329 results about "Data dimensionality reduction" patented technology

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.

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 aluminum plate processing, and discloses an aluminum plate processing cleaning intelligent monitoring system, which comprises a three-dimensional deformation sensing module, a surface residue detection module, a thermal stress distribution analysis module, a vibration spectrum identification module, a processing track optimization module, an environmental parameter fusion module, an intelligent cleaning regulation module and the like. The three-dimensional deformation sensing module realizes aluminum plate surface three-dimensional reconstruction and deformation characteristic spectrum generation through an industrial camera and an algorithm; the surface residue detection module identifies residue distribution and classification by using a laser speckle interferometer and the like; the thermal stress distribution analysis module inverts thermal stress by means of an infrared focal plane array; the vibration spectrum identification module analyzes the equipment vibration signal; the processing track optimization module generates an optimized track based on an ant colony algorithm and the like; the environmental parameter fusion module generates a fusion decision index through data dimension reduction and correlation analysis; and the intelligent cleaning regulation and control module combines feedback and learning to optimize cleaning parameters. The system achieves multi-dimensional monitoring and intelligent cleaning regulation and control, and the machining precision and efficiency are improved.

The present application relates to the technical field of financial risk management, and in particular to a big data-based financial risk assessment and control system. By means of acquiring and preprocessing multi-source financial data of an enterprise, comprising data cleaning, data fusion, and data dimensionality reduction, the system generates standardized financial data. On the basis of the standardized financial data, constructing a multi-dimensional risk assessment model, comprising a market risk assessment model, a credit risk assessment model, an operation risk assessment model and a compliance risk assessment model. Using the multi-dimensional risk assessment model to perform risk assessment, generating a risk assessment report, and according to the report, generating a financial risk management suggestion, thereby implementing real-time alert and dynamic adjustment. The present invention improves the comprehensiveness and accuracy of financial risk assessment, achieves real-time monitoring and dynamic adjustment of financial risks, and improves the integration and reliability of the system.

The invention discloses an autonomous inspection and homeward voyage control method and system for a blow-off pipeline robot, belongs to the technical field of intelligent pipeline detection and robot control, and aims to solve the technical problems of how to realize a stable, reliable and continuous clogging exploration task in a complex blow-off pipeline environment and realize safe self-rescue in an abnormal state. According to the technical scheme, the method comprises the following steps: data dimension reduction: receiving high-dimensional environment and motion state data from a multi-modal sensor, performing real-time dimension reduction on the high-dimensional environment and motion state data by adopting a dynamic principal component analysis method, extracting key features of state information of a robot and internal state information of a pipeline, and constructing a low-dimensional state vector; performing deep reinforcement learning control based on a liquid neural network: modeling dynamic environment state change by using the liquid neural network, and realizing path planning and control in combination with a deep deterministic strategy gradient algorithm; tracing the track; and a return flight strategy based on a graph attention network and reinforcement learning.

The invention provides a wind power plant dynamic inertia partition evaluation method based on improved clustering, and relates to the technical field of wind power plants. The method comprises the following steps: taking a Canopy clustering algorithm as a preprocessing step through improved Canopy-Kmeans; the mean value point of the collected data set is calculated, and the measuring point closest to the mean value point is selected as the initial clustering center, so that the result is more reasonable and stable. The grouping precision is improved by considering the wake effect and the time delay effect and measuring multi-dimensional wind turbine generator state variables, in order to avoid a large amount of data dimensionality disasters, PCA principal component analysis is adopted for data dimensionality reduction, components with the variance contribution rate being 85% or above are selected as dominant variables, and the complexity of data collection is reduced. According to the method, the improved Canopy algorithm and the k-means algorithm are combined, the optimal classification number k value and the clustering center can be recognized more accurately, and then large-area wind turbine generator partitioning is achieved quickly and accurately.

The invention discloses a communication equipment fault intelligent diagnosis method and device, equipment and a medium. The method comprises the steps of collecting equipment operation logs in real time, and extracting an abnormal point set; clustering the abnormal points, and matching a hardware fault, a software error and a network congestion type in combination with a preset mode library; dimensionality reduction is carried out on temperature, voltage and current sensor data based on principal component analysis, and a time-synchronized fusion feature vector is constructed; analyzing features and abnormal modes, and outputting potential fault points; associating the classification model to generate a fault priority by extracting the dynamic characteristics of the network flow; and a dependency network is constructed in combination with Bayesian reasoning, so that accurate fault positioning is realized. The system supports incremental learning, updates an abnormal mode library and automatically expands a knowledge base. Through multi-dimensional data fusion and spatio-temporal feature joint modeling, the problems that a traditional method depends on manual rules and is high in false alarm rate are solved, and the method is suitable for complex scenes such as a 5G base station and a data center.

