2262 results about "Data matching" patented technology

The invention provides a charger fault diagnosis method based on a knowledge graph, which realizes efficient and self-adaptive fault diagnosis and maintenance through multi-technology fusion, constructs a structured knowledge base, integrates charger parts, fault modes and detection methods, stores entities and causal relationships and detection association by adopting a graph database, extracts information through a BERT model, and realizes fault diagnosis and maintenance based on the knowledge graph. Marking causal strength and dynamically adjusting the causal strength, converting spoken description of a user into structured data, matching a fault path in a knowledge graph, dynamically adjusting a weight, analyzing time sequence data, matching a time sequence mode in the knowledge graph in combination with dynamic time warping, generating a candidate path, optimizing a diagnosis path weight, and designing a multi-target reward function; a visual report is generated, the fault probability in the future seven days is predicted, maintenance suggestions are generated in combination with risk levels, knowledge maps and models are continuously optimized through user feedback, diagnosis accuracy and maintenance efficiency are improved, and manual intervention requirements are reduced.

The invention relates to the technical field of artificial intelligence, and discloses a rapid labeling method based on an artificial intelligence large model, which comprises the following steps of: 1, extracting an entity set and a relationship set of a specific field through a document set of a target field, identifying nouns, terms and keywords related to the field in a document by using an entity identification technology to form an entity set, and marking the entity set and the relationship set; the method comprises the following steps of: establishing an entity set, analyzing logic and semantic relevance among entities by utilizing a relation extraction technology, generating a relation set, combining the entity set with the relation set to construct a knowledge triad, and the knowledge triad comprises an association relation among the entities. By introducing the domain knowledge graph, the entity and relation information extracted from the target domain document is structured into the knowledge triad, and the knowledge triad is matched with the to-be-annotated data to optimize the annotated semantic information, so that the domain adaptability and semantic consistency of the annotation result are realized, and higher accuracy and consistency effects of the annotation data in the professional field are obtained.

The invention relates to the technical field of fluid valve actuator equipment diagnosis, in particular to a fluid valve actuator monitoring and diagnosing system based on AI intelligence. According to the method, the target opening control instruction is generated through fluid medium characteristic data matching, and accurate regulation and control of the fluid valve are achieved; an opening degree change data set and a torque time sequence data set are captured in real time through a deployed sensor group, multi-dimensional dynamic fluctuation analysis is carried out, and intelligent recognition and screening of fluid valve abnormity are achieved; the medium temperature, the pipeline pressure and the medium flow velocity in the dynamic working condition parameters are substituted into a multiple regression linear model to generate reference torque and opening, and dynamic diagnosis of clamping stagnation abnormity is achieved by combining fusion calculation of a torque fluctuation coefficient and an opening deviation coefficient; through the correlation between the opening degree and the torque time sequence data and the traceability analysis of the current dynamic working condition parameters, the accurate positioning of the abnormal source is realized, and a systematic solution is provided for the state diagnosis and reliability evaluation of the fluid valve actuator.

The invention discloses an electromechanical engineering construction information operation and maintenance management method and system based on the Internet, and particularly relates to the technical field of data management. Constructing an initial BIM model based on the design drawing, and generating three-dimensional model data containing equipment geometric information and space coordinates; a three-dimensional laser scanning device or a high-precision positioning sensor is used for obtaining the geometrical morphology and actual coordinates of a real object on site; the collected point cloud data is compared with the initial BIM model through a spatial data matching algorithm, deviation is recognized, the BIM model is automatically updated, and model precision dynamic correction is achieved; binding the updated equipment information and an equipment entity with a two-dimensional code, and synchronously binding an actual position coordinate through an indoor positioning label; uploading the corrected model and equipment information to a cloud operation and maintenance platform to realize information centralized management and multi-terminal sharing; the method improves electromechanical engineering operation and maintenance efficiency and information precision, and has good application value.

