183 results about "Least squares support vector machine" patented technology

The invention relates to the technical field of information, and discloses a multi-factor space-time coupling water environment prediction method, which comprises the following steps: acquiring water environment internal data and water environment external data; preprocessing the water environment internal data and the water environment external data to obtain preprocessed multi-source data; the preprocessed multi-source data serve as a training set to be input into a random forest regression model, importance selection is conducted on the multi-source data, an influence factor set with the minimum error of the random forest regression model is selected, and the influence factor set serves as an optimal feature selection set; and taking the optimal feature selection set as a training set, inputting the training set into a least square support vector machine model, training the least square support vector machine model, obtaining a multi-factor coupled water quality prediction model, and obtaining a water environment prediction result based on the multi-factor coupled water quality prediction model. According to the method, the dimensionality of the least square support vector machine training model is reduced, and the prediction precision is improved.

The invention discloses a rolling bearing fault diagnosis method based on a WSETCNNLBKALSSVM, and the method comprises the steps: S1, carrying out the modal decomposition of a rolling bearing vibration signal collected by a sensor through employing the high time-frequency resolution characteristic of wavelet synchronous extraction transformation, obtaining a decomposed time-frequency image, and carrying out the lossless compression processing of the time-frequency image; s2, dividing the time-frequency image data after lossless compression processing into a training set and a test set according to a set proportion; s3, inputting the generated time-frequency image data in the training set into a two-dimensional convolutional neural network, and performing adaptive fault feature extraction; and S4, taking an output result of a full connection layer of the two-dimensional convolutional neural network as an input of a least square support vector machine, optimizing hyper-parameters of the LSSVM by adopting an improved black-wing optimization algorithm, and completing classification of fault types through the LSSVM. The rolling bearing fault diagnosis method has higher classification precision and diagnosis accuracy in rolling bearing fault diagnosis.

The invention discloses a high-sensitivity carbendazim detection method based on terahertz metamaterial resonance enhancement, and provides a terahertz metamaterial sensor with a cross-shaped composite double-peak structure, a terahertz spectrum technology is utilized to collect transmission spectrums, the difference of different concentrations among terahertz spectrums is explored, and the detection accuracy is improved. The analysis finds that the transmission peak amplitude of the terahertz transmission spectrum of the structure is in a decreasing trend along with the increase of the concentration of a carbendazim solution, data is preprocessed through multiple scatter correction (MSC), feature extraction is performed by adopting an iterative information variable reservation (IRIV) method, and a quantitative detection model of the carbendazim terahertz transmission spectrum is established based on an LS-SVM (least squares support vector machine). The invention provides a novel approach for quantitative detection of carbendazim, and experimental results show that the RP of the model reaches 0.9825, the RMSEP is 0.2001, and the detection limit LOD is 0.672 mu g / ml. The invention provides a new method for rapid, lossless and high-sensitivity detection of carbendazim in food.

The invention provides an EIS on-line detection system and method for a high-power fuel cell system, a main loop of the EIS on-line detection system adopts two Boost type converter topological structures which are connected in parallel, and small signal excitation injection is carried out through an auxiliary DC / DC converter 2; the control system comprises a sampling system, a PWM drive circuit, a communication module and an embedded controller. Establishing a battery equivalent circuit model; when a high-power fuel cell system runs, a multi-frequency sine periodic signal optimized by a non-dominated sorting genetic algorithm improved based on Tent chaotic mapping is injected into an output current of an auxiliary DC / DC converter 2; sampling the voltage of a single body or a galvanic pile, measuring current data, calculating the voltage drop corresponding to the internal resistance of the current data, converting a time domain signal into a frequency domain signal, and obtaining the battery impedance information in the corrected Foouquet equivalent circuit model; importing the characteristic parameter data into a least square support vector machine (LSSVM) for training and testing, and performing in-situ detection and fault diagnosis on line; according to the invention, a new way can be provided for active diagnosis and early warning of faults.

