797 results about "Simulated data" patented technology

Disclosed are a rapid inversion method and apparatus for icing thickness of a power transmission line, and a related device. The method comprises: acquiring meteorological data of the place where the power transmission line is located, wherein target meteorological factors in the meteorological data are determined in advance by means of association analysis, and the meteorological data includes meteorological monitoring data and / or meteorological forecast data; and inputting the meteorological data into a trained rapid inversion model to obtain an icing thickness inversion value, wherein the model is obtained by training by using the meteorological monitoring data as a training sample and icing thickness data as a sample label, and the icing thickness data comprises icing thickness monitoring data and icing thickness simulation data obtained by means of numerical simulation calculation. Abundant icing thickness simulation data is obtained by means of numerical simulation calculation, solving the problem of insufficient icing thickness data, meeting the high-precision training requirements of deep learning, and improving the generalization ability of the model; and using the trained rapid inversion model to implement rapid inversion of the icing thickness of the power transmission line achieves relatively high accuracy.

The invention discloses a PINNs high Reynolds number flow field solving method based on regulatory factor optimization, and the scheme comprises the steps: firstly building a physical model, on one hand, building a geometric model according to the actual condition of a reactor core rod bundle channel, and on the other hand, building a fluid mechanics model containing continuity, momentum conservation and energy conservation equations; a data set is obtained, and experimental data is preprocessed and then integrated with simulated data through experimental measurement and numerical simulation; and then training a physical information neural network, designing a neural network model of specific input and output, introducing a regulatory factor to balance the difference between a physical equation and data, constructing a comprehensive loss function containing internal data points, boundary data points and equation loss, and training the network by using a training data set until convergence. And finally, solving a flow field by using the trained neural network, outputting predicted values of a velocity field, a pressure field and a temperature field, and solving the high Reynolds number flow field of the reactor core.

The invention relates to an intelligent inversion method and system for well drilling overflow shaft gas distribution based on a self-encoder, and belongs to the technical field of oil gas and geothermal development drilling and completion engineering. Comprising the following steps: step 1, accurately solving multiphase flow parameters of a shaft; 2, in combination with the drilling working condition and the geological condition of a specific overflow high-risk well section, based on a Monte Carlo sampling method, eight parameters are changed, uniform sampling is carried out, and a high-precision simulation data set is formed; step 3, constructing a neural network model for gas cut state inversion based on an auto-encoder neural network; 4, training is carried out; 5, standardizing one-dimensional time sequence parameters in the monitoring data; and inputting into a trained neural network model for gas cut state inversion based on an auto-encoder neural network to obtain distribution data of the overflow gas in the shaft in a time period in which the time sequence parameter at the current moment is located. According to the invention, the real-time rapid inversion of the gas distribution in the parallel cylinder during the drilling overflow parallel control period is realized.

The invention belongs to the technical field of finite element simulation optimization, and provides a hot upsetting process multi-target collaborative optimization method and system based on finite element simulation, and the method comprises the steps: firstly constructing a hot upsetting process multi-field coupling finite element model, and then deploying a sensor network in hot upsetting equipment to collect actual process data. Actual data and simulation data are matched through a space-time registration algorithm, then a parameter optimization model is constructed based on a dual-channel depth deterministic strategy gradient algorithm, and a technological parameter solution set is obtained through calculation; and finally, a multi-objective optimization function is defined according to a preset optimization index, and optimal hot upsetting process parameters are screened out in combination with an entropy weight method and a reference point-based non-dominated genetic algorithm. According to the method, high-precision simulation, real-time monitoring and multi-target collaborative optimization of the hot upsetting process are achieved, and the production efficiency and the product quality of the hot upsetting process are remarkably improved.

The invention discloses a nondestructive testing method and system and a data processing method, and relates to the technical field of nondestructive testing. Comprising the steps of obtaining initial detection data of a detected component; establishing at least one reference model according to the initial detection data, and obtaining simulation data corresponding to each reference model; determining target data according to the initial detection data and simulation data; and obtaining subsequent detection data, and comparing the subsequent detection data with the target data to form an evaluation result. According to the invention, high and low frequency mixed excitation signals are adopted, so that the penetrating power and resolution of detection are improved, surface defects and buried defects can be detected at the same time, and the comprehensiveness and accuracy of detection are remarkably enhanced; a picked complex response signal is decoupled by using an orthogonal phase locking method, a magnetic field signal and an electric field signal are effectively separated, the signal-to-noise ratio and the detection precision are improved, and accurate discrimination of defect types, depths and orientations is realized by establishing a reference model and acquiring analog data.