The invention discloses a numerical control lathe tool wear monitoring method and system. The method comprises the steps that numerical control lathe tool operation process data are monitored in real time; a corresponding preset operation parameter threshold value is matched according to the operation process data, and when it is monitored that the preset operation parameter threshold value is exceeded, the operation process data of the numerical control lathe tool is transmitted to an edge computing node; matching a corresponding preset operation parameter combination and finishing operation parameter screening according to the operation process data by utilizing the edge computing node; based on the screened operation parameters, key features are selected and extracted by using a feature selection algorithm, and an optimal feature set is acquired and uploaded to a cloud platform; and matching a corresponding tool wear prediction model according to the operation process data at the cloud platform, inputting the optimal feature set into the matched tool wear prediction model, obtaining a wear value, a wear degree and a life prediction result, and performing early warning decision and early warning prompt. According to the invention, efficient, accurate and real-time tool wear monitoring under complex working conditions is realized.

The invention discloses a substation equipment rare defect simulation and identification method and system and a storage medium. The method comprises the following steps: constructing an equipment reference feature library; marking dynamic features of rare defects in historical inspection according to a spatial-temporal feature enhancement algorithm, generating a knowledge graph, and constructing a dynamic defect learning library; the method comprises the following steps: learning space association and environmental factor influence of defects and equipment through a bimodal generation network, and generating initial defect data matched with a weak area of the equipment; generating high-credibility defect fusion data through physical constraint-intelligent detection double screening; constructing a three-dimensional mixed data set, and screening high-quality training samples through a dynamic defect evolution algorithm and hierarchical cognitive evaluation; and constructing a multi-algorithm collaborative fine tuning network by using a federated learning framework, simulating and labeling defect information, and outputting a multi-dimensional identification prediction report. The method aims at solving the problems that the model is insufficient in rare defect recognition precision and lack of evolution prediction ability, and high-quality simulation of rare defect samples and high-precision recognition of the model are achieved.

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention discloses a touch and talk pen intelligent children education method based on emotion recognition. The method comprises the steps that S1, facial expression data, voice and intonation data and touch behavior data of children are collected; s2, multi-modal high-quality emotion data matched with the actual emotion state is generated; s3, generating deep emotional features related to facial expressions, voices and tones and touch behaviors; s4, generating an emotional state label corresponding to the current emotional state of the child; s5, the generation model generates voice feedback content, image animation content and interactive game content which are adaptive to the current emotional state; s6, generating a personalized recommendation content list conforming to the emotional state of the child; and S7, displaying the education content in the personalized recommendation content list through a display screen, a voice output unit and a touch interaction unit of the touch and talk pen. According to the method, the fuzzy classification problem of the emotional state is solved in a touch and talk pen emotion recognition scene, and the adaptability of the model to the complex emotional state is remarkably improved.

The invention relates to the technical field of visual management and control, in particular to an essential oil production digital visual management and control system and method based on data analysis. The method comprises the following steps: obtaining essential oil raw material data and carrying out component content identification, removing abnormal values to generate standard raw material components, obtaining historical process data, matching the standard raw material components according to the data, obtaining a reference process, carrying out production simulation to generate essential oil parameters, and carrying out multi-dimensional quality evaluation on simulation process parameters. The method comprises the following steps: optimizing a matching reference process, forming a quality compensation essential oil process, carrying out scene simulation on the process, evaluating process risks, implementing safety compensation treatment, obtaining a safety compensation essential oil process, carrying out production control adjustment on standard raw material components based on the process, carrying out visual rendering, and constructing a digital visual view of essential oil production. According to the invention, a more efficient and safer digital visual management and control method for essential oil production is realized.