The invention belongs to the technical field of welding, discloses an electric arc welding process defect identification method based on multi-sensor information, and provides an electric arc welding process defect monitoring system based on welding process multi-sensor information. According to the system, current signals, temperature signals and visual image signals in the welding process are collected, the information theory and machine learning are combined, and real-time recognition of welding defects is achieved. The system aims at solving the problems of massive high-dimensional data volume generated by multi-sensor information fusion, rapid solving of appropriate model parameters and the like in a traditional welding defect detection method, and provides a welding defect identification method combining a mutual information method and a particle swarm optimization algorithm-least square support vector machine. Feature selection is carried out on multi-source sensing high-dimensional data through a mutual information method, optimization training is carried out on a particle swarm optimization algorithm-least square support vector machine model by utilizing a simplified data set, and a classification model established by the method has the characteristics of low complexity, short training and prediction time, relatively strong generalization ability and the like.

The invention discloses an improved integrated learning rock debris element content analysis system and method based on laser spectrum information. The method comprises the following steps: step 1, collecting a rock debris element LIBS spectrum data set; 2, dividing the rock debris element LIBS spectral data set into a training set and a test set, and carrying out noise reduction processing on original spectral data; 3, selecting characteristic spectral lines of the rock debris analysis elements according to the correlation coefficients, then selecting correlation interference spectral lines of the analysis elements, respectively combining the characteristic spectral lines with different correlation interference spectral lines to obtain more than one spectral line combination, and taking the spectral line combination as input of an integrated learning device; 4, constructing an ant colony optimization algorithm to optimize and improve the least square support vector machine ensemble learning base learner as an ensemble learner; and 5, designing a weighted distribution integration strategy of the base learners, and completing the optimal combination of the base learners. According to the method provided by the invention, a strong learner is constructed by combining a plurality of base learners, so that the accuracy and robustness of prediction are improved.

The invention discloses an electricity-heat-vibration multi-dimensional data fusion oil-immersed transformer abnormal state identification method, which comprises the following steps: analyzing multi-physical field coupling data on a transformer, extracting key characteristic parameters, and carrying out abnormal state identification by using an improved BES-LSSVM model. According to the method, a multi-source fusion data mining technology and a machine learning algorithm are combined, parameters are optimized by using a hybrid model of a BES optimization algorithm and a least square support vector machine, and classification and identification of abnormal working conditions are realized through a k-means clustering algorithm. According to the method, the accuracy and reliability of transformer abnormal state evaluation can be remarkably improved, and the fault occurrence rate and the maintenance cost are effectively reduced.

The invention discloses a lithium battery SOC-SOH joint estimation method based on mechanism and data fusion driving, and the method comprises the following steps: firstly, carrying out the training of a prediction model of a least square support vector machine (LSSVM, LSSVM) through a multi-dimensional data set after phase space reconstruction; and then, establishing a state equation based on an ampere-hour integral method and a capacity fading empirical model, establishing an IUPF observation equation through an LSSVM prediction model, and substituting the IUPF observation equation into an improved unscented particle filter algorithm (IUPF, Intermediate UPF) to perform state estimation and parameter updating. And finally, the updated LSSVM model parameters are iterated to a training data set, so that the combined prediction of the SOC (State OfCharge) and the SOH (State OfHealth) of the battery and the online updating of the data driving model are realized. According to the lithium battery SOC-SOH joint estimation method based on mechanism and data fusion driving provided by the invention, joint estimation of the state of charge SOC and the state of health SOH of the lithium battery is realized, and state noise and measurement noise of the system are considered, so that the precision and stability of a battery state estimation result are ensured.