The invention discloses a low-altitude atmosphere three-dimensional wind field inversion method based on wind profile radar vertical observation, relates to the technical field of three-dimensional wind field inversion, and aims to solve the problem of poor inversion effect caused by inaccurate model parameters in the wind field inversion process. The difference between simulation data and actual observation data is evaluated from multiple angles, a comprehensive optimization basis is provided, parameter calculation optimization is performed by adopting a particle swarm optimization method, the method has the characteristics of high convergence speed and high global search capability, is suitable for optimization of complex problems, and is used for confirming model requirements according to a wind field inversion rule. The model is selected by combining the evaluation result of the model library, it is ensured that the selected model has high pertinence and applicability, the prediction precision and reliability of the model are improved, model initialization is conducted through wind field fusion data, data information of multiple sources is fully fused, and the comprehensiveness and accuracy of the data are improved.

An earth and rockfill dam termite nest detection method based on MSCCEAUNet and physical constraint combined driving comprises the following steps that a miscellaneous geologic model of an earth and rockfill dam structure with a termite nest is simulated, and corresponding b-scan data and underground dielectric constant data are generated through a finite difference method FDTD to serve as a simulation data set; 2, constructing an MSCCEAUNet network with multi-scale cascade convolution (MSC) and an efficient channel attention mechanism (ECA), and inputting the complex geologic model data generated by simulation in the step 1 into the MSCCEAUNet network for training; according to the method, the MSCCEAUNet is trained again by adding Gaussian noise and random medium disturbance data with different signal-to-noise ratios, the structure of the MSCCEAUNet is optimized according to the training condition of the model, and the anti-jamming capability and robustness of the model are improved; a physical residual equation meeting physical information constraints of Maxwell equations of the ground penetrating radar is used as a physical loss function to be fused into an MSCCEAUNet framework, and data and physics combined drive inversion is achieved.

The invention discloses a hydroelectric generating set operation fault data enhancement method and device, and the method comprises the steps: firstly collecting a set vibration signal, selecting a time-frequency transformation method to convert a one-dimensional vibration signal into a two-dimensional time-frequency image, enhancing the feature dimension of the signal, constructing a diffusion feature migration model, gradually disturbing the data distribution to Gaussian noise through forward diffusion, and carrying out the recognition of the Gaussian noise. The method comprises the following steps of: performing inverse denoising to generate simulation data highly similar to a real fault sample, realizing relevance learning and migration sharing of fault features among different working conditions in combination with an adversarial feature migration architecture, and finally evaluating an enhancement effect by calculating similarity among samples, and inputting enhanced data into a fault diagnosis model to verify precision improvement. Through the combination of time-frequency transformation and a diffusion model, sample scarcity and working condition barriers are broken through, a remarkable effect is shown in the aspects of expanding the fault sample scale and enriching the sample dimension, the similarity of generated data and a real sample is improved, the diagnosis precision is improved, and the model generalization ability is remarkably enhanced.

The invention discloses a numerical simulation-based roadway surrounding rock deformation monitoring method and device. The method comprises the following steps of: constructing a three-dimensional geologic model of a roadway area; based on the three-dimensional geologic model, establishing a numerical model of heat conduction, stress action and seepage multi-physics field coupling, and based on elastic-plastic characteristics of rocks in the roadway area, simulating simulation data of surrounding rocks in the roadway area under the multi-physics field action by the numerical model; acquiring real-time monitoring data of various types of sensors arranged in a risk monitoring area of surrounding rock deformation corresponding to the simulation data; denoising, filtering and abnormal value processing are carried out on the real-time monitoring data, and then multi-source data fusion is carried out to calibrate the numerical model; and predicting the deformation trend of the surrounding rock based on the calibrated target numerical model and the real-time monitoring data. Therefore, by means of geological modeling, numerical model simulation, sensor arrangement optimization, numerical model optimization and surrounding rock deformation trend prediction, roadway surrounding rock monitoring precision is improved, and safe operation of a roadway is guaranteed.

