2987 results about "Data mapping" patented technology

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

A method for structuring the latent space of an autoencoder is provided. The method includes analyzing natural language descriptions related to input data; creating language-guided libraries that categorize and abstract data features based on the analyzed descriptions; mapping input data into the categorized and abstracted features within the latent space of the autoencoder; and training the autoencoder to minimize reconstruction loss while adhering to the structure imposed by the language-guided libraries.

The invention discloses an expressway digital twinborn dynamic construction method, which relates to the technical field of traffic, acquires and preprocesses multi-dimensional data of an expressway system, and constructs a traffic flow model of an expressway based on a deep learning technology and in combination with the multi-dimensional data of the expressway system. The method comprises the steps of mapping multi-dimensional data, collected in real time, of an expressway system to a traffic flow model, dynamically updating a traffic state in the traffic flow model, performing short-term traffic flow prediction by using the traffic flow model, simulating influences when different traffic events occur, and analyzing existing risks. According to the method, the valuable traffic information is extracted from the mass data by capturing and analyzing the traffic flow data in real time and utilizing the machine learning and data mining technology, so that the digital twinborn body can accurately predict the future traffic flow and provide a decision-making basis, the occurrence of traffic congestion and accidents is reduced, and the traffic flow prediction efficiency is improved. And the traffic capacity and the safety of the expressway are improved.

The disclosed embodiments provide systems and methods for automated mapping of raw data into a data fabric. An innovative approach leveraging Artificial Intelligence (AI)-powered tools and a data fabric to automate the ingestion, transformation, and integration of raw data into a unified model is introduced. By automating the data mapping process, organizations can reduce reliance on manual methods and accelerate their ability to utilize robust insights for exposure management and attack surface reduction. The disclosed solution provides a scalable architecture for unifying cybersecurity signals across cloud and hybrid environments, enabling real-time decision-making and improved organizational resilience against cyber threats

The invention discloses a road and bridge parameter anomaly detection method, and belongs to the technical field of civil engineering. Comprising the following steps: step 1, establishing space-time relevance and a data mapping relation between monitoring points; 2, self-adaptive updating of the judgment rule is achieved so as to adapt to state evolution of a bridge service stage; step 3, performing intelligent attribution analysis on abnormity in the monitoring data; step 4, carrying out joint identification and comprehensive evaluation on the sudden structural damage and the slow degeneration degradation process; and 5, carrying out quantitative analysis on the deviation degree of the bridge health state, and generating a standardized bridge health index. According to the method, the space-time correlation network and the environment-structure mapping function between the monitoring points are established, and a multi-dimensional feature extraction and dynamic weight distribution mechanism is combined, so that environment interference elimination, abnormal attribution intelligent analysis and joint identification of sudden damage and slow degeneration are realized, and finally, a standardized health index is quantified and generated.

The embodiment of the invention discloses an intelligent contract auditing method and device based on a multi-modal large model and a medium, and relates to the technical field of contract auditing, the method comprises the steps that a to-be-audited target contract document is acquired, multi-modal data in the target contract document is analyzed, and the multi-modal data comprises text data, table data and image data; performing feature extraction on the multi-modal data to obtain multi-modal feature data, mapping the multi-modal feature data to a unified dimension space so as to calculate an association weight between each modality through a cross-modal attention mechanism, establishing cross-modal bidirectional link data, and obtaining multi-modal data; the multi-modal feature data comprises any one or more of text semantic features, table relation maps and image visual features; and based on the multi-modal feature data and the cross-modal bidirectional link data, performing single-modal self-consistency verification and cross-modal contradiction detection on the target contract document to generate auditing information of the target contract document.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention discloses a digital twinning-based power distribution network relay protection evaluation test method, system and equipment and a storage medium, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the site selection optimization according to a distribution network topological structure and load distribution, configuring a data collection terminal, and building a data transmission network to collect the operation data of a power distribution network; preprocessing operation data of the power distribution network, constructing a digital twinning model, and realizing twinning synchronous updating through data mapping; simulation and fault test verification are carried out, and analysis and optimization feedback are carried out on verification data. According to the digital twinborn-based power distribution network relay protection evaluation test method provided by the invention, the problems are effectively solved by optimizing data acquisition distribution points, constructing a twinborn model in real time and performing multi-scene simulation verification, and an innovative solution is provided for relay protection strategy evaluation and optimization of a complex power distribution network.

