3192 results about "Temperature measurement" patented technology

The invention relates to the technical field of electronic manufacturing equipment, in particular to an adaptive thermal compensation system and method for a high-precision mounting head of a chip mounter, and the system comprises a temperature-deformation sensing unit, a thermal-mechanical coupling analysis unit, a dynamic compensation control unit, and a closed-loop execution unit. The temperature-deformation sensing unit collects temperature and deformation data of multiple parts of the mounting head in real time, the thermal-mechanical coupling analysis unit reconstructs a three-dimensional temperature field based on a finite element algorithm, the thermal expansion distribution quantity is dynamically calculated, the problem of rough model of traditional single-point temperature measurement is solved, and the measurement precision is improved. The dynamic compensation control unit predicts the thermal drift amount in the future 5 ms through online parameter identification and a long-short-term memory network model, a compensation strategy is adaptively adjusted in combination with the motion working condition, the closed-loop execution unit decomposes the compensation amount into displacement and torsion correction instructions, accurate offset of thermal deformation is achieved, and a whole-process thermal compensation closed loop is constructed. The precision stability of the mounting head in a complex thermal environment is improved, and the production efficiency is improved.

The invention relates to the technical field of power equipment state monitoring, in particular to an oil-immersed transformer distributed temperature measurement method based on a fluorescent optical fiber sensor, and the method comprises the steps: arranging the fluorescent optical fiber sensor in a key temperature rise region in a transformer in a partitioned and layered three-dimensional topological structure, and forming a distributed temperature measurement network; exciting light is injected through a pulse laser to excite a fluorescence signal; a dual-channel phase-locked amplification technology is used to collect signals, and a dual-weight adaptive attenuation model and an oil flow coupling compensation function are combined to demodulate the temperature; reconstructing a dynamic temperature field based on a three-dimensional thermodynamic inversion algorithm, and marking high-gradient hot spots; and temperature, load current and oil flow velocity data are fused to realize graded alarm. According to the invention, the limitation of traditional single-point monitoring is broken through, the strong electromagnetic interference resistance is excellent, a global temperature field can be accurately reconstructed, dynamic early warning is realized, and the operation safety and the operation and maintenance efficiency of the transformer are remarkably improved.

The invention relates to the technical field of mode recognition, in particular to a transformer substation equipment temperature measurement method and system based on visible light guidance. Firstly, a visible light image, an infrared thermogram and vibration spectrum data are synchronously collected, a dynamic compensation coefficient matrix is constructed through illumination intensity analysis, the dynamic compensation coefficient matrix is applied to the infrared thermogram, and illumination interference compensation is achieved. And then, inputting the corrected infrared thermogram and visible light image into a pre-trained heterogeneous atlas matching model, and identifying the category and three-dimensional coordinates of the target equipment. And based on the equipment category information, calling a standard temperature field template in the digital twin model library, establishing a heat conduction path model in combination with the vibration spectrum data, and generating a temperature field prediction reference map of the equipment. And finally, through pixel-by-pixel deviation analysis, inputting the temperature deviation value, the image features and the vibration data into the probability graphic model, and outputting equipment fault information. The method can realize high-precision temperature measurement and fault diagnosis.

The invention discloses a temperature measurement precision optimization method based on multiple sensors, and belongs to the technical field of temperature measurement, and the method specifically comprises the steps: deploying a temperature monitoring array which comprises a fixed position basic sensor group and a position adjustable auxiliary sensor group; scanning the target area through an infrared thermal imaging device to generate a thermal field temperature gradient distribution map; identifying a high dynamic change region boundary based on the thermal field temperature gradient distribution map, and extracting geometric feature parameters of the boundary; constructing a heat flow propagation prediction model according to the boundary geometric feature parameters, and calculating a key monitoring node space coordinate set on a heat conduction path; generating a sensor deployment instruction according to the key monitoring node space coordinate set, and dynamically scheduling an auxiliary sensor group to move to a target position; fusing the temperature data streams of the basic sensor group and the auxiliary sensor group, and executing space-time calibration calculation to generate an optimized temperature field distribution diagram; according to the invention, the coverage integrity and the result accuracy of temperature measurement in a dynamic scene are improved.

