546 results about "Synthetic data sets" patented technology

The invention provides an automobile part spraying control method and system, and relates to the technical field of automobile manufacturing, and the method comprises the steps: collecting spraying environment parameters in real time, generating a comprehensive digital model containing the environment parameters and part characteristics, and carrying out the intelligent partitioning of the surface of an automobile part through a clustering partitioning algorithm, and dividing the surface into a plurality of sub-regions; on the basis of each sub-region, combining with the current environment parameters, generating an optimal spraying parameter for each sub-region by utilizing an intelligent agent trained by a reinforcement learning algorithm, and on the basis of the spatial relationship between the optimal spraying parameters and the sub-regions, optimizing a global spraying sequence by adopting an ant colony path planning algorithm, and generating an optimal spraying path; the spraying robot sprays, a real-time quality evaluation result of spraying is generated, a pre-constructed fuzzy logic controller is adopted, spraying parameters are adjusted, sprayed parts are obtained, a full-period comprehensive data set is integrated, and spraying control parameters are iteratively updated.

The invention relates to the technical field of structure health monitoring, in particular to a breakwater structure health monitoring method based on a sensing network, which comprises the following steps: S1, collecting stress, acceleration, displacement and environmental parameter data of a breakwater; s2, preprocessing the sensing data acquired in the step S1; s3, integrating the data preprocessed in the S2 into a comprehensive data set reflecting the overall structure state of the breakwater; s4, analyzing and calculating the comprehensive data set in combination with a structural mechanical model of the breakwater so as to extract characteristic parameters; s5, constructing a breakwater health assessment model; s6, evaluating the real-time data based on the health evaluation model constructed in the S5, and calculating the health score of the breakwater; according to the invention, through real-time data acquisition based on the sensor network and the sparse Bayesian learning algorithm, comprehensive monitoring and intelligent early warning of the health state of the breakwater structure are realized, and the monitoring precision, the real-time performance and the risk prediction capability are effectively improved.

The invention relates to the technical field of mineral resource reserve simulation, in particular to a geological mineral resource reserve simulation method. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition on a target geological area, and carrying out comprehensive feature extraction to obtain a comprehensive data set; constructing an initial static geologic model according to the comprehensive data set to obtain the initial static geologic model; building a multi-physics field coupling model according to the initial static geologic model to obtain a multi-physics field coupling model grid; performing metallogenic fluid migration and enrichment simulation according to the multi-physics field coupling model grid to obtain a metallogenic fluid migration simulation result; carrying out geologic body geometrical morphology updating on the initial static geologic model according to a metallogenic fluid migration simulation result to obtain a dynamic geologic model; and performing reserve prediction model training according to the dynamic geologic model to obtain a reserve prediction model. According to the method, the mineral resource reserves are evaluated more accurately and dynamically through a mineral resource reserve simulation technology.

The invention provides an automatic driving vehicle scheduling method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of an automatic driving vehicle, and generating a comprehensive data set; and based on the comprehensive data set, carrying out dynamic allocation and fleet collaborative planning on loading and unloading tasks to generate a task allocation scheme. And through a hierarchical search algorithm and a multi-objective optimization algorithm, the path of the vehicle is planned and optimized in combination with the task allocation scheme and the comprehensive data set, and an optimized path scheme is generated. And according to the task allocation scheme, the optimized path scheme, the integrated data set and the vehicle real-time state data, utilizing a predictive analysis model to evaluate and deal with potential risks in the transportation process, and generating and executing a real-time scheduling instruction. According to the invention, the scheduling method of multi-dimensional data acquisition and processing, dynamic task allocation, path optimization and risk prediction is combined, the scheduling efficiency and safety of the autonomous vehicle are comprehensively improved, and efficient logistics management is realized.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

Disclosed are a method, system, and device for removing smoke from laparoscope images based on a conditional diffusion model. The method includes: segmenting a video of a laparoscopic surgery according to the number of frames to form a data set; performing smoke rendering on the obtained laparoscope smokeless images, and synthesizing paired smoky images to obtain a synthetic data set containing the smokeless images and the smoky images; inputting the smokeless images into the conditional diffusion model for forward noise addition, and continuously adding noise until the smokeless images are completely noised; inputting the smoky images into a smoke sensing module to obtain smoke concentration and position information, then training a neural network, and continuously performing reverse denoising on the completely noised images using the trained neural network until clear smokeless images are outputted; and optimizing a smoke removal model through a multi-loss function fusion strategy.