The invention relates to the technical field of identity authentication, and discloses an identity authentication method and system based on a large-model multi-mode venue, and the method comprises the steps: obtaining a fingerprint image, a face image and behavior interaction data of a user; performing fingerprint feature extraction according to the fingerprint image to obtain a fingerprint feature vector; performing facial feature extraction according to the facial image to obtain a facial feature vector; performing feature fusion on the fingerprint feature vector and the face feature vector, and generating a fusion feature vector after data dimension reduction; performing behavior feature extraction according to the behavior interaction data to obtain a behavior feature vector; and after cross-modal mapping is carried out according to the fusion feature vector and the behavior feature vector, matching degree analysis is carried out in a preset unified semantic space, and an identity authentication result is obtained. The method has the following effect that the identification security of identity authentication can be improved.

The invention discloses a new energy power generation equipment intelligent regulation and control method and system based on big data. The method comprises the steps of obtaining operation data of new energy power generation equipment, performing data dimension reduction processing to obtain structured data, and performing feature extraction and clustering to obtain an influence factor candidate set; according to the influence factor candidate set, performing spatial-temporal feature fusion analysis to obtain a multi-dimensional feature vector, and performing continuous sampling and quantization to obtain a quantization sequence; on the basis of the quantization sequence, potential risks are predicted through a long-short term memory network, risk marking is carried out, risk distribution map dynamic adjustment control parameters are obtained, and an optimization strategy set is obtained; based on the optimization strategy set, equipment operation parameters are selected and adjusted through a real-time feedback mechanism, a system stability index is obtained, multi-dimensional evaluation verification is carried out, and if an expected threshold value is not reached, regulation and control parameters are optimized through a genetic algorithm, and a final control instruction is output. According to the invention, real-time prediction of risks and intelligent regulation and control of equipment can be realized.

The invention belongs to the technical field of industrial flue gas purification, and discloses an ammonia desulfurization optimization control system based on a machine learning algorithm, which comprises a feature extraction module, a working condition clustering module, a dynamic optimization module and a self-adaptive feedback module, the feature extraction module is used for collecting multi-dimensional operation parameters in a coal burning process, performing data dimension reduction through a PCA algorithm, and extracting key working condition features; the working condition clustering module identifies different operation working condition modes; the dynamic optimization module can construct a multi-modal optimization neural network based on different operation condition modes, and generates optimal control parameters in real time. Through PCA dimension reduction processing of the feature extraction module and in combination with a working condition clustering algorithm, automatic mode recognition and strategy switching under complex working conditions are achieved, fluctuation of desulfurization efficiency is reduced, and meanwhile the problems that the ammonia water adding amount depends on experience setting, raw material waste and secondary pollution are likely to be caused, and the operation and maintenance cost is large are solved.

The invention provides a rare earth element content quantitative estimation method and device, equipment and a storage medium. The method relates to the technical field of hyperspectrum, machine learning and quantitative estimation. The method comprises the following steps: acquiring hyperspectral data of each sample, eliminating gross error points, performing data dimension reduction and K-means clustering analysis to obtain effective spectral data of each sample, performing denoising processing, averaging to obtain an average spectral curve, and extracting spectral characteristics; analyzing the correlation between the characteristic data of each wave band and the element content by utilizing Pearson correlation, selecting a high-correlation wave band range, carrying out importance test on the high-correlation wave band range by utilizing a random forest, and screening out a characteristic wave band corresponding to each element; and constructing a data set by using the characteristic wave bands corresponding to the elements and the chemical test contents of the elements, and training and evaluating the machine learning model based on the data set to obtain a quantitative estimation model. The rare earth element content can be quickly scanned and evaluated in time.

The invention discloses a sudden drought identification method and system based on a space-time double-branch fusion model. The method comprises the steps that meteorological data are acquired and preprocessed; calculating a composite sudden drought index according to the obtained data; performing data dimension reduction on the obtained data through singular value decomposition (SVD); a deep learning model is adopted to construct a time branch model, a graph attention network GAT is adopted to construct a space branch model, and the time branch model and the space branch model are dynamically fused through a cross attention mechanism to construct a space-time double-branch fusion model; according to the method, data dimensions are compressed and model complexity is reduced by fusing multi-source variables and combining singular value decomposition (SVD), meanwhile, geographic neighborhood weights are dynamically learned by adopting Transforme and based on a graph attention network (GAT), dynamic fusion of spatial-temporal characteristics is finally realized through a cross attention mechanism, a sudden drought recognition result is generated, and the method has the advantages of being high in robustness, high in accuracy and high in reliability. The limitation of a traditional method on nonlinear feature capture, space-time modeling splitting and generalization ability is broken through.

