354 results about "Ordinary differential equation" patented technology

The invention provides a mesoscale convection parameter optimization method and system based on a genetic algorithm, and relates to the technical field of weather forecast, and the method comprises the steps: modeling a rainfall evolution state through a Sheng differential equation, inferring and recognizing power system parameters in combination with variation, and extracting features through a space-time heterogeneous graph neural network and a diffusion probability model; the parameter threshold is corrected by adopting the physically guided neural network, and the optimization objective function is constructed through the deep neural network to realize parameter optimization, so that the accuracy of rainfall forecasting can be improved, the forecasting error can be reduced, and the method has relatively strong adaptability and generalization ability.

The invention discloses a fault diagnosis and detection system for production automation equipment. The fault diagnosis and detection system comprises a data sensing layer which is used for carrying out multi-mode signal acquisition and real-time preprocessing; the feature extraction layer is used for constructing a recursive block convolution module, capturing transient impact features in four time steps by using an L1-layer gating convolution unit, associating a 16-time-step cross-block periodic degradation mode with an L2-layer sparse attention mechanism, aggregating multi-sensor spatial-temporal features by using an L3-layer global context node, and performing multi-scale feature extraction; the causal reasoning layer is used for establishing a physical constraint driven causal graph engine and outputting a fault propagation path with probability weight; the state modeling layer is used for constructing a continuous health evolution model by adopting a Shenchang differential equation, embedding a physical constraint loss function, and performing equipment full life cycle health state prediction and residual service life estimation in combination with a three-stage memory fusion mechanism of LSTM short-term memory, differentiable neural dictionary medium-term memory and knowledge graph long-term memory; and the decision support layer is used for generating a personalized maintenance work order.

The invention provides a weight metering automatic classification and calibration method and system based on image recognition, and relates to the technical field of weight metering, and the method comprises the steps: carrying out the imaging of the surface of a weight through dual-light-path high-speed camera shooting, carrying out the gamma correction, and extracting contour features and surface defect features; internal density distribution is obtained through X-ray imaging; performing multi-scale feature fusion on the surface features and the density distribution to construct a holographic feature model; analyzing the dynamic change trend of the characteristic parameters based on a deep variational Bayesian network, and performing evaluation and scoring in combination with a gradient boosting decision forest to obtain an initial classification; establishing a dynamic evaluation model by adopting a self-organizing competitive learning network, determining a weight grade and setting calibration parameters; selecting a corresponding reference weight to establish a grading calibration compensation model, and determining an adaptive weight coefficient for dynamic adjustment by combining defect distribution; and predicting a performance degradation trend by adopting a Shenchang differential equation network, and outputting calibration parameters and generating early warning information when the calibration precision meets a threshold value requirement.

The invention discloses a cross-system fault diagnosis method and system combined with multi-dimensional anomaly detection, and relates to the technical field of fault diagnos.The method comprises the steps that a graph neural network with a liquid time constant network unit as a node is constructed through a dynamic topology dependency relationship and a multi-dimensional key performance index flow; each unit describes state evolution through a coupled ordinary differential equation system, and a liquid state time constant can be adaptively adjusted. A time back propagation algorithm is adopted to train a model to learn a normal behavior track contour reference, and anomaly is detected through a dynamic time warping distance. And determining a fault propagation path and a root cause through anti-fact intervention and forward integral solution. And generating an optimal diagnosis action sequence in a liquid graph neural network simulation environment, and calculating a reward value based on execution efficiency, accuracy and a repair effect to carry out strategy optimization. The abnormal detection accuracy and the root cause positioning precision are improved, the fault repair time is shortened, the operation and maintenance cost is reduced, and an intelligent fault diagnosis solution is provided for a complex information technology system.

