6904results about "Radiation pyrometry" patented technology

The invention, which relates to the technical field of structure health monitoring, discloses a sensing data-based on-line intelligent detection system for the whole body and the periphery of a house, comprising: a distributed sensor module, which is composed of a fiber grating sensor array, an MEMS environment sensor and an image acquisition unit, the system is arranged in a house bearing structure, a peripheral foundation, a drainage system and a greening area. The edge computing node module is configured to preprocess sensor data, including noise filtering, temperature compensation and abnormal data marking; the dynamic topology communication network module adopts a LoRaMesh and 5G hybrid networking technology and supports node self-repairing and bandwidth self-adaptive distribution; and the cloud analysis platform module integrates BIM model reconstruction, multi-source data fusion analysis and early warning decision, and outputs structure deformation prediction, an energy consumption anomaly map and a disaster risk assessment report.

The invention discloses a temperature monitoring method for a photovoltaic grid-connected cabinet, and particularly relates to the technical field of photovoltaic grid-connected cabinets. The method comprises the following steps: arranging multiple types of temperature sensors in a grid-connected cabinet, collecting real-time temperature data, constructing a heat distribution model, comparing a current heat map with a historical steady-state heat field template, and identifying heat field offset and an atypical temperature rise trend; when the system detects that early warning is not triggered by an existing measuring point in a heat abnormal area, redundancy alarm logic is automatically started, temperature abnormal early warning is generated, and the speculated abnormal heat source position is recorded; according to the method, the sensing capability of the thermal anomaly in the blind area of the sensor is effectively improved, and the safety and the intelligent level of the system are enhanced.

The invention discloses an intelligent detection method for fault detection of an outdoor power line, and the method comprises the following steps: 1, carrying out the collection and preprocessing of multi-modal data, and carrying out the collection and preprocessing of the multi-modal data through an unmanned plane cluster, a distributed optical fiber sensor, a laser radar and meteorological monitoring equipment; visible light image data, infrared image data, laser point cloud data, vibration waveforms, temperature distribution and environmental parameters of the power line are synchronously obtained, and multi-source image data are processed, namely the visible light image data, the infrared image data and the laser point cloud data are processed; and 2, intelligent fault diagnosis: inputting the data acquired in the step 1 into a multi-task neural network model, and outputting a fault positioning and type identification result. According to the novel detection method based on multi-modal data fusion, an intelligent algorithm and closed-loop optimization, the fault identification precision, the dynamic decision-making capability and the comprehensive protection efficiency are improved, and the intelligent operation and maintenance requirements of a modern power grid are met.

The invention provides an intelligent video monitoring system and method based on deep learning, and belongs to the technical field of video monitoring, and the system comprises a heterogeneous sensing array, a cognitive driving feature fusion module, a dynamic evolvable detection model, a causal reasoning tracking engine, a meta-knowledge enhancement behavior analysis module and a quantum-classical hybrid computing architecture. The heterogeneous sensing array comprises a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array and a millimeter wave radar, and is configured to generate multi-physical field sensing data; and the cognitive driving feature fusion module adopts a space-time-frequency spectrum joint coding technology, and integrates a three-dimensional convolutional network and a graph attention mechanism to realize cross-modal feature interaction. According to the invention, through multi-dimensional technical innovation, the intelligent video monitoring system with autonomous evolution capability is constructed, the perception capability, reasoning precision, resource efficiency and safety are obviously superior to those of a traditional scheme, and the high-standard requirements of smart cities, security and other scenes are met.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention discloses a perioperative hypothermia prediction and prevention system based on multi-modal data and deep learning. The system integrates skin temperature, physiological parameters (such as heart rate, blood pressure and oxyhemoglobin saturation) and operating room environment data (temperature, humidity, airflow and the like), and adopts a deep learning method (CNN-LSTM model, Transformer model and multi-modal fusion model) to carry out data analysis and hypothermia prediction. The system comprises a data acquisition module, a data processing module, a deep learning prediction module and a personalized temperature control module, can realize real-time low body temperature early warning, and provides a personalized intraoperative temperature control strategy. Experimental results show that the system abandons complexity, invasiveness and hysteresis of traditional hypothermia monitoring, hypothermia occurrence can be predicted 20-30 minutes in advance, and the accuracy of intraoperative management is improved. The method can be widely applied to the fields of anesthesia monitoring, operating room intelligent management and perioperative patient body temperature optimization control.

