172results about How to "Reduce data dimensionality" patented technology

The invention discloses an SCL field safety accident analysis and early warning method and system, a medium and a program product, and the method comprises the steps: carrying out the distributed cleaning and feature extraction of multi-source heterogeneous monitoring data, and obtaining a multi-dimensional time sequence feature flow; constructing a dynamic association network of a security state; identifying a key influence factor chain, and establishing a semantic analysis model; constructing a safety threshold system; evaluating the multi-dimensional time sequence characteristic flow by using a safety threshold system; when the multi-dimensional time sequence characteristic flow triggers early warning, performing tracing analysis by using a semantic analysis model to obtain a root event triggering the early warning, and simulating the development trend of the root event based on a preset evolution model; determining a predicted risk range and a risk type according to the tracing cause analysis result and the accident development trend; and the corresponding accident early warning scheme is matched in the preset database according to the risk range and the risk type, and the accident early warning scheme is sent to the monitoring terminal, so that the accuracy of judgment and classified early warning of different types of safety accidents is improved.

The invention discloses a power equipment fault prediction method based on support vector regression, and aims to improve the prediction accuracy. The method comprises the following steps: firstly, collecting multi-dimensional operation parameter data of power equipment, and constructing an original monitoring data set; a data set is segmented through a sliding time window, wavelet transform is used for noise reduction, and then a normalization method is applied to generate a standardized data set. Time domain and frequency domain features are further extracted, a feature vector space is constructed, an optimal feature subset is screened through a mutual information method, and a training data set is generated; performing preliminary training on a support vector regression model by using the training data set to generate candidate support vectors, and screening a support vector set with the maximum contribution through an optimization algorithm; a grid search method is adopted to adjust kernel function parameters and penalty factors, and a support vector set is further optimized. And evaluating the performance of the model through cross validation, applying the optimized support vector regression model to power equipment fault prediction, and generating a fault occurrence probability prediction value.

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention relates to the technical field of intelligent scene recognition, and discloses an intelligent scene recognition method and equipment for an intelligent interaction control unit, and the method comprises the steps: carrying out the collection and preprocessing of an operation sequence, video content and audio data of the intelligent interaction control unit in a digital set-top box, and obtaining a standard multi-mode data set; inputting the standard multi-modal data set into a multi-branch deep neural network model, and extracting time sequence, visual and audio features through parallel processing to obtain a fusion scene feature vector; carrying out dimensionality reduction and semantic mapping on the fused scene feature vector to obtain a compressed semantic feature vector; performing similarity calculation on the compressed semantic feature vector and a predefined scene query vector to obtain a scene matching score; and based on the scene matching score, integrating the output of the plurality of sub-classifiers to obtain a target scene classification result, and the method can be efficiently operated on the digital set top box with limited resources to realize real-time scene recognition.

The invention provides an anomaly detection and response method and system based on intelligent perception driving, and is particularly applied to cloud service information security monitoring. The method comprises the following steps: acquiring multi-type data in real time and fusing the multi-type data through Kalman filtering and weighted average; constructing a self-encoding network to carry out feature extraction and dimension reduction, detecting abnormity through a reconstruction error and setting a dynamic threshold value; a reinforcement learning optimization algorithm is introduced, a dynamic response strategy is designed, and a reward mechanism is defined, so that the response effect is improved; designing an automatic response process, including alarm, isolation and repair measures; migrating the optimized self-encoding network knowledge to a new scene by adopting a transfer learning technology; a cloud-edge collaborative architecture is constructed, and data are analyzed in a centralized manner and synchronized in real time. According to the method, the real-time performance and accuracy of information security monitoring are effectively improved, potential threats can be quickly responded, the security risk is reduced, and the method is suitable for various information security monitoring scenes.

