A temperature sensor detection data analysis method and system

Abstract

The application discloses a temperature sensor detection data analysis method and system, relates to the field of temperature sensor detection data analysis, and is used for improving the accuracy of temperature monitoring and the timeliness of control, and comprises the following steps: acquiring temperature data curves of a pipeline system output by a temperature sensor and process operation state information related to temperature change; determining whether the slope of the temperature data curves reaches a peak value and whether the measurement time of the peak value is delayed according to the temperature data curves; when the slope of the temperature data curves reaches the peak value and the measurement time of the peak value is delayed, determining the source causing the delay according to the process operation state information; the source includes process change and sensor fouling; and correcting the temperature data curves in the case that the source of the delay is the sensor fouling.

Description

Technical Field

[0001] This invention relates to the field of temperature sensor detection data analysis, and in particular to a method and system for analyzing temperature sensor detection data. Background Technology

[0002] In automated chemical production, monitoring the temperature of the medium within the pipeline system is crucial. This is typically achieved through continuous monitoring of the medium temperature using temperature sensors with protective sleeves installed on the pipeline. The system periodically collects temperature data and analyzes the trends in the data sequence to identify inflection points and peak values ​​of the temperature rise, thereby controlling the start-up and shutdown of heating or cooling equipment. During this process, the analysis system usually compensates for the constant response delay parameters obtained from factory calibration or initial testing by shifting the sensor output waveform forward on the time axis to reconstruct the true temperature sequence of the medium.

[0003] However, in actual chemical processes, high-temperature materials inside pipelines are prone to polymerization or crystallization, leading to the gradual formation of a scale layer on the outer wall of the sensor's protective sleeve. This scale layer is located on the heat conduction path between the medium and the sensor's temperature-sensing element, and its heat capacity and thermal resistance slowly change the overall thermal dynamic characteristics of the protective sleeve as the scale layer thickens. When the process is in a rapid heating phase, the actual thermal response delay caused by the scale layer will be significantly greater than the initial calibration value, and this delay will dynamically change with the rate of change of the medium temperature and the degree of scaling.

[0004] At this point, due to the non-constant changes in the thermal dynamic characteristics of the protective sleeve, the lag time of the sensor output waveform relative to the actual temperature on the time axis continuously increases. Existing analysis systems still rely on fixed time relationships under clean sleeve conditions to infer the actual temperature, making it difficult to perceive and adapt to this delayed dynamic change caused by the gradual change in the external physical state of the sleeve. Therefore, in chemical pipeline temperature monitoring, when the thermal response delay of the protective sleeve becomes non-constant and gradually increases due to scaling, the analysis system still relies on fixed calibrated time relationships to determine the temperature rise inflection point. This causes the inflection point detection time lag to accumulate with the degree of scaling, leading to a delay in the heating control switching time, making it easy for the actual temperature of the medium inside the pipeline to exceed the target peak value. Summary of the Invention

[0005] This application discloses a method and system for analyzing temperature sensor detection data, aiming to solve the problem in existing chemical pipeline temperature monitoring where, when the thermal response delay of the protective sleeve is not constant and gradually increases due to scaling, the analysis system still relies on a fixed calibrated time sequence relationship to determine the temperature rise inflection point. This results in a delay in the inflection point detection time that accumulates with the degree of scaling, leading to a delay in the switching time of heating control, which makes it easy for the actual temperature of the medium in the pipeline to exceed the target peak value.

[0006] In a first aspect, this application discloses a method for analyzing temperature sensor detection data, comprising the following steps: Acquire temperature data curves within the piping system from temperature sensors, along with process operation status information related to temperature changes; based on the temperature data curves, determine whether the slope of the temperature data curves has reached its peak value, and whether there is a delay in the measurement time to reach the peak value; when the slope of the temperature data curves reaches its peak value and there is a delay in the measurement time to reach the peak value, determine the source of the delay based on the process operation status information; this source includes process changes and sensor scaling; if the source of the delay is sensor scaling, correct the temperature data curves.

[0007] Optionally, based on the temperature data curve, determine whether the slope of the temperature data curve has reached its peak, including: Based on a preset sliding time window, the slope of the temperature data curve is determined in different time windows; Based on the slope of the temperature data curve at different time windows, determine whether the slope of the temperature data curve has reached its peak.

[0008] Optionally, determine if there is a delay in the measurement time to reach the peak, including: Obtain the reference heating time and the start time corresponding to the temperature data curve; Based on the start time and the reference heating duration, determine the reference time to reach the peak value; Based on the reference time and measurement time, determine whether there is a delay in the measurement time.

[0009] Optionally, based on process operation status information, determine the sources causing the delay, including: Based on the process operation status information, determine whether there is a decrease in heat in the injected pipeline system; In the absence of heat attenuation in the injected pipeline system, the source of the delay was determined to be sensor scaling. When the heat injected into the piping system decays, the source of the delay is determined to be a change in the process.

[0010] Optionally, based on process operation status information, determine whether there is heat attenuation in the injected pipeline system, including: Based on the process operation status information, determine whether the first preset condition is met; the first preset condition is that the opening of the heating steam valve has not decreased, the output power of the main heater has not decreased, and the rate of change of the medium flow rate in the pipeline system is less than the rate of change threshold. Under the condition that the first preset condition is met, it is determined that there is no heat loss in the injected pipeline system; If the first preset condition is not met, it is determined that there is a decrease in the heat of the injected pipeline system.

[0011] Optional, temperature data curve correction, including: Determine the current thermal response characteristics of the sensor; Calculate the timing correction based on the current thermal response characteristic parameters; The temperature data curve is corrected based on the time-series correction.

