for the enterprise multi-cloud environment.

The invention discloses an environment-friendly operation and maintenance robot vision system, and the system comprises an image collection module which comprises a 3D camera disposed on a mechanical arm of a robot, and is used for collecting a plane image and a depth image of a target object; the image processing module is used for preprocessing the plane image acquired by the image acquisition module and transmitting the plane image to the target detection module; the target detection module is provided with a rotating target detector obtained by training based on a YOLOv8OBB network model, and is used for detecting the image transmitted by the image processing module and outputting a target frame center point, a target frame width, a target frame height and a rotating angle; the data conversion module is used for obtaining a rotation translation matrix of the target relative to the 3D camera; and the result output module is used for converting the rotation translation matrix into a relative motion matrix of the mechanical arm and outputting the relative motion matrix to a mechanical arm control unit of the robot. Based on the scheme of the invention, an efficient and accurate water quality station robot vision system can be obtained.

The invention discloses a quick and automatic layout method for a warehouse logistics conveying system based on Unity 3D and CAD. The method comprises the following steps: extracting detail data of warehouse logistics conveying equipment; exporting and optimizing warehouse logistics conveying equipment detail data, and adjusting data in exported equipment detail table grids and a list arrangement sequence; the optimized details of the warehouse logistics conveying equipment are imported into Unity; and querying and analyzing data in the equipment details on the basis of a linear traversal algorithm in combination with matrix transformation, and automatically substituting corresponding equipment position information, rotation parameters and scaling into matrix transformation to complete spatial layout and attribute assignment of the warehouse logistics conveying system. Compared with an existing traditional conveying system layout technology, the data conversion step can be reduced, and data interaction of Unity3D and CAD is improved; the modeling cost is reduced, and the project economic benefits are improved; errors of spatial layout and attributes are reduced, and the model precision is improved; compared with the prior art, the layout efficiency of the conveying system can be improved by 70%-85%.

The invention relates to an on-satellite disaster risk area lightweight extraction method and system, and belongs to the cross technical field of satellite remote sensing data processing and emergency disaster response. The method comprises the following steps: receiving a satellite remote sensing image and performing on-satellite preprocessing, and then performing on-satellite extraction and geospatial data conversion on a disaster risk area; performing data lightweight: performing boundary simplification on the data subjected to format conversion by using a boundary simplification function, performing centroid extraction by using a centroid calculation function, and generating simplified vector data; and downloading data based on the Beidou short message, and receiving and visually displaying ground data. According to the invention, core processing can be completed on a satellite, lightweight data is adapted to Beidou, and minute-level downloading to the ground is realized, and the timeliness bottleneck of disaster emergency response is broken through.

The invention discloses an ultrasonic partial discharge signal feature extraction and classification method and system based on deep learning, and relates to data collection and enhancement, preprocessing, deep learning model construction and training, feature extraction and classification, and classification result and anomaly diagnosis. Collecting data through an ultrasonic sensor, generating a standardized file, and performing enhancement in combination with laboratory and field data; in the preprocessing stage, amplitude and flight atlas features are extracted and normalized. The deep learning model based on the CNN is constructed for feature extraction and classification, signal features can be automatically captured, efficiency and accuracy are improved, a closed-loop technical framework is formed, data conversion complexity is reduced, rapid and accurate diagnosis is achieved, the real-time monitoring requirement is met, and the method is suitable for partial discharge anomaly detection of the power equipment switch cabinet.

The invention discloses a method for analyzing a complex system by using a surface enhanced Raman spectrum three-dimensional code and application of the method. The method specifically comprises the following steps: obtaining SERS sensing channels with different affinity characteristics by using an unmarked or functionally-marked SERS substrate; collecting an SERS spectrum data set by utilizing the constructed SERS sensing channel, and integrating the SERS spectrum data set into a'three-dimensional code 'image through data conversion and integration of three dimensions of Raman vibration band distribution, the SERS sensing channel and SERS intensity, so as to realize imaging of the molecular landscape; the feature information in the SERS three-dimensional code is screened and extracted through a convolutional neural network algorithm, and a mapping relation between the SERS three-dimensional code and a complex system label is established, so that a complex system classification / regression model is established, rapid qualitative or quantitative analysis of a complex system is realized, and the accuracy of the SERS three-dimensional code is improved. More applications can be expanded in the fields of molecular recognition, biological sample classification and identification, disease diagnosis, cell monitoring and the like.

The invention discloses an industrial robot data acquisition method based on programming and intelligent cooperation, relates to an industrial robot control method, and provides an industrial robot data acquisition system and method based on programming and intelligent cooperation. The system comprises a programming mechanical arm (main arm), a robot demonstrator, five RGBD cameras and a humanoid robot (slave arm). The master mechanical arm and the slave mechanical arm conduct cooperative collection, the master arm executes pre-programming actions, and the slave arm receives the joint angle and the tail end pose of the master arm through the ROS and calculates the track of the slave arm. And efficient data acquisition and processing are realized by applying algorithms such as master-slave mechanical arm cooperation, image acquisition and processing, data conversion standardization, model training optimization and cloud edge cooperation. Meanwhile, an alarm mechanism is arranged, and data are recorded and analyzed. The method is high in expansibility, can be applied to the fields of industrial automation, intelligent manufacturing and the like, and helps to improve the industrial production efficiency and quality.

The invention provides a heterogeneous data mapping optimization method, and relates to the technical field of software development. A JSON Schema model library containing business and voucher objects is constructed by obtaining heterogeneous source data, the construction process comprises data format analysis and feature extraction, and construction and classified storage are completed by means of DSL rules and visual editing; based on the model library, generating a mapping relation description document by utilizing a preset abstract mapping rule template and an expanded JSON Schema grammar; a mapping engine analyzes the document to construct a field-level dependency graph, data conversion is executed according to the field-level dependency graph, and an inerting execution and caching mechanism is adopted in the conversion process; and verifying the converted data and the mapping process by adopting a mode of combining static pre-verification and incremental verification, dynamically adjusting a mapping rule according to a verification result or model change, and performing version management. Through the steps, the data mapping efficiency, flexibility and accuracy are improved, and the system performance is optimized.