307 results about "Non linear dynamic" patented technology

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention belongs to the technical field of crossing of environmental engineering and ocean dynamics, and particularly relates to a tidal dynamics prediction method and control device for a seawater desulfurization system, and the method comprises the steps: constructing a time-space coupling prediction model fusing multi-source hydrological observation data, and introducing a nonlinear dynamic weight distribution mechanism; the influence of terrain constraint, wind stress disturbance and upstream runoff on tidal propagation is quantified in real time, and a rolling prediction sequence of tide level phase, flow velocity gradient and salinity disturbance in the next three hours is output; the prediction result drives the scheduling of a desulfurization pump set, the adjustment of spraying density and the matching of aeration intensity, and the model weight is corrected on line based on the actually measured feedback of desulfurization efficiency to form closed-loop control. By means of the technical scheme, accurate cooperation of the operation parameters of the desulfurization system and tidal dynamics is achieved, meaningless energy consumption is reduced while the desulfurization efficiency is guaranteed, and the utilization rate of the desulfurization agent and the stability of the system are improved.

The invention discloses a high-temperature surface contact thermal resistance elimination method based on double thermocouple compensation, and belongs to the technical field of material thermal property characterization and testing. The method comprises the following steps: synchronously acquiring double-thermocouple temperature, and obtaining a temperature difference signal through filtering calibration and environment correction; calculating the real-time heat flux density according to the corrected temperature difference, the Seebeck coefficient and the dynamic correction factor; establishing a nonlinear dynamic coupling model containing contact thermal resistance parameters, and performing temperature interval division; identifying a contact thermal resistance parameter on line by adopting a self-adaptive recursive least square algorithm, and generating a dynamic thermal resistance compensation value; and applying the compensation value to a double-thermocouple signal according to the weight, and outputting accurate high-temperature surface temperature. According to the method, data acquisition and preprocessing, dynamic heat flow correction, nonlinear segmented modeling, self-adaptive online identification and refined compensation output strategies are adopted, the influence of high-temperature surface contact thermal resistance can be effectively eliminated, and the accuracy and reliability of temperature measurement in the high-temperature environment are remarkably improved.

The invention relates to the technical field of oral health monitoring, and discloses an oral detection method based on an intelligent tooth socket. The method comprises the following steps: acquiring an original oral cavity pressure time sequence and frequency domain characteristics through an intelligent tooth socket, determining an optimized decomposition parameter, and performing multi-scale decomposition on an original signal to generate a multi-scale oral cavity signal component; nonlinear dynamic features are extracted, a component complexity index is obtained, and key signal components related to oral health are screened out; generating a personalized physiological response frequency in combination with the user oral cavity baseline features, the real-time occlusion state and the component complexity index; reconstructing an oral cavity physiological feature sequence based on the key signal component and the personalized physiological response frequency, and separating a low-frequency component and a high-frequency component through modal decomposition; identifying oral cavity abnormity categories according to the two components, establishing a mapping relation with a diagnosis result, and generating an oral cavity health detection instruction. According to the method, convenient and comprehensive oral cavity detection can be realized, the detection adaptability and precision are improved, and support is provided for oral cavity health management.

A method and system are provided for identifying time-varying suspension characteristics of heavy-load vehicles. The method includes collecting sequential control state data of a mining truck using sensors, predicting parameter-related factors through a deep learning network, estimating suspension stiffness and damping coefficients via a linear dynamic model considering longitudinal-vertical coupling, and predicting future system states through a nonlinear dynamic model based on the estimated parameters and learned factors. According to the method, a deep learning network is integrated into a physical model of the mining truck, an accurate longitudinal-vertical dynamical model of the mining truck is established, accurate suspension parameters are identified, the stiffness damping time-varying characteristics of the suspension of the mining truck are given through a physical model-data driving method, and the model has certain interpretability and generalization; the rigidity and damping of the four suspensions can be obtained only through sprung information.

