Self-adaptive temperature control method

Abstract

The invention relates to the technical field of temperature control, in particular to a self-adaptive temperature control method, which comprises the following steps of: acquiring a high-precision snapshot of thermal characteristics of a target object at the moment of power failure when a power failure early warning signal is sent; determining a temperature control parameter for re-evaluating the target object based on the downtime of the target device and / or the abnormal fluctuation of the heat flow change curve of the target object within a preset time before power failure; determining a temperature control parameter evaluation method based on the downtime of the target device and / or the abnormal fluctuation of the key component content of the target object in the power failure process; and based on whether a target object bonding phenomenon occurs in the temperature control process and / or whether a target object temperature change rate abnormal phenomenon occurs, re-evaluation standard parameters aiming at the temperature control parameters are determined for adjustment. According to the invention, the accuracy of analyzing the power failure downtime process of the target device is improved, so that the accuracy of temperature control during subsequent power supply is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to an adaptive temperature control method. Background Art

[0002] The power supply in industrial production environments is not always stable and reliable, and factory power outages occur frequently, which poses a huge challenge to the ongoing temperature control process. After a short power outage, the temperature, thermal state, and composition characteristics of the target object may undergo subtle changes. Traditional temperature control methods often lack effective response mechanisms for such emergencies. After power is restored, if the temperature control parameters before the power outage are still used, it is likely that the target object's temperature requirements will not be met, and the target object may even be damaged.

[0003] For example, Chinese patent application publication number CN107065967A discloses a method and an automatic control system for automatic temperature control of a dryer, wherein the method includes periodically collecting the conveying speed inside the dryer and the temperature of the heat source supplying heat to the dryer, thereby obtaining a detection speed and a detection temperature; comparing the above detection speed and detection temperature according to preset speed-temperature matching information, and if the detection temperature does not match the detection speed, an alarm is issued; if the detection temperature and detection speed continue to mismatch for a preset time period, the execution component is automatically controlled to match the detection temperature with the detection speed, and the temperature of the heat source inside the dryer is automatically controlled according to the preset speed-temperature matching information based on the conveying speed of the gypsum board inside the dryer, so that the temperature matches the speed, thereby achieving the purpose of improving the production quality of the gypsum board.

[0004] However, the existing technology has the problem that when the equipment stops due to power outage, the accuracy of analyzing the characteristics of the target object during the downtime and the downtime duration is low, resulting in inaccurate temperature control process during subsequent power supply. Summary of the Invention

[0005] To this end, the present invention provides an adaptive temperature control method to overcome the problem in the prior art that when the equipment shuts down due to power outage, the accuracy of the analysis of the target object characteristics and the downtime duration during the downtime process is low, resulting in inaccurate temperature control during subsequent power supply.

[0006] To achieve the above object, the present invention provides an adaptive temperature control method, comprising:

[0007] Acquiring target object characteristic data and power system data, wherein the target object characteristic data includes heat flow change curve data of the target object and component content data of the target object;

[0008] When a power outage warning signal is issued, data is collected at the highest sensor data feedback frequency, and the heat flow change curve of the target object is quickly scanned to obtain a high-precision snapshot of the thermal characteristics of the target object at the moment of power outage;

[0009] Re-evaluating the temperature control parameters of the target object based on the downtime of the target device and / or the abnormal fluctuation of the heat flow change curve of the target object within a preset time before the power outage;

[0010] Determine a temperature control parameter evaluation method based on the downtime of the target device and / or abnormal fluctuations in key component content of the target object during the power outage;

[0011] Based on whether a sticking phenomenon of the target object occurs during the temperature control process and / or whether an abnormal temperature change rate phenomenon of the target object occurs, a re-evaluation standard parameter for the temperature control parameter is determined and adjusted.

[0012] Furthermore, determining to re-evaluate the temperature control parameter of the target object includes:

[0013] If the downtime of the target device is longer than the first preset downtime or the heat flow change curve of the target object within the preset time before the power outage has abnormal fluctuations, it is determined to re-evaluate the temperature control parameters of the target object.

[0014] Furthermore, it is determined that the heat flow change curve of the target object within the preset time before the power outage has abnormal fluctuations, including a sudden change in slope of the heat flow change curve of the target object displayed by the high-precision snapshot.

[0015] Furthermore, determining the temperature control parameter evaluation method includes:

[0016] If the downtime of the target device is less than the second preset downtime, determining that the temperature control parameter evaluation method is a historical experience fine-tuning method;

[0017] If the downtime of the target device is greater than or equal to the second preset downtime and the target object has abnormal fluctuations in the content of key components during the power outage, determining that the temperature control parameter evaluation method is a heat flow combined with component comprehensive evaluation method;

[0018] If the downtime of the target device is greater than or equal to the second preset downtime and there is no abnormal fluctuation in the content of key components of the target object during the power outage, the temperature control parameter evaluation method is determined to be a heat flow-dominated adjustment method.

