443 results about "Thermal inertia" patented technology

The invention relates to the technical field of public safety and intelligent fire protection, in particular to a real-time processing method and system based on fire alarm data, and the method comprises the steps: environment steady state reconstruction: accessing non-fire environment dynamic parameters; presetting thermotechnical static parameters of the building space; constructing a space thermal inertia differential equation; generating an ideal reference baseline; knowledge-driven simulation: receiving the ideal reference baseline; calling a synthesis operator in a preset disaster interference knowledge base; executing dynamic superposition injection; generating a virtual sensor state flow which has physical characteristics and contains environmental background characteristics; homomorphic judgment: executing double difference calculation; obtaining a real residual feature vector; geometric homomorphism verification is executed; outputting fire alarm triggering, interference filtering or fault prompting instructions; according to the method, the problem of false alarm caused by non-stable fluctuation of the environment background in the background technology is solved, and dynamic fusion and scene adaptation of the standard signal model and the real environment background are realized.

The invention discloses a temperature measurement sensor data processing method and system in a chemical synthesis environment. The method comprises the steps of original data acquisition and preprocessing, sensor drift compensation, dynamic response optimization and anomaly detection and adaptive calibration. The system comprises an original data acquisition and preprocessing module, a sensor drift compensation module, a dynamic response optimization module and an anomaly detection and adaptive calibration module. Through multi-module deep fusion of LSTM drift compensation and adaptive Kalman filtering, + / -0.5 DEG C measurement precision is realized in a range of 80-300 DEG C; the MPC predictive control compresses the thermal inertia lag of the sensor from 500ms to less than or equal to 80ms, so that the sudden rising / dropping temperature in the synthesis reaction can be effectively captured, and the product decomposition caused by local overheating is avoided; isolated forest anomaly detection and least square calibration are combined, so that the maintenance period of the sensor in a strong corrosion environment is prolonged from 7 days to 90 days, and the annual maintenance cost is reduced by 65%.

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention relates to the technical field of fault detection, in particular to an electrical equipment fault diagnosis method and system, and the method comprises the following steps: constructing a ratio group of a trigger incremental sequence quantity and a frequency spectrum cluster set based on a partial discharge pulse interval, deducing impedance frequency boundary migration, analyzing a conductor heating and cooling rate difference, and forming thermal response lag information. According to the method, frequency spectrum energy evolution characteristics are extracted through partial discharge pulse interval dynamic behaviors, a frequency migration path is constructed to reflect an impedance boundary fluctuation process, hysteresis effect characteristics are extracted in combination with thermal inertia response differences in a temperature rise stage, and the impedance boundary fluctuation is determined. And analyzing a voltage and current phase fluctuation rule and deducing a change trend of a combined feature, establishing a layer-by-layer progressive judgment logic between multi-stage dynamic coupling features, realizing evolutionary process tracking from local disturbance to a global abnormal state, and improving a fine-grained identification level of an abnormal mechanism in an electrical equipment operation behavior.

The invention relates to the field of building energy automation control, and discloses a building energy consumption optimization method based on artificial intelligence, which comprises the following steps: acquiring an energy loop state, generating a reference control instruction, superposing a detection signal of which the amplitude is lower than the upper limit of a mechanical dead zone, and sending the superposed detection signal to an execution mechanism; extracting a phase difference of the feedback response relative to the detection signal by using orthogonal correlation logic; according to the matching relation between the instruction gradient direction and the phase difference curvature evolution trend, an asymmetric damping rule is configured for a loop, and control parameters are corrected, the asymmetric physical characteristics of building thermal inertia are matched, phase mismatch between the instruction and physical response is eliminated, energy backflow and overshoot oscillation generated in the adjusting process are restrained, and the stability of the building thermal inertia is improved. Therefore, energy consumption adjustment is converted from oscillation approximation to smooth cut-in, and on-line identification of the deterioration state of the actuating mechanism is realized on the premise that hardware cost is not increased.