The invention discloses the technical field of constructional engineering and intelligent control, and relates to a concrete hydration heat self-adaptive control method and system based on digital twinning. The method comprises the following steps: S1, constructing a concrete finite element model, simulating a hydration heat process, and outputting simulation data of temperature and temperature stress; s2, based on the analog data, optimizing the arrangement scheme of the cooling water pipe and the sensor through a composite convolutional neural network; s3, arranging and collecting real-time temperature and temperature stress data according to the scheme; s4, in combination with simulation and real-time data, predicting temperature change by adopting a BP neural network and a long-short term memory network, and constructing a dynamic prediction model; s5, based on the prediction result and the real-time data, constructing a WebGL digital twinborn platform, visualizing the data and correcting a prediction model; and S6, dynamically adjusting parameters of the cooling water pipe according to a correction result to realize hydration heat self-adaptive control. The invention further provides a system for realizing the method, the concrete temperature and the temperature stress can be accurately controlled, and the engineering structure safety and the quality control efficiency are improved.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

The invention relates to the technical field of engineering intelligent algorithms, in particular to a jet grouting pile forming quality and construction optimal parameter prediction method and system, and the method comprises the following steps: building a data sample set based on similar historical engineering cases; the construction stratum soil bodies of the jet grouting piles are classified to obtain different stratum soil body categories, and construction process sensitive parameters are obtained; performing an orthogonal test to obtain an orthogonal test result so as to establish a jet grouting pile construction numerical simulation model to obtain a simulation data sample set; a jet grouting pile strength prediction model is constructed, and a jet grouting pile forming quality prediction result is obtained; establishing constraint conditions, and optimizing the pile-forming quality prediction result of the jet grouting pile according to an optimization algorithm to obtain an optimal solution set; and in combination with actual jet grouting pile construction conditions, optimal construction parameters are obtained according to an optimal solution screening method. According to the method, the optimal construction parameters of the jet grouting pile project can be rapidly and efficiently obtained, so that the construction scheme is optimized, and the pile forming quality of the jet grouting pile is guaranteed.

The invention discloses a hydrological data prediction method based on a convolutional neural network and a physical information neural network. Comprising the following steps: 1) utilizing an HEC-RAS hydrological model to generate cross sectional area, flow and water level elevation data of a key section of a target river channel in a preset time period as training reference truth values; 2) constructing space-time coordinates corresponding to the data as an input data set; 3) space-time input is processed through a physical information neural network model based on a convolutional neural network, the convolutional neural network extracts local heterogeneity features of a spatial position of a river channel through one-dimensional convolution, and a continuity equation and a momentum equation of a Saint-Venant equation set are embedded into a training process in a residual form through physical information constraint; and 4) finally, directly outputting a hydrological parameter prediction result of the target river channel. Therefore, under the environment that the observation data is deficient or missing, the hydrological parameter prediction precision and the model generalization ability are remarkably improved by fusing the simulation data and the physical mechanism constraint.

The invention discloses an oil reservoir production dynamic prediction method fusing discrete gradient information, and belongs to the technical field of oil reservoir development and artificial intelligence crossing, and the method comprises the steps: building a heterogeneous oil reservoir oil-water two-phase flow numerical simulation data set based on a numerical simulation method; designing a double-branch network structure and extracting spatial and physical characteristics of input field data in parallel, wherein the spatial and physical characteristics comprise a main characteristic coding branch and a differential operator branch; designing a backbone network to carry out deep nonlinear modeling; an efficient pressure and saturation field prediction neural network model is constructed based on a double-branch network structure and a backbone network, in a model training stage, spatial region observation points of part of time steps are used to participate in data item loss calculation, and meanwhile, physical control equation residuals are introduced into all time steps and a whole space to serve as physical loss items; a trained efficient pressure and saturation field prediction neural network model is obtained, and high-precision prediction of a full-time-sequence pressure field and a saturation field is achieved.

The invention relates to the technical field of crane stress analysis, in particular to a crane structure parameter stress analysis method and system. The method comprises the following steps: constructing an accurate three-dimensional structure simulation model by obtaining crane device data and operation logs, and carrying out crane operation analogue simulation based on the model; performing boom structure division on the simulation data, respectively obtaining simulation data of the truss boom type crane and the telescopic boom type crane, evaluating stress coupling increment of the truss boom type crane and telescopic boom type mechanical conduction response characteristics, and comprehensively detecting dynamic mechanical response of the crane; based on the dynamic response data, identifying the breakage risk of crane components, determining the chain type degradation condition of the components, and evaluating the operation stability gradient attenuation of the crane in combination with the three-dimensional structure model; carrying out structural mechanics optimization treatment according to the stability attenuation condition; through crane stress analysis, the crane can run more stably and efficiently.