The invention relates to the field of cold-chain transportation, and discloses an aquatic product cold-chain quality monitoring and tracing method, which comprises the following steps: acquiring multivariable environmental data in cold-chain transportation; based on the multivariable environment data, constructing a dynamic coupling model of a cold chain environment for representing a nonlinear dynamic relationship among environment variables; performing real-time estimation on the cold chain environment state by using a dynamic monitoring algorithm, and predicting the future change of the cold chain environment state; constructing a target function, and optimizing the operation parameters of the cold chain equipment according to the dynamic change of the cold chain environment; constructing a mapping model between the cold chain environment and the aquatic product quality, and dynamically evaluating the quality index of the aquatic product according to the historical change of the environment; and generating a traceability report based on the evaluation result, wherein the traceability report comprises a dynamic record of the cold chain environment and an aquatic product quality change curve. Dynamic monitoring, quality tracing and system optimization of the whole cold-chain transportation process can be realized, and the stability of the cold-chain environment and the aquatic product quality guarantee capability are improved.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

The invention relates to the technical field of industrial control systems, in particular to an ink path control system of a full-color printer, which comprises the following specific implementation steps of: measuring a plurality of physical quantities in a controlled physical process in real time, and outputting the physical quantities as state variables and external disturbance signals; receiving a state variable and an external disturbance signal, performing future state prediction through a nonlinear dynamic process model, and generating a prediction result; constructing a process performance index function, optimizing the performance index function through a sparrow search optimization algorithm based on a prediction result, and generating an optimal target set value; through a model based on Bayesian reasoning, parameters of a PID control law are updated in a self-adaptive mode according to external disturbance signals; the controller is used for receiving the optimal target set value and the adaptive PID control law parameters, executing the PID control law in combination with a preset undisturbed switching module, and generating a final control instruction; and receiving a final control instruction, and converting the final control instruction into driving signals acting on a plurality of physical actuators so as to realize closed-loop feedback control of the controlled physical process.

The invention relates to the technical field of image processing and traffic safety monitoring, in particular to a dynamic traffic marking inverse reflectivity intelligent monitoring system and a method thereof.According to the system, marking images are collected through a multispectral imaging technology, a precise space mapping relation is established through laser ranging, marking feature parameters are extracted from multi-band images, and the dynamic traffic marking inverse reflectivity intelligent monitoring system is obtained. A multi-dimensional feature matrix including reflection features, space geometry, time change and environmental influence is constructed, a nonlinear dynamic model of a traffic marking state is established based on the multi-dimensional feature matrix, a critical point and a bifurcation point in a degradation process are identified, and the system can calculate the change trend of the inverse reflectivity of the marking and generate multiple possible degradation paths. According to the method, the real-time dynamic monitoring of the inverse reflectivity of the traffic marking is realized, the measurement precision is improved, the prediction accuracy is enhanced, scientific decision support is provided for road maintenance, and the road traffic safety performance is remarkably improved.

The invention provides an LSTM underwater robot modeling method based on TPE hyper-parameter optimization, and relates to the technical field of underwater robot modeling, and the method comprises the steps: carrying out the time sequence feature extraction through a memory unit comprising a forgetting gate, an input gate and an output gate, and predicting the state change amount delta Yt'at a t + 1 moment through an output layer; performing iterative optimization on the number of layers and the number of units of the LSTM network and training the model to obtain an optimized LSTM model; evaluating the optimized LSTM model through a multi-step cumulative prediction error, inputting an initial real state into the model to carry out T-step recursive prediction, updating a current state by utilizing a prediction state increment in each step, and finally calculating an average position error and an attitude error in a T-step window on a verification set; and selecting the hyper-parameter combination with the minimum comprehensive error of the verification set as a final model parameter, and completing the dynamic modeling of the underwater robot. According to the method, the problem that the model is inaccurate due to excessive parameterization in a nonlinear dynamic model can be solved.