[0012] Optionally, determine the current thermal response characteristics of the sensor, including: Identify and extract multiple local segments with different slopes and curvature characteristics in the temperature data curve; For each of the multiple local segments, the equivalent heat conduction parameters corresponding to the local segment are estimated through an iterative optimization process by using the temperature change rate and second-order change rate information of the local segment, combined with the duration of the local segment. The equivalent thermal conduction parameter is determined as the current thermal response characteristic parameter of the sensor.

[0013] Optionally, the current thermal response characteristic parameters include equivalent heat conduction parameters. The time-series correction is calculated based on these parameters, including: The equivalent heat conduction parameters estimated from multiple local sections are weighted and fused. The equivalent thermal conductivity parameters after fusion are compared with the baseline parameters under the initial clean state, and the drift of the thermal conductivity parameters is calculated. Calculate the timing correction amount based on the drift amount.

[0014] Optionally, the method also includes: Based on the corrected temperature data curves, determine the temperature inflection point time within the piping system; Based on the temperature inflection point time, the switching action of the heating equipment is executed.

[0015] Secondly, this application also discloses a temperature sensor detection data analysis system, which includes: The data acquisition module is used to acquire temperature data curves within the pipeline system output by the temperature sensor, as well as process operation status information related to temperature changes. The data processing module is used to determine whether the slope of the temperature data curve has reached its peak value, and whether there is a delay in the measurement time when the peak value is reached, based on the temperature data curve. The source determination module is used to determine the source of the delay when the slope of the temperature data curve reaches its peak and there is a delay in the measurement time of reaching the peak, based on the process operation status information. The sources include process changes and sensor scaling. The correction module is used to correct the temperature data curve when the source of the delay is sensor fouling. Beneficial effects

[0016] The temperature sensor detection data analysis method disclosed in this application acquires the temperature data curve and process operation status information of the pipeline system output by the temperature sensor. Based on the temperature data curve, it can determine whether the slope has reached its peak value and whether there is a delay in the measurement time to reach the peak value. When the slope reaches its peak value and there is a delay in the measurement time, the method can accurately determine whether the source of the delay is a change in the process or sensor scaling, based on the process operation status information. Specifically, when the source of the delay is sensor scaling, the method can correct the temperature data curve.

[0017] This technical solution effectively solves the problem in existing technologies where scaling on the sensor protective sleeve causes a non-constant and gradually increasing thermal response delay. The analysis system still relies on fixed calibrated timing relationships to determine the temperature inflection point, resulting in a lag in inflection point detection time and consequently, a delay in heating control switching, making it easy for the actual temperature of the medium in the pipeline to exceed the target peak value. By dynamically identifying and distinguishing the sources of delay and adaptively correcting the delay caused by scaling, this application can provide more accurate data closer to the actual medium temperature, thereby achieving more precise temperature inflection point judgment and heating equipment switching control. Therefore, this method significantly improves the accuracy of temperature monitoring and the timeliness of control, avoiding production risks and energy losses caused by temperature overshoot or undershoot, and has significant practical value and technological advancement. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a temperature sensor detection data analysis method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another temperature sensor detection data analysis method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a temperature sensor detection data analysis system provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0022] The "temperature data curve" refers to the graph showing the temperature values ​​continuously collected by the temperature sensor over a period of time, reflecting the dynamic change in the medium temperature within the pipeline system. "Process operation status information" refers to various process parameters related to temperature changes within the pipeline system, such as the opening degree of the heating steam valve, the output power of the main heater, and the medium flow rate. This information helps determine the external causes of temperature changes. "Slope reaching its peak" means that the rate of change of the temperature data curve reaches its maximum at a certain point in time, usually corresponding to a stage of rapid temperature rise in the medium. "Delay in peak measurement time" means that the actual temperature peak measured by the sensor is later than the actual peak temperature of the medium. This delay may be caused by the sensor's own characteristics or external factors. "Sensor scaling" refers to the adhesion of an additional layer of material to the outer wall of the sensor's protective sleeve, which alters the sensor's thermal response characteristics and leads to measurement delay. "Process changes" refer to temperature changes caused by adjustments or anomalies in the production process, such as changes in heating power or fluctuations in medium flow rate.

[0023] The following specific embodiments will provide a detailed description and explanation of a temperature sensor detection data analysis method provided in this application.

[0024] Reference Figure 1 This invention provides a method for analyzing temperature sensor detection data, comprising the following steps: S1 acquires the temperature data curve of the pipeline system output by the temperature sensor, as well as the process operation status information related to temperature changes.

[0025] Temperature data curves can be acquired in various ways. For example, temperature sensors can directly convert analog signals into digital signals and transmit them to a data acquisition system via industrial Ethernet or fieldbus protocols (such as Modbus TCP, PROFIBUS, etc.). After receiving these digital signals, the data acquisition system can store them in a database and present them as temperature data curves in time-series format. Alternatively, wireless sensor network (WSN) technology can be used to transmit temperature sensor data wirelessly to a gateway, which then uploads the data to a cloud platform or local server to form temperature data curves. Process operating status information related to temperature changes, such as the opening degree of heating steam valves, the output power of the main heater, and the flow rate of media in the piping system, can be read in real time from a distributed control system (DCS) or programmable logic controller (PLC). These systems typically record and store various process parameters and can exchange data via communication protocols such as OPC UA and Modbus to obtain the required process operating status information.

[0026] S2. Based on the temperature data curve, determine whether the slope of the temperature data curve has reached its peak, and whether there is a delay in the measurement time when the peak is reached.

