331 results about "Temperature error" patented technology

The invention relates to the technical field of temperature control, and discloses a temperature error compensation method and system for a multi-heating-section coffee maker, and the method comprises the steps: synchronously collecting the real-time temperature value of each heating section, and calculating a temperature error index; constructing a thermal coupling interference model based on the physical distance between adjacent sections and the heat conduction characteristic of the medium; generating a dynamic decoupling compensation parameter according to the model and the error index (including constructing a symmetric interference matrix, calculating a coupling interference component, applying negative feedback and identifying a main interference component); a decoupling parameter is converted into a power adjustment instruction through a multivariable control algorithm (superposition of basic compensation and decoupling compensation, amplitude limiting and phase compensation); and heating output is updated according to the power instruction, and iterative execution is performed based on the error index change trend until the temperatures of all sections reach steady-state balance. The method can effectively model and compensate thermal coupling interference between sections, improves the efficiency of temperature error compensation, and achieves the quick and stable cooperative temperature control of multiple sections.

The invention provides a temperature monitoring method and system for a vertical buried pipe ground source heat pump, and belongs to the technical field of temperature measuration.The method and system comprise the steps that firstly, original temperature data are collected through a distributed optical fiber sensor, a temperature fluctuation matrix is constructed, and then time domain decomposition is conducted to obtain temperature fluctuation characteristic quantity; based on this, a temperature anomaly parameter is calculated and a temperature anomaly matrix is constructed. And inputting the temperature anomaly matrix into a pre-trained temperature error prediction model to obtain an error prediction value, and generating a temperature compensation matrix according to the error prediction value. And then a three-level compensation strategy is adopted, first-level compensation, second-level compensation and final correction of the temperature data are achieved in sequence, the pre-training model constructs training data through a conditional generative adversarial network embedded into a physical equation, and the reliability of the compensation effect is ensured. The technical problem that the temperature error compensation effect is poor due to the fact that temperature field distribution around the vertical buried pipe of the ground source heat pump is difficult to accurately measure in the prior art is solved.

The invention provides a full-temperature automatic calibration and compensation method for an electric energy meter, which belongs to the technical field of electric energy meter embedding, and is characterized in that a multi-segment and non-uniform temperature segment point method is adopted, an instruction is sent to the electric energy meter through an upper computer, and the electric energy meter can be automatically calibrated and compensated. And then the target temperature of the high-low temperature box is set, and full-temperature calibration is carried out. By adopting the design of multi-section and non-uniform temperature segmentation points, the method can adapt to various temperature-error curves. The method is simple to operate, only an instruction needs to be sent to the electric energy meter through the upper computer, then the target temperature of the high-temperature and low-temperature box is set, full-temperature calibration can be automatically completed, and frequent command interaction with the electric energy meter or frequent manual adjustment of the temperature of the high-temperature and low-temperature box is not needed.

The invention discloses a belt type roasting machine operation control method and system. The method comprises the steps that roasting machine operation control parameters including the material level, the fan rotating speed and the air bellow temperature target value are set; collecting the blanking amount, the machine speed, the material level and the temperature measurement values of the roasting section air bellow and the soaking section air bellow in real time; calculating a speed target value based on the blanking amount change rate and the material level error, and dynamically adjusting the speed of the trolley; and a fuzzy control strategy or a regular control strategy is selected according to the blanking fluctuation state, the historical control mode and the fan-speed matching degree. In the fuzzy control mode, slope characteristics are extracted through temperature curve fitting, fuzzy decision is carried out in combination with temperature errors, and the fan rotating speed adjustment amount is output; and in the rule control mode, the fan rotating speed adjustment amount is determined according to a predefined rule table. And finally, the fan target rotating speed is updated according to the fan rotating speed adjustment amount, and the rotating speeds of the main induced draft fan and the backheating fan are adjusted according to the fan target rotating speed. The stability of thermal parameters of the belt type roasting machine can be improved.

The invention discloses a sensor temperature error compensation method and device based on artificial intelligence. The method comprises the following steps: collecting measurement data and temperature data of a sensor; based on the temperature data, performing temperature error compensation on the measurement data by using a neural network model based on deep learning to obtain error-compensated measurement data; wherein the neural network model is obtained based on the following training: preprocessing training data in a training data set, and calculating an error value of the neural network model based on the preprocessed training data; and network parameters of the neural network model are updated based on the error value. The technical problem that the precision of the sensor is not high due to temperature influence is solved.

