4441 results about "Data transformation" patented technology

A computing system is disclosed for transforming document data into schema-conformant structured outputs. The system obtains document data comprising multi-format structured documents and classifies each document by type and class using vector-based modeling and structural feature analysis. An extraction configuration is selected for each document, the configuration comprising machine-executable instructions for parsing based on semantic and layout characteristics. The system extracts semantic data using structured inference, transforms the semantic data into schema-conformant outputs, and validates the outputs using temporal and domain-specific constraints. Validated structured data may be used for downstream processing, visualizations, or optimization based on performance metrics.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

Method and system for analyzing software or firmware of computing systems to assess security properties includes loading predicate device input data including characteristics about predicate devices; translating predicate device input data into predicate device model data describing characteristics or dependencies of the predicate device input data relevant to the analysis; determining digital twin configuration data used to configure digital twin; loading the digital twin configuration data onto the digital twin; storing configuration data in the memory; instructing the digital twin to configure itself to implement the loaded digital twin configuration data; determining security analysis to be carried out on the digital twin; simulating the predicate device; executing security analysis on the digital twin; generating output data describing the result of execution of the security analysis; storing output data pertaining to the result; and determining if the result satisfies a predetermined condition, and if so, executing action corresponding to the result.

The present disclosure provides a method and system for orchestrating automated data processing and transformation. A centralized orchestrator receives a request to process a client dataset and initiates a data ingestion process to obtain sample data. A semantic analysis module analyzes the sample data to determine semantic types of data fields. A transformation module generates data transformation instructions based on the determined semantic types. The orchestrator deploys a data processing pipeline to a client-controlled environment and configures privacy preservation parameters to identify and obfuscate potential personally identifiable information. The pipeline applies the transformation instructions and privacy parameters to the dataset. A configuration module determines data storage configurations for the transformed dataset. The transformed dataset is stored according to the configurations in a client-controlled or cloud environment. A machine learning module generates a model based on the transformed dataset, which is stored in a model repository accessible to the client.

The invention provides a non-performing asset cross-scene question and answer framework based on a knowledge graph, and relates to the field of artificial intelligence and legal science and technology, and the framework comprises a framework, a knowledge fusion layer is provided with a multi-source data access module which is used for obtaining heterogeneous data of a law and regulation database, a judicial case library, an enterprise financial statement system and an asset transaction platform; configuring a legal entity analysis engine, and converting the unstructured legal text into an entity-relationship-attribute triple which can be represented by a knowledge graph based on an ontology mapping technology; setting a financial data standardization component, and converting the financial data in different formats into unified semantic representation through a predefined financial index mapping rule; the knowledge fusion layer of the framework can integrate multi-source heterogeneous data such as laws and regulations, financial data, business processes and the like through a multi-source data access module, a legal entity analysis engine, a financial data standardization component and the like.

The invention discloses a multi-source heterogeneous data dynamic protocol security exchange adaptation system, which comprises a protocol analysis engine, a data encryption module, a data format standardization engine and a routing strategy engine, and is characterized in that the protocol analysis engine is respectively connected with a data source, the data encryption module and the data format standardization engine; the data format standardization engine is connected with the routing strategy engine, and the routing strategy engine is connected with the nodes. The invention discloses a multi-source heterogeneous data dynamic protocol secure exchange adaptation method. Comprising the following steps of protocol analysis and data conversion, data encryption and sensitive data desensitization, integrity verification and anomaly detection, routing strategy and intelligent scheduling, data distribution and storage, and dynamic extension and protocol adaptation, and the dynamic conversion of different protocol data ensures that various types of data can be smoothly interacted. Meanwhile, an advanced data security enhancement technology is integrated, multiple protections such as encryption and authentication are carried out on transmitted and stored data, and the data security is effectively improved.

The invention relates to the technical field of oil field databases, and discloses a dynamic data management method, device, equipment and medium for an oil and gas field database, and the method comprises the steps: docking multi-source data of the oil and gas field database, the multi-source data comprising a shaft sensor real-time monitoring data flow, geologic modeling gridding structure data and an equipment management tree coding table; the method comprises the following steps: performing abnormal value cleaning and timestamp alignment on real-time monitoring data flow of a shaft sensor, converting geologic modeling gridding structure data into a reservoir unit set with a coordinate system label, extracting a set membership chain of well IDs and equipment IDs in an equipment management tree-shaped coding table, generating an identifier system with a unified coding format, and storing the identifier system in a database; the identifier system comprises a well identifier, a reservoir unit identifier and a device identifier; generating a structured data pool based on the identifier system; and when the data pool is updated, sending a change instruction to a plurality of subsystems of the oil and gas field database, and updating each subsystem based on the change instruction.