The invention belongs to the field of intelligent monitoring, and discloses a photovoltaic flexible support load deformation monitoring system and method, and the system and method achieve the safe and precise management and control of a structure through the deep fusion of multi-source sensing, mechanism modeling and data driving. Structural deformation parameters are synchronously obtained by combining a laser displacement sensor and a double-axis tilt angle sensor, and the limitation that a traditional monitoring means is single in parameter and lack of dimensionality is broken through. After data is gathered in real time through a wireless transmission module, a physical constraint-data learning hybrid framework is adopted for processing, a physical model constructs a deformation calculation equation based on a moment balance and deflection theory, a theoretical lower limit conforming to an engineering mechanics law is provided, and an LSTM neural network captures nonlinear dynamic association such as wind load sudden change through time sequence learning. A prediction result with both theoretical compliance and data adaptability is generated, and the operation reliability and maintenance efficiency of the photovoltaic flexible support in a complex environment are improved while high real-time performance is maintained.

The invention relates to the crossing field of mechanical engineering and intelligent prediction technologies, particularly discloses a coal mill efficiency and service life prediction method and device, and aims to solve the problem that a traditional model is difficult to deal with nonlinear coupling prediction of equipment performance degradation under variable load and coal quality fluctuation. The method comprises the following steps: receiving a multi-source sensing data stream and constructing a structured feature matrix with aligned time sequences; efficiency degradation implicit features are extracted through a nonlinear dynamic encoder, and the interaction influence of grinding roller abrasion, lining plate fatigue and bearing degradation is quantified in combination with a multi-failure-mode coupling analysis module; and cooperatively predicting a network output efficiency attenuation curve and residual life probability distribution through a bidirectional attention mechanism. According to the method, through fusion of multi-source time sequence characteristics and multi-failure coupling modeling, limitation of a static threshold value and linear extrapolation is broken through, prediction precision and timeliness are remarkably improved, intelligent maintenance decision support is provided for a coal-fired power plant, non-planned shutdown risks are reduced, and operation economy and system reliability are optimized.

The invention provides an energy complex flexible dynamic aggregation method and system based on reinforcement learning, and the method comprises the steps: collecting the real-time operation parameters of electric, gas, heat and multi-energy coupling equipment in an energy complex through an industrial Ethernet protocol, and constructing a continuous time dynamic system model through employing a Shenchang differential equation process modeling technology; and the nonlinear dynamic characteristics of various energy resources are accurately described. Modeling an aggregation process into a Markov decision process, and training and optimizing by adopting an improved twin delay depth deterministic strategy gradient algorithm to generate a reinforcement learning aggregation strategy model. The system realizes self-adaptive dynamic optimization through an online learning updating mechanism, and the renewable energy consumption rate and the system operation efficiency are remarkably improved.

According to the cooperative control method and system of the non-linear multi-agent system, under the condition that communication link faults are considered, a non-linear dynamic model of each single-connecting-rod mechanical arm is established, and unknown uncertain non-linear dynamic states in the single-connecting-rod mechanical arms are fuzzified through a fuzzy logic system; a mixed event triggering mechanism is introduced, so that state information of a leader is estimated, and the communication frequency is effectively reduced; and constructing a virtual controller and an actual controller by utilizing a backstepping method based on the estimated state information, so as to control the position of the following mechanical arm to be synchronous with the expected trajectory of the leading mechanical arm under the condition of being influenced by the communication link fault. According to the method, the state of the multi-single-connecting-rod mechanical arm system can be kept consistent and stable tracking control can be achieved under the condition that communication link faults and nonlinear uncertainty exist without global information.

The invention discloses a self-adaptive neural network continuous reaction kettle control method based on an observer, and the method comprises the following steps: constructing a kinetic model used for estimating the conversion rate of reaction components in a continuous reaction kettle and the change rate of reaction temperature, and calculating the change rate of the reaction temperature as the conversion rate of the reaction components and the change rate of the reaction temperature are unmeasurable system states; a state observer needs to be used for estimation; introducing a nonlinear mapping method to ensure that all states meet designed time-varying constraints; an unknown nonlinear dynamic function in a continuous reaction kettle is approximated by using a neural network, and a self-adaptive controller is designed based on a Lyapunov stability theory so as to deal with faults of an actuator and a sensor. According to the method, the internal states such as the conversion rate of reaction components of the continuous reaction kettle system can be estimated under the condition that the system state is unknown, the cost of the control system is reduced, and the efficiency is improved.

