223 results about "Non linear response" patented technology

The invention relates to the technical field of agricultural information perception and decision making, in particular to a multi-source perception fused agricultural monitoring method and system. The method comprises the following steps: converting a multi-source heterogeneous agricultural sensing signal into a space-time tensor, constructing a semantic resonance field to simulate nonlinear coupling between modals, and driving multi-modal data to adaptively aggregate by using a gravitational evolution mechanism to form a fused semantic field; calculating non-linear response to generate an agricultural state emergence index, and according to the index, identifying a potential risk area and constructing a binary risk map; for the risk area, semantic disturbance is mapped into an agricultural variable disturbance vector through a modal decoupling matrix, a minimum intervention strategy is generated in combination with sparse optimization of an operation response matrix, and feasible operation suggestions are output after verification of an agricultural knowledge graph; a drift potential energy function is constructed based on strategy execution feedback, strategy parameters are dynamically updated through gradient descent, and closed-loop self-evolution optimization is achieved in combination with trend prediction. According to the invention, full-link adaptive optimization from multi-source sensing to regulation and control decision is realized.

The invention relates to the technical field of tunnel engineering, and discloses a tunnel multi-field coupling nonlinear deformation analysis method and system.The method comprises the steps that geological environment information of a tunnel area is collected, a multi-source physical field boundary condition model is built based on collected data, and a heat-seepage-stress-time four-field coupling control model is built based on the collected data; specifying a nonlinear response model for the geological medium to truly reflect the stress-strain behavior of the geotechnical material; solving a coupling equation set, and optimizing the four-field coupling control model; introducing measured data to correct the model; the system comprises a geological data acquisition module, a boundary condition modeling module, a coupling model establishment module, a numerical solution module, a measured data correction module and a visual output and control interface module. According to the method, high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under the complex geological condition are realized based on comprehensive acquisition of the geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

The invention belongs to the technical field of nondestructive testing, and particularly relates to a detection method for quantitatively evaluating fatigue damage in a pipeline through a nonlinear ultrasonic technology. 316 stainless steel and SA335GRP-11 ferrite steel samples are adopted, a welding seam area is formed through V-shaped groove welding, fatigue loading is conducted, and actual pipeline fatigue damage is simulated; for the samples with different fatigue cycles, nonlinear response signals of the samples are obtained; fourier transform is carried out on the obtained signal to obtain an energy spectrum of the signal, and energy changes of the signal on a fundamental frequency band and a second harmonic frequency band are analyzed; and calculating a nonlinear coefficient, and analyzing a change rule of the nonlinear coefficient along with fatigue cycles, so that the evolution process of fatigue damage in the pipeline is evaluated. According to the method, energy is used for replacing the amplitude value, on the basis of the formula principle, integration is used for replacing the amplitude value to improve the intensity of the characterization nonlinear ultrasonic signal, the early stage of damage and the later stage of crack initiation are detected, and reliable guarantee is provided for safe operation of an industrial pipeline system.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention discloses a detection method for judging the quality of a cable-stayed pipe, relates to the technical field of cable-stayed pipe quality detection, and is used for solving the problem of poor cable-stayed pipe quality detection precision. The optical fiber sensors are arranged on the surface of the cable-stayed pipe to collect strain data, a continuous space-time strain field is constructed, local gradient changes are extracted, identification of a micro-damage area is achieved, a node response frequency spectrum is obtained in combination with a multi-frequency-band acoustic excitation means, nonlinear energy and frequency coupling characteristics are extracted, and the micro-damage area is identified. Calculating a nonlinear display probability to locate a potential nonlinear response concentration area of the structure, constructing a topological graph model of the structure, reflecting a mechanical connection relation between nodes and monitoring abnormal response, when an abnormal structure behavior is detected, carrying out topological structure state analysis on the area where the abnormal structure behavior is located, extracting key information such as structure coupling and state propagation capability, and carrying out state analysis on the abnormal structure behavior; and high and low risk signals are generated according to a judgment result, so that the accurate identification and intelligent regulation and control of the quality change of the cable-stayed pipe are realized.