The invention relates to the technical field of combined fleet navigation, and discloses a combined fleet intelligent navigation data processing system, which comprises a data acquisition unit used for acquiring navigation data and ship lock data of each ship of a combined fleet in real time, fusing the preprocessed navigation data and ship lock data to obtain a comprehensive data set, through dynamic data fusion and self-adaptive grid division, the spatial-temporal characteristics of a flow velocity abrupt change area during opening and closing of a ship lock are captured in real time, the limitation that a traditional algorithm depends on a static environment is broken through, a dynamic power distribution strategy is generated based on flow field characteristics and navigation data, and precise power matching is achieved in combination with a propeller efficiency priority and a self-adaptive weight algorithm. The response time is shortened, the cooperation efficiency is improved, course deviation is avoided, a multi-stage early warning and time difference judgment mechanism is constructed, the power margin is evaluated in real time, the weight, the navigational speed and the formation are adjusted according to the risk level, it is guaranteed that power distribution adjustment of the whole fleet does not lag behind water flow changes, and normal course of the fleet is guaranteed.

The invention discloses a water quality monitoring method and system based on artificial intelligence. The method comprises the following steps: acquiring a comprehensive data set composed of sensor data, satellite images and meteorological parameters; according to the water flow velocity and pollution concentration gradient in the comprehensive data set, adopting a dynamic sampling algorithm to adjust the sampling frequency and position, and outputting adjustment data; performing space-time interpolation processing on the adjusted data to obtain a preprocessed data set with high-density space-time coverage; key features of sensor values, image textures and meteorological parameters are extracted from the preprocessed data set by adopting a principal component analysis method, a weighted feature matrix is constructed, and a fusion feature set is obtained; and judging whether the dimension of the fusion feature set exceeds a preset threshold value, if the dimension of the fusion feature set exceeds the preset threshold value, performing dimension reduction and classification on the fusion features by adopting a random forest algorithm, and optimizing model parameters through cross validation to obtain a pollution concentration prediction result. Effective technical support is provided for water environment protection, and important ecological and social benefits are achieved.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.

The invention provides an electronic evidence correlation analysis method which comprises the following steps: analyzing and standardizing heterogeneous data of electronic evidences, extracting timestamp information according to data source characteristics, and uniformly converting data of each time precision into millisecond-level timestamps; extracting space information, including a physical space and a virtual space, according to the timestamp, and performing format unification and coordinate system conversion on the extracted information to obtain a comprehensive data set containing a standardized time sequence and multi-scale space information; constructing a space-time association graph model based on the electronic evidence group, setting the weight of an edge in combination with the space-time association distance between nodes, mapping the high-dimensional space-time association feature to a low-dimensional vector space, and obtaining the space-time association feature vector of the electronic evidence; and performing similarity calculation on the space-time association feature vectors, calculating the space-time association degree between different electronic evidences, and screening out associated electronic evidences as electronic evidence pairs according to a similarity threshold to form an association relationship network.

The invention discloses an equipment fault intelligent early warning system based on abnormal voiceprint AI analysis of energy equipment, and relates to the technical field of equipment health management, the equipment fault intelligent early warning system comprises an equipment fault early warning platform, and the equipment fault early warning platform is in communication connection with the following modules: a data sensing fusion module, a voiceprint AI analysis module and a voiceprint AI analysis module; the data acquisition module is used for acquiring high-frequency voiceprint signals, temperature field distribution and vibration data in real time during operation of energy equipment through a distributed sensor network to form a comprehensive data set; and the voiceprint AI analysis module is used for extracting voiceprint features from the comprehensive data set, and identifying whether the equipment emits abnormal voiceprints or not by using a pre-trained voiceprint AI model. According to the method, early abnormity is identified through the high-precision AI model, sudden equipment faults are effectively prevented, equipment physical field interaction is simulated in combination with the digital twin technology, and a fault evolution path is dynamically deduced, so that operation and maintenance personnel can take intervention measures at the initial stage of the faults, the stability and reliability of equipment operation are remarkably improved, and the non-planned downtime is shortened.

