93 results about "Svm regression" patented technology

The invention relates to the technical field of motor state monitoring and fault prediction, and provides a motor residual life analysis method and system based on a support vector machine, and the method comprises the steps: building a multi-dimensional degradation feature set of a motor, and calculating a health index degradation rate based on multi-source sensor data and a failure threshold; constructing a support vector machine regression model of an adaptive kernel function, predicting a health index change track by using the model and real-time data, and generating a residual life evaluation result if a prediction deviation is within an allowable error range; otherwise, starting an incremental learning mechanism to update the training data set, dynamically adjusting kernel function parameters, and recalculating the trajectory; and if the error requirement is still not met, model regularization parameters are optimized in combination with the working condition data until the residual life evaluation result is converged. According to the method, the accuracy and dynamic adaptability of motor residual life prediction can be improved, and the robustness of the model to complex working conditions is enhanced.

The invention relates to an intelligent production management system and method for high-concentration polyacrylamide fracturing fluid, and the method comprises the steps: obtaining multi-source heterogeneous data through designing a unique algorithm model and an innovative data processing flow: an innovative data collection and fusion strategy, and designing a comprehensive and high-precision data collection system; based on intelligent control algorithm innovation of a fuzzy neural network and MPC, the fuzzy neural network and model prediction control are combined to carry out production process control, a robust optimization method is introduced to enhance the robustness of a control strategy, and meanwhile, a rule-based emergency processing mechanism is established; intelligent mechanism innovation of quality monitoring and optimization is realized, and real-time monitoring and analysis of product quality are realized by establishing a quality prediction model based on combination of support vector machine regression (SVR) and a particle swarm optimization (PSO) algorithm.

The invention relates to a mold 3D analogue simulation analysis method, which comprises the following steps: acquiring a three-dimensional CAD model of a mold, carrying out preprocessing and grid division, encrypting grids in a key area, calculating a stress value by utilizing finite element analysis, optimizing the grids through iterative calculation until a convergence requirement is met, and obtaining a stress distribution condition. A support vector machine regression algorithm is adopted to establish a nonlinear prediction model of stress characteristics and fatigue life, mold structure optimization is carried out, an optimization scheme is converted into a CAD model, a machining program is generated, accurate prediction and automatic optimization design of the fatigue life of the mold are achieved, the service life of the mold is prolonged, and the reliability of the mold is improved. And important technical support is provided for 3D analogue simulation analysis of mold design and manufacturing.

The invention discloses a magnetostriction liquid level meter temperature compensation method and system based on a support vector machine. The method comprises the steps that the currently captured original pulse number is obtained through a magnetostriction liquid level meter; the current environment temperature is detected in real time through a high-precision temperature sensor in the magnetostrictive liquid level meter; taking the original pulse number and the current environment temperature as input features, sending the input features to a pre-trained support vector machine regression model (SVM), and outputting a displacement error prediction value in the current temperature environment; and correcting the original measurement result according to the error prediction value to obtain a real pulse measurement value after temperature compensation. The system comprises a time-to-digital conversion chip, a temperature chip temperature measurement module, a support vector machine reasoning module, a pulse compensation module and a distance calculation module. According to the method, nonlinear modeling can be carried out on measurement errors caused by environment temperature changes, so that the liquid level measurement result is dynamically corrected, and the overall robustness and precision of a measurement system are improved.

The invention discloses a wafer polishing laser interference closed-loop pressure control method, and relates to the technical field of semiconductor manufacturing, and the method comprises the following steps: S1, triggering an automatic measurement program after a ceramic plate and a wafer are overturned; s2, driving a laser interference measuring head to perform non-contact scanning on T2 / T4 point positions of the wafer through a six-axis mechanical arm positioning system to obtain thickness distribution data; s3, fitting the thickness distribution data by adopting a Zernike polynomial to generate a three-dimensional distribution diagram, and calculating a pressure correction amount based on a process dynamic model and a pressure prediction model constructed by a support vector machine regression algorithm; s4, detecting a data outlier in real time through an exception handling module, and triggering an equipment protection strategy when the deviation exceeds a set threshold value; and S5, a pressure control instruction is transmitted to the polishing head through the PROFINET bus by means of the PLC. Through non-contact laser interference measurement, the laser interference technology is applied to thickness detection of the polished wafer for the first time, the single measurement time is shorter than 20 seconds, and the detection efficiency is remarkably improved.