The invention provides an underground pipeline leakage detection method and system based on physical enhancement thermal inertia imaging, and belongs to the technical field of pipeline detection, and the method comprises the steps: obtaining an earth surface thermal field and environment meteorological parameters of a current target area; the surface thermal field and the environmental meteorological parameters are input into a double-flow physical information neural network model, a predicted background thermal field is obtained, the double-flow physical information neural network model is obtained through training based on leakage-free historical data and a total loss function, and the total loss function comprises physical loss items determined based on a one-dimensional heat conduction partial differential equation; calculating a space-time residual image of the earth surface thermal field and the predicted background thermal field, and obtaining an apparent thermal inertia distribution map of the earth surface based on the space-time residual image and environmental meteorological parameter inversion; and detecting whether the underground pipeline in the target area leaks based on the space-time residual map, the apparent thermal inertia distribution map and a preset condition. According to the invention, the accuracy of underground pipeline leakage detection can be improved.

The invention relates to the technical field of engineering monitoring, in particular to an unmanned aerial vehicle dynamic monitoring and early warning system and method for the whole subway tunnel excavation process, and the system comprises a virtual base station differential positioning module which is used for carrying out the unmanned aerial vehicle dynamic monitoring and early warning according to a carrier phase observation value transmitted by a ground active control target and a satellite signal observation value obtained by an airborne receiving end, and calculating ionosphere residual values and troposphere residual values between the current position of the unmanned aerial vehicle and the three adjacent ground active control targets. According to the method, the pixel gradient vector of the thermal inertia matrix is further calculated, the edge candidate points exceeding the threshold value are extracted, and the edge candidate points are closed in the gradient vertical direction to form the geometric region, so that the fuzzy thermal field anomaly is converted into the quantized graph with the definite physical boundary, and the hidden danger area and the internal gradient mean value are combined for calculation; the risk early warning level can be dynamically generated according to the tunnel design tolerance, so that recognition and hierarchical management and control of hidden geological disasters are realized on the premise of not contacting the ground.

The invention discloses a state monitoring method and system based on artificial intelligence, relates to the technical field of intelligent monitoring of industrial equipment, is applied to high-temperature industrial equipment, and is used for monitoring the state of high-temperature industrial equipment by fusing visible light and infrared heat map data and combining a thermal inertia prediction model and a dynamic threshold analysis technology. Real faults and transient external interference of the equipment are effectively distinguished, the accuracy and reliability of state monitoring of the high-temperature industrial equipment can be improved, and the false alarm rate and the missing report rate are reduced.

The invention relates to the technical field of data processing, in particular to a heat supply data monitoring system based on artificial intelligence. The system comprises a return water temperature prediction module, a deviation monitoring module, a thermal load analysis module, a first pipe network state monitoring module, a second pipe network state monitoring module and a parameter correction module. An expected return water temperature target value is calculated, and a deviation monitoring module captures the difference between an actual operation state and a theoretical value based on a heat balance principle; the thermal load analysis module accurately draws a system total load curve through data of a user side intelligent heat meter, when the system is abnormal, fault sources can be intelligently distinguished, whether set parameters are misaligned or not is judged according to a thermal inertia mechanism by recognizing near-end overheating and tail-end underheating, hydraulic instability and physical blockage are accurately positioned, and the system reliability is improved. Meanwhile, self-adaptive optimization of set parameters is achieved, the accuracy and timeliness of fault diagnosis of the heat supply system are improved, and stable and efficient operation of the heat supply system is effectively guaranteed.

