Intelligent control system and method for fermentation process

Abstract

The invention provides an intelligent control system and method for a fermentation process. The method comprises the following steps: acquiring the fermentation temperature of a fermentation tank in the last fermentation process; dividing to obtain a plurality of temperature change intervals; the temperature fluctuation gradient of each temperature change interval is determined, and then the temperature constraint coefficient of each temperature change interval is determined based on all the optimal temperatures obtained through temperature fluctuation gradient extraction; the fermentation temperature of the fermentation tank in the fermentation is automatically collected, the temperature deviation between the adjacent fermentation temperatures is determined, and then the energy loss of the fermentation temperature of the fermentation tank in the fermentation is determined according to all the temperature deviations; determining the confidence compensation amount of the fermentation tank in the fermentation according to all the temperature constraint coefficients and the energy loss; and performing confidence compensation on the current fermentation temperature of the fermentation tank based on the confidence compensation amount. By adopting the scheme provided by the invention, confidence compensation on the fermentation temperature can be realized in the fermentation process, so that the fermentation quality of a fermented finished product is improved.

Description

Technical Field

[0001] This application relates to the technical field of fermentation temperature control. More specifically, this application relates to an intelligent control system and method for the fermentation process. Background Art

[0002] In modern bio-industry, fermentation is widely used in industries such as medicine, food, and agriculture, for example, to produce antibiotics, enzymes, amino acids, and food additives. The control of the fermentation process refers to improving the yield and quality of fermentation products by monitoring and regulating key parameters in the fermentation process. The key parameters in the fermentation process include temperature, pH value, dissolved oxygen concentration, stirring rate, substrate concentration, fermentation strain state, etc. These factors interact with each other and determine the fermentation efficiency and product yield.

[0003] During the fermentation process, temperature is crucial for the growth and metabolic activities of microorganisms. Early fermentation control mainly relied on manual operation and empirical judgment, which had problems such as low efficiency and large errors, and it was difficult to ensure a stable fermentation environment. However, in the prior art, since different fermentation stages require different temperatures and the fermentation temperature changes frequently, if the fermentation temperature cannot be adjusted in time, it will affect the fermentation effect. If the fermentation temperature of the fermentation tank is adjusted in advance, it may also lead to poor fermentation effect due to the sensitivity of microorganisms in the fermentation tank to temperature. Therefore, how to achieve confidence compensation for the fermentation temperature during the fermentation process to improve the fermentation quality of the final product has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides an intelligent control system and method for the fermentation process, which can achieve confidence compensation for the fermentation temperature during the fermentation process to improve the fermentation quality of the final product.

[0005] In a first aspect, this application provides an intelligent temperature control method for the fermentation process, including the following steps: Obtain the fermentation temperature of the fermentation tank during the previous fermentation process; Construct a fermentation temperature curve based on all the obtained fermentation temperatures, and then divide it into multiple temperature change intervals; For each temperature change interval, determine the temperature fluctuation gradient of the temperature change interval, and then extract all the optimal temperatures in the temperature change interval based on the temperature fluctuation gradient. Determine the temperature constraint coefficient of the temperature change interval through all the extracted optimal temperatures, and then obtain the temperature constraint coefficients of each temperature change interval; Automatically collect the fermentation temperature of the fermentation tank during this fermentation, determine the temperature deviation between each adjacent fermentation temperature, and then determine the energy loss of the fermentation temperature of the fermentation tank during this fermentation based on all the temperature deviations, where the two corresponding fermentation temperatures with adjacent collection times are used as adjacent fermentation temperatures; Determine the confidence compensation amount of the fermenter in this fermentation based on all temperature constraint coefficients and the energy loss; Perform confidence compensation on the current fermentation temperature of the fermenter based on the confidence compensation amount.

[0006] In some embodiments, constructing a fermentation temperature curve based on all acquired fermentation temperatures, and then dividing to obtain multiple temperature change intervals specifically includes: Generate a fermentation temperature curve from all acquired fermentation temperatures; Extract multiple local extreme points from the fermentation temperature curve; Divide the temperature curve into multiple temperature change intervals during the fermentation process based on each local extreme point.

[0007] In some embodiments, determining the temperature fluctuation gradient of the temperature change interval specifically includes: Determine the trend characteristics of the fermentation temperature in the temperature change interval; Determine the temperature fluctuation gradient of the temperature change interval according to the trend characteristics.