The invention discloses a dynamic digital compensation method for the metering error of wide-temperature-interval electric energy meter equipment, and the method comprises the steps: collecting the metering error data of the equipment at different temperatures through a temperature sensing module, so as to construct a historical temperature-error relation library; training an adaptive compensation model based on a historical temperature-error relation library to establish a nonlinear mapping relation between the temperature and the error; in the real-time operation process of the equipment, the current equipment temperature is collected through a temperature sensing module, and the current equipment temperature and the real-time load rate are input into the self-adaptive compensation model to obtain error correction; and the metering result is dynamically adjusted in real time according to the current equipment temperature and the error correction amount so as to output a compensated accurate metering value. According to the method, a temperature sensor does not need to be newly added, dynamic error correction is realized only by utilizing the existing temperature sensing capability of equipment, the metering precision and stability in a wide-temperature environment can be improved, the comprehensive cost is reduced, and the high-precision metering requirements in special environments such as extremely cold and high temperature environments are met.

The invention relates to a soft package lithium battery internal and external temperature real-time monitoring method, which comprises the following steps of: in a soft package battery packaging process, implanting a plurality of superfine thermocouples into a key area in a battery to obtain a temperature change condition in the battery; the key area comprises an area below the current collector, a maximum thickness position and an area close to the bottom of the shell; meanwhile, a plurality of distributed temperature measuring points are arranged on the outer surface of the battery, superfine thermocouples are arranged respectively, a distributed monitoring network is formed, and the temperature change condition outside the battery is obtained; the proportion of the total volume of the implanted superfine thermocouple to the internal space of the battery is lt; 0.03% by weight; temperature data are recorded in real time through high-precision acquisition equipment, the sampling frequency is adjustable, and the temperature error does not exceed + / -0.01 DEG C. By optimizing a sensor arrangement strategy, the original performance of the battery is maintained to the maximum extent while accurate temperature monitoring is achieved, and the method is suitable for temperature field accurate management and thermal safety control in the fields of power batteries, energy storage batteries and the like.

The invention provides a microbial fermentation temperature control method based on PID (Proportion Integration Differentiation), which relates to the field of temperature control, and specifically comprises the following steps: constructing microbial fermentation temperature control models including a temperature monitoring model, an improved PID control model, a temperature driving model, a temperature error model and a target function model; the maternal line optimization algorithm is improved, and the improved maternal line optimization algorithm is guided through the target function to set the coefficient of the traditional PID control algorithm to obtain an improved PID control algorithm; normalizing the deviation value of the target temperature and the real-time temperature in a range of [0, 1] to obtain a normalized temperature deviation value of the real-time temperature and the target temperature; the normalized temperature deviation value is input into an improved PID control model, a temperature control signal is output after adjustment through an improved PID control algorithm, the temperature control signal controls and adjusts the microbial fermentation temperature through a temperature driving model, and the microbial fermentation temperature is accurately and rapidly adjusted.

The invention is suitable for the technical field of intelligent control, and discloses a water cooling control method, system and device and a medium. Based on the preprocessed data, constructing a thermal load trend prediction model based on LSTM to obtain a cold load prediction value; based on the cold load prediction value, a PID adjusting signal is generated in combination with the temperature error value; based on the PID adjusting signal, a water pump target frequency instruction is obtained in combination with the cooling load predicted value; based on the water pump target frequency instruction, a valve target opening instruction is obtained in combination with the actually-measured water flow; based on the valve target opening instruction, the fault alarm and emergency instruction is generated, the preset emergency measures are executed, the PID control parameters are updated, the performance evaluation report and the feedback information are obtained, and the response speed, the operation stability and the autonomous decision-making capacity of the water cooling system under the dynamic load and abnormal working conditions are improved.

The invention provides a real-time calibration method and system for an inertial measurement unit, and a terminal, and the method comprises the steps: carrying out the first correction of an acceleration measurement value and an angular velocity measurement value which are output by a to-be-calibrated inertial measurement unit and are obtained in real time based on a temperature error compensation data table, and carrying out the second correction through employing a multi-sensor fusion filtering algorithm; on one hand, a temperature variable is introduced to carry out a multi-temperature zero offset test and a multi-temperature multi-axis rotation excitation test, a temperature error compensation data table is established, and the influence of temperature on the performance of the to-be-calibrated inertial measurement unit is fully considered; on the other hand, a multi-sensor fusion filtering algorithm is adopted, interference of vibration noise can be suppressed, the attitude calculation precision of the to-be-calibrated inertial measurement unit is improved, and therefore the calibration precision of the to-be-calibrated inertial measurement unit is guaranteed; the technical problems that an existing inertial measurement unit calibration method is low in calibration precision and poor in real-time performance are solved.

