1385 results about "Data dimension" patented technology

The invention relates to the technical field of data processing, and discloses a multi-dimensional financial pressure testing and financial early warning method and system. The method comprises the following steps: forming a financial situation awareness data set through multi-dimensional data acquisition integration and standardization processing; adaptive weight distribution and data dimension reduction are carried out on the five dimension indexes, and a risk measurement standard spectrum is output; a third-level pressure scene is established according to the standard spectrum, a risk conduction path is analyzed, and a financial pressure fluctuation spectrum is generated; a deep learning early warning model is constructed based on the fluctuation pedigree, an early warning threshold value is determined, and a risk pulsation graph is formed; constructing a multi-level early warning system based on the pulsating diagram, and establishing a financial early warning intelligent chain; and matching historical cases based on the early warning intelligent chain, designing a prevention and control strategy, and constructing a risk solution scheme library. According to the invention, by constructing a multi-level risk assessment system, accurate identification, dynamic monitoring and intelligent disposal of financial risks are realized, and the accuracy and timeliness of risk early warning are improved.

The invention relates to the field of internet attack detection, in particular to a data security event real-time monitoring method and system, and the method comprises data collection, multi-modal fusion, threat detection, causal reasoning, dynamic response and feedback optimization. Compared with the traditional security analysis which depends on isolated data dimension or simple rule association, is difficult to capture a cross-data-source complex attack mode, and is faced with the limitations of low calculation efficiency, slow link traceability, storage access bottleneck and the like in mass data association analysis, the scheme integrates multi-source heterogeneous data into a dynamic association network through graph structure modeling, so that the security analysis efficiency is improved. Hidden association and behavior patterns among users, equipment and IPs are deeply mined by utilizing a GNN framework, the suspicious degree among entities can be accurately quantified, and hidden attack chains can be identified; and meanwhile, a hybrid storage architecture and a query optimization technology are adopted, so that a security analyst can backtrack a complex attack path in a second level while breaking through the bottleneck of large-scale graph data access performance, and the threat hunting efficiency and the high-level attack traceability are remarkably improved.

The invention discloses a power distribution area anomaly positioning method and system based on multi-modal dynamic feature analysis, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining area multi-source fusion data according to a sliding time window, and determining a hidden danger path corresponding to an instantaneous drop event in combination with a line topology; s2, performing state fluctuation analysis according to the real-time state data and the historical state data of the monitoring point to determine a state fluctuation factor, and constructing an impedance prediction model according to the path disturbance data to determine an environment disturbance factor; s3, performing activation processing on the hidden danger path according to the state fluctuation factor and the environment disturbance factor to obtain a transformer area degradation graph; and S4, dynamically cutting the transformer area degradation graph through the hidden danger risk values corresponding to the monitoring points to determine degradation blocks, and determining corresponding early warning measures according to the degradation degrees corresponding to the degradation blocks. According to the scheme, the limitation of single data dimension analysis and static evaluation is overcome, and the monitoring point hidden danger recognition accuracy and intelligent processing efficiency in a complex scene are remarkably improved.

The invention discloses a network traceability data processing method, system and device and a medium, and the method comprises the steps: collecting network data, carrying out the preprocessing of the network data, and generating the preprocessed network data; extracting behavior characteristics from the preprocessed network data to obtain network behavior characteristics; constructing an attack graph according to the obtained network behavior characteristics, and forming a dynamic graph representing an attack path; and carrying out traceability decision on the dynamic atlas, and positioning an attack source and an attack path through the traceability decision. Through the technical means of six-dimensional feature vector construction, quantum derivation genetic algorithm optimization detection and attack atlas construction through the tense graph convolutional network, the problems of data dimension missing, correlation analysis lagging and insufficient traceability precision are effectively solved.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The invention relates to the technical field of textile machinery fault detection, and discloses a spinning machine fault detection method and system based on deep learning, and the method comprises the steps: collecting vibration, temperature and current signals through a plurality of sensors, constructing a data stream after principal component analysis and dimension reduction, employing a variational auto-encoder to reconstruct an error positioning abnormal data segment, and obtaining a spinning machine fault detection result. The method comprises the following steps: extracting local features of a multi-source signal in combination with a convolutional neural network, dynamically distributing feature weights by using an attention mechanism, screening time sequence key features through a forgetting gate, introducing an LSTM network to model a feature sequence dynamic relationship, completing fault category mapping based on a support vector machine, and triggering LSTM secondary classification calibration for samples with insufficient confidence. A closed-loop technical system of data dimension reduction, anomaly detection, feature optimization, time sequence modeling and classification verification is formed, and efficient analysis and accurate recognition of multi-modal fault features under complex working conditions are achieved.

