853 results about "Data conversion" patented technology

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 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 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.

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 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 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 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.

The invention relates to the technical field of cross-platform collaboration of an ERP (Enterprise Resource Planning) system, in particular to an ERP operation method for realizing cross-platform collaboration, which comprises a data conversion module, a protocol adaptation module, a task scheduling module and a resource allocation module. The data conversion module unifies heterogeneous data formats, the protocol adaptation module analyzes communication protocols, the task scheduling module dynamically adjusts task priorities, and the resource allocation module coordinates calculation and storage resources. According to the method, through improvements of a weight distribution algorithm, dynamic mapping updating, security verification, load balancing and the like, data integration and task collaboration among multiple platforms are optimized. The flexibility and adaptability of the system can be improved, the technical threshold is reduced, and the efficient and intelligent operation requirement of enterprises is met.

The invention relates to the technical field of large language model deployment, and discloses a large language model weight inverse quantization reasoning device and method.The method comprises the steps that low-precision weight data is transmitted to a high-bandwidth storage from a host and then transmitted to an on-chip storage through the high-bandwidth storage; data conversion from a low-precision format to a high-precision format is completed in the on-chip memory, the data is multiplied by an inverse quantization factor to obtain recovered high-precision weight data, and the functional unit is responsible for executing general matrix multiplication of input data and the high-precision weight data after inverse quantization. And pipeline parallel execution of the inverse quantization operation and the general matrix multiplication operation is realized through a double-buffering technology. According to the invention, on the basis of a dual-path inverse quantization architecture of the vector processing unit and a dual-buffer mechanism in the on-chip memory, the problem of hardware adaptation of low-precision calculation is solved, and efficient execution of a low-precision conversion algorithm is realized under the condition of limited hardware resources.

The invention provides a graphic display agent device based on a PCIE (Peripheral Component Interface Express) bus, which is characterized in that an instruction analysis module receives a graphic drawing instruction containing an operation type and a corresponding coordinate parameter through a PCIE bus interface, and generates first pixel data based on the instruction; meanwhile, the data conversion module receives original image data containing pixel position information and sensor echo information through a radar video interface, analyzes the original image data and converts the original image data into second pixel data consistent with the first pixel data in format, and the format difference of the two types of data is eliminated to meet the superposition condition; a graph superposition processing module generates a primary and secondary clear composite image according to the logic that the first pixel data covers the second pixel data; then, the image output control module receives the synthesized image by using a first buffer area and a second buffer area which alternately work to ensure that a complete image frame is transmitted to a subsequent module; and finally, the video signal generation module converts the complete image frame into an analog video signal conforming to a preset video time sequence and outputs the analog video signal.

The invention relates to a radio frequency transceiving multichannel amplitude-phase calibration system, which comprises a radio frequency receiver, a radio frequency transmitter, a data converter, an FPGA (Field Programmable Gate Array) processor and a clock network, wherein the radio frequency receiver adopts a zero intermediate frequency architecture and is used for receiving a multichannel radio frequency signal and down-converting the multichannel radio frequency signal into an analog baseband signal; the radio frequency transmitter adopts a zero intermediate frequency architecture and is used for up-converting the analog baseband signal into a radio frequency signal and transmitting the radio frequency signal; the data converter is connected with the radio frequency receiver and the radio frequency transmitter and is used for realizing conversion between an analog signal and a digital signal; the FPGA processor is connected with the data converter through a JESD204B interface and is used for aligning the data of each receiving and transmitting channel; and the clock network consists of a clock chip with JESD204B characteristics and a clock Buffer, and is used for providing a synchronous clock and an SYSREF signal for the whole system and supporting an SYNC synchronous trigger signal. By adopting the method, the problem of multi-channel amplitude-phase consistency can be solved.

The invention discloses an image transmission method and system based on a Polar code, and relates to the technical field of image transmission. According to the image transmission system based on the Polar code, an image acquisition module acquires image data, a data conversion module performs format conversion, a Polar code encoding module combines channel data to perform dynamic encoding and generate an encoding bit stream data packet, and a wireless transmission module transmits the encoding bit stream data packet to a Polar code decoding module through the wireless transmission module. According to the invention, the image reconstruction module is used for carrying out quality evaluation analysis on the transmitted image data to obtain the quality evaluation index of the transmitted image and carrying out image reconstruction based on the quality evaluation index of the image, so that the transmission reliability of the image data in wireless communication is effectively improved; and therefore, the integrity and transmission quality of the image data can be ensured in an environment with more noise or stronger interference.

The invention relates to the technical field of privacy computing, and discloses a privacy computing protocol composition method based on parameter security conversion, comprising clients for data conversion, a plurality of MPC servers and a communication interface, S100, each client executes model training on a local private data set, generates local model update parameters, and sends the local model update parameters to the client; the method comprises the following steps: S200, a parameter encryption and conversion stage: a client calls an MPC encryption module to carry out security encryption on local model parameters, secret state conversion is carried out on the parameters by adopting a secret sharing and homomorphic encryption mechanism, and ciphertext parameter representation is generated, and S300, an encryption parameter transmission stage: the client transmits the secret state parameters to an MPC server through a communication interface, and the client transmits the encrypted parameters to the MPC server through a communication interface. The data transmission is protected by adopting an encrypted channel, S400, a security aggregation stage: the MPC server side receives encrypted state parameters from each client side, and S500, an aggregation result decryption and synchronization stage, and the device has the advantages of realizing protocol deep fusion, enhancing security and robustness and the like.