The invention discloses a method for predicting the concentration of dissolved gas in transformer oil based on an EVO-VMD-LSSVM. An energy valley optimization algorithm EVO, variable mode decomposition VMD and a least square support vector machine LSSVM are combined and used. The method comprises the following steps: decomposing an original dissolved gas concentration signal into a plurality of intrinsic mode functions IMFs with different frequency characteristics through a VMD method, and extracting multi-scale characteristics of the signal; a plurality of factors such as transformer load, operating oil temperature, environment temperature and operating time are introduced into the model, an LSSVM prediction model is established for IMF in combination with characteristic parameter correlation, LSSVM model parameters are optimized by using an EVO algorithm, and prediction precision is improved; reconstructing the prediction result of each IMF to obtain a final dissolved gas concentration prediction value; the prediction performance is obviously better than that of a traditional method, and evaluation indexes such as mean square error (MSE), mean absolute error (MAE) and decision coefficient (R) show that the method has high prediction precision and robustness.

The invention discloses an ultra-short-term wind power prediction method based on scene real-time switching. The method comprises the following steps: firstly, extracting a multi-dimensional feature vector capable of representing a wind speed fluctuation degree from a historical wind speed time sequence; and then clustering the multi-dimensional feature vectors by adopting a K-means clustering algorithm, and dividing the wind power operation state into a plurality of scenes. Then distinguishing a strong fluctuation scene and a weak fluctuation scene according to the sample entropy of each scene, wherein the strong fluctuation scene is predicted by adopting a CNN-BIGRU model; and the weak fluctuation scene adopts a least square support vector machine (LSSVM) model. And finally, according to the real-time wind speed data, identifying fluctuation characteristics of a previous time sequence segment of a to-be-predicted moment, calculating an Euclidean distance with a scene clustering center, judging a scene to which the to-be-predicted moment belongs, selecting a corresponding prediction model, and dynamically generating a wind power prediction result. According to the method, adaptive switching of different scenes can be realized in an environment with frequent and complex wind speed fluctuation, and the accuracy and stability of ultra-short-term wind power prediction are effectively improved.

The invention relates to a method for optimizing cutting machining parameters of a cast thin-wall cabin, which solves the problems of low machining efficiency and unstable machining precision of the current cast thin-wall cabin in numerical control machining, and comprises the following steps of: 1, classifying machining features of a cast thin wall, and determining feature allowance based on a three-dimensional scanning result of a casting; step 2, aiming at the divided machining features, based on machining deformation test data, establishing a feature machining deformation prediction model by using a least square support vector machine; and step 3, for the divided machining features, establishing a cutting machining parameter optimization model of the features, and performing optimization solution by using a genetic algorithm to realize cutting machining parameter optimization. According to the method, cutting machining parameter optimization selection is carried out on different machining characteristics, meanwhile, the influence of cutting machining parameters on machining deformation is considered when the cutting machining parameters are optimized, the machining efficiency and precision are considered, and the optimized cutting machining parameters have more guiding significance for actual production.

The invention discloses a combined model pollutant concentration prediction method and system based on multiple strategies, a storage medium and electronic equipment. The method comprises the following steps: firstly, performing multi-pollutant signal decomposition and feature fusion; then realizing data set division and standardization processing; time sequence window division and data tensor conversion are carried out; then training and preliminarily predicting an LSSVM (Least Square Support Vector Machine) model based on DBO optimization; and finally, carrying out residual error compensation and model performance improvement of an error correction module. According to the method, the hybrid kernel function least square support vector machine and the computational fluid mechanics correction model are innovatively combined, and the improved dung beetle algorithm is utilized to perform multi-objective optimization on the kernel parameters and the error compensation coefficient; and multi-modal feature mapping is realized through a dynamic weight distribution layer, and a photocatalytic reaction kinetic equation is introduced to implement prediction value inversion. The method successfully overcomes the technical bottlenecks that a traditional model lags in response to sudden emission and is insufficient in multi-pollution coupling effect modeling, and provides accurate decision support for air pollution prevention and control.