The invention discloses a multi-platform store normalizing method, which relates to the technical field of computers, and comprises the following steps: extracting a standardized data set through a multi-modal feature extraction technology to generate a multi-platform feature vector; the feature vectors of the multiple platforms are input into a multi-algorithm fusion engine, parallel matching calculation is executed, meanwhile, a dynamic weight distribution mechanism is designed, normalized platform IDs and a cross-platform mapping relation table are output, and a matching confidence report is generated; a normalization result is stored in a distributed database based on a block chain, an incremental synchronization mechanism is designed, data are divided and stored according to geographic areas, node loads are dynamically distributed through a consistent Hash algorithm, and updated normalized platform data are obtained; through a multi-modal feature extraction technology and a multi-algorithm fusion engine, deep mining of a standardized data set is realized, an accurate feature vector set is generated, and the accuracy of cross-platform data matching is remarkably improved.

The invention provides a business data sharing fusion system and method based on a trusted data space, and relates to the technical field of data sharing. According to the method, sharing and fusion of service data are realized by constructing a trusted data space infrastructure. The method comprises the following steps: firstly, establishing a node path planning model for priority evaluation and data matching; then forming a model collaborative deployment scheme to distribute tasks to edge nodes; then perception scoring and resource scheduling are carried out on the tasks, and a multi-task concurrent transmission scheme is generated; constructing a multi-domain network service level protocol evaluation model to perform service decomposition and business collaborative management; and finally carrying out heterogeneous processing and data fusion on the business data. According to the method, the problems of information islands, multi-source heterogeneous data fusion and cross-domain business collaboration among regional mechanisms are solved, and the system performance and the user experience are improved.

The invention relates to a psychological counseling interaction method and device based on an autonomous psychological planning architecture. The method comprises the following steps: acquiring initial user data of a target user; constructing a dynamic target map of a target user through a three-layer nested target structure, and pre-loading a matched personalized decision tree; after receiving the real-time interaction data, executing real-time path re-evaluation based on the real-time interaction data to obtain a treatment value function of each intervention path; constructing an intervention response prediction model by adopting a Bayesian network and a Monte Carlo tree search algorithm, adjusting the priority and the execution sequence of the intervention paths in the personalized decision-making tree in combination with the treatment value function of each intervention path, and selecting a target intervention path matched with the real-time interaction data from the adjusted intervention paths; the real-time interaction data is mapped into the multi-dimensional psychological state space to serve as the session trajectory, session trajectory planning is conducted based on the target intervention path, real-time interaction with the target user is achieved, and the accuracy of intervention decision making and the session fluency are improved.

Embodiments of the present disclosure provide a database management method, a device, a storage medium, and a program product. The method comprises the following steps: obtaining a database problem input by a user and a current state of a database; on the basis of the current state, querying historical operation and maintenance case data matching the current state, the historical operation and maintenance case data comprising: the state of the database when an abnormal event occurred in history and an operation and maintenance operation adopted for the abnormal event; constructing a prompt word on the basis of the database problem, the current state and the historical operation and maintenance case data; and inputting the prompt word into a trained large language model to obtain a database operation and maintenance suggestion for the database problem.

The invention belongs to archives, and particularly relates to an investment and financing project archive evidence intelligent management platform based on big data, which comprises the following steps: step 1, a system uploads and preprocesses archives through an upload and storage module, and converts project files in different formats into structured data; 2, a data processing and intelligent analysis module automatically extracts key information and performs archive classification, data matching and compliance review by using natural language processing and a machine learning algorithm on the basis of data of the uploading and storage module; 3, the system carries out automatic examination based on the compliance model, an evidence chain meeting legal requirements is generated, real-time risk assessment is carried out, and when potential risks are recognized, the system triggers early warning and provides suggestions; step 4, in combination with risk early warning and legal examination data, a system workflow and task scheduling module dynamically schedules a workflow according to a task priority and a dependency relationship; and 5, providing decision support by the system through a user interface, and helping a manager to make a precise decision.