The invention belongs to the field of mass spectrum imaging, and particularly provides an image segmentation method for a mass spectrum imaging image of an ESI source, and the method comprises the steps: (S1) dividing the mass spectrum imaging image into a plurality of mass spectrum imaging channels according to a mass-to-charge ratio; (S2) selecting a mass spectrum imaging channel where target ions are located, and preprocessing each pixel; (S3) carrying out preliminary segmentation on the spectrogram data; (S4) performing interval division on the mass spectrum data of the channel where the target ions of each pixel are located, calculating the lower area of each interval, selecting the central mass-to-charge ratio of each interval as the abscissa of the intensity value of each interval, and setting the intensity value of the interval with the ratio of the central mass-to-charge ratio to the highest intensity value lower than a set value as 0; (S5) analyzing, training and optimizing the transformed mass spectrum data; and (S6) the optimal combination model obtained in the step (S5) is used for classification of other pixel points of the mass spectrum imaging image, and an image segmentation result is obtained. The method has the advantages of accurate segmentation and the like, and is applied to mass spectrum imaging of the ESI source.

The invention relates to the technical field of data analysis, and discloses a laboratory data analysis method and system based on a machine learning technology. The method comprises the following steps: firstly, introducing an adaptive threshold, and identifying and deleting abnormal data in original laboratory data by using an improved isolated forest algorithm to obtain a processed laboratory data set; secondly, improving the generative adversarial network to obtain an improved generative adversarial network filling model, and performing data filling on the processed laboratory data set for multiple times to obtain a processed laboratory data set; according to errors of experimental result data, an improved black-wing optimization algorithm is used for searching for the minimum error, and optimization of laboratory data is achieved; and finally, establishing a laboratory database, performing data storage on the processed laboratory data set, and completing laboratory data analysis. By analyzing and processing the laboratory data, the purpose of analyzing the laboratory data is achieved, and the method is accurate and objective.

The invention relates to an enterprise park meteorological fire risk assessment method based on an adaptive random forest model, and the method comprises the steps: building a risk assessment model and a visual early warning platform, enabling the risk assessment model to collect alarm data, meteorological data, enterprise data and park division data, and forming a risk assessment data set, and integrating internal characteristics and external data of the park into a data set, inputting new data into the self-adaptive random forest model by means of a real-time data flow technology, updating and evaluating a fire risk index value of the park in real time, and optimizing a model decision tree to automatically adjust a risk evaluation model and adapt to changes of environment and operation. Accurate early warning information is provided for a visual early warning platform, the risk level of the park is displayed in real time, and a risk alarm is given out in time.

The invention provides a mixed precursor chemical gas identification method based on feature construction, and relates to the technical field of mixed gas identification, and the method comprises the steps: determining a division criterion of a transient response stage and a steady-state response stage through a working mode of fixed temperature modulation as an inverted step wave; and taking the average value in the steady-state response stage as the characteristic of the structure, establishing a sample library of the structure characteristic and the mixed gas concentration, and fitting the relationship between the structure characteristic and the mixed gas concentration by using a regression model with a simple structure, so that the function of identifying the mixed precursor chemical gas by using the simple model is realized. The current situation that the model for identifying the mixed precursor chemical gas is more complex and the algorithm scale is more huge is changed, and the working efficiency of mixed gas detection is improved.

The invention relates to the technical field of tumor patient management, in particular to a tumor patient hospitalization full-period management system, which acquires and manages personal information, physiological data and treatment data of tumor patients through a data management module and ensures the real-time performance and integrity of the data. The data processing module improves the accuracy of the data by supplementing missing values and screening out abnormal values, optimizes the calculation efficiency through dimension reduction processing, retains main information features, and provides high-quality data input for intelligent prediction. The intelligent prediction module is based on a neural network algorithm, the future physiological parameter change trend of the patient is accurately predicted, the physiological anomaly occurrence probability is calculated, the prediction accuracy and timeliness are remarkably improved, and medical staff are helped to recognize potential risks in advance and provide personalized treatment. And the full-cycle visualization module displays patient data through a timeline and generates a risk object attention set, so that key monitoring of high-risk patients is realized, and the safety management level during hospitalization is enhanced.