[0019] Furthermore, determining that the target object has abnormal fluctuation in key component content during the power outage includes that the similarity between the key component content of the target object displayed by the high-precision snapshot and the key component content of the target object at the moment power supply is started is less than a preset similarity.

[0020] Furthermore, calculating the similarity between the key component content of the target object displayed by the high-precision snapshot and the key component content of the target object at the moment of power supply start includes:

[0021] Obtain key component content data of the target object displayed as a high-precision snapshot and record it as power outage data;

[0022] Obtain key component content data of the target object at the moment power supply starts, recorded as power supply data;

[0023] Standardize power outage data and power supply data;

[0024] The cosine similarity formula is used to calculate the similarity between power outage data and power supply data.

[0025] Furthermore, the preset similarity is determined based on the average similarity of the key component content before and after the power outage when the difference in power outage duration during the historical temperature control process of the same type of target object is less than 10 seconds.

[0026] Furthermore, determining the adjustment of the re-evaluation standard parameters for the temperature control parameters includes:

[0027] If the target object appears to be sticking during the subsequent temperature control process, it is determined that the preset similarity is adjusted;

[0028] If an abnormal temperature change rate of the target object occurs during the subsequent temperature control process, it is determined that the first preset downtime duration is adjusted.

[0029] Further, determining that an abnormal temperature change rate phenomenon of the target object occurs includes that a deviation value between an actual temperature change rate of the target object and a predicted temperature change rate is greater than a preset deviation value.

[0030] Furthermore, the adjustment amount of the preset similarity is positively correlated with the proportion of the agglomeration area when the target object sticks, and the adjustment amount of the first preset downtime is negatively correlated with the deviation value between the actual temperature change rate of the target object and the predicted temperature change rate obtained by the time series analysis method.

[0031] Compared with the existing technology, the beneficial effect of the present invention is that when the power outage warning signal is issued, the present invention collects data at the highest sensor data feedback frequency and quickly scans the heat flow change curve, which can obtain a high-precision snapshot of the thermal characteristics of the target object at the moment of power outage, and accurately record key information such as the heat distribution and heat transfer trend inside the target object at the moment of power outage. Since the temperature and thermal state of the target object will change rapidly after the power outage due to factors such as heat loss and internal thermal balance disruption, capturing this instantaneous state in advance provides a reliable benchmark for subsequent analysis of changes in the target object during the power outage.

[0032] Furthermore, the present invention determines that there is no need to re-evaluate the temperature control parameters when the downtime of the target device is less than or equal to the first preset downtime and the target object has no abnormal fluctuations within the preset time before the power outage, effectively avoiding blindly performing complex and unnecessary parameter adjustments when the target object is minimally affected by the power outage, reducing equipment start-up and shutdown, energy consumption and time waste caused by frequent parameter adjustments, and ensuring production efficiency. When the downtime of the target device is greater than the first preset downtime or the heat flow change curve of the target object has abnormal fluctuations within the preset time before the power outage, it is determined to re-evaluate the temperature control parameters. After a long power outage, the target When heat is lost and the temperature is unevenly distributed inside the object, re-evaluation of parameters can be used to adjust the heating strategy in a targeted manner, such as using segmented slow heating instead of the original rapid heating to prevent the target object from generating stress, deformation or even damage due to sudden temperature changes; if the slope of the heat flow change curve before the power outage suddenly changes, it indicates that the thermal characteristics of the target object have changed. Re-evaluation can optimize the heat transfer method based on the new heat flow trend to ensure that the target object is heated evenly and accurately adapt to the state of the target object. The above method improves the accuracy of the analysis of the target object characteristics and the downtime duration during the downtime due to power outage, thereby improving the accuracy of the temperature control process during subsequent power supply.

[0033] Furthermore, the present invention adopts a historical experience fine-tuning method when the downtime of the target object within the preset time before the power outage is less than the second preset downtime, because a shorter power outage usually has limited impact on the characteristics of the target object, and the internal thermal and chemical state of the target object changes less. At this time, with the help of a large amount of temperature control data of the same type of target objects under similar working conditions in the past, the current temperature control parameters can be quickly and accurately adjusted in small amounts. When the downtime of the target object within the preset time before the power outage is greater than or equal to the second preset downtime and there is an abnormal fluctuation in the content of key components during the power outage, on the one hand, from the perspective of heat flow, the long power outage causes the thermal balance of the target object to be broken, and the slope, peak value and other characteristics of the heat flow change curve change, which reflect the changes in the heat transfer path and rate inside the target object, such as the heat flow. Unevenness may cause local overheating or overcooling. On the other hand, abnormal fluctuations in the content of key components mean that the chemical composition of the target object has changed. When the downtime of the target object within the preset time before the power outage is greater than or equal to the second preset downtime and there is no abnormal fluctuation in the content of key components during the power outage, the target object is mainly affected by the power outage duration and its thermal properties change, and its chemical composition is relatively stable. By in-depth analysis of the heat flow change curve, such as a sudden change in the heat flow slope indicating that heat conduction is blocked, the heating strategy is adjusted in a targeted manner, the heating is suspended and slowly restarted after the heat flow returns to stability, or the stirring rate is adjusted to improve the uniformity of heat transfer. The above method improves the accuracy of the analysis of the target object characteristics and downtime duration during the downtime process when the equipment is down due to power outage, thereby improving the accuracy of the temperature control process during subsequent power supply.

