38 results about "Polynomial regression model" patented technology

The invention belongs to the field of phase change heat storage, and discloses a phase change heat exchange fin parameter optimization method and a related device.According to the method, a three-dimensional parameterized model of a tree-shaped fractal fin containing the length ratio, the width fractal dimension and the fin number is established, representative parameter sample points are efficiently generated through Box-Behnken experimental design, and the representative parameter sample points are optimized; and a liquid phase rate and a total heat storage capacity response value are obtained by combining numerical simulation of phase change heat transfer, and multi-target parameter global optimization is realized based on a second-order polynomial regression model. By the adoption of the method, optimization efficiency and precision are remarkably improved, on the premise that a large number of repeated experiments or simulations are not needed, the optimal fin structure parameters capable of cooperatively strengthening heat conduction, promoting melting of the phase-change materials and maximizing the heat storage capacity are rapidly obtained, and a reliable design basis is provided for engineering application of the fractal fin strengthening phase-change heat storage technology.

The invention provides a process parameter optimization method and system for a functionally graded material and a medium, which can be applied to the technical field of process parameter optimization. According to the method, after corresponding technological parameters and forming response data under different component gradients are obtained through an orthogonal test, a quadratic term polynomial regression model used for representing the mapping relation between the technological parameters and the forming response data in the different component gradients is constructed according to the technological parameters and the forming response data. A quadratic term polynomial regression model is used as a virtual simulation environment, meanwhile, after a multi-dimensional continuous strengthening model is constructed based on a double-delay depth deterministic strategy gradient algorithm, based on the multi-dimensional continuous strengthening model, through dynamic interaction of an intelligent agent and the virtual simulation environment, an optimal process parameter combination is searched in different component gradients; and thus, cross-component gradient process parameter optimization is realized. And finally, verifying the optimal process parameter combination through a physical experiment, and determining the accuracy of model prediction.

The invention discloses a sight tracking method and system based on cascade deep learning and distance correction, and a storage medium. The method comprises the following steps: processing an eye image through a cascade deep learning model, and obtaining pupil center coordinates and cornea reflection light spot coordinates of two eyes; calculating a binocular pixel distance as a distance correction factor based on the pupil center coordinates, and calculating a relative position difference vector based on the pupil and light spot coordinates; establishing a three-dimensional nonlinear polynomial regression model taking the relative position difference vector and the distance correction factor as input; obtaining calibration data through a multi-distance sampling strategy and solving a model coefficient; and in the real-time tracking stage, calculating the coordinates of the fixation point of the screen by using the solved model. According to the method, the distance correction factor and the three-dimensional mapping model are introduced, so that geometric errors caused by forward and backward movement of the head of the user are effectively eliminated, meanwhile, the feature point positioning precision is ensured through cascaded deep learning detection, and the precision and robustness of a sight line tracking system are remarkably improved.

The invention relates to the technical field of earphone equipment, in particular to a Bluetooth earphone non-contact type dynamic control system and method based on gesture recognition, and the system comprises an induction signal collection module which obtains the total number of clock cycles and generates an original count value, a signal standardization module which carries out the operation of the original count value to generate a standardization intensity value, and a control module. The spatial feature distinguishing module is used for generating a spatial curvature gradient coefficient based on the standardized intensity numerical value and screening an effective intensity numerical value; and the linear mapping control module is used for performing nonlinear correction mapping on the effective numerical value, generating a vertical distance coordinate numerical value and generating a volume gain control parameter based on a change rate. According to the method, environment interference is eliminated through standardized data differential operation, effective fingertip control gestures and large-area skin contact are distinguished through space curvature gradient coefficients, a polynomial regression model is called to establish the corresponding relation between signal intensity and distance coordinates, non-contact hovering interaction is achieved, and ear canal compression and mechanical abrasion are avoided.