The invention discloses a method for detecting industrial hazardous gas leakage based on a multispectral infrared imaging system formed by a micro-lens array and a partition optical filter, which is used for solving the problem that the current infrared imaging equipment cannot identify various leaked gases, and belongs to the technical field of infrared imaging. The multispectral infrared imaging system is constructed on the basis of the microlens array and the partition optical filter, multispectral band design is carried out on common gas in the current petrochemical industry field, and the types of common leaked gas are identified by means of spectral characteristics of the leaked gas in an infrared multispectral channel. Firstly, a snapshot type infrared imaging system is constructed based on a micro-lens array and a partition optical filter, and the system comprises a plurality of spectral intervals which respectively correspond to optical absorption peaks of specific gas; imaging the hazardous gas based on a multispectral infrared imaging system to obtain a multispectral infrared image of a hazardous gas leakage scene; and finally, based on a foreground detection method, extracting gray information of the gas cloud target, constructing a spectral absorption normalization curve of the gas cloud target in each channel, and comparing the spectral absorption normalization curve with the spectral absorption curve in the database to judge the gas type of the current gas cloud target. The invention provides a reliable multispectral imaging scheme for a hazardous gas imaging system based on infrared imaging, gas types can be effectively distinguished during actual detection, interference of safe gases such as carbon dioxide and water vapor is eliminated, and the detection capability and practicability of an infrared imaging hazardous gas detection system are improved.

The invention discloses a drift rapid compensation method used in an infrared radiation calibration system, which is characterized in that temperature drift and path attenuation are respectively corrected based on calibration of two environment temperatures and calibration of two test distances, and at least two values of three factors of black body temperature, environment temperature and test distance are respectively set; in a detector linear response interval of the infrared radiation calibration system, values of two factors are fixed, infrared image gray values of the black body when the third factor is in the two values are obtained, and at least four infrared image gray values are obtained; solving black body radiation brightness response, a temperature drift compensation coefficient, a distance drift coefficient and detector response bias by combining expression equations of four infrared image gray values; and according to a solving result, rapid compensation of gray value attenuation caused by the change of the environment temperature and the test distance is realized.

The invention discloses a defect detection method for transformer substation unmanned aerial vehicle inspection equipment, and belongs to the technical field of transformer substation defect detection. Performing temperature field inversion correction on the monitoring data, establishing an incidence matrix according to sub-pixel displacement quantification mechanical deformation, and matching a preset defect feature library to obtain a first detection result; according to the first detection result and an assembly digital twin model, correcting the incidence matrix and positioning an abnormal area; inputting the corrected incidence matrix into a defect detection model embedded with the component prior constraint, and outputting a second detection result; and constructing a causal graph of the inspection records and the assembly working conditions, and outputting a predictive maintenance strategy according to the second detection result and the abnormal region. According to the invention, the problems of low detection precision and insufficient association degree with the component state in the aspect of component defect detection in the prior art are solved.

The invention discloses a collaborative diagnosis method for partial discharge and overheating faults of a high-voltage switch cabinet, and particularly relates to the technical field of fault diagnosis of the high-voltage switch cabinet. Signals are synchronously acquired through ultrahigh frequency, acoustic emission and infrared sensors, and an abnormal candidate sequence is extracted through adaptive baseline segmentation; through laser calibration and time interpolation calibration, uniform event position information and timestamps are mapped; identifying potential coupling pairs of partial discharge and hot spot events based on dynamic threshold and time difference matching; generating a high-risk linkage region through spatio-temporal clustering, trajectory analysis and region fusion; updating the diagnosis model based on the high-frequency data increment, and generating a collaborative diagnosis report; the method breaks through the limitation of a static threshold value and a single sensor, and remarkably improves the diagnosis precision and adaptability of a composite fault under a complex working condition.

Disclosed are an apparatus, system, and method of a visual sensor of a tactical gear to use facial recognition technology to identify a person of interest and to cause a responsive device on the tactical gear to notify a wearer. In one embodiment, a personal protective equipment includes a responsive device integrated in a tactical gear and a visual sensor. The visual sensor of the tactical gear identifies a target person using an identity artificial intelligence model. The responsive device notifies a wearer of the tactical gear when the visual sensor identifies the target person. Further, the responsive device notifies an additional person when the visual sensor identifies a threat to a protectee of the wearer and the additional person. The responsive device may vibrate when the visual sensor of the tactical gear detects an ambient threat to the protectee of the wearer.