The invention belongs to the field of computer vision, and particularly relates to an unmanned aerial vehicle aerial photography night image enhancement method based on long and short exposure fusion. Comprising the following steps: firstly, constructing a low-illumination synthetic data set; secondly, carrying out data preprocessing on the long-exposure image and the short-exposure image in the low-illumination synthesized data set to obtain a long-exposure image and a short-exposure image containing blurring and noise; then, a lightweight denoising and deblurring network is constructed, the first stage comprises a multi-scale input module, a dual-core feature fusion module and a feature reconstruction module, and the second stage adopts a channel attention mechanism module for fine tuning; designing a multi-scale perception loss function, and guiding network training; and finally, using the trained final model to enhance the to-be-tested unmanned aerial vehicle aerial night image. According to the method, the visual effect and fidelity of the aerial image of the low-illumination unmanned aerial vehicle are effectively improved by utilizing complementary information of continuous short exposure and long exposure.

The invention relates to the technical field of electric power fault diagnosis, and discloses a transformer area line loss abnormity diagnosis method and system based on a large model, and the method comprises the steps: collecting multi-dimensional data used for supporting transformer area line loss abnormity diagnosis, carrying out the cleaning, correlation fusion and standardization processing of the multi-dimensional data, and obtaining a comprehensive data set; a multi-layer diagnosis system based on a rule model, a random forest model and a large model is constructed, and the rule model identifies the simple and conventional anomalies of the transformer area according to a preset anomaly diagnosis rule based on the basic attribute data and the power operation state data in the comprehensive data set; the random forest model locates complex anomalies and novel anomalies by mining a coupling relationship among energy access condition data, external environment influence data and line loss fluctuation in the comprehensive data set; the big model carries out fusion verification on diagnosis results of the rule model and the random forest model, and outputs a final transformer area abnormity diagnosis result; and based on the diagnosis result, a differential loss reduction strategy adaptive to the actual data characteristics of the transformer area is recommended. The working efficiency is improved.

The invention discloses a common concrete and ultra-high performance concrete performance prediction and mix proportion multi-objective optimization design method and system, and the method comprises the steps: obtaining a comprehensive data set which covers the mix proportion, performance data and cost data of common and ultra-high performance concrete; correlation analysis is carried out, and influence factors strongly correlated with the target variable are determined; constructing a concrete performance prediction model by using a machine learning algorithm, training and testing, then carrying out interpretability analysis, and selecting an optimal concrete performance prediction model; constructing a mix proportion optimization design objective function and an ultra-high performance concrete cost function; establishing a multi-objective optimization design mathematical model in combination with constraint conditions, a cost function and an optimization objective function; and solving by adopting a multi-objective evolutionary algorithm, generating an approximate Pareto frontier solution set, and determining an optimal mix proportion optimization design scheme. According to the method, the concrete performance can be predicted, the mix proportion with the optimal comprehensive performance and cost effectiveness is obtained, and multi-objective optimization design of the concrete mix proportion is achieved.

The invention discloses an intelligent hospital guide and tracking return visit recording integrated system for medical examination, which relates to the technical field of intelligent medical treatment, and comprises the following steps: based on a comprehensive data set, using an A-star algorithm to identify a hospital guide path; in the process that the patient waits for the hospital according to the hospital guide path, the system collects behavior data of the patient in real time; based on historical data, training a waiting time difference prediction model, inputting the patient behavior data into the waiting time difference prediction model, and predicting a waiting time deviation value; dynamically adjusting a hospital guide path according to the waiting time deviation value; and after the patient finishes visiting according to the adjusted hospital guide path, the system automatically provides health propaganda and education contents, collects patient feedback data and stores the patient feedback data in the tracking return visit system. Through the A-star algorithm and the waiting time prediction model, the system can perceive waiting delay in advance and adjust a path in time, the waiting time of a patient is remarkably shortened, and the doctor-seeing experience is improved.