The invention relates to the technical field of power equipment state monitoring, in particular to a power equipment operation state evaluation and prediction method based on big data, which comprises a multi-source data acquisition module, a feature engineering processing module, an intelligent evaluation and prediction module and a decision support output module. By setting a multi-source sensing end, when the operation state of the power equipment is evaluated, the definiteness of state evaluation of different types of equipment is ensured by formulating multi-modal data fusion standard parameters and setting different state sensing weights for different types of equipment; and meanwhile, real-time state sensing is performed by fusing electrical parameters, mechanical vibration, thermal distribution and environmental stress data, so that whether a sensing blind area problem caused by a single data dimension occurs in an equipment evaluation process or not can be detected in real time, the comprehensiveness and accuracy of equipment operation state evaluation are ensured, and state sensing fragmentation errors are further reduced.

The invention relates to the technical field of industrial equipment state monitoring, and provides a servo cable bending stress prediction method based on multi-sensor fusion. Aiming at the problems of single data dimension, poor generalization ability of a machine learning model and insufficient real-time performance in the prior art, the method comprises the following steps of: firstly, acquiring and preprocessing a multi-source historical data set by deploying a multi-modal sensor; and then, constructing a neural network fusion layer to fuse the preprocessed data set, and generating comprehensive time-frequency domain fusion data. Thirdly, constructing a trainable deep neural network hybrid architecture, performing deep feature extraction on the fused data, and executing pattern correlation analysis to obtain a comprehensive correlation matrix; and finally, directly inputting new data acquired in real time into the prediction neural network, and performing time sequence prediction reasoning in combination with the comprehensive incidence matrix to obtain final stress prediction data. According to the method, the accuracy of predicting the bending stress of the servo cable is remarkably improved, and the adaptability and robustness of the system are enhanced.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention provides a Gaussian modeling method combined with laser scanning point cloud data processing, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: carrying out the registration of a plurality of point cloud data sets through employing a point cloud fusion algorithm, and obtaining a target point cloud data set; performing data precision, position deviation and noise influence analysis on the plurality of point cloud data sets, determining point cloud data precision, a position deviation coefficient and a noise influence coefficient, and comprehensively evaluating and determining point cloud acquisition precision; setting a data dimension reduction proportion according to the point cloud acquisition precision, and performing covariance matrix dimension reduction on the target point cloud data set; and selecting a predetermined kernel function, performing data fitting and Gaussian process regression modeling on the dimension reduction point cloud data set, and outputting a surface fitting model. According to the method, the technical problems that the requirements for storage and computing resources are too high and efficient three-dimensional modeling cannot be realized due to the fact that a large number of covariance matrixes need to be calculated in a traditional Gaussian modeling method can be solved; the dimensionality of data can be effectively reduced, and the efficiency of three-dimensional modeling is remarkably improved.

The invention relates to a spare part service life prediction method, device and equipment and a computer readable medium. The method comprises the steps of collecting multi-mode state data of a target spare part; verifying the historical consistency of the multi-modal state data; and under the condition that the historical consistency verification of the multi-modal state data is passed, inputting the multi-modal state data into a target residual life prediction model so as to predict a degradation track of the target spare part based on the multi-modal state data by using the target residual life prediction model, the target residual life prediction model is a neural network model obtained by training by taking a physics degradation mechanism of the spare part as priori knowledge; and determining the predicted remaining life of the target spare part based on the degradation trajectory. According to the method, the evaluation one-sidedness caused by insufficient single data dimension is avoided, the prediction credibility is improved through a data verification and physical mechanism constraint model, and the technical problem of low life prediction accuracy caused by spare part life counterfeiting is effectively solved.