The present application relates to a kind of shale oil carbon dioxide fracturing production dynamic intelligent prediction method, the method is applied to the improved AHP-GRA-EWM method quantitatively calculate the influence weight of different categories factors to different stage carbon dioxide fracturing-throughput shale oil well production dynamic parameter, obtain the main control influence factor of different production stage production dynamic parameter;The main control influence factor of different production parameters of different production stages is respectively introduced into different artificial neural network system, nonlinear polynomial regression model system and different percolation mathematical model, establishes prediction model, selects the model with highest prediction accuracy to correct improvement, combination generates the production dynamic intelligent prediction model of the entire production cycle of target shale reservoir block carbon dioxide fracturing-throughput shale oil well;Based on the intelligent prediction model, the different production parameter values of different production stages of new well are calculated, and the production dynamic curve is drawn.The method designed by the present application can realize the fine and efficient prediction of carbon dioxide fracturing-throughput shale oil well production dynamics, provide basis for the development of development plan and optimization, improve the single-well production and recovery ratio of target shale reservoir block.

The present application relates to the technical field of prosthesis lattice design, in particular to a kind of orthopedic implant lattice design method, system and application, by establishing parameterized lattice model sample set, and the polynomial regression model of elastic modulus and permeability coefficient is combined with the bayesian optimization framework with comprehensive score objective function as optimization goal, so that it can be iteratively optimized in parameter range efficiently, finally output optimal design parameter to make the mechanical property and mass transfer performance reach the best balance, fundamentally solve the problem that existing PEEK lattice implant design lacks double performance collaborative optimization, further improve lattice prosthesis postoperative recovery effect.

The invention discloses a method for evaluating the influence of data-driven distributed photovoltaic grid connection on feeder line voltage fluctuation. According to the method, the thought of combining simulation data modeling and actual measurement data correction is comprehensively utilized, and accurate quantitative evaluation can be carried out on node voltage fluctuation characteristics in the absence of comprehensive monitoring conditions; a voltage variation polynomial regression model is constructed through a simulation sample, regression model parameter correction is carried out in combination with actually measured historical data, and finally distributed photovoltaic output is input to obtain a whole-network voltage variation sequence. And accurate prediction of node voltage fluctuation amplitude and frequency index caused by distributed photovoltaic output under the condition of incomplete monitoring data is realized through data processing. The method is high in accuracy and high in engineering applicability, is particularly suitable for being applied to a medium and low voltage power distribution system which comprises a plurality of photovoltaic access points and is relatively high in fluctuation characteristic, and has a good popularization prospect.

The invention relates to the technical field of intelligent beekeeping, and discloses a mature honey standardized production control system based on strong group multiple box bodies, which comprises a dielectric spectrum sensing array integrated on a comb foundation, a data acquisition module, a central processing module and a user interface module. According to the system, the micro interdigital capacitor unit is used for independently detecting electromagnetic characteristics of substances in cells, the central processing module obtains complex impedance spectroscopy and temperature data, a content classification model and a polynomial regression model containing temperature cross terms are operated in parallel, the types of the contents in the cells are recognized, and the water content of honey is accurately calculated. The system establishes data mapping between a super production area and a hive breeding area, respectively calculates a mature honey harvest index and a breeding health degree score, and automatically generates a honey extraction recommendation or a health alarm. According to the invention, lossless, in-situ and high-resolution monitoring of the internal state of the beehive is realized, and the problems that the bee colony is interfered by manual box opening and the honey maturity is lack of quantitative standards are solved.

The application provides a configuration method and device of a composite substrate for sewage treatment and a server, relates to the technical field of water treatment material preparation, and comprises the following steps: performing central combination treatment on a ternary composite substrate for sewage treatment to generate a test formula set; obtaining test data corresponding to the test formula set, taking the removal rates of various substrates as response values, and obtaining target test data; performing regression analysis processing on the target test data in a quadratic polynomial regression model of the ternary composite substrate to obtain model prediction values, and performing model verification and update processing on the quadratic polynomial regression model by using the model prediction values and the target test data to determine a target quadratic polynomial regression model; and performing optimization processing on the proportioning of the ternary composite substrate by using the target quadratic polynomial regression model to determine a target mass proportioning of the ternary composite substrate. The application can significantly improve the pollutant removal rate of the collaborative pollutant removal of various pollutants in the sewage treatment process.

