Industrial furnace intelligent temperature control system based on data acquisition

Abstract

The invention relates to the technical field of industrial furnace temperature control, and discloses an industrial furnace intelligent temperature control system based on data acquisition, which comprises a data acquisition module, a temperature analysis module, an intelligent control module, an early warning feedback module, a user interaction module and a remote maintenance module, according to the method, trend analysis is carried out in combination with historical temperature data, and the short-term temperature trend is predicted, so that the system can pre-judge the future change trend of the temperature, a control instruction is generated in advance, the problems of large inertia and large delay in the thermal process in the furnace are solved, temperature overshoot and adjustment oscillation are restrained under the complex and changeable working conditions, and the control efficiency is improved. The technical requirements of high-precision constant-temperature control are met, the temperature data are monitored in real time, a statistical process control method is used for anomaly detection, the system can conduct intelligent diagnosis, anomaly identification and root cause analysis triggering at the initial stage of abnormal temperature fluctuation, and therefore the product quality stability is guaranteed, and equipment safety faults are prevented.

Description

Technical Field

[0001] This invention relates to the field of industrial furnace temperature control technology, specifically to an intelligent temperature control system for industrial furnaces based on data acquisition. Background Technology

[0002] Industrial furnaces are thermal equipment that use heat from fuel combustion or electrical energy conversion to heat materials or workpieces. They encompass types such as cupola furnaces, electric arc furnaces, and annealing furnaces, and are used in industries such as casting, metal heat treatment, metallurgy, and petrochemicals. They perform functions such as smelting and sintering and are one of the main sources of air pollution in the industrial sector. Temperature control is a crucial aspect of industrial furnace operation, significantly impacting the heat treatment effect on materials or workpieces and affecting subsequent processes and product quality.

[0003] Currently, in the temperature control process of industrial furnaces and kilns, due to the complex and ever-changing production conditions, existing control methods often rely on feedback adjustment of the current temperature, making it difficult to predict future temperature change trends. This results in control commands being delayed. When there is a large inertia and a large delay in the thermal process inside the furnace, it is easy to cause temperature overshoot and regulation oscillation, which cannot meet the process requirements of high-precision constant temperature control.

[0004] Therefore, a smart temperature control system for industrial furnaces based on data acquisition is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent temperature control system for industrial furnaces based on data acquisition, which solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent temperature control system for an industrial furnace based on data acquisition, the system comprising a data acquisition module, a temperature analysis module, an intelligent control module, an early warning feedback module, a user interaction module, and a remote maintenance module; The data acquisition module is used to collect temperature data in real time through temperature sensors set at multiple locations in the industrial furnace and transmit the data to the temperature analysis module. The temperature sensors include thermocouples, infrared sensors and resistance temperature detectors, covering the furnace chamber, heating zone and cooling zone of the industrial furnace. The temperature analysis module is used to receive real-time temperature data, compare it with preset standard temperature parameters, calculate the temperature deviation value, and analyze it in combination with historical temperature data trends to generate adaptive control commands. The preset standard temperature parameters include the target temperature curve, the allowable deviation range, and the temperature change rate. The intelligent control module is used to adjust the power of the heating elements, the opening of the fuel supply valve, and the speed of the cooling system fan in real time according to the control commands generated by the temperature analysis module. The early warning feedback module is used to monitor the system's operating status. When it detects excessive temperature, abnormal data acquisition, or control failure, it immediately triggers a voice alarm, a display screen warning, and a remote notification system to remind operators to handle the situation promptly. The user interaction module provides real-time temperature visualization, parameter setting, and manual control functions via touchscreen and web interface, and supports multilingual interfaces. The remote maintenance module is used to enable remote monitoring, fault diagnosis, and software updates through a cloud platform, reducing on-site maintenance costs and integrating data analysis tools to provide decision support for process optimization.