[0034] Furthermore, in the present invention, when the target object sticks together during the subsequent temperature control process, the preset similarity is adjusted. The sticking of the target object will destroy the originally uniform distribution of components inside the target object, resulting in abnormal local component concentration. By adjusting the preset similarity in a manner that is positively correlated with the proportion of the agglomeration area, that is, the larger the agglomeration area, the larger the preset similarity adjustment amount, the more accurately the degree of deterioration of the target object characteristics can be reflected. When the target object temperature change rate is abnormal, it is very important to adjust the first preset downtime duration. If the target object temperature change rate deviates from the normal range, it indicates that the heat transfer inside the target object is obstructed, which may be due to target object agglomeration, heat flow turbulence, etc. At this time, the first preset downtime duration is adjusted according to the principle of negative correlation with the deviation value of the target object temperature change rate. The larger the deviation value, the shorter the duration, thereby ensuring the stability of raw material quality. The above method improves the accuracy of the analysis of the target object characteristics and downtime duration during the downtime process when the equipment is down due to power outage, thereby improving the accuracy of the temperature control process during subsequent power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flowchart of the adaptive temperature control method according to an embodiment of the present invention;

[0036] Figure 2 This is a flowchart of a process for calculating the similarity between the key component content of a target object displayed by a high-precision snapshot and the key component content of the target object at the moment power is started in the adaptive temperature control method according to an embodiment of the present invention;

[0037] Figure 3 This is a flowchart of a process for determining whether to re-evaluate the temperature control parameters of a target object in the adaptive temperature control method according to an embodiment of the present invention;

[0038] Figure 4 This is a workflow diagram for determining whether to adjust the first preset downtime duration or the preset similarity in the adaptive temperature control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0040] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0041] See also Figures 1-4 As shown, Figure 1 This is a flowchart of the adaptive temperature control method according to an embodiment of the present invention; Figure 2 This is a flowchart of a process for calculating the similarity between the key component content of a target object displayed by a high-precision snapshot and the key component content of the target object at the moment power is started in the adaptive temperature control method according to an embodiment of the present invention; Figure 3 This is a flowchart of a process for determining whether to re-evaluate the temperature control parameters of a target object in the adaptive temperature control method according to an embodiment of the present invention; Figure 4 This is a workflow diagram for determining whether to adjust the first preset downtime duration or the preset similarity in the adaptive temperature control method according to an embodiment of the present invention.

[0042] The adaptive temperature control method according to an embodiment of the present invention includes:

[0043] Step S1, acquiring target object characteristic data and power system data, wherein the target object characteristic data includes heat flow change curve data of the target object and component content data of the target object;

[0044] Step S2: When the power outage warning signal is issued, data is collected at the highest sensor data feedback frequency, and the heat flow change curve of the target object is quickly scanned to obtain a high-precision snapshot of the thermal characteristics of the target object at the moment of the power outage;

[0045] Step S3, re-evaluating the temperature control parameters of the target object based on the downtime of the target device and / or the abnormal fluctuation of the heat flow change curve of the target object within a preset time before the power outage;

[0046] Step S4, determining a temperature control parameter evaluation method based on the downtime of the target device and / or abnormal fluctuations in key component content of the target object during the power outage;

[0047] Step S5 : determining and adjusting re-evaluation standard parameters for temperature control parameters based on whether the target object sticks and / or the target object temperature change rate is abnormal during the temperature control process.

[0048] In the embodiments of the present invention, the power system data includes, but is not limited to, "power system voltage data, current data of the power system voltage data, and power data of the power system." The temperature control parameters include, but are not limited to, "heating rate, temperature range, and pressure in the space where the target object is located." The key components of the target object (including, but not limited to, "chemical synthetic materials, metal alloy products, and electronic components") are the most temperature-sensitive chemical substances or substances with the greatest impact on thermal conductivity in the target object. The key components of the target object can be determined through thermal analysis experiments, using differential scanning calorimetry (DSC) to measure the thermal effects (such as melting, crystallization, decomposition, etc.) and mass changes of each component in the target object at different temperatures. Temperature-sensitive components will exhibit significant thermal effects within a specific temperature range. For example, if the target object is PET, a common thermoplastic polyester, determining the key components includes thermal analysis of the PET raw materials and intermediates using differential scanning calorimetry (DSC). It was found that ethylene glycol, as the main reactive monomer, has a significant effect on the melting point and crystallization behavior of PET in its residual content. At temperatures above 260°C, excessive residual ethylene glycol can cause premature melting and decomposition of PET, thus determining ethylene glycol as the key component.