The invention relates to the technical field of temperature control, in particular to a heating and ventilation heating power constant-temperature variable-flow control method, and provides the following scheme that circulating pump frequency instructions and water temperature response data of multiple historical regulation and control periods are collected, and a dynamic track of the current water temperature in a temperature inertia field is constructed; calculating an attraction potential energy state between the current water temperature and a target constant temperature value in combination with the disturbance residual matrix; and outputting the frequency bandwidth interval of the circulating pump based on the state to realize dynamic adjustment. An adjacency matrix and a Laplacian matrix are constructed by introducing a hydraulic communication relation, spatial convolution and weighted updating are performed on disturbance residues in combination with a topological diffusion core, and accurate propagation and suppression of disturbance are realized. The method can effectively solve the problems of nonlinear response, delay effect and disturbance coupling of the water temperature to the pump frequency, and the temperature stability and the energy utilization efficiency of a heating system are improved.

The invention discloses a mine card simulation method and system based on log data, and relates to unmanned driving. The method comprises the following steps: obtaining operation log data of a mine card under multiple working conditions; extracting nonlinear response feature data according to the running log data; taking the historical control instructions and historical vehicle states of continuous N periods as input characteristics, taking the vehicle state of the current period as an output label, and constructing a training set; using the training set to train an LSTM model, inputting a current control instruction, a historical vehicle state and a current load value, and outputting a predicted vehicle response state; and inputting the predicted vehicle speed, acceleration, front wheel turning angle or hinge angle into a vehicle kinematic model, and calculating the position and attitude of the vehicle under the current load condition. Aiming at the problem that the time delay between a control instruction and an actual response in a super-large inertial system cannot be processed due to the fact that a non-linear dynamic response caused by the fact that mine truck large load change cannot be accurately simulated by adopting fixed dynamic parameters in mine truck simulation, the simulation precision is improved.

The invention discloses a multi-target blending optimization method for refined oil of a refining enterprise, and particularly relates to the technical field of target optimization. Constructing an original physical property feature set by identifying non-linear response sensitive variables in the blending component proportion; respectively constructing an oil product performance prediction model and a blending component interaction influence model based on the original physical property feature set; utilizing an oil product performance prediction model to analyze the influence of non-linear coupling of simultaneous change of a plurality of blending component proportions on the change trend of key performance indexes of the refined oil product; analyzing the influence of the proportion change of a single or individual blending component on the stability of the key performance index of the product oil by utilizing the blending component interaction influence model; and according to the key performance change trend characteristics and the fluctuation sensitivity indexes, constructing a constraint equation, generating a blending solution set, performing target performance achievement degree evaluation and global optimal solution screening, obtaining an optimal blending scheme, improving the blending prediction precision, and realizing stable optimization control of the product oil quality.

The invention discloses a PEDOT sensor nonlinear response compensation optimization system, and relates to the technical field of PEDOT sensors, the system comprises a cloud management center, and the cloud management center is in communication connection with a data acquisition module, a feature extraction module, a response compensation analysis module, a cloud compensation module, a compensation deviation analysis module and a feedback adjustment module. The method combines the neural network model through the cloud, precisely learns the nonlinear characteristics of the sensor, carries out precise compensation, carries out initial linear compensation at the end side, reduces the data amount transmitted to the cloud, improves the real-time performance of the system, carries out the model training and optimization at the cloud through a large amount of historical data, and improves the real-time performance of the system. According to the PEDOT sensor, the error introduced by nonlinear response is remarkably reduced, and the measurement precision and accuracy of the PEDOT sensor are effectively improved in an end-cloud cooperation mode, so that the sensor can provide high-reliability measurement data under different environmental conditions, and the requirement of high-precision measurement is met.

The invention relates to the technical field of electronic component testing for investigating or analyzing internal microstructures and physical properties of materials by utilizing an electrical means, and discloses an online testing and analyzing method for physical properties of electronic component materials, which comprises the following steps of: constructing a composite excitation signal formed by linearly superposing a main carrier voltage and a perturbation wave voltage; applying the signal to a to-be-tested electronic component and acquiring response current; and extracting an intermodulation current amplitude at an intermodulation frequency point in the frequency spectrum of which the linear response interval is excluded, calculating a nonlinear response coefficient of the material in combination with the excitation amplitude, and comparing the nonlinear response coefficient with a preset degradation threshold so as to judge whether the nonlinear response coefficient is qualified or not. By extracting intermodulation components which cannot be generated by linear contact resistance, physical isolation of external contact noise in a frequency domain is realized, and the problem that contact resistance fluctuation and material intrinsic aging cannot be distinguished in a traditional linear impedance test is solved.

