Industrial furnace temperature and atmosphere intelligent optimization control system

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an industrial furnace temperature and atmosphere intelligent optimization control system, which comprises a temperature monitoring module, an atmosphere monitoring module, an intelligent optimization module and a central control module, the system is also integrated with a safety monitoring unit. The temperature distribution states of different areas in the industrial furnace are monitored in real time through the temperature monitoring module, temperature gradient abnormity and overshoot phenomena are recognized, dynamic changes of atmosphere components are monitored in real time through the atmosphere monitoring module, the uniformity and stability of the atmosphere are evaluated, and real-time accurate monitoring of the temperature and atmosphere parameters in the industrial furnace is guaranteed. A reliable data basis is provided for intelligent optimization control, and the comprehensiveness and timeliness of monitoring are improved; the thermal process in the furnace is optimized through the intelligent optimization module according to temperature and atmosphere monitoring data, and the control precision and the self-adaptive capacity are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, in particular to an industrial furnace temperature and atmosphere intelligent optimization control system. BACKGROUND

[0002] Industrial furnaces are thermal equipment that use fuel combustion or electrical energy conversion to heat materials or workpieces, but are not generally considered to include boilers. They are mainly composed of a furnace body, a heating system, and an exhaust system. They are divided into flame furnaces and electric furnaces according to the heating method, and are divided into intermittent furnaces and continuous furnaces according to the thermal process. They include cupola furnaces, electric arc furnaces, and annealing furnaces, and are used in casting, metal heat treatment, metallurgy, petrochemical, and other industries.

[0003] Currently, temperature and atmosphere parameters need to be monitored independently during the control process of industrial furnaces. Traditional systems cannot analyze temperature distribution abnormalities and atmosphere composition dynamic changes in real time. If local overheating or atmosphere fluctuations occur during the thermal process in the furnace, the control response may be delayed, and the accuracy of temperature and atmosphere collaborative optimization cannot be guaranteed.

[0004] Therefore, the present application provides an industrial furnace temperature and atmosphere intelligent optimization control system to solve the above problems. SUMMARY

[0005] To overcome the deficiencies of the prior art, the present application provides an industrial furnace temperature and atmosphere intelligent optimization control system to solve the problem of control response lag and inability to guarantee the accuracy of temperature and atmosphere collaborative optimization.

[0006] To achieve the above purpose, the present application provides the following technical solution: an industrial furnace temperature and atmosphere intelligent optimization control system, the system comprising a temperature monitoring module, an atmosphere monitoring module, an intelligent optimization module, and a central control module; The temperature monitoring module is used to monitor the temperature distribution state of different areas in the industrial furnace in real time. High-precision temperature sensor arrays are used to collect temperature data of the furnace, workpiece surface, and exhaust gas outlet, and to identify temperature gradient abnormalities and overshoot phenomena. The atmosphere monitoring module is used to monitor the dynamic changes of the atmosphere composition in the industrial furnace in real time, including oxygen concentration, carbon monoxide concentration, carbon dioxide concentration, and humidity parameters. Data is obtained through a multi-channel gas analyzer, and the atmosphere uniformity and stability are evaluated. The intelligent optimization module is used to dynamically adjust the heating power, fuel supply rate, and ventilation volume based on temperature and atmosphere monitoring data using a machine learning-based predictive control algorithm to optimize the thermal process in the furnace. The central control module is used to coordinate the running sequence of various modules in the system, integrate and process monitoring data and execute control commands, drive the operation of actuators according to preset optimization strategies, and provide a graphical human-machine interface to support experimental parameter configuration, real-time data visualization and historical record query. The system also integrates a safety monitoring unit, which triggers an automatic protection mechanism when temperature or atmosphere parameters exceed safety thresholds and records fault logs for analysis.

[0007] Preferably, the temperature monitoring module includes a sensor deployment unit, a data acquisition unit, and a temperature analysis unit; The sensor deployment unit covers key areas of the industrial furnace, including the heating zone, soaking zone, and cooling zone, in a grid layout using high-temperature thermocouples and infrared thermometers. The data acquisition unit is used to simultaneously acquire multiple temperature signals and perform signal amplification, filtering, and analog-to-digital conversion. The temperature analysis unit calculates the temperature distribution uniformity index based on the heat conduction model and real-time data, identifies local overheated and undercooled areas, and predicts the temperature evolution trend.