[0049] The present invention installs an intelligent power monitoring system (IPMS) in the factory's power system control room. The system connects to the grid access point and the power supply lines of major electrical equipment (including the target device) via a dedicated data acquisition terminal. The system possesses real-time, high-precision data acquisition and analysis capabilities. The IPMS continuously samples and monitors the grid voltage at a sampling frequency of up to once per millisecond. Simultaneously, it uses a built-in fast Fourier transform (FFT) algorithm to perform real-time spectrum analysis on the collected voltage signal to detect harmonic components in the voltage signal. When the effective value of the monitored grid voltage drops by more than a set threshold (e.g., 15% based on the target device's sensitivity to voltage fluctuations and statistical analysis of past power outage data) within a short period of time (e.g., 10 seconds), and the content of specific harmonics in the voltage signal (e.g., the 5th and 7th harmonics, which are typically associated with grid faults and the start-up and shutdown of large equipment) rises sharply to more than three times the normal operating range, the IPMS immediately issues a power outage warning signal.

[0050] In this embodiment of the present invention, when a power outage warning signal is issued, data is collected at the highest sensor data feedback frequency (the heat flow sensor collection frequency is increased to 1 time per second, and the near-infrared spectrum analyzer immediately performs a rapid test), and the heat flow change curve of the target object is quickly scanned. The CCU drives the array heat flow sensor to complete full-area heat flow data collection within 1 second. Combined with the built-in thermal model algorithm, it infers the internal heat distribution state of the target object and obtains a high-precision snapshot of the thermal characteristics of the target object at the moment of power outage; at the same time, the near-infrared spectrum analyzer quickly detects the content of key components of the target object and records the component data at the moment of power outage.

[0051] When issuing a power outage warning signal, the present invention collects data at the highest sensor data feedback frequency and quickly scans the heat flow change curve, which can obtain a high-precision snapshot of the thermal characteristics of the target object at the moment of power outage, and accurately record key information such as the heat distribution and heat transfer trend inside the target object at the moment of power outage. Since the temperature and thermal state of the target object will change rapidly after the power outage due to factors such as heat loss and the disruption of internal thermal balance, capturing this instantaneous state in advance provides a reliable benchmark for subsequent analysis of changes in the target object during the power outage.

[0052] Specifically, in step S3, when it is determined to re-evaluate the temperature control parameters of the target object, the temperature control parameters of the target object are determined to be re-evaluated according to the downtime of the target device and / or the abnormal fluctuation of the heat flow change curve of the target object within a preset time before the power outage;

[0053] When the downtime of the target device is longer than a first preset downtime or there is abnormal fluctuation in the heat flow change curve of the target object within a preset time before the power outage, determining to re-evaluate the temperature control parameters of the target object;

[0054] When the downtime of the target device is less than or equal to the first preset downtime and there is no abnormal fluctuation in the heat flow change curve of the target object within the preset time before the power outage, it is determined that there is no need to re-evaluate the temperature control parameters of the target object.

[0055] In the embodiment of the present invention, the value range of the first preset downtime is set to 4 minutes to 6 minutes (which can be determined through experiments, for example, allowing the target device to operate normally, and controlling the temperature of the target object according to the actual production or operation process, recording the heat flow change curve data, component content data and other related parameters of the target object during normal operation, artificially simulating a power outage, and causing the target device to stop operating within several set downtimes. When a power outage warning signal is issued, data is collected at the highest sensor data feedback frequency, and the heat flow change curve of the target object is quickly scanned to obtain a high-precision snapshot of the thermal characteristics of the target object at the moment of the power outage. After the power outage is over, the operation of the target device is resumed, the temperature of the target object is continued to be controlled, and the heat flow change curve of the target object is continuously recorded. The target object is subjected to a plurality of downtimes, and the target object is subjected to a plurality of downtimes, and the plurality of downtimes are subjected to a plurality of downtimes. The plurality of downtimes are ...

[0056] In the embodiment of the present invention, it is determined that the heat flow change curve of the target object within the preset time before the power outage has abnormal fluctuations, including the occurrence of a sudden change in the slope of the heat flow change curve of the target object displayed by the high-precision snapshot. For example, during the normal heating process, the heat flow change curve shows a relatively stable trend for a period of time (15 minutes before the power outage), and the slope remains within a small fluctuation range, indicating that the internal heat transfer of the target object is uniform and the heating is stable. At this time, the heat flow change rate monitored by the heat flow sensor is approximately 0.03W / (m 2 K), which is about 0.06W / (m 2 ·K), which means that the target object is absorbing heat stably according to the established temperature control parameters. However, near the time of the power outage, in the last two minutes before the power outage, the slope of the heat flux change curve suddenly increased. Through high-frequency monitoring of the array heat flux sensor (acquisition frequency of once per second), it was found that the heat flux change rate jumped to 0.05W / (m2 / s) per 10 seconds in just 10 seconds. 2 ·K), which is equivalent to 0.3W / (m 2 ·K), which is nearly 4 times higher than the previous stable slope, and is manifested as a sudden change in slope on the heat flux curve.

