An adaptive temperature control method

Abstract

The present application relates to the technical field of temperature control, and particularly relates to a self-adaptive temperature control method, which comprises the following steps: obtaining a high-precision snapshot of the thermal characteristics of a target object at the moment of power failure when a power failure early warning signal is sent; determining the re-evaluation of the temperature control parameters of the target object based on the downtime of the target device and / or abnormal fluctuations in the heat flow curve of the target object within a preset time before power failure; determining the temperature control parameter evaluation method based on the downtime of the target device and / or abnormal fluctuations in the content of key components of the target object during power failure; and adjusting the re-evaluation standard parameters for the temperature control parameters based on whether the target object appears to be sticking and / or whether the target object temperature change rate appears to be abnormal. The present application improves the accuracy of the analysis of the power failure downtime of the target device, and further improves the accuracy of the temperature control during subsequent power supply.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of temperature control, and in particular to a self-adaptive temperature control method. BACKGROUND

[0002] The power supply in the industrial production environment is not always stable and reliable, and power failure occurs in the factory from time to time, which is a great challenge to the ongoing temperature control process. After a short power failure, the temperature, thermal state and component characteristics of the target object may change subtly, and the traditional temperature control method often lacks an effective response mechanism for such sudden situations. After the power is restored, if the temperature control parameters before the power failure are still followed, the temperature requirements of the target object may not be met, or even the target object may be damaged.

[0003] For example, Chinese patent application publication No. CN107065967A discloses a drying machine temperature automatic control method and automatic control system, wherein the method includes collecting the conveying speed inside the drying machine and the temperature of the heat source supplying heat to the inside of the drying machine at regular intervals; thereby obtaining the detection speed and the detection temperature; according to the preset speed-temperature matching information, if the detection temperature and the detection speed are not matched, an alarm is given; if the detection temperature and the detection speed continue to be unmatched for a preset time period, the automatic control execution component is automatically controlled to match the detection temperature and the detection speed, according to the conveying speed of the gypsum board inside the drying machine, the temperature of the heat source inside the drying machine is automatically controlled according to the preset speed-temperature matching information, so that the temperature and the speed are matched, thereby achieving the purpose of improving the production quality of the gypsum board.

[0004] However, the prior art has the problem that the accuracy of analyzing the characteristics of the target object during the downtime and the downtime duration when the equipment is down due to power failure is low, resulting in inaccurate temperature control process after power supply. SUMMARY

[0005] Therefore, the present application provides a self-adaptive temperature control method to overcome the problem that the accuracy of analyzing the characteristics of the target object during the downtime and the downtime duration when the equipment is down due to power failure is low, resulting in inaccurate temperature control process after power supply.

[0006] To achieve the above-mentioned purpose, the present application provides a self-adaptive temperature control method, comprising:

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

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

[0009] The temperature control parameter of the target object is re-evaluated based on the downtime of the target device and / or abnormal fluctuations in the heat flow change curve of the target object within a preset time before power failure.

[0010] The temperature control parameter evaluation method is determined based on the downtime of the target device and / or abnormal fluctuations in the content of the key component of the target object during power failure.

[0011] Based on whether the target object appears to be sticking and / or whether the target object temperature change rate is abnormal during the temperature control process, the re-evaluation standard parameter for the temperature control parameter is adjusted.

[0012] Further, the temperature control parameter of the target object is re-evaluated, including:

[0013] If the downtime of the target device is greater than the first preset downtime or the heat flow change curve of the target object within a preset time before power failure has abnormal fluctuations, the temperature control parameter of the target object is re-evaluated.

[0014] Further, determining that the heat flow change curve of the target object within a preset time before power failure has abnormal fluctuations includes that the heat flow change curve of the target object displayed by the high-precision snapshot appears to have a sudden change in slope.

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

[0016] If the downtime of the target device is less than the second preset downtime, 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 the key component during power failure, the temperature control parameter evaluation method is a heat flow combined component comprehensive evaluation method.

[0018] If the downtime of the target device is greater than or equal to the second preset downtime and the target object does not have abnormal fluctuations in the content of the key component during power failure, the temperature control parameter evaluation method is a heat flow dominant adjustment method.

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

[0020] Further, the similarity between the key component content of the target object in the high-precision snapshot display and the key component content of the target object at the power supply start moment comprises:

[0021] Obtain the key component content data of the target object in the high-precision snapshot display, denoted as outage data;

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

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

[0024] Calculate the similarity between the outage data and the power supply data by using a cosine similarity formula.