The invention provides a self-adaptive real-time image quality enhancement method and system, and the system comprises an image data conversion module which is used for converting YUV format data of a video stream into an RGB format through OpenGL, and further converting the RGB format into an HSL color space; the adaptive brightness enhancement module dynamically adjusts an enhancement coefficient according to the brightness component of the current frame; the multi-frame joint skin color detection module is used for determining a skin color area based on the chromaticity and saturation threshold range of the HSL color space; the local self-adaptive color enhancement module is used for inputting image generation dynamic parameters and performing color enhancement; the noise analysis and noise reduction module is used for performing soft threshold processing on the high-frequency signal; the texture adaptive detail enhancement module is used for adjusting sharpening strength based on texture complexity; and the GPU acceleration processing framework is used for rendering and outputting the enhanced RGB image in real time. Through a series of technical innovations, the image quality of video playing and the user experience are remarkably improved in multiple aspects, and the method has important practical application value.

The invention discloses a printing color closed-loop control system based on spectral analysis, and relates to the technical field of computer processing, and the system comprises the following steps: S01, collecting the spectral reflectivity data of a plurality of key color blocks on a printed matter measurement and control strip in real time through an online high-speed spectrometer at the frequency of not less than 100 color blocks per second; s02, constructing a color prediction model, wherein the model receives the spectral reflectivity data and outputs absorption scattering characteristics, the spectral transmittance of ink and dot expansion, trapping and overprinting effects in the printing process; s03, setting a dynamic tolerance threshold value delta Et based on a chromatic aberration formula according to the color precision requirement of the printed matter, and judging the absorption and scattering characteristics, the spectral transmittance of the ink and a grading tolerance interval in which the dot expansion, trapping and overprinting effects in the printing process are consistent with visual perception; and S04, converting the spectral reflectivity data acquired in real time into a CIELAB color space irrelevant to equipment, and comparing the spectral reflectivity data with a preset standard color target value. According to the method, the problems of different standards and color drift caused by artificial experience difference and visual fatigue are solved.

A communication-middleware-implemented architecture system and control method applicable to an automatic parking planning and control algorithm. By using an SOA concept to perform service-oriented division on data conversion logic, and by optimizing the real-time performance of software by means of EventLoop and dynamic thread pool technology, a high-performance general-purpose middleware solution compatible with a plurality of vendor perception solutions is implemented to ensure the decoupling of an automatic parking algorithm from upstream perception data, such that a vendor can freely access, in the form of a standard part, middleware designed in the present application, thereby greatly shortening a vehicle model adaptation period and reducing the mass production costs for automatic parking.

An image processing apparatus comprises: a superimposition unit that performs superimposition processing for superimposing an object on first image data of a first dynamic range, the first image data being associated with a first gain map for converting the first dynamic range into a second dynamic range, to generate superimposed image data; a generating unit that obtains a conversion characteristic for converting image data of the object into image data of the second dynamic range based on a luminance of the object, and generates a second gain map for converting the superimposed image data into second image data of the second dynamic range using the conversion characteristic; and an updating unit that updates an image file consisting of the first image data and the first gain map by using the superimposed image data and the second gain map.

The invention discloses a two-color printing method based on a thermal printer. The two-color printing method comprises the steps that a two-color printing mode is started; performing color configuration on different types of to-be-printed information, and respectively setting the to-be-printed information as a first color and a second color; converting the character data to be printed into dot matrix data; according to the color configuration, converting the dot matrix data into double-color printing dot matrix data according to a double-color printing data encoding format; the control chip of the thermal printer separates second color printing data from the two-color printing dot matrix data, and respectively restores all the two-color printing dot matrix data and the second color printing data into 1-bit single-color printing dot matrix data; calculating the first color heating time and the second color heating time of the double-color thermo-sensitive paper according to the restored double-color printing dot matrix data and the second color printing data; and according to the first color heating time and the second color heating time, the printing head is controlled to heat the two-color thermo-sensitive paper in a time-sharing mode, and two-color printing is achieved. The printing information identification degree can be improved.

The invention discloses a medical record file classification early warning method based on artificial intelligence, which belongs to the technical field of artificial intelligence, and comprises the steps of data acquisition, standardization processing, primary and secondary classification prediction, data fusion, early warning judgment and report generation. The technical problems of real-time early warning of potential hospitalization demands in medical records and effective early warning of hospitalization resource shortage are solved, historical data and model prediction are fully utilized, final decisions of doctors are considered, the early warning result is more reasonable and effective, long-term retention prediction information is prevented from affecting early warning, timely data conversion is guaranteed, and the early warning efficiency is improved. According to the method, data acquisition, model calculation, data fusion and early warning report generation are carried out, the whole process can be recorded and traced, and a complete basis is provided for manual intervention, system tuning and subsequent model iteration.