The invention relates to the technical field of bridge detection, and discloses a detection data acquisition system and method for bridge support design, and the method comprises the steps: constructing a slippage trend prediction model with a disturbance weight gating structure, fusing historical slippage trajectory data and pressure gradient data, and carrying out the prediction of the slippage trend; the time sequence modeling capability of nonlinear response of the sliding support in a vibration state is effectively enhanced, the perception precision of the model on short-time disturbance and inertia reversal behaviors is improved, then a disturbance change rate sequence is constructed based on a predicted sliding trend sequence, a disturbance scoring factor is calculated by using trend fluctuation amplitude and direction reversal times, and the disturbance change rate sequence is calculated. According to the method, the disturbance time window is dynamically marked, so that the problem of lack of support disturbance behavior dynamic division means in the prior art is solved, finally, different filtering processing modes are adopted for the disturbance time window and the non-disturbance time window, and differentiation processing is performed on real-time strain data. And the availability of the strain data under the low-frequency disturbance background is improved.

The invention discloses a liquid hydrogen container heat leakage prediction and boost characteristic detection method and system. The method comprises the following steps: establishing a three-dimensional heat flow field model of a liquid hydrogen container; calculating the heat flow density of a gas-liquid interface based on the three-dimensional heat flow field model so as to adjust the liquid hydrogen evaporation capacity; total heat leakage is obtained through finite element simulation and experimental data collaborative calibration; calculating a static evaporation rate according to the total heat leakage; establishing a nonlinear mapping model of the static evaporation rate and the boost rate through a response surface regression method; the wall temperature distribution of the liquid hydrogen container is monitored in real time, thermophysical parameters are calibrated through the LSTM neural network by adopting a dynamic calibration algorithm, and the output result of the three-dimensional heat flow field model is corrected. By establishing a three-dimensional heat flow field model of the liquid hydrogen container, determining a nonlinear response relationship between the evaporation rate and the pressure rising rate of the liquid hydrogen container, and constructing a cloud-edge collaborative detection system, the heat leakage performance of the liquid hydrogen container is accurately predicted, and efficient pressure rising rate detection is realized.

The invention relates to optical plane correction, and discloses an infrared focal plane array non-uniformity correction method based on integral time adjustment, which comprises the following steps: for each pixel of an infrared focal plane array, respectively establishing a nonlinear response model representing an incidence relation among integral time, radiation flux and detector output response; according to the radiation distribution characteristics of the current scene and the independent response characteristics of each pixel, the optimal integral time is determined for each pixel through an adaptive optimization algorithm; dynamically updating the parameters of the nonlinear response model of each pixel based on the deviation between the actual output of the detector and the output of the nonlinear response model; and compensating the response nonlinear difference between different pixels based on the optimal integral time, and performing further linear compensation on the compensated signal based on a two-point correction formula. With the adoption of the method, the defects of a traditional correction method in the aspects of nonlinear correction, scene adaptation, drift adaptation and the like are overcome, and the imaging quality of the infrared focal plane array is effectively improved.

The invention provides a wind-induced response prediction method and device for a large-span bridge structure and electronic equipment, and the method comprises the steps: obtaining the wind power response data of a bridge in a wind environment; decomposing the wind power response data by adopting a dynamic mode decomposition algorithm to obtain first mode information in the current wind environment; and inputting the mode information into a pre-trained target neural network model, and outputting a wind-induced response prediction result. According to the method, through the synergistic effect of the physical insight of the DMD and the data analysis capability of the neural network, the multi-scale flow mode can be accurately analyzed in real time, meanwhile, the dependence on a large number of computing resources is reduced, the DMD and the neural network model are fused, and the fusion strategy not only solves the limitation of the traditional DMD on nonlinear response prediction, but also improves the prediction accuracy of the DMD. And the defects of the neural network in physical law understanding are overcome, and high efficiency, accuracy and intelligence of wind-induced response prediction are realized.

The invention discloses a digital stethoscope system based on a piezoelectric film, and relates to the technical field of medical electronics and biomedical engineering. An existing digital stethoscope based on a piezoelectric film has the challenges of signal noise and interference, limitation of a signal processing algorithm, nonlinear response and time migration, insufficient diagnosis accuracy and automation and the like. A piezoelectric film and microphone dual audio acquisition mode is fused, various physiological signals such as low-frequency vibration and high-quality heart and lung sound are captured, and background noise is removed through a self-adaptive filter and a frequency spectrum subtraction technology; an MFCC feature extraction and dynamic time warping algorithm is utilized to carry out fine feature analysis and time axis alignment on the preprocessed audio signal, and high-precision matching with a storage template is realized; different physiological states are accurately distinguished through intelligent feature processing, real-time and visual diagnosis feedback is achieved through wireless transmission and state indication, and the reliability and efficiency of medical detection are improved.