The invention provides a collaborative optimization method for a magnetic-thermal composite field assisted laser cladding process. According to the method, a magnetic-thermal composite field-assisted laser cladding device is designed based on an energy field-assisted laser cladding process principle and is used for improving the structure and performance of a coating; the method comprises the following steps: generating a quadratic polynomial regression model on the basis of an RSM method, magnetic-thermal composite field assisted laser cladding process parameters and nickel-based composite coating quality evaluation indexes, performing multi-target optimization on the coating quality evaluation indexes on the basis of the quadratic polynomial regression model and an IMOGWO algorithm, determining a magnetic-thermal composite field assisted laser cladding target process parameter combination, and determining the quality of the nickel-based composite coating. And score calculation is carried out on the target process parameter combination based on an EWM-TOPSIS method to obtain a comprehensive evaluation sequence of the target process parameter combination, so that collaborative optimization of the magnetic-thermal composite field assisted laser cladding process is realized, and the performance optimization efficiency and quality of the nickel-based composite coating are improved.

This invention provides a method and system for controlling the stability of steel slag aggregate, belonging to the technical field of steel slag aggregate stability control. The method determines the degree of specimen damage under different combinations of maximum steel slag particle size and dosage through autoclaving accelerated testing. The degree of damage is quantified into a continuous stability index according to a preset grading standard. A quadratic polynomial regression model is established between the stability index and the maximum steel slag particle size and dosage, which also includes interaction terms for particle size and dosage. Based on the model and a preset engineering acceptable threshold, the maximum safe dosage or maximum allowable particle size of steel slag that meets the threshold requirement is calculated. The mix design of road base materials is then performed based on the calculation results. This invention achieves continuous quantitative characterization of the influence of steel slag particle size and dosage on stability, overcoming the shortcomings of existing technologies that rely on single-factor empirical limits and lack engineering back-calculation methods. It can improve the resource utilization rate of steel slag while ensuring stability.

The invention discloses a gangue concrete filling material ratio improving method based on a multi-objective optimization algorithm, which comprises the following steps: step 1, determining raw material composition and key influence factors of a gangue concrete filling material, step 2, carrying out test design and performance test based on RSM-BBD, the method comprises the following steps of 1, establishing a multi-objective optimization model, 2, establishing a quadratic polynomial regression model and carrying out reliability analysis, 3, determining a multi-objective optimization objective, a constraint condition and an optimization algorithm, 5, solving the multi-objective optimization algorithm and screening an optimal ratio, 6, carrying out performance verification and engineering application of the optimal ratio, and 7, carrying out dynamic updating and iteration of an optimization model. According to the method, the RSM-BBD test design and the multi-objective optimization algorithm are fused, the comprehensive influence and interaction of the mass fraction of the rubber material, the mixing amount of the alkali activator and the mass fraction of the solid material on the bleeding rate, the strength and the slump of the filling material are comprehensively considered, the limitation of a traditional single-factor experiment method is overcome, and the scientificity and the reliability of a matching optimization result are improved.

The present disclosure provides an image color correction method and apparatus, a color correction robot, and a storage medium. The method comprises: acquiring an image to be processed; extracting color values of pixels in a plurality of color blocks in the image to be processed to obtain a color value list; setting a weight coefficient for each color value in the color value list; using the color value list having the weight coefficients to fit a polynomial regression model to obtain a fitted polynomial regression model; using the fitted polynomial regression model to generate a color conversion matrix, wherein the color conversion matrix is used for representing a mapping relationship between the color values in the image to be processed and standard color values in a color chart; and using the color conversion matrix to correct the color values of the pixels in the image to be processed, to obtain a corrected image. The present embodiment improves the color accuracy of an image and ensures the reliability and authenticity of image information, thereby providing reliable data support for subsequent intelligent analysis and assisted diagnosis.