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

The invention relates to the technical field of temperature measurement, in particular to a multi-zone temperature distribution real-time calibration method based on a heating body, which comprises the following steps of: 1, etching on the surface of a metal alloy heating body of which the resistance temperature coefficient is greater than a preset material threshold value to form N discrete series resistance heating units which are electrically connected in series but thermally discrete; 2, establishing a resistance-temperature dynamic relation library of each discrete series resistance heating unit; 3, synchronously measuring the real-time resistance value of each unit when the heating body works; 4, reversely deducing the temperature of a unit center point through a unit exclusive relational expression according to the real-time resistance value, and reconstructing a continuous temperature distribution matrix on the surface of the heating body in combination with a heat conduction equation; and 5, comparing the target temperature field with the real-time temperature field matrix, and adjusting the driving power of the corresponding unit in the out-of-tolerance region. Through continuous calibration and temperature adjustment, accurate control of a temperature field can be maintained in different use environments.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention discloses a hydration heat monitoring and dynamic regulation and control method and system in a mass concrete pouring process, and relates to the technical field of concrete pouring, and the method comprises the steps: pre-burying a plurality of dual-mode temperature measurement probes in concrete, collecting dual-mode temperature field data in the concrete in real time, and synchronously collecting environmental parameters; carrying out preprocessing, dynamic weight distribution and data fusion on the obtained dual-mode temperature field data; according to the unsteady state heat transfer equation and the hydration heat model, a finite element heat conduction model is constructed, and the total hydration heat and the temperature difference trend are inverted through actually measured temperature field data; predicting a concrete core and surface temperature difference and a surface and environment temperature difference in a future set time period by combining an LSTM neural network model; and according to the predicted temperature difference result, the cooling water flow rate and the cooling water temperature are subjected to closed-loop regulation. Through the combination of dual-mode sensor temperature measurement and heat inversion, the hydration heat temperature difference in the concrete can be predicted in advance, a cooling scheme is regulated and controlled in real time in a linkage mode, and temperature stress cracks are avoided.

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The present disclosure provides a method for measuring flow velocity of multilayer groundwater using distributed optical fiber with point-source active heating, comprising: setting and optimizing field test parameters; conducting a background temperature monitoring test; performing a point-source active heating distributed temperature measurement test, including conducting multiple rounds of heating tests; denoising the obtained multi-point source thermal plume attenuation signal data of the groundwater; searching for the peak value of temperature-permeability curve based on an automatic multi-scale peak search algorithm; performing secondary processing on the peak data; estimating the groundwater flow velocity. The method of the present disclosure solves the problem that the existing groundwater flow velocity measurement technology cannot simultaneously measure multiple points and multiple layers, and it can avoid groundwater contamination during the measurement process.

The invention relates to the technical field of fire-fighting engineering, and discloses a real-time fireproof monitoring data transmission system for fire-fighting engineering construction, which comprises a fire parameter analysis module, an environmental data analysis module, a fire source positioning module, a model construction module and a safety database. The fire parameter analysis module generates a temperature change record table through gridding temperature measurement nodes, estimates the smoke diffusion rate, and evaluates the thermal stress and deformation risk of the building structure; the environment data analysis module associates the air velocity with the smoke diffusion rate and corrects the parameter weight of the evaluation model; the fire source positioning module generates a fire source three-dimensional positioning map by using multispectral imaging equipment; the model construction module constructs a dynamic fire assessment model according to multi-dimensional data and outputs a comprehensive threat level; the security database stores building material parameters, historical case characteristics, and dynamic threshold rules. According to the system, through multi-module cooperation and multi-data fusion, the accuracy and timeliness of fire-fighting monitoring are improved, and the system is suitable for safety management of fire-fighting engineering.