[0057] The present invention determines that there is no need to re-evaluate the temperature control parameters when the downtime of the target device is less than or equal to the first preset downtime and the target object has no abnormal fluctuations within the preset time before the power outage, effectively avoiding blindly performing complex and unnecessary parameter adjustments when the target object is minimally affected by the power outage, reducing equipment start-up and shutdown, energy consumption and time waste caused by frequent parameter adjustments, and ensuring production efficiency. When the downtime of the target device is greater than the first preset downtime or the heat flow change curve of the target object within the preset time before the power outage has abnormal fluctuations, it is determined that the temperature control parameters need to be re-evaluated. After a long power outage, the target object Internal heat loss and uneven temperature distribution can be caused by re-evaluating parameters to adjust the heating strategy in a targeted manner. For example, adopting segmented slow heating instead of the original rapid heating to prevent the target object from generating stress, deformation or even damage due to sudden temperature changes. If the slope of the heat flow change curve before the power outage suddenly changes, it indicates that the thermal characteristics of the target object have changed. Re-evaluation can optimize the heat transfer method based on the new heat flow trend to ensure that the target object is heated evenly and accurately adapt to the state of the target object. The above method improves the accuracy of the analysis of the target object characteristics and the downtime duration during the downtime of the equipment due to power outage, thereby improving the accuracy of the temperature control process during subsequent power supply.

[0058] Specifically, in step S4, when determining the temperature control parameter evaluation method, the temperature control parameter evaluation method is determined based on the downtime of the target device and / or the abnormal fluctuation of the key component content of the target object during the power outage;

[0059] When the downtime of the target device is less than the second preset downtime, determining that the temperature control parameter evaluation method is a historical experience fine-tuning method;

[0060] When the downtime of the target device is greater than or equal to the second preset downtime and the target object has abnormal fluctuations in the content of key components during the power outage, determining that the temperature control parameter evaluation method is a heat flow combined with component comprehensive evaluation method;

[0061] When the downtime of the target device is greater than or equal to the second preset downtime and there is no abnormal fluctuation in the content of key components of the target object during the power outage, the temperature control parameter evaluation method is determined to be a heat flow-dominated adjustment method.

[0062] In an embodiment of the present invention, the value range of the second preset downtime duration is set to 9 to 11 minutes (the method for determining the second preset downtime duration range may be the same as the method for determining the value range of the first preset downtime duration), and the value of the second preset downtime duration is preferably 10 minutes, but the above value is not limited to this, and technical personnel in this field may also adjust the value according to actual needs.

[0063] Specifically, in step S4, it is determined that the target object has abnormal fluctuation in key component content during the power outage, including that the similarity between the key component content of the target object displayed by the high-precision snapshot and the key component content of the target object at the moment of power supply start is less than a preset similarity.

[0064] Specifically, in step S4, calculating the similarity between the key component content of the target object displayed by the high-precision snapshot and the key component content of the target object at the moment of power supply start includes:

[0065] Step S4401, obtaining key component content data of the target object displayed by a high-precision snapshot, and recording it as power outage data;

[0066] Step S4402, obtaining key component content data of the target object at the moment power supply starts, and recording it as power supply data;

[0067] Step S4403, standardizing the power outage data and power supply data;

[0068] Step S4404: Calculate the similarity between the power outage data and the power supply data using the cosine similarity formula.

[0069] The preset similarity described in the embodiment of the present invention is the average similarity of the key component content before and after the power outage of the same type of target object under the same temperature control conditions and the power outage duration difference is less than 10 seconds during the historical temperature control process, but the above value is not limited to this, and technical personnel in this field can also adjust the value according to actual needs.

[0070] In the embodiment of the present invention, at the moment of power supply, the target object is scanned for the first time by a high-precision near-infrared spectrometer installed inside the target device to obtain the key component content data of the target object at this time. For example, the active protein content is 35% (mass fraction). At the same time, a small amount of impurity content is also detected, such as a metal ion impurity content of 0.05% (mass fraction). These data are recorded as power supply data. When a power outage warning signal is received, the near-infrared spectrometer is quickly started again at the moment of power outage to quickly collect the key component content data of the target object. At this time, it is found that the active protein content becomes 32%, and the metal ion impurity content is up to 0.05%. The data rose to 0.07%. These data were recorded as power outage data. Since the units of active protein and metal ion impurity content were the same (mass fraction), they were directly converted into dimensionless relative values ​​for the convenience of subsequent calculations. For active protein, the relative value of power supply data was 0.35, and the relative value of power outage data was 0.32; for metal ion impurities, the relative value of power supply data was 0.05, and the relative value of power outage data was 0.07. Vectors were constructed, the key component content vector at the moment of power supply and the key component content vector at the moment of power outage. According to the cosine similarity formula, the numerator was first calculated, that is, the dot product of the two vectors, and then the denominator was calculated, and finally the similarity was obtained.