[0008] In some embodiments, extracting all the optimum temperatures in the temperature change interval based on the temperature fluctuation gradient specifically includes: Determine the effective temperature boundary value of the temperature change interval according to the temperature fluctuation gradient; Compare the magnitudes of all fermentation temperatures in the temperature change interval with the effective temperature boundary value, and then regard all fermentation temperatures in the temperature change interval that are higher than or equal to the effective temperature boundary value as the optimum temperatures, so as to obtain all the optimum temperatures in the temperature change interval.

[0009] In some embodiments, determining the energy loss of the fermentation temperature of the fermenter in this fermentation according to all temperature deviations specifically includes: Determine the influence coefficient of the temperature response of the fermenter in this fermentation; Determine the energy loss of the fermentation temperature of the fermenter in this fermentation according to the influence coefficient and all temperature deviations.

[0010] In some embodiments, determining the confidence compensation amount of the fermenter in this fermentation according to all temperature constraint coefficients and the energy loss specifically includes: Determine the proportional coefficient and differential coefficient for regulating the fermentation temperature during this fermentation process from the energy loss; Determine the confidence compensation amount of the fermenter in this fermentation through all temperature constraint coefficients, the proportional coefficient, and the differential coefficient.

[0011] In some embodiments, automatically collect the fermentation temperature of the fermenter in this fermentation through a temperature sensor arranged in the fermenter.

[0012] In a second aspect, the present application provides an intelligent control system for a fermentation process, including an intelligent temperature regulation unit, and the intelligent temperature regulation unit includes: An acquisition module, configured to acquire the fermentation temperature of the fermenter during the previous fermentation process; A processing module, configured to construct a fermentation temperature curve based on all the acquired fermentation temperatures, and then divide it into multiple temperature change intervals; The processing module is further configured to, for each temperature change interval, determine the temperature fluctuation gradient of the temperature change interval, and then extract all the optimum temperatures in the temperature change interval based on the temperature fluctuation gradient, determine the temperature constraint coefficient of the temperature change interval through all the extracted optimum temperatures, and thus obtain the temperature constraint coefficients of each temperature change interval; The processing module is further configured to automatically collect the fermentation temperature of the fermenter during the current fermentation, determine the temperature deviation between each adjacent fermentation temperature, and then determine the energy loss of the fermentation temperature of the fermenter during the current fermentation according to all the temperature deviations, where the two corresponding fermentation temperatures with adjacent acquisition times are used as adjacent fermentation temperatures; The processing module is further configured to determine the confidence compensation amount of the fermenter during the current fermentation according to all the temperature constraint coefficients and the energy loss; An execution module, configured to perform confidence compensation on the current fermentation temperature of the fermenter based on the confidence compensation amount.

[0013] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned intelligent temperature regulation method for the fermentation process.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes run on a computer, the computer is caused to execute the above-mentioned intelligent temperature regulation method for the fermentation process.

[0015] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects: In this application, the fermentation temperature of the fermenter during the previous fermentation process is obtained; a fermentation temperature curve is constructed based on all the obtained fermentation temperatures, and then multiple temperature change intervals are divided; for each temperature change interval, the temperature fluctuation gradient of the temperature change interval is determined, and then all the optimal temperatures in the temperature change interval are extracted based on the temperature fluctuation gradient. The temperature constraint coefficient of the temperature change interval is determined through all the extracted optimal temperatures, and then the temperature constraint coefficients of each temperature change interval are obtained; the fermentation temperature of the fermenter in the current fermentation is automatically collected, the temperature deviation between each adjacent fermentation temperature is determined, and then the energy loss of the fermentation temperature of the fermenter in the current fermentation is determined according to all the temperature deviations, where the corresponding two fermentation temperatures with adjacent collection times are used as adjacent fermentation temperatures; the confidence compensation amount of the fermenter in the current fermentation is determined according to all the temperature constraint coefficients and the energy loss; the current fermentation temperature of the fermenter is confidence-compensated based on the confidence compensation amount.