The invention discloses a method for constructing a prediction model of pan-apoptosis related biomarkers of asthma based on a convolutional neural network algorithm and application, and the method comprises the steps: firstly obtaining related data from a database, and determining characteristic pan-apoptosis related genes PDGs in asthma through an SVM support vector machine, LASSO regression analysis and RF random forest regression; the method comprises the following steps: establishing an lncRNA-miRNA-mRNA network by using interaction STRING and Cytoscape software, constructing a prediction model of the pan-apoptosis related biomarkers of asthma through a convolutional neural network, and analyzing the diagnosis accuracy of potential target genes through an ROC curve. 10 characteristic PDGs playing an important role in asthma are finally obtained, the difference of the genes between a normal group and an asthma group is proved, and the enrichment pathway and immune cell infiltration of the genes have a certain relation with the occurrence and development of asthma, so that a prediction model of the pan-apoptosis related biomarkers of asthma is constructed, and the prediction model is used for predicting the pan-apoptosis related biomarkers of asthma. And a new research direction is provided for studying the pathogenesis and treatment of asthma.

The invention discloses a saline-alkali cultivated land information monitoring method based on spectral analysis. The method comprises the following steps: step 1, data preparation: collecting and processing data covering a target area; step 2, spectral index calculation and fusion: carrying out unary linear regression fusion on the salt index subjected to ground hyperspectral transformation and the salt index subjected to simultaneous Sentinel-2 multispectral calculation to generate a high-multispectral fusion index data set, and constructing a characteristic variable library; 3, feature variable selection: screening out key features from a feature variable library; 4, constructing and training a support vector machine model: training a support vector machine regression model; 5, salinity inversion and precision verification: outputting a soil salinity prediction value, generating a soil salinity spatial distribution diagram of the cultivated land in the Hetao irrigation area according to the soil salinity prediction value, and dividing cultivated land salinization grades by combining an inversion result; and 6, dynamic monitoring: repeating the steps to the step 5, and generating a salinization dynamic change diagram.

The invention discloses a temperature prediction method and system for a prestressed reinforced concrete long-span beam bridge, and relates to the field of bridge temperature effect analysis. The method comprises the following steps of: 1, acquiring a construction design position coordinate, and monitoring a structure temperature in the construction design position coordinate; step 2, carrying out ANSYS modeling on the structure temperature to divide grids, and establishing a structure temperature field according to the single structure temperature in each grid; 3, acquiring a time sequence corresponding to the single structure temperature, establishing a mapping relation between the time sequence and the structure temperature field, and establishing a coordinate relation between the time sequence and the structure temperature field; and step 4, predicting the temperature at the next moment according to the coordinate relationship between the time sequence and the structural temperature field by using an LS-SVM regression algorithm based on the time sequence. Through temperature monitoring, the distribution rule of a temperature field is mastered, and the influence of the predicted temperature on the deformation of the main beam in the construction process is theoretically analyzed, so that the stress and deformation in the construction process can be timely and intuitively evaluated.