[0027] Specifically, determining whether the slope of the temperature data curve has reached its peak can be achieved by numerically differentiating the temperature data curve. For example, the finite difference method can be used to calculate the ratio of the temperature change between adjacent time points to the time interval, thus obtaining the instantaneous slope. Then, by comparing the slope values ​​at different time points, the maximum slope value is found, which is the peak value. Another method is to smooth the temperature data curve (e.g., using a moving average filter) to reduce noise interference, then calculate the slope of the smoothed curve and identify the peak value. Determining whether there is a delay in the measurement time of reaching the peak value can be done by comparing it with a reference time. For example, an ideal temperature rise model or reference curve can be pre-set, which can accurately reflect the true temperature change of the medium under clean conditions. When the slope of the actual measured temperature data curve reaches its peak value, the corresponding measurement time is recorded. Then, this measurement time is compared with the peak time predicted by the reference model or curve. If the measurement time is later than the reference time, a delay is considered to exist.

[0028] S3. When the slope of the temperature data curve reaches its peak and there is a delay in the measurement time to reach the peak, determine the source of the delay based on the process operation status information.

[0029] The sources include changes in the manufacturing process and scaling on sensors.

[0030] Specifically, based on the process operation status information, it can be determined whether the heat injected into the pipeline system has decreased; In the absence of heat attenuation in the injected pipeline system, the source of the delay was determined to be sensor scaling. When the heat injected into the piping system decays, the source of the delay is determined to be a change in the process.

[0031] Specifically, "determining whether there is a decrease in heat input to the pipeline system based on process operation status information" refers to assessing changes in heat input within the pipeline system by analyzing process operation status information related to temperature changes. For example, parameters such as the power output of heating equipment, the flow rate of the heating medium, and valve opening can be monitored to determine whether the heat received by the system remains stable or decreases. "Determining sensor scaling as the source of delay when there is no decrease in heat input to the pipeline system" means that if a delay is still observed in the peak slope of the temperature data curve even when it is confirmed that the heat input received by the pipeline system has not decreased, it can be inferred that the delay is caused by scaling on the sensor surface. Sensor scaling increases the resistance to heat transfer, causing the sensor response to slow down, thus exhibiting a measurement delay when the temperature changes. Furthermore, "determining process changes as the source of delay when there is a decrease in heat input to the pipeline system" means that when a decrease in heat input received by the pipeline system is detected, the observed delay in the peak slope of the temperature data curve is attributed to changes in the process itself. For example, changes in process parameters such as a reduction in heating power or a decrease in the flow rate of the heating medium will directly affect the rate of temperature rise within the pipeline system, thus leading to a delay in temperature response.

[0032] Thus, by combining the determination of the source of delay with the heat attenuation of the injected piping system, the causes of delay in temperature sensor data analysis can be effectively and accurately distinguished. When the slope of the temperature data curve reaches its peak and a measurement time delay exists, the process operating status information is first evaluated to determine whether heat input attenuation exists. If the heat input does not attenuate, the possibility of process changes causing the delay is ruled out, and the delay is attributed to sensor scaling, as scaling is a common cause of sensor response lag. Conversely, if heat input attenuates, it indicates that the process itself has changed, and the delay is reasonably attributed to this process change. This heat attenuation-based judgment mechanism makes the identification of the source of delay more accurate and reliable.

[0033] S4. If the source of the delay is sensor fouling, correct the temperature data curve.

[0034] Correcting temperature data curves can be achieved in several ways. For example, the additional thermal resistance and heat capacity can be estimated based on the degree of fouling on the sensor, and a dynamic model can be built to simulate the impact of fouling on the sensor response. This model can calculate the real-time time delay caused by fouling and apply it to the original temperature data curve, shifting the curve forward on the time axis to compensate for the delay, thus obtaining the corrected temperature data curve. Another approach is to use historical data and machine learning algorithms to train a predictive model that can predict the delay caused by fouling based on the current temperature data curve characteristics and process operating status information. The temperature data curve can then be corrected based on the predicted delay.

[0035] This application's temperature sensor detection data analysis method, by dynamically analyzing the slope peak and measurement time delay of the temperature data curve and combining it with process operation status information, can accurately identify measurement errors caused by sensor scaling, and thus correct the temperature data curve. Specifically, the method first acquires the temperature data curve of the pipeline system output by the temperature sensor, as well as process operation status information related to temperature changes. Then, based on the temperature data curve, it determines whether its slope has reached a peak value, and further determines whether there is a delay in the measurement time to reach the peak value. When a slope peak is detected and a measurement time delay exists, the system uses process operation status information, such as the opening degree of the heating steam valve, the output power of the main heater, and the medium flow rate, to distinguish whether the delay originates from process changes or sensor scaling. If it is determined that the delay is caused by sensor scaling, the temperature data curve is corrected in real time according to the degree of scaling to eliminate the measurement lag caused by scaling. In this way, this application can provide more accurate medium temperature data, thereby avoiding heating control switching lag and medium temperature overshoot problems caused by the failure of traditional fixed compensation methods.

[0036] The temperature sensor detection data analysis method proposed in this application has significant advantages and innovations compared to existing technologies. Traditional methods typically rely on constant response delay parameters obtained from factory calibration or initial testing to compensate for a fixed time advance in the waveform output by the sensor. However, this fixed compensation mechanism cannot adapt to the non-constant changes in the thermal dynamic characteristics of the sensor's protective sleeve caused by scaling. In actual chemical processes, as the scale layer gradually thickens, the actual thermal response delay will be significantly greater than the initial calibration value, and this delay will dynamically change with the rate of change of medium temperature and the degree of scaling. This makes it difficult for existing analysis systems to detect and adapt to this dynamic change in delay caused by the gradual change in the external physical state of the sleeve, resulting in a lag in the detection time of the temperature rise inflection point, causing a delay in the switching time of heating control, and making it easy for the actual temperature of the medium inside the pipeline to exceed the target peak value.