The invention discloses a loss diagnosis and health management system and method for a transformer, and belongs to the technical field of intelligent transformers, and the system comprises a thermal fingerprint module which is used for recording a temperature drop curve of each part of the transformer after planned power failure, and obtaining a thermal inertia fingerprint of the transformer based on the fitting of multiple temperature drop curves; the theoretical temperature module is used for calculating the theoretical cold body temperature of each part of the transformer based on the real-time environment temperature and the thermal inertia fingerprint when the transformer runs; the net temperature rise module is used for acquiring the actual temperature value of each part of the transformer and subtracting the corresponding theoretical cold body temperature from the actual temperature value; weak thermal characteristic signals caused by insulation aging and loss abnormity of the transformer can be accurately extracted from strong background noise such as load fluctuation and environment temperature through technologies such as thermal inertia fingerprint construction and net temperature rise separation, early diagnosis of progressive health deterioration is realized, support is provided for intervening potential faults in advance, and the fault diagnosis accuracy is improved. And the passive situation of post-treatment of operation and maintenance is changed.

The invention provides an intelligent heat supply regulation and control system and method based on building thermal inertia and waste heat recovery. The method comprises the following steps that building thermal parameters, indoor and outdoor environment data, user heat consumption preference and waste heat resource parameters are collected; based on the collected data, obtaining a building thermal inertia coefficient and a building temperature attenuation prediction curve in a future time period; the collected waste heat is subjected to heat exchange with a municipal heat source, and a hybrid heat source is prepared; based on the temperature attenuation prediction curve, the state of the hybrid heat source and user requirements, adjusting instructions for the municipal heat source, the waste heat recovery unit and the heat supply pipe network are generated; the problems of energy waste and comfort reduction caused by adjustment lag of a traditional system are effectively solved.

The invention discloses a real-time monitoring control system based on a thermal flow sensor and a controller, and relates to the technical field of sensor monitoring. According to the real-time monitoring control system based on the thermal flow sensor and the controller, the method comprises the steps that S1, sensor monitoring data and historical statistical data are preprocessed; s2, lagging and response time dynamic characteristics are extracted; performing modal decomposition, phase estimation, peak calculation and steady-state noise analysis; s3, executing phase compensation, peak value recovery and enhancement compensation judgment; recording a residual sequence and a compensation parameter, and outputting optimized flow data; s4, constructing a credibility interval and dynamic consistency score synthesis control credibility mark; and executing security constraint and prediction filtering and archiving. The problems that an existing thermal flow sensor generates obvious phase lag and peak smoothing distortion due to the influence of thermal diffusion thermal inertia under the rapid fluctuation and pulsation working conditions, and the real-time monitoring precision and the closed-loop control stability are difficult to guarantee due to the lack of an online dynamic response analysis and compensation mechanism are solved.

The invention belongs to the technical field of automatic control, and particularly relates to an intelligent temperature control method for a bisphenol F type epoxy resin reaction kettle, which comprises the following steps: acquiring the temperature in the kettle, the frequency of a stirring motor and the flow of cooling water, and carrying out de-noising treatment; obtaining a dynamic viscosity thermal resistance operator based on the processed frequency of the stirring motor and the processed temperature in the kettle, and quantifying the heat transfer obstruction degree caused by the viscosity change of the material; obtaining a thermal inertia compensation driving operator by combining the target set temperature and the dynamic viscosity thermal resistance operator; the power of the heating actuator is obtained based on the thermal inertia compensation driving operator and the static balance power, and the input current of the heating actuator is adjusted through mapping and the cooling water flow is cooperatively adjusted. By sensing the thermal resistance dynamic state in a variable viscosity environment and performing feedforward compensation, the problem of temperature overshoot caused by large lag of the reaction kettle is solved, and the stability of the reaction process is improved.