[0016] It can be seen that in this application, first, the entire fermentation process is divided into different stages according to the change of temperature to obtain multiple temperature change intervals. Compared with the prior art where the fermentation stage is artificially divided according to past experience, the temperature requirements of different stages can be more precisely identified, and the impact of temperature change on microorganisms in the fermenter can be reduced; second, all the optimal temperatures are extracted from each temperature change interval, which can ensure the growth and metabolism of microorganisms at the optimal temperature, thereby improving the fermentation efficiency and product quality; third, the temperature constraint coefficient of each temperature change interval is further determined, which can avoid the adverse effects of too high or too low temperature on microorganisms while ensuring the fermentation efficiency; finally, the confidence compensation amount of the current fermentation is determined by combining the energy loss and temperature constraint coefficient generated during the current fermentation process, so as to perform confidence compensation on the current fermentation temperature of the fermenter, enabling the heating or cooling system in the fermenter to automatically adjust the temperature according to real-time data and historical experience to achieve more accurate and stable temperature control, thereby improving the fermentation efficiency and product quality; in summary, this solution can achieve confidence compensation for the fermentation temperature during the fermentation process to improve the fermentation quality of the finished product after fermentation. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is an exemplary flowchart of an intelligent temperature control method for a fermentation process shown in some embodiments of the present application; Figure 2 An exemplary flowchart for determining a temperature change range as shown in some embodiments of the present application; Figure 3 An exemplary flowchart for determining energy loss as shown in some embodiments of the present application; Figure 4 A schematic structural diagram of an intelligent temperature control unit as shown in some embodiments of the present application; Figure 5 A schematic structural diagram of a computer device for implementing an intelligent temperature control method for a fermentation process as shown in some embodiments of the present application. Detailed implementation manners

[0019] For a better understanding of the technical solution, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0020] Refer to Figure 1 , this figure is an exemplary flowchart of an intelligent temperature control method for a fermentation process as shown in some embodiments of the present application. The intelligent temperature control method 100 for the fermentation process mainly includes the following steps: In step 101, obtain the fermentation temperature of the fermentation tank during the previous fermentation process.

[0021] Specifically, the fermentation temperature of the fermentation tank during the previous fermentation process can be obtained from the temperature records of the temperature sensor in the fermentation tank. Among them, the temperature sensor collects the temperature of the fermentation tank during the fermentation process at a set collection frequency and automatically enters it into the temperature record.

[0022] It should be noted that the fermentation temperature obtained in this step is the temperature collected by the temperature sensor in the fermentation tank during the previous fermentation process; the setting of the collection frequency can be adjusted according to the dynamics of the current fermentation process. For example: for a situation where the temperature change during the fermentation process is relatively slow, a lower collection frequency (such as once per minute) can be set, while when the temperature changes violently during the fermentation process, a higher collection frequency (such as once per second) can be set. In other embodiments, other methods can also be used to set the collection frequency, which is not limited here.

[0023] In step 102, construct a fermentation temperature curve based on all the obtained fermentation temperatures, and then divide it into multiple temperature change ranges.

[0024] In some embodiments, refer to Figure 2 shown, this figure is an exemplary flowchart for determining the temperature change range in some embodiments of the present application. In this embodiment, constructing a fermentation temperature curve based on all the obtained fermentation temperatures and then dividing it into multiple temperature change ranges can be implemented by the following steps: First, in step 1021, generate a fermentation temperature curve from all the acquired fermentation temperatures; Second, in step 1022, extract multiple local extreme points from the fermentation temperature curve; Finally, in step 1023, divide the temperature curve into multiple temperature change intervals during the fermentation process based on each local extreme point.

[0025] When specifically implemented, generating a fermentation temperature curve from all the acquired fermentation temperatures can be achieved in the following manner, that is: the acquired fermentation temperatures can be plotted into a fermentation temperature curve during the fermentation process with the acquisition time of the fermentation temperature as the abscissa and the temperature value of the fermentation temperature as the ordinate through the matplotlib library function of Python. In other embodiments, other methods can also be used for generation, which is not limited here; extracting multiple local extreme points from the fermentation temperature curve can be achieved in the following manner, that is: the first derivative detection method can be used to judge the rising or falling trend of the temperature change in the fermentation temperature curve, and then the points corresponding to the first derivative being zero and the second derivative being less than zero in the fermentation temperature curve are used as local maxima, and the points corresponding to the first derivative being zero and the second derivative being greater than zero in the fermentation temperature curve are used as local minima. All the points corresponding to the obtained local maxima and local minima are used as local extreme points. In other embodiments, other methods can also be used for determination, which is not limited here; dividing the temperature curve into multiple temperature change intervals during the fermentation process based on each local extreme point can be achieved in the following manner, that is: all the local extreme points are arranged in the order of the acquisition time corresponding in the fermentation temperature curve, and then the fermentation temperature curve is divided into multiple temperature change intervals during the fermentation process based on the sorted local extreme points. Among them, the starting point and the ending point of each temperature change interval are determined by adjacent local maxima and local minima, that is, the boundary of each temperature change interval is the part between one local extreme point and the next local extreme point. In other embodiments, other methods can also be used for determination, which is not limited here.