[0037] This application achieves real-time sensing and adaptive correction of sensor scaling by introducing dynamic judgment of the slope peak and measurement time delay of temperature data curves, combined with process operation status information to determine the source of delay. Specifically, this application no longer relies on a single fixed delay parameter, but identifies rapid temperature change phases by analyzing the dynamic characteristics of temperature data curves, such as slope peaks. Simultaneously, it dynamically assesses the existence of delay by comparing the actual measurement time with the reference time. More importantly, this application utilizes process operation status information related to temperature changes, such as the opening degree of heating steam valves, the output power of the main heater, and the medium flow rate, to intelligently distinguish whether the delay is caused by process changes or sensor scaling. This distinguishing ability is not available in existing technologies. Once it is determined that the delay originates from sensor scaling, this application can correct the temperature data curve in real time, effectively eliminating the measurement lag caused by scaling and providing more accurate medium temperature data. This dynamic and adaptive analysis and correction mechanism significantly improves the accuracy of temperature monitoring and the responsiveness of the control system, avoiding temperature overshoot and control errors caused by scaling, and providing strong protection for the safe and stable operation of chemical production.

[0038] like Figure 2 As shown, the above determination of whether the slope of the temperature data curve has reached its peak based on the temperature data curve includes: S101. Based on a preset sliding time window, determine the slope of the temperature data curve in different time windows.

[0039] The preset sliding time window refers to a fixed-length time period defined on the temperature data curve, which slides forward along the time axis. By analyzing the temperature data within each sliding time window, the slope of the temperature data curve within that window can be calculated. Specifically, the length of the sliding time window can be set according to the actual application scenario and the required detection accuracy, for example, it can be set to several seconds to several minutes. Its purpose is to smooth out data noise and capture the trend of local temperature changes.

[0040] Furthermore, determining the slope of the temperature data curve across different time windows can be achieved by performing linear regression analysis on the data points within each sliding time window, or by approximating it by calculating the ratio of temperature change to time change within the window. This yields a series of slope values ​​representing the rate of temperature change over different time periods.

[0041] S102. Based on the slope of the temperature data curve in different time windows, determine whether the slope of the temperature data curve has reached its peak.

[0042] For example, the calculated slope value can be compared with a preset threshold. When the slope value exceeds the threshold and then begins to decrease, the slope can be considered to have reached its peak. Alternatively, the first or second derivative of the slope sequence can be analyzed. When the first derivative is close to zero and the second derivative is negative, it is identified as a peak point. The aim is to accurately identify the point of fastest change during the temperature rise, which typically corresponds to critical transition moments in the process.

[0043] The proposed solution introduces a preset sliding time window and calculates the slope of the temperature data curve within different time windows. This effectively smooths the original temperature data locally, reducing the impact of instantaneous noise on the slope calculation. By analyzing these local slopes, the peak points of the temperature data curve's slope can be identified more accurately. This method avoids the errors and instabilities that may arise from directly performing global differentiation on the original data, making peak detection more robust and reliable.

[0044] Specifically, in the above-mentioned temperature sensor detection data analysis method, the step of determining whether there is a delay in the measurement time to reach the peak value can be further refined into the following operations: S201. Obtain the reference heating time and the start time corresponding to the temperature data curve.

[0045] This duration can be preset based on historical data, theoretical models, or experimental results. The start time corresponding to the temperature data curve usually refers to the moment when the heating process begins or the temperature begins to rise significantly. The measurement time refers to the moment when the temperature data curve actually detected by the temperature sensor reaches its peak.

[0046] S202. Based on the start time and the reference heating time, determine the reference time to reach the peak value.

[0047] Specifically, after obtaining the reference heating time and the start time of the temperature data curve, the reference time when the temperature will theoretically or expected to reach its peak can be calculated by adding the start time to the reference heating time.

[0048] S203. Based on the reference time and measurement time, determine whether there is a delay in the measurement time.

[0049] Compare this reference time with the actual measured time when the temperature reaches its peak. If there is a significant difference between the two, such as the measurement time reaching a preset threshold later than the reference time, it can be determined that there is a delay in the measurement time when the peak temperature is reached.

[0050] This application's solution establishes a benchmark—the reference time to reach the peak temperature—by introducing a reference heating time and start time. By comparing the actual measured peak time with this benchmark, the existence of abnormal delays during the temperature rise process can be objectively and quantitatively assessed. This comparison mechanism enables the system to identify hysteresis in the temperature response, which is often caused by factors such as sensor fouling or changes in the process. Through explicit calculation and comparison, the inaccuracies that may arise from relying solely on experience can be avoided, providing a reliable basis for subsequent fault diagnosis and correction.

[0051] In some embodiments described above in this application, a method is proposed to determine whether there is heat attenuation in the injection piping system based on process operating status information. Specifically, the steps for determining whether there is heat attenuation in the injection piping system include: S301. Based on the process operation status information, determine whether the first preset condition is met.

[0052] The first preset condition is that the opening of the heating steam valve has not decreased, the output power of the main heater has not decreased, and the rate of change of the medium flow rate in the pipeline system is less than the rate of change threshold.

