105 results about "Feature model" patented technology

The invention discloses a PLC controller fault detection system, and relates to the technical field of industrial control equipment fault detection.The system is characterized in that PLC operation environment data is acquired through a multi-physical-quantity holographic acquisition module, and after the PLC operation environment data is cleaned and subjected to feature fusion through a data preprocessing module, a fault model is constructed through a multi-physical-quantity fusion model module; the self-adaptive threshold value judgment module dynamically calculates and judges a threshold value and evaluates a state; the fault traceability analysis module constructs a propagation path diagram based on a model and historical cases, and realizes accurate traceability of a fault source and a propagation process; according to the invention, multi-physical-quantity holographic acquisition and feature fusion algorithms are integrated, multi-dimensional parameters are monitored synchronously, a comprehensive feature model is constructed, the fault identification precision is improved, and misjudgment is avoided; dynamic optimization is achieved through self-adaptive threshold judgment, the early warning accuracy is improved, meanwhile, accurate traceability is achieved through an element fault propagation algorithm, a path is optimized in combination with historical cases, the downtime is shortened through full-process intelligent support, and the maintenance cost is reduced.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention relates to the technical field of deep learning and computer vision, and discloses a histogram interactive attention-based pavement crack segmentation network processing method and system, so as to enhance the edge detail fidelity and improve the crack segmentation precision. The method comprises the steps of image preprocessing, up-sampling, down-sampling, feature fusion and image reconstruction processing. Wherein global feature modeling in the intensity sub-boxes and among the sub-boxes is realized by constructing a histogram interactive attention module (HIA); a double-branch detail enhancement feedforward module (DDEF) is introduced to enhance spatial detail and high-frequency edge information expression; meanwhile, a Fourier jump enhancement module (FFSM) is adopted to jointly refine jump connection features in a spatial domain and a frequency domain. Through the synergistic effect of the modules, the network can realize continuous recovery and structural consistency modeling of a crack boundary in a complex pavement environment, so that the accuracy and the stability of a segmentation result are remarkably improved.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention provides an itinerary generation method and system based on a large language model. The itinerary generation method comprises the steps of obtaining a user social portrait, wherein the user social portrait is fused with at least one of historical activity information, geographic position information and social information of a user; performing multi-dimensional feature modeling on the interest points in the area where the user is located to obtain interest point embedding; the social portraits and the interest points of the users are embedded and converted into natural language descriptions, and the natural language descriptions and real-time context information are fused to generate context prompts; reasoning an activity intention of the user based on context prompt through a large language model, generating a natural language travel description, and then generating a structured activity plan by using the natural language travel description; according to the structured activity plan, candidate interest points are matched in a preset range in an area where the user is located, and recommended interest points are determined in the candidate interest points; and performing path optimization on the recommended interest points to generate a trip.

The invention discloses a shore power load prediction method and system based on a self-attention mechanism, and the method comprises the steps: building a high-precision load prediction model through combining a historical load time sequence, an environment feature and a ship feature as input features, and employing a multi-head self-attention mechanism of a Transform model; multi-modal feature representation is generated through feature embedding and position coding, a global dependency relationship between a time sequence and multi-modal features is captured by using a multi-head self-attention mechanism, deep features are further extracted in combination with a feedforward neural network, and an accurate load prediction result can be generated after the model is subjected to multiple rounds of training and verification. According to the method, the load prediction precision, the prediction efficiency and the robustness and adaptability of the model are improved, and energy optimization of a green port is supported.

The invention relates to the technical field of computers, and discloses a test case generation method, device and equipment and a storage medium, and the method comprises the steps: constructing a scene feature model based on the multi-source sensing data of a vehicle collected in a to-be-tested target scene, and matching a target source scene from a pre-constructed test template library based on the scene feature model; mapping test configuration information corresponding to the target source scene to the target scene to generate an initial test template adaptive to the target scene; and generating a target test case in combination with the scene feature model, the initial test template, the multi-modal interaction rule base and the target test case evaluation rule. The scene coverage rate, the scene adaptability and the test efficiency of the intelligent cabin test can be improved.

The present disclosure extracts contextual product feature model from requirement specification documents where the conventional methods fail to perform. Initially, the system receives a plurality of requirement specification documents pertaining to a product, a domain dictionary, a plurality of configuration parameters, and a plurality of extraction patterns. A product feature model is generated using a NLP based feature extraction technique. The product feature model includes a plurality of product feature elements comprising a feature area, a major feature and a plurality of features arranged hierarchically and classified into feature types. Further, a plurality of ContextType associations like core, client, geography and market are extracted for each of the plurality of features using a ContextType extraction technique. Finally, the plurality of ContextType associations is updated in the product feature model to obtain a contextual product feature model. Various types of feature exports can be generated using a natural language interface.