The invention discloses a method for predicting a composite biological effect of a carbon nanomaterial. The method comprises the following steps: measuring the growth inhibition toxicity of different types of carbon-based nanomaterials to scenedesmus obliquus in the presence of singly; the growth inhibition toxicity is calculated by taking the effect multiple change generated by a treatment group compared with a control group as a toxicity index; based on the obtained growth inhibition toxicity data, constructing an SEML prediction model through a support vector machine regression algorithm or an artificial neural network regression algorithm; and predicting the composite biological effect of the carbon nanomaterial according to the SEML prediction model. According to the method, the effect multiple change FC is adopted as a toxicity index, biotoxicity evaluation is more accurate, meanwhile, the prediction model is directly constructed based on growth inhibition toxicity data, material characteristic parameters of the carbon-based nano material do not need to be measured, and the modeling process is more convenient.

The invention discloses a PID (Proportion Integration Differentiation)-based support vector machine regression monitoring model for VOCs (Volatile Organic Compounds) concentration in water-soil-gas, which comprises the following steps of: performing characteristic parameter extraction on a PID signal according to acquired PID signal data; performing principal component extraction on the characteristic parameters by adopting a principal component analysis method; normalizing a training set and a test set in the process of performing VOCs concentration quantitative analysis by using support vector machine regression; selecting a Gaussian radial basis kernel function as a kernel function of SVM regression, and seeking an optimal parameter delta and an optimal penalty factor C of the radial basis kernel function through a genetic algorithm; and carrying out model construction on PID signals under the condition of different concentrations of VOCs by applying a support vector machine regression method on the optimal parameter delta and the optimal penalty factor C. According to the method, the precision of analyzing the concentration of VOCs is improved, so that the prediction effect on the concentration of VOCs is improved, and the monitoring precision of VOCs is improved.

The invention relates to the technical field of battery management, and discloses a battery fault diagnosis method and system for a CCS module, and the method comprises the steps: obtaining a multi-dimensional data sequence of the voltage, current and temperature of a battery pack, carrying out the abnormality detection, and obtaining a potential abnormal time period and a multi-dimensional data subset; performing clustering analysis on the multi-dimensional data subset to obtain an abnormal feature clustering center, and performing dynamic association analysis to obtain an association mode vector set; calculating the similarity of each single data and a fault propagation path, and matching the propagation path with a preset mode to obtain a fault propagation score; fusing the clustering center and the propagation score, positioning a fault monomer through a support vector machine regression model, and determining a fault monomer identifier; and carrying out segmented clustering and logic judgment on the data corresponding to the identifier, and determining a fault type. According to the invention, accurate positioning and type determination of fault monomers can be realized.

The invention discloses a CFD (computational fluid dynamics) and energy consumption simulation-based thermal performance optimization method for a transformer substation enclosure structure, relates to the technical field of transformer substation building energy conservation, and aims at solving the problem of excessive CFD simulation examples when an annual meteorological parameter-near wall surface temperature prediction model is constructed based on annual typical representative meteorological parameter identification of a clustering algorithm. A meteorological parameter-near wall surface temperature prediction model is constructed based on SVM regression, and the coupling problem of hour-level simulation step length between CFD simulation and energy consumption simulation is solved. According to the method, CFD and energy consumption simulation are coupled, and the problem that in the current energy consumption simulation process, the influence of a high heating source on the outer enclosure structure and building energy consumption is not considered is solved. Therefore, the accuracy of evaluation and optimization of the thermal performance of the enclosure structure of the transformer substation can be improved.

The invention discloses a tire wear depth intelligent analysis method and system, and relates to the technical field of tire detection. According to the method, the resolution of the image sensor is automatically adjusted to adapt to different environment temperatures, and the K-means clustering algorithm and the support vector machine regression model are combined, so that the real-time performance and accuracy of tire tread image data acquisition are improved, the data processing efficiency is optimized, and the real-time performance of tire tread image data acquisition is improved. A complex model capable of accurately mapping the wear rate and the change of the feature points is established, the model has good generalization ability and can adapt to various tire types and use environments, the contradiction between high precision and real-time performance, data processing efficiency and accuracy and model complexity and generalization ability is effectively solved, and the real-time performance of the model is improved. Accurate analysis and prediction of the tire wear depth are achieved, and the intelligent level of tire monitoring is improved.