The invention belongs to the technical field of artificial intelligence, and relates to an attribution analysis method and device based on artificial intelligence, computer equipment and a storage medium, and the method comprises the steps: carrying out the information analysis of an alarm notification based on a data perception agent when the alarm notification corresponding to a target system is monitored, and obtaining key entity information; acquiring associated data corresponding to the key entity information based on the data acquisition agent; performing data fusion on the associated data based on the data processing agent to obtain a comprehensive data set; constructing a target prompt text corresponding to the comprehensive data set based on the data interaction agent; based on a large language model, performing association analysis and intelligent reasoning on the comprehensive data set according to the target prompt text, and generating a root cause analysis result; and outputting an attribution analysis result. In addition, the attribution analysis result can be stored in the block chain. The method can be applied to attribution analysis scenes in the financial field and the medical health field, and the processing efficiency and accuracy of attribution analysis are improved.

The invention relates to the technical field of artificial intelligence, and discloses a neural network model training method based on virtual reality and simulation and related equipment. The method comprises the steps of generating a three-dimensional visual virtual reality scene based on a target real scene and constructing a dynamic physical simulation environment; deploying the neural network model to a test vehicle digital twin execution model for reasoning, and outputting a reasoning result; identifying a to-be-optimized target test scene type of the neural network model based on a reasoning result; dynamically adjusting scene parameters of the dynamic physical simulation environment to simulate a target test scene type, and generating a high-fidelity synthetic data set; and performing iterative training on the neural network model by mixing the high-fidelity synthetic data set and the real data set to form a closed-loop optimization mechanism until the generalization ability of the neural network model in the complex scene reaches a preset threshold. Based on the method, the training efficiency and generalization ability of the neural network model in a complex scene can be improved.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The invention provides an intelligent operation and maintenance strategy making method and device and a related system. The method comprises the steps that a first operation data set of target equipment in a historical time period and a second operation data set of the target equipment in an operation and maintenance time period are acquired; determining a first fault type data set according to the first operation data set; determining a first synthetic data set according to the first operation data set and the first fault type data set; determining an operation fault data set according to the first operation data set, the first fault type data set and the first synthetic data set; training a fault prediction model through the operation fault data set to obtain a fault prediction model, and inputting the second operation data set into the fault prediction model to obtain a fault prediction result; and generating an operation and maintenance strategy according to the fault prediction result, and scheduling corresponding operation and maintenance personnel to perform equipment operation and maintenance through the operation and maintenance strategy. By executing the method, high efficiency and reliability in the operation and maintenance process of the equipment can be improved.

The invention relates to the technical field of automobile air conditioner control, in particular to an automobile air conditioner combined management regulation and control method and system.The method comprises the steps that automobile interior and exterior environment data and automobile state information are obtained, and an automobile comprehensive data set is obtained; passenger comfort demand data are obtained; calculating an energy consumption demand and a comfort demand of the air conditioner based on the vehicle comprehensive data set and the passenger comfort demand data, and generating a preliminary optimization control signal; the optimized control signal is adjusted in real time through a self-adaptive algorithm according to the change of the environment inside and outside the vehicle and the sensible temperature feedback of passengers; and continuously collecting passenger feedback data and environment change data, and optimizing a control signal based on a reinforcement learning model to obtain a final dynamic management regulation and control scheme. By means of the air conditioning system, the problems that an existing air conditioning system cannot comprehensively consider changes of the environment inside and outside the automobile and individual requirements of passengers and lacks dynamic regulation and control and self-learning optimization capacity are effectively solved, the comfort of the passengers is improved, and the overall performance of the air conditioning system is optimized.

The invention belongs to the technical field of data processing, discloses a statistical data association mining platform and method based on a knowledge graph, and aims to provide a comprehensive platform for extracting, integrating, analyzing and explaining complex economic data from various heterogeneous data sources so as to support more effective decision making. Collecting original data from various heterogeneous data sources, and preprocessing the original data to generate a comprehensive data set; secondly, mapping the comprehensive data set into a predefined knowledge graph to form a network structure of nodes and edges, and updating the knowledge graph in real time; then, feature extraction is carried out on the updated knowledge graph, and a comprehensive feature vector is generated; then, the comprehensive feature vector is used as input, a potential mode in the data is obtained, and statistical association is mined through the potential mode; and finally, generating an interpretation report, displaying the potential mode in the knowledge graph and the mined statistical association, and increasing the transparency of mode interpretation.