[0008] Preferably, the sensor deployment unit is also equipped with a self-calibration mechanism to periodically verify the sensor accuracy using a standard temperature source; The data acquisition unit supports high-speed sampling rates and has data compression and redundant backup functions to prevent data loss; The temperature analysis unit uses time series analysis to compare the current temperature curve with the historical baseline curve. When the deviation continues to exceed the tolerance value, it is judged as an abnormal state.

[0009] Preferably, the atmosphere monitoring module includes a gas sampling unit, a component analysis unit, and an atmosphere assessment unit; The gas sampling unit extracts gas samples from different locations inside the furnace using multiple pumps and filters to avoid cross-contamination. The component analysis unit uses electrochemical sensors and spectroscopic analysis technology to quantitatively measure the concentration of each gas component and performs temperature and pressure compensation. The atmosphere assessment unit calculates the oxidation-reduction potential and dew point temperature based on the atmosphere balance model to determine whether the atmosphere control effect meets the standards.

[0010] Preferably, the gas sampling unit is equipped with an automatic cleaning function to regularly remove dust from the pipeline and ensure the representativeness of the sampling. The component analysis unit integrates a multi-point calibration algorithm, which automatically adjusts the measurement accuracy based on the standard gas. The atmosphere assessment unit generates an atmosphere stability report by comparing real-time atmosphere data with the ideal process curve, and provides feedback input to the intelligent optimization module.

[0011] Preferably, the intelligent optimization module includes a data fusion unit, an algorithm execution unit, and a strategy adjustment unit; The data fusion unit is used to integrate the heterogeneous data from the temperature monitoring module and the atmosphere monitoring module, and to perform normalization and feature extraction. The algorithm execution unit is equipped with a deep learning neural network and a fuzzy logic controller. It learns the optimal control rules through training data and outputs heater power setpoint, fuel valve opening degree and damper position commands in real time. The strategy adjustment unit dynamically refreshes and optimizes parameters based on the control effect, supporting multi-objective optimization modes, including minimizing energy consumption, maximizing temperature uniformity, and prioritizing emission control.

[0012] Preferably, the data fusion unit uses principal component analysis to reduce dimensionality and highlight key feature variables; The algorithm execution unit has online learning capabilities, using real-time data to incrementally update model parameters and adapt to process changes; The strategy adjustment unit is equipped with an adaptive weighting mechanism that automatically adjusts the priority ratio of temperature control and atmosphere control according to production needs.

[0013] Preferably, the central control module includes a main control unit, a communication scheduling unit, and an interface management unit; The main control unit adopts a distributed microprocessor architecture to achieve multi-task parallel processing and modular expansion; The communication scheduling unit supports industrial Ethernet and wireless communication protocols, and supports low-latency data transmission between modules; The interface management unit provides a touch screen interface that supports recipe management, trend curve plotting, and alarm information push.

[0014] Preferably, the main control unit has a fault self-diagnosis function, which monitors the hardware status in real time and switches the backup module accordingly; The communication scheduling unit integrates data encryption and integrity verification mechanisms to prevent unauthorized access; The interface management unit allows users to customize control logic and report formats, and supports remote mobile access.

[0015] Preferably, the safety monitoring unit includes a threshold early warning subunit and an emergency response subunit; The threshold warning subunit sets the upper and lower limits of temperature and the safe range of atmosphere composition according to the process standard. When the parameters exceed the limit, it triggers an audible and visual alarm and SMS notification. The emergency response subunit automatically executes the cooling, nitrogen charging, and flameout procedures, and generates an accident report recording the operation time, cause, and handling results.