[0025] Further, the preset similarity is determined according to the average similarity of the key component content before and after the outage when the difference between the outage durations of the target objects of the same type in the historical temperature control processes is less than 10 seconds.

[0026] Further, the adjustment of the re-evaluation standard parameter for the temperature control parameter comprises:

[0027] If the target object sticking phenomenon occurs in the subsequent temperature control process, it is determined to adjust the preset similarity;

[0028] If the target object temperature change rate abnormality phenomenon occurs in the subsequent temperature control process, it is determined to adjust the first preset outage duration.

[0029] Further, the determination of the target object temperature change rate abnormality phenomenon comprises that the deviation value between the actual temperature change rate of the target object and the predicted temperature change rate is greater than a preset deviation value.

[0030] Further, the adjustment amount of the preset similarity is positively correlated with the block area ratio when the target object sticking phenomenon occurs, and the adjustment amount of the first preset outage duration 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 prior art, the present application has the beneficial effects that when the power outage warning signal is issued, the data is collected at the highest sensor data feedback frequency and the heat flow change curve is quickly scanned, the high-precision snapshot of the thermal properties of the target object at the power outage moment can be obtained, and the key information such as the heat distribution and heat transfer trend inside the target object at the power outage moment can be accurately recorded. 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 breaking, the instant state is captured in advance, which provides a reliable benchmark for subsequent analysis of the changes of the target object during the power outage.

[0032] Further, when the downtime of the target device is less than or equal to a first preset downtime and the target object does not have abnormal fluctuations within a preset time before the power failure, the application determines that the temperature control parameters do not need to be re-evaluated, effectively avoiding blind parameter adjustment in the case where the target object is minimally affected by the power failure, reducing device start-stop, energy consumption and time waste caused by frequent parameter adjustment, and ensuring production efficiency. When the downtime of the target device is greater than the first preset downtime or the heat flow curve of the target object within the preset time before the power failure has abnormal fluctuations, the temperature control parameters are re-evaluated. After a long power failure, the heat inside the target object is lost and the temperature distribution is uneven. Re-evaluating the parameters can adjust the warming strategy, such as using segmented slow warming instead of original fast warming, to prevent the target object from being stressed, deformed or even damaged due to sudden temperature changes. If the heat flow curve before the power failure has a sudden change in slope, it indicates that the thermal properties of the target object have changed. Re-evaluation can optimize the heat transfer mode based on the new heat flow trend to ensure uniform heating of the target object and accurately adapt to the state of the target object. The above method improves the accuracy of analyzing the characteristics of the target object during downtime and the downtime when the device is down due to power failure, thereby improving the accuracy of the temperature control process during subsequent power supply.

[0033] Further, when the downtime of the target object within the preset time before the power failure is less than a second preset downtime, the application uses a historical experience fine-tuning method. Because a short power failure usually has limited impact on the characteristics of the target object, the thermal and chemical states inside the target object change little. At this time, with the help of a large amount of data from temperature control of similar target objects under similar working conditions, the current temperature control parameters can be adjusted slightly quickly and accurately. When the downtime of the target object within the preset time before the power failure is greater than or equal to the second preset downtime and there are abnormal fluctuations in the content of key components during the power failure, on the one hand, from the heat flow point of view, a long power failure breaks the thermal balance of the target object, changing the slope and peak value of the heat flow curve and reflecting changes in the heat transfer path and rate inside the target object, such as uneven heat flow that 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 failure is greater than or equal to the second preset downtime and there are no abnormal fluctuations in the content of key components during the power failure, the thermal properties of the target object are mainly affected by the length of the power failure, and the chemical composition is relatively stable. By deeply analyzing the heat flow curve, such as a sudden change in the heat flow slope indicating blocked heat conduction, the warming strategy is adjusted accordingly, such as pausing the warming and slowly restarting after the heat flow stabilizes, or adjusting the stirring rate to improve heat transfer uniformity. The above method improves the accuracy of analyzing the characteristics of the target object during downtime and the downtime when the device is down due to power failure, thereby improving the accuracy of the temperature control process during subsequent power supply.