[0026] It should be noted that the fermentation temperature curve in this application reflects the change trend of temperature over time in the previous fermentation process; the local extreme point represents the point where the change trend of temperature reverses within a period of time, and it is used to divide the temperature change intervals during the fermentation process; the temperature change interval reflects the temperature change pattern in the corresponding interval during the fermentation process, where the temperature change pattern is divided into an ascending pattern and a descending pattern.

[0027] In step 103, for each temperature change interval, determine the temperature fluctuation gradient of the temperature change interval, and then based on the temperature fluctuation gradient, extract all the optimal temperatures in the temperature change interval. Determine the temperature constraint coefficient of the temperature change interval through all the extracted optimal temperatures, and then obtain the temperature constraint coefficients of each temperature change interval.

[0028] In some embodiments, the determination of the temperature fluctuation gradient of the temperature change interval can be implemented by the following steps: Determine the trend characteristic of the fermentation temperature in the temperature change interval; Determine the temperature fluctuation gradient of the temperature change interval according to the trend characteristic.

[0029] Specifically, in the present application, the trend characteristic reflects the central tendency of the fermentation temperature in the temperature change interval. As a preferred embodiment, the determination of the trend characteristic of the fermentation temperature in the temperature change interval can be implemented in the following manner, that is: the temperature value corresponding to the mode of all the fermentation temperatures in the temperature change interval can be used as the trend characteristic of the temperature change interval. In other embodiments, other methods can also be used for determination, which is not limited here.

[0030] In addition, specifically, in the present application, the temperature fluctuation gradient reflects the dispersion degree of all the fermentation temperatures in the temperature change interval. The larger the temperature fluctuation gradient, the greater the dispersion degree of all the fermentation temperatures in the temperature change interval; the smaller the temperature fluctuation gradient, the smaller the dispersion degree of all the fermentation temperatures in the temperature change interval. As a preferred embodiment, the determination of the temperature fluctuation gradient of the temperature change interval according to the trend characteristic can be implemented in the following manner, that is: calculate the absolute value of the difference between the temperature value of each fermentation temperature in the temperature change interval and the trend characteristic of the temperature change interval, and then take the average value of all the calculated absolute values as the temperature fluctuation gradient of the temperature change interval. In other embodiments, other methods can also be used for determination, which is not limited here.

[0031] In some embodiments, the extraction of all the optimal temperatures in the temperature change interval based on the temperature fluctuation gradient can be implemented by the following steps: Determine the temperature effective boundary value of the temperature change interval according to the temperature fluctuation gradient; Compare the sizes of all the fermentation temperatures in the temperature change interval with the temperature effective boundary value, and then regard all the fermentation temperatures in the temperature change interval that are higher than or equal to the temperature effective boundary value as the optimal temperatures, and then obtain all the optimal temperatures in the temperature change interval.

[0032] In specific implementation, the effective temperature threshold is a temperature threshold, which is used to extract the fermentation temperature that plays a major role in the fermentation process. As a preferred embodiment, the effective temperature threshold for determining the temperature change range according to the temperature fluctuation gradient can be implemented in the following manner, that is: relevant experts can determine the effective temperature threshold for the temperature change range based on the temperature fluctuation gradient of the temperature change range, the fermentation characteristics corresponding to each temperature change range in the fermentation process, and the fermentation requirements. Additionally, in some embodiments, the actual situation during the fermentation process can be continuously monitored to adjust the effective temperature threshold, so as to precisely improve the efficiency of the fermentation process and the product quality; furthermore, all the fermentation temperatures in the temperature change range are compared with the effective temperature threshold, and then all the fermentation temperatures in the temperature change range that are higher than or equal to the effective temperature threshold are taken as the optimum temperatures, and then all the optimum temperatures in the temperature change range are obtained. In other embodiments, other methods can also be used for determination, which is not limited here.

[0033] It should be noted that the optimum temperature in this application represents the fermentation temperature that plays a major role in the fermentation process within the temperature change range.