The invention discloses a laser intelligent management system of a laser particle analyzer, particularly relates to the technical field of laser control, and is used for solving the problem that pollution monitoring is not accurate due to intermittent work of a laser when an existing laser particle analyzer is in an energy-saving mode. By synchronously acquiring a laser working state signal and a background scattered light signal of an optical window, whether the laser is in an initial stage awakened from a low-power-consumption state is judged, and a nonlinear response stage dominated by pollutant state change in the background scattered light signal is identified; analyzing the symmetry of the signal change track at the stage to generate a symmetry breaking index, and synchronously executing Granger causal relationship test on the driving current and background scattered light signal time sequence data to obtain a causal relationship index; and distinguishing signal abnormal sources based on a symmetry breaking index and a causal relationship index so as to evaluate the current pollution level of the optical window, adaptively adjusting pollution monitoring parameters and controlling the working mode of a laser.

The invention relates to the technical field of equipment calibration, in particular to a multi-channel pressure sensor calibration method and system, and the method comprises the steps: constructing a flexible pressure response platform, carrying out the channel calibration according to the flexible pressure response platform, and carrying out the preliminary deviation modeling; generating a nonlinear response correction factor according to a result output by the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; in response to completion of local regression adjustment of each channel, performing unified response surface fusion processing; and in response to completion of unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function. According to the method, the limitation that a traditional method depends on a linear model and static single-point calibration is overcome, and the problem of error accumulation caused by inter-channel interaction interference and delay response is solved.

The invention relates to the technical field of computing power consumption prediction, in particular to a unit computing power energy consumption prediction method based on workload identification and a power regression model, which comprises the following steps of: acquiring a hardware resource time sequence signal and an instruction mixed feature vector of a computing node in real time; inputting the instruction mixed feature vector into a working load fingerprint identifier, and outputting a load type energy influence factor; and inputting the hardware resource time sequence signal and the load type energy influence factor into an adaptive power ridge regression model to generate a unit computing power energy consumption predicted value. According to the method, the problem of large deviation of a traditional fixed coefficient regression model in a voltage fluctuation scene is effectively solved, the power consumption prediction error in a high-voltage scene is reduced, and the method has good nonlinear response capability and platform adaptability.

The invention discloses a real-time reserve pool calculation method and system based on pulse amplitude information coding, and belongs to the technical field of reserve pool calculation of space-time sequence tasks. The method comprises the steps that modal information in a real environment is received and preprocessed, encoding is carried out according to time steps, and the codes are pulse sequences with different pulse amplitudes; feeding the pulse sequence into an SL end of a volatile memristor array, writing the pulse sequence into the volatile memristor array, and receiving nonlinear response current of dynamic volatile memristor array space projection at a BL end in real time; receiving the response current by the FPGA, sampling at equal intervals to construct a virtual node, converting the response current into a voltage signal through a TIA, and feeding the voltage signal into a pre-trained nonvolatile memristor array for reasoning; and receiving the response current, outputting a maximum value corresponding neuron index through the WTA winner eating circuit, representing a classification or prediction result, and transmitting the result back to an upper computer or performing action decision at a local end.

The invention discloses a method for detecting pollutant concentration based on deep ultraviolet Raman spectrum of deep learning, and belongs to the technical field of spectral analysis. According to the method, aiming at the problems of spectral line overlapping interference, weak trace signals, strong background noise, nonlinear response and the like when a traditional deep ultraviolet Raman spectrum technology is used for detecting pollutants, high-precision quantitative analysis is realized by constructing an SSA-CNN-LSTM-Attention fusion model and combining a deep ultraviolet Raman spectrum pretreatment technology. The model adopts 1D-CNN to extract spectral local features, LSTM to capture sequence long-range dependence, an Attention mechanism to enhance feature peak response weight, SSA to optimize hyper-parameters, and a concentration prediction value is output through a weighted loss function optimization model. And meanwhile, by combining a synthetic data expansion strategy and environment matrix standard sample library training, the adaptability of the model to a complex scene is improved. According to the method, the anti-interference capability and generalization of new pollutant detection are effectively improved, and the method has important application value in the fields of environmental monitoring, industrial safety and the like.

Aspects and embodiments disclosed herein include a surface acoustic wave resonator comprising a multilayer piezoelectric substrate and interdigital transducer (IDT) electrodes disposed on an upper surface of the multilayer piezoelectric substrate, the IDT electrodes having at least partially rounded upper sides.