[0071] The historical experience fine-tuning method described in the embodiment of the present invention is based on the successful temperature control case data of the same target object under the same temperature control conditions (including but not limited to heating rate, pressure and heating range) in the past, and makes a small adjustment to the current temperature control parameters. For example, during normal heating, the temperature range is set to 40°C-50°C, the heating rate is 0.3°C per minute, and the pressure is maintained at 80kPa. The target object ran smoothly within 10 minutes before the power outage, and the power outage lasted only 6 minutes (the second preset downtime is 10 minutes). The control system retrieved the historical database and found that when a similar target object had a power outage of 6 to 7 minutes without obvious abnormal fluctuations, the heating rate was appropriately reduced by 10% (i.e., adjusted to 0.27°C per minute), the upper limit of the temperature range was fine-tuned by -1°C (to 40°C-49°C), and the degree remained unchanged. The quality of the subsequent target problem can be optimized. Therefore, the temperature control parameters (including but not limited to heating rate, pressure and temperature range) are fine-tuned based on this experience.

[0072] The heat flow combined with component comprehensive evaluation method described in the embodiment of the present invention is to simultaneously and deeply analyze the heat flow change curve and the key component content data. On the one hand, the thermal stability of the target object is judged from the slope of the heat flow curve and the peak-to-valley change, and whether the heat transfer characteristics inside the target object have changed is understood; on the other hand, the influence of the change in the chemical composition of the target object on the temperature control process is determined according to the fluctuation amplitude and direction of the key component content, such as the loss of heat-sensitive components, the change in viscosity of high-sugar target objects, etc. Combining these two factors, the temperature control parameters are comprehensively re-planned. For example, the normal temperature control parameters are a temperature range of 60°C-70°C, a heating rate of 0.4°C per minute, and a pressure of 90kPa. During a power outage, within 15 minutes before the power outage, The target object showed an abnormality and the power outage lasted for 12 minutes (the second preset downtime was 10 minutes), and the high-precision snapshot showed that the key component (the polymer content that determines the viscosity) decreased by 10%. The slope of the heat flow change curve increased in the later period of the power outage. The control system determined that a comprehensive evaluation method combining heat flow and components was required. First, considering that the viscosity decreased as the polymer content decreased, the heating rate was appropriately increased by 20% to 0.48°C per minute to speed up the heating. At the same time, in view of the fact that the increase in the heat flow slope may cause local overheating, the temperature range was tightened to 62°C-68°C, and the stirring intensity was increased, and the stirring frequency was doubled. After restarting the equipment, real-time monitoring found that the heat flow of the target object tended to be stable, the viscosity returned to an acceptable range, and the final product quality met the standards.

[0073] The heat flow-dominated adjustment method described in the embodiment of the present invention is to adjust the temperature control parameters according to the characteristics of the heat flow change curve. By analyzing the sudden change in the slope of the heat flow curve, the fluctuation period, etc., the internal thermal equilibrium state of the target object is judged, and the heating uniformity of the target object, the change in the heat conduction path, etc. are inferred. Then, the temperature range and the heating rate are adjusted in a targeted manner to restore the stable thermal environment of the target object. For example, the initial temperature control parameters are set to a temperature range of 50°C-60°C, a heating rate of 0.35°C per minute, and a pressure of 85kPa in the space where the target object is located. 12 minutes before the power outage, the target object was The target object was normal, and the power outage lasted 13 minutes (the second preset downtime was 10 minutes). There was no obvious fluctuation in the content of key ingredients (sugar, vitamins, etc.) during the power outage, but the slope of the heat flow change curve decreased in the later stage of the power outage, indicating that heat conduction was blocked. The control system adopted a heat flow-dominated adjustment method, first suspending the heating for 3 minutes, using the system to balance the temperature of the target object, and then slowly heating at a rate of 0.2°C per minute, while increasing the temperature from 5kPa to 90kPa, promoting water evaporation to take away heat and improve heat transfer. After the slope of the heat flow curve returned to normal, the heating rate was gradually adjusted back.