The invention discloses a color consistency control method in a bamboo kitchen ware carbonization process, and belongs to the technical field of bamboo processing. The method comprises the following steps: acquiring a multi-dimensional spectral data vector of the surface of the bamboo kitchen ware; determining a multi-dimensional change rate vector of the spectral data; respectively generating a first candidate control signal and a second candidate control signal based on the spectral data and the change rate vector; determining a system dynamic state index based on the change rate vector; generating a dynamic weighting coefficient according to the dynamic state index of the system; and performing weighted fusion on the first candidate control signal and the second candidate control signal by using a dynamic weighting coefficient to generate a final heat regulation control signal. According to the invention, the method achieves the self-adaptive optimal control of all working conditions through the construction of a dual-mode decision fusion composite control system, guarantees the quick response in a dynamic stage, guarantees the smooth convergence in a steady-state stage, and fundamentally improves the thermal inertia problem of the system.

The invention relates to the field of switch cabinet heating control, and particularly discloses an intelligent self-adaptive heating system for a medium-voltage switch cabinet, which actively captures and quantifies the change rate of an external environment by introducing environment trend analysis to form a temperature risk slope and a humidity risk slope representing a future condensation risk. And generating a target temperature matched with the risk change rate based on the dynamic state. When the external environment is stable, the system adopts smaller safety margin to save energy; when it is monitored that the environment is sharply changed towards the easy condensation direction, the system can foreseeably and greatly improve the safety margin, and start and increase the power heating in advance, so that the thermal inertia of the equipment and the response delay of the heating system are effectively counteracted. In this way, the defects that in the prior art, response is delayed and condensation risks exist when environment sudden changes are dealt with are overcome, and meanwhile the energy-saving requirement under the stable working condition is considered.

The invention provides a multi-cavity mold PET bottle preform production consistency optimization control method, which comprises the following steps of: arranging high-response infrared thermal imaging and piezoelectric pressure sensors to realize high-precision real-time synchronous acquisition of temperature and pressure data of each cavity, and establishing a cavity exclusive thermal history fingerprint model by using time sequence feature extraction and a deep neural network; combining the thermal historical fingerprints and the real-time filling data, dynamically calculating the thermal interference intensity between the cavities, and generating a multivariable coupling relation matrix; decomposing an overall consistency target by using principal component analysis and a multi-target optimization algorithm, generating a personalized compensation instruction adapted to thermal inertia and sensitivity of each cavity, and realizing multivariable decoupling cooperative control through adaptive PID parameter adjustment and virtual decoupling variables; wall thickness detection quality feedback is introduced, a model closed-loop calibration mechanism is constructed, adaptive iterative optimization of a control strategy and forming quality is guaranteed, and the consistency of multi-cavity injection molding products and the robustness of the system are remarkably improved.

The invention relates to the technical field of plasma discharge control, in particular to a hot cathode filament discharge control method and system and controllable nuclear fusion equipment, and the method comprises the steps: obtaining a target arc current value and target discharge time of a hot cathode filament, and calculating a voltage feed-forward value of the hot cathode filament according to the target arc current value and the target discharge time; in response to a discharge control instruction of the thermionic cathode filament, calculating a voltage feedback value of the thermionic cathode filament according to the actual arc current value and the target arc current value of the thermionic cathode filament; and controlling the hot cathode filament to discharge according to the voltage feed-forward value and the voltage feedback value. Therefore, the problems that when the hot cathode filament discharges, due to the fact that the thermal inertia of the filament is large and a control method in the related technology is difficult to adapt to parameter changes under different discharge conditions, plasma discharge response is slow, and adaptability is poor are solved.

The invention relates to the technical field of underground water state evaluation, and discloses an underground water level dynamic evaluation method based on multi-source data mapping and fluid model correction. The invention aims to solve the technical problems of insufficient underground water level monitoring precision and lack of space details caused by low spatial resolution of existing satellite gravity detection data and difficulty in accurately separating a deep underground water change signal from a land total water reserve signal. According to the method, through space-time reference unification of multi-source heterogeneous data, high-resolution surface texture features are utilized to guide a low-resolution gravitational field to carry out space downscaling reconstruction; surface water body and soil water reserve signals are removed layer by layer in combination with spectral characteristics and a thermal inertia principle; and introducing measured data to construct a spatial error correction curved surface of specific yield parameters, and identifying the underground water funnel by using an image connected domain analysis technology.