The invention discloses a listed company operation risk early warning method based on multi-source auditing and text semantic fusion, and relates to the technical field of auditing, and the method comprises the steps: S1, crawling and converging multi-source heterogeneous data of listed company financial newspapers, auditing suggestions, supervision announcements, inquiry letters, news public opinions and market transactions; according to the method, unstructured texts are subjected to cleaning, blocking and semantic vectorization processing, each text segment is embedded into a high-dimensional semantic space, a vector index is established, a bottom-layer knowledge base of an RAG framework is formed, in the stage, it is ensured that the data structure is uniform, the source is traceable, standardized input is provided for subsequent semantic retrieval and modeling, and the reliability of the system is improved. S2, a query expression is constructed based on a target company, a time window and a risk topic, dense semantic retrieval and sparse BM25 retrieval methods are comprehensively used, a time decay and source credibility weighting mechanism is introduced, and the problems that a traditional method is single in data dimension and information is split are solved.

The invention belongs to the technical field of computer information processing, and relates to a scientific research performance information analysis and evaluation system and a processing method thereof.The scientific research performance information analysis and evaluation system comprises a data collection module, a weight distribution module, a real-time feedback module and an execution engine function module; weight distribution receives the first signal and analyzes a current strategic target priority identifier and a historical performance evaluation record contained in the first signal, a weight parameter space dynamically matched with a target priority is constructed through an online learning algorithm, and a second signal containing a real-time weight mapping relation is generated; real-time feedback is carried out, a multi-dimensional performance evaluation matrix is constructed based on the weight mapping relation in the second signal, and a third signal is generated; the execution engine receives the third signal, analyzes an abnormal positioning code of the third signal, and aggregates intervention records in a period to form a fourth signal; according to the method, the defects that traditional performance information analysis depends on static weight, the data dimension is single, and real-time dynamic adjustment is lacked can be overcome, and the analysis efficiency is improved.

The invention relates to the technical field of fruit detection, in particular to a blueberry acidity detection method based on hyperspectral imaging, which comprises the following steps: randomly selecting blueberry samples to be detected, and constructing a reference sample database; acquiring full-band hyperspectral image data of the to-be-detected blueberry sample by using hyperspectral imaging equipment; performing spectrum preprocessing on the hyperspectral image data; eliminating redundant information of redundant wave bands by combining a continuous projection algorithm to obtain a final characteristic wave band set, and extracting a spectral intensity matrix corresponding to the final characteristic wave band set from the hyperspectral image data based on the final characteristic wave band set; constructing a blueberry acidity detection model; and outputting the acidity predicted value of the blueberries. According to the method, the data dimension is greatly reduced, the sensitivity and modeling efficiency of the characteristic wave band are improved, and compared with a traditional destructive detection method, the method has the advantages of being high in speed, free of damage and suitable for large-scale sample detection.

The invention belongs to the technical field of machine room safety monitoring, and discloses an image review method fusing semantic understanding and visual recognition, which comprises the following steps: calculating visual / semantic feature dynamic credibility in real time through an exponential weighted moving average algorithm in combination with environment interference and equipment state parameters; resNet50 is adopted to extract visual features in global and local branches, and a BERT model is adopted to encode semantic features; correcting the feature correlation degree according to the scene, calculating a dynamic weight, carrying out heterogeneous calibration through an attention mechanism, and calling priority rules such as'physical security features are higher than behavior features' for arbitration during conflicts; the method is advantaged in that low-credibility feature interference fusion is avoided, abnormity identification accuracy in a complex scene of a machine room is improved, multi-modal data cooperation demands are adapted, scene labels are marked based on time, work orders and historical data dimensions, an exclusive sub-model is constructed for a high-density scene through DBSCAN density clustering, and low-frequency scene parameters are migrated to a similar model.