The invention relates to the field of computer big data, and discloses a self-adaptive parallel computing high-dimensional data dimension reduction and classification optimization system which comprises a quantum tensor decomposition module, a dynamic routing module, a heterogeneous computing classification module and a feedback regulation and control module. The quantum tensor decomposition module realizes nonlinear feature extraction of high-dimensional data through super-adjacency tensor modeling and entanglement entropy constraint; the dynamic routing module generates an adaptive communication path based on pulse time sequence weight optimization and quantum key verification; the heterogeneous calculation classification module dynamically schedules a quantum processor according to data characteristics, and FPGA and GPU resources execute mixed gradient aggregation; and the feedback regulation and control module guarantees the robustness of the system through multi-dimensional parameter closed-loop correction and a multi-stage fault-tolerant mechanism. All the modules cooperate to form a closed loop of dimension reduction, transmission, classification and regulation, the problems of feature extraction distortion, low resource utilization rate and poor dynamic environment adaptability in the prior art are solved, and the real-time performance and reliability of high-dimensional data processing are remarkably improved.

The invention discloses a tractor transportation operation working condition construction method based on improved particle swarm optimization and KMeans fusion, and relates to the technical field of agricultural machinery working condition analysis. The method comprises the following steps: acquiring original data of tractor transportation operation through a plurality of data acquisition modes, and carrying out preprocessing and three-stage screening to obtain an effective kinematics fragment; selecting multi-dimensional characteristic parameters to construct a characteristic matrix, and performing data dimension reduction by adopting principal component analysis; optimizing a KMeans clustering initial center by using an improved particle swarm optimization (IPSO) algorithm which introduces a dynamic inertia weight and a Gaussian mutation strategy, and performing clustering analysis on the feature space after dimension reduction; and selecting representative fragments based on feature similarity, and synthesizing a standardized working condition curve by taking the sum of average relative errors of all feature dimensions as a target function. According to the method, the problems that a traditional clustering algorithm is prone to falling into local optimum and the working condition construction accuracy is insufficient are solved, the constructed working condition can truly and comprehensively reflect the actual transportation operation characteristics of the tractor, and a reliable basis is provided for tractor power system optimization, operation efficiency improvement and energy consumption reduction.

The invention discloses a risk early warning method, training method and device of a risk identification model. The method comprises the steps that N kinds of performance index data of an information system in a target time period are collected, and a target index data set is obtained; the target index data set is input into a risk recognition model, a risk recognition result is output, the risk recognition model is obtained after an initial recognition model is trained based on a target training sample, and the target training sample comprises data obtained by conducting dimension raising on historical index data in a historical index data set and data obtained by conducting dimension raising on the historical index data in the historical index data set; the historical index data comprises N kinds of performance index data of the information system in a historical time period; and generating early warning prompt information under the condition that the risk identification result indicates that the information system has the risk. According to the method and the device, the problem that a risk prediction result of an information system is inaccurate due to data dimension reduction when a machine learning algorithm in related technologies processes multi-dimensional data is solved.

The invention relates to the technical field of remote sensing image processing, in particular to a joint sparse representation hyperspectral image classification method based on dual domain constraints, which comprises the following steps: preprocessing: carrying out spectral feature-based wave band grouping on hyperspectral image data, and then carrying out MNF data dimension reduction on the grouped hyperspectral image data; extracting a main component feature map by using morphology; performing superpixel segmentation on the hyperspectral image by using an improved watershed algorithm, and performing FCM clustering on the hyperspectral image; adaptive selection of the neighborhood is carried out through weight calculation under double constraints of the obtained superpixel neighborhood and the clustering field; multi-view angles of the superpixel field, the clustering field and the constraint field are used for joint sparse representation; and a majority voting method is adopted to integrate classification results, and the classification results are adjusted through a correction rule, so that the classification effect of the hyperspectral image is improved, and the classification precision of edge pixels is improved.