[0007] Preferably, the data acquisition module includes a sensor deployment unit, a signal conditioning unit, and a real-time transmission unit; The sensor deployment unit is used to deploy redundant temperature sensor arrays in the high-temperature zone, medium-temperature zone, and low-temperature zone according to the heat distribution characteristics of the industrial furnace, and to identify them by digital numbers to form a sensor network covering different temperature zones. The signal conditioning unit is used to filter, linearize, compensate and amplify the analog signal output by the sensor, eliminate environmental interference and sensor drift error, and output a standard signal that conforms to the preset voltage and current range. The real-time transmission unit is used to package and transmit the conditioned temperature data to the temperature analysis module via industrial Ethernet and wireless communication protocols. The data transmission protocol supports data packet verification and retransmission mechanisms.

[0008] Preferably, the sensor deployment unit includes a location planning subunit and a calibration and maintenance subunit; The location planning subunit is used to optimize the sensor placement based on computational fluid dynamics simulation results to cover heat-sensitive areas; The calibration and maintenance subunit is used to periodically calibrate the temperature sensor automatically and manually, correct the sensor readings by comparing them with a standard temperature source, and record calibration history data and the corresponding sensor identifier.

[0009] Preferably, the temperature analysis module includes a data preprocessing unit, a trend analysis unit, and a control generation unit; The data preprocessing unit is used to perform outlier removal, moving average filtering, and normalization on the received temperature data; The trend analysis unit is used to identify temperature change patterns through time series analysis, including periodic fluctuations, upward trends, and abrupt events, and to predict short-term temperature trends. The control generation unit is used to generate control commands based on trend analysis results and preset parameters, using a rule base and fuzzy logic, and dynamically adjust the control strategy to adapt to different working conditions.

[0010] Preferably, the trend analysis unit includes a short-term prediction subunit and an anomaly detection subunit; The short-term forecasting subunit is used to output temperature forecasts for a specific future time period based on an autoregressive integral moving average model. The anomaly detection subunit is used to monitor temperature data using statistical process control methods. When consecutive data points exceed the control limits, an anomaly is identified and root cause analysis is triggered.

[0011] Preferably, the intelligent control module includes an execution drive unit, a feedback adjustment unit, and an energy consumption optimization unit; The actuator unit is used to convert control commands into electrical and pneumatic signals to drive heaters, valves and actuators. The feedback adjustment unit is used to compare the actual temperature with the target value in real time, calculate the error, and dynamically correct the control parameters through the closed-loop control principle. The energy consumption optimization unit is used to monitor the energy consumption data of the industrial furnace and, in conjunction with the control commands generated by the temperature analysis module, dynamically calculates and outputs the power setpoint of the heating element.

[0012] Preferably, the feedback adjustment unit includes an adaptive tuning subunit and a fault-tolerant control subunit; The adaptive tuning subunit is used to automatically adjust the proportional gain coefficient and integral time constant of the control algorithm based on the system delay time and response slope in the temperature response characteristics; The fault-tolerant control subunit is used to switch to backup sensors and actuators when hardware failures occur in the sensors and actuators. When the sensor data is abnormal but communication is normal, it enables the temperature estimation algorithm based on the historical data model and outputs the corresponding control commands.

[0013] Preferably, the early warning feedback module includes a status monitoring unit, a multi-level alarm unit, and a log recording unit; The status monitoring unit is used to assess the system health in real time, including sensor online status, communication link quality, and control effectiveness indicators; The multi-level alarm unit is used to trigger alarms according to the severity of the anomaly, providing visual cues for minor deviations and activating voice broadcasts and SMS notifications for serious faults. The log recording unit stores the status, trigger time, and operator processing records of all alarm events by timestamp.

[0014] Preferably, the multi-level alarm unit includes a threshold setting subunit and a response management subunit; The threshold setting subunit provides a user interface for receiving and storing user-defined upper and lower limits for temperature alarms, delay time parameters, and conditions for triggering repeated alarms. The response management subunit is used to track the status of alarm responses. If the alarm is not handled in a timely manner, the alarm level will be automatically escalated and the superior management personnel will be notified.