[0034] In some embodiments, the temperature constraint coefficient reflects the degree of constraint of the fermentation temperature on the fermentation process within the temperature change range. The larger the temperature constraint coefficient, the greater the degree of constraint of the fermentation temperature on the fermentation process within the temperature change range; the smaller the temperature constraint coefficient, the smaller the degree of constraint of the fermentation temperature on the fermentation process within the temperature change range. As a preferred embodiment, the temperature constraint coefficient for determining the temperature change range by extracting all the optimum temperatures can be implemented in the following manner, that is: First, obtain the maximum fermentation temperature and the minimum fermentation temperature within the temperature change range, and calculate the difference between the maximum fermentation temperature and the minimum fermentation temperature to obtain the first difference. Second, calculate the average value of all the optimum temperatures within the temperature change range. Then, subtract the minimum fermentation temperature from the calculated average value to obtain the second difference. Finally, take the ratio of the second difference to the first difference as the temperature constraint coefficient for the temperature change range. In other embodiments, other methods can also be used for determination, which is not limited here.

[0035] In step 104, the fermentation temperature of the fermenter during this fermentation is automatically collected, the temperature deviation between each adjacent fermentation temperature is determined, and then the energy loss of the fermentation temperature of the fermenter during this fermentation is determined based on all the temperature deviations, where the two corresponding fermentation temperatures with adjacent collection times are used as adjacent fermentation temperatures.

[0036] In specific implementation, the fermentation temperature of the fermenter during the current fermentation is collected in real time by a temperature sensor in the fermenter according to a preset collection frequency. Among them, the setting of the collection frequency can be adjusted according to the dynamics of the current fermentation process. For example, for the case where the temperature change during the fermentation process is relatively slow, a lower collection frequency (such as once per minute) can be set, while when the temperature changes violently during the fermentation process, a higher collection frequency (such as once per second) can be set. In other embodiments, other methods can also be used to set the collection frequency, which is not limited here.

[0037] It should be noted that in this application, two adjacent fermentation temperatures with adjacent collection times are regarded as adjacent fermentation temperatures.

[0038] In specific implementation, the temperature deviation represents the degree of temperature difference between adjacent fermentation temperatures. The larger the temperature deviation, the greater the temperature difference between adjacent fermentation temperatures, and the smaller the temperature deviation, the smaller the temperature difference between adjacent fermentation temperatures. As a preferred embodiment, the temperature deviation between each adjacent fermentation temperature can be determined by the following method, that is: calculate the absolute value of the difference between two adjacent fermentation temperatures with adjacent collection times, and then use this absolute value as the temperature deviation between adjacent fermentation temperatures. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0039] In some embodiments, refer to Figure 3 As shown, this figure is an exemplary flowchart for determining energy loss in some embodiments of this application. In this embodiment, the energy loss of the fermentation temperature of the fermenter during the current fermentation can be determined according to all the temperature deviations by the following steps: First, in step 1031, determine the influence coefficient of the temperature response of the fermenter during the current fermentation; Then, in step 1032, determine the energy loss of the fermentation temperature of the fermenter during the current fermentation according to the influence coefficient and all the temperature deviations.

[0040] In specific implementation, the influence coefficient in this application reflects the influence degree of the fermentation temperature change on the current fermentation process. The greater the influence coefficient, the greater the influence of the fermentation temperature change on the current fermentation process; the smaller the influence coefficient, the smaller the influence of the fermentation temperature change on the current fermentation process. As a preferred embodiment, the influence coefficient of the temperature response of the fermenter in the current fermentation can be determined in the following manner, that is: the relationship between the change in fermentation temperature and the microbial growth and metabolic activities during the current fermentation process can be quantified through an evaluation algorithm, and the quantified result is used as the influence coefficient of the temperature response of the fermenter in the current fermentation. Among them, the evaluation algorithm is, for example: regression analysis, machine learning model or other statistical methods. In other embodiments, other methods can also be used for determination, which is not limited here.

[0041] In addition, in specific implementation, the energy loss of the fermentation temperature of the fermenter in the current fermentation can be determined according to the influence coefficient and all temperature deviations in the following manner, that is: first calculate the average value of all temperature deviations, and then multiply the influence coefficient by the calculated average value as the energy loss of the fermentation temperature of the fermenter in the current fermentation. In other embodiments, other methods can also be used for determination, which is not limited here.

[0042] It should be noted that the energy loss in this application represents the degree of reduction in fermentation efficiency during the temperature regulation process.

[0043] In step 105, the confidence compensation amount of the fermenter in the current fermentation is determined according to all temperature constraint coefficients and the energy loss.