The invention relates to the technical field of memory channel testing, in particular to a multi-channel parallel testing system and method based on a memory. The method comprises the following steps: acquiring original data of a memory test channel; performing topology analysis on the original data of the memory test channel to generate node distribution data of the memory channel; performing dynamic weight calculation on the storage channel node distribution data to obtain channel priority data; performing resource competition arbitration on the storage channel node distribution data according to the channel priority data to generate channel resource allocation data; performing control plane mapping on the channel resource allocation data to generate network topology virtualization data; and performing routing strategy optimization processing on the network topology virtualization data to generate dynamic routing table data. By optimizing channel resource allocation, topology virtualization management, elastic scheduling synchronization, fault prediction and data matching, the multi-channel parallel test efficiency and accuracy of the memory are improved.

The invention relates to a large-model-driven KPI (Key Performance Indicator) management and service analysis system and method, and the method comprises the steps: S1, carrying out the definition of an enterprise KPI through a low-code configuration tool, and building an index value network; s2, semantic analysis is carried out on the input natural language instruction of the user, key entities and query intentions in the natural language instruction of the user are recognized, and key indexes comprise a target index and a time range; s3, index data matching is carried out according to the key entities, and correlation analysis and root cause positioning based on an index value network are carried out according to the query intention so as to identify related indexes and potential root causes; and S4, outputting an analysis report and a decision suggestion according to index data matching and correlation analysis and root cause positioning results. According to the invention, the business context can be understood and the demand can be analyzed through natural language dialogues, and more intelligent, flexible and efficient index management and analysis support can be provided for enterprise decision makers.

The invention discloses a person and post matching method and device, equipment, a medium and a product, and relates to the technical field of information processing, and the method comprises the steps: receiving recruitment post data and resume data; and performing data matching on the recruitment post data and the resume data through a pre-constructed person-post matching model to obtain a matching result, the person-post matching model being constructed based on a recruitment vector model, an information extraction model, a weight evaluation model and a matching degree scoring model. Therefore, data matching is carried out on the recruitment post data and the resume data through the human-post matching model constructed by the recruitment vector model, the information extraction model, the weight evaluation model and the matching degree scoring model to obtain the matching result, and the problem that post requirements and resume advantages are not aligned in the prior art is solved. Therefore, the recruitment quality of the enterprise is not high and the talents are lost can be solved, and the accuracy of man-post matching can be improved.

The invention relates to a four-way shuttle vehicle based on ontology state perception and a self-calibration positioning method of the four-way shuttle vehicle. The method comprises the following steps: establishing a body state sensing system, collecting three-axis acceleration, three-axis angular velocity, wheel set displacement and attitude angle data of a shuttle vehicle body, and synchronously forming a standardized sensing data set; performing fusion calculation on the standardized sensing data set to obtain real-time position and attitude parameters; calculating an initial value of a positioning error based on matching of the real-time position and attitude parameters with pre-stored high-precision track map data; according to the positioning error initial value, adaptive correction calculation is executed, a real-time positioning error correction amount is determined, and positioning information is corrected; parameters of a shuttle vehicle track control system are updated according to the corrected positioning information, and track navigation control is carried out; accumulated positioning error correction data are periodically analyzed, self-calibration model parameters are optimized, and positioning accuracy and stability are improved. According to the invention, the positioning reliability and path tracking capability of the four-way shuttle vehicle in a complex environment can be improved.

The invention relates to a data lake-based industry templated modeling and dynamic access control method, which comprises the following steps of: acquiring multi-source heterogeneous data of a data center in real time, and performing data verification, key field extraction and metadata extraction through an analyzer; according to the data classification and grading key points, grading protection is carried out on the currently obtained data matching industry grading and classification template, and the protection grade is dynamically adjusted according to the data dynamic access control rule, the sensitivity and the service importance; performing threat detection and risk prediction on the abnormal behavior data by using a machine learning algorithm; constructing a distributed intelligent data lake storage architecture, and storing the currently acquired data into a data lake in a layered manner according to a classification result; and displaying the security situation and the early warning information through a visual interface. The invention also relates to a corresponding system. By adopting the industry templated modeling and dynamic access control method and system based on the data lake, the limitation of traditional data lake modeling is effectively solved.