The invention discloses a laser control system and a spectral analysis system. The laser control system comprises an ARM module and a laser control module, the laser control module comprises a TEC module and a DA module; the TEC module is in communication connection with the ARM module, when the laser is started to work, the ARM module judges whether the detected laser shell temperature is within a preset range, if the detected laser shell temperature is within the preset range, the TEC module does not work, otherwise, the ARM module controls the TEC module to start to work until the TEC module reaches a preset target temperature, and stable work is kept; and the DA module is in communication connection with the ARM module, an LD current feedback circuit is arranged in the DA module, the ARM module calculates the error between the current power and the target power according to the current data acquired by the LD current feedback circuit, and then the DA module is driven to adjust the output power of the laser in real time, so that the laser stably operates near the target power. And the power loss of the laser is effectively reduced.

The invention relates to the technical field of sensor calibration, and particularly discloses a high-precision automatic calibration system and method for a multispectral sensor. Comprising a multispectral signal acquisition module, a sensor response optimization module, a spectral feature matching module, a calibration parameter optimization module, an automatic calibration execution module, a calibration error evaluation module and a multi-node cooperative calibration module. The method comprises the following steps: acquiring an original spectral signal, and performing dynamic compensation to generate an optimized signal; extracting spectral feature points and matching the spectral feature points with a standard library to quantify feature deviation; fusing the environment data and the deviation value, and generating an optimal calibration parameter through optimization; real-time calibration is implemented based on parameters, and a cloud report is automatically generated; and constructing a calibration error evaluation model, evaluating the calibration error of the multispectral sensor, and dynamically adjusting a calibration strategy through cooperation of the distributed nodes and the central control unit. According to the invention, the automatic calibration process of the sensor is realized, and the calibration precision of the multispectral sensor in a complex environment is improved.

The invention relates to the technical field of remote sensing monitoring equipment, and discloses tropical agricultural ecology remote sensing monitoring equipment which comprises an unmanned aerial vehicle platform, a remote sensing unmanned aerial vehicle is arranged on the unmanned aerial vehicle platform, and a monitoring sensor assembly is loaded on the remote sensing unmanned aerial vehicle. The monitoring sensor assembly comprises a high-resolution camera device, a hyperspectral sensor, a thermal infrared sensor and a laser radar, a data receiving module, a data processing module and a display module are arranged in the unmanned aerial vehicle platform, and the monitoring sensor assembly is mainly used for obtaining soil images and data information. The data processing module can judge soil ecological information according to images and data, and the monitoring sensor assembly and the data processing module are matched to obtain the erosion degree and fertility condition of soil in the planting area, the moisture content, the soil type, the moisture evaporation condition and the slope and direction information of the soil. The growth state of plants in a planting area and the ecological environment of soil are monitored, and producers are better assisted in formulating targeted agricultural management measures.

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses a high-precision small unmanned aerial vehicle real-time tracking method which comprises the steps that a multi-modal sensor array is constructed through a millimeter wave radar, a binocular vision module and an infrared thermal imager, and the multi-modal sensor array is used for collecting three-dimensional motion data of a target object in real time; constructing a fusion tracking model based on improved Kalman filtering by using the collected three-dimensional motion data, dynamically distributing the weight of a multi-source sensor through a convolution attention mechanism, and compensating a motion trail prediction error by using an adaptive noise covariance matrix; through cooperative work of the millimeter wave radar, the binocular vision module and the infrared thermal imager, challenges such as illumination change and target shielding can be effectively handled, three-dimensional motion data of a target object are collected in real time, and the target positioning and tracking precision is improved. Meanwhile, the improved Kalman filtering algorithm is combined with a convolution attention mechanism, so that the weight of the sensor can be dynamically adjusted, and a motion track prediction error is compensated.

A temperature control device includes a temperature detector, a difference operation unit, a controller, a saturation processing circuit unit, a rewritable storage unit, and a conversion unit. The difference operation unit operates a digital value corresponding to a difference value between a detected temperature value and a target temperature. The controller calculates a manipulated variable using the digital value operated by the difference operation unit. The saturation processing circuit unit includes a digital circuit to limit an output value of the controller to a pre-set upper limit value. The rewritable storage unit stores the upper limit value read from a storage area of the rewritable storage unit and input into the saturation processing circuit unit. The conversion unit converts the output value of the saturation processing circuit unit into an analog signal to output the converted value as a control command value to the heater.