The invention relates to an airport aid-to-navigation light inspection method based on an unmanned aerial vehicle technology, and relates to the technical field of aid-to-navigation light inspection, and the method comprises the following steps: S1, setting standard parameters; s2, the unmanned aerial vehicle executes a flight task, and images are collected from multiple angles; s3, obtaining the mass center of the navigation aid lamp; s4, calculating actual geographic coordinates of the navigation aid lamp; s5, comparing the position coordinate information of the navigation aid lamp; s6, brightness attenuation detection of the navigation aid lamp; and S7, carrying out statistical feedback, and carrying out the step S2. The airport navigational light inspection method has the effects of reducing and improving the airport navigational light inspection cost and improving the airport navigational light inspection efficiency.

The invention relates to a vehicle track and traffic flow prediction method based on checkpoint data, and belongs to the field of intelligent traffic, the checkpoint data is adopted and combined with GCN, GRU and KAN models to carry out vehicle track prediction, and the predicted vehicle tracks are superposed to obtain a flow statistical result of each checkpoint. Therefore, the purposes of accurately reflecting the real traffic condition, enhancing the road traffic safety and relieving the traffic jam problem are achieved. Comprising the steps of (1) definition, (2) data preprocessing, (3) checkpoint data information extraction, (4) vehicle track construction and division, (5) selection of vehicles meeting conditions, (6) vehicle track prediction, (7) traffic flow prediction, and superposition of the predicted vehicle tracks to obtain a traffic flow prediction value of each checkpoint in the future.

The invention belongs to the technical field of network security, and discloses an Internet of Things attack detection method based on deep learning, which eliminates redundant features through feature selection, enhances inter-class difference and intra-class similarity of selected feature subsets, solves the problem of class imbalance in an attack detection data set by utilizing EQLv2, and improves the attack detection efficiency. And meanwhile, constructing a space-time model to extract space-time information of the sample. According to the method, multi-classification performance evaluation is carried out on an Internet of Things attack detection reference data set NSL-KDD and a CIC-IDS-2017, the advantages of the method are verified from five indexes including the accuracy rate, the precision rate, the recall rate, the F1 score and the false alarm rate, and the effectiveness and feasibility of the method are reflected when the method is compared with a plurality of methods.

The invention relates to an intelligent industrial data clustering and grouping method, system and device and a medium, and the method comprises the steps: carrying out the standardization preprocessing of collected original industrial data, obtaining standard industrial data, and constructing a high-dimensional feature space according to the standard industrial data; performing industrial feature extraction and dimension reduction on the high-dimensional feature space to obtain a low-dimensional feature vector; performing preliminary grouping on the low-dimensional feature vectors according to a preset clustering algorithm to obtain a first clustering result; performing secondary adjustment on the first clustering result to obtain a second clustering result; and performing industrial scene simulation verification on the second clustering result to obtain a verification result, and performing optimization adjustment on the second clustering result according to the verification result to obtain a target clustering combination. According to the method, a more reasonable clustering result can be obtained, and the accuracy and applicability of the clustering effect are improved.

The invention discloses a deep learning-based pipeline structure stress proxy model construction method, which belongs to the field of engineering mechanics, energy transmission and artificial intelligence, and comprises the following steps of: firstly, establishing a numerical model of a pipeline, simulating different environmental conditions by applying different loads, and solving to generate a large amount of simulation data; then, feature unit extraction is carried out on the simulation data so as to compress feature dimensions; then, a deep learning initial proxy model of pipeline stress is constructed, and initial training is carried out through finite element simulation data; and finally, transfer learning is carried out based on the initial agent model, the transfer learning is divided into two stages, enhanced actual measurement sample data is used for training in the pre-training stage, and accurate actual measurement samples are used for further training and fine adjustment in the strengthening stage, so that the generalization ability of the model is improved. According to the method, the stress distribution can be rapidly and accurately solved by constructing the stress agent model, the generalization ability is high, and the problem that a traditional physical modeling method is insufficient in real-time performance and precision in stress monitoring and prediction can be solved.