The invention relates to a temperature control method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring the real-time temperature of a controlled object, determining a temperature deviation value and a temperature deviation change rate according to the real-time temperature and a preset reference temperature, determining a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the real-time temperature and a preset temperature interval, and determining a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the fuzzy control weight coefficient and the proportional-differential-integral control weight coefficient; the preset temperature interval is determined based on a preset reference temperature, a preset dual-mode switching temperature threshold value and a preset temperature buffer area, and the temperature of the controlled object is adjusted based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient and the proportion-differential-integral control weight coefficient. And the temperature deviation value between the real-time temperature and the preset reference temperature is within the preset temperature error range. By adopting the method, the precision of temperature control can be improved.

The invention relates to the technical field of automatic control, in particular to an electric food warmer control system which comprises a temperature data monitoring module, a thermal response prediction and adjustment module, a warmer bottom power balance regulation and control module, a food material heating opportunity optimization module and a heating strategy execution module. According to the method, the heat conduction characteristic of the pot body material and the specific heat capacity parameter of the food material are combined, the temperature change trend is accurately calculated, accurate temperature control is achieved, the pot bottom temperature gradient is analyzed, heat distribution is balanced, local overheating or insufficient heating is reduced, the temperature error interval is predicted through historical thermal response data, the heating strategy is dynamically adjusted, and the temperature control precision is improved; power distribution is optimized according to the temperature difference of the partition at the bottom of the pot, heat energy utilization efficiency is improved, heat absorption characteristics of food materials are matched, heating opportunity is optimized, uniform heating is ensured, cooking quality is improved, starting and stopping states of heating elements are dynamically adjusted, power output is optimized, system stability is improved, energy waste is reduced, and the service life of the heating elements is prolonged.

The invention discloses a fuzzy neural network PID photovoltaic series welding temperature control method based on improved PSO optimization. The method comprises the steps that a target photovoltaic series welding equipment heating transfer function model is acquired; setting an initial PID parameter; adjusting a PID increment parameter of the PID control module in real time according to the temperature error, the temperature error change rate and a preset fuzzy rule base; the neural network is combined with fuzzy control, and parameters and rules of fuzzy control are automatically modified through training data; according to the photovoltaic series welding temperature condition, the fitness function of the PSO algorithm is improved, the adjustment mode of the inertia weight is improved, and the improved PSO algorithm is used for optimizing the initial PID parameters of fuzzy neural network PID photovoltaic series welding temperature control. Self-adaptive precise control and robust control of the welding temperature control system are achieved, the temperature fluctuation phenomenon in the photovoltaic series welding process is effectively improved, and the robustness and control reliability of the system are enhanced.

The invention relates to the field of data processing, in particular to an injection mold temperature control method and system, and the method comprises the steps: uniformly dividing the surfaces of injection molds of the same model and different batches into regions based on the area, and obtaining a historical temperature sequence and a real-time temperature sequence of each region; a sliding window is preset for the historical temperature sequence, the temperature fluctuation degree is calculated, and the temperature sensitivity degree of each area is determined. And calculating the similarity between the real-time temperature sequence and the historical production stage temperature sequence, selecting the historical stage with the maximum similarity as the best matching stage, and calculating the comprehensive temperature error. And according to the temperature sensitivity degree and the comprehensive temperature error, a temperature control signal of the PID control center is corrected, a comprehensive temperature control signal is obtained, and accurate temperature control is realized. By calculating the temperature sensitivity degree and dynamically adjusting the temperature control strategy, accurate and stable temperature control is achieved, manual intervention is reduced, and the production efficiency and the product quality are improved.

The invention relates to the field of temperature measurement, in particular to a temperature change monitoring system and method in a metal raw material melting process, and the temperature change trend is captured by collecting temperature data in a melting furnace in real time and combining a temperature change rate calculation formula. A DBSCAN clustering algorithm is adopted to carry out segmentation processing on temperature data to obtain different temperature change stages, curve fitting is carried out through a least square method, and error factors at all moments are calculated. And finally correcting temperature data based on the error factor to ensure that the measured value is closer to the actual temperature so as to judge whether the smelting furnace reaches the target temperature and avoid the influence of temperature error on the melting effect. The high-precision sensor, the clustering algorithm and error correction are adopted, the temperature change trend is dynamically captured, precise monitoring of the temperature in the metal melting process is achieved, and the melting uniformity and the product quality are ensured.