The invention discloses a variable-frequency driving access system suitable for a coal mine high-voltage power grid and a control method, and particularly relates to the technical field of access control of a coal mine power system. The method comprises the steps of firstly obtaining a voltage grade, a power grid topological structure, a grounding mode, system impedance, a power grid load change curve and a bus transient stability coefficient of a coal mine high-voltage power grid, and constructing a dynamic stability model; the output voltage, the current harmonic component, the impact current, the voltage change rate and the carrier frequency of the variable frequency driving equipment are obtained and serve as disturbance input for multi-scene simulation, and a bus response index is extracted; a polynomial regression model is utilized to predict an interference level coefficient, the interference level coefficient is compared with a safety threshold value, a target access parameter group is screened, an access path and a buffer modulation strategy are determined, a control instruction is generated and transmitted to a main control unit, and stable access of equipment in a high-voltage power grid is achieved; according to the invention, the adaptability and safety of frequency conversion equipment access are improved, and the method is suitable for dynamic control requirements in a complex power grid environment.

The invention discloses a power consumption management method, device and equipment for a desulfurization plant, a storage medium and a program product. The method comprises the following steps: acquiring current uncontrollable working condition parameters influencing power consumption of a target desulfurization plant and an actual power consumption rate; according to the uncontrollable working condition parameters, predicting by using the trained polynomial regression model to obtain a theoretical power consumption rate; and determining the power consumption rate deviation between the actual power consumption rate and the theoretical power consumption rate, so as to evaluate the regulation and control effect based on the controllable working condition parameters influencing the power consumption of the target desulfurization plant according to the power consumption rate deviation. The power consumption rate is predicted by using the polynomial regression model, a nonlinear relation can be accurately described, so that the prediction precision is improved, meanwhile, the regulation and control effect of the controllable working condition parameters is evaluated through the deviation between the theoretical power consumption rate obtained through prediction based on the uncontrollable working condition parameters and the corresponding actual power consumption rate, uncontrollable factors are well stripped, and the method is suitable for large-scale popularization and application. Therefore, the actual regulation and control capability of the operation team on the controllable factors can be evaluated more objectively and fairly.

This invention relates to an automatic focusing method for laser galvanometers based on a polynomial regression algorithm, including variable definition, data collection of sample data within different power ranges from 10% to 80%, and data preprocessing; employing a piecewise polynomial regression model, and adding an L2 regularization term to the model to prevent overfitting; and minimizing the observed value D. i The model parameters are estimated using the sum of squared errors between the predicted and actual values. The model is used to predict values ​​in real time. When the absolute value of the deviation ∈ between the predicted and actual roundness values ​​D exceeds the threshold Th, the galvanometer height H or laser power P is dynamically adjusted to make D approach the target value D. target =0; the system is based on the optimal height H * The Z-axis position of the galvanometer is adjusted in real time to focus the laser beam at the optimal position; the deviation between the actual value D and the predicted value ∈ is periodically collected to update the regression model coefficients. This invention enables precise focusing of the laser beam, improving product consistency and yield.

The invention provides a long-span bridge soft flutter response prediction method and device, and relates to the technical field of bridge safety, and the method comprises the steps: obtaining the parameter information of a long-span bridge; constructing a physical model and an initial numerical model based on the parameter information of the long-span bridge; performing a soft flutter wind tunnel test based on the physical model to obtain actually measured vibration responses at different test wind speeds; parameter optimization is carried out on a multivariate polynomial regression model of preset optimization parameters, an optimal numerical model suitable for different test wind speeds is obtained, and a target function of parameter optimization is constructed based on the simulated vibration response and the actually measured vibration response; performing interpolation on the optimal numerical model to obtain a digital twinborn body of the long-span bridge; and carrying out soft flutter response prediction on the large-span bridge according to the digital twin. According to the method, the problems of limitation and uncertainty when the soft flutter response of the long-span bridge is predicted in the prior art are solved.

