291 results about "Non linear model" patented technology

The invention provides a thermal power plant flexibility transformation and deep peak regulation optimization control method and system, and relates to the technical field of control engineering, and the method comprises the steps: collecting the omnibearing operation data of a thermal power plant to construct a multi-level thermal power plant dynamic nonlinear model, carrying out the real-time identification of model parameters, generating an identification result, and building a multi-dimensional thermal power plant operation characteristic database; based on the thermal power plant operation characteristic database, designing a transformation scheme, and performing visual simulation and optimization to obtain a flexible transformation result; constructing a multi-objective optimization function, formulating an adaptive deep peak regulation optimization control strategy, carrying out short-term and medium-term prediction on a power grid load, generating an initial control strategy based on a prediction result, carrying out online optimization and adjustment, and continuously evaluating and optimizing the control strategy.

The invention provides a multi-mode intelligent propulsion control method, device and equipment for a short vertical aircraft and a storage medium, and the method comprises the steps: firstly obtaining flight state data in real time, and classifying the flight state data according to a state classification model of a decision tree to generate a current flight mode; next, calling a control strategy group and performing parallel processing on the current flight mode by taking the lowest energy consumption as an optimization target so as to generate a plurality of control instructions, and calling a fusion device weight network to process the current flight mode so as to generate a fusion weight of each control instruction, and generating a final control quantity according to the plurality of control instructions and the fusion weight, and sending the final control quantity to a pre-constructed hybrid propulsion structure nonlinear model to control the short vertical aircraft, thereby solving the problem that the short vertical aircraft cannot be adaptively adjusted when a plurality of propulsion units have differential dynamic characteristics.

The invention relates to the technical field of flour processing, and discloses an intelligent control method and system for a flour workshop, and the method comprises the following steps: collecting raw material characteristic data and equipment operation state data in real time through a sensor, calculating the state variable of the current system according to the collected data, and calculating the state variable of the current system according to the current state variable. Performing rolling optimization on a control strategy in a future time domain, dynamically adjusting grinding parameters according to a rolling optimization result, correcting a prediction model according to a data deviation between real-time acquired data and nonlinear model prediction data, and calculating an optimal control parameter combination based on a stochastic optimization theory; the system comprises a sensing module, a control module, an execution module, a feedback correction module and a random optimization module. By adopting the technical scheme that a high-precision sensor is combined with intelligent control, comprehensive automation and accurate control of the flour production process are achieved. The stability and consistency of flour quality are remarkably improved, and quality fluctuation is avoided.

The invention relates to the technical field of digital printing machines, in particular to an intelligent ink droplet control method and system of a digital printing machine and the digital printing machine. The method comprises the steps of real-time data acquisition, multi-dimensional parameter modeling, real-time ink droplet behavior prediction, self-adaptive ink jet parameter adjustment, closed-loop feedback control, medium characteristic self-adaptive matching, intelligent multi-region optimization and environmental factor compensation. Ink droplet behaviors and environmental data are collected in real time through a high-speed camera and various sensors, a nonlinear model is constructed by using a deep learning technology, and a dynamic parameter optimization curve is generated. According to the method, the printing quality and efficiency are remarkably improved, and the method is suitable for various media and complex scenes.

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for video coding. In one method, a decoder obtains, for a current chroma block, a non-linear model (non-LM) mode and a linear model (LM) mode, and the LM mode includes a cross component linear model (CCLM) mode and a multi-model linear model (MMLM) mode. The decoder then combines the non-LM mode and the LM mode for a multi-hypothesis-based chroma prediction (MCP) for the current chroma block.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention discloses an aircraft control surface oscillation fault monitoring method based on multi-scale wavelet transformation, and the method comprises the steps: carrying out the calculation through a PID (Proportion Integration Differentiation) control law through an autopilot according to an input flight path command, and obtaining a control surface control instruction deltadepth; a control surface deflection prediction value delta est is generated based on the nonlinear model and the control surface control instruction, the control surface deflection prediction value and an actual deflection value delta mea collected by the sensor module are subtracted to generate a residual signal Rn, and the residual signal is decomposed into a plurality of sub-band signals through wavelet decomposition; wavelet energy calculation is carried out on the sub-bands possibly containing the control surface oscillation fault signals, whether a fault occurs or not is judged according to the magnitude of an energy comparison threshold value, and the judgment threshold value is determined through Monte Carlo simulation. According to the method, instruction signals are separated by generating residual errors, wavelet transform processing is carried out on residual error signals, interference of noise and irrelevant frequency signals on monitoring performance is reduced, meanwhile, small-amplitude oscillation signals can be effectively monitored according to sub-band wavelet energy serving as a fault criterion, and the monitoring coverage rate of oscillation faults is remarkably increased.