The invention discloses a judicial scene-oriented multi-modal data fusion method and system. The method comprises the following steps: 1) collecting multi-modal data in a judicial application scene and converting the multi-modal data into data in a uniform format; 2) mapping the data to a unified semantic space to realize cross-modal alignment; then associating the multi-modal data to obtain the text description of the same case and the corresponding image evidence as the multi-modal features of the corresponding case; 3) constructing a knowledge graph of a judicial application scene based on the multi-modal features; driving multi-modal data fusion based on the knowledge graph and constructing a multi-modal evidence chain of each entity; 4) quantifying the integrity of the knowledge graph and the reliability of the multi-modal evidence chain according to a preset index, marking abnormal nodes in the knowledge graph according to a quantification result, and adjusting the abnormal nodes; 5) generating a structured knowledge graph according to the knowledge graph and creating a dynamic desensitization report; edges in the structured knowledge graph represent relationships between legal entities, and each relationship is bound with a multi-modal evidence chain.

The invention relates to the technical field of power batteries, in particular to a battery aging state detection system, method and device. The method comprises the following steps: obtaining battery pack structure design data of the unmanned aerial vehicle; according to the battery pack structure design data, carrying out single battery health monitoring, and generating a multi-parameter feature vector; establishing a data mapping relation of the battery monomer indexes through the multi-parameter feature vectors, thereby obtaining initial health state data of the battery pack; collecting charging and discharging sample data of the unmanned aerial vehicle under different flight conditions; a dynamic current excitation sequence is constructed based on charging and discharging sample data, and current load changes in different flight stages are simulated, so that a multi-working-condition current disturbance sequence is generated. By simulating the load change of the unmanned aerial vehicle in different flight stages and combining the dynamic response capture capability of the high-frequency current sensor, the electrochemical characteristics of the battery can be extracted in real time, and the aging state of the battery under the complex working condition can be accurately identified.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle battery endurance flight capability prediction system, which comprises a multi-dimensional data acquisition module, a feature mapping module, a prediction module, an optimization module and a feedback optimization module, and can be additionally provided with an early warning module. The multi-dimensional data acquisition module acquires battery data and cleans the battery data to generate standardized data; the feature mapping module maps the data to a feature space, and generates a feature sequence cluster containing a multi-dimensional association relationship by using a time sequence segmentation algorithm; the prediction module divides prediction intervals based on a support vector machine algorithm and extracts prediction indexes; the optimization module generates an endurance prediction strategy by predicting and optimizing the network model; and the feedback optimization module performs multi-source data fusion optimization and outputs a prediction instruction. The early warning module can associate the prediction instruction with the battery health degree, output a grading early warning signal and trigger a response mechanism.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention provides a digital asset security verification and information monitoring method and system, and the method comprises the steps: carrying out the classification of digital assets through employing a multi-dimensional classification model according to the type attributes of the digital assets, obtaining the classification labels and feature vector representations of the digital assets, and building a digital asset feature database; obtaining transaction historical data of the digital assets from the digital asset association map, discovering association rules and abnormal modes of digital asset transactions by using a time sequence mode mining technology, and forming a transaction verification rule base; according to the data structure of the algorithm adaptation layer, an intelligent data analysis and conversion technology is adopted, heterogeneous digital asset data is mapped to a unified feature space, standardized digital asset data is generated, and a digital asset feature database is formed; and performing deep verification analysis by using the digital asset feature database to obtain security attribute information of the digital assets, and performing dynamic assessment on the security level of the digital assets by using a multi-factor risk assessment model to generate the security rating of the digital assets.

