346 results about "Synthetic data sets" patented technology

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 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 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 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 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 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.

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 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 discloses an agricultural meteorological disaster monitoring and early warning method and system based on a remote sensing technology, and the method comprises the steps: generating a multi-dimensional data set through obtaining and processing multi-source remote sensing data, and generating a comprehensive data set through weighted average fusion. Then, extracting land surface temperature data, comparing the land surface temperature data with a historical value, calculating a temperature deviation value, and generating temperature anomaly distribution data; and calculating a disaster intensity index by combining the soil humidity and vegetation index data change trend, and generating disaster intensity distribution data. The data meteorology is imported into a driving simulation system, disaster evolution is simulated, and the disaster influence range and duration are predicted. And if the prediction data exceeds an early warning threshold, generating early warning data including disaster categories, influence areas and prediction time, and generating disaster risk distribution data by using a spatial interpolation method for dynamic monitoring. According to the invention, the accuracy and timeliness of disaster monitoring and early warning are improved.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention provides a psychological health data generation method based on multi-source data fusion analysis. The method comprises the following steps: acquiring associated data of a user from a plurality of data sources; preprocessing the associated data to obtain preprocessed associated data; performing feature extraction processing and data fusion processing on the preprocessed associated data to obtain a comprehensive data set; training the pre-training model by using the comprehensive data set, and constructing a mental health risk assessment model; and analyzing and processing the user data by using the psychological health risk assessment model to output the psychological health data of the user, the method automatically generates the psychological health data for assessing the psychological health risk of the individual by integrating the associated data of the user from the data source. According to the method, the accuracy and the real-time performance of mental health assessment can be effectively improved, the influence of multi-dimensional factors on mental health is comprehensively considered through data fusion, deviation possibly caused by a single data source is avoided, and the comprehensiveness and the reliability of an assessment result can be ensured.

The invention discloses a differential privacy data set distillation method and system based on image generation data, and belongs to the technical field of data privacy protection and machine learning. The method comprises the following steps: firstly, synthesizing a synthetic data set meeting Gaussian differential privacy, training a feature extractor on the synthetic data set, and finely adjusting an expert model by using original data differential privacy; multiple rounds of iterative optimization are carried out on the distillation data set initialized according to the classes, wherein a feature extractor is randomly selected according to the classes in each round to align the features of the original data added with the noise and the distillation data, and an expert model is utilized to align the semantics of the distillation data hard labels and the semantics of the synthetic data soft labels; and finally outputting a distillation data set meeting the total privacy budget. According to the method, effective priori is provided by utilizing generated data, extra noise injection in the alignment process is reduced, convergence is accelerated, and better privacy protection and data availability balance compared with a previous method is realized under the same differential privacy budget.

The invention discloses a distribution line abnormity monitoring and early warning method and system, and relates to the technical field of distribution line intelligent monitoring. The method comprises the following steps: acquiring operation data acquired by multiple types of sensors, and performing time alignment, normalization and fusion processing to form a comprehensive data set; inputting the comprehensive data set into a generative denoising model based on noise and abnormal signal distribution separability learning, and outputting a clean data sequence through noise suppression and abnormal feature fidelity joint optimization; identifying an abnormal evolution trend with a nonlinear amplification characteristic by using a Lyapunov index and a Hurst index, and generating a risk assessment result; and constructing and dynamically adjusting a self-adaptive early warning threshold set according to a risk assessment result, and outputting abnormal early warning information when a risk index exceeds a threshold. The method realizes parallel noise suppression and abnormal feature fidelity, has dynamic identification and self-learning capabilities, and significantly improves the accuracy of distribution line anomaly detection and the stability of an early warning system.