The embodiment of the invention discloses an interconnection communication method and device based on a power terminal cross-operating system. The method comprises the following steps: acquiring an original data set of a cross-operating system channel of the power terminal in each modulation mode; training a preset quantitative relation model based on the original data set and a support vector machine regression algorithm, and determining a to-be-selected quantitative relation model; performing performance evaluation on the to-be-selected quantitative relation model based on the original data set, and determining a target quantitative relation model; determining the current crosstalk intensity corresponding to the current channel interval parameter based on the current channel interval parameter in the current communication environment and the target quantitative relation model in the cross-operating system interconnection communication process of the power terminal; according to the method, the current channel interval parameter is dynamically adjusted based on the current crosstalk intensity and the preset crosstalk intensity threshold value, so that the dynamic adjustment of the channel interval parameter is accurately and conveniently realized, the crosstalk between channels is avoided, and the communication quality of cross-operating system interconnection communication of the power terminal is improved.

The invention belongs to the technical field of power grid electric quantity prediction, and discloses a temperature regulation electric quantity prediction method, system and equipment based on a support vector machine, and a medium, so as to solve the problem of temperature regulation electric quantity prediction. The method comprises the following steps: acquiring historical temperature data, historical daily electricity consumption data and a preset temperature time window of a target industry in a predicted target area, and performing moving weighted average based on the historical temperature data, the historical daily electricity consumption data and the preset temperature time window so as to respectively obtain an accumulated highest temperature, an accumulated lowest temperature and an accumulated average temperature; after abnormal data in the historical daily electricity consumption data are removed, polynomial regression models are sequentially constructed by combining the accumulated highest temperature, the accumulated lowest temperature and the accumulated average temperature for fitting, so that a corresponding polynomial regression model fitting curve and corresponding historical temperature adjustment electricity consumption data are obtained; and using support vector machine regression to predict the future temperature regulation electric quantity of the target industry in the target region. According to the method, the defect that nonlinear characteristics are neglected during data processing in the prior art is overcome, and more accurate prediction of the temperature regulation electric quantity is realized.

The invention provides a video picture and virtual information projection registration and fusion method and system based on a digital twinning scene, and relates to the technical field of digital twinning, and the method comprises the steps: obtaining a virtual three-dimensional coordinate in the digital twinning scene, and a time sequence video stream and an actual three-dimensional coordinate sequence synchronously collected by multiple cameras; then, space pose calculation is carried out through a Perspective-n-Point algorithm, and an initial mapping relation is obtained; performing deviation registration on the relation by using a support vector machine regression model of time sequence perception to obtain an accurate target mapping relation; determining a target fusion region and extracting visual features of the target fusion region; and finally, inputting the visual features and the image features of the virtual information into a random forest model, generating fusion control parameters in combination with a dynamic change index, and then dynamically projecting the virtual information and fusing the virtual information into a video picture, so that the accuracy of virtual and real projection registration and the naturalness of dynamic fusion can be improved.

The invention provides a photovoltaic power generation prediction method and device, and the method comprises the steps: collecting an atmospheric motion vector diagram (including a lower-layer wind field, a middle-layer wind field and an upper-layer wind field), and determining a target region and an adjacent search region. The method comprises the following steps: extracting cloud factors from a search area, and converting the cloud factors into cloud influence factors of each wind field on the cloud amount change of a target area; carrying out support vector machine (SVM) regression training by taking the cloud influence factor and the cloud factor as inputs and taking the cloud cover and the irradiance prediction value of the target area as outputs; and evaluating the prediction performance of the trained SVM and then deploying the SVM to predict photovoltaic power generation. Through extraction and analysis of an atmospheric motion vector diagram (AMV) and combination of SVM regression training, accurate prediction of the cloud cover and irradiance of a target area is realized, and then prediction of photovoltaic power generation is realized.