The invention relates to the technical field of industrial control, in particular to an error compensation method for numerical control machining of an automobile stamping die, which comprises the following steps of: acquiring temperature of a plurality of thermo-sensitive points of a numerical control machine tool and thermal displacement data of a tool nose; the effective thermal driving potential is calculated by considering the time lag effect of the temperature level and the historical temperature change; calculating a thermal phase direction gradient in combination with the temperature difference value and the nonlinear gain; combining the effective thermal driving potential and the thermal phase direction gradient into a feature vector to train a support vector regression model; and inputting the feature vector in real time to obtain a compensation value, and superposing the compensation value to the coordinate instruction by dynamic origin offset. The method can accurately reflect the thermal inertia, capture the dynamic evolution of the thermal state, comprehensively represent the thermal state to improve the adaptability of the model, and correct the thermal error in real time, thereby improving the machining precision and surface quality of the automobile stamping die.

The invention belongs to the technical field of data processing, and discloses a Mars rover non-contact trafficability estimation method and system based on thermal inertia, and the method comprises the steps: obtaining a ground surface temperature time sequence and environment parameters of a target region; constructing a thermal inertia estimation model, and inputting the earth surface temperature time sequence and the environmental parameters into the thermal inertia estimation model to obtain a thermal inertia estimation value of the fire soil; constructing a thermal inertia and trafficability index relation model, inputting the thermal inertia estimation value of the fire soil into the thermal inertia and trafficability index relation model, and outputting a predicted trafficability index; and determining a trafficability state grade according to the predicted trafficability index, and generating a navigation control suggestion, so that the Mars rover passes through the target area according to the Mars rover navigation control suggestion. According to the method, the trafficability index of the Mars rover is predicted by extracting the thermal inertia estimation value of the fire soil in the target area, the evaluation precision of the Mars rover passing through the target area can be effectively improved, and the driving safety of the Mars rover is ensured.

The invention discloses a heating system feedforward control method integrating building thermal inertia and meteorological prediction, which comprises the following steps of: firstly, dynamically identifying building thermal parameters of each room so as to quantify the thermal inertia; then, inputting the parameters and future meteorological prediction data into a feedforward controller; and the controller predicts a room temperature change trend caused by future meteorological disturbance based on a thermodynamic model, calculates a required feedforward compensation control quantity, and adjusts a heating system in advance so as to counteract the disturbance. Through combination of dynamic identification and predictive control, heating control is changed into active intervention from passive response, the problem of control lag caused by thermal inertia is fundamentally solved, the stability and comfort of indoor temperature are remarkably improved, and meanwhile energy waste is avoided through accurate compensation.

The invention provides a collaborative model prediction control method for a regional cooling system penetrating through a source network load full chain, and belongs to the technical field of intelligent control of cooling systems. Collecting and preprocessing meteorological and operation characteristic data of the system; inputting the data into an Attention-LSTM cold load prediction model, determining an optimal step length in combination with thermal inertia analysis, and outputting a cold load prediction sequence; based on the unified energy path theory, a pipe network quasi-steady-state hydraulic and thermal coupling mechanism model is constructed; by taking load prediction as input and the coupling model as constraint, establishing an optimization model containing three targets of pump consumption power and the like; rolling optimization is carried out by adopting a multi-target particle swarm optimization algorithm, and optimal operation parameters meeting thermal unbalance degree constraints are obtained; and in combination with the peak-valley electricity price, a cold source unit and cold storage device collaborative scheduling strategy is formulated, and a final control instruction is generated. The phenomena of insufficient cold supply at the tail end and local supercooling caused by hydraulic imbalance of a pipe network and large hysteresis characteristics are effectively solved, and optimal control over the operation cost is achieved.