[0044] In some embodiments, the confidence compensation amount of the fermenter in the current fermentation can be determined according to all temperature constraint coefficients and the energy loss by the following steps: Determine the proportional coefficient and differential coefficient for regulating the fermentation temperature during the current fermentation process from the energy loss; Determine the confidence compensation amount of the fermenter in the current fermentation through all temperature constraint coefficients, the proportional coefficient and the differential coefficient.

[0045] In specific implementation, the proportionality coefficient in the present application is used to control the response intensity to the temperature deviation, and the differential coefficient is used to control the prediction ability for the temperature change trend. The proportionality coefficient and the differential coefficient for regulating the fermentation temperature during the current fermentation process determined from the energy loss can be implemented in the following manner, that is: the P value and the D value in the PID parameters can be determined based on the energy loss through existing PID parameter tuning methods, and then the optimal P value and D value can be determined by minimizing a cost function (such as the mean square error). Thus, the finally obtained P value is used as the proportionality coefficient for regulating the fermentation temperature during the current fermentation process, and the finally obtained D value is used as the differential coefficient for regulating the fermentation temperature during the current fermentation process. Among them, the PID parameter tuning methods include, for example, the Ziegler–Nichols tuning method, the manual tuning method, or the automatic tuning algorithm, etc. In other embodiments, other methods can also be used for determination, which is not limited here; the confidence compensation amount of the fermenter during the current fermentation can be determined by all the temperature constraint coefficients, the proportionality coefficient, and the differential coefficient in the following manner, that is: first, screen out the temperature constraint coefficients corresponding to the start of the current fermentation to the current moment from all the temperature constraint coefficients, and then calculate the cumulative value of all the screened temperature constraint coefficients and the current fermentation temperature (for example, if the temperature constraint coefficients are 0.1, 0.2, and 0.3 respectively, the cumulative value = the current fermentation temperature × (1 + 0.1) × (1 + 0.2) × (1 + 0.3)); second, calculate the product of the proportionality coefficient and the cumulative value respectively, and the product of the differential coefficient and the energy loss; then, subtract the current fermentation temperature from the sum of the two calculated products; finally, use the calculated difference as the confidence compensation amount of the fermenter during the current fermentation. In other embodiments, other methods can also be used for determination, which is not limited here.

[0046] It should be noted that the confidence compensation amount in the present application represents the credible temperature difference between the current fermentation temperature in the fermenter and the ideal fermentation temperature, and can be used to compensate the output power of the heating or cooling system in the fermenter during the current fermentation process, that is, to compensate the current fermentation temperature during the current fermentation process.

[0047] In step 106, confidence compensation is performed on the current fermentation temperature of the fermenter based on the confidence compensation amount.

[0048] In specific implementation, first, the current fermentation temperature is collected by the temperature sensor in the fermenter, and then the output power of the heating or cooling system in the fermenter is adjusted according to the confidence compensation amount, so as to reach the ideal fermentation temperature. And during the subsequent period of the current fermentation, the fermentation temperature of the fermenter will be continuously monitored, and the confidence compensation amount will be adjusted in real time to improve the fermentation efficiency and product quality.

[0049] In addition, on the other hand of the present application, in some embodiments, the present application provides an intelligent control system for a fermentation process. The system includes an intelligent temperature regulation unit. Refer to Figure 4 , which is a schematic structural diagram of the intelligent temperature regulation unit shown according to some embodiments of the present application. The intelligent temperature regulation unit 400 includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described as follows: The acquisition module 401. In the present application, the acquisition module 401 is mainly used to acquire the fermentation temperature of the fermenter during the previous fermentation process. The processing module 402. In the present application, the processing module 402 is mainly used to construct a fermentation temperature curve based on all the acquired fermentation temperatures, and then divide it into multiple temperature change intervals. In the present application, the processing module 402 is further used to, for each temperature change interval, determine the temperature fluctuation gradient of the temperature change interval, and then extract all the optimum temperatures in the temperature change interval based on the temperature fluctuation gradient. The temperature constraint coefficient of the temperature change interval is determined by all the extracted optimum temperatures, and thus the temperature constraint coefficients of each temperature change interval are obtained. In the present application, the processing module 402 is further used to automatically collect the fermentation temperature of the fermenter during the current fermentation, determine the temperature deviation between each adjacent fermentation temperature, and then determine the energy loss of the fermentation temperature of the fermenter during the current fermentation according to all the temperature deviations, where the two corresponding fermentation temperatures with adjacent acquisition times are used as adjacent fermentation temperatures. In the present application, the processing module 402 is further used to determine the confidence compensation amount of the fermenter during the current fermentation according to all the temperature constraint coefficients and the energy loss. The execution module 403. In the present application, the execution module 403 is mainly used to perform confidence compensation on the current fermentation temperature of the fermenter based on the confidence compensation amount.