The invention discloses an automatic optimization system for an administrative approval process, which relates to the field of administrative approval and comprises a twin modeling module, a simulation prediction module, a scheme generation module, a feature modeling module, a dynamic routing module, a semantic analysis module, a change analysis module, a behavior intervention module, a fusion inference module and a decision support module. According to the method, a real process structure and an operation rule can be accurately copied, bottleneck nodes can be identified in different pressure scenes, intervention measures are ensured to consider efficiency, throughput and resource utilization rate, task allocation is efficient and fair by dynamically generating a routing instruction, the response speed and accuracy of approval to regulation updating are improved, and the method is suitable for popularization and application. Quantitative policy implementation effect prediction is provided for decision makers, the scheme is ensured to have transparency and auditing performance, and manual review and risk control are facilitated.

The invention relates to the technical field of big data analysis and user behavior modeling, and provides a user portrait generation method and system based on big data. The method comprises the following steps: extracting a time sequence and interaction frequency characteristics from user behavior data, constructing a user access mode characteristic set and analyzing data missing distribution; boundary features are extracted based on missing distribution, and if the correlation between the boundary features and complete data is significant, surrounding data are detected to identify behavior rules; extracting a potential association path of the missing area, and deducing a user behavior pattern prototype; spreading the behavior rule to the missing region, and generating a data inference value; fusing the path and the boundary features, determining the internal structure of the missing region, and generating a user activity sequence; and verifying the consistency of the internal structure and the inferred value to form a complete user feature model. The missing data is inferred through the context, and the accuracy and integrity of the user portrait are improved.

The invention belongs to the technical field of intelligent education and personalized learning, and provides a personalized learning path recommendation method based on a course knowledge point sequence. The method aims at solving the problems that existing learning path recommendation logic association is insufficient, the personalized matching degree is low, and the optimization efficiency is not high. The method comprises the following steps: constructing a knowledge point sequence model by adopting a topological sorting algorithm on the basis of a curriculum knowledge point pre-modification and post-modification relationship, a difficulty level and dependence intensity, and generating a structured knowledge relationship graph; a learner feature model and a knowledge resource model are established, learner features are described from the dimensions of learning targets, cognitive levels, learning styles and the like, and the matching degree with knowledge points is calculated; and constructing an optimization model taking learning cost minimization, mastering degree maximization and path continuity optimization as targets, performing iterative optimization by using an improved intelligent optimization algorithm, and generating a learning path with logic rationality and personalized adaptability. According to the method, the scientificity and learning efficiency of learning path recommendation can be effectively improved.

The application discloses a design method, device and equipment for rotary body part turning processing and a storage medium. The processing feature model is established based on the processing features of an aviation rotary body part and in units of a part family, and then a part processing database is formed, so that the features of various aviation rotary body parts are effectively summarized and integrated, a standardized and parameterized turning processing program can be quickly formed, the programming quality is improved, the automation of aviation rotary body part programming is realized, and then the production cycle is shortened and the production efficiency is improved. Geometric feature analysis is performed on a part to be designed, part family classification, part size and part material are obtained, a corresponding processing feature model is selected in the part processing database according to the part family classification, and the feature elements of the part to be designed are input, so that a parameterized primary turning processing program can be directly obtained, and the programming efficiency is effectively improved. Furthermore, simulation verification is performed, and the accuracy of the turning processing program is ensured.

The application provides a data acquisition method and system based on flexible design of structured table, comprising: obtaining table data; establishing a structured table feature model according to the table data; constructing a table knowledge database, and calling table related attributes from the table knowledge database to perform corresponding table flexible design and obtain a corresponding feature matrix; aggregating the corresponding feature matrix according to the structured table feature model to generate a multifunctional structured table; and establishing an associated mapping relationship between a production process and the structured table, integrating the multifunctional structured table with a data terminal for application, and realizing acquisition of structured table data. The application establishes a multifunctional structured table model and performs flexible design, realizes software and hardware integrated application with a diversified data acquisition terminal, and effectively improves data structured, integrated acquisition and management capability in a satellite development process.