The invention discloses a charging pile load prediction method based on deep learning. The method comprises the following steps: S1, constructing a regional grid and initializing a cellular basic attribute vector; s2, collecting historical load data and external influence factors, and unifying the historical load data and the external influence factors into an input tensor; s3, generating a time continuous evolution state by using an ordinary differential equation modeling module; s4, constructing a state vector at the current moment; s5, the differentiable cellular automaton module executes primary spatial state propagation; s6, performing multiple rounds of spatial propagation iteration, and outputting enhanced state representation; s7, the load prediction module outputs a future multi-moment prediction value; s8, errors are calculated, and end-to-end training optimization model parameters are executed; and S9, outputting a final optimization model as a load prediction method. According to the method, the response capability of the model to sudden disturbance and the stability of multi-region collaborative prediction are remarkably improved, and the method can be widely applied to multiple application scenes such as smart energy management, electric traffic scheduling and urban public infrastructure intelligent optimization.

The invention discloses a water quality prediction system and method based on machine learning, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-source monitoring data; the teacher model module is used for constructing a Shenchang differential equation model as a teacher model and outputting a prediction result of the target water quality index; the student model module is used for constructing a lightweight neural network model as a student model to perform structure optimization training; the trajectory evaluation module is used for calculating predicted trajectory similarity of the teacher model and the student model and optimizing structural parameters of the student model; the model reasoning module is used for calling the optimized student model to generate a prediction result of the target water quality index; and the result output module is used for outputting a prediction result and providing a display and storage interface. According to the method, high-precision, light-weight and sustainable self-adaptive prediction of water quality time sequence data is realized by fusing Shenchang differential modeling and a trajectory similar distillation optimization mechanism.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention discloses a large-scale heterogeneous computing power cluster training and pushing acceleration method, which comprises the following steps: S1, collecting operation characteristics of each node of a heterogeneous cluster, and constructing an equipment state parameter set; s2, loading a training and reasoning model, constructing an intermediate representation graph structure, and generating graph structure information; s3, inputting the graph structure information and the equipment state parameter set into a scheduling strategy model constructed based on a neural network ordinary differential equation, and generating an optimization decision result; s4, reconstructing a model graph according to an optimization decision result, and dividing the model graph into a training sub-graph and a reasoning sub-graph; s5, mapping the sub-graphs to heterogeneous nodes, performing resource matching and task distribution according to the equipment state parameter set, and generating a scheduling result; s6, collecting performance feedback information; and S7, inputting feedback information into the scheduling model to update parameters, and adjusting evolution function strategy parameters. According to the method, the scheduling acceleration of the training and pushing task of the deep model in the heterogeneous cluster is realized.

The invention provides a thyroid cancer auxiliary diagnosis and metastasis risk prediction method based on deep learning, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the steps: extracting ultrasonic image multi-scale features through a self-adaptive neural architecture search network, combining clinical examination data, fusing diagnosis and treatment knowledge through a neural symbol inference device, and carrying out the prediction of the metastasis risk. Generating a knowledge enhancement feature map; constructing a feature propagation field by using a dynamic neural field network, solving a dynamic evolution equation, and generating a spatial-temporal feature field representing the dynamic change of focus features; constructing a tumor diffusion kinetic model by using an implicit neural representation network and a nerve ordinary differential equation network, calculating a transition probability based on an optimal transmission algorithm, solving an optimal control equation, and outputting a metastasis risk prediction result of each organ; the thyroid cancer diagnosis accuracy and metastasis risk prediction reliability can be effectively improved, and doctors can be assisted in accurate diagnosis and treatment.

The invention provides a rotating machine fault diagnosis method and a related system. The rotating machine fault diagnosis method comprises the following steps: S1, constructing a rotor vibration equation according to a vibration behavior of a rotor system under an operation condition; s2, acquiring real-time information of a to-be-diagnosed rotating machine, performing modal analysis based on a rotor vibration equation according to the real-time information of the to-be-diagnosed rotating machine to determine a sensitive frequency band and key features, and forming multi-dimensional feature data; s3, carrying out denoising processing on the multi-dimensional feature data and carrying out signal reconstruction to obtain a reconstructed signal; s4, inputting the reconstruction signal into a pre-trained rotating machine fault model to obtain a fault diagnosis result; and the rotating machine fault model is fused by adopting a neural network ordinary differential equation and a convolutional neural network-gating cycle unit. The method can accurately simulate and predict the continuous dynamic change of the system, improves the diagnosis precision, and enhances the real-time performance and robustness of the model.