The invention provides a lithium ion battery control method and device based on temperature regulation and control, and relates to the field of data processing. The method comprises the following steps: acquiring temperature measurement data sent by a thermistor and an infrared temperature sensor for a lithium ion battery; performing data processing on the temperature measurement data to obtain current temperature data; inputting the current temperature data into a preset neural network model to generate predicted temperature data; acquiring temperature error data of the lithium ion battery; generating a temperature regulation and control mechanism according to the predicted temperature data and the temperature error data; and controlling a temperature control assembly corresponding to the lithium ion battery to carry out temperature regulation and control according to the temperature regulation and control mechanism. By implementing the technical scheme provided by the invention, the temperature of the lithium ion battery can be conveniently and accurately regulated and controlled.

The invention provides a temperature error compensation method and system for a strong current pulse measurement system, and belongs to the technical field of pulse power, and the method comprises the steps: analyzing the influence of a device on the output comprehensive error of a to-be-compensated strong current pulse measurement system according to a device temperature coefficient function and interference weight ratio recognition, and recognizing a system key device; taking the temperature coefficient function of the key device as the input, taking the goal that the root mean square of the temperature coefficient in the system operation domain reaches the minimum, and adopting a first deep convolutional neural network to carry out optimal distribution of the temperature coefficient of the key device; according to the distributed optimal temperature coefficient of the key device, adjusting the actual temperature coefficient of the key device through a second deep convolutional neural network by using a temperature sensor and a programmable device; and based on the difference value between the actual temperature coefficient of the key device and the distributed optimal temperature coefficient of the key device, compensating the output overall temperature of the measurement system. According to the invention, effective compensation of the temperature coefficient of the full-temperature operation domain of the measurement system is realized.

The invention relates to the technical field of MEMS sensors, and discloses a temperature drift real-time compensation and correction method and system of an MEMS gyroscope. The method comprises the following steps: acquiring an original output signal and synchronous temperature time sequence data of the MEMS gyroscope under a preset temperature gradient, dividing a dynamic temperature change interval, extracting zero-bias stability and scale factor nonlinearity parameters, and constructing a temperature-error mapping table; checking output signals of multiple gyroscopes through array layout, and screening effective sensor nodes; and a multi-sensor fusion compensation coefficient matrix is generated by combining error calibration test weighted fusion parameters. A corresponding coefficient matrix is loaded according to a real-time temperature query mapping table, and a parameter weight is analyzed and updated by using a sliding time window residual error; when the temperature changes suddenly, an adaptive filtering optimization coefficient matrix is triggered, and finally optimization parameters are written into an embedded storage unit of the PCB integrated chip to complete mass production calibration. The method adapts to a dynamic temperature change scene, enhances the anti-interference capability, and adapts to a mass production process.

The invention relates to a PID (Proportion Integration Differentiation) control method for coupling heating of a multi-cavity diffusion furnace based on a BP (Back Propagation) neural network, which comprises the following steps of: acquiring temperature measurement data of each cavity in the diffusion furnace, and calculating an error between the temperature of each cavity and a preset target temperature; taking the current moment temperature error, the historical moment temperature error, the current moment temperature value, the historical moment temperature error and the control quantity of the previous moment as the input of the trained BP neural network, and predicting to obtain PID control parameters; based on the PID control parameters, through incremental PID control, heating unit power output values of all cavities of the diffusion furnace are calculated, the power of the corresponding heating units is adjusted, and hyper-parameters of the BP neural network are determined by constructing an ARX model of the diffusion furnace. The method can reduce the training cost of equipment operators, improves the stability of the diffusion furnace under different working conditions, and has a wide application prospect in the field of semiconductor manufacturing.