The invention relates to an injection molding product quality prediction method based on a random forest algorithm. The method comprises a model training stage and a prediction stage. The model training stage comprises the following steps: acquiring a historical data set, wherein the historical data set comprises a process parameter set and a corresponding product quality label; performing principal component analysis on the historical data to obtain a principal component set and a first transformation matrix; screening the principal component set through a gradient lifting decision tree algorithm; training the random forest prediction model by using the sample data set; the prediction stage comprises the following steps: collecting a process parameter set in a production process of a to-be-predicted product; performing transformation and screening; and predicting the product quality by using the trained random forest prediction model. According to the invention, a prediction model having significant advantages in the aspects of data dimension reduction, feature selection and nonlinear data processing can be provided, the accuracy of quality prediction of the injection product is improved, and the processing quality of the injection product is improved in the aspect of improving real-time process parameters.

The invention relates to the field of cucumber disease-resistant breeding analysis, in particular to a disease-resistant gene typing result analysis method and system, which are applied to a cucumber disease-resistant breeding analysis environment, and the method comprises the following steps: obtaining multi-source heterogeneous microscopic phenotype data for cucumber disease-resistant breeding analysis, the multi-source heterogeneous microscopic phenotype data is converted into a unified quantitative index set; multi-source heterogeneous microscopic phenotype data is converted into a unified quantitative index set, and a plurality of resistance components are identified through data dimension reduction processing based on the unified quantitative index set, so that complex microscopic resistance responses are abstracted into quantifiable component feature vectors; the feature vector is further subjected to correlation analysis with genotyping data and macroscopic disease-resistant phenotype data, so that the problem that microscopic data and a resistance mechanism are difficult to effectively integrate and reveal in the prior art is solved, and the effects of deeply understanding the disease-resistant mechanism and realizing mechanism-based precise breeding are achieved.

The invention discloses a state monitoring method and monitoring system for photovoltaic panel waste heat coupling power generation equipment. The method comprises the following steps: acquiring operation original data; classifying and denoising the original data; cleaning and fusing the de-noised data to obtain standardized data; establishing a health index model based on the standardized data, calculating a health index and comparing the health index with a threshold value; when the health index is abnormal, data dimension reduction is carried out, and the fault type and position are determined; system parameters are adjusted or graded alarming is performed according to fault types; through multi-sensor data fusion and dynamic health index evaluation, and in combination with multi-stage fault diagnosis of principal component analysis dimensionality reduction, support vector machine classification and isolated forest detection, the crossing from single parameter alarm to system state comprehensive diagnosis is realized, the fault identification accuracy is improved, and the fault diagnosis efficiency is improved. And the problems of high false alarm rate and difficulty in positioning multiple faults caused by a static threshold and a single algorithm in the prior art are solved, and the operation reliability and the energy recovery efficiency of the waste heat coupling power generation system are remarkably improved.

The invention relates to the technical field of big data resource services, in particular to a quality control rule generation method based on artificial intelligence. The method comprises the following steps: acquiring medical record data, determining a sample point, and performing principal component analysis on the sample point to obtain a first principal component direction and a second principal component direction; according to the similarity degree of the first principal component direction projection and the second principal component direction projection, the type contribution degree of the to-be-detected type is determined, and then the dimension correlation of the two types is determined; determining a mapping adjustment value of the to-be-tested type according to the dimension correlation; high-dimensional mapping data of the medical record data are obtained according to the mapping adjustment value of each type, dimensionality reduction data are obtained through data dimensionality reduction, and the dimensionality reduction data are used for assisting in determining the quality control rule. According to the method, generation of the quality control rule can be combined with distribution characteristics of data of the same type and relevance characteristics of data of multiple types; the objectivity and reliability of the quality control rule are enhanced, and the robustness of the quality control rule is improved.

The invention discloses an equipment remaining service life prediction method and system, and belongs to the technical field of equipment state monitoring, and the method comprises the steps: obtaining the whole life cycle degradation data of equipment as a data set; performing data dimension reduction by using an automatic encoder to obtain an equipment health index HI; inputting the HI into a fusion type RUL prediction network model, and predicting a probability density curve of the RUL; wherein the model comprises a probability prediction network, a Wiener process model and a fusion module; the probability prediction network is used for obtaining a probability density curve in a numerical form; the Wiener process model is used for obtaining a probability density curve in an analysis form; and the fusion module is used for dynamically weighting the two curves to realize RUL prediction. According to the method, the RUL probability density curve prediction is realized, the problem that an existing prediction model based on machine learning can only obtain a point prediction result is solved, and the interpretability of the model is improved in combination with a random process.