The invention discloses a security monitoring abnormal behavior image recognition method based on time sequence convolution, and the method comprises the following steps: S1, obtaining monitoring video data, and constructing a key point time sequence; s2, modeling the key point time sequence by using a manifold embedded Sheng differential equation, and performing smooth change in a manifold space by using an exponential mapping method; s3, based on a self-adaptive step size solving strategy, dynamically adjusting the time step size according to the change rate of the optimized key point time trajectory; s4, constructing an adaptive topology time sequence convolutional network, and generating enhanced key point time sequence features; s5, using local topology regularization to constrain the amplitude of the topological change of the key points, and extracting the feature representation of the human behavior; s6, carrying out abnormal behavior classification by adopting a classification network; and S7, setting an alarm threshold value, and providing early warning information through the monitoring platform. According to the method, the manifold embedding Shenchang differential equation and the adaptive topology time sequence convolution technology are utilized to accurately identify abnormal behaviors.

The invention provides a consistency path matching method for super-resolution, which is an efficient generative super-resolution method, and can generate a super-resolution result with high reality through one-step reasoning without distillation. Firstly, a consistency training method suitable for super-resolution is introduced, and deterministic mapping from a low-resolution image with noise to a high-resolution image is directly learned, so that a probability flow ordinary differential equation path is established. In order to better keep the distribution of the super-resolution result consistent with the distribution of the natural image, distribution path matching is provided, and the method matches the super-resolution result with a corresponding path of the natural image distribution based on a PF-ODE path learned by consistency training, so that the perception quality of the super-resolution result is remarkably improved. A large number of experimental results show that compared with an existing diffusion model method, the method can achieve equivalent or better performance while the reasoning speed is fastest.

Techniques are described for optical flow estimation. For example, a computing device can obtain images including at least a first image and a second image. The computing device can process the first image and the second image using a first neural network to obtain a set of features representing the first image and the second image. The computing device can predict, based on the set of features, a latent representation of a change in an optical flow between at least the first image and the second image using a neural ordinary differential equation that uses a second neural network to generate a predicted latent representation. The computing device can estimate the optical flow based on the predicted latent representation to generate an estimated optical flow, wherein the optical flow is associated with movement of pixels from at least the first image to the second image.

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

The invention discloses an end-to-end oil reservoir history fitting method based on flow matching, which belongs to the field of petroleum engineering, and comprises the following steps: step 1, collecting and sorting oil reservoir geological parameters and oil-water well production dynamic data, constructing a data set, and carrying out data preprocessing; step 2, constructing a flow matching infrastructure; step 3, constructing an improved U-shaped network for flow matching velocity field prediction; 4, constructing an ordinary differential equation solver to realize data generation; and 5, integrating and packaging the flow matching infrastructure, improving the U-shaped network and an ordinary differential equation solver to obtain a complete flow matching model, and realizing end-to-end dynamic history fitting of the oil reservoir. And directly inputting a production dynamic condition by utilizing the trained end-to-end flow matching model to generate an oil reservoir parameter field sample conforming to the condition. In the process, additional intermediate link conversion is not needed, and dynamic matching and accurate parameter adjustment of the oil reservoir model can be achieved.

The invention discloses a continuous time dynamics prediction method, system, terminal and medium fusing a diffusion model and a graph frequent differential equation, and relates to the technical field of dynamics prediction.The method comprises the steps that a multi-node time sequence is obtained, network structure inference is conducted on the multi-node time sequence through the diffusion model, and a potential graph structure between nodes is obtained; carrying out continuous time dynamic modeling by adopting a Scheng ordinary differential equation, and predicting a node state at any time point; diffusion reconstruction loss, dynamic prediction errors and structure sparsity constraints are constructed, total loss is established, network structure inference based on a diffusion model and dynamic modeling based on a Shenzheng differential equation are coupled based on the total loss, and collaborative training optimization is achieved. According to the method, the potential graph structure of the system can be stably recovered in a complex noise environment, high-precision and continuous prediction can be carried out on dynamic evolution of the potential graph structure, and the limitation that structure inference and continuous time modeling cannot be considered in the prior art is overcome.