The invention discloses a tumor chemoradiotherapy reaction automatic prediction system, and particularly relates to the technical field of medical automation, the tumor chemoradiotherapy reaction automatic prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction and selection module, a model training module, a prediction analysis module, an early warning module and a feedback module; the multi-source data acquisition module comprehensively gathers patient information, the quality is improved through the preprocessing module, a foundation is built for the feature extraction and selection module to accurately screen key features, the data dimension is greatly reduced, and the model training efficiency is remarkably improved; the model training module applies a machine learning algorithm to deeply mine a data complex relationship, so that the prediction is more accurate and reliable; on the basis, the prediction analysis module outputs detailed chemoradiotherapy reaction prediction and provides confidence evaluation to help doctors to make decisions and select; and the early warning module is responsible for predicting, warning abnormity in time and helping doctors to plan intervention in advance.

The invention belongs to the technical field of crucible coating optimization, and relates to an intelligent coating optimization method for quartz crucible surface treatment. According to the method, coating material data, surface treatment environment data, coating process data and performance test data are collected, the performance test data serve as a real result label of a training set, denoising, missing value processing and normalization are carried out on the collected data, and through a Pearson's correlation coefficient and a principal component analysis method, the coating material data, the surface treatment environment data, the coating process data and the performance test data are obtained. According to the method, a group of key features are screened out, data dimensions are reduced, main information influencing the coating performance is reserved, a deep neural network model is trained, the coating performance is predicted through the model, and coating process parameters are optimized by adopting a particle swarm optimization algorithm based on the predicted coating performance, so that the coating performance is optimized. According to the method, the optimal parameter combination can be effectively searched, blindness and high cost of a traditional optimization method are avoided, a closed-loop intelligent coating optimization process is formed, and the optimization efficiency and performance of the coating process are remarkably improved.

The invention discloses a road de-duplication method based on GPS positioning and comprehensive consideration of disease types and disease sizes, and the method comprises the specific steps: S1, data collection and processing: collecting road image data through an edge detection device, carrying out the road disease recognition of the road image data, obtaining disease data, and processing and storing the disease data; and S2, disease duplicate removal: matching the positioning information, the disease type and the size information of the processed disease data, if the positioning information, the disease type and the size information are all within a preset matching threshold, determining that the disease data are duplicated items, removing the duplicated items, and then storing the disease data after duplicate removal. According to the method, duplicate removal is performed by integrating multiple data dimensions, so that the accuracy of road disease data can be remarkably improved, and errors and redundant information are reduced; the intelligent and automatic level of road maintenance is improved, technical support is provided for sustainable development of road maintenance, and the intelligent road maintenance management system becomes an effective road maintenance management tool.

The invention discloses a power grid dynamic scheduling decision-making method and device based on multi-modal prediction, electronic equipment and a storage medium, and belongs to the field of power system regulation and control operation, and the method comprises the steps: obtaining internal state data and external working condition data of each target power grid device, and a future load change curve of a related power transmission and distribution line, and an equipment feature matrix is constructed through space-time alignment. And inputting the feature matrix into a multi-modal neural network, and outputting the health index, the remaining service life and the fault probability. When the equipment health index is lower than a threshold value, a multi-objective optimization model is constructed, a preventive scheduling strategy is generated, and scheduling is executed; and when the equipment fault probability exceeds a set threshold value, updating the power grid line weight based on load prediction, generating a topology reconstruction scheme of the minimum power failure range, and scheduling according to the topology reconstruction scheme. By implementing the method and the device, the problem that the long-term degradation trend and the short-term sudden risk of the equipment cannot be accurately predicted due to single data dimension in the prior art can be solved.

The invention relates to the technical field of intelligent assessment, and discloses a dam risk assessment method, device and equipment based on multi-source data fusion, and the method comprises the steps: determining a monitoring project type of dam multi-source monitoring data, building a risk assessment index system through selecting a key index of the monitoring project type, and obtaining a risk assessment result; the method comprises the steps of dividing a dam region by combining measuring point spatial distribution to obtain a dam partitioning result, processing single measuring point actual measurement data of the same region in the dam partitioning result through a dam risk assessment model to obtain a region basic probability distribution value, and further obtaining a risk assessment result. According to the method, the limitation of single data dimension is broken through by constructing a risk assessment index system, accurate mastering of risk heterogeneity of different regions is realized through region division, a dam risk assessment model combining a cloud model and an evidence theory is adopted, the problem of dimension unification of multi-source data is solved, the risk assessment comprehensiveness is ensured, and the risk assessment efficiency is improved. And the accuracy of risk assessment is also improved.