[0034] Further, the preset similarity is adjusted when the target object is stuck in the subsequent temperature control process. The sticking of the target object will destroy the original uniform composition distribution inside the target object, resulting in abnormal local composition concentration. By adjusting the preset similarity in a positive correlation with the blockage area ratio, that is, the larger the blockage area, the larger the preset similarity adjustment amount, the deterioration degree of the target object characteristics can be accurately reflected. When the target object temperature change rate is abnormal, adjusting the first preset downtime length is crucial. 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 caused by target object blockage, heat flow disorder, etc. At this time, the first preset downtime length 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 time length, so as to ensure the stability of the raw material quality. Through the above method, the accuracy of the target object characteristics and the downtime length during the downtime caused by power failure is improved, and the accuracy of the subsequent temperature control process during power supply is improved. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The working flowchart of the adaptive temperature control method of the embodiment of the present application is shown in the figure.

[0036] Figure 2 The working flowchart of the adaptive temperature control method of the embodiment of the present application is shown in the figure.

[0037] Figure 3 The working flowchart of the adaptive temperature control method of the embodiment of the present application is shown in the figure.

[0038] Figure 4 The working flowchart of the adaptive temperature control method of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0039] In order to make the purpose and advantages of the present application clearer and more apparent, the present application will be further described below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0040] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and do not limit the protection scope of the present application.

[0041] Please refer to Figures 1-4 shown in the figure, Figure 1 The working flowchart of the adaptive temperature control method of the embodiment of the present application is shown in the figure.Figure 2 A work flow chart for calculating the similarity between the key component content of the target object in the high-precision snapshot display of the embodiment of the adaptive temperature control method and the key component content of the target object at the power supply start instant; Figure 3 A work flow chart for determining whether to reevaluate the temperature control parameters of the target object in the embodiment of the adaptive temperature control method; Figure 4 A work flow chart for determining whether to adjust the first preset downtime or the preset similarity in the embodiment of the adaptive temperature control method.

[0042] The adaptive temperature control method of the embodiment of the present application comprises:

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

[0044] Step S2, collecting data at the highest sensor data feedback frequency when issuing a power outage warning signal, and rapidly scanning the heat flow change curve of the target object to obtain a high-precision snapshot of the thermal characteristics of the target object at the power outage instant;

[0045] Step S3, determining to reevaluate 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 power outage;

[0046] Step S4, determining the 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 during power outage;

[0047] Step S5, determining to adjust the reevaluation standard parameters for the temperature control parameters based on whether the target object sticking phenomenon occurs and / or whether the target object temperature change rate abnormal phenomenon occurs during the temperature control process.

[0048] The power system data in the embodiments of the present application include, but are not limited to, voltage data of the power system, current data of the voltage data of the power system, and power data of the power system, the temperature control parameters include, but are not limited to, a temperature rising rate, a temperature interval, and a pressure of a space where the target object is located, and the key component of the target object (including, but not limited to, a chemical synthesis material, a metal alloy product, and an electronic component) is a chemical substance that is most sensitive to temperature or a substance that has the greatest influence on heat conduction performance in the target object, the key component of the target object can be determined by a thermal analysis experiment method, a differential scanning calorimeter (DSC) is used to measure thermal effects (such as melting, crystallization, decomposition, etc.) and mass changes of each component in the target object at different temperatures, and the temperature-sensitive component will show significant thermal effects in a specific temperature interval, for example, if the target object is PET, which is a common thermoplastic polyester, the key component determination includes thermal analysis of PET raw materials and intermediate products by a differential scanning calorimeter (DSC), it is found that ethylene glycol as the main reaction monomer has a significant influence on the melting point and crystallization behavior of PET, and when the temperature is higher than 260 DEG C, too high a residual amount of ethylene glycol will cause the PET to melt and decompose in advance, and therefore ethylene glycol is determined as the key component.

[0049] The present application installs an intelligent power monitoring system (IPMS) in the power system general control room of a factory, the system is connected with a power grid access point and power supply lines of each main power consumption equipment (including a target device) through a dedicated data acquisition terminal, has real-time and high-precision data acquisition and analysis capabilities, the IPMS continuously samples and monitors the power grid voltage, the sampling frequency is as high as 1 time per millisecond, at the same time, the IPMS uses a built-in fast Fourier transform (FFT) algorithm to perform real-time spectrum analysis on the collected voltage signals to detect harmonic components in the voltage signals, when the effective value of the power grid voltage is monitored to decrease by more than a set threshold (for example, 15%, the threshold is obtained according to the sensitivity of the target device to voltage fluctuation and past power failure event data statistical analysis) in a short time (for example, within 10 seconds), and specific harmonic content (such as 5th and 7th harmonics, which are usually related to power grid faults and large equipment start-stop impact) in the voltage signal sharply rises and exceeds 3 times of the normal operating range, the IPMS immediately issues a power failure warning signal.