The application discloses an automatic auxiliary programming system for die electrode processing, and belongs to the technical field of computer-aided manufacturing; the automatic auxiliary programming system comprises an electrode feature recognition module, a processing scheme prediction module, a scheme optimization module, a simulation verification module and a post-processing module; the electrode feature recognition module is used for acquiring a three-dimensional model of a die, and obtaining electrode feature parameters and a feature model of a plurality of electrodes to be processed according to the three-dimensional model; the processing scheme prediction module is used for obtaining an initial processing scheme according to the electrode feature parameters; wherein the processing scheme comprises a processing cutting point, a processing track and processing parameters; the scheme optimization module is used for optimizing the initial processing scheme according to a preset historical case database to obtain an optimized processing scheme; the simulation verification module is used for simulating and verifying the optimized processing scheme, and obtaining a target processing scheme according to a verification result; and the post-processing module is used for converting the target processing scheme into a code executable by a numerical control machine tool.

The application discloses a kind of train vehicle fault image intelligent analysis methods based on lightweight deep learning technology, comprising: obtaining train image data, then the train fault component to be detected in image is labeled, then the overall data is divided into training set, test set two parts;Data enhancement operation is carried out, the hyperparameter required by algorithm is configured, and the training sample is input into the MobileDetectNet neural network model formed after improvement to carry out feature learning;The learned feature model is input to the new vehicle image to identify faults, and finally the area of the fault occurs is framed and the corresponding alarm information is output.The MobileDetectNet network intelligent identification model of the application has few parameters, occupies less memory and has high recognition rate, can improve the recognition rate of EMU fault detection under the original equipment CPU environment, reduce the work intensity of artificial, shorten the maintenance operation time, reduce the missed detection probability, so as to ensure the safe operation of EMU.

The application discloses a kind of civil aviation safety report theme modeling methods based on large language model and semantic enhancement, its method includes: civil aviation safety theme semantic feature model extracts the text semantic vector and event structure vector of safety report text, and obtains the fusion feature vector of safety report text by fusion;Safety report text database is carried out cluster clustering processing and obtains initial cluster set and noise sample set;Select representative front p% as the representative sample set of cluster;Construct noise sample evaluation repair mechanism module, and select the candidate cluster to which noise sample belongs using noise sample evaluation repair mechanism module;Comprehensive gain function is constructed using representative sample set in cluster, and representative sample iteration screening processing of candidate sample is carried out in cluster.The application realizes the theme modeling goal of semantic accuracy, comprehensive coverage and stable result by multi-module collaborative innovation, and provides reliable technical support for management decision.

The method includes receiving spectral data of a substrate and measurement data corresponding to the spectral data of the substrate. The method includes determining multiple feature model configurations for each of a plurality of feature models, further including determining that each of the multiple feature model configurations includes one or more feature model conditions. The method includes determining multiple feature model combinations, further including determining that each of the multiple feature model combinations includes a subset of the multiple feature model configurations. The method includes generating multiple input datasets, each of which is generated based on applying spectral data to each of the multiple feature model combinations. The method includes training multiple machine learning models, each of which is trained to produce an output using an input dataset from a plurality of input datasets and measurement data. The method further includes selecting from the trained machine learning models a trained machine learning model that satisfies one or more selection criteria.

The invention discloses a digital method for accurate duplication, transfer and verification of an in-place track, and belongs to the technical field of oral cavity restoration digitization. The method comprises the following steps: carrying out in-place path observation of a digital model in oral rehabilitation computer-aided design software, and generating an undercut filling model along the observation direction; constructing a parameterized three-dimensional feature model in reverse modeling software according to the undercut filling model; and automatically identifying the projection contour of the feature model through an algorithm, calculating the deviation angle between the current observation direction and the target in-position track, and guiding the model to observe and adjust. According to the invention, a track consistency verification system is constructed, accurate copying and transferring of the track direction are realized in a digital mode, the system can be adapted to dental restoration computer-aided design software with different function configurations, and a standardized and verifiable track digital transmission scheme is provided for clinical diagnosis and treatment of dental restoration.

The invention provides a business processing method which can be applied to the technical field of artificial intelligence. The business processing method comprises the following steps: in response to a received user operation, obtaining interaction data of a user in real time; performing deep intention recognition on the interaction data by using a large language model to obtain a target analysis result, the target analysis result comprising a service request of a user; according to the target analysis result, using the knowledge graph to update a user portrait of the user, the user portrait being a user feature model pre-constructed according to historical interaction data; and based on the updated user portrait, determining and executing a service processing flow corresponding to the service request. The invention further provides a business processing device, equipment, a storage medium and a program product.