The invention discloses a PID temperature control system and method for a semiconductor etching machine, and relates to the field of temperature control, and the system comprises a coupling relation construction module, a temperature sensing confidence module, a decoupling compensation module, a coupling relation dynamic updating module, and a transient temperature change control module. According to the invention, by constructing the coupling relation matrix and the inverse matrix thereof, decoupling control of multi-area temperature control is realized, cross interference is effectively suppressed, and the temperature control precision is improved. And a multi-point temperature measurement fusion and anti-interference algorithm is introduced, so that the temperature measurement accuracy and the system robustness are enhanced. A response residual vector is associated with a control vector, and a coupling matrix is dynamically optimized, so that the system has self-learning and model self-adaption capabilities and adapts to working condition changes. And a transient temperature change quick response mechanism is set, so that the control priority can be improved and the PID parameters can be adjusted when the temperature changes suddenly, the response speed and the control stability of the system to abnormal temperature change are remarkably improved, and the thermal uniformity and the process consistency in the etching process are ensured.

The invention relates to a turbine blade global temperature field reconstruction method based on a multi-modal physical constraint network, and belongs to the technical field of aero-engine thermal management. Aiming at the problems of insufficient global heat conduction path modeling, lack of physical rules and blank region differentiation perception, carrying out normalization and position coding on sparse temperature measurement points to form a Transform encoder input sequence; extracting features through an encoder and compressing the features into global feature vectors through self-attention pooling; decoding the image into a temperature field image by adopting a U-Net generator fused with jump connection; a multi-scale discriminator is constructed, a semantic weighting mechanism is introduced, and differentiated weights are distributed to cooling holes, tenons and pressure surface areas; a physical constraint loss and local energy conservation loss are constructed based on a heat conduction equation, and a weighted training model is combined with confrontation loss and temperature error loss. The method is used for providing high-precision temperature distribution data support for turbine blade structure optimization design, cooling system efficiency evaluation and thermal fatigue prevention.

The invention discloses a method for online reconstruction of a temperature field of a cutting area of a temperature measurement end mill. The method comprises the following steps: constructing a milling cutter three-dimensional model; establishing a milling heat conduction model; finite element analysis software is used for conducting heat conduction positive problem simulation under different heat flow densities; extracting a temperature value of each node and calculating a temperature sensitivity coefficient; acquiring a temperature data sequence at the temperature measurement position; establishing and training a heat conduction inverse problem algorithm model; acquiring real-time temperature data and predicting heat flux density through a temperature measurement module; and on-line reconstruction of the temperature field of the cutting area is realized in combination with the temperature sensitivity coefficient and the predicted heat flux density. The method can monitor the cutting temperature in real time, optimize the machining parameters, prolong the service life of the cutter and improve the machining precision.

The invention relates to a mass concrete raft foundation three-dimensional temperature field reconstruction method, and relates to the technical field of concrete raft foundation structure three-dimensional temperature field reconstruction. The method comprises the following steps: arranging temperature sensing optical fibers in a plurality of temperature measurement areas of a concrete raft foundation to be measured; constructing an optical fiber temperature measurement monitoring system, and obtaining space-time temperature data; a temperature field prediction model is constructed, the temperature field prediction model is based on a physical information neural network, and a data loss function, a physical loss function and an initial condition loss function are fused; performing multi-objective loss function joint optimization training on the temperature field prediction model based on the space-time temperature data; utilizing the trained temperature field prediction model to predict the temperature value of the concrete raft foundation to be measured at any time and space point coordinates; and reconstructing a three-dimensional temperature field based on a prediction result output by the temperature field prediction model. By adopting the method, high-precision reconstruction of the three-dimensional temperature field of the mass concrete raft foundation can be realized.