[0050] The above has introduced in detail the examples of the intelligent control system and method for the fermentation process provided by the embodiments of the present application. It can be understood that, in order to implement the above functions, the corresponding device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0051] In some embodiments, the present application further provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned intelligent temperature control method for the fermentation process.

[0052] In some embodiments, referring to Figure 5 , the dashed line in this figure indicates that the unit or module is optional. This figure is a schematic structural diagram of a computer device for implementing the intelligent temperature control method for the fermentation process of the present application. The above-mentioned intelligent temperature control method for the fermentation process in the embodiments can be implemented by Figure 5 the computer device shown. The computer device 500 includes at least one processor 501, a memory 502, and at least one communication unit 505. The computer device 500 can be a terminal device, a server, or a chip.

[0053] The processor 501 can be a general-purpose processor or a special-purpose processor. For example, the processor 501 can be a central processing unit (CPU). The CPU can be used to control the computer device 500, execute software programs, and process data of the software programs. The computer device 500 can also include a communication unit 505 for implementing signal input (reception) and output (transmission).

[0054] For example, the computer device 500 can be a chip, and the communication unit 505 can be the input and / or output circuit of the chip. Alternatively, the communication unit 505 can be the communication interface of the chip. The chip can be a component of a terminal device, a network device, or other devices.

[0055] Again, for example, the computer device 500 can be a terminal device or a server, and the communication unit 505 can be the transceiver of the terminal device or the server. Alternatively, the communication unit 505 can be the transceiver circuit of the terminal device or the server.

[0056] The computer device 500 can include one or more memories 502, on which there is a program 504. The program 504 can be run by the processor 501 to generate an instruction 503, so that the processor 501 executes the method described in the above method embodiments according to the instruction 503. Optionally, data (such as a target audit model) can also be stored in the memory 502. Optionally, the processor 501 can also read the data stored in the memory 502. The data can be stored at the same storage address as the program 504, or the data can be stored at a different storage address from the program 504.

[0057] The processor 501 and the memory 502 can be set separately or integrated together. For example, they can be integrated on a system on chip (SOC) of a terminal device.

[0058] It should be understood that each step of the above method embodiments can be completed by a logic circuit in hardware form or an instruction in software form in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices. For example, discrete gates, transistor logic devices, or discrete hardware components.

[0059] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] For example, in some embodiments, the present application further provides a computer-readable storage medium. Instructions or code are stored in the computer-readable storage medium. When the instructions or code run on a computer, the computer is caused to execute the above-mentioned intelligent temperature control method for the fermentation process.

[0061] In summary, in the intelligent control system and method for the fermentation process disclosed in the embodiments of the present application, the fermentation temperature of the fermenter in the previous fermentation process is obtained; a fermentation temperature curve is constructed based on all the obtained fermentation temperatures, and then a plurality of temperature change intervals are divided; for each temperature change interval, the temperature fluctuation gradient of the temperature change interval is determined, and then all the optimum temperatures in the temperature change interval are extracted based on the temperature fluctuation gradient, the temperature constraint coefficient of the temperature change interval is determined through all the extracted optimum temperatures, and then the temperature constraint coefficients of each temperature change interval are obtained; the fermentation temperature of the fermenter in the current fermentation is automatically collected, the temperature deviation between each adjacent fermentation temperature is determined, and then the energy loss of the fermentation temperature of the fermenter in the current fermentation is determined according to all the temperature deviations, wherein the two corresponding fermentation temperatures with adjacent collection times are used as adjacent fermentation temperatures; the confidence compensation amount of the fermenter in the current fermentation is determined according to all the temperature constraint coefficients and the energy loss; the confidence compensation is performed on the current fermentation temperature of the fermenter based on the confidence compensation amount; by adopting the solution of the present application, the confidence compensation for the fermentation temperature can be realized during the fermentation process, thereby improving the fermentation quality of the finished product after fermentation.