The invention relates to the technical field of rapid quantitative and nondestructive detection of food, and particularly provides a rapid quantitative detection method for lactose content and crystal form proportion based on a near infrared spectrum technology, which comprises the following steps: preparing a lactose-whey protein mixture sample; scanning through an instrument to obtain near infrared spectrums of the samples prepared at different gradients; preprocessing the spectrum to remove noise information, establishing a least square support vector machine method model, and screening out optimal interval data of modeling; establishing a prediction model by applying a principal component analysis method; a correlation coefficient value (R2) and a predicted root-mean-square error (RMSEP) are used as key indexes of model robustness test, so that the lactose content and the crystal form proportion in the lactose-whey protein mixture can be rapidly and accurately predicted within a short time (several seconds) on the premise of not damaging a sample. The method has important reference value for regulating and controlling the structure function and application of the whey protein.

The invention relates to the field of artificial intelligence, and discloses a phase modifier operation data analysis fault diagnosis method, device and equipment, and the method comprises the steps: obtaining a historical operation data sample of a phase modifier, constructing the multi-dimensional operation characteristics of the phase modifier based on a non-coding ratio method, and carrying out the dimension reduction of the multi-dimensional operation characteristics through a kernel principal component method, obtaining a first sample data set; generating a new fault feature sample based on the auxiliary confrontation generative network to balance the sample data set and obtain a second sample data set; and training a phase modifier fault diagnosis model based on a least square support vector machine by using the second sample data set, inputting to-be-diagnosed operation data of the phase modifier, extracting multi-dimensional operation features, performing kernel principal component analysis dimension reduction processing, and inputting the trained phase modifier fault diagnosis model to perform fault diagnosis. According to the method, the problems of few fault samples, unbalanced category distribution, high feature dimension and redundancy of the phase modifier are effectively solved, and the accuracy, generalization and real-time performance of fault diagnosis are improved.

The invention belongs to the technical field of underwater acoustic navigation, and provides an underwater acoustic navigation enhancement method and system, a storage medium and equipment, and the method comprises the steps: carrying out the standardized preprocessing of obtained sonar data and seawater parameter data, carrying out the partitioning of the preprocessed data, considering the propagation time of sound waves, constructing a time observation equation, and solving the time observation equation. Joint estimation is carried out on the position of the seabed beacon of the azimuth and the nadir total delay error parameter; taking the estimated nadir total delay error parameter and the corresponding time sequence as a training sample set, constructing and training a nadir total delay error prediction model by adopting a least square support vector machine method, and taking the model as a target function by minimizing the sum of double absolute values of all error variables and weight vectors; and correcting the sound velocity time-varying error in the long-baseline underwater acoustic navigation by using the trained model, and carrying out navigation calculation by using extended Kalman filtering based on the corrected sound velocity time-varying error. According to the invention, the precision of underwater acoustic navigation is improved.

The invention discloses a method and a device for monitoring the temperature of a busbar of a flexible direct-current transmission power sub-module. The method comprises the following steps: acquiring operation parameters of a converter valve under different temperature conditions; inputting the operation parameters into the busbar temperature prediction model, and outputting a corresponding busbar temperature prediction value; the busbar temperature prediction model is obtained by training a least square support vector machine model by taking historical operation parameters as input and historical busbar temperature as output; acquiring temperature values at a plurality of contact points of the surface of the busbar and the sensing optical fiber, and selecting a maximum temperature value from the temperature values at the plurality of contact points as an actual temperature value of the busbar; and calculating a temperature difference value between the busbar temperature prediction value and the busbar temperature actual value, and monitoring the temperature state of the busbar according to a temperature difference value calculation result. The operation safety and stability of the converter valve can be improved.

The invention relates to a method for predicting a PM2.5 concentration field of a pavement area. The method comprises the following steps: using simulation software FLUENT to obtain two-dimensional flow field data when a vehicle moves under a plurality of different working conditions, and extracting target physical quantities of a plurality of sampling points on a plane under the different working conditions through numerical simulation post-processing software so as to construct a multi-working-condition vehicle movement flow field snapshot matrix; carrying out order reduction operation on the snapshot matrix by adopting an intrinsic orthogonal decomposition method to obtain main modes of the flow field and mode coefficients of the main modes; constructing a mapping relation between a modal coefficient and a working condition through a least square support vector machine model to quickly obtain a modal coefficient corresponding to a target working condition, and linearly combining the modal coefficient with the main modal to obtain flow field data corresponding to the target working condition; and verifying the accuracy of the reduced-order model by calculating an error between the reduced-order model and the full-order model. The method can be suitable for a dynamic mesh model, and provides an order reduction method for dynamically changing flow field calculation.