The invention provides a non-contact multi-mode thermal imager temperature measurement method, and relates to the technical field of non-contact temperature measurement, and the method comprises the steps: collecting the three-dimensional structure information of a measured object, obtaining infrared thermal radiation data, and collecting meteorological parameters; identifying the material and the surface state of the object, and adjusting an infrared emissivity parameter by using a deep learning model; performing cross-modal feature alignment on the related data; and inputting the meteorological parameters into the environment interference model to correct the infrared signal, determining whether the temperature measurement data is qualified, and if yes, repeating until the temperature measurement of the measured area is completed. According to the invention, by fusing the multi-modal data and utilizing the deep learning model, the space-time attention mechanism, the environment interference model and the like, the dynamic calibration of the infrared emissivity, the feature alignment of the multi-modal data and the effective compensation of the environment interference are realized; the problems of large environmental interference, difficult emissivity calibration, insufficient complex scene adaptability and the like of a traditional method are solved, and the temperature measurement precision and reliability 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 discloses a platinum resistor high-precision temperature measuring device capable of automatically identifying a wiring mode. The platinum resistor high-precision temperature measuring device comprises a power supply module, a signal detection module, an isolation module, a signal processing module and a cold end temperature measuring module. The power supply module supplies power to the signal detection and processing module. The signal detection module comprises a sensing probe, a wiring identification circuit, a constant current source, a measuring circuit and an analog-to-digital conversion circuit. The sensing probe measures the measured temperature, the wiring identification circuit automatically judges a two-wire system, a three-wire system or a four-wire system and feeds back a result, the constant current source provides positive and negative constant current excitation, and the measuring circuit collects and processes feedback voltage and then sends the feedback voltage to the analog-to-digital conversion circuit. The analog-to-digital conversion circuit is connected with the signal processing module through the isolation module, and the signal processing module calls a corresponding algorithm according to a wiring identification result and outputs final temperature data. According to the invention, high-precision temperature measurement at the grade of + / -0.02 DEG C can be realized in the range of-200 DEG C to 850 DEG C under the three / four-wire system and low-speed sampling conditions.

The invention relates to the technical field of industrial temperature measurement, in particular to a high-temperature heating body hearth temperature distribution measurement method, which comprises the following steps of: establishing a quantitative mapping relation model between real-time input electric power and theoretical heating intensity of a heating body; a limited number of temperature sensor arrays are arranged at key positions of the internal space of the hearth; synchronously acquiring a real-time temperature measurement value of the temperature sensor array and real-time input electric power data of the heating body; calculating and generating theoretical temperature field distribution covering the three-dimensional geometric model; obtaining a corrected temperature value; calculating the temperature residual error of the corrected temperature value and the theoretical temperature field distributed at the corresponding position of the sensor; expanding the discrete temperature residual error into a full-space three-dimensional residual error distribution field; and outputting the three-dimensional real-time temperature distribution field of the hearth. Dynamic lag compensation and convective heat transfer compensation algorithms are adopted, lag and errors in measured values can be corrected in real time, and it is ensured that measured data at high temperature have high response speed and accuracy.

The invention relates to the technical field of fault equipment detection, and discloses a power equipment fault detection method and system based on high-precision temperature measurement, and the method comprises the steps: scanning the surface temperature of power equipment through a high-precision temperature measurement device, obtaining a temperature distribution image, processing the temperature distribution image, and obtaining a temperature distribution matrix; carrying out gradient analysis and temperature difference identification on the temperature distribution matrix to obtain a hot spot candidate area; performing clustering analysis on the hot spot candidate region to obtain an initial hot spot region, and generating a static boundary by adopting a boundary tracking algorithm; correcting the static boundary according to the temperature change of each boundary point to obtain a hot spot area; and performing fault risk assessment according to the load data of the power equipment and the temperature data of the hot spot area. According to the invention, through temperature data analysis and region boundary correction, the hot spot region can be accurately identified, and in combination with a machine learning algorithm, the accuracy of power equipment fault risk assessment is improved, and safe and stable operation of a power system is ensured.

The invention belongs to the field of data processing and deep learning, particularly relates to a temperature field prediction model training method and a temperature field reconstruction method, and aims to solve the problem of insufficient boiler furnace temperature measurement accuracy. The temperature field prediction model training method comprises the steps that a geometric model of a target boiler is obtained, grid division is conducted on the geometric model, and grids of the geometric model correspond to grid data used for positioning; acquiring temperature field data of the target boiler based on the fluid numerical simulation model of the target boiler; a target data set constructed by the grid data and the temperature field data is used as a training sample of the unit model, and a node temperature updating model and an edge feature updating model included in the unit model are respectively iterated for training; and synchronizing the model parameters of the trained unit model to the plurality of layers, and generating a temperature field prediction model composed of the plurality of layers. According to the invention, high-precision reconstruction of the internal temperature field of the target boiler can be realized through a small amount of actually measured temperature data.