The invention relates to the technical field of municipal comprehensive GIS data management, in particular to a municipal comprehensive GIS data integration and management system for hub airport construction. The system comprises a data acquisition unit which is used for acquiring spatial geographic parameters, construction parameters and traffic operation parameters to construct an original multi-source data set, preprocessing the original multi-source data set and outputting a standardized data set; the equipment scheduling and resource management unit generates a total conflict set based on the spatial fusion data set and the dynamic progress data set, and obtains a Pareto optimal solution set by constructing a multi-objective optimization function; and the multi-dimensional conflict early warning unit obtains a security conflict set based on the spatial fusion data set and the dynamic progress data set in combination with the total conflict set, and finally generates dynamic early warning information through risk grade division. The coordinated scheduling scheme can be automatically generated under the scene of progress conflict of multiple contractors, complete dependence on manual decision making is avoided, and the construction efficiency and collaboration are improved.

The invention discloses a synthetic data set construction method and electronic equipment, and relates to the technical field of artificial intelligence, and the synthetic data set construction method comprises the following steps: dividing an original multi-source document of a target field into a plurality of word segmentation units by using a word segmentation device; obtaining representative scores of the plurality of word segmentation units on the original multi-source document; based on the representative scores, determining the word segmentation units with the representative scores higher than a first score threshold as candidate keywords; determining importance degree scores of the candidate keywords based on the representative scores of the candidate keywords; based on the importance score, determining the candidate keyword of which the importance score is higher than a second score threshold as a target keyword; and calling a pre-training language model, and based on the target keyword, generating a question and answer pair corresponding to the target keyword to obtain a synthetic data set of the target field. The technical problem that the data coverage rate and the field correlation of the generated synthetic data set are low in the prior art is solved, and the technical effect of improving the data coverage rate and the field correlation of the generated synthetic data set is achieved.

A remote sensing image and ground surveying and mapping fused ecological protection area planning system specifically relates to the field of image processing, and comprises a multi-source data acquisition and fusion module for acquiring multi-source data of an ecological protection area through satellite remote sensing, aerial remote sensing and ground surveying and mapping technologies, and an ecological evaluation parameter calculation module for calculating ecological evaluation parameters of the ecological protection area based on a fused ecological comprehensive data set. Sequentially evaluating and determining an ecological vulnerability index, an ecological restoration priority index and an ecological protection effect evaluation index, generating a function partition, a restoration plan and a protection strategy optimization scheme of an ecological protection area by a planning decision module according to an evaluation result, periodically updating multi-source data by a dynamic monitoring and feedback module, and re-determining the evaluation result. According to the method, remote sensing images and ground surveying and mapping are fused, the ecological vulnerability, the restoration priority and the protection effect are evaluated through quantitative indexes, the ecological planning scientificity is improved, and reasonable resource allocation is achieved.

The invention is suitable for the field of education data analysis, and provides a learning cycle data acquisition and evaluation management method and system, and the method comprises the steps: automatically collecting and fusing the full life cycle data of students, and forming a comprehensive data set; dynamically constructing a student digital portrait based on the data set, and constructing a personal knowledge graph taking knowledge points as nodes; in combination with the portrait and the knowledge graph, analyzing relevance between browsing behaviors and course selection and practice, and evaluating a career behavior mode and track; calculating a quantitative career-academic fitness index, identifying an imbalance risk and generating an early warning; personalized resources are pushed to the student terminal based on early warning, early warning is sent to the teacher terminal, and the intervention effect is continuously tracked. The system correspondingly comprises five modules. According to the method, the problems of data islands, portrait statics, lack of fitness and the like in the prior art are solved, and the accuracy, systematicness and effectiveness of learning cycle evaluation and management are improved.

The invention is suitable for the technical field of air conditioners, and provides an air conditioner intelligent regulation and control method which comprises the steps that outdoor environment data, indoor environment data and personnel state data are collected in real time, all the data are fused, and a comprehensive data set is generated; constructing a dynamic temperature gradient field based on the comprehensive data set; dividing functional areas, and calculating an influence relation matrix in combination with the dynamic temperature gradient field; taking the influence relation matrix as a core constraint condition, and adopting a multi-objective optimization algorithm to obtain an air conditioner regulation and control parameter set; and on the basis of the air conditioner regulation and control parameter set, the output parameters of all the air conditioner single bodies are gradually adjusted through a switching mechanism, dynamic global optimization of the air conditioner system on multiple targets such as energy saving, comfort and equipment service life is achieved, and the problems that regulation and control are lagged, energy consumption is too high, the comfort degree is not uniform, and equipment loss is large are effectively solved.