The invention discloses an antenna bandwidth extension optimization method and device which can be used in the field of railway communication, and the method comprises the steps: obtaining geometric parameters of a to-be-optimized antenna, extracting data features of the geometric parameters, and obtaining a feature data set; after the feature data set is subjected to normalization processing, a learning label is added to each group of data features in the feature data set; determining the plurality of groups of data features added with the learning labels as a plurality of groups of sample sets, and for a ridge regression model, a polynomial regression model and a random forest model, training each model by using each group of sample sets; and constructing a feature set according to the resonance bandwidth response value predicted by each model and the multiple groups of sample sets, performing intensive training on each model by using the feature set, determining a model with an optimal test result, and optimizing antenna bandwidth extension by using the model with the optimal test result. The antenna design optimization cost can be reduced, and the antenna performance can be improved.

The invention discloses a conductivity prediction and condition backstepping system based on a multi-parameter regression model, which relates to the technical field of material conductivity prediction and comprises a data input module, a regression model processing module, a calculation engine module, a result output module and a user interface module. By constructing the multi-parameter third-order polynomial regression model and combining with a software system, accurate prediction and condition back-stepping of the material conductivity are realized, the experimental efficiency and reliability are remarkably improved, nonlinear mapping of parameters and conductivity can be described with high precision, and the system integrates forward prediction and reverse derivation functions, so that the system has high practical value. The conductivity can be output in real time by inputting parameters by a user, or feasible experiment conditions are quickly and reversely deduced according to the target conductivity, so that the optimization period is greatly shortened, the number of blind tests is reduced, a data-driven technical support is provided for performance optimization of a multi-parameter material, and the method has high popularization value.

The invention discloses a multi-solvent system tobacco essence flash point regulation and control method. The method comprises the following steps: acquiring multi-solvent content distribution ranges in different tobacco essence systems, and establishing a data set; obtaining flash point distribution ranges of different tobacco essence systems; constructing a multivariate polynomial regression model based on the influence of the content of the multivariate solvent on the flash point of the tobacco essence; improving and optimizing the multivariate polynomial regression model; based on the optimized regression model and the target essence flash point, obtaining the optimal ratio of each solvent in the essence system; adjusting the formula of the essence solvent according to the optimal ratio of each solvent; and evaluating and verifying the flash point of the essence after the solvent formula is adjusted. According to the invention, the flash point regulation and control of the multi-component solvent system tobacco essence can be realized, the flammable and explosive risks of the low-flash-point essence are reduced, and the transportation, storage and management safety of the tobacco essence is improved.

The invention discloses a temperature compensation method of a capacitive force / torque sensor and a sensor structure of the capacitive force / torque sensor. The sensor comprises an upper-layer substrate, a middle substrate and a lower-layer substrate, and an interdigitated electrode is arranged between the upper-layer substrate and the middle substrate and used for tangential force detection; a strip-shaped dielectric layer and a capacitor electrode array are arranged between the middle substrate and the lower substrate and are used for normal force and torque detection; the lower substrate is provided with a temperature compensation reference electrode used for collecting the environment temperature. The system takes a capacitance digital conversion chip as a core to realize multi-channel signal sampling and real-time resolving, and establishes a mapping relation between capacitance variation and five-dimensional force / torque components. In addition, in order to reduce the influence of the environment temperature on the output of the sensor, a temperature compensation method based on a binary polynomial regression model is used, a capacitance-temperature characteristic curved surface is established through a temperature and load experiment matrix, temperature compensation parameters are calculated in real time, and dynamic correction is achieved. The method is small in calculation amount, and improves the precision and stability.