The invention discloses an unmanned aerial vehicle navigation reasoning method and system based on visual perception and a large language model, and belongs to the field of unmanned aerial vehicle navigation and artificial intelligence, and the method comprises the steps: collecting an image of a target area through a camera carried by an unmanned aerial vehicle; pixel-level perception is carried out on the image through a pre-trained visual perception model, and visual information is converted into structured data; converting the structured data into key statements which can be processed by a large language model by adopting retrieval enhancement generation and combining with a predefined knowledge base; adopting a prompt project and thinking chain technology, and outputting a navigation instruction by the large language model according to the key statement and the current task; when a target of an unknown type is encountered, an active learning process is started, and real-time updating of an edge end knowledge base is realized through human intervention; according to the method, the problems of understanding and decision making of the unmanned aerial vehicle on a dynamic scene in a complex environment are effectively solved, data-driven intelligent support is provided for autonomous flight of the unmanned aerial vehicle, and the method has the advantages of being high in real-time performance, high in environmental adaptability and good in decision making reliability.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention relates to the technical field of fault detection, and particularly discloses a prediction reconstruction framework causal perception space-time network for explaining anomaly monitoring in a complex industrial process, comprising the following steps: S01, constructing graph data E (V) and a causal graph; automatically adjusting the fusion proportion of the time-frequency characteristics according to the data characteristics so as to ensure that the model can comprehensively capture the information of the data in the time domain and the frequency domain; secondly, introducing a residual image attention network (RGAT), and converting the image data E (V) into image structure data G (S (V), E (V)); and S03, reconstructing a prediction error by adopting a variational automatic encoder (VAE), learning an error mode of normal data, providing an anomaly judgment AD (V) for anomaly detection, analyzing a causal relationship between data in combination with a causal graph, and positioning an anomaly reason according to an anomaly score, so as to form a prediction result. The network solves the problem that a traditional monitoring network is high in false alarm rate.

The invention relates to a server testing method, electronic equipment, a storage medium and a program product, which are applied to the technical field of computer testing, and the method comprises the following steps: in the process of executing a pressure test on a tested server in a composite testing scene, based on the standard exceeding condition of the performance index of the tested server, according to a degradation strategy matched with the standard exceeding condition, determining the performance index of the tested server according to the degradation strategy matched with the standard exceeding condition; the access traffic injected into the tested server is adjusted, and the degradation strategy is used for performing overload protection on the tested server; converting the acquired running state data of the tested server into multi-dimensional feature data according to a plurality of performance dimensions; determining weights of different performance dimensions based on a test mode of a pressure test; and determining a test result of the tested server according to the weight and the multi-dimensional feature data.

The invention provides a cloud platform docking service implementation method, system and device based on an adaptation layer and a medium, belongs to the technical field of cross-platform resource management and service integration, and particularly relates to a method for configuring an adapter for each cloud platform and mapping an API (Application Program Interface) of the platform to a standard RESTful API interface; performing standardization processing on interface return formats of different cloud platforms through a data conversion module, converting a heterogeneous format into a unified format, and outputting a unified data format; packaging and abstracting services of different cloud platforms; identity authentication is realized by randomly generating a secret key in cooperation with an encryption and decryption algorithm, and a verification mechanism of a source request IP is preset; and recording an operation log and an error log in the docking process, and recording an interface calling frequency, a requester IP and error information of an abnormal logic node. According to the method, the adaptation layer is introduced, the isomerism of the bottom cloud platform is shielded, and unified interfaces and services are provided, so that efficient and flexible cloud platform docking is realized.

The invention relates to the technical field of data processing, and discloses a stress monitoring and early warning method and system in a net rack dismantling process. The method comprises the following steps: scanning a net rack to obtain geometric data, and constructing a three-dimensional model; establishing a finite element model based on the three-dimensional model to analyze stress distribution; key point installation sensors are determined; converting the collected data into a stress value, a displacement value and a support reaction value; constructing a third-level early warning threshold model; and when the monitoring value exceeds the threshold value, early warning is triggered and emergency disposal is executed. Accurate monitoring, real-time evaluation and graded early warning of the whole dismantling process are achieved, and the safety and controllability of dismantling operation are remarkably improved.