The invention discloses a qualitative and quantitative analysis method and system for explosives based on terahertz time-domain spectroscopy, sample preparation is carried out by using unified standards, and the method comprises the steps of grinding, weighing and pressing; the method comprises the following steps: measuring a terahertz time-domain spectrogram of an explosive sample by using a terahertz time-domain spectroscopy system, and obtaining a terahertz frequency-domain spectrogram through Fourier transform; data preprocessing algorithms such as data expansion, SG smoothing, multivariate scatter correction (MSC) and competitive adaptive reweighting (CARS) are used for preprocessing the data; and performing classification / regression analysis by using machine learning algorithms such as a support vector machine classification (SVC) model, a support vector machine regression (SVR) model and a one-dimensional convolutional neural network model (1D-CNN). According to the method, the problems of difficulty in constructing a spectrum database, insufficient algorithm robustness, limited practicability and the like in the prior art are solved, and a more efficient and reliable solution is provided for safety detection and prevention and control of explosives.

The application provides a video picture and virtual information projection registration and fusion method and system based on a digital twin scene, relates to the technical field of digital twinning, and obtains a virtual three-dimensional coordinate in a digital twin scene and a time sequence video stream and an actual three-dimensional coordinate sequence synchronously collected by multiple cameras; then, a space pose solution is performed through a Perspective-n-Point algorithm to obtain an initial mapping relationship; subsequently, a time sequence sensing support vector machine regression model is used to perform deviation registration on the relationship to obtain an accurate target mapping relationship; then, a target fusion region is determined and a visual feature thereof is extracted; finally, the visual feature and an image feature of virtual information are input into a random forest model, a fusion control parameter is generated by combining a dynamic change index, and then the virtual information is dynamically projected and fused into the video picture, which can improve the accuracy of virtual-real projection registration and the naturalness of dynamic fusion.

The present application relates to the technical field of complex equipment component repair, in particular to a device component performance and unit body maintenance level relationship mining method capable of effectively improving the use efficiency of an aero-engine, which first carries out expansion processing of repair samples, and then selects a support vector machine regression method which is better in the condition of small sample problems to solve the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair. Since a component is generally composed of multiple unit bodies, each component has multiple maintenance levels, and the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair is a many-to-one mapping relationship. In order to improve the accuracy of support vector machine regression, a hybrid kernel function method is used to optimize it, and a particle swarm algorithm is used to optimize the related parameters.

The invention belongs to the technical field of hydrology and water resource and climate prediction crossing, and discloses a medium-and-long-term runoff prediction method fusing a data enhancement technology and a machine learning model, which comprises the following steps: collecting runoff data and climate system index data of a target area, performing factor screening by adopting a replacement accuracy method, the method comprises the following steps: dividing data into a training set and a test set, constructing a time lag factor, carrying out SMOTE data enhancement on the training set, carrying out runoff prediction model construction by using a support vector machine regression model, carrying out parameter optimization by using grid search, calculating an evaluation index to carry out model performance evaluation, and finally predicting and outputting a medium and long-term runoff time sequence according to the model. The method effectively solves the problem of low prediction precision caused by data scarcity and insufficient factor selection in a traditional method. The method overcomes the problems of data scarcity, imbalance and insufficient model generalization ability in medium and long term runoff prediction, and has wide application prospects in the fields of medium and long term runoff prediction and water resource management.

The invention discloses an industrial transfer analysis method of a support vector machine based on energy data, which depends on the consistency of industrial transfer and regional energy consumption trend, extracts energy consumption data as key feature data, and further simplifies the industrial transfer analysis method. The industrial transfer analysis method is designed from two dimensions: 1, constructing an industrial geographic concentration index, and measuring the concentration or diffusion trend of key industries in a regional layout; 2, industrial output values are extracted as independent variables, a support vector machine regression model is adopted to construct a prediction model, and regional power consumption demands are predicted; and the two are combined to analyze the internal relationship between the industry transfer and the energy consumption data to assist in decision making.