[0050] When the power-off early warning signal is sent, the 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 detection), and the target object heat flow change curve is rapidly scanned. The CCU drives the array type heat flow sensor to complete one full area heat flow data collection in 1 second, combined with the built-in thermal model algorithm, the internal heat distribution state of the target object is calculated, and a high-precision snapshot of the thermal characteristics of the target object at the power-off moment is obtained. At the same time, the near-infrared spectrum analyzer rapidly detects the content of the key components of the target object, and records the component data at the power-off moment.

[0051] When the power-off early warning signal is sent, the data is collected at the highest sensor data feedback frequency and the heat flow change curve is rapidly scanned, so that a high-precision snapshot of the thermal characteristics of the target object at the power-off moment can be obtained, and the key information such as the internal heat distribution and heat transfer trend of the target object at the power-off moment can be accurately recorded. Since the temperature and thermal state of the target object will change rapidly after power-off due to factors such as heat loss and internal thermal balance breaking, the instant state is captured in advance, which provides a reliable reference for subsequent analysis of the changes of the target object during power-off.

[0052] Specifically, in step S3, when it is determined to reevaluate the temperature control parameter of the target object, the temperature control parameter of the target object is reevaluated according to the downtime of the target device and / or abnormal fluctuations of the heat flow change curve of the target object within a preset time before power-off.

[0053] 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 a preset time before power-off has abnormal fluctuations, it is determined to reevaluate the temperature control parameter of the target object.

[0054] When the downtime of the target device is less than or equal to the first preset downtime and the heat flow change curve of the target object within a preset time before power-off has no abnormal fluctuations, it is determined that the temperature control parameter of the target object does not need to be reevaluated.

[0055] The value range of the first preset downtime is set to 4-6 minutes (which can be determined by experiment, for example, the target device is normally operated, and the temperature control of the target object is carried out according to the actual production or operation process, the related parameters such as the heat flow change curve data, component content data and the like of the target object in the normal operation process are recorded, the power failure situation is artificially simulated, the target device is stopped to operate in a plurality of set downtime, the data is collected at the highest sensor data feedback frequency when the power failure warning signal is sent, 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 power failure moment, after the power failure ends, the operation of the target device is restored, the temperature control of the target object is continued, and the heat flow change curve, temperature change, whether the target object is bonded and the like of the target object are continuously recorded, whether the heat flow change curve of the target object before power failure under different downtime has abnormal fluctuation, whether the target object bonding phenomenon occurs in the subsequent temperature control process, whether the target object temperature change rate is abnormally changed or not is observed and recorded, the number and proportion of adverse conditions such as abnormal fluctuation of the heat flow change curve of the target object, bonding phenomenon and abnormal change of the temperature change rate of the target object under each downtime value are counted, and the downtime range in which the target object remains relatively stable in the temperature control process is determined), and the value of the first preset downtime is preferably 5 minutes, and the preset time is preferably 15 minutes, but the above values are not limited thereto, and the values can be adjusted according to actual needs by those skilled in the art.

[0056] In the embodiment of the application, the abnormal fluctuation of the heat flow change curve of the target object within the preset time before power failure includes the heat flow change curve of the target object displayed by the high-precision snapshot showing a sudden change in slope, for example, in the normal heating process, the heat flow change curve shows a relatively stable trend within a period of time (15 minutes before power failure), the slope is maintained within a small fluctuation range, indicating that the heat transfer inside the target object is uniform and stable, at this time, the heat flow change rate monitored by the heat flow sensor is about 0.03 W / (m 2 ·K) per 30 seconds, that is, about 0.06 W / (m 2 ·K) per minute, which means that the target object absorbs heat stably according to the established temperature control parameters, however, near the power failure moment, the slope of the heat flow change curve suddenly increases within the last 2 minutes before power failure, through high-frequency monitoring of the array type heat flow sensor (1 collection frequency per second), it is found that the heat flow change rate jumps to 0.05 W / (m 2 ·K) per 10 seconds within 10 seconds, which is equivalent to 0.3 W / (m 2 ·K) per minute after conversion, which is nearly 4 times larger than the previous stable slope, and is shown as a sudden change in slope on the heat flow change curve.