[0053] The fact that the opening of the heating steam valve did not decrease indicates that the supply of heating steam did not decrease, thus ensuring a continuous and stable input of heat. The output power of the main heater did not decrease, further confirming that the heating capacity of the main heating equipment did not decline. The rate of change of the medium flow rate within the pipeline system is less than the rate of change threshold, indicating that the flow state of the fluid within the pipeline is relatively stable, without drastic flow fluctuations, thus avoiding changes in heat transfer efficiency due to flow rate variations. The aforementioned rate of change threshold can be set according to specific process requirements and system characteristics; for example, it can be set to a relative change in flow rate within a certain time window not exceeding 5%.

[0054] S302. Under the condition that the first preset condition is met, it is determined that there is no heat loss in the injected pipeline system.

[0055] S303. If the first preset condition is not met, it is determined that the heat injected into the pipeline system is attenuated.

[0056] The solution proposed in this application can effectively determine whether there is heat attenuation in the injected pipeline system by setting and detecting a first preset condition. When the opening degree of the heating steam valve, the output power of the main heater, and the rate of change of the medium flow are all in a stable state, i.e., when the first preset condition is met, it can be reasonably inferred that the heat input received by the pipeline system is stable and there is no attenuation. Conversely, if any condition is not met, it indicates that there may be attenuation in the heat input, which helps to distinguish whether the delay is caused by changes in the process or by sensor scaling. This method based on multi-parameter comprehensive judgment improves the accuracy of heat attenuation judgment.

[0057] In some embodiments described above, when the source of the delay is determined to be sensor fouling, the temperature data curve needs to be corrected. However, in practical applications, sensor fouling can alter the sensor's thermal response characteristics, reducing its response speed and accuracy to temperature changes. Simple corrections may not adequately compensate for the thermal response delay and distortion caused by fouling, thus affecting the accuracy of subsequent temperature-based decision-making and control. Therefore, this application proposes a more accurate temperature data curve correction method that dynamically evaluates the sensor's current thermal response characteristics and calculates timing correction amounts to more effectively eliminate the impact of fouling on temperature data.

[0058] In the above-mentioned temperature sensor detection data analysis method, correcting the temperature data curve includes: S401. Determine the current thermal response characteristic parameters of the sensor.

[0059] Specifically, determining the current thermal response characteristics of a sensor involves analyzing its response behavior during actual operation to quantify its thermal conduction and thermal response performance under current fouling conditions. These parameters may include, but are not limited to, the sensor's thermal time constant, equivalent thermal conductivity, or thermal resistance, reflecting the sensor's response speed and accuracy to changes in ambient temperature. The aim is to obtain the sensor's true dynamic response characteristics under the influence of fouling, providing a basis for subsequent precise corrections.

[0060] S402. Calculate the timing correction amount based on the current thermal response characteristic parameters.

[0061] The calculation of the timing correction based on the current thermal response characteristics can be understood as deriving the necessary time-dimensional adjustment to the original temperature data curve using a mathematical model or algorithm, based on the determined current thermal response characteristics of the sensor. This timing correction aims to compensate for thermal response delays and signal distortions caused by sensor fouling, enabling the corrected data to more accurately reflect the actual temperature changes within the piping system. For example, an inverse system model or a transfer function-based inversion method can be used, taking the sensor's current response characteristics as input, to calculate the timing adjustment required to "push forward" or "reshape" the original measurement data to eliminate delays and distortions.

[0062] S403. Correct the temperature data curve based on the timing correction amount.

[0063] In practical applications, correcting temperature data curves based on time-series corrections specifically refers to applying the calculated time-series corrections to the original temperature data curves. For example, this can be achieved through time-axis shifting, data point interpolation or resampling, or by applying specific digital filters to adjust the temperature data curves to synchronize them with actual temperature changes in time and eliminate signal attenuation or distortion caused by scaling. The goal is to obtain a temperature data curve that more closely approximates the actual temperature changes within the piping system, compensated for by scaling effects, thereby improving the accuracy of subsequent analysis and control.

[0064] The proposed solution first dynamically determines the current thermal response characteristic parameters of the sensor, enabling real-time capture of changes in the sensor's thermal conductivity caused by scaling. This dynamic assessment allows subsequent time-series corrections to accurately reflect the impact of the current scaling level on the sensor response. By applying this precisely calculated time-series correction to the original temperature data curve, time delays and signal distortion caused by scaling can be effectively compensated, overcoming the limitations of traditional simple correction methods in adequately addressing dynamic sensor changes. This correction mechanism based on real-time thermal response characteristic parameters ensures that even with continuously changing scaling levels, the obtained temperature data curve more accurately reflects the true temperature within the piping system, providing a reliable data foundation for subsequent process control and fault diagnosis.

[0065] In some preferred embodiments, a specific example is given below. Suppose that in a piping system, a temperature sensor is used to monitor the temperature of the medium. As operating time increases, scale gradually forms on the sensor surface, causing its response to temperature changes to become sluggish. When the system detects that the slope of the temperature data curve has reached a peak, and there is a delay in the measurement time to reach the peak, and determines that the source of the delay is sensor scaling, the correction method of this application is initiated.

[0066] Specifically, firstly, the system analyzes the temperature response data of the temperature sensor over a recent period, for example, the actual response curve output by the sensor when a known temperature step change or ramp-up occurs. By fitting or inverting these response curves, the current thermal response characteristic parameters of the sensor can be determined, such as its equivalent thermal time constant or thermal conductivity. Assume that the calculated current thermal time constant has increased from T0 in the initial clean state to T1.

[0067] Next, based on this current thermal time constant T1, the system calculates a timing correction. For example, an inverse filter can be constructed to compensate for the signal delay and attenuation caused by the thermal time constant T1. This timing correction can be expressed as shifting the currently measured temperature data point forward by a specific time interval Δt on the time axis, or by reshaping the data through a digital signal processing algorithm.