The invention relates to the technical field of data processing, in particular to a state prediction-oriented full-solid waste concrete detection data processing method, which adopts a generative adversarial network algorithm based on adversarial enhancement to carry out data expansion, generates a new sample with trueness, and solves the problem of poor model generalization ability caused by insufficient training data. A neural network algorithm of dynamic group evolution optimization is adopted for feature extraction, the problems of gradient disappearance, explosion and the like of a traditional neural network are effectively solved, accurate feature capture of high-dimensional data is achieved, and the model generalization ability and the training efficiency are improved; feature dimension reduction is carried out through the self-encoding neural network based on feature refinement, important data information is effectively reserved, the data dimension is reduced, and the subsequent classifier processing efficiency is improved; and a support vector machine algorithm of quantum phase space mapping is adopted to optimize kernel function parameters, so that the model is more flexible when processing nonlinear features, and the classification precision and the adaptability of decision boundaries are improved.

The invention discloses an efficient intelligent data compression and feature extraction method and system. The method comprises the following steps: S1, data preprocessing: performing data cleaning, denoising and standardization processing on to-be-processed data; s2, data compression and storage: determining a compression algorithm according to the type of the data, and uploading a compressed data file to a database; s3, feature extraction: deploying a machine learning model, and extracting representative features from the compressed data; and S4, data post-processing: further processing and optimizing the extracted features. According to the method, the data compression technology and the feature extraction technology are combined, the most representative information is extracted from the original data, meanwhile, the data storage and transmission cost is reduced, key features in the data are automatically recognized and reserved through an intelligent algorithm, and therefore efficient data compression and feature extraction are achieved.

The invention provides a hyperspectrum-based tea tree canopy leaf nitrogen estimation method, which comprises the following steps of: firstly, smoothing original spectral data of a tea tree canopy by using a Savitzky-Golay method so as to reduce random noise and improve a signal-to-noise ratio; then, the original spectrum is converted into 600 wave bands at the interval of 1 nm through spline interpolation, so that the continuity and convenience of calculation are ensured; the method comprises the following steps of: firstly, carrying out first-order derivative processing on a spectrum, then carrying out second-order derivative processing on the processed spectrum, calculating a normalized difference spectrum index (NDSI) by using a derivative spectrum, calculating a Pearson's correlation coefficient (PCC) between the NDSI and a measured nitrogen element, carrying out principal component analysis (PCA) on the highest 20 indexes of the correlation coefficient, and finally constructing a multiple linear regression model by using the principal components with the first 10 PCA scores as parameters. The method is used for predicting the nitrogen content (LNC) of tea tree canopy leaves. Prediction experiments of a plurality of tea gardens show that the model shows a good prediction effect which is close to the actual nitrogen content, and the generalization performance is good.

The invention discloses an industrial equipment operation state analysis method and system based on machine learning, and relates to the technical field of equipment detection. The method comprises the following steps: obtaining a plurality of types of detection items and abnormal behaviors, respectively combining the detection items and the abnormal behaviors, respectively inputting corresponding data in historical detection records and historical abnormal records into different deep networks for training, and screening out a plurality of detection items sensitive to each abnormal behavior; obtaining the type of a to-be-monitored abnormal behavior of the target; acquiring parameters of the target to-be-monitored detection item; and according to the type of the target to-be-monitored abnormal behavior and the plurality of detection items sensitive to each abnormal behavior, identifying and analyzing parameters of the target to-be-monitored detection items to obtain state parameters of the target to-be-monitored abnormal behavior of the equipment. According to the method, the data dimension of the detection item in the analysis process is reduced, so that the equipment abnormal behavior analysis speed is increased.