[0074] The present invention adopts a historical experience fine-tuning method when the downtime of the target object within the preset time before the power outage is less than the second preset downtime, because a shorter power outage usually has limited impact on the characteristics of the target object, and the internal thermal and chemical state of the target object changes less. At this time, with the help of a large amount of temperature control data of the same type of target objects under similar working conditions in the past, the current temperature control parameters can be quickly and accurately adjusted in small amounts. When the downtime of the target object within the preset time before the power outage is greater than or equal to the second preset downtime and there is an abnormal fluctuation in the content of key components during the power outage, on the one hand, from the perspective of heat flow, the long power outage causes the thermal balance of the target object to be broken, and the slope, peak value and other characteristics of the heat flow change curve change, which reflects the changes in the heat transfer path and rate inside the target object, such as uneven heat flow. It may cause local overheating or overcooling. On the other hand, abnormal fluctuations in the content of key components mean that the chemical composition of the target object has changed. When the downtime of the target object within the preset time before the power outage is greater than or equal to the second preset downtime and there is no abnormal fluctuation in the content of key components during the power outage, the target object is mainly affected by the power outage duration and its thermal properties change, and its chemical composition is relatively stable. By in-depth analysis of the heat flow change curve, such as a sudden change in the heat flow slope indicating that heat conduction is blocked, the heating strategy is adjusted in a targeted manner, the heating is suspended and waits for the heat flow to return to stability before slowly starting again, or the stirring rate is adjusted to improve the uniformity of heat transfer. The above method improves the accuracy of the analysis of the target object characteristics and downtime duration during the downtime process when the equipment is down due to power outage, thereby improving the accuracy of the temperature control process during subsequent power supply.

[0075] Specifically, in step S5, when it is determined that the re-evaluation standard parameter for the temperature control parameter is to be adjusted, it is determined that the re-evaluation standard parameter for the temperature control parameter is to be adjusted according to whether a sticking phenomenon of the target object occurs during the temperature control process and / or whether an abnormal temperature change rate phenomenon of the target object occurs;

[0076] When the target object appears to be sticking during the subsequent temperature control process, it is determined to adjust the preset similarity;

[0077] When an abnormal temperature change rate of the target object occurs during the subsequent temperature control process, determining to adjust the first preset downtime duration;

[0078] When no target object sticking phenomenon occurs and no abnormal temperature change rate phenomenon occurs in the subsequent temperature control process, it is determined that there is no need to adjust the preset similarity and the first preset downtime duration.

[0079] In the embodiment of the present invention, the re-evaluation standard parameters of the temperature control parameters include a preset similarity and a first preset downtime duration.

[0080] Specifically, in step S5 , determining that the abnormal temperature change rate phenomenon of the target object occurs includes that a deviation between the actual temperature change rate of the target object and the predicted temperature change rate is greater than a preset deviation value.

[0081] The re-evaluation standard parameters of the temperature control parameters in the embodiment of the present invention include a first preset downtime and a preset similarity. The predicted temperature change rate is the average value of the temperature change rate of the same target object under the same conditions (including but not limited to "the same heating temperature, the same heating rate, and the downtime difference does not exceed 10 seconds") for several times. The preset deviation value is the historical average value of the deviation value between the actual temperature change rate of the target object and the predicted temperature change rate when the target object achieves the expected effect after heating, but the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0082] Specifically, when it is determined to adjust the preset similarity, the preset similarity is adjusted with a first adjustment coefficient; when it is determined to adjust the first preset downtime duration, the first preset downtime duration is adjusted with a second adjustment coefficient.

[0083] In the embodiment of the present invention, the value range of the first adjustment coefficient is set to 1.05-1.19, and the value of the first adjustment coefficient is preferably 1.09. The value range of the second adjustment coefficient is set to 0.86-0.96, and the value of the second adjustment coefficient is preferably 0.92. The adjustment amount of the preset similarity is positively correlated with the proportion of the agglomeration area when the target object adhesion phenomenon occurs, and the adjustment amount of the first preset downtime is negatively correlated with the deviation of the moisture content decrease rate. The value range and preferred value of the first adjustment coefficient can be determined according to the following method. When the target object adhesion phenomenon occurs in the subsequent temperature control process, the preset similarity needs to be adjusted. The value of this coefficient is based on experimental observations of the same type of target objects under different temperature control conditions and analysis of a large amount of historical data. It is found that when the adhesion phenomenon occurs, the agglomeration area proportion has a certain positive correlation with the adjustment of the preset similarity. In order to ensure the adjustment effect while avoiding excessive adjustment After many experiments and data fitting, this value range is determined, so that the adjusted preset similarity can more accurately reflect the actual situation of the target object, thereby optimizing the evaluation of the temperature control parameters; the value range and preferred value of the second adjustment coefficient can be determined according to the following method. When an abnormal temperature change rate of the target object occurs, the first preset downtime needs to be adjusted. The value of this coefficient is based on the study of the thermal characteristics of the target object under different downtimes and temperature control conditions and the statistical analysis of historical data. It is found that the adjustment amount of the first preset downtime is negatively correlated with the deviation of the moisture content decrease rate. In order to make the adjusted first preset downtime more reasonably adapt to the thermal changes of the target object, after repeated experiments and data verification, this value range is determined to achieve accurate evaluation of the downtime and precise optimization of the temperature control process, but the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0084] The present invention adjusts the preset similarity when the target object sticks during the subsequent temperature control process. The sticking of the target object will destroy the originally uniform distribution of components inside the target object, resulting in abnormal local component concentration. By adjusting the preset similarity in a manner that is positively correlated with the proportion of the agglomeration area, that is, the larger the agglomeration area, the larger the preset similarity adjustment amount is, the more accurately the degree of deterioration of the target object characteristics can be reflected. When the target object temperature change rate is abnormal, it is very important to adjust the first preset downtime duration. If the target object temperature change rate deviates from the normal range, it indicates that the heat transfer inside the target object is blocked, which may be due to target object agglomeration, heat flow turbulence, etc. At this time, the first preset downtime duration is adjusted according to the principle of negative correlation with the deviation value of the target object temperature change rate. The larger the deviation value, the shorter the duration, thereby ensuring the stability of raw material quality. The above method improves the accuracy of the analysis of the target object characteristics and downtime duration during the downtime process when the equipment is down due to power outage, thereby improving the accuracy of the temperature control process during subsequent power supply.