The invention relates to the technical field of power system testing, in particular to a transformer simulation test current optimization method based on dynamic impedance matching, which comprises the following steps: performing low-power scanning on a tested transformer to establish a nonlinear model capable of predicting harmonic waves; actively synthesizing the virtual impedance of the target power grid; and a feed-forward compensation current is generated by using a harmonic prediction model and is injected into the transformer after being superposed with a fundamental wave instruction. According to the method, accurate identification modeling is carried out on nonlinear characteristics of a tested unit, active synthesis of virtual impedance of a target power grid is combined, and harmonic compensation current is generated in real time by utilizing a feedforward control thought so as to optimize finally injected test current. According to the method, the problems of inaccurate power grid simulation and insufficient nonlinear response coupling of the transformer in a traditional test are solved, high-fidelity current injection is realized, real electrical behaviors of the transformer under various complex power grid working conditions can be accurately simulated, and the flexibility, accuracy and safety of the test are remarkably improved.

The invention relates to the technical field of non-electrical variable control, and discloses an industrial water chiller temperature intelligent adjusting system, which comprises a refrigeration circulation loop and a control unit, the control unit calculates a steady state control component based on the water outlet temperature deviation, performs differential processing on the refrigerant pressure to extract the gradient, and outputs the gradient. Generating a transient compensation component when the gradient exceeds a dynamic noise threshold; according to the method, the problem of thermal lag is solved by utilizing the pressure wave transmission speed advantage, aggressive response is prevented from triggering low-pressure shutdown through a safety margin constraint mechanism, the pressure safety margin is calculated in real time, a gain coefficient is determined according to a nonlinear model, and a final driving instruction is generated. And the dynamic balance of the control response speed and the system physical security boundary is realized.

The invention particularly relates to a robot outdoor large-scale scene autonomous navigation method based on an open digital map, which comprises the following steps: extracting a road network structure, constructing a road network topological graph, and modeling a road network space by using an R tree; associating a starting point with a target point according to the road network topological graph of the R-tree structure, and performing global path planning based on a Dijkstra algorithm to obtain path points from the starting point to the target point; acquiring laser radar sensor point clouds, and generating a local voxel map by using the point clouds after the ground points are filtered out; performing local target point mapping by combining the global path line segment and a local voxel map to generate a local target point; performing local front-end path search based on a JPS algorithm to obtain a local front-end path from the current position of the robot to a local target point; a safety corridor is constructed according to the local front-end path, and trajectory optimization is carried out; and tracking the optimized trajectory, and planning the speed by considering the nonlinear model of the robot and the actual motion state. The method provided by the invention can realize outdoor large-range long-distance autonomous navigation.

The invention belongs to the technical field of fine chemical reaction safety testing and automatic chemistry, and particularly discloses a nonlinear model predictive temperature control method of a semiconductor temperature control type synthesis reactor. The method comprises the following steps: constructing a dynamic heat transfer model of the system based on the semiconductor temperature control type synthesis reactor; designing a nonlinear model prediction controller according to the heat transfer model of the synthesis reactor; constructing a multi-constraint objective function for nonlinear predictive control according to the characteristics of the controlled object; and solving an optimal control sequence for the nonlinear model prediction controller by adopting a projection gradient method. Compared with a traditional PID temperature control algorithm, the temperature of the reactor can be accurately predicted and controlled through the designed nonlinear model prediction temperature control algorithm, it is ensured that the chemical reaction is conducted at the optimal temperature, and the robustness and adaptability of a control system are improved.