The invention relates to the field of oil field heating furnace control, and discloses an oil field heating furnace intelligent digital implementation method and control method.The control method comprises the following steps that S1, a temperature sensor is used for monitoring the temperature of an oil field heating furnace in real time; s2, calculating a temperature error between the upper limit and the lower limit of the real-time temperature and the set temperature according to the monitoring data of the temperature sensor; s3, a self-adaptive PID control algorithm is applied, and heating power is calculated based on the temperature error; and S4, dynamically adjusting the heating power to control the temperature of the oil field heating furnace. The precision and stability of temperature control can be improved, the oil field heating furnace can control the target temperature more accurately, and the production efficiency is improved. Meanwhile, the self-adaptive PID control algorithm can automatically adjust parameters according to actual working conditions and environment changes, the adaptability and robustness of the system are improved, and the requirements for manual intervention and adjustment are reduced. In addition, the stable and efficient heating process can be achieved by dynamically adjusting the heating power, and energy and cost are saved.

The invention relates to the technical field of temperature control, in particular to an intelligent temperature control system of an assembly type metal low-temperature granary, which comprises a data acquisition module, a regional square granary analysis module, an overall square granary analysis module and an intelligent temperature regulation module, the data acquisition module acquires first environment data and second rice data; the regional square bin analysis module obtains a first germination coefficient of each square bin region according to the first environment data; the overall square bin analysis module is used for acquiring a second overall square bin germination coefficient and the first overall square bin temperature error of the overall square bin to obtain a third overall square bin abnormal coefficient; and the intelligent temperature adjusting module obtains the first fan adjusting power by comparing the abnormal coefficient with a preset threshold value so as to control the temperature of the whole square bin and enable the temperature to reach the target temperature of the whole square bin, so that intelligent regulation and control of the temperature of the metal square bin are achieved, and the quality of rice is further guaranteed.

The invention discloses a thin film thermocouple cold end temperature error compensation method and a related device. The method comprises the following steps: S1, acquiring real-time thermoelectric force of a thin film thermocouple; s2, inputting the real-time thermoelectric force into a pre-trained cold end error compensation model, and calculating to obtain a compensation thermoelectric force; and S3, according to the compensation thermoelectric force obtained through calculation of the cold end error compensation model and the real-time thermoelectric force of the thin film thermocouple, real-time thermoelectric output quantity is obtained through calculation, and the cold end temperature of the thin film thermocouple is compensated in real time through the thermoelectric output quantity. According to the method, the error thermoelectric force under the temperature gradient from the reference temperature to the real-time cold end temperature is taken as an error term, and the error term and the real-time thermoelectric force obtained through the thin film thermocouple are summed, so that the thermal electromotive force of the thin film thermocouple is compensated and corrected, and the method can be suitable for different sensitive material systems, and has a wide application prospect. The compensation of different sensitive systems is in favor of reducing the cold end error of various non-standard film thermocouples in the temperature test on the premise that the process is not changed.

Disclosed example pressure transducers include: a housing; a fluid inlet configured to provide a fluid to a first cavity within the housing; a pressure measurement assembly comprising a microelectromechanical pressure sensor configured to output a signal representative of the pressure in the first cavity; and a heater configured to heat the pressure measurement assembly based on a target temperature.

The invention discloses a data center air conditioning equipment control method and system. Comprising the steps of obtaining a first environment temperature and a second environment temperature of a target space where the data center air conditioning equipment is located at a first sampling moment and a second sampling moment, and determining a temperature error and an error change rate of the target space at the first sampling moment by combining an expected environment temperature, the temperature error and the error change rate serve as target input variables to be matched with a preset fuzzy rule base, a target output fuzzy set is obtained, defuzzification processing is conducted on the target output fuzzy set, and a corresponding target output variable is obtained; and a control signal is determined according to the temperature error, the error change rate and the target output variable, and equipment operation parameters of the data center air conditioning equipment are adjusted through the control signal. The technical problem that the effect of controlling the equipment operation parameters of the data center air conditioning equipment through the control signals generated through the fixed PID parameters is poor is solved.

The invention provides a PI control method and device for laser soldering based on observation compensation, a controller and a program product, and the method comprises the steps: building a laser soldering thermodynamic model containing a thermal disturbance term based on a lumped parameter method, and constructing a state-space equation; designing a state observer based on the state-space equation, and estimating a state variable of the state-space equation and external disturbance acting on the state variable in real time; calculating a feedforward control quantity according to a target temperature curve and the thermodynamic model, calculating a compensation control quantity according to a disturbance value estimated by an observer, and calculating a PI control quantity according to the deviation between the output temperature and the target temperature to obtain a total control quantity; and in each sampling period, the total control quantity is dynamically adjusted according to the deviation between the current welding spot temperature and the target temperature, so that the temperature error tends to zero. According to the method, the disturbance observation technology and the feedforward-PI control are combined, and high-precision and high-robustness control over the welding temperature is achieved.