The invention relates to the technical field of numerical control machining, in particular to a CNC closed-loop control method based on a dynamic feature model in a CAD / CAM environment. The method comprises the following steps: extracting a tolerance mark value of each processing surface in a CAD model, and mapping the tolerance mark value into an error tolerance budget of a corresponding track segment; the geometric curvature and the feeding speed set value of each track section in the machining program are analyzed from the CAM system, and the predicted contour error of each track section at the current speed is calculated according to the geometric curvature and the feeding speed set value; comparing the predicted contour error of each trajectory segment with the corresponding error tolerance budget, and calculating a tolerance margin; and marking the track section of which the tolerance margin is a negative value as an error overrun section. According to the method, tolerance transfer is carried out from the tolerance surplus section to the error over-limit section, so that error tolerance resources are redistributed among different track sections, and the stability and adaptability of processing beat control under a complex processing path are enhanced.

The application relates to the technical field of computers and discloses a full-movement simulator fault self-recovery optimization method and system, an electronic device and a storage medium. The method comprises the following steps: collecting multi-dimensional state data, identifying a fault type and locating a root cause, performing cross verification and confirmation through remote display picture and local rendering data comparison, performing hierarchical self-recovery according to the fault type and the severity, backing up configuration, cache and log to support rollback, performing bidirectional connectivity test and link health degree evaluation on network faults, executing adapter reset, driver reload and master / standby link / network port switching and outputting deterioration early warning, grading and caching terrain data according to distance, preloading tiles in combination with flight situation to predict a path range, regularly cleaning the cache and synchronizing with a data source, controlling a start timing based on a node dependency relationship and adjusting parameters in a maintenance mode, training a time sequence feature model based on historical fault data to realize trend early warning and preventive maintenance, and archiving disposal data to update a diagnosis rule and a self-recovery strategy.

The application discloses a pipe network operation and maintenance management system based on data analysis, and relates to the technical field of municipal infrastructure operation and maintenance management.The system comprises a device feature modeling module, a group anomaly identification module, a common mode risk traceability module, a dynamic risk modeling module, a regulation and control strategy optimization module, and a closed-loop adaptive updating module.The device feature modeling module is based on historical operation data and real-time acquisition parameters, and encodes the type, batch, position and time sequence of sensors to construct a device attribute mapping matrix.The application realizes intelligent adjustment of regulation and control parameters and avoidance of abnormal interference by constructing the device attribute mapping matrix and the group anomaly identification mechanism, combining external induced factor positioning and a dynamic risk weight graph, constructing a closed-loop optimization process, and improving the identification accuracy, control safety and operation resilience of the pipe network system in a multi-disturbance scenario.

The invention relates to a stock average line similar mode searching method. The method comprises the following steps of 1, extracting and preprocessing data; 2, establishing a feature model; step 3, calculating a single mean line feature; step 4, multi-mean line feature calculation; step 5, feature similarity calculation; and step 6, feature result weighted fusion. The method has the advantages that the multi-dimensional analysis system covering the single-mean-line form, the data features and the multi-mean-line cross features is constructed, and the weighted fusion strategy and the deep cross mode analysis model are combined; compared with a traditional method, the influence of the problems that single feature analysis robustness is poor, multi-feature integration is insufficient, cross mode mining is shallow and the like is effectively relieved. Experimental verification on stock historical average line data shows that by means of the searching method, the similarity degree of different stock average line systems can be quantified more accurately and comprehensively, and the accuracy and practicability of similar fragment searching are further improved.

The invention discloses an optical storage charging and discharging integrated intelligent scheduling system based on deep learning, and the system comprises a data collection module which is used for collecting and preprocessing multi-source operation data; the feature modeling module is used for performing feature coding and time sequence modeling on the preprocessed multi-source data to generate a time sequence input tensor; the deep learning prediction module is used for inputting the time sequence input tensor into the improved NASNet model to generate a photovoltaic power prediction value; the energy scheduling optimization module is used for establishing an energy scheduling optimization model and solving an optimal scheduling strategy by adopting a gradient search algorithm; the execution control module is used for forming system feedback data; and the model adaptive updating module is used for correcting model parameters by adopting a rolling training mechanism, outputting the updated improved NASNet model, realizing adaptive correction of the model parameters, improving the photovoltaic power prediction precision and reducing the light abandoning rate.

The invention discloses a load curve-based calculation and power cooperative scheduling system and method. Comprising a multi-source data acquisition and preprocessing module, a load curve feature extraction and prediction module, a computing power task feature modeling module, a computing power resource state monitoring module, a multi-target collaborative scheduling decision module, a scheduling instruction generation and execution module and a scheduling effect evaluation and self-optimization module. Based on feature extraction and prediction of a load curve, a dynamic matching mechanism of a computing power task and power resources is established, the single-dimension limitation of traditional scheduling is broken through, and accurate cooperation of computing power and power resources is achieved; a multi-objective optimization function including the power cost, the energy consumption intensity and the green power utilization rate is constructed, through quantitative modeling and algorithm solving, multiple scheduling objectives are balanced, and the requirements of green and low-carbon development policies are met.