[0068] Finally, based on the calculated time-series correction, the original temperature data curve is corrected. For example, if the original data point is (t_i, Temp_i), the corrected data point might become (t_i - Δt, Temp_i), or an inverse filter could be applied to make the corrected curve reflect the actual temperature changes within the piping system more quickly and accurately. In this way, even if the sensor has scale buildup, its output temperature data can be effectively calibrated, thus providing reliable temperature information.

[0069] Specifically, the parameters for determining the current thermal response characteristics of the sensor include: S501. Identify and extract multiple local segments with different slopes and curvature characteristics in the temperature data curve.

[0070] Identifying and extracting multiple local segments with different slopes and curvatures from the temperature data curve refers to the detailed division of temperature data from the sensor under different operating conditions or response stages. These local segments may correspond to different dynamic response processes such as rapid temperature rise, slow temperature rise, stabilization, or temperature drop. By analyzing the slope (i.e., the rate of temperature change) and curvature (i.e., the rate of change of the rate of temperature change) of these segments, the dynamic response characteristics of the sensor to temperature changes can be captured. For example, during heating or cooling processes, the temperature data curve will exhibit different slopes and curvatures, and these characteristics reflect the thermal conduction and convection states of the sensor and its surrounding medium.

[0071] S502. For each of the multiple local segments, using the temperature change rate and second-order change rate information of the local segment, combined with the duration of the local segment, the equivalent heat conduction parameters corresponding to the local segment are estimated through an iterative optimization process.

[0072] Specifically, the rate of temperature change (first derivative) reflects how quickly temperature changes over time, while the second rate of temperature change (second derivative) describes the rate of temperature change, i.e., the curvature of the curve. This information, along with the duration of the local segment, constitutes key data describing the sensor's thermal response characteristics. The iterative optimization process can employ least squares, gradient descent, or other numerical optimization algorithms. It compares the sensor's actual response in the current local segment with the response predicted based on a certain heat conduction model, continuously adjusting the model parameters until the error between the two is minimized, thereby estimating the equivalent heat conduction parameters that best represent the heat conduction characteristics of that local segment. These equivalent heat conduction parameters may include, but are not limited to, heat capacity, thermal resistance, and heat transfer coefficient, which comprehensively reflect the heat exchange efficiency between the sensor probe, the scale layer, and the surrounding medium.

[0073] S503. The equivalent thermal conduction parameters are determined as the current thermal response characteristic parameters of the sensor.

[0074] This means that by carefully analyzing and estimating the temperature data curves, one or a set of parameters can be obtained that can accurately characterize the current thermal response state of the sensor. These parameters can reflect whether the sensor is affected by factors such as scaling, thus providing a basis for subsequent temperature data correction.

[0075] This application's solution involves refined analysis of temperature data curves to identify and extract local segments with different dynamic characteristics. Equivalent heat transfer parameters are then estimated for each of these segments, enabling a more accurate capture of the sensor's thermal response characteristics under various operating conditions. Traditional methods may rely solely on the overall curve or a few points for analysis, failing to comprehensively reflect the sensor's dynamic behavior under complex conditions such as scaling. By utilizing temperature change rate and second-order change rate information, combined with duration for iterative optimization, the sensor's current heat transfer state can be inferred from the dynamic response. This method effectively distinguishes the impact of changes in sensor characteristics (such as increased thermal resistance due to scaling) on ​​temperature data from changes in the manufacturing process, providing more accurate input for subsequent time-series correction calculations and ensuring the accuracy and reliability of the correction.

[0076] In some embodiments described above in this application, a method is proposed to determine the current thermal response characteristics of a sensor by identifying multiple local segments in a temperature data curve and estimating the equivalent heat conduction parameters for each segment. However, in its implementation, these equivalent heat conduction parameters estimated from different local segments may exhibit some fluctuations or inconsistencies. Directly calculating the time-series correction based on a single or unprocessed estimate may lead to insufficient accuracy and robustness of the correction results, thereby affecting the accuracy of the final temperature data curve correction.

[0077] In response, this application further proposes that the current thermal response characteristic parameters include equivalent heat conduction parameters, and that time-series corrections are calculated based on the current thermal response characteristic parameters, including: S601, Weighted fusion of the estimated equivalent heat conduction parameters for multiple local sections.

[0078] Specifically, weighted fusion of equivalent heat conduction parameters estimated from multiple local segments refers to combining the equivalent heat conduction parameters estimated separately from multiple local segments identified from the temperature data curve according to preset weights. These weights can be dynamically or statically allocated based on factors such as the reliability of the local segment, data quality, duration, or degree of conformity with the overall trend. The aim is to reduce the impact of estimation errors in individual local segments and improve the accuracy and stability of the overall parameter estimation.

[0079] S602. Compare the fused equivalent thermal conductivity parameters with the baseline parameters under the initial clean state, and calculate the drift of the thermal conductivity parameters.

[0080] The process involves comparing the fused equivalent thermal conductivity parameters with the baseline parameters under initial clean conditions to calculate the drift of the thermal conductivity parameters. This can be understood as comparing the current equivalent thermal conductivity parameters, after weighted fusion, with the baseline parameters representing ideal thermal response characteristics, measured by the sensor in a brand-new or scale-free state. This comparison quantifies the degree of change in the sensor's thermal response characteristics relative to its initial state, i.e., the drift, and aims to accurately identify and quantify the thermal response delay caused by factors such as scaling. The baseline parameters under initial clean conditions can be obtained from sensor factory calibration data, historical scale-free operation data, or theoretical models.