The invention provides a tunnel event and warning lamp acousto-optic linkage strategy generation system based on AI decision, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting tunnel traffic event data, vehicle structured data, vehicle coordinates, vehicle flow, vehicle speed, and brightness data inside and outside a tunnel in real time, so as to obtain multi-source heterogeneous data; according to the multi-source heterogeneous data, taking the data matching degree, the space-time consistency and the event confidence as multi-objective optimization dimensions, constructing a weighted objective function, generating a Pareto optimal solution set through a multi-objective optimization algorithm, screening the solution set based on space-time constraint conditions, and removing conflict hypotheses; and determining the final event type and the structured event description of the three-dimensional geographic coordinates. The problems of large illumination energy consumption and poor system linkage in existing tunnel safety management are solved.

The invention relates to the technical field of information management, in particular to a student enrollment information management system and method, and the system comprises a student enrollment data integration module, a student enrollment risk assessment module, a student enrollment resource optimization module, a data flow adjustment module and a student enrollment effect analysis module. And carrying out data matching on the basic information, the score and the professional intention. According to the method, when the data are screened and matched, the risk management capability is enhanced through identification and quantitative processing of the abnormal points, analysis of enrollment resources and demand differences and identification of performance bottlenecks caused by wrong decisions are avoided, the information processing flow is optimized, the system response time is shortened, the operation efficiency is improved, and the system reliability is improved. By monitoring the deviation between the data stream and the expected target in real time and adjusting the data stream processing process in time, the accuracy and real-time performance of the data are ensured, so that the transparency of the enrollment process and the evaluability of the execution effect are improved, and the management efficiency of the whole enrollment process is optimized.

Embodiments of the present application relate to speech processing technology. The embodiments of the present application provide a data processing method and apparatus based on artificial intelligence, an electronic device, a computer program product, and a computer readable storage medium. The method comprises: acquiring speech, and acquiring emotion data corresponding to the speech; performing first speech content feature extraction processing on the speech to obtain a first speech content feature corresponding to the speech, and performing emotion feature extraction processing on the emotion data to obtain emotion features corresponding to the speech; performing fusion processing on the first speech content feature and the emotion features to obtain a fusion feature corresponding to the speech; and performing animation parameter mapping processing on the fusion feature to obtain controller parameters corresponding to the speech, wherein the controller parameters are used for controlling a virtual object model to be presented in a target image, and the target image matches the content of the speech and the emotion data.

A blockchain-based system for managing music rights and royalties is disclosed. The system enables artists and contributors to upload music, assign revenue percentages, and track usage via smart contracts on a distributed ledger. Play counts are immutably recorded across distributor platforms. A contributor dashboard provides real-time access to royalty data. The platform incorporates Majority Reports to validate metadata matches and calculate royalties, and Minority Reports to flag discrepancies without payments, thereby ensuring data integrity. A Golden Box smart contract module replicates song rankings and applies predictive classification models to identify features associated with musical success. These mechanisms improve transparency, accuracy, and fraud prevention in royalty distribution.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of data center operation and maintenance, financial science and technology, medical treatment and health and the like, and discloses a task processing method and device based on a domain knowledge base, equipment and a medium. Cleaning and formatting to generate standardized task list data, inputting the standardized task list data and a domain knowledge base into a language model to generate structured task list data, and matching task list types based on the structured task list data to generate a task list execution plan, and determining an execution mode according to the task list execution plan and executing to generate a task list execution state. Through cooperative processing of the domain knowledge base and the language model, standardized and structured processing of task list data is realized, and in combination with task type matching and execution mode control, processing automation and accuracy are improved, manual participation is reduced, and response speed and efficiency are improved.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention discloses a multi-scene internet of things off-site dynamic inspection self-adaptive monitoring system, and relates to the technical field of intelligent inspection and path planning, the system is composed of a plurality of functional modules, and the system comprises a moving object detection module for detecting a moving object by adopting a Gaussian mixture background modeling and optical flow method; meanwhile, a light-weight MobileViT-XXS model is deployed, and a moving object target is tracked; the moving object classification and identification module is used for judging the type of a moving object through a multi-modal classification decision tree: when the moving object is judged to be a non-living body, carrying out ROI extraction and optimal frame interception on the image saliency of the moving object, and generating EXIF metadata through super-resolution reconstruction; when the judgment result is a biological object, biological category judgment is carried out: when the judgment result is an animal, geo-fencing data matching is carried out, and a spatial-temporal feature fusion model is adopted; and when human beings are judged, face quality evaluation is carried out through multi-frame fusion super-resolution.