The invention relates to the technical field of computer data management, and discloses a data management method and system capable of reducing computing power occupancy, and the method comprises the steps: S1, based on data distribution analysis, employing a sparse representation algorithm and a principal component analysis method, carrying out the operation elimination of the redundancy of high-dimensional data features through setting an L1 regularization constraint condition, and obtaining an L1 regularization constraint condition; decomposing the data correlation matrix in combination with a matrix operation mode, extracting feature vectors with top-ranked contribution values, and converting the feature vectors into a sparse low-dimensional matrix; and generating a sparse low-dimensional data matrix. Through combination of sparse representation and principal component analysis, L1 regularization is creatively applied to perform feature screening on high-dimensional data, redundant data items are removed, and principal component features with high correlation are extracted, so that the data dimension is effectively reduced, consumption of computing resources is reduced, and meanwhile, main information features of the data are reserved; the efficiency and accuracy of subsequent processing are improved, and distributed scheduling of tasks and efficient utilization of equipment computing power are realized.

The invention provides a data cleaning method and device based on time sequence similarity, a medium and equipment. The method comprises the steps that firstly, a to-be-cleaned time sequence data set and multiple historical time sequence curves are acquired; discretizing the to-be-cleaned time sequence data based on a symbol clustering method to obtain a character sequence data set; performing similarity measurement on the character sequence data set and a historical character sequence data set corresponding to the historical time sequence curve by using an Euclidean distance algorithm, and determining a target time sequence curve; and finally, comparing the target time sequence curve with to-be-cleaned time sequence data in the to-be-cleaned time sequence data set, and determining abnormal data points. And correcting the time sequence data corresponding to the abnormal data points based on the target time sequence curve to obtain a target time sequence data set. According to the invention, through dimension reduction, similarity measurement optimization and accurate identification of abnormal data, missing data is reasonably filled up, and the accuracy and efficiency of power load data cleaning are improved.

The invention provides an industrial control protocol abnormal behavior analysis and detection method and device, a medium and a program product. The method comprises the steps of network flow collection, network flow analysis, data preprocessing, behavior baseline model construction, real-time flow access and detection and dynamic optimization of a baseline model. According to the invention, the process service behavior baseline model can be established for specific process service communication links and industrial protocols, and automatic construction and dynamic adjustment of the process service baseline model can be realized; the problems that in existing industrial control protocol abnormal behavior analysis and detection, a model is not established for a specific process service communication link, when the service changes, real-time dynamic adjustment cannot be achieved, and the abnormal detection rate is low are solved. The abnormal condition of each device in the industrial network traffic can be rapidly detected by performing abnormal behavior analysis detection on the industrial control protocol transmission traffic by using the process business behavior baseline model.

The invention relates to the technical field of energy storage, and discloses an energy storage power station abnormity alarm method, device and equipment based on multi-modal data, and the method comprises the steps: obtaining the original sampling data of an energy storage power station, and carrying out the sampling frequency alignment processing of the original sampling data based on the resampling frequency, so as to obtain the multi-modal data; performing feature extraction and fusion processing on the multi-modal data to obtain a comprehensive feature vector, and performing dimension reduction processing on the comprehensive feature vector to obtain a key feature vector; and inputting the key feature vector into a pre-trained anomaly monitoring model, and generating an alarm signal under the condition that the energy storage power station is monitored to run abnormally according to an output result of the anomaly monitoring model. The method has the beneficial effects that the multi-modal data of different data sources are kept consistent in the sampling time dimension, the availability of the multi-modal data is improved, and the accuracy of subsequent feature extraction and analysis is also ensured.

The invention relates to the technical field of intelligent manufacturing, in particular to a fault prediction method and system for factory manufacturing equipment based on data driving, and the method comprises the steps: collecting vibration signal data and tool wear data, carrying out the preprocessing of the collected data, carrying out the time-frequency domain feature extraction of the collected vibration signal data, and extracting the wear features of a tool; fusing the extracted tool wear features, vibration signal data features and cutting parameter features to form a high-dimensional feature vector, selecting first k feature vectors through principal component analysis (PCA), and reducing the dimension of the high-dimensional feature vector; constructing and training a numerical control machine tool fault prediction model based on a support vector machine SVM model; and performing fault prediction on the numerical control machine tool, namely factory manufacturing equipment. Potential faults of the numerical control machine tool can be predicted, maintenance and overhaul can be arranged in advance, the equipment reliability is improved, the accidental shutdown time caused by the faults is shortened, and the production continuity is guaranteed.