An image color migration network weak light image enhancement method fusing language features comprises the steps that S1, based on the rectification theory, a low light content image and a reference style image are modeled into RGB space three-dimensional probability distribution, and smooth migration of color distribution is achieved through an ordinary differential equation and linear interpolation; s2, decomposing the low-light image based on the Retinex theory, and respectively extracting brightness details and color information by a double-branch encoder to generate a super-resolution image; s3, analyzing a user text instruction, decomposing a region descriptor and an intensity coefficient, generating an enhanced mask, and realizing local region optimization; and S4, pre-training related modules in stages, and performing end-to-end fine tuning through a composite loss function to enable color distribution, structural details and style reference of an output image to be consistent. Compared with the traditional method, the method provided by the invention can realize personalized enhancement and improve the weak light image quality and the style migration effect, and has the remarkable advantages of flexible input, high fidelity, no artifact, detail reservation, strong noise suppression capability and the like.

The invention belongs to the technical field of intelligent transportation, and particularly relates to a multi-vehicle alliance collaborative path decision method based on interoperation, which comprises the following steps: acquiring scene information; acquiring a temporary terminal point of the gaming vehicle; grouping the vehicles according to the temporary terminal points of the game vehicles and the current positions of the game vehicles to form sub-communities; obtaining the strategy action of each vehicle in the sub-community, and adjusting to obtain the state of the sub-community; based on the adjusted sub-community state, obtaining the road section duration when all vehicles in the sub-community arrive at the temporary destination; based on the road section duration and the intersection time consumption, calculating the income of the strategy action corresponding to the sub-community and the average income until the sub-community state corresponding to the optimal income is obtained; establishing an ordinary differential equation based on the earnings of the policy actions corresponding to the sub-communities, the average earnings of the policies and the states of the sub-communities, solving by using a fourth-order Runge-Kutta method, and converging to a stable point capable of reaching a Nash equilibrium state; and distributing the route of the vehicle based on the sub-community state corresponding to the Nash equilibrium state.

The invention provides an image defogging enhancement and intelligent perception collaborative optimization method based on stream matching, and the method comprises the steps: obtaining a foggy input image, and constructing an ordinary differential equation for defining image transformation; the input image is input into a fog perception vector field, the fog perception vector field comprises an atmospheric scattering purifier and a defogging perception color lookup table, and the atmospheric scattering purifier is used for extracting multi-scale features from the foggy input image to generate clearer output; the defogging perception color lookup table adaptively adjusts the image color through a nonlinear color transformation mechanism; and solving the ordinary differential equation through an RK4 solver, iteratively updating the foggy input image at each time step, and ensuring that the input image can be stably converted into a clear image from an initial foggy state. The technical problems that in the prior art, the existing method is forced to reduce the model capacity due to the calculation efficiency constraint in the ultra-high-definition image defogging, so that the fog concentration estimation is incomplete, and the perception quality and the calculation efficiency cannot be considered at the same time are solved.

The invention relates to the technical field of water resource management and forecasting, in particular to a deep learning model building and forecasting method based on hydrological mechanism fusion. The method specifically comprises the following steps: constructing a snow melting calculation module and a soil calculation module, splicing to form a runoff production model, constructing a confluence calculation module, executing confluence evolution simulation of a calculation result of the runoff production module by using the confluence calculation module, and constructing a confluence model. In confluence model simulation calculation, according to the number of calculation units, a broadcast calculation strategy and an array-based ordinary differential equation solving method are adopted, parallel calculation of the multiple calculation units is achieved, and runoff production calculation results of the multiple units are obtained; and integrating and calculating runoff production calculation results of each unit through a confluence model to obtain a runoff prediction result of the modeled drainage basin after confluence. The method provided by the invention solves the problems of low model construction efficiency and poor practical application effect faced by the application of the deep learning technology in the hydrological model at present.