In some examples, a system monitors input / output (I / O) operations to identify data matching a honeypot pattern. The system determines storage location information associated with the data identified as matching the honeypot pattern, and detects an access of the data at a storage location indicated by the storage location information. The system indicates a potential attack based on detecting the access of the data at the storage location indicated by the storage location information.

The invention relates to the field of vehicle control, in particular to a vehicle control method and a vehicle. According to the method provided by the invention, when the first vehicle door and / or the second vehicle door rotate, the passive rotation data of the first vehicle door and / or the second vehicle door are firstly obtained, and then the passive rotation data and the preset abnormal rotation data are subjected to matching detection, so that the judgment accuracy is improved. And if the position of the vehicle door is changed due to normal environmental factors, the passive rotation data of the vehicle door is not consistent with the preset abnormal rotation data, so that misjudgment cannot be triggered. According to the method, the active rotation function can be forbidden only when the real abnormal movement of the vehicle door occurs and the passive rotation data is matched with the preset abnormal rotation data, so that the normal condition and the abnormal condition are accurately distinguished, unnecessary misjudgment caused by normal environment interference is avoided, and stable operation of the vehicle door is guaranteed.

The invention belongs to the technical field of pipelines, and provides an intelligent natural gas pipeline leakage monitoring system and method. The method comprises the following steps: calling monitoring data acquired by multiple types of monitoring sensors corresponding to a potential area and pipeline distribution structure data; acquiring environment data corresponding to the potential area, and obtaining a parameter weight group based on environment data matching; performing prediction processing on the pipeline pressure data set, the pipeline flow data set, the pipeline audio data set and the pipeline temperature data set by using the corresponding convolution model to obtain a leakage region set corresponding to each data set; performing joint probability analysis on each group of leakage areas and leakage probabilities based on the parameter weight group to obtain a plurality of target leakage areas; and outputting the target leakage area. According to the invention, through fusion and intelligent analysis of multi-source data, a leakage area can be accurately positioned; meanwhile, the parameter weights of different sensing data are dynamically adjusted based on the environmental data, the interference influence is reduced, and the prediction accuracy is improved.

The invention discloses a geological park geological relics information acquisition method and system based on multi-source data, and the method comprises the steps: obtaining a GIS ground surface image, geological relics environment parameters and geological relics updating data, updating the flight path of an unmanned aerial vehicle, executing the flight strategy of the unmanned aerial vehicle, obtaining a low-altitude aerial image, carrying out the data matching and data correction, and obtaining the geological park geological relics information. Determining a data fusion area according to the multi-source correction data, constructing a geological relic multi-source data fusion model based on the multi-source correction data, and inputting to-be-collected geological park multi-source sensing data into the geological relic multi-source data fusion model to obtain geological relic collection information, and determining an acquisition scheme according to the data fusion area and the acquisition time. The method not only can improve the efficiency and accuracy of geological park geological relic information acquisition based on multi-source data, but also has good interpretability, and can be directly applied to a geological park geological relic information acquisition system based on multi-source data.