The invention discloses an intelligent port scheduling and energy efficiency optimization method based on digital twinning. The method comprises the steps that S1, three-dimensional point cloud data and sensor data of a target port are acquired; s2, constructing a port digital twin model comprising a quay crane model, an AGV model and a storage yard model; s3, agents are constructed for a quay crane model, an AGV model and a storage yard model in the port digital twinborn model, and a multi-agent reinforcement learning environment is built; s4, training each agent in a multi-agent reinforcement learning environment through a multi-agent reinforcement learning algorithm; s5, mapping the real-time task data and state data of the port to each trained agent to simulate the scheduling of the target port, and generating a port scheduling strategy; and S6, realizing operation scheduling of the quay crane equipment, the AGV equipment and the storage yard equipment of the target port based on the port scheduling strategy. According to the invention, the operation efficiency and the energy efficiency level of the port can be improved, and powerful technical support is provided for sustainable development of the intelligent port.

The invention provides a dam safety perception fusion association method based on a multi-modal space-time diagram neural network. The method comprises the following steps: dividing a dam into a plurality of structural units, and mapping various data into a three-dimensional coordinate system; a heterogeneous graph structure is defined, and a dynamic adjacency matrix is calculated based on the real-time stress gradient so as to reflect physical connection, mechanical conduction and geological association relationships among nodes; carrying out fusion modeling on multi-source data in the heterogeneous graph structure by utilizing a multi-modal space-time diagram neural network, constructing a causal inference engine based on an output result of the multi-modal space-time diagram neural network, and updating a three-level modeling system through structural equation modeling, anti-factual inference and dynamic weight to obtain the heterogeneous graph structure. According to the method, the dynamic coupling rule among the dam structure, geology and material states is excavated, cross-modal space-time fusion of manual inspection and sensor monitoring data can be realized, the early recognition capability and early warning accuracy of dam potential safety hazards are improved, and the problems of data islands and insufficient relevance in a traditional monitoring method are effectively solved.

The invention relates to an intelligent semantic analysis knowledge graph system for multi-source heterogeneous data. The system comprises a data preprocessing module used for preprocessing to-be-processed multi-source heterogeneous data to obtain format-standardized text data; the semantic analysis module is used for performing natural language processing on the text data to obtain words, and then performing vocabulary layer, syntactic layer and semantic layer analysis on the text data and the words to obtain structured data; the structured data comprises a triple consisting of entities, relationships and events and a network diagram of the entities and the relationships; the model structure mapping module is used for mapping the structured data into a predefined structure of a knowledge graph to obtain structured graph data; and the knowledge graph module analyzes entities and relationships in the graph data, performs reasoning to obtain potential relationships in the graph data by utilizing a logic rule and a machine learning algorithm, realizes cross-data-source and cross-document deep semantic association, and improves the efficiency of large-scale data processing and knowledge management.

The invention relates to the technical field of cold chain management, and discloses a multi-modal AIGC cold chain logistics abnormal event early warning method and system, and the method comprises the steps: collecting the multi-modal data of a cold chain node through an Internet of Things sensing system, carrying out the data mapping through a cross-modal alignment algorithm, generating a multi-modal monitoring feature sequence, and carrying out the early warning of the abnormal event of the cold chain logistics. According to the method, cold chain network evaluation feature distribution is obtained through high-dimensional feature analysis, local and global abnormal features are extracted by using a space-time diagram convolutional network, an abnormal probability information set is generated, the information set is subjected to multi-strategy deduction and diffusion simulation, prediction data are corrected, and finally accurate abnormal event early warning information is generated. According to the method, the abnormal early warning precision and response speed of cold-chain logistics can be improved, the risk and loss are reduced, the spatial topology and time evolution of a cold-chain network are captured, the method is superior to traditional isolated node detection, and the problem that in the prior art, single monitoring data prediction has the limitation of recognition capacity is solved.

The invention relates to the technical field of collaborative optimization, in particular to a geometric parameter collaborative optimization method of a taper hole machining cutter, which comprises the following steps: by constructing a high-fidelity digital twin model, integrating multi-physics field coupling and machine tool dynamic characteristics based on a finite element method, generating a geometric parameter-performance data mapping set and training and calculating an agent model; outputting a Pareto solution set through multi-target global optimization; and constructing a constraint range based on the solution set, and calling a digital twin model to carry out local optimization to obtain an optimal geometric parameter combination. The method comprises a self-correction mechanism: correcting a material constitutive relation and a friction coefficient through experimental data; staged adaptive learning, NSGA-II and DBSCAN clustering are adopted, and the efficiency is optimized along with the method; and a Pareto stability index and transfer learning are introduced, so that the result robustness and the cross-task reusability are improved. According to the method, high-precision and high-efficiency geometric parameter collaborative optimization of the taper hole machining tool can be realized.