The invention discloses a crop ralstonia solanacearum disease prediction method based on an integrated learning model, and the method comprises the following steps: S1, collecting a published 16s rRNA gene sequence related to solanaceae crop bacterial wilt, and carrying out the preprocessing of original sequencing data based on an EasyAmplicon standardized process; s2, performing data dimension reduction by using a principal component analysis algorithm, and retaining 95% of variance; s3, performing hyper-parameter search based on 5-fold cross validation and grid search on the Light GBM model, the CatBoost model and the XGBoost model respectively, and selecting three groups of optimal hyper-parameters of each model; s4, constructing a model according to three groups of optimal hyper-parameters of each selected model, performing prediction, analyzing model result difference based on a Pearson correlation coefficient, and retaining Pearson correlation coefficient mean < lt > with other eight model prediction values; a model of 0.8; and S5, inputting the screened model prediction result into a second-layer element learner RF for integrated learning to obtain a final prediction result.

The invention discloses a five-directional stressometer monitoring data anomaly identification method based on PCA confidence interval analysis, and relates to the technical field of hydropower engineering. According to the method, circle centers (zt1, zt2) and radiuses r1r2 of two-dimensional coordinate data points in each time period are obtained, all the circle centers are subjected to arithmetic average to obtain a final circle center, all the radiuses are subjected to arithmetic average to obtain a final radius, and therefore a global confidence circle is formed; and when new data appear, performing the same PCA transformation and coordinate mapping on the new data, and judging whether a new point is in the final confidence circle so as to realize anomaly detection. According to the method, data dimensionality reduction is performed on data of each monitoring point of the five stress meters by adopting a principal component analysis method, and a final confidence circle is formed by performing arithmetic averaging on two-dimensional circle center coordinates and radiuses of monitoring data in multiple periods; judging whether the data of each monitoring point is normal or not by judging whether the circle center coordinate of the new confidence circle is in the final confidence circle or not; the whole method is suitable for hydropower engineering dam monitoring, equipment diagnosis and data analysis scenes of rapid anomaly detection.

The invention belongs to the technical field of battery health degree evaluation, and particularly relates to a battery health degree evaluation method and system, electronic equipment and a storage medium. Based on the original sample data of the battery health degree evaluation indexes, establishing a standardized decision matrix to eliminate the dimensional difference between the indexes; constructing a correlation matrix according to the matrix, and evaluating index correlation; extracting common factors of a correlation matrix, calculating a core quantitative index, determining a main factor by taking standard reaching of an accumulated variance contribution rate as a standard, realizing data dimension reduction and retaining original index core information; and calculating a main factor score in combination with the original data and the rotated factor matrix, performing weighting according to a variance contribution rate, and performing weighted summation to obtain a health degree comprehensive score. And the health difference of the batteries can be judged in an auxiliary manner through clustering analysis, and data support is provided for reasonable configuration and fine management of the batteries. According to the method, dimension reduction weighting is carried out on various related indexes through a factor analysis method, a result is objectively deduced based on data features, subjective influences are reduced, and evaluation scientificity and reliability are improved.

The invention discloses a white spirit flavor recognition method based on machine learning, and belongs to the technical field of food detection and artificial intelligence. Aiming at the problems of high subjectivity of manual evaluation, low efficiency of mass spectrometry, noise sensitivity of a machine learning model and the like in the prior art, the method provides a solution integrating solvent background deduction and feature weight screening. The method specifically comprises the following steps: diluting a white spirit sample with methanol according to a volume ratio of 1: 10, collecting mass spectrum data through GC-MS, and dynamically deducting a methanol background peak; a BP neural network (GABP) optimized by a genetic algorithm is utilized to automatically analyze a weight coefficient of each molecular peak to flavor classification, and key features are screened; a classification model is constructed based on XGBoost, and parameters are optimized through cross validation, so that automatic judgment of the flavor type, authenticity and quality of the white spirit is realized. According to the method, through data dimension reduction and model collaborative optimization, the overfitting problem caused by high noise and high redundancy of mass spectrum data is solved, the classification accuracy is remarkably improved, and the method can be extensively applied to white spirit brand identification, process optimization and market quality supervision.