The invention relates to the technical field of cultural tourism guide, in particular to a marine culture intelligent guide system based on artificial intelligence and the Internet of Things, which integrates multi-source data acquisition, context semantic understanding, user portrait construction, personalized content recommendation and adaptive content presentation, and the system acquires user voice, position, facial image and environment information, so as to realize the intelligent navigation of the marine culture. Accurate data acquisition is realized; the system combines semantic understanding and user portraits, dynamically generates interest models, recommends personalized marine culture contents to users, ensures effective information transmission through a self-adaptive presentation module, optimizes resource allocation through a distributed processing module, guarantees stable operation of the system, constructs a user-environment-cultural relic three-dimensional perception network, improves the acquisition precision by 65%, and improves the real-time performance of the system. The data dimension is obviously expanded, a comprehensive data basis is provided for intelligent navigation, and efficient and personalized cultural tourism experience is achieved.

The invention relates to the technical field of man-machine interaction, and discloses an LED display screen interaction method and an LED display screen interaction system.The method comprises the following steps that user interaction data are collected through a multi-sensor module, and the user interaction data comprise visual data, touch data, infrared data and environment data; preprocessing the collected multi-modal user interaction data, including data noise reduction, feature extraction and unified coding; performing time sequence modeling on the multi-modal user interaction data based on the dynamic data fusion model to generate a fusion state; and identifying the user interaction intention based on the user interaction intention prediction model. Through the technical scheme of combining multi-modal data fusion and deep learning, all-around acquisition and comprehensive perception of user interaction information are realized by collecting visual, touch, infrared and environmental data of a user, and the defects of limited data dimension, poor environmental adaptability and insufficient interaction precision in single-modal interaction are overcome.

The invention discloses a large-amount consumption scene intelligent customer obtaining method, system and device based on AI multi-modal data and a medium, relates to the technical field of multi-modal data processing, and solves the technical problems of low customer obtaining efficiency, poor customer conversion quality and lagging credit risk management and control in a large-amount consumption scene. According to the technical scheme, the method is characterized in that a trans-modal feature fusion model based on a Transform model and a'large-amount consumption demand prediction-credit adaptation degree evaluation 'dual-prediction model are constructed by fusing four types of multi-modal data of client texts, images, behaviors and time sequences; the technical problems of single data dimension, disjunction of demand and qualification pre-judgment and insufficient strategy dynamics in traditional customer acquisition are solved, accurate identification of potential customers and pre-management and control of credit risks are realized, and customer acquisition efficiency, customer conversion quality and risk management and control capability of a large-amount consumption scene are improved.

The invention relates to the technical field of food and beverage, in particular to a food and beverage network sales trend prediction model construction system and method based on multi-source data fusion and deep learning. Comprising a data acquisition unit; a data processing unit; the model construction unit is used for constructing a deep learning prediction model, and an improved LSTM-Transform fusion algorithm is adopted to realize nonlinear mapping modeling of the food and beverage sales trend by integrating time sequence feature modeling and a global dependency relationship analysis technology; a model training verification unit; and a prediction output unit. According to the method, multi-source data such as network sales platform data, social media emotion texts, weather information and industry information are integrated, cross-correlation features such as time dimension features, text emotion features and price elasticity-weather influence are extracted in combination with a feature engineering technology, influence factors of food and beverage sales are comprehensively covered, and the sales quality is improved. The problem that a traditional scheme is single in data dimension is solved.

The invention discloses a memory writing method and device, a storage medium and a program product, and relates to the technical field of memory access, and the method comprises the following steps: determining a memory step length of an initial image tensor corresponding to a target image, determining a target vectorization width according to the memory step length and a memory data width of a target graphics processor, and determining a target data dimension of the target graphics processor, determining a target thread grid corresponding to the initial image tensor according to the target data dimension, performing image tensor slicing by using the target thread grid to obtain a target image tensor, and writing the target image tensor into a memory. According to the method, the memory layout (step length) of the input tensor can be dynamically analyzed, so that the optimal vectorization width is adaptively determined, the memory access efficiency is maximized, slicing is performed according to the data dimension when the target graphics processor outputs the data, the method has adaptability and high efficiency, the GPU video memory bandwidth is fully utilized, and the memory bandwidth utilization rate is improved.