The invention discloses an intracranial pressure intelligent monitoring system, which relates to the technical field of intracranial pressure monitoring, and comprises a continuous pressure conduction chain construction module for establishing a continuous pressure conduction chain of an intracranial pressure signal, carrying out time sequence segmentation analysis on the intracranial pressure signal acquired in real time, and obtaining a continuous pressure conduction chain of the intracranial pressure signal; identifying a section with lagging and smooth abnormity in the pressure feedback curve, and determining the section as a nonlinear response positioning range; and the elastic pressure buffer window adjusting module introduces an elastic pressure buffer window in the nonlinear response positioning range and adaptively adjusts the width of the elastic pressure buffer window according to the pressure change rate of the adjacent time slices. By constructing a continuous pressure conduction chain and introducing an elastic buffering and layering rearrangement mechanism, it is ensured that an intracranial pressure signal accurately reflects true pressure changes in the abnormal stage. Through dynamic weight band and rhythmicity balance regulation and control, self-adaptive stability of signal energy and a time structure is achieved, and continuity and early warning accuracy of intracranial pressure monitoring are improved.

The invention discloses a 5G microwave amplifier performance test method based on automatic spectrum analysis, and relates to the technical field of data analysis, by introducing a disturbance excitation and recovery behavior comparison mechanism, the short-time dynamic response of an amplifier spectrum is actively induced, the point-by-point difference of a reference spectrum, a disturbed spectrum and a recovery spectrum is analyzed, and the performance of a 5G microwave amplifier is tested. A response-recovery ratio indicator is constructed to identify a local indentation region that is difficult to perceive in the steady-state spectrum. Compared with a traditional method depending on a frequency spectrum smoothing strategy, the method has the advantages that the problem of slight indentation in a high-frequency bandwidth is easy to cover, specific frequency point response is excited through frequency perturbation, power fluctuation or narrow-band interference, and the disturbance response and recovery capability difference of the amplifier become an evaluation basis; therefore, the recognition accuracy of nonlinear response and early failure characteristics is improved, and the stability pre-judgment capability of the 5G amplifier is enhanced.

The invention provides a quartz enhanced photo-thermal spectral concentration data acquisition method and a light intensity correction method and device, and the light intensity correction method comprises the following steps: selecting data of non-absorption positions at two sides of an original 2f signal as base lines, and adjusting the value range of the base lines at the two sides according to an actually measured spectral signal; fourth-order polynomial fitting is carried out according to the base line, and a light intensity nonlinear response background signal in the whole spectral range is obtained through fitting; a background obtained by fourth-order polynomial fitting is deducted from the measured harmonic signal, and the peak-to-peak value of the signal is in direct proportion to the light intensity and the concentration of the gas to be measured; and dividing the peak-to-peak value of the 2f signal from which the background is deducted by the amplitude of the background signal to obtain a normalized peak-to-peak value of the 2f signal. According to the invention, light intensity correction of quartz enhanced photo-thermal spectrum concentration data is realized, light intensity jittering and drifting are prevented from introducing errors to system measurement, light noise can be inhibited, and the long-term stability of the system can be improved.

The invention relates to the technical field of transformer substation operation environment regulation and control, and discloses a transformer substation operation environment low-carbon energy-saving intelligent regulation and control method, system and device and a medium. The method comprises the steps that transformer substation operation environment parameters and device state signals are collected, and a first environment device coupling dynamic representation model is constructed; and in combination with the time sequence prediction and condensation risk assessment sub-model, the nonlinear response relationship of various factors to the heat and humidity distribution and condensation risk of the cabinet is described, and a foundation is laid for accurate regulation and control. And in collaborative optimization, the second multi-target collaborative optimization regulation and control model comprises an upper-layer global energy consumption optimization and lower-layer local environment precise control target function, so that the overall energy consumption is reduced. And low-carbon operation targets and equipment safety constraints are considered, so that low-carbon energy conservation is realized during safe operation of the equipment. The solving strategy is a first improved solving strategy based on model predictive control and particle swarm optimization improvement, the efficiency and precision of processing the complex multi-target collaborative optimization regulation and control problem are improved, and the optimal regulation and control scheme is found quickly and accurately.