[0016] Compared with the prior art, the present invention provides an intelligent optimization control system for temperature and atmosphere in industrial furnaces, which has the following beneficial effects: 1. In this invention, the temperature monitoring module monitors the temperature distribution in different areas of the industrial furnace in real time, identifies abnormal temperature gradients and overshoot phenomena, and the atmosphere monitoring module monitors the dynamic changes of atmosphere composition in real time, assesses atmosphere uniformity and stability, ensuring real-time and accurate monitoring of temperature and atmosphere parameters in the industrial furnace, providing a reliable data foundation for intelligent optimization control, and improving the comprehensiveness and timeliness of monitoring.

[0017] 2. In this invention, the intelligent optimization module dynamically adjusts the heating power, fuel supply rate and ventilation volume based on temperature and atmosphere monitoring data using a machine learning-based predictive control algorithm, thereby achieving coordinated control of temperature and atmosphere, optimizing the thermal process inside the furnace, improving control accuracy and adaptability, and supporting multi-objective optimization modes, including minimizing energy consumption, maximizing temperature uniformity and prioritizing emission control.

[0018] 3. In this invention, the central control module coordinates the running sequence of each module of the system, integrates and processes monitoring data and executes control commands, provides a graphical human-machine interface to support parameter configuration and real-time data visualization, and triggers an automatic protection mechanism when temperature and atmosphere parameters exceed safety thresholds through the safety monitoring unit, records fault logs, ensures the safe and stable operation of the system, and improves the convenience of operation and the efficiency of emergency fault handling. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the intelligent optimization control system for temperature and atmosphere of an industrial furnace according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] For specific implementation examples, please refer to: Figure 1 An intelligent optimization control system for temperature and atmosphere in an industrial furnace, comprising a temperature monitoring module, an atmosphere monitoring module, an intelligent optimization module, and a central control module; The temperature monitoring module is used to monitor the temperature distribution in different areas of the industrial furnace in real time. It collects temperature data of the furnace chamber, workpiece surface and exhaust gas outlet through a high-precision temperature sensor array, and identifies abnormal temperature gradients and overshoot phenomena. The atmosphere monitoring module is used to monitor the dynamic changes of atmosphere composition in the industrial furnace in real time, including oxygen concentration, carbon monoxide concentration, carbon dioxide concentration and humidity parameters. It acquires data through a multi-channel gas analyzer and evaluates atmosphere uniformity and stability. The intelligent optimization module is used to dynamically adjust the heating power, fuel supply rate and ventilation volume based on temperature and atmosphere monitoring data and a predictive control algorithm based on machine learning, thereby optimizing the thermal process in the furnace and achieving coordinated control of temperature and atmosphere. The central control module is used to coordinate the running sequence of various modules in the system, integrate and process monitoring data and execute control commands, drive the operation of actuators according to preset optimization strategies, and provide a graphical human-machine interface to support experimental parameter configuration, real-time data visualization and historical record query. The system also integrates a safety monitoring unit, which triggers an automatic protection mechanism when temperature or atmosphere parameters exceed safety thresholds and records fault logs for analysis.

[0022] The temperature monitoring module includes a sensor deployment unit, a data acquisition unit, and a temperature analysis unit; The sensor deployment unit covers key areas of the industrial furnace, including the heating zone, soaking zone, and cooling zone, in a grid layout using high-temperature thermocouples and infrared thermometers. The data acquisition unit is used to simultaneously acquire multiple temperature signals, and perform signal amplification, filtering and analog-to-digital conversion to eliminate environmental interference; Signal amplification is achieved using a multi-stage operational amplifier circuit. An instrumentation amplifier is used to amplify the weak voltage signal generated by the thermocouple. The amplification factor can be set according to the sensor range via an adjustable resistor to ensure that the signal voltage is compatible with the subsequent processing range. Filtering uses an analog low-pass filter to remove high-frequency noise. The cutoff frequency is set according to the temperature change rate of the industrial furnace, typically between 0.1Hz and 10Hz, to retain the effective signal. Analog-to-digital conversion uses a high-resolution ADC chip to convert the analog signal into a digital signal at a sampling rate of over 1000 times per second. A reference voltage source is added during the conversion process to improve accuracy. The temperature analysis unit calculates the temperature distribution uniformity index based on the heat conduction model and real-time data, identifies local overheated and undercooled areas, and predicts the temperature evolution trend. The uniformity index is calculated using statistical methods, and the formula is: ; ; in It is the temperature distribution uniformity index. The standard deviation of temperature, This represents the average temperature. The number of temperature monitoring points, This represents the temperature value at the k-th grid point.