The invention provides a molten pool temperature field in-situ monitoring method and system based on multi-view light field imaging, and the method employs a light field camera multi-view system, obtains molten pool images of R, G and B channels through building a light field sub-aperture Bayer model, and calibrates the relative spectral responsivity of a light field camera and the proportion of dual-wavelength intensity under a coaxial temperature measurement system. Molten pool images of two channels are selected from the molten pool images of the R channel, the G channel and the B channel, calibration parameters are combined, and a molten pool temperature field is derived through a dual-wavelength temperature measurement method. A blackbody furnace is adopted to calibrate a multi-view light field temperature measurement system, and the error of a monitored temperature field is kept below 3%. The method simplifies the monitoring process, improves the temperature measurement precision, has a wide temperature range and low complexity, can perform in-situ monitoring on the high-temperature metal molten pool, and has a wide application prospect in the field of additive manufacturing.

Methods and systems are disclosed that use a temperature sensor integral to a drill bit during drilling. The temperature sensor is configured to measure temperature associated with a cutting element of the drill bit over time during drilling. The temperature sensor is used to generate temperature data representing temperature of the cutting element of the drill bit over time during drilling. The temperature data is processed to determine and monitor at least one condition of the wellbore and / or the drill bit during drilling, such as i) detection of faults or openings or cracks or other geological rock features of the wellbore being drilled, ii) estimation of one or more drilling parameters of the drill bit (such as rate of penetration (ROP), iii) detection of lost cutting element(s), iv) detection of bit balling, and v) detection of drilling efficiency.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine bearing temperature and lubricating oil state monitoring and fault diagnosis method and system. According to the method, triple-redundancy temperature measurement values of a water turbine bearing are adopted, multiple parameters are fused, redundancy design is adopted, sensor failure diagnosis is executed, a temperature-vibration-oil pressure combined feature space is constructed according to the triple-redundancy temperature measurement values and online oil quality parameters, a fault propagation map is established, and the fault propagation map is analyzed. And finally, based on the multi-level diagnosis model and in combination with the fault propagation atlas, composite fault mode recognition is realized, a fault classification result is obtained, and the composite fault mode recognition is realized based on the multi-level diagnosis model, so that the problems of low reliability, single monitoring dimension and insufficient fault recognition capability of a sensor in the prior art are effectively solved. And the equipment reliability and the operation safety are obviously improved.

The invention relates to the technical field of thermotechnical measurement and signal processing, in particular to a multi-sensor linkage heat flux density-temperature real-time acquisition and error compensation method, which comprises the following steps: S1, constructing an embedded sensing array on a surface to be measured; s2, synchronously acquiring original measurement values of the sensors by taking 10ms as a period, and generating an original data matrix with a timestamp identifier; s3, extracting a three-dimensional temperature gradient field, and calculating to obtain a convective disturbance intensity coefficient of each measurement point; s4, establishing a dynamic error compensation model, and performing point-by-point error correction on the original data matrix; s5, cross validation is carried out, and credibility evaluation parameters are generated; and S6, carrying out weighted fusion, and outputting a final heat flux density-temperature coupling measurement value. According to the invention, through multi-sensor array cooperative acquisition, dynamic error compensation and credibility weighted fusion, high-precision, strong-anti-interference and high-credibility output of heat flux density and temperature measurement is realized.

The invention provides a thermal imager intelligent temperature measurement method based on a neural network, and relates to the technical field of thermal imaging temperature measurement, and the method comprises the steps: firstly collecting an infrared image and a visible light image of a target object, and environment temperature and humidity data; then, the infrared image is preprocessed; then calculating an initial temperature value through a temperature compensation formula, correcting energy attenuation by utilizing humidity data, and processing an abnormal heating signal in combination with the visible light image and the humidity data; and finally, related data are analyzed by means of a neural network, and a corrected temperature value is output. According to the method, a temperature compensation formula combining heat exchange and atmospheric radiation difference is constructed, correction factors are adjusted according to humidity and atmospheric pressure, a visible light image is used for identifying the dirt accumulation level, infrared image features are analyzed in combination with a neural network, and multi-source data fusion and environmental factor dynamic compensation are achieved. The interference of environmental factors on the temperature measurement of the thermal imager is effectively overcome, and the temperature measurement precision and the equipment defect detection accuracy in a complex environment are improved.