[0057] The application determines that the temperature control parameter does not need to be re-evaluated when the downtime of the target device is less than or equal to the first preset downtime and the target object does not have abnormal fluctuations within a preset time before the power failure, effectively avoiding blind parameter adjustment in a complex and unnecessary manner in the case that the target object is slightly affected by the power failure, reducing equipment start-stop, energy consumption and time waste caused by frequent parameter adjustment, 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 a preset time before the power failure has abnormal fluctuations, the temperature control parameter is re-evaluated. After a long power failure, the heat in the target object is dissipated, and the temperature distribution is uneven. Re-evaluating the parameter can adjust the heating strategy, such as using segmented slow heating instead of original rapid heating, to prevent the target object from being stressed, deformed or even damaged due to sudden temperature changes. If the heat flow change curve before the power failure has a sudden change in slope, it indicates that the thermal properties of the target object have changed. Re-evaluation can optimize the heat transfer mode in combination with the new heat flow trend to ensure uniform heating of the target object and accurately adapt to the state of the target object. The above method improves the accuracy of analyzing the characteristics of the target object during the downtime and the downtime when the equipment is down due to power failure, and further improves 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 according to the downtime of the target device and / or the abnormal fluctuation of the content of the key component of the target object during the power failure process;

[0059] When the downtime of the target device is less than the second preset downtime, the temperature control parameter evaluation method is determined to be 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 the key component during the power failure process, the temperature control parameter evaluation method is determined to be a heat flow combined component comprehensive evaluation method;

[0061] When the downtime of the target device is greater than or equal to the second preset downtime and the target object does not have abnormal fluctuations in the content of the key component during the power failure process, the temperature control parameter evaluation method is determined to be a heat flow dominant adjustment method.

[0062] The value range of the second preset downtime in the embodiment of the application is set to 9-11 minutes (the determination method of the second preset downtime range can be the same as the determination method of the first preset downtime value range), and the value of the second preset downtime is preferably 10 minutes, but the above value is not limited thereto, and a person skilled in the art can also adjust the value according to actual needs.

[0063] Specifically, in step S4, determining that the target object has a key component content fluctuation abnormality during the power outage process includes that the similarity between the key component content of the target object in the high-precision snapshot display and the key component content of the target object at the power supply start moment is less than a preset similarity.

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

[0065] Step S4401, obtaining the key component content data of the target object in the high-precision snapshot display, denoted as outage data;

[0066] Step S4402, obtaining the key component content data of the target object at the power supply start moment, denoted as power supply data;

[0067] Step S4403, performing standardization processing on the outage data and the power supply data;

[0068] Step S4404, calculating the similarity between the outage data and the power supply data by using a cosine similarity formula.

[0069] The preset similarity in the embodiment of the application is the average similarity of the key component content before and after the power outage of the same type of target object in the historical temperature control process under the same temperature control condition and with a power outage time difference of less than 10 seconds, but the above-mentioned value is not limited thereto, and a person skilled in the art can also adjust the value according to actual needs.

[0070] In the embodiment of the application, at the power supply start moment, the high-precision near-infrared spectrum analyzer installed in the target device is used to perform initial scanning on the target object, to obtain the key component content data of the target object at this moment, for example, the active protein content is 35% (mass fraction), and a small amount of impurity component content is also detected, such as the content of a certain metal ion impurity is 0.05% (mass fraction), and these data are recorded as power supply data. When receiving a power outage early warning signal, the near-infrared spectrum analyzer is quickly started again at the power outage moment to rapidly collect the key component content data of the target object, at this moment, it is found that the active protein content becomes 32%, and the metal ion impurity content rises to 0.07%, and these data are recorded as outage data. Since the active protein and the metal ion impurity content are the same (mass fraction), in order to facilitate subsequent calculation, they are directly converted into dimensionless relative values. For the active protein, the relative value of the power supply data is 0.35, and the relative value of the outage data is 0.32; for the metal ion impurity, the relative value of the power supply data is 0.05, and the relative value of the outage data is 0.07. The key component content vector at the power supply start moment and the key component content vector at the power outage moment are constructed, the numerator is calculated according to the cosine similarity formula, that is, the dot product of the two vectors, the denominator is calculated, and finally the similarity is obtained.