The invention relates to the technical field of geophysical exploration, in particular to a multichannel deep learning magnetotelluric inversion method based on physical information constraint. The method comprises the following steps: generating a synthetic data set containing a geoelectric model and forward modeling response thereof, and adding a noise simulation actual observation condition; constructing a hybrid network architecture combining Transform and U-Net, taking apparent resistivity and impedance phase as dual-channel input, extracting global features by using an encoder, gradually recovering spatial resolution through a decoder, and outputting an underground resistivity model; network training adopts a composite loss function fusing model loss and data loss, and an inversion process is constrained by introducing a magnetotelluric forward modeling physical rule, so that a result is ensured to fit observation data and conform to a physical mechanism; after training is completed, preprocessed actual measurement data are input into the model, and a resistivity image can be directly obtained. The method is used for geological structure identification and reservoir interpretation, and the inversion precision and reliability are effectively improved.

The invention relates to the technical field of advertisement data marketing, in particular to an advertisement marketing system based on data updating and sorting, which comprises a data acquisition and transmission unit, a data processing and analysis unit, an advertisement strategy generation and optimization unit and an advertisement putting monitoring unit. The data processing and analyzing unit is used for calculating user behaviors and market trend indexes, mining atlas association and predicting the user behaviors by applying memory calculation, a parallel processing framework and various algorithms, and the advertisement strategy generating and optimizing unit is used for constructing a strategy generating engine to generate an initial strategy and dynamically optimizing by utilizing deep reinforcement learning. And the advertisement putting monitoring unit applies the optimized strategy to actual putting and monitors the conversion rate in real time, and detects abnormity and adjusts the strategy through an auto-encoder model, so that the accuracy and efficiency of advertisement marketing are improved, the input-output ratio of advertisement putting is improved, and the advertisement effect is enhanced.

The invention provides a digital twin model correction method and system based on machine vision. The method comprises the steps that a comprehensive data set is constructed by collecting sounding data, tidal flow velocity data and unmanned aerial vehicle images, wave disturbance deviation and water surface reflection distortion are eliminated based on geometric feature matching, and a dynamic compensation coefficient is generated in combination with tidal parameters; after time deviation of sounding data is corrected, a dynamic incidence relation between terrain elevation and sediment migration rate is calculated, a digital twinborn model fusing sedimentary layer thickness distribution and tidal flow velocity driven erosion boundary prediction is established, and a visual terrain curved surface is generated; through reverse verification of real-time sediment accumulation characteristics and terrain curved surfaces, migration rate weight parameters are adjusted in a self-adaptive mode, and dynamic optimization of the model is achieved. High-precision dynamic coupling simulation of sea area terrain evolution and sediment migration is achieved, the real-time performance and reliability of island surface change prediction are improved, and coastal zone safety and economic collaborative decision making is supported.

The invention provides a method for calculating migratory parent fish quantity by using fish early-stage resource quantity, which comprises the following steps: collecting early-stage resource quantity data, migratory parent fish quantity data and various parameters, and processing the collected data to obtain a comprehensive data set; constructing a basic model for representing the relationship among the number of roes, the number of larva fishes and the number of migratory parent fishes in combination with various parameters; designing a model architecture of the basic model by using a long-short-term memory network to obtain an initial prediction model, and quantifying the initial prediction model by using a Bayesian method to obtain a calculation prediction model; in combination with an MCMC method, performing parameter estimation and model training on the reckoning prediction model to realize optimization of the reckoning prediction model; and calculating the number of migratory parent fishes by using the optimized calculation and prediction model. According to the method, multiple data sources can be combined, the ecological environment complexity, the time sequence dynamics and the prediction uncertainty are fully considered, and accurate calculation of the migratory parent fish quantity is achieved.