[0015] Preferably, the remote maintenance module includes a remote monitoring unit, a diagnostic update unit, and a data support unit; The remote monitoring unit is used to establish a persistent connection with the cloud platform through an encrypted communication link, upload system operating status, temperature data and alarm information in real time, and receive query commands from remote terminals. The diagnostic update unit is used to perform automated fault diagnosis and analysis based on the operational data received from the cloud platform, and supports remote push of control algorithm parameter optimization packages and system software incremental update packages; The data support unit is used to integrate historical data storage and analysis tools on the cloud platform, generate process optimization suggestion reports based on long-term operating data, and provide data support for decision-making.

[0016] Compared with existing technologies, this invention provides an intelligent temperature control system for industrial furnaces based on data acquisition, which has the following beneficial effects: 1. In this invention, by combining historical temperature data for trend analysis and predicting short-term temperature trends, the system can make predictions about future temperature changes, thereby generating control commands in advance. This overcomes the large inertia and large delay problems in the furnace thermal process, ensuring that temperature overshoot and regulation oscillation are suppressed under complex and variable operating conditions, and meeting the process requirements of high-precision constant temperature control.

[0017] 2. In this invention, by monitoring temperature data in real time and using statistical process control methods for anomaly detection, the system can perform intelligent diagnosis and identify anomalies and trigger root cause analysis at the initial stage of abnormal temperature fluctuations. At the same time, combined with a multi-level early warning and response management mechanism, it ensures that deviations caused by material changes, equipment malfunctions and environmental interference factors are warned and dealt with in a timely manner, thereby ensuring product quality stability and preventing equipment safety failures.

[0018] 3. In this invention, by adopting a fuzzy logic dynamic adjustment control strategy and automatically tuning the key parameters of the control algorithm based on the temperature response characteristics, the system can adapt to the differentiated control characteristics requirements of different process stages such as heating, heat preservation, and sintering. This ensures that the control parameters can be optimized online without human intervention, solving the problems of low efficiency and poor consistency caused by relying on manual experience for adjustment, and realizing intelligent and optimized control of the entire process. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the architecture of an intelligent temperature control system for an industrial furnace based on data acquisition, 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 temperature control system for industrial furnaces based on data acquisition, comprising a data acquisition module, a temperature analysis module, an intelligent control module, an early warning feedback module, a user interaction module, and a remote maintenance module; The data acquisition module is used to collect temperature data in real time through temperature sensors set at multiple locations in the industrial furnace and transmit the data to the temperature analysis module. The temperature sensors include thermocouples, infrared sensors and resistance temperature detectors, covering the furnace chamber, heating zone and cooling zone of the industrial furnace. The temperature analysis module is used to receive real-time temperature data, compare it with preset standard temperature parameters, calculate the temperature deviation value, and analyze it in combination with historical temperature data trends to generate adaptive control commands. The preset standard temperature parameters include the target temperature curve, the allowable deviation range, and the temperature change rate. The intelligent control module is used to adjust the power of the heating elements, the opening of the fuel supply valve, and the speed of the cooling system fan in real time according to the control commands generated by the temperature analysis module. The early warning feedback module is used to monitor the system's operating status. When it detects excessive temperature, abnormal data acquisition, or control failure, it immediately triggers a voice alarm, a display screen warning, and a remote notification system to remind operators to handle the situation promptly. The user interaction module provides real-time temperature visualization, parameter setting, and manual control functions via touchscreen and web interface, and supports multilingual interfaces. The remote maintenance module is used to enable remote monitoring, fault diagnosis, and software updates through a cloud platform, reducing on-site maintenance costs and integrating data analysis tools to provide decision support for process optimization.