The invention relates to the technical field of aero-engines, and discloses an aero-gas turbine engine complete machine stability margin evaluation method, which comprises the following steps of: analyzing physical characteristics and working principles of all parts of a gas turbine engine to construct an engine complete machine model, and constructing an engine experiment table to carry out an experiment; judging whether the compressor enters a surge state and recording experimental data, carrying out numerical simulation based on the constructed engine complete machine model and recording simulation data, comparing the simulation data with the experimental data, verifying the accuracy of the model, if so, constructing a surge boundary, and if not, optimizing the model. The method comprises the following steps: judging whether an engine has a stall hysteresis phenomenon or not in real time, generating a normal operation signal or a stall hysteresis signal, if the stall hysteresis signal is generated, updating a surge boundary, calculating a stability margin value of the engine based on the surge boundary, and evaluating the stability margin of the whole engine.

The invention provides a power distribution network source network storage cooperative control method and system, and relates to the technical field of power distribution networks, and the method comprises the steps: collecting multi-dimensional power grid information of a power distribution network, and setting a target constraint condition according to the multi-dimensional power grid information; establishing a target optimization index and a cooperative control simulation model in cooperation with the target constraint condition, and performing cooperative control simulation on the power distribution network through the cooperative control simulation model to obtain a simulation record; a fitness evaluation strategy is introduced to analyze a plurality of simulation data sets in the simulation records, and an optimal cooperative control scheme is obtained through screening; and performing source network storage cooperative control of the power distribution network through the optimal cooperative control scheme. According to the invention, the technical problem that the power distribution network is difficult to realize effective coordination of the power generation source, the energy storage equipment and the load in the prior art can be solved, and the technical effects of improving the new energy consumption capability, reducing the operation cost, guaranteeing the operation safety of the power grid and improving the overall stability of the system are achieved.

The invention relates to the technical field of water conservancy informatization flood control, in particular to a reservoir flood control simulation method, system and equipment based on digital twinning and a medium. Constructing a three-dimensional digital twinborn platform based on the target reservoir; the method comprises the following steps: acquiring physical data of a target reservoir and simulation data under a three-dimensional digital twin platform, and utilizing the three-dimensional digital twin platform to construct an incoming water forecasting model and a scheduling model of the upstream of the reservoir and a downstream flood routing model; in the simulation process, upstream incoming water data is collected, and the future incoming water amount is predicted by using an incoming water forecasting model; based on the prediction result, reservoir scheduling simulation is carried out to obtain an optimal scheduling scheme; then, combining downstream rainfall and incoming water data, and utilizing a flood routing model to simulate a flood routing condition; the whole process is automatically joined and driven through a digital twin platform, a simulation result is displayed in a visual form, and the method solves the problem that the joining degree of all links of reservoir flood control is not high.

The invention discloses a multi-mode rainy day area road flatness detection method by means of Carla training, and relates to the technical field of road flatness detection, and the method comprises the steps: collecting real rainy day road surface images and vehicle parameters, and constructing a real data set; a rainy day scene is simulated in Carla, and terrain, wet and slippery materials and weather are configured; the method comprises the following steps: acquiring multi-modal data such as simulation point cloud and acceleration through a laser radar and a vibration sensor, and constructing a simulation data set; fusing real and simulation data to train a multi-modal neural network; extracting and fusing point cloud and acceleration features; road height variances and ranges are predicted to assess flatness. According to the method, the problem of scarcity of real data in a rainy day environment is solved by using Carla to generate synthetic data, the real acquisition cost is reduced, the detection precision is remarkably improved, various rainfall intensities and terrains can be covered, data complementation of the laser radar and the vibration sensor is realized, and the robustness in a rainy day is improved through dynamic weighting of an attention mechanism.