The invention discloses an intelligent environment monitoring method, system and device and a readable storage medium, and the method comprises the steps: obtaining the current environment data of a versatile laboratory, carrying out the intelligent data processing of the current environment data, obtaining the accurate current environment data, carrying out the intelligent analysis of the accurate current environment data, and obtaining a current analysis result; acquiring historical environment data of the versatile laboratory, performing optimization processing on the historical environment data, and analyzing the optimized historical environment data and the accurate current environment data by adopting a versatile laboratory environment prediction model to obtain a predictive analysis result; based on the current analysis result and the predictive analysis result, the test environment state of the versatile laboratory is evaluated, and environment monitoring of the versatile laboratory is achieved. The problems that the current environment monitoring technology cannot capture the subtle change of the environment in real time, the prediction capability on the future environment condition change is lacked, and the accuracy of the generated environment management decision is low are solved.

The invention discloses a fault prediction method and device and a computer readable storage medium, and relates to the technical field of data processing, and the fault prediction method comprises the steps: obtaining the operation data of each to-be-tested device based on a sensor; determining features of the operation data by calculating quantiles of the operation data, the features including time features and event features; taking the features as input sequence data of a long short-term memory network, processing each input sequence data through the long short-term memory network according to the sequence of the input sequence data, and calculating the hidden state of each time step as an output sequence; and determining a predicted fault probability and / or a predicted fault period according to the output sequence. The technical effect of improving the accuracy of fault prediction is achieved.

The invention discloses a log compression method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining original log data, carrying out the preprocessing of the original log data, and analyzing the importance degree of each field in the log data through employing a feature selection algorithm; according to the importance degree, key log features in the log data are extracted, and dimension reduction processing is conducted on the extracted log features through a dimension reduction algorithm to obtain compressed log data; and storing the compressed log data in a database in a binary form. According to the technical scheme, the data dimension in the log compression process can be remarkably reduced, the storage capacity occupied by the compressed data is reduced, meanwhile, important information in the log data is reserved, and therefore the log analysis efficiency and the troubleshooting efficiency are improved.

The invention discloses a fault diagnosis method and device for a large die forging press, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal sensor data of the large die forging press; the data of the multi-mode sensor are preprocessed; inputting the preprocessed multi-modal sensor data into a multi-modal attention convolutional neural network model for feature extraction to obtain multi-modal features; carrying out fusion processing on the multi-modal features; and inputting the fused multi-modal features into a classifier for classification to obtain a fault diagnosis result.

The invention discloses a time series prediction method of a multi-time scale channel mixed attention mechanism, and relates to the technical field of time series prediction and deep learning. Specifically, weighted processing is carried out on extracted features of time sequence data through a channel mixed attention mechanism. The mechanism can dynamically adjust the weight of the feature according to the importance of the feature, and ensures that the key feature plays a greater role in prediction. Inputting the time sequence data into a multi-time scale convolutional neural network to obtain feature vectors under different time scales; and fusing the weighted features to obtain comprehensive multi-time scale features, and completing the training process of the time sequence prediction model. According to the method, the accuracy and adaptability of time sequence prediction are improved, and complex modes and trends in time sequence data can be captured more accurately. Through the method, the method can play a role in various application scenes needing high-precision prediction.

The invention provides an audio coding method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: performing first-level feature extraction processing on an audio signal to obtain a first-level signal feature; for the ith hierarchy in the N hierarchies, splicing processing is carried out on the audio signals and the signal features of the (i-1) th hierarchy to obtain spliced features, feature extraction processing of the ith hierarchy is carried out on the spliced features to obtain the signal features of the ith hierarchy, N and i are integers larger than 1, and i is smaller than or equal to N; the i is traversed, the signal feature of each level in the N levels is obtained, and the data dimension of the signal feature is smaller than the data dimension of the audio signal; respectively encoding the signal feature of the first level and the signal feature of each level in the N levels to obtain a code stream of the audio signal at each level; according to the invention, the audio coding efficiency can be improved and the audio coding quality can be ensured.