[0085] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An adaptive temperature control method, characterized in that: include: Acquiring target object characteristic data and power system data, wherein the target object characteristic data includes heat flow change curve data of the target object and component content data of the target object; When a power outage warning signal is issued, data is collected at the highest sensor data feedback frequency, and the heat flow change curve of the target object is quickly scanned to obtain a high-precision snapshot of the thermal characteristics of the target object at the moment of power outage; Re-evaluating the temperature control parameters of the target object based on the downtime of the target device and / or the abnormal fluctuation of the heat flow change curve of the target object within a preset time before the power outage; Determine a temperature control parameter evaluation method based on the downtime of the target device and / or abnormal fluctuations in key component content of the target object during the power outage; Based on whether a sticking phenomenon of the target object occurs during the temperature control process and / or whether an abnormal temperature change rate phenomenon of the target object occurs, a re-evaluation standard parameter for the temperature control parameter is determined and adjusted.

2. The adaptive temperature control method according to claim 1, characterized in that: Determining to re-evaluate the temperature control parameters of the target object includes: If the downtime of the target device is longer than the first preset downtime or the heat flow change curve of the target object within the preset time before the power outage has abnormal fluctuations, it is determined to re-evaluate the temperature control parameters of the target object.

3. The adaptive temperature control method according to claim 2, characterized in that: It is determined that there is abnormal fluctuation in the heat flow change curve of the target object within a preset time before the power outage, including a sudden slope change phenomenon in the heat flow change curve of the target object displayed by a high-precision snapshot.

4. The adaptive temperature control method according to claim 3, characterized in that: The evaluation method for determining temperature control parameters includes: If the downtime of the target device is less than the second preset downtime, determining that the temperature control parameter evaluation method is a historical experience fine-tuning method; If the downtime of the target device is greater than or equal to the second preset downtime and the target object has abnormal fluctuations in the content of key components during the power outage, determining that the temperature control parameter evaluation method is a heat flow combined with component comprehensive evaluation method; If the downtime of the target device is greater than or equal to the second preset downtime and there is no abnormal fluctuation in the content of key components of the target object during the power outage, the temperature control parameter evaluation method is determined to be a heat flow-dominated adjustment method.

5. The adaptive temperature control method according to claim 4, characterized in that: Determining that the target object has abnormal fluctuation in key component content during the power outage includes that the similarity between the key component content of the target object displayed by the high-precision snapshot and the key component content of the target object at the moment power supply is started is less than a preset similarity.

6. The adaptive temperature control method according to claim 5, characterized in that: Calculating the similarity between the key component content of the target object shown in the high-precision snapshot and the key component content of the target object at the moment power supply is started includes: Obtain key component content data of the target object displayed as a high-precision snapshot and record it as power outage data; Obtain key component content data of the target object at the moment power supply starts, recorded as power supply data; Standardize power outage data and power supply data; The cosine similarity formula is used to calculate the similarity between power outage data and power supply data.

7. The adaptive temperature control method according to claim 6, characterized in that: The preset similarity is determined based on the average similarity of the key component content before and after the power outage when the difference in power outage duration during the historical temperature control process of the same type of target object is less than 10 seconds.

8. The adaptive temperature control method according to claim 7, characterized in that: Re-evaluation of standard parameters for temperature control parameters to determine adjustments include: If the target object appears to be sticking during the subsequent temperature control process, it is determined that the preset similarity is adjusted; If an abnormal temperature change rate of the target object occurs during the subsequent temperature control process, it is determined that the first preset downtime duration is adjusted.

9. The adaptive temperature control method according to claim 8, characterized in that: Determining that an abnormal temperature change rate phenomenon of the target object occurs includes that a deviation value between an actual temperature change rate of the target object and a predicted temperature change rate is greater than a preset deviation value.

10. The adaptive temperature control method according to claim 9, characterized in that: The adjustment amount of the preset similarity is positively correlated with the proportion of the agglomeration area when the target object sticks, and the adjustment amount of the first preset downtime is negatively correlated with the deviation value between the actual temperature change rate of the target object and the predicted temperature change rate obtained by the time series analysis method.

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