In one embodiment, a system includes a pump light source configured to produce a pump beam of light at a pump frequency and a Stokes light source configured to produce a Stokes beam of light at a Stokes frequency, where the pump and Stokes frequencies are offset by a frequency offset Ω. The system further includes a photonic integrated circuit (PIC) that includes multiple optical waveguides configured to: direct the pump and Stokes beams of light to a sample; receive a Raman signal produced by coherent Raman scattering of the pump and Stokes beams of light at the sample; and direct the Raman signal to an optical receiver. The optical receiver is configured to detect the Raman signal. The optical receiver includes a probe light source configured to produce a probe beam of light at a probe frequency, an optical detector, and an electronic circuit.

The invention relates to the field of power system intelligent monitoring, and discloses a surface temperature sensor and a cable joint temperature monitoring system, and the system comprises the surface temperature sensor which is used for obtaining the temperature data of a cable joint, and transmitting the data to a data processing module; the data processing module is used for receiving the temperature data from the surface temperature sensor, storing the temperature data, formatting the received temperature data, removing invalid data and preliminarily screening abnormal values; and the temperature modeling module is used for calculating temperature distribution of the cable joint based on a heat conduction model, and establishing a temperature distribution model of the cable joint through a heat conduction equation in combination with the effective data output by the temperature data preprocessing module. According to the method, an optimization technical scheme combining Fourier transform and dimension reduction calculation is adopted, efficient processing and analysis of temperature data are achieved, the system response speed is increased, and the method is more efficient especially in a large-scale data processing environment.

The invention provides a data detection method and system, a detection device, computer equipment and a storage medium, and the method comprises the steps: obtaining a detection signal of each wafer surface in a planetary reaction cavity, and cutting the detection signal based on a trigger signal to obtain a first signal sequence and a second signal sequence; processing the first signal sequence by adopting a classification model to obtain a material type on each wafer; querying a corresponding optical model based on the material type; and performing iterative optimization on the optical model, the first signal sequence and the second signal sequence by adopting a decoupling algorithm to obtain a temperature detection value and a reflectivity detection value. According to the method, the original detection signal is segmented into the independent signal sequences based on the trigger signal, then the material type is determined according to the independent signal sequences, the corresponding optical model is selected according to the material type, and finally the temperature detection value and the reflectivity detection value with high accuracy are obtained through decoupling according to the optical model. Accurate analysis of a single wafer signal is realized, and process repeatability and stability are greatly improved.

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.

An apparatus may include: (a) a processing chamber including chamber walls and a chamber heater; and (b) a pedestal positioned within the chamber interior and including: (i) a substrate heater having a plurality of light emitting diodes (LEDs), (ii) a window positioned above the substrate heater comprising a material transparent to light from the LEDs, (iii) three or more substrate support pads configured to support a substrate such that the window and the substrate are offset by a nonzero distance; and (iv) a temperature sensor comprising a first detector configured to measure thermal radiation emitted by the substrate, and a second detector configured to measure light transmitted through the substrate.

The invention relates to the technical field of terrain reconstruction and path planning, and particularly provides a night terrain reconstruction and path planning method and system based on multi-sensor collaboration, and the method comprises the steps: obtaining the multi-source sensing data of a night environment through a data collection module; performing multi-source data fusion processing on the multi-source sensing data to generate a night environment reconstruction model fusing multi-dimensional features; performing obstacle semantic analysis on the night environment reconstruction model, extracting static obstacle distribution information and a dynamic obstacle movement track, and constructing a composite environment map; according to the composite environment map, a passable path is generated through a dynamic optimization algorithm in combination with terrain passability parameters and kinematics constraint conditions of a mobile carrier, and path parameters are dynamically adjusted based on real-time update data of a night environment reconstruction model; when it is detected that the obstacle distance or the motion state exceeds a safety threshold value, path real-time correction and graded obstacle avoidance instruction output are triggered through multi-sensor confidence evaluation and path collision risk prediction.

The invention relates to an electrical key equipment fault early warning system and method based on voiceprint recognition and infrared temperature measurement. The method comprises the following steps: collecting voiceprint signals and infrared images of electrical equipment, extracting voiceprint features and infrared image features of different scales by using related technologies, carrying out feature fusion based on a cascade channel attention and space attention mechanism, establishing a feature fusion fault diagnosis model, establishing a detection and segmentation multi-task network by using two multi-layer perception mechanisms, and carrying out fault diagnosis on the electrical equipment. Therefore, the fault type and position are predicted. According to the system and method, infrared image information and voiceprint information are organically combined, the diagnosis accuracy of complex faults can be improved, accurate and rapid diagnosis of the faults of the electrical key equipment can be achieved, the safety and reliability of the equipment can be effectively improved, and the system and method are suitable for state monitoring and fault early warning of the electrical equipment such as transformers and switch cabinets. The method has a wide application prospect and an important practical value.