The invention discloses a pulverized coal boiler SNCR reaction area temperature field prediction method and prediction system. The pulverized coal boiler SNCR reaction area temperature field prediction method comprises the steps that a numerical simulation model of a pulverized coal boiler is established; a temperature field database under the full working condition of the pulverized coal boiler is simulated and obtained, the temperature field database at least comprises a temperature measurement position temperature field and an SNCR reaction area temperature field, and the temperature measurement position is located in the pulverized coal boiler and located on the upstream side and / or the downstream side of the SNCR reaction area; based on the temperature field database, taking the temperature measurement position temperature field as an input quantity and the SNCR reaction area temperature field as an output quantity to train a neural network, and obtaining an SNCR reaction area temperature field neural network prediction model; and measuring the temperature field of the temperature measurement position, inputting the temperature field into the SNCR reaction area temperature field neural network prediction model, and outputting the SNCR reaction area temperature field through the SNCR reaction area temperature field neural network prediction model. According to the prediction method provided by the invention, the measurement difficulty of the flue gas temperature of the SNCR reaction area can be reduced.

The invention relates to a boiler flue gas temperature dynamic compensation method based on CFD-reinforcement learning, and belongs to the technical field of boiler operation control. The method comprises the steps that boiler operation parameters and flue gas temperature data are collected in real time, a multi-scale coupling three-dimensional CFD model is constructed, a macroscopic gas-solid two-phase flow-combustion reaction-radiation heat transfer coupling algorithm and a microcosmic pulverized coal particle combustion dynamics algorithm are fused, and hearth and flue temperature distribution is simulated and output; building a CFD-reinforcement learning fusion agent based on a CFD model, taking similar working condition agent parameters as initial values, combining historical and virtual data staged training, and reinforcing the weight of a key temperature measurement point through a focusing regulation and control strategy; an echo state network-predictive control collaborative algorithm is adopted, real-time data are input to obtain an initial adjustment amount, and after security constraint optimization, a compensation execution mechanism is controlled to act. The flue gas temperature is accurately and dynamically compensated, the adaptability to complex working conditions is improved, and equipment safety and operation efficiency are both considered.

The invention relates to the technical field of power modules, and discloses a power module detection method and system based on temperature measurement, and the method comprises the steps: obtaining the temperature data of an air inlet outside a power module, in a heat dissipation channel, other units outside the heat dissipation channel, and beside the air inlet, and calculating an air inlet temperature value, a channel temperature value, a unit temperature value, and an inlet temperature value; and calculating a curve matching value of the two thermal difference temperature value change curves according to the thermal difference temperature values, the first thermal difference temperature value and the second thermal difference temperature value, comparing the curve matching value with a preset reference matching value, and if the curve matching value is smaller than the preset reference matching value, performing early warning prompt. The temperature conditions of different positions of the power module are comprehensively considered, the detection deviation is reduced, the accuracy of temperature state detection is improved, the temperature abnormal change trend is captured, potential faults are found in advance, and reliable guarantee is provided for stable operation of the power module.

The invention discloses a multi-dimensional data-based temperature prediction method and system for a power integrated module, particularly relates to the technical field of temperature measurement, and is used for solving the problems of temperature field distortion and unreliable measurement result caused by thermal disturbance introduced by a sensor in temperature monitoring of an existing power integrated module. Multi-dimensional temperature data are formed by acquiring measurement data of a plurality of temperature sensors, a virtual heat flow path is reconstructed based on temperature gradient distribution, deviation degree analysis is carried out on the virtual heat flow path and an inherent structure heat flow path of a module so as to judge thermal disturbance influence, and reference isothermal surface topological structure features are acquired when disturbance occurs. A real-time isothermal surface is constructed, a thermal disturbance space position and an influence range are identified by detecting a curvature distribution sudden change area, a local thermal conductivity correction field is established to perform thermal field reconstruction on multi-dimensional temperature data to generate correction temperature field data, and finally a temperature change trend is analyzed according to the correction temperature field data to predict a future temperature state.