A method for constructing a digital twin monitoring model for slopes based on a proxy model includes S101-S104. S101, a slope geometric model consisting of M triangular facets is constructed. S102, a first training dataset is constructed for pretraining a pre-proxy model by using the slope geometric model. S103, monitoring data of the M triangular facets are collected in real-time to construct a second training dataset for training the proxy model. S104, a warning cloud map of the slopes is generated. The method uses the slope geometric model to obtain simulation data under multiple physical field conditions. It constructs training dataset of pretraining pre-proxy model based on the simulation data, which greatly reduces training samples. Moreover, the multiple base learners in the proxy model can reduce the computational bias of a single time-series analysis model, improving the prediction accuracy of the proxy model.

The invention provides a high-strength aluminum alloy selective laser melting forming thermal stress prediction method based on deep learning. The method comprises the steps of 1, finite element model establishment and simulation, wherein a heat transfer model and a thermal coupling model are established; an SLM process is used as a simulation object, a Gaussian model is adopted to define a laser heat source, and thermal stress distribution under different process parameters and different sample sizes is simulated; wherein the process parameters comprise laser power, scanning speed and hatch spacing; step 2, data processing and deep learning model training; comprising the steps of data preprocessing, neural network model architecture determination, adversarial network part generation and model configuration and training. 3, evaluating and optimizing the model; and 4, thermal stress prediction and process optimization. By learning a complex mode in finite element simulation data through a deep learning model, efficient and accurate thermal stress prediction is achieved, and the problems that traditional finite element analysis is complex in calculation and large in resource consumption are solved.

The invention relates to the technical field of remote sensing image processing, in particular to an end-to-end neural network InSAR (Interferometric Synthetic Aperture Radar) phase unwrapping method based on mixed attention, which takes U-Net as a basic framework, extracts key features and improves resolution through down-sampling and up-sampling operations, thereby effectively recovering detail information. Specifically, a convolutional block attention module (CBAM) and two receptive field modules, namely cavity spatial pyramid pooling (ASPP) and a receptive field module (RFB) are combined to construct an RFAUNet network model, and then simulation data sets generated by two methods of digital elevation inversion and random matrix generation are used for training a neural network until a good unwrapping effect is obtained. And finally, carrying out a phase unwrapping experiment on simulation data and real data to verify the effectiveness and robustness of the RFAUNet network model.

A calibration method of the magnetic field model and a controller for electromagnetic positioning are provided. In the method, a magnetic field is established through a magnetic field transmitter; measurement data is obtained through a magnetic field sensor, where the measurement data includes the amplitude of induced voltage at multiple measurement positions in the magnetic field; and the magnetic field model is corrected according to the error between the measurement data and simulated data correction, where the magnetic field model is used to generate simulation data corresponding to multiple measurement locations. The steps of modifying the magnetic field model include: establishing one or more objective functions through the nonlinear least squares method based on the error between the measured data and the simulated data; and determining the modified magnetic field model based on one or more solutions of the objective function. Therefore, positioning accuracy can be improved.

The invention provides a foundation pit excavation deformation prediction method and device based on finite elements, and relates to the technical field of foundation pit construction.The method comprises the steps that firstly, a digital geologic model is built based on geological survey drilling data, and a foundation pit excavation finite element numerical simulation model is built in combination with a foundation pit supporting design drawing of a design institute; orthogonal design parameters are combined to obtain multiple groups of simulation data, and a twinborn database is established; constructing an initial prediction model and training by using a twin database to obtain a target prediction model; obtaining foundation pit monitoring data through a sensor, extracting data corresponding to the twin database to form an effective parameter combination, and inputting a target model to predict a foundation pit excavation deformation result in the next stage; and judging whether a foundation is excavated or not according to a deformation result, and if not, continuing to predict until the foundation is reached. According to the method, modeling is performed by combining geology and design data, the model is trained by using a twin database, deformation can be accurately predicted, dynamic monitoring and real-time prediction are realized by combining monitoring data, and construction safety is guaranteed.

The invention discloses a transformer substation switching operation risk pre-control method and system based on deep reinforcement learning, and relates to the technical field of power system automation, and the method comprises the steps: collecting transformer substation equipment data in real time, processing the collected data, generating a standardized time series data set, building a system topology structure represented by multiple graphs based on the processed data, and carrying out the pre-control of the transformer substation switching operation risk. Integrating dynamic characteristics, based on a system topological structure, generating a switching operation decision sequence, dynamically adjusting a risk boundary threshold, based on a real-time system state, evaluating the security of the decision sequence, intercepting operations which do not conform to a security boundary, performing reward attribution on contributions of operation sequence steps, and constructing a digital twin environment; the model performance is improved through interactive calibration of real data and simulation data. According to the substation switching operation risk pre-control method based on deep reinforcement learning, high-precision risk prediction is realized, the early warning time is sufficient, and the operation sequence is optimized, so that the safety of substation switching operation is remarkably improved.