The invention discloses a BIM-driven tunnel construction full life cycle management method and system, and relates to the technical field of industrial data processing and analysis, and the method comprises the steps: building a BIM model covering the full life cycle of a tunnel and a data mapping linkage relation; collecting monitoring data, accessing the monitoring data to the BIM model, and performing dynamic linkage evaluation to obtain a construction state result; and constructing a safety management mechanism according to an evaluation result and feeding back field guidance. The technical problem that in tunnel construction full-life-cycle management, a traditional industrial data processing mode cannot achieve full-life-cycle integration and cross-stage linkage of multi-source industrial data in the stages of design, construction and operation and maintenance is solved. The technical effects of full-life-cycle integration and cross-stage linkage processing of the industrial data in tunnel design, construction and operation and maintenance stages, improvement of the comprehensiveness, collaboration and timeliness of industrial data processing, and accurate acquisition of the construction state to optimize on-site safety management are achieved.

The invention discloses a control method and system for remote monitoring of the Internet of Things, and relates to the technical field of intelligent monitoring of the Internet of Things, and the method comprises the steps: inputting an operation data set of Internet of Things equipment into a space-time diagram convolution model, carrying out the local space-time feature extraction of an edge layer, carrying out the cross-equipment cooperation mode analysis of a cloud layer, and generating an abnormal propagation path; the method comprises the following steps: mapping three-dimensional space coordinate parameters in an operation data set of Internet of Things equipment into nodes of a topological structure, mapping interaction data between the equipment into edges of the topological structure, constructing a dynamic knowledge graph, injecting an abnormal propagation path into the dynamic knowledge graph, and updating a fault influence weight between the nodes by applying an improved graph convolution fusion algorithm. Acquiring propagation risk nodes, and performing dynamic sorting and community clustering analysis on the propagation risk nodes. According to the invention, through the improved graph convolution fusion algorithm and the space-time graph convolution model, the capability of identifying the fault behavior in the Internet of Things equipment is enhanced, and the efficiency of edge and cloud collaborative analysis is improved at the same time.

The invention relates to the technical field of application of information technology in constructional engineering, and discloses a method for intelligently extracting engineering quantity application in highway engineering. The method comprises the following steps: standardizing an engineering scale in highway engineering design to form a standardized engineering scale; establishing a preliminary budget project reference template; establishing a connection relationship between the preliminary budget project reference template and the standardized engineering scale, wherein the connection relationship at least comprises a semantic correspondence relationship between project sections and engineering scale fields, a logic matching rule between quota subitems and engineering parameters, and a self-adaptive adjustment mechanism for table format differences; when a new engineering quantity table is imported, automatically identifying and analyzing a data field and a structure of the new engineering quantity table based on a connection relationship, mapping data in the new engineering quantity corresponding table to a corresponding project section and a quota sub-target of a preliminary budget project reference template through semantic analysis and a logic matching rule; and generating a budget file meeting the cost compilation requirement.

The invention provides a root cause positioning method which can be applied to the technical field of artificial intelligence. The root cause positioning method comprises the following steps: acquiring data in a configuration management database, a network topology tool, a monitoring system and a work order system to form a multi-source heterogeneous data set; performing knowledge extraction on the multi-source heterogeneous data set, extracting equipment attributes, network topological relations, fault event entities and timestamps, and storing the equipment attributes, the network topological relations, the fault event entities and the timestamps as structured knowledge; mapping real-time index data in the structured knowledge into dynamic attributes of an entity, and constructing a dynamic knowledge graph; based on a graph neural network and in combination with time sequence features, learning a time sequence dependency relationship and a propagation path between fault events in the dynamic knowledge graph; and outputting a root cause entity, a confidence score and a fault propagation path of the fault event through a causal inference algorithm in combination with the multi-dimensional evidence. The invention further provides a root cause positioning device and equipment, a storage medium and a program product.