[0022] The data acquisition module includes a sensor deployment unit, a signal conditioning unit, and a real-time transmission unit; The sensor deployment unit is used to deploy redundant temperature sensor arrays in the high-temperature zone, medium-temperature zone, and low-temperature zone according to the heat distribution characteristics of the industrial furnace, and to identify them by digital numbers to form a sensor network covering different temperature zones. The signal conditioning unit is used to filter, linearize, compensate and amplify the analog signal output by the sensor, eliminate environmental interference and sensor drift error, and output a standard signal that conforms to the preset voltage and current range. The real-time transmission unit is used to package and transmit the conditioned temperature data to the temperature analysis module via industrial Ethernet and wireless communication protocols. The data transmission protocol supports data packet verification and retransmission mechanisms.

[0023] The sensor deployment unit includes a location planning subunit and a calibration and maintenance subunit; The location planning subunit is used to optimize the sensor placement based on computational fluid dynamics simulation results to cover heat-sensitive areas; The calibration and maintenance subunit is used to periodically and automatically and manually calibrate the temperature sensor, correct the sensor reading by comparing it with a standard temperature source, and record calibration history data and the corresponding sensor identifier. Specifically, the temperature sensor is periodically and automatically calibrated. The subunit controls the calibration device to apply a standard temperature source to the sensor to be calibrated according to a preset time period; Read the output value of the sensor to be calibrated Compared with the actual value of the standard temperature source Compare and calculate the error; When the error exceeds the allowable range, the calibration compensation coefficient of the sensor is generated and stored. When collecting data subsequently; The coefficient is automatically applied for correction, and the corrected temperature value is... The calculation is as follows: ; in, This is the uncalibrated temperature value calculated directly from the sensor's original output value.

[0024] The temperature analysis module includes a data preprocessing unit, a trend analysis unit, and a control generation unit; The data preprocessing unit is used to perform outlier removal, moving average filtering, and normalization on the received temperature data; The trend analysis unit is used to identify temperature change patterns through time series analysis, including periodic fluctuations, upward trends, and abrupt events, and to predict short-term temperature trends. The control generation unit is used to generate control commands based on trend analysis results and preset parameters, using a rule base and fuzzy logic, and dynamically adjust the control strategy to adapt to different working conditions. The control instructions are generated using a rule base, which is a set of condition-action rules preset based on expert experience. Specific rules include: Receives and stores user-defined rule parameters, including temperature deviation thresholds. Deviation change rate threshold and the corresponding control action amount; The current temperature deviation calculated by the real-time temperature analysis module is obtained. and the rate of change of deviation ; Will and The system matches the data against preset rules and executes the control action corresponding to the first rule that matches successfully. These rules include: when and Then, the output will be a power reduction control command primarily aimed at eliminating static errors; when and Then, the output will be a power increase control command primarily aimed at eliminating static errors; when and and Then, the output will be a power reduction control command that primarily suppresses dynamic fluctuations; when and and Then, the output will be a power increase control command primarily aimed at suppressing dynamic fluctuations; when and If so, then output a control command to maintain the current power. The matched and generated control commands are output to the control generation unit; Control commands are generated using fuzzy logic, specifically including: Define the input variable as temperature deviation. and the rate of change of deviation Fuzzy sets and membership functions; Define output variables to control the amount of change in commands. fuzzy sets; Establish a fuzzy rule base based on expert experience; Through fuzzy inference and defuzzification, the input variables are converted into control command changes. The defuzzification uses the centroid method, and the calculation formula is as follows: ; in, To control the amount of change in instructions, For the number of rule activations, For the first The activation strength of the rule, For the first The rule outputs the center value of the fuzzy set.