[0071] The historical experience fine-tuning method in the embodiment of the present application is to make small adjustments to the current temperature control parameters based on the successful temperature control case data of the same target object under the same temperature control conditions (including but not limited to temperature rising speed, pressure and temperature rising range). For example, when normally rising, the temperature range is set to 40-50℃, the temperature rising rate is 0.3℃ per minute, the pressure is maintained at 80kPa, the target object runs smoothly within 10 minutes before the power failure, and the power failure lasts only 6 minutes (the second preset downtime is 10 minutes). The control system retrieves the historical database and finds that when the similar target object is powered off for 6-7 minutes and there is no obvious abnormal fluctuation, the temperature rising rate is moderately reduced by 10% (i.e. adjusted to 0.27℃ per minute), the upper limit of the temperature range is fine-tuned by-1℃ (changed to 40-49℃), and the degree is kept unchanged. The quality of the target object can reach the best after the subsequent target problem, so the temperature control parameters (including but not limited to temperature rising speed, pressure and temperature range) are fine-tuned according to the experience.

[0072] The heat flow combined component comprehensive evaluation method in the embodiment of the present application is to analyze the heat flow change curve and the key component content data at the same time. On the one hand, the heat stability of the target object is judged from the slope and peak-valley change of the heat flow curve to understand whether the heat transfer characteristics in the target object are changed. On the other hand, the influence of the chemical composition change 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 of the viscosity of high-sugar target objects, etc. The temperature control parameters are comprehensively re-planned according to the two factors, for example, the normal temperature control parameters are temperature range 60-70℃, temperature rising rate 0.4℃ per minute, and pressure 90kPa. In a power failure process, the target object appears abnormal within 15 minutes before the power failure, the power failure lasts for 12 minutes (the second preset downtime is 10 minutes), and the high-precision snapshot shows that the key component (polymer content determining viscosity) decreases by 10%. The slope of the heat flow change curve increases in the later stage of the power failure. The control system judges that the heat flow combined component comprehensive evaluation method needs to be used. First, considering that the polymer content decreases the viscosity, the temperature rising rate is appropriately increased by 20% to 0.48℃ per minute to speed up the temperature rising. At the same time, since the heat flow slope increases, the temperature range is tightened to 62-68℃, and the stirring intensity is increased to double the stirring frequency. After restarting the equipment, it is found that the heat flow of the target object tends to be stable, the viscosity is restored to an acceptable range, and the final product quality meets the standards.

[0073] The heat flow dominant adjustment method in the embodiment of the present application is to adjust the temperature control parameters according to the characteristics of the heat flow change curve. By analyzing the slope mutation and fluctuation period of the heat flow curve, the internal thermal equilibrium state of the target object is judged, the uniformity of the target object under heat and the change of the heat conduction path are inferred, and then the temperature interval and the heating rate are adjusted accordingly to restore the stable thermal environment of the target object. For example, the initial temperature control parameter sets the temperature interval to 50-60 DEG C, the heating rate is 0.35 DEG C per minute, the space pressure of the target object is 85 kPa, the target object is normal 12 minutes before the power failure, the power failure lasts for 13 minutes (the second preset downtime is 10 minutes), and the content of the key components (sugar, vitamins, etc.) does not fluctuate obviously during the power failure. However, the slope of the heat flow change curve decreases in the late power failure, indicating that the heat conduction is blocked. The control system uses the heat flow dominant adjustment method to pause the heating for 3 minutes, balances the temperature of the target object, and then slowly heats up at a rate of 0.2 DEG C per minute, while increasing the degree by 5 kPa to 90 kPa, promoting water evaporation to take away heat and improving heat transfer. When the slope of the heat flow curve returns to normal, the heating rate is gradually adjusted.