A temperature power adjusting method based on double-closed-loop active-disturbance-rejection control comprises the steps that real-time temperature data of the surface of a heated workpiece are obtained, and a temperature error signal between the real-time temperature data and a target temperature value is calculated; and calling an outer ring active-disturbance-rejection control unit to estimate the external disturbance of the heated workpiece to obtain an outer ring disturbance estimator, and compensating the outer ring disturbance estimator to dynamically adjust the inner ring power given value. And acquiring an actual power value output by the inner-loop inverter, and calculating a power error signal between the actual power value and the inner-loop power given value. And calling an extended state observer to estimate inner loop interference in real time according to the power error signal, and performing instantaneous correction on a switching instruction of the inverter according to an inner loop interference estimator to generate a switching control signal set. And executing corresponding power output in response to each switch control signal in the set, and performing frequency tracking on the heating process of the heated workpiece through the digital phase-locked loop so as to obtain the reference frequency calibrated by the digital phase-locked loop.

The invention discloses an ADRC-based air cooling PEMFC temperature control method and system, and relates to the technical field of fuel cell control, and the method comprises the following steps: inputting the actual temperature at the current moment and the temperature control quantity at the previous moment into a linear expansion state observer, and obtaining the estimated temperature and estimated total disturbance at the next moment; the estimated temperature error is input into a linear state error feedback controller, and the initial control quantity of the next moment is obtained; performing feedforward compensation on the estimated total disturbance at the next moment to the initial control quantity at the next moment to obtain the temperature control quantity at the next moment; in the feed-forward compensation process, the temperature control quantity gain at the next moment is optimized through extremum search; and controlling the temperature of the air-cooled proton exchange membrane fuel cell through the temperature control quantity at the next moment. The ADRC controller provided by the invention has very strong anti-interference capability and a good control effect, and can solve the problem of model mismatch.

The invention provides a temperature control method of a power battery, electronic equipment and a storage medium, and the method comprises the steps: determining a temperature error value and an error change rate value of the power battery based on a temperature value of the power battery and a set temperature value; based on the temperature error value and the error change rate value of the power battery, determining an error expansion and contraction factor and a PID expansion and contraction factor; inputting the error scaling factor and the PID scaling factor into a fuzzy controller to determine a PID target correction coefficient; and inputting the PID target correction coefficient into a PID controller to obtain a target temperature value output by the PID controller, so that the power battery temperature control execution system adjusts the temperature of the power battery.

The invention provides a boiler wall temperature prediction method and system based on deep learning. The method comprises the steps of obtaining characteristic parameters related to boiler wall temperature and prediction parameters during boiler operation; dividing the feature parameters and the prediction parameters into a training set, a verification set and a test set; constructing a boiler wall temperature prediction model; the boiler wall temperature prediction model comprises an empirical mode decomposition module, a deep learning module and an error correction module, the empirical mode decomposition module is used for decomposing prediction parameters into a plurality of component sequences, the deep learning module is used for predicting the boiler wall temperature according to the component sequences, and the error correction module is used for correcting the predicted boiler wall temperature; using the training set to train the boiler wall temperature prediction model, and adjusting the boiler wall temperature prediction model through the verification set; and evaluating the boiler wall temperature prediction model by using the test set, and if an evaluation result meets a preset standard, deploying the boiler wall temperature prediction model into an operation monitoring system of the boiler to predict the boiler wall temperature, thereby realizing accurate prediction of the boiler wall temperature.

The invention belongs to the field of temperature detection, relates to a data analysis technology, is used for solving the problem that an existing temperature controller cannot reduce the error influence through a temperature error test, and particularly relates to a temperature error detection system adaptive to a temperature controller. Comprising an error test module, an error evaluation module, an error analysis module and an operation optimization module which are connected in sequence, and the error evaluation module, the error analysis module and the operation optimization module are in communication connection with a database; the error test module is used for marking the temperature controller subjected to temperature error test analysis as a test object and sending detection data of all acquisition time points in the test process to the error evaluation module; the temperature error of the temperature controller can be tested and analyzed, temperature compensation in different environments is tested in a double-probe mode, the control accuracy of the temperature controller is improved, errors caused by environmental factors are reduced to the minimum, and therefore the more accurate room temperature is obtained.