[0025] The trend analysis unit includes a short-term prediction subunit and an anomaly detection subunit; The short-term forecasting subunit is used to output temperature forecasts for a specific future time period based on an autoregressive integral moving average model, including the following steps: Based on historical temperature data sequences, stationarity tests are performed, and non-stationary sequences are transformed into stationary sequences through differencing operations, specifically including: Stationarity test: Calculate the mean and variance of the historical temperature data series and observe its characteristics over time; If the mean of a sequence shows a trend over time and the variance of the sequence shows non-constant fluctuations over time, then the sequence is considered a non-stationary sequence. Difference operation: The difference operation is performed on sequences determined to be non-stationary. Its mathematical expression is: ; in For the new sequence in The value at time, For the original time series data in The observed value at time, For the same time series in The previous moment Observed values; After performing a difference operation, the resulting new sequence Perform the stationarity test again until a stationary sequence is obtained, and record the final required difference order d; Identify and determine the autoregressive order p, differencing order d, and moving average order q of the model, and construct the ARIMA(p,d,q) model, whose general expression is: ; in, For lag operators, For the original time series data in The value at time, These are the autoregressive coefficients. The moving average coefficient is... It is a residual sequence. Let the order be the autoregressive order. Let be the difference order. The moving average order; The maximum likelihood estimation method is used to estimate the model parameters. and Estimate the parameters by maximizing the log-likelihood function to find the optimal parameters: ; in, Let be the log-likelihood function. For the sample size, For residual variance, It is a residual sequence. and These are the parameter vectors for autoregression and moving average, respectively; Verify that the residual sequence is white noise, i.e., the autocorrelation function of the residuals satisfies: ; in, Lagging The autocorrelation coefficient of order, For the sample size, The maximum lag order, The lag order; The finalized model is used to make rolling predictions of temperature values ​​for specific future time periods, and the predicted values ​​are output to the anomaly detection subunit. The anomaly detection subunit monitors temperature data using statistical process control methods. When consecutive data points exceed control limits, it identifies the anomaly and triggers root cause analysis. Specifically, it monitors temperature data using a mean control chart. During the stable operation phase of the system, the data collection size is... Calculate the population sample mean from the sample subgroups. and range mean Then calculate the center line of the control chart. Upper control limit and lower control limit : ; in, For subgroup size Relevant control chart coefficients; Real-time monitoring of temperature data points; when a data point exceeds the control limit and exhibits a non-random pattern, an anomaly is identified. When an anomaly is identified, the root cause analysis process is triggered.

[0026] The intelligent control module includes an execution drive unit, a feedback adjustment unit, and an energy consumption optimization unit; The actuator unit is used to convert control commands into electrical and pneumatic signals to drive heaters, valves and actuators. The feedback adjustment unit is used to compare the actual temperature with the target value in real time through closed-loop control, calculate the error, and dynamically correct the control parameters. The closed-loop control principle adopts a proportional-integral-derivative control algorithm, and its output control quantity... Calculated by the following formula: ; in, This represents the temperature error at the current moment. This is the proportional gain coefficient. The integral coefficient is... The differential coefficients are... For integration variables; The energy consumption optimization unit is used to monitor the energy consumption data of the industrial furnace and, in conjunction with the control commands generated by the temperature analysis module, dynamically calculates and outputs the power setpoint of the heating element.

[0027] The feedback adjustment unit includes an adaptive tuning subunit and a fault-tolerant control subunit; The adaptive tuning subunit is used to automatically adjust the proportional gain coefficient and integral time constant of the control algorithm based on the system delay time and response slope in the temperature response characteristics. This is achieved through the following steps: A step signal is applied to the control system, and the temperature response curve is recorded; Calculate the system delay time based on the response curve. and time constant ; Based on the Ziegler-Nichols tuning rule, a new proportional gain coefficient is calculated. and integration time constant : ; in This is the proportional gain coefficient. The integral time constant of the controller, It is a time constant. For delay time; The new parameters are applied to the proportional-integral-derivative control algorithm; The fault-tolerant control subunit is used to switch to backup sensors and actuators when hardware failures occur in the sensors and actuators. When sensor data is abnormal but communication is normal, it activates a temperature estimation algorithm based on a historical data model and outputs corresponding control commands. Specifically, it uses a multiple linear regression model. Pre-establish fault sensor location temperature Temperature at other normal sensor locations The relationship model between them: ; in, The estimated temperature at the location of the faulty sensor. For the regression intercept term, These are the model coefficients obtained through regression analysis of historical data. For the temperature at other normal sensor locations, This is the regression error term; When sensor data is abnormal, the readings of other normal sensors are substituted into the model to estimate the temperature value at the location of the faulty sensor. ; Valuation The abnormal measured value is replaced and input to the feedback adjustment unit.