The invention relates to the technical field of under-actuated crane system motion control, in particular to an enhanced coupling positioning anti-swing control method and system for a crane with distributed mass loads. Based on a Lagrange kinetic equation, establishing a six-degree-of-freedom bridge crane nonlinear model with distributed loads, and analyzing the kinetic characteristics of the six-degree-of-freedom bridge crane nonlinear model; establishing an energy function of the model based on the bridge crane nonlinear model, constructing a new coupling signal based on a displacement signal of the model to enhance dynamic coupling among a trolley, a lifting hook and a load, and constructing a new state vector based on the constructed coupling signal to enhance dynamic coupling among the trolley, the lifting hook and the load. Substituting the constructed new state vector into an energy function of the model so as to obtain a new class energy function; establishing a nonlinear coupling controller with swing angle information according to the proposed new energy-like function; according to the method, linear processing does not need to be carried out on a kinetic equation of a crane system in controller design, and finally efficient trajectory tracking and positioning and load swing suppression are achieved.

The invention provides a structural damage detection method and device for a nonlinear system, and relates to the technical field of nondestructive detection. The method comprises the following steps: applying excitation force to a to-be-detected test piece, vibrating the to-be-detected test piece based on the excitation force, performing signal acquisition on the to-be-detected test piece to obtain an excitation signal and a response signal, constructing initial NARX models of different orders by combining the excitation signal and the response signal with a plurality of imported orders respectively, and calculating the initial NARX models according to the initial NARX models. And screening a target NARX model from the plurality of initial NARX models based on an akaike information criterion, optimizing the target NARX model, comparing order information in the NARX model to be detected obtained by optimization with order information in the reference NARX model, and determining whether the test piece to be detected has defects according to an order comparison result. The nonlinear model is constructed and optimized through the signal data of the to-be-detected test piece, so that the model accurately represents the structural characteristics of the to-be-detected test piece, and then whether the to-be-detected test piece has defects or not is judged.

The invention discloses a lithium battery health state prediction method based on a feature screening technology. The lithium battery health state prediction method based on the feature screening technology comprises the following steps: obtaining a data set; extraction and primary processing of initial features; screening a feature set; preliminarily training a machine learning model; evaluating and optimizing a machine learning model; and predicting and verifying the health state of the lithium battery. The method indirectly reveals the complex relationship between the features and the target output by evaluating the uncertainty of the parameter estimation value by using the effective Fisher information matrix-based feature screening technology, not only can effectively capture the linear and nonlinear interaction between the features, but also can reflect the combined influence of each feature in the multi-parameter model, and can effectively improve the target output. A more comprehensive and accurate feature selection basis is provided; besides, the Fisher information matrix-based feature screening technology is suitable for linear and nonlinear models, and particularly shows relatively high robustness and precision when processing a multi-parameter model with complex interaction.

The invention discloses a controllable gain RCS active enhancement method and device, and the method comprises the steps: carrying out the geometric feature extraction and electromagnetic scattering feature extraction of a target three-dimensional CAD model, carrying out the fusion of a tensor network projection algorithm to generate joint feature representation data, and carrying out the dynamic updating of features according to the posture of a target; according to the feature representation data, establishing a nonlinear model by adopting multi-dimensional Lie algebraic dynamics mapping, and generating a prediction model through a chaotic system prediction algorithm; based on the prediction model, a multi-objective constraint optimization algorithm is adopted to construct a control objective function, and a self-adaptive control strategy is generated through a hybrid control law synthesis algorithm; and according to the environment parameters, an environment disturbance compensator is adopted to execute real-time compensation, and controlled quantity conversion is realized through hardware constraint mapping. According to the method, the RCS feature extraction precision and the control response speed are improved, and the system environment adaptability is enhanced.