The invention belongs to the technical field of agricultural product detection, and discloses a rapid detection device and method for the sugar degree of prunes, and the rapid detection device for the sugar degree of the prunes comprises a sugar degree instrument shell, a power supply unit, a sugar degree detection unit, a sugar degree instrument control unit, a sugar degree display unit and an optical fiber probe. The signal sending module, the signal receiving module and the power supply unit are electrically connected with the information processing module, and the optical fiber probe is connected with the signal sending and receiving module through an optical fiber. The detection method comprises the following steps: sending an optical signal to the prune by using the light source, collecting reflected light spectrum information by using the micro spectrometer, transmitting the reflected light spectrum information to the Raspberry Pi to construct a two-dimensional spectrum characteristic matrix, and inputting a prune regression model based on a least square support vector machine regression model to obtain a sugar degree detection result. According to the technical scheme provided by the invention, rapid sugar degree detection can be carried out on prunes with different sugar degrees.

The invention provides a ground temperature field change prediction method based on a fuzzy least square support vector machine, and belongs to the technical field of ground temperature field prediction, and the method comprises the following steps: S1, based on a drill hole distribution diagram, arranging shallow ground temperature monitoring points, and obtaining initial monitoring parameters; s2, performing Pearson correlation analysis based on the initial parameters, and determining input parameters of the prediction model; s3, performing fuzzification processing on the input parameters, and establishing a fuzzy least square support vector machine model; s4, parameters are optimized based on a fruit fly optimization algorithm, and self-training and self-verification of a fuzzy least square support vector machine model are carried out; and S5, performing ground temperature field prediction based on the trained and verified fuzzy least square support vector machine model. According to the method, fuzzy processing is carried out on the data, subsequent effective processing of the data is facilitated, and the rapidity and accuracy of model prediction are effectively improved by fully utilizing the least square support vector machine.

The invention discloses a thermal power plant boiler combustion control method and system, and relates to the field of boiler combustion system control technical equipment. By collecting control loop data of a boiler combustion system in real time, dynamic characteristics, stability and response efficiency of a loop can be analyzed through a multi-dimensional performance evaluation model (including six indexes of steady-state tracking performance, static tracking performance, maximum control deviation, steady-state error, over-limit time and comprehensive tracking performance); based on the evaluation result, the air-coal ratio, the oxygen amount set value and the over-fire air door opening degree are dynamically adjusted through a combustion linkage algorithm, and intervention correction is conducted on set values of loops such as superheat degree control, primary air, secondary air and the coal amount of a coal mill when the performance of the control loop is deteriorated; combining a least square support vector machine to predict NOx concentration and boiler efficiency; and finally, combustion optimization is realized through closed-loop control. The problems that traditional PID control lags behind and loop coordination is poor are solved, boiler efficiency is improved, and pollutant emission is reduced.

The invention relates to the field of vehicle route planning, in particular to a dynamic multi-target vehicle route planning method and system based on prediction and recombination, and the method comprises the steps: building a mathematical model of dynamic multi-target vehicle route planning, initializing a population through real number coding, adjusting the population to meet a constraint, carrying out the iteration through a genetic algorithm, and obtaining a candidate solution set; a Pareto front set is obtained and clustered, and a clustering center is recorded as a special individual; and when environment change is detected, predicting individuals in a new environment by using a gray model or a least square support vector machine, and constructing a new population until an optimal solution set is output. According to the method, the convergence speed of the algorithm is high, the response to the environment change is more sensitive and timely, the dynamic multi-target vehicle routing problem is effectively solved, and the application effect and the economic benefit of the vehicle routing in the actual life are effectively promoted.