The invention belongs to the field of LED display control, particularly relates to a software-definable LED driving chip gray scale display control method, and aims to solve the problems that the refresh rate of each gray scale cannot be flexibly adjusted, the nonlinear response of an LED driving chip and a lamp bead cannot be compensated, first scanning darkness cannot be solved and ghosting cannot be effectively eliminated in the prior art. The method comprises the following steps: determining the number of refresh cycles of one frame of display time and the number of gray scales displayed in each refresh cycle; calculating the initial gray scale weight of each gray scale bit, and adjusting the gray scale weight; scanning the transformation moment line by line according to each refresh cycle, and adjusting the position of the gray scale OE position in the subfield; and when to-be-displayed data is received, controlling the LED driving chip to complete gray scale display of the LED display screen. According to the method and the device, the refresh rate of each gray scale is flexibly adjusted, the nonlinear response is compensated, and the first scanning darkness and the ghosting are effectively eliminated.

A system configured to reduce loudspeaker distortion by performing nonlinear signal processing is provided. A device may include preprocessing component(s) that apply nonlinear signal correction prior to sending a playback audio signal to a driver in order to compensate for a nonlinear response of the driver. While the driver response may be nonlinear, a combination of the preprocessing and the nonlinear driver response results in a combined response that is linear and / or compensates for the nonlinear driver response. For example, applying the nonlinear driver response to a processed audio signal may result in output audio generated by the driver accurately reproducing the playback audio signal input to the preprocessing components. To train the preprocessing components to apply the nonlinear signal correction, a deep neural network (DNN) is trained to model the driver response.

The invention is applicable to the technical field of data processing, and provides an air adaptive purification control method and device, terminal equipment and a storage medium, and the method realizes dynamic adaptive power adjustment by constructing a pollution gas coupling kinetic model. A historical concentration mean value, a nonlinear response mechanism and periodic change characteristic parameters are introduced into the model, low power can be kept in low pollution, the purification efficiency can be rapidly improved in high pollution, the pollution trend can be pre-judged, the power can be adjusted in advance, and energy waste is avoided. The multi-pollutant synergistic effect is considered, interaction of different gases is treated in a cross term mode, the purification priority is distributed according to hazard index differentiation, and energy consumption generated by excessive treatment of low-hazard pollutants is reduced. According to the method, dual-mode switching is achieved in combination with hazard index comparison, dynamic balance of energy consumption and the purification effect is achieved while the purification effect is guaranteed, compared with a traditional linear control and single pollutant treatment mode, energy consumption control is more intelligent and efficient, and energy consumption of the air purification system can be effectively reduced.

The invention discloses a soil water and salt dynamic prediction system and method. A data acquisition unit acquires soil underground water burial depth data of a to-be-predicted area; the model optimization unit determines a machine model for respectively predicting the soil water content and the soil salt content; and the coupling prediction unit predicts the soil water content and the soil salt content of the to-be-predicted region according to the soil groundwater burial depth data of the to-be-predicted region to obtain the soil water content and the soil salt content of the to-be-predicted region. According to the invention, the long-time-sequence, high-resolution and in-situ monitoring of key elements such as soil water content, soil salinity and underground water level is realized by constructing an Internet of Things monitoring network, and the defects of the existing method on temporal-spatial resolution and monitoring indexes are overcome. Meanwhile, a prediction model coupling underground water numerical simulation and machine learning is innovatively provided, the prediction precision and efficiency are improved, the nonlinear response relation between shallow soil water and salt and the buried depth of underground water is disclosed, and a new perspective is provided for understanding a soil salinization mechanism.

The invention discloses a construction method of a river network hydrodynamic condition visualization system based on digital twinning, and the method comprises the steps: employing a dual gradual change dynamic visual angle switching algorithm, and carrying out the smooth transition to a three-dimensional visualization environment through user interaction in a two-dimensional interface; a semantic deployment modeling mechanism and a three-dimensional river mechanism based on water level interpolation are utilized to automatically generate a building model and a river path in a river-side area, and a water flow material and a flow animation are constructed through a Cesium platform to realize flow effect rendering; water depth and flow velocity data under a continuous time sequence are dynamically acquired, real-time rendering is performed on a Cesium platform, continuity and smoothness of a visual effect are guaranteed by using a nonlinear response interpolation algorithm based on animation driving, and a simulation time step length is controlled through a timer mechanism, so that real-time simulation of the water depth and the flow velocity is realized. And accurate visual expression of the hydrodynamic force change process under different time scales is supported. And users are supported to carry out interactive operation under different visual angles and time dimensions, so that the visual expressive power of the river network hydrodynamic data and the system response efficiency are effectively improved.