The invention relates to the technical field of foreign matter recognition, in particular to a foreign matter early warning method and device, electronic equipment and a storage medium. Carrying out dimensionality reduction on the plurality of first spectral vectors through a dimensionality reduction conversion matrix to obtain a plurality of second spectral vectors; classifying each second spectrum vector according to the relationship between the second spectrum vectors and a plurality of spectrum classes to obtain a plurality of spectrum class identifiers; and finally, selecting a plurality of target identifiers from the plurality of spectrum identifiers, determining a first foreign matter probability according to the plurality of target identifiers, the perimeter area ratio of the first region and a conditional probability equation, and performing early warning according to the first foreign matter probability. According to the invention, based on the visual identification area, the spectral data is extracted, dimensionality reduction and classification are carried out on the spectral data, the foreign matter risk is determined based on data dimensionality reduction, classification and statistics, the accuracy of foreign matter early warning is improved, and the probability of false alarm when the foreign matter is found is reduced.

The invention discloses a composite material reflectivity spectral information classification method and system based on a PCA-SVM algorithm. The method comprises the following steps: collecting reflectivity spectral data of a composite material sample; preprocessing data to eliminate measurement deviation and unify numerical scale; carrying out dimensionality reduction on the preprocessed high-dimensional spectral data through principal component analysis, and extracting feature components retaining main variance information; based on dimension reduction features, a support vector machine is adopted to construct a multi-label classification model according to a'one-to-other 'strategy; predicting the test sample, and generating a multi-label classification result through probability output and threshold processing; and analyzing the classification performance by using the multi-label evaluation index. The data processing module of the system executes preprocessing, dimension reduction, modeling, prediction and evaluation operations. The method is suitable for lossless identification of multi-component composite samples, both interpretability and identification precision are considered, the performance bottleneck of a traditional method under the conditions of feature overlapping, insufficient samples and the like is effectively overcome, and efficient and accurate classification of the composite materials is achieved.

The invention discloses a battery cell abnormity determination method and system based on BMS data driving, and the method comprises the steps: collecting multi-dimensional battery cell operation data through employing a plurality of sensors and a BMS, and carrying out the preprocessing of the collected data, and forming a basic data set; performing data dimension reduction and feature extraction on the obtained basic data set, comparing an extracted feature value with a dynamic adaptive threshold obtained based on a dynamic threshold adjustment mechanism to obtain a cell health state evaluation result, generating an abnormal early warning signal according to the cell health state evaluation result, and positioning a potential fault point; and performing exception classification on the exception early warning signals, generating maintenance suggestions, generating exception records and storing the exception records in a cloud database. According to the invention, accurate identification and rapid response to the abnormity of the battery cell are realized.

The invention discloses a small sample non-uniform multi-mode clutter modeling and partition suppression method, which is applied to the technical field of radars, performs multi-mode clutter modeling aiming at a complex environment, partitions the non-uniform clutter environment based on the difference of non-uniform clutter covariance matrix geometric matching, and improves the robustness of the multi-mode clutter modeling. Then, the problem that the performance of a traditional clutter suppression method is reduced under the small sample condition is solved through a dimensionality reduction self-adaptive filtering method in different areas; according to the method, firstly, multi-mode clutter echoes are generated for a complex environment, different-mode clutter region boundaries are judged through the difference of different clutter covariance matrix geometric matching, clutters are partitioned, and then the degree of freedom is reduced through data dimension reduction in different regions, and filter weight vectors are designed to achieve self-adaptive filtering; according to the method, effective partitioning and suppression of multimode clutters can be realized under the conditions that the number of samples is less than the degree of freedom of a radar system and the clutter environment is not uniform.

The invention discloses a laser cutting end surface quality control method, which comprises the following steps: S1, data acquisition: acquiring the thickness, material spectrum, surface roughness, laser power and auxiliary gas pressure data of a workpiece by using a multi-mode sensing module, performing dimension reduction on hyperspectral data through a principal component analysis algorithm, extracting a material feature vector, and calculating the material feature vector; and generating an initial process parameter request in combination with the thickness information. Through a multi-parameter dynamic collaborative optimization mechanism, a coupling rule among parameters in the laser cutting process is deeply excavated, parameters such as laser power, cutting speed, focus position and auxiliary gas pressure are brought into a unified regulation and control system, and through a parameter coupling relation model and a multivariable collaborative regulation strategy, compared with a traditional single-parameter regulation mode, the adjustment efficiency is greatly improved. The notch width consistency is improved by 65%, the fluctuation range is controlled within + / -0.02 mm, the end face roughness is reduced by 52%, the Ra value can reach 3.2 [mu] m or below, and the problems of thick plate slag residue, thin plate overburning deformation and the like are effectively solved.