The invention discloses an Internet of Things panoramic perception data conversion method. The method comprises the following steps: acquiring a multi-source heterogeneous Internet of Things perception data set; performing space-time alignment preprocessing on the multi-source heterogeneous Internet of Things sensing data set to generate a standardized space-time data set; inputting the standardized spatio-temporal data set into a feature fusion model, and generating a panoramic perception feature vector set through a dynamic weight distribution algorithm; performing adaptive compressed encoding on the panoramic sensing feature vector set to generate a compressed sensing data stream; and inputting the compressed sensing data stream into the multi-modal decision model to generate a real-time abnormal decision instruction. The method has the following advantages and effects that the problem of panoramic feature expression distortion caused by dynamic value imbalance of the multi-source heterogeneous sensing data is effectively solved, and then the reliability of real-time decision making is improved.

The invention provides a multi-source data fusion intelligent control method for logarithmic curved surface machining, and relates to the technical field of data processing, and the method comprises the steps: 1, in a product machining process, through a geometric morphology detection sensor, a material characteristic perception sensor and a cutting state monitoring sensor, a cutting state is detected; obtaining multi-dimensional data reflecting the geometric morphology, the material characteristics and the cutting state of the curved surface, and forming a multi-source information stream; 2, carrying out real-time fusion on the multi-source information flow, and generating a comprehensive state description through space-time alignment and association of sensor data; the method comprises the following steps: converting data reflecting the geometric morphology of a curved surface into a point cloud set, identifying a feature point of a curvature mutation region as an original reference point, constructing two space reference straight lines, and calculating a space included angle between the two space reference straight lines to obtain an included angle region; through multi-source data fusion and intelligent control, the precision, efficiency and stability for logarithmic curved surface machining are improved, and the machining cost and the error rate are reduced.

The invention relates to a multi-sensor fusion positioning method and device based on environmental characteristics. The method comprises the following steps: respectively acquiring RTK data, laser radar data and IMU data; converting the RTK data into RTK positioning data, converting the laser radar data into laser radar positioning data, and obtaining IMU positioning data according to IMU output; monitoring the quality state of the output signal of each sensor in the current environment, and calculating to obtain a corresponding quality score; constructing and forming an input matrix according to the quality score of each sensor, mapping the input matrix to a high-dimensional feature space through an embedded layer and adding a position code, inputting the input matrix into a Transform model for time sequence modeling and extracting environment dynamic features, and finally outputting a real-time fusion weight of each sensor; and performing weighted fusion by using the real-time fusion weight to obtain final pose output. The method has the advantages of being simple in implementation method, high in positioning precision and stability, high in environmental adaptability and robustness and the like.

The invention discloses a government affair data automatic classification and grading method, and relates to the technical field of data management and information processing. According to the method, data of different sources and formats are converted into structured formats by adopting a regular expression and a pattern matching technology, and the consistency and accuracy of the data are improved by means of a machine learning algorithm, data quality detection, anomaly correction and the like; by constructing a government domain ontology and a knowledge graph and combining a natural language processing technology, semantic classification is effectively carried out on government affair data, overlapping and uncertainty between categories are solved, robustness and accuracy of classification decision are further improved through an integrated learning algorithm, high efficiency and reliability of classification are ensured, and the method is suitable for large-scale popularization and application. By establishing a data grading dynamic adjustment and update mechanism and combining real-time data monitoring, sensitivity evaluation and automatic adjustment of access control rules, the flexibility and security of data management are ensured.

The invention relates to the technical field of airport safety monitoring, and discloses an image-based airport runway foreign matter intelligent detection and clearance auxiliary processing system and method. The acquisition module is used for acquiring original image data of a runway and a clearance area in real time through a high-definition camera; adopting an improved YOLOv11 target detection algorithm to identify birds, unmanned aerial vehicles or foreign matter targets in the clearance area; an improved YOLOv11 backbone is used as a main body feature extractor, and original image data is converted into a multi-scale feature map; the neck assembly combines feature maps of different scales and transmits the feature maps to the head assembly for prediction; the head assembly performs target prediction based on the reconstructed feature map; wherein a C2f block in the YOLOv11 neck assembly is replaced by a C3k2 block, and an intersection stage part and a space attention block are introduced; and a bird repelling or cleaning module is dispatched according to the detection result. According to the invention, the monitoring efficiency can be effectively improved, the detection precision is improved, clearance auxiliary processing is optimized, and the safety of the airport runway is improved.