The invention provides a predetermined time distributed optimal inclusion control method for an unmanned aerial vehicle cluster, and belongs to the technical field of collaborative decision of an unmanned cluster system. The method comprises the steps that unmanned aerial vehicles in an unmanned aerial vehicle cluster are divided into virtual leader unmanned aerial vehicles and follower unmanned aerial vehicles; according to the single integral linear model of the virtual leader unmanned aerial vehicle, generating a preset formation pattern through a preset time distributed optimization theory; constructing a nonlinear model of a follower unmanned aerial vehicle in the unmanned aerial vehicle cluster; and designing an inclusion control protocol of the follower unmanned aerial vehicle based on a distributed inclusion control algorithm according to the nonlinear model of the follower unmanned aerial vehicle, so that the follower unmanned aerial vehicle converges to a preset formation. Through a distributed optimal formation control algorithm and a distributed inclusion control algorithm, a virtual leader unmanned aerial vehicle formation generation strategy within a preset time is realized, follower unmanned aerial vehicles converge to a preset formation, and thus unmanned aerial vehicle cluster security cooperative control under a complex sea condition is completed.

Disclosed is a rapid load test method for a pile by use of a method resulting from extending a well-known SULP method, wherein a rapid load test is implemented on a pile head more than once in the manner of changing a drop height h of a weight, with the pile instrumented with strain gages and accelerometers at more than one depth level, followed by the processing of obtaining a rapid load Frapid-pile displacement w relation for each pile segment for each time of the rapid load test using measurement data measured by the strain gages and the accelerometers, of obtaining a soil resistance Rsoil-pile displacement w relation on each pile segment for each time of the rapid load test, of calculating an unloading point that can be a point of maximum displacement of the pile, together with an unloading point load RULP at that moment, and of obtaining a static soil resistance Rw-pile displacement w relation on each pile segment by use of an Unloading Point Connection method using the calculated unloading point and unloading point load RULP for each time of the rapid load test. Accordingly, an analysis on a load-displacement relation is performed by use of a load transfer method with respect to the whole pile, using a nonlinear model of the thus obtained soil resistance on each pile segment.

The invention relates to a four-rotor unmanned aerial vehicle pose control method based on a global sliding mode. The invention relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: carrying out the modeling of a dynamic model of a quad-rotor unmanned aerial vehicle, and obtaining the nonlinear state space description of the quad-rotor unmanned aerial vehicle; secondly, respectively designing global sliding mode control laws for the position subsystem and the attitude subsystem according to the nonlinear model; according to the output of a position subsystem controller, the expected acceleration is mapped into expected lift force and attitude angle by designing control distribution; and finally, constructing a nonlinear programming problem corresponding to a nonlinear model prediction controller by taking a previously designed global sliding mode control law as a contraction constraint, and solving the problem to obtain a control quantity. According to the method, the quadrotor unmanned aerial vehicle can effectively perform trajectory tracking under the conditions of output constraint and convergence guarantee, and the control precision, the response speed and the safety in the flight process are improved.

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.

The invention discloses a safety risk assessment method and system for a power generation enterprise, and relates to the technical field of enterprise safety assessment, and the method comprises the steps: collecting the equipment data and environment data of the power generation enterprise through a sensor, and carrying out the data preprocessing; performing batch and hierarchical analysis on the preprocessed data, extracting multi-dimensional features, and forming a comprehensive feature set through feature optimization; respectively processing the comprehensive feature set by using a linear model and a nonlinear model, and outputting a prediction result; and carrying out dynamic combination on linear and nonlinear prediction results, and outputting a final risk assessment result. According to the invention, through the multi-level data processing and dynamic combination model, the linear and non-linear relationship in the power generation enterprise equipment and environment data can be comprehensively and accurately captured, so that the accurate assessment of the equipment safety risk is realized. Compared with a traditional method, the accuracy and the real-time performance of risk assessment are greatly improved.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitter device may transmit, to each receiver device of a plurality of receiver devices communicating with the transmitter device over a wideband channel using frequency division multiplexing, an indication of a bandwidth portion of the wideband channel to be used by the receiver device for performing measurements associated with a non-linear model of the transmitter device. The transmitter device may transmit, to each receiver device of the plurality of receiver devices, one or more wideband reference signals associated with training the non-linear model. The transmitter device may receive a plurality of digital pre-distortion (DPD) coefficients, obtain a set of DPD coefficients based at least in part on combining the plurality of DPD coefficients, and perform DPD processing in accordance with the set of DPD coefficients. Numerous other aspects are described.

