3365 results about "Data matching" patented technology

The embodiment of the invention relates to the technical field of large language modules, and discloses a multi-task processing method of a large language model fused with LoRA, which comprises the following steps: receiving task processing information input by a user, and performing feature extraction on the task processing information to determine each scene task feature; performing data matching with a pre-LoRA module parameter set based on the scene task features to obtain a LoRA module parameter set associated with the scene task parameters, and performing parameter updating operation on the large language model according to the LoRA module parameter set; and inputting the scene task features into the large language model subjected to the parameter updating operation for identification to obtain corresponding identification result information, and outputting the corresponding identification result information. According to the scheme of the invention, by fusing the LoRA module and the routing mechanism, the adaptive selection and optimization capability for specific tasks is realized, and the performance of the model in processing diversified tasks is improved.

Examples of file analytics systems are described that may obtain metadata data and events data from a virtualized file server. The file analytics systems may detect one or more events from the events data matching a criteria indicating malicious activity. The file analytics systems may validate the detection of malicious activity. The validation may be performed by comparing the file type, such as the MIME type, of sample files before and after the suspected malicious activity. The systems may recover a share of the distributed file server including the one or more affected files by replacing the one or more affected files with stored versions of the one or more affected files from a snapshot of the share taken prior to the detected malicious activity.

The invention discloses a construction progress detection system and method based on three-dimensional model and field data fusion, and relates to the technical field of power grid construction engineering. The system comprises a data acquisition module, a data preprocessing module, a data matching module, a data fusion module, a progress detection module and a feedback module, the data matching module matches point cloud data with a panoramic image, a clonal selection algorithm is applied to optimize matching parameters, and on-site three-dimensional basic data is constructed; the data fusion module performs deep fusion on the BIM model and on-site three-dimensional basic data, and constructs a three-dimensional fusion model for construction progress detection by using a GeoBIM technology; and the progress detection module compares the real-time acquired panoramic image of the construction site with the three-dimensional fusion model frame by frame, and automatically detects the construction progress, so that efficient acquisition, accurate processing and real-time monitoring of construction site data are realized, and the efficiency and decision ability of construction management are remarkably improved through automatic progress detection and real-time feedback.

The invention discloses a multi-protocol intelligent interaction heterogeneous device full interconnection method and system, and belongs to the technical field of computers, and the method comprises the steps: S1, collecting data, and deploying edge computing nodes; s2, preprocessing the collected data; s3, performing classification according to task requirements of the data; s4, after classification, determining an optimal communication protocol suitable for the current network condition; s5, realizing data format standardization and communication protocol unification by adopting an OPC UA protocol; s6, on the basis of an OPC UA publishing / subscribing mechanism and a QoS priority mechanism, allowing the equipment to publish or subscribe data in real time and ensuring priority transmission of high-priority data; s7, performing data matching and association, and fusing the data; s8, realizing a global decision; according to the multi-protocol intelligent interaction heterogeneous equipment full interconnection method and system provided by the invention, by introducing protocol unification, intelligent interaction and distributed optimization technologies, the interoperability of multi-protocol equipment and the overall management efficiency of a traffic system can be effectively improved.

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 relates to the technical field of human resource information processing, in particular to a position resume intelligent matching method and system based on multi-dimensional analysis, and the system comprises a data collection module, a data processing module and a data matching module, and the data collection module is used for obtaining resume information data of job seekers and recruitment information data of enterprises; and the data processing module is used for extracting information dimensions in the resume information data and the recruitment information data. According to the method, all the information dimension scores in the hard information data and the soft information data are quantitatively calculated, and the matching score is calculated after the scores are combined, so that the matching degree of the job seekers and the positions can be more comprehensively evaluated, the demand difference of the job seekers is automatically identified according to the attention degree of the job seekers on the information dimensions, and the job seekers can be more accurately evaluated. And the weight coefficient is updated, so that the personalized demand of the job seeker is accurately met, the accurate matching rigidity requirement is provided for the job seeker and the enterprise, and a flexible secondary selection scheme can also be provided for the job seeker and the enterprise.

The invention discloses a method and a system for adjusting process parameters based on historical data, and a storage medium. The method comprises the following steps: constructing an equipment pool and a standard process parameter pool based on production equipment combinations and process parameters corresponding to production of different injection molding products; the production equipment combination comprises an injection molding machine and a plurality of auxiliary equipment; according to the injection molding product type and the key parameters of the injection molding machine to be adjusted, multiple sets of production equipment combinations consistent with the key parameters are selected from the equipment pool to serve as alternatives, and one set of production equipment combinations is recommended to serve as the optimal production equipment combination; and matching the process parameters of the optimal production equipment combination for producing the injection molding product of the type from a standard process parameter pool, and updating the process parameters to the injection molding machine to be adjusted and the auxiliary equipment. The parameter adjusting method does not need complex means such as model building, corresponding equipment and parameters are matched according to historical data, and the parameter adjusting method is simple, rapid and suitable for the field of injection molding.

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 provides a ground surface settlement prediction and early warning method based on a neural network, and relates to the technical field of foundation pit settlement prediction, and the method comprises the steps: obtaining the structure data of a foundation pit and a soil body in a preset range around the foundation pit, obtaining the monitoring data of a plurality of preset point positions in the foundation pit in real time, and summarizing and outputting the initial data; selecting historical data matched with the initial data, outputting historical reference data, performing comprehensive analysis on the initial data and the historical reference data in combination with a preset analysis model, and outputting a real-time analysis result; and performing prediction analysis on the initial data and the historical reference data in combination with a preset prediction model, outputting a prediction analysis result, obtaining a real-time state and a prediction state of the foundation pit structure in a result-state comparison table in combination with a real-time analysis result, outputting a state analysis table, and performing early warning judgment according to a preset threshold condition. According to the method, prediction and risk early warning can be carried out on the ground surface settlement data around the foundation pit in the construction period in real time, and corresponding precautionary measures are taken in time.