The application discloses a kind of integrated modeling methods comprising mechanical simulation and virtual maintenance attributes, comprising the following processes: respectively establishing geometric feature model, interactive feature model and state feature model, form integrated equipment model comprising mechanical simulation and virtual maintenance attributes;Integrated equipment model is divided into state execution layer, motion logic layer, multi-dimensional presentation layer;State execution layer is used to execute control command, external control command data is first transmitted to state execution layer, with the output of state execution layer as the input of motion logic layer, the result is obtained by processing motion logic layer, then the result is transmitted to multi-dimensional presentation layer, simulation result is obtained by processing by multi-dimensional presentation layer, and is presented by multi-dimensional way.The application can not only realize multi-dimensional presentation equipment / device geometric characteristics, but also can express motion logic and execution state in equipment / device simulation process completely, and can be realistically simulated mechanically and virtually maintained.

The invention relates to the technical field of meteorological prediction, in particular to a meteorological prediction method and device based on dual-channel modeling, equipment and a storage medium. The method comprises the steps that historical meteorological observation data of a target area are collected, the historical meteorological observation data are meteorological time sequences, and meteorology comprises at least one of temperature, precipitation, humidity, wind speed and air pressure; performing feature enhancement on the meteorological time sequence; the feature-enhanced meteorological time sequence is input into a first feature modeling channel and a second feature modeling channel, the first feature modeling channel is a multi-scale gated convolutional neural network, and the second feature modeling channel is an extended long-short-term memory network; and carrying out fusion processing on the output features of the first feature modeling channel and the output features of the second feature modeling channel to generate meteorological data in the target time period. According to the method, the meteorological long-period evolution rule can be captured, and multi-scale and multi-variable meteorological characteristics can be considered, so that the meteorological prediction precision is greatly improved.

This invention presents an AI-generated text detection method based on graph structure features, belonging to the fields of artificial intelligence and natural language processing. The method includes: dataset construction, entity relation extraction and graph structure construction, graph structure feature extraction, graph structure feature model training, and text detection. It further incorporates traditional text feature extraction and model training, adaptively fusing the traditional text feature model and the graph feature model based on confidence-weighted entropy, and then performing text detection based on the fused model. This invention is the first to perform AI text detection from the perspective of graph structure features, breaking through the limitations of existing research that focuses on surface features such as vocabulary, syntax, and perplexity. The fusion strategy dynamically adjusts the fusion weights by quantifying the uncertainty of model predictions, maintaining a high level of performance on both original data and adversarial examples, achieving a balance between detection accuracy and adversarial robustness. It can be widely applied to the detection of AI-generated content such as news content and academic papers.

The invention discloses a new energy scene intelligent risk prediction system based on cross-modal perception. The system comprises a data acquisition module which is used for acquiring multi-modal operation data of target new energy equipment; the data preprocessing module is used for preprocessing the multi-modal operation data to generate a multi-modal data set; the modal feature modeling module is used for generating a multi-modal feature tensor through an improved Meta-Transform network; the risk prediction modeling module is used for predicting a risk expected value and a risk standard deviation through an improved NGBoost algorithm and generating a confidence risk score; the risk level evaluation module is used for judging a risk level label of the target new energy equipment; the control strategy generation module is used for generating and executing a response control strategy; and the feedback updating module is used for collecting operation state feedback information and carrying out incremental updating on the improved Meta-Transform network. According to the invention, the risk prediction accuracy and the response control real-time performance in the new energy scene are improved.

The invention discloses a parameterized model reconstruction method and system based on an onshape API, and relates to the technical field of model reconstruction, and the specific steps are as follows: reading regularized three-dimensional modeling vector data in an H5 format file, and converting the three-dimensional modeling vector data into a Numpy array; analyzing the Numpy array to obtain sketch geometry, plane and stretching operation parameters, carrying out de-regularization on the sketch geometry, plane and stretching operation parameters, and storing the sketch geometry, plane and stretching operation parameters through Python examples; creating a reference straight line in the cloud three-dimensional modeling document through the API, and creating a new plane based on the reference straight line; and performing data conversion on the parameters according to the new plane to generate conversion parameters, constructing a sketch entity and a stretching body on the new plane based on the conversion parameters, and generating a reconstruction model. According to the method, data in a vector format can be converted into an editable parameterized feature model by calling an Onshape API (Application Program Interface).