The application discloses a variable frequency drive access system and control method suitable for a coal mine high-voltage power grid, and particularly relates to the technical field of coal mine power system access control. Firstly, the voltage grade, power grid topology structure, grounding mode, system impedance, power grid load change curve and bus transient stability coefficient of the coal mine high-voltage power grid are acquired to construct a dynamic stability model; then the output voltage, current harmonic component, impact current, voltage change rate and carrier frequency of the variable frequency drive equipment are acquired as disturbance input for multi-scene simulation to extract bus response indexes; the polynomial regression model is used to predict the interference level coefficient, and the interference level coefficient is compared with a safety threshold value to screen a target access parameter group, determine an access path and a buffer modulation strategy, generate a control instruction and transmit the control instruction to a main control unit to realize stable access of the equipment in the high-voltage power grid; and the application improves the adaptability and safety of the variable frequency equipment access and is suitable for dynamic control requirements in a complex power grid environment.

The invention discloses a plasma optical image boundary marking method and device, and belongs to the field of plasma diagnosis. The method comprises the following steps: presetting a plurality of groups of line segments crossing a boundary on a plasma optical image, carrying out strip-shaped region sampling with the width of w pixels on each line segment, extracting a gray intensity value of a sampling point, and taking a point with the maximum intensity as a candidate boundary point; automatically dividing the candidate points into a left side set and a right side set according to the horizontal coordinate position, and performing curve fitting on the point sets on the two sides by adopting a cubic polynomial regression model; dynamically setting a threshold value by iteratively calculating a residual mean value and a standard deviation, and removing abnormal points until a termination condition is met; and finally, uniformly sampling in a vertical coordinate range to generate a smooth and continuous boundary curve and outputting a label file. According to the method, automatic and accurate identification of the plasma optical boundary is realized, subjective errors caused by manual labeling are eliminated, configuration reconstruction precision is improved, and the method is suitable for related research work of identification and reconstruction of the plasma optical boundary.

The invention belongs to the technical field of quality control and component analysis of traditional Chinese medicinal materials, and particularly relates to a method for rapidly predicting the content of effective components of radix sophorae flavescentis based on an electronic tongue technology. The invention discloses a sophora flavescens effective component content rapid prediction method based on an electronic tongue technology, and the method comprises the following steps: (1) measuring a taste sensor response signal of a to-be-detected sophora flavescens sample by using an electronic tongue, and obtaining characteristic values such as CPS, SCS and the like; and (2) substituting the measured characteristic value into a quadratic polynomial regression model, and calculating to obtain a predicted value of the content of the effective components of the sophora flavescens. The method can rapidly, losslessly and accurately predict the content of the effective components of the sophora flavescens through the combination of the electronic tongue taste features and the regression model, is suitable for quality control and component analysis of traditional Chinese medicinal materials, provides an efficient solution for modern detection of traditional Chinese medicinal materials, and has a good application prospect.

This invention provides a method and system for predicting the potential of bovine in vitro embryo production based on AMH concentration, relating to the field of livestock reproductive technology. The method includes: obtaining a blood sample from a donor cow before the start of the in vitro embryo production cycle; performing serum separation processing on the blood sample to obtain a serum sample; detecting the anti-Müllerian hormone concentration in the serum sample to obtain a serum AMH concentration value; and predicting the number of follicles in the donor cow based on the serum AMH concentration value using a cubic polynomial regression model. This invention establishes serum AMH concentration as an objective biomarker for predicting bovine in vitro embryo production potential and constructs a corresponding quantitative prediction model, achieving early, non-invasive, and quantitative prediction of the individual potential of donor cows before incurring high production costs.

The present application relates to the technical field of aero-engine sealing, and particularly relates to a data-driven floating ring seal multi-source feature fusion life evaluation method and device, wherein the method comprises: obtaining and preprocessing multi-channel operation data of a floating ring graphite seal to obtain a smoothed signal; extracting, reducing dimensions and fusing multi-domain degradation features in the smoothed signal to obtain a fused health index; respectively adopting a support vector regression model, an exponential degradation model and a polynomial regression model to fit the fused health index to obtain a final mixed prediction model; obtaining a real-time health index sequence and inputting the real-time health index sequence into the final mixed prediction model to predict a degradation trajectory; extrapolating the degradation trajectory until the degradation trajectory intersects with a preset health index threshold to determine a predicted failure time point, and calculating a remaining service life and a confidence interval thereof according to the predicted failure time point. Thus, the problems that the floating ring graphite seal cannot efficiently and accurately quantify the nonlinear influence of actual complex and changeable working conditions on the sealing life in the prior art are solved.