The invention discloses a virtual power plant control method and device for resource optimization, and relates to the technical field of virtual power plant control. The method comprises the steps of collecting multi-dimensional monitoring data of a virtual power plant, inputting the multi-dimensional monitoring data into a virtual power plant state prediction model, and performing state prediction to obtain a virtual power plant state prediction result; inputting the virtual power plant state prediction result into a virtual power plant resource allocation optimization model, and generating a resource allocation scheme to obtain a virtual power plant resource allocation scheme; inputting the virtual power plant resource allocation scheme into a virtual power plant digital twinborn model, and performing digital twinborn simulation verification to obtain a corrected virtual power plant resource allocation scheme; and inputting the corrected virtual power plant resource allocation scheme into a virtual power plant cooperative control model, performing control instruction generation, obtaining a virtual power plant control instruction, and issuing the virtual power plant control instruction to an execution terminal. The problems of single data dimension, insufficient prediction precision, extensive resource allocation, lack of closed-loop verification and complex cooperative control in the prior art are solved.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention discloses a central air conditioner energy consumption big data intelligent analysis method based on data mining. The method comprises the following steps that historical data of central air conditioner energy consumption is collected; performing data preprocessing on historical data of the energy consumption data of the central air conditioner; establishing an analysis engine, namely establishing an energy consumption model and establishing a load prediction model; establishing a central air conditioner energy efficiency detection strategy model; establishing a fault prediction model; after energy consumption data of the central air conditioner are collected for data preprocessing, energy efficiency detection is conducted through the energy efficiency detection strategy model of the central air conditioner according to predicted energy consumption and load data output by an analysis engine, fault prediction is conducted through the fault prediction model, and finally the data are summarized to obtain an analysis report. Through data mining and intelligent analysis, the problem that in the prior art, data dimensions are complex is solved, and the problem of overfitting of the position where the central air conditioner is located due to building specificity is solved.

The invention provides an industrial production Internet of Things data anomaly detection method, medium and system, and belongs to the technical field of industrial production Internet of Things. Industrial equipment sensor data is collected and preprocessed to establish a multi-dimensional data set, and principal component analysis and a mutual information algorithm are used to construct a dimension reduction feature data set; a virtual sensor algorithm is utilized to make up for data missing to form an extended data set, a simulation statistical mechanical anomaly analysis model is established based on a statistical mechanical law to convert the microscopic state of massive high-dimensional sensor data into a macro thermodynamic parameter, and a statistical mechanical feature vector is calculated through a virtual particle ensemble simulation equipment operation state. A state evaluation model and a dynamic threshold function are adopted to identify an abnormal mode and perform grading marking, a feedback optimization mechanism is constructed to continuously improve the system performance, and the technical problem that a traditional algorithm has a curse of dimensionality and cannot perform effective anomaly detection due to extremely high data dimensionality of an industrial equipment sensor is solved.

The invention discloses an unmanned vehicle accident judgment and responsibility judgment system and method based on roadside perception, and the system comprises a roadside perception module which is used for obtaining target scene image data, physical environment information and point cloud data in real time, the accident identification and evaluation module is used for judging whether an accident exists in the target scene and classifying the accident type, and through multi-dimensional analysis of the accident, the problems that the accident judgment response is not timely and the data dimension is not comprehensive in the unmanned vehicle accident handling process are solved. Besides, the invention provides an accident escape system and an accident judgment system based on the accident judgment system, an escape strategy is formulated by combining a steering wheel control technology, and accident liability is judged, so that the problems that an unmanned driving strategy cannot reasonably avoid obstacles and accident liability processing is complex are solved, the claim settlement time is shortened, and the safety is improved. And a whole-process solution from accident perception, intelligent decision-making to responsibility judgment is provided for the unmanned vehicle.