[0023] The sensor deployment unit is also equipped with a self-calibration mechanism that periodically verifies the sensor accuracy using a standard temperature source. Self-calibration is achieved by automatically starting a calibration program every 24 hours. During calibration, the control unit exposes the temperature sensor to a known standard temperature source and records the deviation between the sensor reading and the standard value. The data acquisition unit supports high-speed sampling rates and has data compression and redundant backup functions to prevent data loss; Data compression employs the lossless Huffman coding algorithm to compress temperature data streams in real time, reducing storage space. Before compression, data is grouped into frames, and the compression ratio is adjustable. Redundancy backup is achieved through RAID1, with local storage using dual hard drive mirroring, while data is periodically uploaded to a remote server. Backup strategies include full backup and incremental backup to ensure data recoverability. The temperature analysis unit uses time series analysis to compare the current temperature curve with the historical baseline curve. When the deviation continues to exceed the tolerance value, it is judged as an abnormal state. The time series analysis method uses an autoregressive integral moving average model to analyze temperature trends. The model form is as follows: ; in The temperature value at time t. For the backoff operator, Autoregressive coefficient, These are the autoregressive coefficients. Let be the difference order. The number of terms in the moving average. The moving average coefficient, For constant terms, This is the white noise error term.

[0024] The atmosphere monitoring module includes a gas sampling unit, a component analysis unit, and an atmosphere assessment unit; The gas sampling unit extracts gas samples from different locations inside the furnace using multiple pumps and filters to avoid cross-contamination. The component analysis unit uses electrochemical sensors and spectroscopic analysis technology to quantitatively measure the concentration of each gas component and performs temperature and pressure compensation. The atmosphere assessment unit calculates the oxidation-reduction potential and dew point temperature based on the atmosphere balance model to determine whether the atmosphere control effect meets the standard. The redox potential is calculated using the Nernst equation, and the formula is as follows: ; in Redox potential, This is the standard potential. The gas constant is 8.314 J / (mol·K). Absolute temperature For electron transfer number, The Faraday constant is 96485 C / mol, [Ox] is the concentration of the oxidant, and [Red] is the concentration of the reducing agent; The dew point temperature is calculated using the August-Roche-Magnus approximation formula: ; in Dew point temperature, Relative humidity, This represents the current ambient temperature.

[0025] The gas sampling unit is equipped with an automatic cleaning function to regularly remove dust from the pipeline and ensure the representativeness of the samples. The component analysis unit integrates a multi-point calibration algorithm, which automatically adjusts the measurement accuracy based on the standard gas. Calibration uses at least three standard gas points: 0%, 50%, and 100% of the measurement range concentration. For each gas component, the standard gas is introduced, the sensor output value is recorded, and then a polynomial fitting is used to establish the concentration-output relationship. The calibration curve formula is: ; in For gas concentration, For sensor voltage output, These are the polynomial fitting coefficients, which are obtained using the least squares method and are in matrix form: ; in For the coefficient vector, For sensor output matrix, For concentration vectors, This is the matrix transpose operator; after calibration, the coefficients are stored in memory and automatically applied during real-time measurements. The atmosphere assessment unit generates an atmosphere stability report by comparing real-time atmosphere data with the ideal process curve, and provides feedback input to the intelligent optimization module.