This invention presents a computer-implemented method for answering polls and surveys using large language models (LLMs), a novel approach that leverages an LLM's ability to emulate a group of human responses for diverse data collection. The system involves training a single or multi-modal LLM on a comprehensive dataset comprising one or more categories of text, image, sound and other sensory input data, which can be continuously updated with current events and trends, to ensure accurate representation of human behaviors and opinions. Utilizing a user interface, the method includes receiving survey and poll questions, posing these questions to a unique instance of the trained LLM and receiving the answers, recording the question-answer sessions, and compiling the results for presentation. Additionally, the system prioritizes data privacy and integrates a feedback mechanism for continuous improvement, providing an efficient solution to answering surveys or polls in the digital age.

The invention relates to the technical field of market research data analysis, in particular to a market research data analysis method and system.The method comprises the steps that multi-source data is collected and cleaned through a multi-channel self-adaptive data integration technology, and a comprehensive data set is generated; constructing a graph network structure based on a graph neural network, updating node features through a message passing mechanism, generating market prediction data, and optimizing an investigation path based on a reinforcement learning algorithm; comparing the market feedback data with the prediction data through a feedback self-learning mechanism, and adjusting an investigation strategy to obtain an optimized investigation path; the performance of the market under extreme conditions is simulated through Monte Carlo simulation, the abnormal behaviors of the market are identified through dynamic outlier detection, a coping strategy is generated, and an investigation path is adjusted. The method effectively improves the accuracy of investigation data and the allocation efficiency of investigation resources, and adapts to a complex market environment.

The invention relates to the technical field of injection mold monitoring, and discloses an injection mold cavity state monitoring method, system, equipment and medium, the injection mold cavity state monitoring method comprises the following steps: multiple sensors collect mold state data in real time, and the mold state data are preprocessed and fused to generate a comprehensive data set; training a machine learning model based on historical samples, and updating model parameters in real time through an incremental learning algorithm; analyzing current data by adopting dynamic fuzzy logic reasoning, and outputting a state evaluation result; real-time data and results are uploaded to the cloud for deep analysis and distributed storage; a user feedback mechanism is integrated, and model parameters and a fuzzy inference rule base are optimized. According to the method, model parameters are updated in real time through an incremental learning algorithm, a multi-parameter nonlinear coupling relation is analyzed in combination with dynamic fuzzy logic reasoning, and based on cloud collaboration and a user feedback closed-loop mechanism, self-adaptive monitoring and continuous optimization of the injection mold state are achieved, and the anomaly detection precision and the system robustness are improved.

The invention belongs to the technical field of seismic exploration and artificial intelligence, and relates to a physical perception collaborative fusion-based full waveform inversion neural network construction method, which comprises the steps of required model data construction, full waveform inversion neural network construction, loss function construction, network pre-training, and network fine tuning and application. Physical feature perception and collaborative fusion are carried out through a U-Net network and an adaptive residual learning module, so that the problem that traditional full-waveform inversion depends on synthetic data training and is not matched with an actually measured data field is solved. The designed pre-training strategy not only can get rid of dependence on a large-scale synthetic data set, but also can effectively relieve the domain offset problem through fine adjustment of measured data, thereby improving the generalization ability of the model. And the adaptive residual learning module enables the neural network to adaptively and dynamically optimize feature expression, and improves inversion precision through collaborative fusion of a physical perception mechanism. According to the method, the physical consistency and imaging reliability of speed model prediction can be remarkably improved while the labor cost is reduced.