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 data matching, in particular to an information matching management system and method for financial marketing platform data, and the system comprises a data acquisition module, a data analysis module, a dynamic adaptation module, a marketing strategy module, an anomaly detection module and a strategy implementation module. According to the method, behaviors and demands of different customer groups can be deeply understood through comprehensive analysis of the market data set and clustering analysis, and such statistical analysis can provide a scientific basis for making a marketing strategy, so that a financial institution can identify and utilize market trends and modes on the basis of data driving; according to the invention, the market change is predicted and quickly responded, and the improvement of the risk assessment result and the anomaly detection capability not only optimizes the resource allocation, but also enhances the control of potential risks, improves the overall efficiency and effect of marketing activities, enhances the accuracy of data-driven decisions, improves the customer service experience and brand loyalty, and promotes the sales growth.

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.

The invention discloses a PM2.5 and ozone combined pollution intelligent prediction method and system and a medium. The method comprises the following steps: collecting historical air quality data, environmental meteorological data and atmospheric pollution source emission data of a to-be-predicted area; collecting cross characteristic data formed by combining different environmental meteorological conditions and different associated pollution sources; enabling the collected data to form a training data set, and carrying out the data matching; training a multi-task neural network model; selecting an optimal input feature parameter according to the predicted increment contribution degree of each input feature to the trained multi-task neural network model, and constructing and forming a final combined pollution identification model; and acquiring real-time air quality data, environmental meteorological data and atmospheric pollution source emission data of a to-be-predicted area, and inputting the data into the combined pollution identification model to predict PM2.5 and ozone concentration data. The method has the advantages of being simple in implementation method, low in cost, high in prediction precision and efficiency, high in robustness and the like.

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 provides a power transmission line icing condition monitoring and early warning method and system, and relates to the technical field of power transmission line icing early warning. According to the scheme, line image data are acquired based on a power transmission panoramic system, and grid meteorological data are acquired from a meteorological center; based on a pre-trained icing identification model and in combination with a multi-head attention mechanism, carrying out icing identification on the line image data to obtain an image identification result, and based on a support vector machine algorithm, predicting an icing condition according to grid meteorological data to obtain a meteorological prediction result; and matching the grid meteorological data with the monitoring and photographing device point location to obtain positioning information, fusing a meteorological prediction result with an image recognition result for correction, and performing icing degree division according to a preset threshold value to obtain an icing degree report. According to the method, the traditional machine learning technology, the deep learning technology and the data processing and analysis technology are comprehensively utilized, the precision, the real-time performance and the reliability of icing monitoring are improved, meanwhile, electric power accidents caused by icing are reduced, and support is provided for development of the electric power career.

The invention discloses a waveform debugging method and device, a storage medium and a computer program product, and relates to the technical field of ink-jet printing, and the waveform debugging method comprises the steps: obtaining target ink droplet form data and a parameter prediction model of specified ink-jet printing equipment, wherein the parameter prediction model is a machine learning model for describing a mapping relationship between the waveform parameter of the specified ink-jet printing equipment and the ink droplet form; and inputting the target ink droplet form data into a parameter prediction model to obtain a target waveform parameter, matched with the target ink droplet form data, of the specified ink-jet printing equipment. The technical problem that the existing waveform debugging process is easily influenced by subjective factors, so that the debugging stability is poor is solved.

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 financial data processing, in particular to a financial marketing platform data storage system and method, and the system comprises a data prediction regulation and control module, a hotspot recognition module, a storage medium adjustment module, a sensitive encryption module, a data backup module and a performance evaluation module. According to the method, by collecting and calculating the average value of the data traffic and the access time points of the financial marketing platform, the time period with high access frequency can be accurately predicted, the data traffic can be effectively managed, the probability of system overload can be reduced, and by matching suitable storage media such as SSD and HDD for different types of data, optimization is carried out according to the access speed requirement, the access speed of the financial marketing platform can be greatly improved. The data processing speed and cost efficiency are remarkably improved, differentiated encryption processing and backup are performed on the data, and a security policy is designed according to the sensitivity level of the data, so that the operation efficiency of data storage is effectively monitored and improved, and the efficiency and security of a data storage system are improved.

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 discloses a knowledge base AI original text accurate reading device containing a DLF dynamic layout file. A data acquisition offline module sets an acquisition website and offline webpages according to the hierarchical relationship of the webpages; the DLF file generation module is used for converting each offline webpage file into an OFD file and assembling a plurality of webpages into a dynamic layout DLF file according to a hierarchical relationship, the content extraction knowledge base storage module is used for extracting content information in each individual webpage and generating a file queue according to the extracted content information, the generated file queue is pushed to the inference model, and the dynamic layout DLF file is obtained. Forming a knowledge base containing the DLF file; the AI reasoning module performs logical reasoning on the input problem and returns a result; the traceability interpretation module is matched with an associated DLF file according to the obtained return data; and the combination comparison module compares and checks multiple page areas of different DLF files at the same time, so that AI original text tracing of various types of files is realized.