The invention relates to the fields of artificial intelligence, finance and medical treatment, and discloses an intelligent question and answer accuracy improving method and device, computer equipment and a storage medium, and the method comprises the steps: converting structured supervision fine tuning training data into a high-dimensional vector; constructing a multi-dimensional joint index for the high-dimensional vector, and storing the indexed high-dimensional vector in a distributed vector database; receiving a current dialogue text input by a user, and retrieving an associated question set from the distributed vector database through a similarity matching algorithm; dynamically splicing the retrieved associated questions, the corresponding answers and preset template instructions to generate enhanced cue words containing knowledge contexts; inputting the enhanced cue word into a large language model, and generating N candidate answers in parallel; and carrying out quantitative evaluation on the N candidate answers by adopting a multi-dimensional reward model, and selecting the answer with the highest score and returning the answer to the user. According to the scheme, the answering accuracy of the dialogue robot can be improved, so that the user experience is improved.

The invention discloses a concrete 3D printing path optimization method and system based on reinforcement learning, and the method comprises the following steps: representing a to-be-printed geometric model as a graphic structure composed of nodes and edges, each node being a specific position in a three-dimensional space on a printing surface, and the edge representing a feasible moving path of a printing head between the nodes; performing dynamic optimization on the movement of the spray head through a Q-learning algorithm, performing iterative calculation on a Q value, and completing dynamic adjustment on a printing path on the basis of an instant reward to obtain an optimal printing path with the minimum movement distance, the minimum sharp turn frequency and the minimum start-stop frequency; the optimal printing path data is converted into a standard G code, and the 3D printer is controlled to execute the optimal printing path based on the standard G code; by combining the solving process of the optimal Q value in the Q-learning algorithm and the construction requirements of concrete 3D printing path planning, the accurate optimal printing path planning can be efficiently completed through the self-learning-self-evolution algorithm advantages of the reinforcement learning model.

The invention provides a process data acquisition and evaluation method based on artificial intelligence, and relates to the technical field of intelligent education. The method comprises the following steps: acquiring images, audios, videos and teacher description information of students in learning, living and activity scenes through an acquisition terminal to form multi-modal process data; converting and fusing the data into a unified text data set; calling a semantic recognition model to recognize a behavior type, a situation type and a performance level; carrying out dimension classification on the text data according to a preset education concept; combining the behavior dimension, the situation type and the performance level to look up an evaluation index library, and generating an integral evaluation result; and an evaluation result is returned to the acquisition end in real time and a student growth record is updated. According to the invention, automatic acquisition, semantic recognition and dynamic integral evaluation of process data are realized, and whole-process and structured intelligent evaluation support is provided for the comprehensive quality of students.

The invention provides a mixed data precision matrix multiplication and addition unit and a calculation method, the matrix multiplication and addition unit comprises a calculation unit, and the calculation unit comprises a format division module, a multiplication array module, an addition tree module, an accumulator module, a normalization module and a shift register module. The calculation unit converts the first input matrix and the second input matrix into input data in a middle floating point format; executing parallel multiplication operation on the input data to generate an intermediate product result; performing index alignment and accumulation on the intermediate product result to generate an intermediate accumulated value; accumulating the intermediate product result and the value of the third input matrix in a form of accumulating an intermediate accumulated value, and outputting an accumulated result; and converting an accumulation result into a normalized result and outputting the normalized result. The format division module supports various precisions and converts data with different widths into an intermediate floating point format, so that other hardware units can be reused, and the problems that hardware resources are complex and different model reasoning scenes are difficult to meet are solved.

The invention relates to a business travel data processing system and method based on a large model and an intelligent agent and a medium, and the system comprises a natural language interaction and structured data conversion module which is used for receiving unstructured business travel text information inputted by a user, and carrying out semantic analysis, entity extraction and context association through a multi-modal intention recognition engine, converting the data into structured travel data; the agent-driven dynamic process adaptation module is used for dynamically adapting the approval process and the system operation logic according to the request input by the user and the business process state; the multi-dimensional intelligent compliance verification module is used for verifying the structured travel data; the cross-system collaboration and data communication module is used for realizing automatic synchronization and data consistency guarantee of the travel data and the heterogeneous system; and the system architecture and key technology integration module is used for providing layered architecture design, large-model lightweight deployment and security and privacy protection of the system. The method has the advantages of being high in efficiency, high in adaptability, low in compliance risk, good in collaboration and the like.