The invention discloses a chlorogenic acid component detection method fused with deep learning, and relates to the technical field of chlorogenic acid. The method comprises the following steps: acquiring a chlorogenic acid hyperspectral image; clustering the chlorogenic acid hyperspectral image through a fuzzy C-means clustering algorithm based on a plurality of subclass centers to obtain a chlorogenic acid hyperspectral cluster, and calculating a chlorogenic acid spectral cluster feature vector; the method comprises the following steps: calculating mutual information of chlorogenic acid hyperspectral cluster feature vectors to obtain hyperspectral cluster feature weights; obtaining a chlorogenic acid hyperspectral cluster weighted vector by combining the chlorogenic acid hyperspectral cluster feature vector and the hyperspectral cluster feature weight; inputting the chlorogenic acid hyperspectral cluster weighted vector into a support vector machine regression model based on high-dimensional multiple scales, and outputting to obtain a chlorogenic acid concentration predicted value; and constructing a chlorogenic acid concentration diagram according to the chlorogenic acid concentration predicted value, calculating a concentration abnormal value through a sliding window method, and performing early warning if the concentration abnormal value is greater than a preset threshold value.

The application discloses a wafer polishing laser interference closed-loop pressure control method, and relates to the technical field of semiconductor manufacturing, and comprises the following steps: S1: a ceramic plate is triggered to automatically measure a program after being turned over with a wafer; S2: a laser interference measurement head is driven by a six-axis mechanical arm positioning system to non-contact scan T2 / T4 points of the wafer, and thickness distribution data is acquired; S3: a Zernike polynomial is adopted to fit the thickness distribution data to generate a three-dimensional distribution graph, and a pressure correction amount is calculated based on a pressure prediction model constructed by a process kinetics model and a support vector machine regression algorithm; S4: an abnormal processing module is used to detect data outliers in real time, and a device protection strategy is triggered when a deviation exceeds a set threshold; and S5: a pressure control instruction is transmitted to a polishing head by a PLC controller through a PROFINET bus. By means of non-contact laser interference measurement, the laser interference technology is applied to wafer thickness detection after polishing for the first time, and single measurement time is less than 20 seconds, so that the detection efficiency is significantly improved.

The invention belongs to the technical field of power equipment insulation state detection, and particularly relates to an XLPE cable water branch quantitative evaluation method and system based on frequency conversion pseudo trapezoidal wave excitation, and the method comprises the steps: applying pseudo trapezoidal wave excitation, and collecting voltage and current signals; wavelet and adaptive filtering are combined for denoising; establishing a Wiener-ANN nonlinear dielectric response model, and fitting cable response through the combination of a dynamic linear neural network module and a static neural network module; obtaining a frequency domain dielectric spectrum and extracting a multi-harmonic characteristic parameter, a loss slope, a hysteresis angle and a time domain nonlinearity; and finally, outputting a water treeing aging comprehensive index WTI through the support vector machine regression model to realize quantitative evaluation of the aging degree. The system comprises a pseudo trapezoidal wave excitation unit, a high-voltage amplification unit, a micro-current acquisition unit, a signal processing unit and an aging evaluation unit. The broadband harmonic response can be obtained through a single test, the test efficiency is improved by more than 75%, the anti-interference performance is high, the evaluation precision is high, and the method is suitable for on-site rapid nondestructive diagnosis.

The invention discloses a strip mine transportation equipment multi-source information fusion monitoring method based on automatic driving, and the method comprises the following steps: 1, collecting a position signal and a vehicle speed signal of transportation equipment and a rotation angle of a steering wheel according to a sampling period; 2, performing data preprocessing on the to-be-fused data of the multi-source information; 3, constructing a support vector machine regression model; 4, establishing a particle swarm optimization algorithm model, and carrying out optimal solution solving on a penalty coefficient and a kernel function parameter of the support vector machine regression model to obtain an optimal parameter combination; and step five, the optimal parameter combination is substituted into the support vector machine regression model, so that the monitoring result is more accurate, and the travel route monitoring result of the transportation equipment is obtained. The method has the characteristics that the operation time is greatly shortened, the monitoring precision can be improved, and the accuracy is improved.