The invention belongs to the technical field of information management, and particularly relates to a power grid bearing capacity evaluation method, device and equipment, a medium and a program. The method comprises the steps of obtaining power grid parameters based on a digital twin platform; wherein the data of the digital twin platform is subjected to distributed storage and / or encryption based on a block chain network after being synchronized through an Internet of Things sensor; calculating the bearing capacity of the power grid according to the fusion weight and the power grid parameters; wherein the fusion weight is the weight of each power grid parameter, which is obtained by fusing a first weight and a second weight based on a nonlinear model. The method not only optimizes the efficiency of data processing, but also improves the accuracy, real-time performance and safety of monitoring and managing the carrying capacity of the power grid, thereby further improving the reliability of evaluation.

The invention provides a high-dynamic sliding mode prediction double-layer control method based on an error optimization strategy, a double-layer cooperative control framework is constructed, an outer layer adopts linear model prediction control to generate an optimal reference trajectory meeting performance index requirements in real time, and optimization of performance indexes is realized while calculation efficiency is ensured; a discrete logarithmic sliding mode controller is designed on the inner layer based on a precise nonlinear model, rapidity improvement of logarithmic sliding mode control is provided, a track error optimization mechanism is designed for the strong coupling characteristic of nonlinear systems such as an aircraft tethered system, and the reliability of the system is improved. The error of tracking an optimal reference trajectory generated by outer-layer model prediction control is optimized by aiming at inner-layer discrete logarithmic sliding mode control, traditional planning trajectory tracking is converted into expected trajectory direct tracking, and rapid and accurate tracking of system output to an optimal reference signal is realized.

The invention discloses a general multi-bias noise model parameter extraction method for a microwave compound transistor, and relates to the technical field of semiconductor device modeling, the transistor is tested, and static direct current IV, S parameters and noise parameter actual measurement data under different biases are obtained; establishing a large signal model, and performing nonlinear model parameter extraction in combination with multi-bias static direct current IV and S parameters to obtain nonlinear model parameters; carrying out noise characteristic modeling under different biases, and extracting noise model parameters under different biases in combination with actually measured data and nonlinear model parameters; fitting a noise model parameter through a piecewise function to obtain a bias correlation function of the noise model parameter; and establishing a multi-bias noise model according to the nonlinear model parameters of the device, the noise model parameters and the bias correlation function, and verifying by combining actual measurement data of static direct current IV, S parameters and noise parameters. According to the invention, multi-bias noise modeling can be carried out based on a general noise model parameter bias correlation function equation.

The present invention provides a method and system for fast and accurate modeling of distributed optical fiber channels based on feature decoupling, including: step S1: collecting input and output data under different optical fiber channel conditions as training data sets; step S2: compensating the output data of channels including nonlinear features by linear compensation algorithm; step S3: performing data preprocessing and arrangement to obtain a nonlinear data set suitable for neural network training; step S4: modeling the nonlinear channel features based on the neural network structure and deep learning algorithm, and obtaining a nonlinear channel model through training; step S5: in the transmission process, modeling the nonlinear model, the linear model, and the amplifier model in turn, and then performing multiple iterations to finally realize the long-distance transmission of the optical fiber channel model. The present invention does not require too many iterative steps to achieve low-complexity, high-precision, and strong generalization optical fiber channel modeling.