Disclosed are systems and methods that employ distributed fiber optic sensing (DFOS) / distributed acoustic sensing (DAS) to monitor and provide real-time estimation of ice thickness on fiber optic communications facilities, and which integrate DSA data with a hybrid processing technique that combines frequency domain decomposition (FDD) and stochastic subspace identification (SSI). Aspects of our innovative systems and methods include: i) Hybrid Signal Processing Techniques; ii) Real-time, Continuous Ice Monitoring; iii) Enhanced Noise Robustness and Non-linear Dynamics Handling; and iv) Adaptability and Scalability.

The invention relates to a robot grinding and polishing self-adaptive compensation method based on multi-line laser, and relates to the field of robot positioning precision. According to the technical scheme, online high-resolution measurement of the workpiece is achieved through the multi-line laser scanning sensor, hand-eye parameters are dynamically updated in combination with multi-view calibration and data fusion, the tail end pose of the robot is corrected in real time through the mixed error model fusing MDH kinematics and machine learning, and the grinding and polishing precision and consistency are guaranteed. Wherein the MDH model is responsible for coarse positioning to guarantee interpretability of a kinematic structure, the machine learning model captures nonlinear dynamic errors such as thermal expansion and abrasion, and the MDH model and the machine learning model are combined to achieve self-adaptive real-time compensation of a complex error source. The error compensation precision is improved from the millimeter level to the submillimeter level, the cost of the line laser measurement system is only tens of thousands of yuan, the size is small, installation is easy, and compared with a laser tracker, the cost and the installation difficulty are greatly reduced.

The invention discloses a dissolved oxygen closed-loop regulation and control method used in a sewage treatment process. The method comprises the following steps: acquiring real-time dissolved oxygen concentration in a reaction tank through a distributed sensor array, and performing temperature compensation; an online water quality analyzer is used for collecting water inlet flow, chemical oxygen demand, ammonia nitrogen and water temperature to construct a comprehensive input feature vector; establishing a nonlinear dynamic mapping model based on a deep neural network, and outputting a target dissolved oxygen set value changing along with working conditions; an aeration adjusting instruction is generated by adopting a feedforward-feedback composite adaptive fuzzy PID control algorithm; dynamically adjusting the air volume of an air blower and the opening density of a partition aeration disc to match the aerobic rate of microorganisms; and the control performance is evaluated regularly, and a model parameter online optimization mechanism is triggered to continuously improve the setting precision. According to the method disclosed by the invention, the dissolved oxygen can be accurately controlled, so that the aeration energy consumption is reduced by not less than 18%, the control precision is within + / -0.3 mg / L, and the robustness, the automation level and the low-carbon operation capability of the system are remarkably improved.

The invention provides a power load prediction method and system based on double-layer LSTM (Long Short Term Memory). The method comprises the following steps: constructing a power load prediction model comprising one or more groups of double-layer LSTMs (Long Short Term Memory); wherein in each group of double-layer LSTM, the first layer of LSTM is used for extracting bottom-layer time features, and the second layer of LSTM is used for modeling high-layer time sequence abstract features; historical data in past set time and environmental data in future set time are obtained respectively; and taking the historical data and the environmental data as input data of the power load prediction model, and outputting a predicted load in a future set time through the power load prediction model to complete power load prediction. According to the method, multiple groups of double-layer LSTMs are stacked, so that the method generally has stronger expression ability in the aspects of capturing a complex time dependent structure and nonlinear dynamics, the common influence of short-term disturbance and long-term trend on the load is effectively understood, and accurate prediction of the power load can be realized.