[0081] S603. Calculate the timing correction amount based on the drift amount.

[0082] In practical applications, the timing correction is calculated based on the drift amount. Specifically, the drift amount of the quantified thermal conductivity parameter is converted into the time offset required to correct the temperature data curve through a preset mathematical model or mapping relationship. For example, the larger the drift amount, the more severe the sensor's thermal response delay, and the greater the required timing correction amount. The purpose is to provide a precise basis for subsequent temperature data curve correction.

[0083] This application's solution effectively integrates information from different data segments by weighted fusion of equivalent heat conduction parameters estimated from multiple local segments. This smooths out potential noise and uncertainty in individual segment estimations, improving the accuracy and robustness of current thermal response parameter estimations. Furthermore, by comparing the fused parameters with baseline parameters under initial clean conditions, the degree of drift in the sensor's thermal response characteristics can be precisely quantified. This drift directly reflects the actual delay caused by factors such as sensor fouling. It is precisely this accurate drift calculation that allows for the accurate determination of subsequent time-series corrections, providing a reliable basis for correcting temperature data curves and effectively solving the problem of insufficient correction accuracy that may result from directly using unprocessed estimates.

[0084] In some preferred embodiments, this application is implemented as follows: Assume that when determining the current thermal response characteristic parameters of the sensor, three local segments are identified and extracted from the temperature data curve, and three equivalent heat conduction parameters E1, E2, and E3 are estimated respectively. To improve the reliability of the parameters, these three parameters can be weighted and fused. For example, different weights, such as W1, W2, and W3, can be assigned according to the data quality or duration of each local segment, where W1 + W2 + W3 = 1. The fused equivalent heat conduction parameter E_fused = W1*E1 + W2*E2 + W3*E3.

[0085] The fused parameter E_fused is then compared with the baseline parameter E_baseline measured by the sensor in its initial clean state. For example, if E_baseline is 1.0 and E_fused is 0.8, the drift ΔE of the thermal conductivity parameter is E_baseline - E_fused = 0.2. This drift ΔE reflects the degree of degradation in the sensor's thermal response capability.

[0086] Finally, based on the calculated drift ΔE, the timing correction ΔT is calculated using a pre-established mapping model. For example, a linear relationship can be established: ΔT = k * ΔE + c, where k and c are coefficients determined based on experimental data or experience. If the calculated timing correction ΔT is 5 seconds, it means that the temperature data curve output by the current sensor needs to be shifted forward by 5 seconds to reflect the true temperature change. In this way, response delays caused by factors such as sensor fouling can be accurately compensated, ensuring the accuracy of the temperature data.

[0087] In some embodiments described above, by analyzing temperature sensor data, measurement delays caused by sensor scaling can be identified and corrected, thereby obtaining more accurate temperature data curves within the piping system. However, in actual industrial production processes, simply obtaining corrected temperature data curves may not be sufficient to directly guide subsequent process operations, such as the precise control of heating equipment. Failure to utilize this corrected data in a timely and effective manner to adjust process parameters may lead to energy waste or product quality fluctuations.

[0088] In response, this application further proposes a method for analyzing temperature sensor detection data, the method further including: S701. Based on the corrected temperature data curve, determine the temperature inflection point time within the pipeline system.

[0089] Specifically, after obtaining the corrected temperature data curve, this curve is used to accurately identify the temperature inflection point time within the piping system. The temperature inflection point time refers to the moment when the temperature change trend of the medium within the piping system undergoes a significant change. For example, when the medium reaches a specific phase transition temperature, reaction initiation temperature, or target process temperature, its heating rate may change significantly, thus appearing as an inflection point on the temperature data curve. This inflection point time can be determined through mathematical analysis of the corrected temperature data curve, such as calculating its first or second derivative and identifying the extreme points or zero points of the rate of change of the derivative.

[0090] S702. Based on the temperature inflection point time, perform the switching action of the heating equipment.

[0091] Switching actions for heating equipment may include, but are not limited to, adjusting heating power, turning auxiliary heaters on or off, switching heating modes (e.g., switching from preheating mode to constant temperature mode), or performing other corresponding control operations according to process requirements. For example, when the temperature curve shows that the medium has reached the preset reaction initiation temperature inflection point, the heating equipment can be triggered to reduce power to maintain constant temperature, or switch to heat preservation mode.

[0092] This application's solution directly applies corrected temperature data curves, which accurately reflect the actual temperature changes within the piping system, to the identification and control of key process parameters. This solves the problem in traditional solutions where corrected data fails to effectively guide subsequent process operations. Specifically, by precisely determining the temperature inflection point time, key state changes occurring in the medium during heating can be captured, such as phase transitions, reaction initiation, or reaching the target temperature. Because these inflection point times are based on real and time-free temperature data, subsequent switching actions on heating equipment can respond more promptly and accurately to process requirements, avoiding misjudgments and improper control caused by measurement errors due to sensor scaling.

[0093] In some preferred embodiments, a specific example is given below. Suppose that in a chemical production process, a certain medium within a pipeline system needs to be heated from room temperature to 150°C and maintained at 150°C for a period of time to complete a specific reaction. Traditionally, operators switch the power of the heating equipment based on experience or preset times. However, if the temperature sensor is scaled, its output temperature data will lag behind the actual temperature, causing the operator to switch the heating equipment too early or too late, thereby affecting the reaction effect or causing energy waste.