The invention provides a distributed multi-mode data cross-trust-domain data sharing method and system, and relates to the technical field of data sharing, and the method comprises the steps: carrying out the type recognition and format standardization processing of heterogeneous data provided by a plurality of data source nodes; dividing the data source node into different trust domains based on the security level, the authorization range and the protection strategy; constructing a cross-trust-domain data mapping model to establish a data synchronization channel; verifying the access authority through the smart contract and recording an access log; a distributed consensus mechanism based on a Byzantine fault-tolerant algorithm is adopted to synchronize data states, and data conflicts are solved through version control. According to the invention, secure sharing and state consistency maintenance of heterogeneous data are realized, and the reliability and fault tolerance of cross-trust-domain data sharing are improved.

The invention discloses a multi-modal monitoring and anomaly detection method, system and device for a belt conveying system and a medium, and belongs to the technical field of anomaly detection.The method comprises the steps that a plurality of multi-modal sensors are arranged on the whole path along the path of the belt conveying system, and original signals in the running process of the belt conveying system are collected; the original signals are preprocessed; performing environment compensation calculation to generate engineering data; mapping the engineering data to the same space coordinate system of the belt conveying system, and completing feature extraction to obtain feature vectors; multi-modal fusion analysis is carried out on the feature vectors, abnormal state identification and fault level classification are realized, and an early warning signal and a risk level are output; and executing a matched response control strategy according to the early warning signal, the risk level and the corresponding space coordinate system position. According to the method, the abnormal state can be dynamically recognized under the complex working condition, differential response control is achieved, and the recognition capability and processing precision of early-stage, multi-source and dynamic anomalies are improved.

The invention provides a clinical research medical supervision method and system based on artificial intelligence, and relates to the technical field of medical supervision. The method comprises the following steps: constructing a medical supervision ontology knowledge base by obtaining related medical knowledge texts, and mapping medical supervision data sample data into an ontology framework based on the knowledge base to form a medical supervision knowledge graph. Then, knowledge extraction is carried out on the medical supervision application data to obtain review elements, the review elements are mapped into a knowledge graph to obtain related ontology concept nodes, and a review element graph is formed; then, reasoning and judging the review element graph based on the medical supervision knowledge graph and a preset rule to form a preliminary review result; and finally, performing quantitative scoring on the preliminary review result, and performing review decision based on the score and the preliminary result to obtain a final review decision result. According to the method, the medical inspection efficiency and the inspection quality are improved.

The invention relates to the technical field of multi-modal data processing and intelligent health management, in particular to a safety monitoring and evaluation method and system based on multi-dimensional data, and the method comprises the steps: mapping multi-modal feature data into health state vectors, and generating health state deviation and change trend by combining with time sequence analysis; identifying a health risk and generating a behavior intervention path; the system expands scarce scene data through a generative adversarial network, verifies the validity of a path in a virtual simulation environment, and optimizes parameters and association weights of key nodes in the path; dynamic collection of user feedback data further supports real-time adjustment of paths and models; finally, dynamic monitoring, accurate evaluation and personalized behavior intervention of the health state are achieved, and the method has high adaptability and perspectiveness.

The invention provides an intelligent medicine storage management method and system based on digital twinning. The method comprises the following steps: firstly, generating a virtual storage space, embedding a binding relationship between a medicine batch number and a space coordinate, and then collecting temperature gradient distribution data, humidity fluctuation data and illumination intensity time sequence data of each medicine storage unit in a physical medicine warehouse; the method comprises the following steps: acquiring three kinds of data, mapping the three kinds of data to corresponding coordinate positions in a virtual storage space, generating a three-dimensional thermodynamic map based on temperature gradient distribution data and storage environment standard parameters associated with drug batch numbers, identifying an abnormal area, and reversely positioning a corresponding drug storage unit in a physical medicine warehouse according to the coordinate position of the abnormal area. The directional ventilation device is controlled to adjust the airflow circulation path of the medicine storage unit, a batch medicine migration path planning scheme is generated, the technical scheme provided by the invention realizes fine management and real-time monitoring of the medicine storage environment, and the safety and efficiency of medicine storage are significantly improved.