The present invention discloses a method for automatic fault detection and repair of a self-healing intelligent power line, comprising the following steps: using a K-Means clustering algorithm to perform cluster analysis on fault sensor data, determining fault-frequently occurring areas, and then, for line equipment parameters and electrical parameters in a local repair section, using a convolutional neural network model of heterogeneous data fusion to perform feature extraction and matching analysis on line layout images and parameter data, judging the compatibility of a local repair scheme with the original line planning, using a least squares support vector machine model to perform real-time data analysis during the local repair construction process, performing live debugging and comprehensive evaluation on the reconstructed local line to judge whether the reconstruction effect meets the construction standard of the self-healing intelligent power line, using a knowledge graph-driven long-short-term memory network model to predict and infer equipment health status and fault risk, automatically generating state maintenance and fault repair strategies, and realizing self-healing operation of the local line.

The invention discloses a hydropower station daily runoff prediction method, system and equipment and a readable storage medium, and the method comprises the steps: building a prediction model based on a least square support vector machine, employing a multi-strategy improved cooperative search algorithm, carrying out the parameter optimization through a sine initialization solution space strategy, a dynamic communication strategy and a migration variation strategy, and obtaining a prediction result through a prediction model; therefore, an aggregation effect occurring in a traditional model parameter optimization technology is avoided, and the domain optimization capability and the global search capability are improved; meanwhile, error space-time comprehensive correction is carried out on a prediction result, and a final daily runoff prediction value is obtained. The daily runoff prediction precision of the hydropower station is improved, and reliable data support and technical support are provided for power grid dispatching.

The invention relates to the technical field of power cable intelligent monitoring, and aims to solve the technical problems that in an existing cable state monitoring system, multi-source heterogeneous data fusion is insufficient, the coupling relation between physical quantities is difficult to reveal, and insulation aging, current-carrying capacity and fault probability cannot be quantitatively predicted according to evaluation results. The invention discloses a cable state multi-source data fusion and prediction system. The system comprises a data acquisition module, a feature processing module, a prediction analysis module and a cloud management platform. The system collects partial discharge, surface temperature and vibration data of the cable through the distributed terminal; performing spectrogram decomposition and fusion of the multi-source data by adopting a non-subsampled contourlet transform algorithm to generate multi-scale fusion features; building a prediction model based on a particle swarm optimization least square support vector machine, and outputting an insulation aging state prediction value, a current-carrying capability evaluation value and a fault probability prediction value of the cable; and data management and full-life-cycle service are realized through the cloud platform.

The invention relates to a plug-in hybrid electric vehicle model predictive control energy management method based on reinforcement learning, and aims to solve the energy management optimization problem of a plug-in hybrid electric vehicle. The method comprises the following steps: firstly, predicting a vehicle speed in a future time domain by using a least square support vector machine-neural network (LSSVM-BP) model; then, calculating power required by a vehicle in a prediction time domain based on a vehicle dynamics model, and solving an energy management problem through a model prediction control algorithm; and finally, performing rolling optimization based on a reinforcement learning method to obtain a control variable sequence of each stage in a prediction time domain, and taking a first element of the control variable sequence as output. According to the method, reinforcement learning and a model prediction control algorithm are combined, optimal distribution of the energy source of the plug-in hybrid electric vehicle is achieved, and the fuel economy of the vehicle is improved.

The application discloses a method for monitoring error-related potentials in a brain-computer interface based on mutual information, and relates to a method for identifying error-related potentials, and aims at solving the problem of low recognition rate of error-related potential signals in the existing brain-computer interface. The application extracts time domain features by pre-processing original electroencephalogram signals and performing non-overlapping sliding window analysis; extracts frequency domain features from the signals by using the Welch method; combines the time domain features and the frequency domain features; uses mutual information as a measure between features and positive and negative categories; calculates the mutual information of the features and the categories and sorts them; filters out the features with high rankings; uses a least squares support vector machine to classify initial error-related potentials; performs leave-one-out cross-validation on samples; obtains and retains individual optimal models; and obtains the accuracy rate of final error-related potential classification. The application has the beneficial effect of improving the recognition accuracy of error-related potentials.