[0026] The intelligent optimization module includes a data fusion unit, an algorithm execution unit, and a strategy adjustment unit; The data fusion unit is used to integrate the heterogeneous data from the temperature monitoring module and the atmosphere monitoring module, and to perform normalization and feature extraction. Normalization uses minimum-maximum scaling to map raw data such as temperature and atmosphere to the [0,1] interval, as shown in the formula: ; in These are the normalized data values. The original data values, The minimum value in historical data. This represents the maximum value in historical data. The algorithm execution unit is equipped with a deep learning neural network and a fuzzy logic controller. It learns the optimal control rules through training data and outputs heater power setpoint, fuel valve opening degree and damper position commands in real time. The neural network adopts a Convolutional Neural Network (CNN) structure. The input layer consists of normalized features, the hidden layers include convolutional layers, pooling layers, and fully connected layers, and the output layer contains control commands. Training uses historical data, and the loss function is mean squared error. The fuzzy logic controller is designed with a rule base, the membership function uses a triangular shape, and defuzzification uses the centroid method. The two are combined: the neural network handles complex patterns, and the fuzzy logic handles uncertainty. The optimal control rule implementation steps are based on a reinforcement learning framework, using the Q-learning algorithm. The Q-value update formula is: ; in For state-action value function, This is the current state. For the current action, For learning rate, For instant reward value, As a discount factor, The maximum Q value under the next state s'; The strategy adjustment unit dynamically refreshes and optimizes parameters based on the control effect, supporting multi-objective optimization modes, including minimizing energy consumption, maximizing temperature uniformity, and prioritizing emission control.

[0027] The data fusion unit uses principal component analysis to reduce dimensionality and highlight key feature variables; The dimensionality reduction implementation steps of principal component analysis (PCA) include: PCA projects high-dimensional features into a low-dimensional space; first, the feature covariance matrix is ​​calculated, and eigenvalues ​​are decomposed. ; in For the data covariance matrix, The eigenvector matrix, It is the eigenvalue matrix; Then, the first k eigenvectors are taken to form the projection matrix W, and the dimensionality-reduced data is Z=XW, where X is the original eigenma matrix and k is determined by the cumulative variance contribution rate. The algorithm execution unit has online learning capabilities, using real-time data to incrementally update model parameters and adapt to process changes; Online learning capability employs an incremental learning algorithm: Online Sequence Extreme Learning Machine (OS-ELM); model parameters are updated progressively with new data, avoiding retraining. The update formula is as follows: ; in The updated output weight vector, For the old weight vector, The updated covariance matrix, For the hidden layer output matrix, This is the target value vector.

[0028] The strategy adjustment unit is equipped with an adaptive weighting mechanism that automatically adjusts the priority ratio of temperature control and atmosphere control according to production needs. The weights are dynamically adjusted based on the error, using the following formula: ; in The weight value at time t. The weight value at the previous time step. To learn step length, For loss function Weights The partial derivatives of .

[0029] The central control module includes a main control unit, a communication scheduling unit, and an interface management unit; The main control unit adopts a distributed microprocessor architecture to achieve multi-task parallel processing and modular expansion; The system employs multiple microprocessors working collaboratively, with the main processor responsible for overall scheduling and the slave processors dedicated to module computation. The processors communicate with each other via a CAN bus and use message queues to synchronize data. The architecture supports modular expansion, and new processors can be plugged and played. The firmware design uses the real-time operating system FreeRTOS, and task allocation is based on priority scheduling to ensure real-time performance. The communication scheduling unit supports industrial Ethernet and wireless communication protocols to ensure low-latency data transmission between modules. The interface management unit provides a touch screen interface that supports recipe management, trend curve plotting, and alarm information push.

[0030] The main control unit has a fault self-diagnosis function, monitors the hardware status in real time, and switches backup modules. The communication scheduling unit integrates data encryption and integrity verification mechanisms to prevent unauthorized access; Encryption uses the AES-256 algorithm to encrypt transmitted data blocks. The key is exchanged via the TLS security protocol. The encryption process is as follows: ; in For plain text, It is a ciphertext. The key is used; integrity verification uses the SHA-256 hash algorithm to generate a data digest: ; in For data, It is a hash value; appended during transmission. The receiving end verifies consistency; the mechanism is integrated into the communication driver and executes automatically. The interface management unit allows users to customize control logic and report formats, and supports remote mobile access.

[0031] The safety monitoring unit includes a threshold early warning subunit and an emergency response subunit; The threshold warning subunit sets the upper and lower limits of temperature and the safe range of atmosphere composition according to the process standard. When the parameters exceed the limit, it triggers an audible and visual alarm and SMS notification. The emergency response subunit automatically executes the cooling, nitrogen charging, and flameout procedures, and generates an accident report recording the operation time, cause, and handling results.