The embodiment of the invention relates to the technical field of computer security authentication, in particular to a security authentication method and system based on a secure computer. The method comprises the following steps: carrying out sensing array deployment and data acquisition on a secure computer to obtain calibrated time-space synchronization data; performing temperature and current fusion processing on the calibrated time-space synchronization data to obtain a thermoelectric coupling characteristic spectrum; carrying out point location time sequence correlation analysis on the thermoelectric coupling characteristic spectrum to obtain a point location cross-correlation matrix; performing nonlinear feature extraction on the point location cross-correlation matrix to obtain a nonlinear dynamic feature set; performing dynamic behavior analysis on the nonlinear dynamic feature set to obtain a security calculation behavior dynamic phase spectrum; and performing phase difference calculation on the dynamic phase spectrum of the security calculation behavior to obtain a phase difference vector. According to the method, the early detection capability of threats which are difficult to reproduce and are in a novel hardware level is improved through real-time monitoring and anomaly recognition of computer microcosmic physical characteristics.

The invention belongs to the field of distributed system control, and particularly relates to a vehicle system distributed control method, system and device based on reinforcement learning. Cooperative and competitive dynamic characteristics of a multi-vehicle system are described through a second-order nonlinear dynamic model in combination with a signed directed graph, unknown nonlinear dynamic and external disturbance existing in the vehicle system are considered, and the whole system is modeled as a second-order strict feedback nonlinear system. In order to get rid of dependence of convergence time on initial conditions and optimize transient performance, a preset time performance function is introduced, and a three-part neural network architecture is constructed; and designing a control law cooperating with a preset time parameter, and ensuring the actual preset time boundaries of the multi-vehicle system under the unknown dynamic and external disturbance influence. According to the method, the convergence time can be accurately set, the calculation complexity of a control algorithm is remarkably reduced, and the transient performance and robustness of a vehicle system are improved.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm predictive control method which comprises the following steps: defining the state of a mechanical arm and modeling; collecting data to construct a Koopman linear lifting determinacy prediction model; carrying out probability representation on the error by utilizing Gaussian process regression; constructing a random prediction model by combining a deterministic model and an error model, and deducing a state distribution evolution equation; and converting the joint constraint into probability opportunity constraint, carrying out deterministic equivalent conversion, and solving a rolling optimization problem. According to the method, dependence on a precise dynamic model can be abandoned, precise approximation of nonlinear dynamics is achieved through data driving, high-precision trajectory tracking of the tail end of the mechanical arm is achieved on the premise that it is guaranteed that high probability meets safety constraints, and control conservative property is reduced.

The invention discloses a cooperative control method for controlling a suspension load by multiple unmanned aerial vehicles, and the method comprises the steps: firstly constructing a frame based on a trajectory, combining an online kinematics and dynamics motion planner with an airborne trajectory tracking controller, and generating a feasible trajectory considering dynamics coupling and constraint through a finite time optimal control problem (OCP); then, an extended Kalman filter (EKF) estimator is used for fusing data of multiple unmanned aerial vehicle inertial measurement units (IMU) and a system dynamics model, and the load attitude, torsion and the cable state are estimated in real time; and finally compensating the tension interference of the cable in real time through an INDI (Incremental Nonlinear Dynamic Inversion) controller. According to the invention, the agility and robustness of the suspension load system are cooperatively improved through online motion planning and the trajectory tracking controller, the low-speed limitation of traditional cascade control is broken through, an additional sensor does not need to be deployed at the load end, and the practicability and reliability of the system under the scenes of high-speed obstacle avoidance, complex trajectory tracking and the like are remarkably improved.