The invention discloses a multi-protocol device data conversion method, device and equipment based on a red-mine system, and a medium. The method comprises the following steps: acquiring a first protocol data frame sent by a first protocol host; analyzing the protocol data frame to obtain at least one piece of equipment data information contained in the protocol data frame; according to a predefined mapping relation, mapping each piece of equipment data information to a standard data field based on a second protocol object model; and performing data encapsulation on the mapped data according to a second protocol format to generate a second protocol data frame, and sending the second protocol data frame to a second protocol client application in the Hongyu system through a second protocol server. Seamless joint between the industrial field equipment and the intelligent mine system is realized, and the data transmission efficiency and the system compatibility are obviously improved.

The invention provides an engine oil burning diagnosis and maintenance system, and relates to engine oil burning diagnosis. The method comprises a multi-source data acquisition model, a heterogeneous data deep fusion module, a multi-level diagnosis module and a prediction maintenance module. The multi-source data acquisition model is used for acquiring multi-source data in the operation process of an engine in real time through an integrated sensor array, and the multi-source data comprises vibration data, acoustic data, exhaust component data and temperature distribution data; the heterogeneous data deep fusion module is used for performing data conversion on the multi-source data to obtain unified multi-source feature representation; the multi-level diagnosis module is used for performing multi-level diagnosis on the multi-source feature representation to obtain a multi-level diagnosis result; and the prediction maintenance module is used for carrying out maintenance analysis according to the multi-layer diagnosis result to obtain a maintenance decision suggestion containing a predicted life value, a fault evolution path and optimal maintenance time. By means of the scheme, the comprehensiveness and accuracy of engine oil burning diagnosis and maintenance are improved.

A system and methods for integrated data processing and protocol adaptation using dyadic distribution-based compression. The system transforms input data into a dyadic distribution, enabling efficient compression through either variational autoencoders or Huffman encoding. A novel protocol appendix generator creates transformation rules for adapting the compressed data to various network protocols. The system interleaves transformation information with the compressed data, enhancing security and ensuring comprehensive data transmission. An enhanced codeword decoder, employing a hybrid neural network architecture, decodes the data and adapts it to target protocols. The system features a protocol handler using meta-learning techniques for adapting to unfamiliar protocols. Continuous learning mechanisms optimize performance over time. This integrated approach offers significant advantages in data efficiency, security, and protocol flexibility, making it particularly suitable for complex, heterogeneous data environments such as IoT networks, cloud computing, and big data analytics.

The invention provides a feature extraction method and system for a three-dimensional CAD model and electronic equipment, and the method comprises the steps: a data processing step: generating a corresponding geometric attribute adjacency graph, an extended adjacency matrix, a spatial position matrix and a surface-surface shortest path side chain matrix based on the data of the three-dimensional CAD model; and a feature extraction step: performing semantic segmentation and instance segmentation on the processing features of the three-dimensional CAD model through a processing feature recognition neural network based on the geometric attribute adjacency graph, the extended adjacency matrix, the spatial position matrix and the surface-surface shortest path side chain matrix so as to perform feature extraction. By means of the mode, the geometric attribute adjacency graph and the novel descriptor are generated after the three-dimensional CAD model is recoded, information of the three-dimensional CAD model is prevented from being lost, and information integrity during data conversion is guaranteed. Semantic segmentation and instance segmentation are carried out on a geometric attribute adjacency graph and a novel descriptor through a specific neural network model, and the accuracy of feature extraction is improved.

The invention provides an agricultural problem prediction and decision support system based on a large model, and the system comprises a data collection and preprocessing module which is used for collecting data resources of a plurality of channels, and carrying out the data cleaning, data conversion and data storage through a distributed calculation framework; the intelligent agent large model module comprises a large model for agricultural problem prediction, and the large model is trained by adopting historical data; the policy response module is used for extracting information in the policy file, converting the information into structured data and providing a calling interface; and the decision support module is used for generating specific farming activity suggestions based on the structured data in the policy response module and the prediction result of the agent large model module. The system provided by the invention can dynamically provide suggestions in response to farming activities according to crop growth conditions.