The invention relates to the technical field of software testing, in particular to a software testing data generation method and system based on a multi-chain doliolaria goblaria swarm algorithm, and the method comprises the steps: initializing a population; randomly generating an initial population, and randomly dividing the population into a plurality of sub-chains; performing fitness sorting on individuals in each sub-chain, and dynamically adjusting the proportion of a leader to a follower in the population; updating the position of each sub-chain leader, and obtaining the actual fitness value of the updated leader individual by actually executing an instrumentation program; updating the position of the follower in the sub-chain based on the adaptive inertia factor, and predicting the fitness value of the updated follower individual by using a support vector machine regression model; updating the optimal fitness value and the corresponding position of each sub-chain and the whole population; and judging whether the current iteration reaches the maximum number of iterations or the solving precision requirement. According to the method, the efficiency and coverage rate of software test data generation are remarkably improved, and the calculation overhead is effectively reduced.

This invention discloses a method for optimizing distributed photovoltaic (PV) microgrid systems, belonging to the field of PV microgrid control technology, specifically considering demand-side response and energy storage. The method includes: Step A: establishing a PV day-ahead output prediction model; Step B: establishing an energy storage and load model; Step C: establishing a PV grid integration capacity assessment and optimization model. The day-ahead PV output is predicted using the PSO-SVM regression prediction method. Addressing the severe problem of PV curtailment, the method considers the impact of energy storage and demand response on PV grid integration rate. Starting from constraints such as no node voltage exceeding limits, no power flow overload, and no energy storage exceeding limits, the optimization objective is established by considering the maximum PV grid integration capacity and the minimum network active power loss, forming a distributed PV grid integration capacity assessment and optimization model that comprehensively considers the PV system's grid integration capacity and economic efficiency. Simulation results show that this model can effectively reduce the PV power plant curtailment rate, providing feasibility guidance for the application of PV in the region.

The invention relates to the technical field of tool management and control, and discloses a tool management and control method based on the Internet of Things and an enhanced Apriori algorithm, and the method comprises the steps: collecting tool data through combining the RFID technology of the Internet of Things and a wireless sensor; performing dynamic clustering analysis by using an improved DBSCAN algorithm, screening out a frequent item set by using space-time semantic enhanced Apriori, and generating an association rule; establishing a cost prediction model by using a multivariate adaptive regression spline, constructing a health state prediction model based on an attention mechanism neural network model, and constructing a residual life prediction model by using support vector machine regression; according to the method, the data is deeply mined based on the intelligent algorithm, the association rules are mined in combination with the improved DBSCAN algorithm and the Apriori algorithm fused with the space-time semantics, and decision support is provided for tool management and control. The tool management and control method has the advantages that the data is deeply mined based on the intelligent algorithm, and the association rules are mined in combination with the improved DBSCAN algorithm and the Apriori algorithm fused with the space-time semantics.

The invention discloses a polyurethane ultraviolet aging hardness prediction method based on support vector machine regression, and the method comprises the following steps: collecting hardness data of polyurethane changing with time in an ultraviolet aging process, constructing a data set, and carrying out the preprocessing; establishing a prediction model taking the aging time as input and the hardness value as output based on a support vector machine regression algorithm; the generalization ability of the model is improved through cross validation and hyper-parameter tuning; a plurality of indexes such as mean square error, mean absolute error, decision coefficient and the like are adopted to comprehensively evaluate the model prediction precision; and predicting the hardness of an unknown aging time point by using the trained model to realize reliable estimation of the aging hardness of the polyurethane. The method is high in prediction precision and high in interpretability, and an effective tool is provided for aging performance evaluation and service life prediction of the polyurethane material.