[0032] The operating steps of this system are as follows: First, a temperature monitoring module monitors the temperature distribution in different areas of the industrial furnace in real time. A high-precision temperature sensor array collects temperature data from the furnace chamber, workpiece surface, and exhaust outlet, followed by signal amplification, filtering, and analog-to-digital conversion. Then, based on a heat conduction model and real-time data, a temperature distribution uniformity index is calculated to identify abnormal temperature gradients, overshoot, and localized overheating or undercooling areas, and to predict temperature evolution trends. Simultaneously, an atmosphere monitoring module monitors the dynamic changes in atmosphere composition within the industrial furnace in real time. A multi-channel gas analyzer acquires oxygen, carbon monoxide, carbon dioxide concentrations, and humidity parameters. An atmosphere balance model is used to calculate redox potential and dew point temperature to assess atmosphere uniformity and stability.

[0033] Subsequently, the intelligent optimization module performs data fusion based on the aforementioned temperature and atmosphere monitoring data, normalizes the heterogeneous data, and extracts features. It then employs a machine learning-based predictive control algorithm, using an integrated deep learning neural network and fuzzy logic controller to learn optimal control rules, dynamically adjusting heating power, fuel supply rate, and ventilation volume to optimize the furnace thermal process and achieve coordinated temperature and atmosphere control. This module also dynamically refreshes optimization parameters based on the control effect, supporting multi-objective optimization modes such as minimizing energy consumption, maximizing temperature uniformity, and prioritizing emission control.

[0034] The central control module is responsible for coordinating the runtime sequence of all modules in the system, integrating and processing all monitoring data and executing control commands, and driving the actuators to operate according to preset optimization strategies. This module adopts a distributed microprocessor architecture to achieve multi-task parallel processing, and supports experimental parameter configuration, real-time data visualization, and historical record query through a graphical human-computer interaction interface.

[0035] In addition, the system's integrated safety monitoring unit continuously monitors temperature and atmosphere parameters. Once these parameters exceed the safety threshold, an automatic protection mechanism is triggered, executing emergency procedures such as audible and visual alarms, cooling, nitrogen charging, and flameout. The system also records fault logs for analysis, thereby ensuring the safe and stable operation of the entire system.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent optimization control system for temperature and atmosphere in an industrial furnace, characterized in that: The system includes a temperature monitoring module, an atmosphere monitoring module, an intelligent optimization module, and a central control module; The temperature monitoring module is used to monitor the temperature distribution in different areas of the industrial furnace in real time. It collects temperature data of the furnace chamber, workpiece surface and exhaust gas outlet through a high-precision temperature sensor array, and identifies abnormal temperature gradients and overshoot phenomena. The atmosphere monitoring module is used to monitor the dynamic changes of atmosphere composition in the industrial furnace in real time, including oxygen concentration, carbon monoxide concentration, carbon dioxide concentration and humidity parameters. Data is acquired through a multi-channel gas analyzer and the atmosphere uniformity and stability are evaluated. The intelligent optimization module is used to dynamically adjust the heating power, fuel supply rate and ventilation volume based on temperature and atmosphere monitoring data and a predictive control algorithm based on machine learning, thereby optimizing the thermal process inside the furnace. The central control module is used to coordinate the running sequence of each module in the system, integrate and process monitoring data and execute control commands, drive the operation of the actuators according to the preset optimization strategy, and provide a graphical human-computer interaction interface to support experimental parameter configuration, real-time data visualization and historical record query. The system also integrates a safety monitoring unit, which triggers an automatic protection mechanism when temperature or atmosphere parameters exceed safety thresholds and records fault logs for analysis.

2. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 1, characterized in that: The temperature monitoring module includes a sensor deployment unit, a data acquisition unit, and a temperature analysis unit; The sensor deployment unit covers the key areas of the industrial furnace, including the heating zone, the soaking zone, and the cooling zone, in a grid layout using high-temperature thermocouples and infrared thermometers. The data acquisition unit is used to simultaneously acquire multiple temperature signals and perform signal amplification, filtering, and analog-to-digital conversion. The temperature analysis unit calculates the temperature distribution uniformity index based on the heat conduction model and real-time data, identifies local overheated and undercooled areas, and predicts the temperature evolution trend.

3. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 2, characterized in that: The sensor deployment unit is also equipped with a self-calibration mechanism that periodically verifies the sensor accuracy using a standard temperature source. The data acquisition unit supports high-speed sampling rates and has data compression and redundancy backup functions to prevent data loss. The temperature analysis unit uses a time series analysis method to compare the current temperature curve with the historical baseline curve. When the deviation continues to exceed the tolerance value, it is determined to be an abnormal state.

4. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 1, characterized in that: The atmosphere monitoring module includes a gas sampling unit, a component analysis unit, and an atmosphere assessment unit; The gas sampling unit extracts gas samples from different locations inside the furnace using a multi-channel air pump and a filter device to avoid cross-contamination. The component analysis unit uses electrochemical sensors and spectroscopic analysis technology to quantitatively measure the concentration of each gas component and performs temperature and pressure compensation. The atmosphere assessment unit calculates the oxidation-reduction potential and dew point temperature based on the atmosphere balance model to determine whether the atmosphere control effect meets the standard.

5. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 4, characterized in that: The gas sampling unit is equipped with an automatic cleaning function to regularly remove dust from the pipeline and ensure the representativeness of the sampling. The component analysis unit integrates a multi-point calibration algorithm to automatically adjust the measurement accuracy based on the standard gas. The atmosphere assessment unit generates an atmosphere stability report by comparing real-time atmosphere data with the ideal process curve, and provides feedback input to the intelligent optimization module.

6. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 1, characterized in that: The intelligent optimization module includes a data fusion unit, an algorithm execution unit, and a strategy adjustment unit; The data fusion unit is used to integrate the heterogeneous data from the temperature monitoring module and the atmosphere monitoring module, and to perform normalization processing and feature extraction. The algorithm execution unit is equipped with a deep learning neural network and a fuzzy logic controller. It learns the optimal control rules through training data and outputs the heater power setpoint, fuel valve opening degree and damper position command in real time. The strategy adjustment unit dynamically refreshes the optimization parameters based on the control effect, and supports multi-objective optimization modes, including minimizing energy consumption, maximizing temperature uniformity, and prioritizing emission control.

7. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 6, characterized in that: The data fusion unit uses principal component analysis to reduce dimensionality and highlight key feature variables. The algorithm execution unit has online learning capabilities, which uses real-time data to incrementally update model parameters and adapt to process changes. The strategy adjustment unit is equipped with an adaptive weighting mechanism that automatically adjusts the priority ratio of temperature control and atmosphere control according to production needs.

8. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 1, characterized in that: The central control module includes a main control unit, a communication scheduling unit, and an interface management unit. The main control unit adopts a distributed microprocessor architecture to realize multi-task parallel processing and modular expansion; The communication scheduling unit supports industrial Ethernet and wireless communication protocols, and supports low-latency data transmission between modules. The interface management unit provides a touch screen operation interface, which supports recipe management, trend curve plotting, and alarm information push.

9. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 8, characterized in that: The main control unit has a fault self-diagnosis function, which monitors the hardware status in real time and switches the backup module. The communication scheduling unit integrates data encryption and integrity verification mechanisms to prevent unauthorized access. The interface management unit allows users to customize control logic and report formats, and supports remote mobile access.

10. The intelligent optimization control system for temperature and atmosphere of an industrial furnace according to claim 1, characterized in that: The security monitoring unit includes a threshold early warning subunit and an emergency response subunit; The threshold warning subunit sets the upper and lower limits of temperature and the safe range of atmosphere composition according to the process standard. When the parameters exceed the limit, it triggers an audible and visual alarm and a text message notification. The emergency response subunit automatically executes the cooling, nitrogen charging, and flameout procedures, and generates an accident report recording the operation time, cause, and handling result.

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