The invention relates to the technical field of digital twinning and environment control, and discloses an intelligent office space management method based on a digital twinning technology, which comprises the following steps: constructing a lumped parameter network model to calculate theoretical state data, calculating state residual vectors of actually measured data and theoretical data, and calculating the state residual vectors of the actually measured data and the theoretical data; the residual error is decoupled into a transient load disturbance component and a structure parameter drift component through a dual-band filter, the transient component is reversely projected into an equivalent disturbance parameter injection model by using a model inverse dynamics principle for advanced deduction, and a model structure parameter matrix is identified and corrected by using a gradual state component; according to the method, through frequency domain decoupling and reverse analysis of the state residual error, software-defined perception of an unmeasurable hidden load and internal adaptive correction of model aging drift are realized, response lag of a large thermal inertia system is avoided, and the self-evolution ability of the system in the whole life cycle is improved without extra hardware investment.

The invention relates to the technical field of electric power energy storage control, and discloses a multi-dimensional state driven energy storage cold energy storage optimization control system and method, and the method comprises the steps: collecting the multi-dimensional state parameters of an energy storage system and the price of an electric power market, and judging a current operation time period mode; in the active cold storage mode, target cold storage capacity is determined according to the maximum allowable heat capacity buffer margin, and cold pre-storage is completed; in the cold capacity release mode, cold capacity-heat capacity collaborative multi-stage judgment is executed, the minimum critical mass flow rate meeting the heat exchange requirement is reversely solved, the minimum critical mass flow rate is mapped to a discrete control gear of the liquid cooling circulating pump to generate a final rotating speed instruction, and meanwhile closed-loop flow correction is executed; according to the method, the minimum critical mass flow rate is accurately solved, the rotating speed of the liquid cooling circulating pump is remarkably reduced while battery thermal inertia buffering is utilized, parasitic energy consumption is greatly reduced through the cubic relation between pumping power consumption and the rotating speed, and the problem that electric charge income is counteracted due to excessive pumping is solved.

The embodiment of the invention provides a smoke shielding false alarm intelligent identification system and method, and is applied to the technical field of intelligent fire detection, and the method comprises the steps: synchronously collecting an imaging picture, radiant heat response data and a wind field state parameter of a suspected shielding region; the method comprises the following steps: identifying and identifying an abnormal shielding area in a picture; further analyzing the motion characteristics of the region along with the change of the wind field and the dynamic process of the thermal inertia change of the region; the core is to judge whether a time sequence lag relationship meeting a smoke diffusion physical rule exists between the two, so as to identify whether the shielding object is combustion smoke or not. And if the physical mechanism is not met, the alarm is inhibited, so that real fire smoke and non-fire interferents such as dust and steam are effectively distinguished, and the fire false alarm rate in a complex ventilation environment is remarkably reduced.

The invention discloses a mechanical product multi-field simulation and prediction system based on digital twinning, and relates to the technical field of thermal error digital twinning. A main shaft load current, a rotating speed, a bearing temperature, a workpiece surface temperature and a cooling liquid parameter are obtained in real time; rapidly predicting a first thermal error according to the load current and the rotating speed by using a preset multi-layer feed-forward neural network, dynamically calculating a thermal inertia time coefficient in combination with the cooling liquid efficiency and the thermal state of a workpiece, and filtering and extracting a slowly-changing thermal state from the temperature of a main shaft bearing according to the thermal inertia time coefficient, thereby obtaining second thermal error prediction; a fusion weight is generated according to the load current change rate and the thermal inertia time coefficient, weighted fusion is carried out on the two predicted values, and a compensation instruction is output; and by monitoring the stability of the fusion weight, the thermal inertia time is reversely and dynamically adjusted to suppress oscillation. According to the method, high-robustness and self-adaptive accurate compensation of complex thermal errors is realized in field processing with changeable environments and working conditions, and the long-term stability of processing precision is remarkably improved.