The invention provides a data filling and abnormity monitoring method and system based on multi-task learning, and relates to the technical field of sensor data processing.The method comprises the steps that multiple types of sensor data are integrated and preprocessed, correlation among the multiple types of sensor data is comprehensively analyzed, and a correlation matrix is obtained; the method comprises the following steps: grouping multiple types of sensors, constructing a multi-task learning prediction model based on a correlation matrix, inputting monitoring data of related sensors at corresponding time of a certain measuring point in the trained model to obtain prediction data and simulation data, performing data filling based on the prediction data, and performing anomaly detection based on the simulation data. Through the multi-task learning prediction model, the inherent correlation among multiple types of monitoring data and the correlation of measuring points in time and space can be fully utilized, and the monitoring data prediction and filling precision is improved.

The present invention discloses a target detection method based on deep learning, comprising the following steps: S1: classification, deep generative model, establishing the probability distribution of model data, by sharing parameters at multiple spatial positions, CNN can extract common features and generate new data samples, which is particularly suitable for processing structured data, such as images and videos; S2: positioning, building a deep neural network DNN architecture, extracting spatial features, identifying key points, and recursive neural network RNN ​​is used to process time series data, capture motion patterns, and simulate the dependencies between data; S3: detection, using the data model of positioning to obtain a detection model of deep learning, combined with region nomination and convolutional neural network, it can automatically learn and extract image features and obtain an accuracy data model. The present invention maintains a high accuracy, and it effectively captures the detailed information of the object by predicting bounding boxes and classification confidence on feature maps at different levels.

The invention discloses a microwave monitoring rainfall field reconstruction method based on a generative model. The method comprises the following steps: S1, obtaining rainfall data from different data sources; s2, dividing a training data set and a simulation data set, and performing data preprocessing; s3, a generative adversarial neural network model Mic-GAN is constructed; s4, iteratively training the anti-neural network model Mic-GAN by adopting the training set in the step S2 until a preset number of iterations or convergence conditions are met; and S5, reconstructing the microwave rainfall field of the target area by adopting the simulation data set in the step S2 and using the model in the step S4. According to the microwave monitoring rainfall field reconstruction method based on the generative model, optimization is continuously carried out along with the increase of data, the advantages of microwave big data are effectively utilized, and the method has good expandability.

The invention provides a multi-layer superposition delay correction method and system based on digital twinning, and relates to the technical field of delay correction. According to the identification result of the delay source, selecting a corresponding measurement method to carry out delay measurement to obtain delay measurement data; establishing a delay mathematical model according to the delay measurement data, and adjusting parameters of the delay mathematical model by comparing the delay mathematical model with simulation data of a preset digital twinborn model; adding a timestamp to each piece of data in the delay mathematical model, and ensuring that the timestamps of the processing modules in the physical system are consistent with the timestamps of the corresponding components in the digital twinborn model; and integrating and comparing the actual data curve and the virtual data curve to obtain a delayed characteristic curve, calculating an error caused by delay according to the delayed characteristic curve, and correcting the delayed data. According to the invention, bad consequences of system stability reduction, control precision reduction and dynamic performance deterioration caused by delay are avoided.

The invention relates to the technical field of intelligent temperature control, in particular to a temperature precision intelligent monitoring system and method for a roller way type sintering furnace, and the system comprises a temperature data collection module, a temperature field simulation module, a hot spot recognition and adjustment module and a temperature precision control module. According to the method, temperature is monitored in real time and compared with simulation data in real time, so that temperature fluctuation in the sintering process can be accurately captured and adjusted in time, inconsistency of product quality is minimized, uniformity of material treatment and structural performance of products are guaranteed, hot spot areas are recognized in real time, actual adjustment measures are determined in combination with simulation data, and the accuracy of the sintering process is improved. The local abnormal temperature can be quickly responded, fine regulation and control can be performed, and a dynamic regulation strategy not only improves the response speed of the system, but also enables energy utilization to be more efficient, reduces energy waste, integrally optimizes the temperature control process, enhances the production efficiency and the product quality, and improves the market competitiveness of products.