Systems and processes are disclosed for enhancing cybersecurity and optimizing software repositories through integration of web crawling, web scraping, feature engineering, and advanced machine learning algorithms to detect phishing attempts, prevent account takeover fraud, and identify unused code in repositories. The system collects and refines data from various sources, including transaction logs, customer databases, device details, external data sources, and historical fraud data, to build comprehensive datasets. Feature engineering creates new, meaningful features from the refined data, which are used to train and evaluate machine learning models. The best-performing models are deployed in production to monitor incoming communications and transactions in real-time, flagging suspicious activities and optimizing codebases. This processing ensures timely detection and prevention of security threats while maintaining efficient software development processes. Robust protection is provided against evolving cyber threats and enhances software performance and security through continuous learning and adaptation.

The invention provides a multi-height three-dimensional gravity and gradient joint inversion method based on a physical information neural network, and the method comprises the steps: building a forward neural network and an inversion neural network based on the physical information neural network; pre-training an inversion neural network on the synthetic data set; wherein the synthetic data set comprises multiple pairs of synthetic data, and each pair of synthetic data comprises original three-dimensional gravity anomaly data and real three-dimensional density data; constructing a closed-loop network based on the forward neural network and the pre-trained inversion neural network; and carrying out iterative training on the closed-loop network on given to-be-solved data to obtain an inversion neural network meeting requirements. According to the scheme of the invention, under the condition of introducing limited cost increase, abundant vertical resolution is provided.

The present invention provides a production control method and system for an intelligent clothing industry based on data analysis, which relates to the field of intelligent manufacturing technology, and includes: setting an intelligent data acquisition platform, collecting heterogeneous data for common feature extraction, generating a comprehensive data set, and constructing a digital twin model for clothing production; performing data extraction based on the comprehensive data set, generating triples and constructing a clothing production knowledge graph, constructing a clothing production data analysis model, extracting key features and determining implicit association rules, generating a cross-modal knowledge graph, identifying clothing production control rules, and solving clothing production control parameters; constructing a decision evaluation model to parse clothing production control parameters into production control decisions, perform evaluation, dynamically correct based on the evaluation results, obtain optimized production control decisions, send them to equipment terminals, adaptively optimize clothing production, collect real-time production data, update the clothing production digital twin model and the clothing production knowledge graph, and periodically optimize until the production task is completed.

The invention relates to the technical field of medical evaluation, and discloses an Alzheimer's disease early cognition evaluation system and method based on combination of traditional Chinese medicine and western medicine. The system comprises a tongue image acquisition module, a pulse condition fluctuation signal acquisition module, a western medicine information acquisition module, a data preprocessing module, a feature extraction module, a data fusion module, a comprehensive data set establishment module and an evaluation decision module, wherein the tongue image acquisition module acquires tongue images, pulse condition fluctuation signals, inquiry text data and inquiry sound information; and the western medicine information acquisition module performs multi-modal fusion on feature data of traditional Chinese medicine and western medicine to construct a comprehensive data set. According to the system, multi-modal data is collected by integrating a traditional Chinese medicine four-diagnosis module and a western medicine detection module, the multi-modal data is processed and fused through algorithms including attention mechanism image processing, signal analysis and the like, and then an evaluation model is trained through integrated learning, transfer learning and the like. The method comprises the steps of equipment deployment, data acquisition and processing, model training, evaluation application and the like, multi-dimensional accurate evaluation is realized by combining optimization of a generative adversarial network, fuzzy logic and the like, and technical support is provided for early diagnosis.

The invention provides a sedimentary body lithology identification system and method for oil-gas exploration, and the system comprises a data collection module which is used for collecting seismic data and logging data; the data processing module is used for preprocessing the seismic data and the logging data; the data fusion module is used for fusing the preprocessed seismic data, logging data and a geologic model to generate a comprehensive data set; the lithology identification module is used for analyzing the comprehensive data set by using a machine learning algorithm and identifying the lithology type of the sedimentary body; the result verification module is used for performing comparison verification on the identification result and actual core analysis data; and the display module is used for visualizing the recognition result. The system provided by the invention not only can identify the lithology of the sedimentary body, but also can generate a visual three-dimensional lithology distribution diagram through three-dimensional geological modeling and a visualization technology. The visualization results enable geologists and engineers to clearly understand the underground structure and lithologic distribution so as to make more accurate exploration decisions and development plans.