This invention relates to a battery state-of-charge (SOC) prediction system and method based on multi-model fusion, belonging to the field of battery management technology. The system includes a data acquisition module, a voltage model prediction module, a capacity accumulation calculation module, a charging detection module, an adaptive calibration module, a rationality check module, and a data output module. A fourth-order polynomial regression model is used to predict the SOC based on voltage and temperature for initialization and charging calibration. A capacity accumulation method based on dynamic effective capacity is used for real-time prediction. Through a charging-triggered adaptive calibration mechanism, the SOC is recalibrated using the voltage model, and the effective capacity is updated, eliminating accumulated errors and tracking battery aging. This invention ensures prediction accuracy while having low memory footprint and low computational load, making it suitable for 8-bit / 16-bit microcontrollers. It also features automatic calibration and adaptability to the entire battery lifecycle.

The application discloses a NAND flash read voltage axis dynamic prediction method and device and a storage medium. The method comprises the following steps: selecting sample data covering different types of NAND flash, training, and obtaining a GRUM model; taking the PE number, RET time and VTH distribution in the sample data as the input of the GRUM model, and taking the output of the GRUM model as the optimal read voltage axis prediction value; maintaining the weight matrix of the GRUM model after training as the base weight; taking the normalized PE number and VTH distribution as the input characteristics, and taking the reduced weight adjustment amount as the output, to construct and train a polynomial regression model; and loading the target weight to the GRUM model to predict the optimal read voltage of the NAND flash to be processed. The application reduces the output dimension of the subsequent polynomial regression model, reduces the calculation and storage burden, eliminates invalid weight files, and further improves the resource utilization efficiency.

The invention discloses an optical storage system power prediction method based on a physical information neural network. Constructing a photovoltaic equipment operation model, obtaining photovoltaic working condition data and taking the photovoltaic working condition data as model input, and solving to obtain a photovoltaic operation prediction result; constructing an energy storage equipment operation model, obtaining energy storage working condition data and taking the data as model input, and solving to obtain an energy storage operation prediction result; constructing a polynomial regression model as an environmental influence model, obtaining environmental factor data and taking the environmental factor data as model input, and processing to obtain an environmental influence prediction result; an integrated learning method is adopted: a photovoltaic equipment operation model, an energy storage equipment operation model and an environmental influence model are used as base learners, a regression model is constructed as a meta learner, and a photovoltaic operation prediction result, an energy storage operation prediction result and an environmental influence prediction result are used as inputs of the meta learner; and inputting the data into a meta-learner for processing to obtain a prediction result of the optical storage integrated system. According to the invention, the power prediction model of the optical storage system is simplified and the prediction precision is improved.

This invention discloses a process for optimizing the extraction of total flavonoids from Houttuynia cordata stems and leaves based on response surface methodology, relating to the field of natural product extraction technology. The method includes the following steps: Step 1, obtaining dried and pulverized Houttuynia cordata stem and leaf powder; Step 2, extracting total flavonoids from the powder using ultrasound, selecting ethanol concentration, extraction temperature, extraction time, and liquid-to-solid ratio as single-factor experiments, and employing response surface methodology with total flavonoid content as the response value to establish a second-order polynomial regression model to predict the optimal extraction conditions; Step 3, determining the optimized extraction parameter combination based on the model; Step 4, performing the extraction operation according to the optimized extraction parameter combination. This invention utilizes response surface methodology to optimize the extraction process of total flavonoids from Houttuynia cordata stems and leaves, determining a process that can effectively extract flavonoids from Houttuynia cordata stems and leaves, providing a theoretical basis for the research and utilization of flavonoids from Houttuynia cordata stems and leaves.