[0074] When the downtime of the target object within the preset time before the power failure is less than the second preset downtime, the historical experience fine tuning method is used. Because the power failure for a short time usually has limited effect on the characteristics of the target object, the internal thermal and chemical state of the target object changes little. At this time, with the help of a large amount of data of temperature control of the same type of target object under similar working conditions, the current temperature control parameters can be quickly and accurately adjusted by a small amount. When the downtime of the target object within the preset time before the power failure is greater than or equal to the second preset downtime and there is abnormal fluctuation of the content of the key components during the power failure, on the one hand, from the point of view of heat flow, long-term power failure breaks the thermal balance of the target object, and the characteristics such as the slope and peak value of the heat flow change curve change, reflecting the change of the heat transfer path and rate in the target object. For example, uneven heat flow may cause local overheating or overcooling. On the other hand, the abnormal fluctuation of the content of the key components means that the chemical composition of the target object changes. When the downtime of the target object within the preset time before the power failure is greater than or equal to the second preset downtime and there is no abnormal fluctuation of the content of the key components during the power failure, the thermal properties of the target object are mainly affected by the downtime at this time, and the chemical composition is relatively stable. By deeply analyzing the heat flow change curve, such as the heat flow slope mutation showing that the heat conduction is blocked, the heating strategy is adjusted, the heating is paused until the heat flow returns to normal, and then the heating is slowly started, or the stirring rate is adjusted to improve the uniformity of heat transfer. Through the above method, the accuracy of analyzing the characteristics of the target object during the downtime and the downtime is improved, and then the accuracy of the subsequent temperature control process is improved.

[0075] Specifically, in step S5, when it is determined to adjust the re-evaluation standard parameter of the temperature control parameter, it is determined whether the target object sticking phenomenon occurs and / or the target object temperature change rate abnormality phenomenon occurs in the temperature control process.

[0076] When the target object sticking phenomenon occurs in the subsequent temperature control process, it is determined to adjust the preset similarity;

[0077] When the target object temperature change rate abnormality phenomenon occurs in the subsequent temperature control process, it is determined to adjust the first preset downtime length;

[0078] When the target object sticking phenomenon does not occur and the target object temperature change rate abnormality phenomenon does not occur in the subsequent temperature control process, it is determined that the preset similarity does not need to be adjusted and the first preset downtime length does not need to be adjusted.

[0079] The re-evaluation standard parameter of the temperature control parameter in the embodiment of the application includes the preset similarity and the first preset downtime length.

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

[0081] The re-evaluation standard parameter of the temperature control parameter in the embodiment of the application includes the first preset downtime length and the preset similarity, the predicted temperature change rate is the average value of the temperature change rates of several same target objects under the same conditions (including but not limited to “the same heating temperature, the same heating rate, and the difference in downtime length does not exceed 10 seconds”), and the preset deviation value is the historical average value of the deviation value between the actual temperature change rate and the predicted temperature change rate of the target object when the target object after heating reaches the expected effect, but the above-mentioned value is not limited thereto, and the person skilled in the art can also adjust the value according to the actual needs.

[0082] Specifically, when it is determined to adjust the preset similarity, the preset similarity is adjusted by a first adjustment coefficient, and when the first preset downtime length is adjusted, the first preset downtime length is adjusted by a second adjustment coefficient.

[0083] The value range of the first adjustment coefficient is set to 1.05-1.19, 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, the value of the second adjustment coefficient is preferably 0.92, the adjustment amount of the preset similarity is positively correlated with the block area ratio when the target object sticking phenomenon occurs, the adjustment amount of the first preset downtime is negatively correlated with the moisture content rate deviation, the value range and the preferred value of the first adjustment coefficient can be determined according to the following method: when the target object sticking phenomenon occurs in the subsequent temperature control process, the preset similarity needs to be adjusted, and the value of the coefficient is based on the experimental observation of the same type of target object under different temperature control conditions and the analysis of a large amount of historical data, it is found that when the sticking phenomenon occurs, the block area ratio and the adjustment of the preset similarity have a certain positive correlation, in order to ensure the adjustment effect while avoiding excessive adjustment, after many experiments and data fitting, the 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 parameter; the value range and the preferred value of the second adjustment coefficient can be determined according to the following method: when the target object temperature change rate abnormal phenomenon occurs, the first preset downtime needs to be adjusted, and the value of the coefficient is based on the thermal characteristics research of the target object under different downtime 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 moisture content rate deviation, in order to make the adjusted first preset downtime more reasonably adapt to the thermal change of the target object, after repeated experiments and data verification, the value range is determined to realize the accurate evaluation of the downtime and the precise optimization of the temperature control process, but the above value is not limited to this, the skilled in the art can also adjust the value according to the actual needs.