The invention belongs to the technical field of constructional engineering, and particularly discloses a shear wall external scaffold monitoring method and system based on an intelligent algorithm. Comprising the steps that a monitoring system is built, monitoring data of the shear wall external scaffold are collected according to the monitoring system, and a sample data set is built based on the monitoring data; the sample data set is input into an SD-MOP model, and the accident occurrence probability in the scaffold service process is calculated; on the basis of the accident occurrence probability component constraint condition, taking the constraint condition as a target function of a least square support vector machine model, taking key influence parameters of the strength and the stability of the scaffold as input variables, and performing optimization training on the least square support vector machine model to obtain an accident occurrence probability component; and obtaining an optimal prediction model for monitoring the strength and stability of the scaffold. The safety and reliability of the scaffold are remarkably improved, construction accidents are reduced, and the life safety of workers and smooth proceeding of engineering are protected.

The invention belongs to the technical field of plant factories, and particularly relates to a light environment optimization control method and system and an intelligent illumination regulation and control multi-layer plant factory based on photoelectric agriculture. Light environment optimization control specifically comprises the steps that multi-factor data of a plant growth environment is collected, a photosynthetic rate prediction model based on a least square support vector machine is constructed, and a photosynthetic rate prediction model is constructed; and predicting photosynthetic rate response curves in different light environments. And determining an inflection point of a photosynthetic rate curve through differential operation, and determining an optimal light quality ratio and a light environment regulation strategy based on cost-photosynthetic effect analysis. The factory roof is combined with the photovoltaic panel and the light uniformizing plate, and optimal utilization of natural light and artificial light is achieved. The energy utilization efficiency is improved, the growth efficiency and yield of plants are remarkably improved by precisely regulating and controlling the light environment, and the system is suitable for efficient planting of various plants.

The invention provides a dynamic modeling simulation method and system for the energy efficiency ratio of a solar seawater desalination system, and belongs to the technical field of seawater desalination and system modelling. Membrane surface resistance, selective permeability and direct-current bus voltage ripple data in the photovoltaic electrodialysis process are firstly obtained; secondly, Fourier transform is carried out on ripple data, then the ripple data and membrane parameters are spliced to generate an input matrix, the matrix is imported into a deep belief network, and a restricted Boltzmann machine is used for extracting an unsteady-state ion impedance vector reflecting the influence of voltage fluctuation; a nonlinear regression model of the vector and unit water production energy consumption is established through a least square support vector machine; finally, the water production rate and the energy efficiency ratio are calculated according to the predicted energy consumption and the photovoltaic power, and a dynamic simulation curve is generated. According to the method, the nonlinear influence of the photovoltaic voltage ripples on the membrane impedance can be quantified through deep learning, and the precision of predicting the energy efficiency ratio of the seawater desalination system under the fluctuating power supply working condition is remarkably improved.

The application discloses a kind of power forest fire intelligent monitoring method, device and equipment based on through remote fusion, and storage medium, it is related to forest fire intelligent monitoring technical field, comprising: obtaining the position information of first monitoring node, and obtaining the temperature and humidity data and smoke concentration data corresponding to position information as environmental data;Position information and environmental data are input into least square support vector machine model for processing, and fire risk probability is obtained;When fire risk probability is greater than preset risk threshold, position information, environmental data and fire risk probability are combined to obtain fire preliminary screening information, and fire preliminary screening information is uploaded to cloud server, so that cloud server fuses satellite remote sensing data to complete fire confirmation, realize the non-blind area monitoring coverage of power forest fire along transmission line, realize the preferential transmission of key fire data, reduce communication resource waste, improve system response speed and transmission reliability of key data.