The invention relates to the technical field of data processing, in particular to a relational database intelligent query method based on machine learning, which comprises the following steps of: processing multi-modal flow data through time sequence alignment, generating a unified semantic representation vector, constructing a dynamic psychological state map, and modeling a psychological state evolution track by utilizing a neural common differential equation mechanism. After user query is received, historical dialogue nodes are retrieved from the graph, enhanced query intention representation is generated, the enhanced query intention representation is converted into an execution plan through a neural symbol inference engine, and a graph neural network is adopted to predict execution cost. And finally, a personalized analysis report is generated by combining a causal discovery algorithm, and system adaptive optimization is realized through feedback signals. According to the method, the problems of inconsistent time sequence semantics and strong context dependency of the multi-modal psychological data are effectively solved, and the query accuracy and the personalized level in a psychological dialogue scene are improved.

The invention provides a speedometer sideslip online compensation integrated navigation method based on FPGA (Field Programmable Gate Array) hardware acceleration. Relates to the technical field of train navigation positioning. According to the SINS / DR integrated navigation system, an IMU inertial measurement unit is integrated in an SINS inertial navigation module and specifically comprises a three-axis gyroscope and a three-axis accelerometer, in SINS / DR integrated navigation, mapping from sensor data of the gyroscope, the accelerometer, an odometer and the like to a slip mode is achieved by adopting a Sheng differential equation model, and the SINS / DR integrated navigation system is obtained. Two speedometers are installed on non-steering wheels on the two sides respectively, a speedometer speed error model is established to compensate and correct the actual speed, and acceleration of a Kalman filtering algorithm is achieved through an FPGA.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a speech synthesis method, device, equipment and medium based on an optimization strategy algorithm, comprising: extracting a time sequence processing network unit and a data sampling scheduling unit in a speech synthesis model; mapping a denoising function in the time sequence processing network unit into a multi-step Markov decision function, and converting an ordinary differential equation in the data sampling scheduling unit into a multi-source stochastic differential equation; sampling multiple groups of independent audio tracks corresponding to the input text based on a multi-source stochastic differential equation; calculating a strategy gradient modulation factor by using an optimization strategy algorithm and a multi-step Markov decision function; optimizing strategy parameters of the speech synthesis model according to the strategy gradient modulation factor to obtain an optimized speech synthesis model; and obtaining a to-be-converted text, and synthesizing voice corresponding to the to-be-converted text by using the optimized voice synthesis model. And the speech synthesis accuracy is improved.

The invention discloses a flood peak enhanced physical base flow and residual error correction collaborative runoff prediction method, which belongs to the field of hydrological prediction, and comprises the following steps of: dividing a training set and a verification set according to a proportion, performing oversampling processing on flood peak samples, and constructing a time sequence window; a Xinanjiang model is discretized and expressed by adopting an ordinary differential equation, rainfall and potential evaporation data are input, and intermediate variables are obtained. A physical base flow and residual error correction dual-channel module is constructed, and physical base flow and residual error correction is calculated through two full-connection networks. And calculating a final runoff predicted value by adopting a residual connection structure, taking basic NSE loss as a core, superposing a flood peak sample error weighted item, strengthening flood peak fitting precision, and updating physical parameters and neural network weight through a back propagation algorithm. And verifying the model, and respectively calculating prediction indexes of the training set and the verification set. According to the method, fusion of a traditional hydrological model and a deep learning method is realized, the physical interpretation of the model is enhanced, and the basin runoff prediction precision is improved.