[0062] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. An intelligent temperature control method for a fermentation process, used to control the temperature of a fermentation tank during a fermentation process, characterized in that: The steps include: Obtain the fermentation temperature of the fermentation tank during the last fermentation process; A fermentation temperature curve is constructed based on all the obtained fermentation temperatures, and then a plurality of temperature change intervals are obtained; For each temperature variation interval, determine the temperature fluctuation gradient of the temperature variation interval, and then extract all the optimum temperatures in the temperature variation interval based on the temperature fluctuation gradient, determine the temperature constraint coefficient of the temperature variation interval by extracting all the optimum temperatures, and then obtain the temperature constraint coefficient of each temperature variation interval; Automatically collect the fermentation temperature of the fermentation tank in this fermentation, determine the temperature deviation between each adjacent fermentation temperature, and then determine the energy loss of the fermentation temperature of the fermentation tank in this fermentation according to all the temperature deviations, wherein two corresponding fermentation temperatures with adjacent collection times are taken as adjacent fermentation temperatures; Determining the confidence compensation amount of the fermentation tank in this fermentation according to all temperature constraint coefficients and the energy loss; A confidence compensation is performed on the current fermentation temperature of the fermentation tank based on the confidence compensation amount.

2. The method according to claim 1, characterized in that A fermentation temperature curve is constructed based on all the obtained fermentation temperatures, and then multiple temperature change intervals are obtained, including: Generate a fermentation temperature curve using all the obtained fermentation temperatures; extracting a plurality of local extreme points from the fermentation temperature curve; The temperature curve is divided into a plurality of temperature variation intervals during the fermentation process based on each local extreme point.

3. The method according to claim 1, characterized in that Determining the temperature fluctuation gradient of the temperature change range specifically includes: Determine the trend characteristics of fermentation temperature in the temperature variation range; The temperature fluctuation gradient in the temperature change interval is determined according to the trend characteristics.

4. The method according to claim 1, characterized in that Extracting all the optimum temperatures in the temperature variation range based on the temperature fluctuation gradient specifically includes: Determine the effective temperature boundary value of the temperature variation interval according to the temperature fluctuation gradient; All fermentation temperatures in the temperature variation interval are compared with the effective temperature boundary value, and then all fermentation temperatures in the temperature variation interval that are higher than or equal to the effective temperature boundary value are taken as the optimum temperatures, thereby obtaining all the optimum temperatures in the temperature variation interval.

5. The method according to claim 1, characterized in that The energy loss of the fermentation temperature of the fermentation tank during this fermentation is determined based on all temperature deviations, including: Determine the influence coefficient of the temperature response of the fermentation tank in this fermentation; The energy loss of the fermentation temperature of the fermentation tank in this fermentation is determined according to the influence coefficient and all temperature deviations.

6. The method according to claim 1, characterized in that Determining the confidence compensation amount of the fermentation tank in this fermentation according to all temperature constraint coefficients and the energy loss specifically includes: Determining the proportional coefficient and differential coefficient for regulating the fermentation temperature during the fermentation process according to the energy loss; The confidence compensation amount of the fermentation tank in this fermentation is determined by all temperature constraint coefficients, the proportional coefficient and the differential coefficient.

7. The method according to claim 1, characterized in that The fermentation temperature of the fermentation tank during the current fermentation is automatically collected by a temperature sensor arranged in the fermentation tank.

8. An intelligent control system for a fermentation process, comprising an intelligent temperature control unit, characterized in that: The intelligent temperature control unit comprises: An acquisition module, used for acquiring the fermentation temperature of the fermentation tank during the last fermentation process; A processing module, used for constructing a fermentation temperature curve based on all the obtained fermentation temperatures, and then dividing it into multiple temperature change intervals; The processing module is further used to determine, for each temperature change interval, a temperature fluctuation gradient of the temperature change interval, and then extract all optimum temperatures in the temperature change interval based on the temperature fluctuation gradient, determine a temperature constraint coefficient of the temperature change interval by extracting all the optimum temperatures, and then obtain the temperature constraint coefficient of each temperature change interval; The processing module is further used to automatically collect the fermentation temperature of the fermentation tank in this fermentation, determine the temperature deviation between each adjacent fermentation temperature, and then determine the energy loss of the fermentation temperature of the fermentation tank in this fermentation according to all the temperature deviations, wherein two corresponding fermentation temperatures with adjacent collection times are used as adjacent fermentation temperatures; The processing module is further used to determine the confidence compensation amount of the fermentation tank in this fermentation according to all temperature constraint coefficients and the energy loss; An execution module is used to perform confidence compensation on the current fermentation temperature of the fermentation tank based on the confidence compensation amount.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the intelligent temperature control method for a fermentation process according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the intelligent temperature control method for a fermentation process as described in any one of claims 1 to 7.

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