The invention discloses a reinforcement learning-power hardware-in-the-loop training system and method for a power converter, and belongs to the technical field of power electronic control and artificial intelligence crossing. The system comprises a real-time digital simulator, a power amplifier, a controlled power converter and an AI accelerator. The method comprises the steps of S1, system initialization and environment configuration; s2, performing safe online training; and S3, strategy convergence and solidification. Through the power hardware-in-the-loop architecture, the risk of reinforcement learning exploration is completely avoided, and hardware damage caused by overvoltage and overcurrent is fundamentally eradicated in combination with a real-time hardware protection strategy. According to the method, the real object power converter controller is incorporated into a training closed loop, so that an intelligent agent strategy directly learns and adapts to the nonlinear dynamic state of real hardware, the gap from simulation to reality is thoroughly filled up, and it is ensured that a training result can be directly applied to an actual system. According to the invention, a full-real-time training system is constructed, and synchronous concurrent processing of data acquisition, strategy execution and model updating is realized.

The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing image defogging method and device based on wavelet multi-scale decomposition, and the method comprises the steps: dividing an original foggy remote sensing image into a low-frequency sub-band and a high-frequency sub-band through multi-scale wavelet frequency decomposition, and separating haze interference and image details; secondly, details of the high-frequency sub-bands are enhanced through a nonlinear dynamic enhancement function, multi-scale guiding filtering is conducted on the low-frequency sub-bands through the enhanced high-frequency sub-bands, and fog components in the image are restrained; further integrating feature information under different scales through residual cavity convolution and an SE module to reinforce the perception ability of a global structure and local details, and obtaining fusion features; and finally, decoding and reconstructing the fused features, and recovering to obtain a high-resolution defogged remote sensing image. According to the method, the definition, the structural integrity and the application adaptability of the remote sensing image after recovery under mild to severe haze conditions are effectively improved, the operation consumption is reduced, and the algorithm execution speed is improved.

The invention relates to the technical field of computers, discloses a digital twinborn monitoring method and system for multi-process coupling operation of an assembly type sewage plant, and aims to solve the problem that a traditional model is difficult to capture multi-process coupling nonlinear dynamic characteristics and improve the intelligent optimization operation level of the sewage plant under complex water quality and water quantity changes. According to the method, real-time data are collected and preprocessed, a hybrid digital twinborn model fusing a mechanism and data driving is constructed, state synchronization, prediction simulation, intelligent optimization decision and fault diagnosis early warning are carried out, and a control instruction is output. The system comprises a data acquisition preprocessing module, a digital twin modeling module, a simulation prediction module, an intelligent monitoring diagnosis module, an optimization decision module, a control instruction output module, a man-machine interaction module and the like. Physical digital bidirectional high-precision interaction and hybrid modeling are realized, the prediction and diagnosis capability is remarkably improved, the operation cost is reduced, the effluent is ensured to reach the standard, and the intelligent management of the sewage plant is comprehensively improved.

A video to event prediction pipeline system includes a backbone conversion network having a model that is configured to receive a raw active pixel sensor video sequence and convert it into 3D predicted voxels. An event sampling module is configured to receive the 3D predicted voxels and create event timestamps in a continuous scale by leveraging nonlinear dynamics of event firing trends in each voxel of the 3D predicted voxels. The backbone conversion network comprises a series of training loss function modules, the training loss function modules teaching the backbone conversion network to account for variations in the active pixel sensor video sequence caused by adjustable camera parameters of the active pixel sensor video sequence.

The invention discloses a periodic settlement data prediction method fused with nonlinear dynamic lag modeling, which comprises the following steps: counting the number of manual operation times in unit time as an intervention frequency, comparing the intervention frequency with a preset threshold value, and judging whether a service scene is a periodic scene or a non-periodic scene; for the scene which is determined to be periodic, calibrating a monthly prediction result based on scale change of historical same-period data; for the scene judged to be aperiodic, parameters are updated in combination with a moving average method, and processing delay is dynamically adjusted by incorporating holidays and festivals and resting arrangement, so that sequence prediction is generated and completed; and dynamically updating model parameters and predicted values. According to the method, manual intervention intensity and sequence period feature analysis are fused, the universality and accuracy of service scene judgment are remarkably improved, the recognition limitation of a traditional method on mixed services is broken through, and lagging behavior prediction in the fields of energy, finance and the like can be adapted through the three stages of triggering, processing and completing.