The invention discloses a heterogeneous multi-agent formation control method based on a composite disturbance observer, and the method comprises the steps: building a nonlinear model of heterogeneous multi-agents based on the composite disturbance observer, and enabling the agents to comprise an unmanned plane and an unmanned vehicle; the composite interference observer is used for estimating unknown composite interference of each agent; a heterogeneous multi-agent intelligent plane formation controller with an internal safe collision avoidance function is constructed based on a nonlinear model, a conversion error function is introduced to restrain an initial collaborative error in a specified interval, and a safe distance between two adjacent agents is ensured; meanwhile, a preset time height formation controller is constructed, so that the consistency of expected flight heights of the unmanned aerial vehicles is realized; and controlling the heterogeneous multi-agent formation according to the constructed controller. The adverse effect caused by unknown interference is effectively eliminated through the composite interference observer, the collision avoidance problem between the intelligent agents is solved by using a specified performance control method, and meanwhile, the system is ensured to be stable within a preset time and the control precision is improved.

The invention discloses a non-linear scene multi-target tracking method and system and a storage medium, and the method comprises the steps: constructing a non-linear motion model and a measurement model, and specifically, representing a non-linear state process and a measurement process through a non-linear model; according to multi-target tracking in a two-dimensional plane, determining a motion equation of each target, setting the motion equation as a collaborative turning model, and determining a motion equation of the target; constructing a system state transition matrix, a process noise distribution matrix and a measurement equation; according to the nonlinear motion model and the measurement model, extraction of multi-target state estimation is carried out through an FAUKF-PHD filter, a state vector of each target is obtained, and tracking of each target is completed; and verifying the tracking result of each target based on the multiple pieces of preset scene information. The method is high in tracking precision and low in cost, can improve the effectiveness of the method, and can be widely applied to the technical field of computers.

Systems and methods for enabling unconstrained demand forecast based on accurate lost sales estimation using collective consumer behavior are disclosed. In some embodiments, a disclosed method includes: obtaining raw sales data of an item in a store for a past time period, wherein the item is offered for sale in the store; detecting at least one out-of-stock (OOS) time period within the past time period based on the raw sales data, wherein sale of the item is impacted by an OOS status of the item in the store in the at least one OOS time period; computing, based on a non-linear model, lost sales estimate of the item in the store for each of the at least one OOS time period; generating, based on the raw sales data and the lost sales estimate, recommended inventory for the item in the store for a future time period; and transmitting the recommended inventory to a computing device associated with the store for inventory arrangement in the future time period.

The invention discloses an efficient self-cleaning method for a heat exchanger of an air conditioner, relates to the technical field of efficient air conditioners, and designs the steps of multi-source data acquisition and residual dirt identification, residual cumulative effect prediction and formula optimization decision, directional deep cleaning, dynamic parameter control and the like according to the self-cleaning requirements of the heat exchanger of the air conditioner. Accurate positioning and grading of stubborn dirt are realized by utilizing data of multiple types of sensors; on the basis of machine learning or a nonlinear model, predicting an accumulative effect of residual dirt in subsequent operation, dynamically generating a cleaning formula and optimizing cleaning parameters; strengthening treatment is carried out on key parts through mechanical vibration, high-pressure ultrasound and real-time feedback control; and performing secondary detection on the cleaning effect, comparing the cleaning effect with a predicted value, and if the deviation is obvious, correcting the weight of the model to form a closed-loop self-learning lifting strategy. Damage to materials and energy waste caused by excessive cleaning can be effectively reduced, and long-term efficient operation of the heat exchanger can be kept in various complex scenes.

The application discloses a kind of atmospheric environment ozone concentration prediction system based on chemical reaction mechanism, including obtaining weather forecast parameter, historical observation data cleaning analysis, analysis screening suitable model, model training and prediction, prediction result analysis, data visualization and so on functional module composition.This system is based on Lagrangian coordinate system, establishes the response relationship between meteorological parameter, pollutant concentration and ozone concentration, using thermodynamic equilibrium principle, chemical reaction equilibrium principle and photochemical reaction principle and other basic chemical reaction engineering theory, deduces the non-linear model of meteorological parameter, pollutant concentration and other parameters on ozone concentration influence, by the independent design of each software function module, with low energy consumption, low carbon emission, low operating cost, data automatic processing, easy to deploy, can be connected ecological cloud platform, system expansibility is strong, maintenance is easy, and computing efficiency is high.Therefore, it has broad market application prospect and good social and economic benefits.