Modulation-encoded light, using different spectral bin coded light components, can illuminate a stationary or moving (relative) target object or scene. Response signal processing can use information about the respective different time-varying modulation functions, to decode to recover information about a respective response parameter affected by the target object or scene. Electrical or optical modulation encoding can be used. LED-based spectroscopic analysis of a composition of a target (e.g., SpO2, glucose, etc.) can be performed; such can optionally include decoding of encoded optical modulation functions. Baffles or apertures or optics can be used, such as to constrain light provided by particular LEDs. Coded light illumination can be used with a focal plane array light imager receiving response light for inspecting a moving semiconductor or other target. Encoding can use orthogonal functions, such as an RGB illumination sequence, or a sequence of combinations of spectrally contiguous or non-contiguous colors.

The invention relates to a polarization spectrum imaging device and a polarization spectrum reconstruction method, in particular to a miniature high-energy-efficiency diffraction polarization spectrum imaging device and a polarization spectrum reconstruction method thereof, and solves the technical problems that an existing polarization spectrum imaging device is low in energy utilization rate and regulation and control precision and large in size. According to the miniature high-energy-efficiency diffraction polarization spectrum imaging device provided by the invention, the diffraction spectrum lens module, the electronic control rheological lens module and the polarization module are directly and compactly integrated by utilizing the macro imaging module, so that the space coupling distance can be reduced, and the imaging quality can be effectively improved; the diffraction spectrum lens module realizes time-sharing spectrum regulation and control through the electronic control rheological lens module, and the polarization module performs area array space division polarization regulation and control, so that 0-degree, 45-degree, 90-degree and 135-degree polarization information under different spectrum channels can be directly obtained, the absorption effect of traditional light filtering on spectrums is avoided, the energy utilization rate is improved, and the energy consumption is reduced. And the number of polarization regions can be reduced, and the regulation and control precision is improved.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

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 relates to an LED display screen environment interaction system based on multi-mode perception and a control method. The system comprises a multi-mode sensing module used for acquiring environment information of the LED display screen; the user position distribution comprises a user distribution hot area map, an area target vector and an interference index; the environment fusion sensing module is used for performing fusion reasoning according to the environment illumination intensity, the environment temperature state, the regional target vector and the interference index to obtain an environment state type; the display strategy control module is used for generating a dynamic control parameter based on the environment state type in combination with the user distribution hot area graph; the LED display screen driving module is used for obtaining an adjusting parameter and an adjusting instruction according to the dynamic control parameter; the adjustment parameters comprise an RGB current parameter, a brightness parameter and a color temperature parameter. By adopting the system, the dynamic change of the environment and the crowd is taken as a driving source, perception, analysis and execution driving are integrated, and the energy use efficiency is improved while the visual experience of a user is improved.

The invention provides a multi-sensor fusion welding temperature monitoring method and a related device, and the method comprises the steps: dividing a welding region into a plurality of sub-regions according to the spatial distribution of the welding region, and obtaining the initial temperature data corresponding to each sub-region according to various temperature sensors; performing time alignment on the initial temperature data through difference resampling of different sampling points to generate a time sequence temperature matrix; performing real-time quality detection on the time sequence temperature matrix to generate a real-time quality score, and performing weighted combination on the real-time quality score and a preset confidence coefficient parameter table to obtain a dynamic confidence coefficient result set; fusing the effective temperature data exceeding a preset confidence threshold in the dynamic confidence result set according to a fusion rule corresponding to each sub-region to obtain a fused temperature result; and the fusion temperature results of the sub-regions are subjected to time sequence splicing to obtain a global temperature curve of the welding process, so that the accuracy of temperature detection in the welding process can be effectively improved.

The invention discloses a converter lining thickness determination method and device, electronic equipment and a storage medium. According to the specific scheme, after smelting of a current heat is completed, smelting parameters and an infrared image of a target converter are obtained, and the infrared image is an image of a local area of the target converter; according to the infrared image, the target furnace lining temperature of the target converter is determined, and according to the target furnace lining temperature and the smelting parameters, the corresponding predicted furnace lining stripping amount of the target converter under the current heat is determined; according to the initial furnace lining thickness information and the predicted furnace lining stripping amount, the target furnace lining thickness corresponding to the target converter is determined; wherein the initial furnace lining thickness information is a furnace lining thickness parameter determined by the target converter before the current smelting. The method improves the efficiency and accuracy of determining the thickness of the converter lining.