[0028] The early warning feedback module includes a status monitoring unit, a multi-level alarm unit, and a log recording unit; The status monitoring unit is used to assess the system health in real time, including sensor online status, communication link quality, and control effectiveness indicators; The multi-level alarm unit is used to trigger alarms according to the severity of the anomaly, providing visual cues for minor deviations and activating voice broadcasts and SMS notifications for serious faults. The log recording unit stores the status, trigger time, and operator processing records of all alarm events by timestamp.

[0029] The multi-level alarm unit includes a threshold setting subunit and a response management subunit; The threshold setting subunit provides a user interface for receiving and storing user-defined upper and lower limits for temperature alarms, delay time parameters, and conditions for triggering repeated alarms. The response management subunit is used to track the status of alarm responses. If the alarm is not handled in a timely manner, the alarm level will be automatically escalated and the superior management personnel will be notified.

[0030] The remote maintenance module includes a remote monitoring unit, a diagnostic update unit, and a data support unit; The remote monitoring unit is used to establish a persistent connection with the cloud platform through an encrypted communication link, upload system operating status, temperature data and alarm information in real time, and receive query commands from remote terminals. The diagnostic update unit is used to perform automated fault diagnosis and analysis based on the operational data received from the cloud platform, and supports remote push of control algorithm parameter optimization packages and system software incremental update packages; The data support unit is used to integrate historical data storage and analysis tools on the cloud platform, generate process optimization suggestion reports based on long-term operational data, and provide data support for decision-making. Specifically, it includes: Long-term storage of industrial furnace operating parameters; Based on stored data, correlation analysis is used to identify key quality indicators affecting products. Main temperature control parameters The correlation is measured by the Pearson correlation coefficient. measure: ; in, For the number of data points, For the temperature control parameters in the first The value of the point, For the product's key quality indicators in the first The value of the point, and They are respectively and The mean; Generate a process optimization suggestion report.

[0031] The operation steps of this intelligent temperature control system for industrial furnaces based on data acquisition are as follows: Step 1: System Initialization and Data Acquisition After system startup, initialization is performed first. The data acquisition module begins operation, and its sensor deployment unit activates a redundant array of temperature sensors distributed throughout the industrial furnace's furnace chamber, heating zone, and cooling zone according to a preset scheme. The signal conditioning unit filters, linearizes, and amplifies the raw analog signals output by the sensors to eliminate environmental interference and sensor drift errors, outputting a standard signal. Subsequently, the real-time transmission unit reliably transmits the processed real-time temperature data to the temperature analysis module via industrial Ethernet and wireless communication protocols.

[0032] Step 2: Temperature Data Analysis and Command Generation After receiving real-time temperature data, the temperature analysis module first performs outlier removal and moving average filtering by the data preprocessing unit to ensure data quality. Next, the trend analysis unit intervenes: its short-term prediction subunit analyzes historical data sequences based on an autoregressive integral moving average model to predict temperature trends over specific future time periods; simultaneously, the anomaly detection subunit monitors data points in real time using statistical process control methods, immediately identifying and triggering root cause analysis if an anomaly pattern continuously exceeds control limits. Finally, the control generation unit integrates the current temperature, predicted trend, preset target temperature curve, and allowable deviation range, using a rule base and fuzzy logic to dynamically generate adaptive control commands.

[0033] Step 3: Intelligent Control Execution and Parameter Optimization The intelligent control module performs precise adjustments based on received control commands. The execution drive unit converts digital commands into specific electrical and pneumatic signals, driving the heating elements, fuel supply valves, and cooling system fan actuators. The feedback adjustment unit, based on closed-loop control principles, continuously compares the actual temperature with the target value and uses a proportional-integral-derivative (PI) algorithm to calculate the control input. Its internal adaptive tuning subunit automatically adjusts the proportional gain and integral time parameters of the PID algorithm based on the system's real-time response characteristics to optimize control performance. Simultaneously, the energy consumption optimization unit monitors energy consumption data and collaboratively outputs the optimal power setpoint to achieve energy-saving control.