[0094] The proposed solution first obtains and corrects the temperature data curve output by the temperature sensor using the aforementioned method, eliminating the delay caused by scaling. Subsequently, based on this corrected temperature data curve, the system accurately identifies the temperature inflection point when the medium actually reaches 150°C. For example, when the slope of the temperature curve changes significantly near 150°C, indicating that the medium has entered the isothermal stage, this moment is determined as the temperature inflection point. Once this inflection point is determined, the system immediately performs a switching action on the heating equipment, such as reducing the output power of the main heater from full power to the heat preservation power required to maintain 150°C, or shutting down some heating units, to ensure that the medium enters the isothermal state at a precise moment, thereby optimizing the reaction process and improving product yield and energy efficiency.

[0095] like Figure 3 As shown in the figure, this embodiment of the invention also provides a temperature sensor detection data analysis system. The system includes: The data acquisition module is used to acquire temperature data curves within the pipeline system output by the temperature sensor, as well as process operation status information related to temperature changes. The data processing module is used to determine whether the slope of the temperature data curve has reached its peak value, and whether there is a delay in the measurement time when the peak value is reached, based on the temperature data curve. The source determination module is used to determine the source of the delay when the slope of the temperature data curve reaches its peak and there is a delay in the measurement time of reaching the peak, based on the process operation status information. The sources include process changes and sensor scaling. The correction module is used to correct the temperature data curve when the source of the delay is sensor fouling.

[0096] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0097] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0099] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for analyzing temperature sensor detection data, characterized in that, Includes the following steps: Acquire temperature data curves within the piping system output by temperature sensors, as well as process operation status information related to temperature changes; Based on the temperature data curve, determine whether the slope of the temperature data curve has reached its peak value, and whether there is a delay in the measurement time when the peak value is reached; When the slope of the temperature data curve reaches its peak and there is a delay in the measurement time of reaching the peak, the source of the delay is determined based on the process operation status information; the source includes process changes and sensor scaling. If the source of the delay is sensor fouling, the temperature data curve is corrected.

2. The method for analyzing temperature sensor detection data according to claim 1, characterized in that, The step of determining whether the slope of the temperature data curve has reached a peak value based on the temperature data curve includes: Based on a preset sliding time window, the slope of the temperature data curve is determined in different time windows; Based on the slope of the temperature data curve at different time windows, determine whether the slope of the temperature data curve has reached its peak.

3. The method for analyzing temperature sensor detection data according to claim 1, characterized in that, Determine if there is a delay in the measurement time to reach the peak value, including: Obtain the reference heating time and the start time corresponding to the temperature data curve; Based on the start time and the reference heating duration, a reference time for reaching the peak value is determined; Based on the reference time and the measurement time, determine whether there is a delay in the measurement time.

4. The method for analyzing temperature sensor detection data according to claim 1, characterized in that, The step of determining the source of the delay based on the process operation status information includes: Based on the process operation status information, determine whether there is any heat loss in the injected pipeline system; In the absence of heat attenuation in the injected pipeline system, the source of the delay was determined to be sensor scaling. When the heat injected into the piping system is attenuated, the source of the delay is determined to be a change in the process.

5. The method for analyzing temperature sensor detection data according to claim 4, characterized in that, The step of determining whether there is heat attenuation in the injected pipeline system based on the process operation status information includes: Based on the process operation status information, determine whether the first preset condition is met; the first preset condition is that the opening of the heating steam valve has not decreased, the output power of the main heater has not decreased, and the rate of change of the medium flow rate in the pipeline system is less than the rate of change threshold. Under the condition that the first preset condition is met, it is determined that there is no heat loss in the injected pipeline system; If the first preset condition is not met, it is determined that the heat in the injected pipeline system is attenuated.

6. The method for analyzing temperature sensor detection data according to claim 1, characterized in that, The correction of the temperature data curve includes: Determine the current thermal response characteristics of the sensor; Calculate the timing correction amount based on the current thermal response characteristic parameters; The temperature data curve is corrected based on the time-series correction amount.

7. The method for analyzing temperature sensor detection data according to claim 6, characterized in that, Determining the current thermal response characteristic parameters of the sensor includes: Identify and extract multiple local segments with different slopes and curvature characteristics from the temperature data curve; For each of the multiple local segments, the equivalent heat conduction parameters corresponding to the local segment are estimated through an iterative optimization process by using the temperature change rate and second-order change rate information of the local segment, combined with the duration of the local segment. The equivalent thermal conduction parameter is determined as the current thermal response characteristic parameter of the sensor.

8. The method for analyzing temperature sensor detection data according to claim 7, characterized in that, The current thermal response characteristic parameters include equivalent heat conduction parameters, and the calculation of the time-series correction based on the current thermal response characteristic parameters includes: The equivalent heat conduction parameters estimated from multiple local sections are weighted and fused. The equivalent thermal conductivity parameters after fusion are compared with the baseline parameters under the initial clean state, and the drift of the thermal conductivity parameters is calculated. The timing correction amount is calculated based on the drift amount.

9. The method for analyzing temperature sensor detection data according to claim 1, characterized in that, The method further includes: Based on the corrected temperature data curves, determine the temperature inflection point time within the piping system; Based on the temperature inflection point time, the switching action of the heating equipment is executed.

10. A temperature sensor detection data analysis system, characterized in that, The system includes: The data acquisition module is used to acquire temperature data curves within the pipeline system output by the temperature sensor, as well as process operation status information related to temperature changes. The data processing module is used to determine, based on the temperature data curve, whether the slope of the temperature data curve has reached its peak value, and whether there is a delay in the measurement time of reaching the peak value. The source determination module is used to determine the source of the delay based on the process operation status information when the slope of the temperature data curve reaches a peak and there is a delay in the measurement time of reaching the peak; the source includes process changes and sensor scaling; A correction module is used to correct the temperature data curve if the source of the delay is sensor fouling.

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