[0084] The preset similarity is adjusted when the target object is bonded in the subsequent temperature control process. The bonding of the target object can destroy the original uniform composition distribution in the target object, resulting in abnormal local component concentration. By adjusting the preset similarity in a positive correlation with the blockage area ratio, that is, the larger the blockage area, the larger the preset similarity adjustment amount, the deterioration degree of the target object characteristics can be accurately reflected. When the target object temperature change rate is abnormal, adjusting the first preset downtime length is crucial. If the target object temperature change rate deviates from the normal range, it indicates that the heat transfer in the target object is blocked, which may be caused by target object blockage, heat flow disorder, etc. At this time, the first preset downtime length 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 time length, so as to ensure the stability of the raw material quality. Through the above method, the accuracy of the analysis of the target object characteristics and the downtime length during the downtime caused by power failure of the equipment is improved, and the accuracy of the subsequent temperature control process during power supply is improved.

[0085] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

Claims

1. An adaptive temperature control method, characterized in that, include: Acquire target object characteristic data and power system data, wherein the target object characteristic data includes the target object's heat flow change curve data and the target object's component content data; 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 properties of the target object at the moment of power outage. Based on the downtime of the target device and / or the abnormal fluctuations in the heat flow change curve of the target object within a preset time before the power outage, the temperature control parameters of the target object are determined for re-evaluation. A method for evaluating temperature control parameters is determined based on the downtime of the target device and / or the abnormal fluctuations in the content of key components of the target object during a power outage. Based on whether the target object sticks during the temperature control process and / or whether there is an abnormal rate of temperature change of the target object, the standard parameters for re-evaluation of the temperature control parameters are adjusted. Methods for determining temperature control parameter evaluation include: If the downtime of the target device is less than the second preset downtime, the evaluation method for the temperature control parameters is determined to be the 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 exhibits abnormal fluctuations in the content of key components during the power outage, the temperature control parameter evaluation method is determined to be the 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 the heat flow-dominated adjustment method. The historical experience fine-tuning method is based on successful temperature control case data of the same target object under the same temperature control conditions in the past, and adjusts the current temperature control parameters accordingly. The heat flow combined with component comprehensive evaluation method involves simultaneously analyzing heat flow change curves and key component content data to re-plan temperature control parameters. The heat flow-dominated adjustment method involves adjusting the temperature control parameters based on the characteristics of the heat flow change curve. The adjustments to the re-evaluation criteria for temperature control parameters include: If the target object sticks together during subsequent temperature control, the preset similarity will be adjusted. If an abnormal rate of temperature change of the target object occurs during subsequent temperature control, the first preset downtime will be adjusted.

2. The adaptive temperature control method according to claim 1, characterized in that, Determining the temperature control parameters for re-evaluating the target object includes: If the downtime of the target device exceeds the first preset downtime or if the heat flow change curve of the target object shows abnormal fluctuations within a preset time before the power outage, the temperature control parameters of the target object will be re-evaluated.

3. The adaptive temperature control method according to claim 2, characterized in that, It was determined that the heat flow change curve of the target object within a preset time before the power outage showed abnormal fluctuations, including abrupt changes in the slope of the heat flow change curve of the target object displayed in a high-precision snapshot.

4. The adaptive temperature control method according to claim 3, characterized in that, The study determined that the target object exhibited abnormal fluctuations in the content of key components during the power outage, including a high-precision snapshot showing that the similarity between the content of key components of the target object and the content of key components of the target object at the moment when power was restored was less than the preset similarity.

5. The adaptive temperature control method according to claim 4, characterized in that, The similarity calculation of the key component content of the target object displayed in the high-precision snapshot with the key component content of the target object at the moment of power-on includes: Acquire key component content data of the target object displayed in a high-precision snapshot, and record it as power outage data; Acquire the key component content data of the target object at the moment power supply begins, and record it as power supply data; Standardize the processing of power outage and power supply data; The similarity between power outage data and power supply data is calculated using the cosine similarity formula.

6. The adaptive temperature control method according to claim 5, characterized in that, The preset similarity is determined based on the average similarity of the content of key components before and after the power outage when the difference in power outage duration during historical temperature control is less than 10 seconds for the same type of target object.

7. The adaptive temperature control method according to claim 6, characterized in that, An abnormal rate of temperature change of the target object is identified when the deviation between the actual rate of temperature change and the predicted rate of temperature change of the target object is greater than a preset deviation value.

8. The adaptive temperature control method according to claim 7, characterized in that, The adjustment amount of the preset similarity is positively correlated with the proportion of the agglomerate area when the target object sticks together, and the adjustment amount of the first preset downtime is negatively correlated with the deviation 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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