[0034] Step 4: Full-process status monitoring and early warning feedback The early warning feedback module independently and in parallel monitors the entire system's operational status. The status monitoring unit evaluates the online status of sensors, communication link quality, and control effectiveness health indicators in real time. The multi-level alarm unit classifies temperature exceedances and abnormal data acquisition events according to the preset limits of the threshold setting subunit: minor deviations trigger visual cues, while serious faults activate voice broadcasts and SMS notifications. The response management subunit tracks alarm processing status and automatically escalates alarm levels if a response is not timely. All events are timestamped and logged by the logging unit, forming a complete audit trail.

[0035] Step 5: Human-Computer Interaction and Remote Operation and Maintenance Support The user interaction module provides operators with real-time temperature data visualization, process parameter setting, and manual control functions through a local touchscreen and a remote web interface. Meanwhile, the remote maintenance module maintains a connection to the cloud platform via an encrypted link. The remote monitoring unit uploads all operational data and alarm information; the diagnostic update unit can perform remote fault diagnosis based on cloud data analysis results and push control parameter optimization packages and system updates; the data support unit integrates advanced analysis tools to mine long-term operational data, generate process optimization suggestion reports, provide data support for decision-making, and achieve predictive maintenance and continuous optimization.

[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 temperature control system for an industrial furnace based on data acquisition, characterized in that: The system includes a data acquisition module, a temperature analysis module, an intelligent control module, an early warning feedback module, a user interaction module, and a remote maintenance module. The data acquisition module is used to collect temperature data in real time through temperature sensors set at multiple locations in the industrial furnace, and transmit the data to the temperature analysis module. The temperature sensors include thermocouples, infrared sensors and resistance thermometers, covering the furnace chamber, heating zone and cooling zone of the industrial furnace. The temperature analysis module is used to receive real-time temperature data, compare it with preset standard temperature parameters, calculate the temperature deviation value, and analyze it in conjunction with historical temperature data trends to generate adaptive control commands. The preset standard temperature parameters include the target temperature curve, the allowable deviation range, and the temperature change rate. The intelligent control module is used to adjust the power of the heating element, the opening degree of the fuel supply valve, and the speed of the cooling system fan in real time according to the control commands generated by the temperature analysis module. The early warning feedback module is used to monitor the system's operating status. When it detects excessive temperature, abnormal data acquisition, or control failure, it immediately triggers a voice alarm, a display screen warning, and a remote notification system to remind operators to handle the situation promptly. The user interaction module is used to provide real-time temperature visualization, parameter setting and manual control functions through a touch screen and web interface, and supports multi-language interfaces. The remote maintenance module is used to achieve remote monitoring, fault diagnosis and software updates through the cloud platform, reduce on-site maintenance costs, and integrate data analysis tools to provide decision support for process optimization.

2. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 1, characterized in that: The data acquisition module includes a sensor deployment unit, a signal conditioning unit, and a real-time transmission unit; The sensor deployment unit is used to deploy redundant temperature sensor arrays in the high-temperature zone, medium-temperature zone and low-temperature zone according to the heat distribution characteristics of the industrial furnace, and to identify them by digital numbers to form a sensor network covering different temperature zones. The signal conditioning unit is used to filter, linearize, compensate and amplify the analog signal output by the sensor, eliminate environmental interference and sensor drift error, and output a standard signal that conforms to the preset voltage and current range. The real-time transmission unit is used to package and transmit the conditioned temperature data to the temperature analysis module via industrial Ethernet and wireless communication protocols. The data transmission protocol supports data packet verification and retransmission mechanisms.

3. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 2, characterized in that: The sensor deployment unit includes a location planning subunit and a calibration and maintenance subunit; The location planning subunit is used to optimize the sensor placement based on computational fluid dynamics simulation results to cover heat-sensitive areas; The calibration and maintenance subunit is used to periodically calibrate the temperature sensor automatically and manually, correct the sensor reading by comparing it with a standard temperature source, and record calibration history data and the corresponding sensor identifier.

4. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 1, characterized in that: The temperature analysis module includes a data preprocessing unit, a trend analysis unit, and a control generation unit; The data preprocessing unit is used to perform outlier removal, moving average filtering, and normalization on the received temperature data. The trend analysis unit is used to identify temperature change patterns through time series analysis, including periodic fluctuations, upward trends, and abrupt events, and to predict short-term temperature trends. The control generation unit is used to generate control commands based on trend analysis results and preset parameters, using a rule base and fuzzy logic, and dynamically adjust the control strategy to adapt to different working conditions.

5. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 4, characterized in that: The trend analysis unit includes a short-term prediction subunit and an anomaly detection subunit; The short-term forecasting subunit is used to output temperature forecasts for a specific future time period based on an autoregressive integral moving average model. The anomaly detection subunit is used to monitor temperature data through statistical process control methods. When consecutive data points exceed the control limits, an anomaly is identified and root cause analysis is triggered.

6. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 1, characterized in that: The intelligent control module includes an execution drive unit, a feedback adjustment unit, and an energy consumption optimization unit; The execution drive unit is used to convert control commands into electrical signals and pneumatic signals to drive the heater, valve and actuator to operate. The feedback adjustment unit is used to compare the actual temperature with the target value in real time, calculate the error, and dynamically correct the control parameters through the closed-loop control principle. The energy consumption optimization unit is used to monitor the energy consumption data of the industrial furnace, and, in conjunction with the control commands generated by the temperature analysis module, dynamically calculates and outputs the power setpoint of the heating element.

7. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 6, characterized in that: The feedback adjustment unit includes an adaptive tuning subunit and a fault-tolerant control subunit; The adaptive tuning subunit is used to automatically adjust the proportional gain coefficient and integral time constant of the control algorithm based on the system delay time and response slope in the temperature response characteristics. The fault-tolerant control subunit is used to switch to backup sensors and actuators when hardware failures occur in the sensors and actuators. When the sensor data is abnormal but communication is normal, it enables a temperature estimation algorithm based on historical data models and outputs corresponding control commands.

8. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 1, characterized in that: The early warning feedback module includes a status monitoring unit, a multi-level alarm unit, and a log recording unit; The status monitoring unit is used to assess the system health in real time, including sensor online status, communication link quality, and control effect indicators. The multi-level alarm unit is used to trigger alarms according to the severity of the abnormality, issuing visual prompts when there is a slight deviation and activating voice broadcasts and SMS notifications when there is a serious fault. The log recording unit stores the status, trigger time, and operator processing records of all alarm events by timestamp.

9. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 8, characterized in that: The multi-level alarm unit includes a threshold setting subunit and a response management subunit; The threshold setting subunit provides a user interface for receiving and storing user-defined upper and lower limits for temperature alarms, delay time parameters, and repeated alarm triggering conditions. The response management subunit is used to track the alarm response status. If the alarm is not handled in a timely manner, the alarm level will be automatically escalated and the superior management personnel will be notified.

10. The intelligent temperature control system for an industrial furnace based on data acquisition according to claim 1, characterized in that: The remote maintenance module includes a remote monitoring unit, a diagnostic update unit, and a data support unit; The remote monitoring unit is used to establish a persistent connection with the cloud platform through an encrypted communication link, upload system operating status, temperature data and alarm information in real time, and receive query commands from remote terminals. The diagnostic update unit is used to perform automated fault diagnosis and analysis based on the operating data received from the cloud platform, and supports remote push of control algorithm parameter optimization packages and system software incremental update packages. The data support unit is used to integrate historical data storage and analysis tools on the cloud platform, generate process optimization suggestion reports based on long-term operating data, and provide data support for decision-making.

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