The invention discloses an armored vehicle diesel engine digital twin model reduction method and system, and the method comprises the steps: building a diesel engine twin model which is a high-dimensional nonlinear system and comprises a thermodynamic equation of a diesel engine; operating the diesel engine twin model under different working conditions, collecting input and output data of variables, and performing standardization processing on the input and output data of the variables to obtain simulation data; a neural network is used for constructing a nerve differential equation model, simulation data is used for training the nerve differential equation model to obtain a reduced-order model, and the output of the diesel engine is predicted based on the obtained reduced-order model. Through Simulink modeling, neural network training, incremental learning and real-time data updating, a dynamic model of the diesel engine is effectively simplified, and it is ensured that high-precision prediction capacity is kept under different working conditions and operation environments. The system architecture supports real-time monitoring, online learning and self-adaptive adjustment, and high efficiency and stability of the diesel engine control system in practical application are guaranteed.

The invention provides a quantum system data processing method based on light quantum and a light quantum computer, and the method comprises the steps: obtaining an ordinary differential equation and the attribute information of the ordinary differential equation; according to the frequency spectrum number of the ordinary differential equation and the attribute information of the ordinary differential equation, constructing an initial light quantum circuit corresponding to the ordinary differential equation; measuring the initial light quantum circuit, and determining a target function corresponding to the initial light quantum circuit according to a measurement result; loss information of the target function is determined, whether the initial light quantum circuit is applied or not is determined according to the loss information, and if yes, the initial light quantum circuit serves as a target light quantum circuit; and performing operation on input data of the quantum system based on the target light quantum line. According to the method, the light quantum circuit expression capability can be greatly improved, and high-precision solving of the ordinary differential equation is realized.

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention relates to the technical field of ship underwater positioning, in particular to a dynamic space positioning method and system for a sensor chain body of a ship drag chain, and the method comprises the steps: dispersing the drag chain into a plurality of hinged rigid sections in a two-dimensional inertial reference system, connecting the sensor chain body to the tail end, and taking the motion of a ship as a traction point boundary; according to the given traction speed, the initial pitch angle, depth and tension distribution are obtained through static balance; calculating local speeds in the sections according to translation and rotation states of upstream nodes of all the sections, obtaining distributed hydrodynamic loads in combination with fluid parameters, establishing moment balance equations at all the upstream nodes, and forming a second-order ordinary differential equation set with a pitch angle as an unknown quantity; and numerical solution is carried out under a unified time step length, and the mass center positions of each node and a sensor chain body are obtained according to geometric recursion, so that dynamic positioning in an inertial reference system is realized. According to the invention, the precision and reliability of underwater target motion analysis and target tracking can be improved.

The invention discloses a self-adaptive load balancing and resource scheduling optimization method based on cloud computing. The method comprises the following steps: S1, collecting a resource use state of each computing node in a cloud computing platform; s2, constructing a node resource prediction model based on a Shenchang differential equation; s3, taking the node prediction resource state sequence as a scheduling optimization parameter; s4, solving a scheduling optimization function by using a photosynthesis optimization algorithm; s5, sending the scheduling execution instruction set to a cloud platform scheduling controller; s6, after scheduling execution is completed, scheduling feedback data are collected; s7, performing parameter updating based on the scheduling feedback data; and S8, repeatedly executing optimization based on the updated node resource prediction model. According to the method, intelligent optimization of cloud computing platform resource scheduling is realized, the resource utilization rate, the task response speed and the system stability are remarkably improved, and the method is suitable for a large-scale load scene.

The invention relates to the technical field of boiler control, in particular to a multivariable coupling thermal process regulation and control system and method for carbon pollution governing, and the method comprises the steps: collecting multi-source data such as acoustic emission, temperature, humidity and spectrum, and constructing a feature sequence through time mark alignment and wavelet packet enhancement; a heat value characterization quantity is predicted by using a Shenchang differential equation model fused with dynamic gating, a partition equivalent thermal network model is driven on this basis, and accurate prediction of a future time domain temperature field is realized by dynamically correcting thermal resistance and thermal capacity; based on the prediction result, a control instruction is solved through multi-objective optimization under the condition that the active temperature constraint is met; and in combination with heat flow density feedback, a layered reinforcement learning controller is adopted for online compensation of a pre-feedback instruction, and stable and efficient regulation and control of the boiler are achieved.