Monitoring methods, devices, computer equipment, and storage media for fermentation equipment

Abstract

This application relates to a monitoring method, apparatus, computer equipment, and storage medium for fermentation equipment. The method includes: monitoring the current stirring speed and current aeration rate of the fermentation equipment; determining the target oxygen transfer rate per unit volume for the fermentation equipment based on the current stirring speed and current aeration rate; and displaying the current stirring speed, current aeration rate, and the corresponding target oxygen transfer rate per unit volume on the fermentation equipment, wherein the target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment. Using this method, the fermentation efficiency and quality of the fermentation equipment can be accurately reflected through the target oxygen transfer rate per unit volume.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a monitoring method, apparatus, computer equipment, and storage medium for fermentation equipment. Background Technology

[0002] For bio-fermentation, the process performance of fermentation directly determines the production quality and efficiency. This performance can be evaluated through many indicators such as oxygen transfer efficiency (OTR), oxygen uptake rate (OUR), and respiratory quotient (RQ). Therefore, studying these indicators is the foundation for optimizing fermentation work.

[0003] Currently, traditional methods primarily predict and optimize by establishing correlation models and evaluation criteria for various parameters and indicators. This typically involves using a theoretical equation to fit the relationship between system indicators and parameters. However, this theoretical equation has limitations in the face of diverse and complex environments, and establishing it is a highly complex process requiring continuous trial and error. Consequently, the accuracy of the indicators used to evaluate the efficiency and quality of biofermentation remains low. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, and storage medium for monitoring fermentation equipment that can accurately reflect the fermentation efficiency and quality of fermentation equipment through the oxygen transfer rate per unit volume of the target unit volume, in order to address the above-mentioned technical problems.

[0005] A method for monitoring fermentation equipment, the method comprising:

[0006] Monitor the current stirring speed and current aeration rate of the fermentation equipment;

[0007] Determine the target oxygen transfer rate per unit volume for the fermentation equipment based on the current stirring speed and current aeration rate.

[0008] The fermentation equipment displays the current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

[0009] In one embodiment, determining the target oxygen transfer rate per unit volume corresponding to the fermentation equipment based on the current stirring speed and the current aeration rate includes: acquiring a pre-trained target oxygen transfer rate monitoring model, inputting the current stirring speed and the current aeration rate into the target oxygen transfer rate monitoring model, calculating the current stirring speed and the current aeration rate through the target oxygen transfer rate monitoring model, and outputting the corresponding target oxygen transfer rate per unit volume.

[0010] In one embodiment, the training steps of the target oxygen transfer rate monitoring model include: under cold model conditions, acquiring multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment, each set of training stirring speeds and training aeration rates being associated with a corresponding standard unit volume oxygen transfer rate; acquiring the constructed original oxygen transfer rate monitoring model; inputting each set of training stirring speeds and training aeration rates into the original oxygen transfer rate monitoring model; calculating each set of training stirring speeds and training aeration rates through the original oxygen transfer rate monitoring model; outputting the corresponding output unit volume oxygen transfer rate; calculating the training loss value based on each output unit volume oxygen transfer rate and the corresponding standard unit volume oxygen transfer rate; continuously adjusting the model parameters of the original oxygen transfer rate monitoring model based on the training loss value until the convergence condition is met, thus obtaining the trained target oxygen transfer rate monitoring model.

[0011] In one embodiment, the model parameters of the original oxygen transfer rate monitoring model are continuously adjusted according to the training loss value until the convergence condition is met, and a trained target oxygen transfer rate monitoring model is obtained. This includes: when the training loss value does not meet the convergence condition, adjusting the number of hidden layers of the original oxygen transfer rate monitoring model, returning to the step of obtaining the constructed original oxygen transfer rate monitoring model, until the training loss value meets the convergence condition, and a trained target oxygen transfer rate monitoring model is obtained.

[0012] In one embodiment, after obtaining multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment, and after each set of training stirring speeds and training aeration rates is associated with a corresponding standard unit volume oxygen transfer rate, the method further includes: performing cluster calculations based on the standard unit volume oxygen transfer rates associated with each set of training stirring speeds and training aeration rates to obtain abnormal standard unit volume oxygen transfer rates, removing the training stirring speeds and training aeration rates corresponding to the abnormal standard unit volume oxygen transfer rates, and obtaining cleaned sets of training stirring speeds and training aeration rates.

[0013] In one embodiment, the method further includes: obtaining a preset trend graph, which includes a mapping relationship between different stirring speeds, aeration rates and corresponding oxygen transfer rates per unit volume, and adjusting the corresponding operating parameters of the fermentation equipment according to the mapping relationship in the preset trend graph and the target oxygen transfer rate per unit volume.

[0014] In one embodiment, the step of generating the preset trend chart includes: acquiring multiple sets of different verification ventilation rates and verification stirring speeds; determining the corresponding verification unit volume oxygen transfer rate based on the verification stirring speed and verification ventilation rate of each set; establishing a mapping relationship between the verification stirring speed, verification ventilation rate and the corresponding verification unit volume oxygen transfer rate of each set; and obtaining the preset trend chart.

[0015] A monitoring device for a fermentation apparatus, the device comprising:

[0016] The monitoring module is used to monitor the current stirring speed and current aeration rate of the fermentation equipment.

[0017] The determination module is used to determine the target oxygen transfer rate per unit volume for the fermentation equipment based on the current stirring speed and current aeration rate.

[0018] The display module is used to display the current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume on the fermentation equipment. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

[0019] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps:

[0020] Monitor the current stirring speed and current aeration rate of the fermentation equipment;

[0021] Determine the target oxygen transfer rate per unit volume for the fermentation equipment based on the current stirring speed and current aeration rate.

[0022] The fermentation equipment displays the current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

[0023] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0024] Monitor the current stirring speed and current aeration rate of the fermentation equipment;

[0025] Determine the target oxygen transfer rate per unit volume for the fermentation equipment based on the current stirring speed and current aeration rate.

[0026] The fermentation equipment displays the current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

[0027] The aforementioned monitoring method, apparatus, computer equipment, and storage medium for fermentation equipment monitor the current stirring speed and aeration rate of the fermentation equipment. Based on the current stirring speed and aeration rate, the target oxygen transfer rate per unit volume for the fermentation equipment is determined. The current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume are displayed on the fermentation equipment. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment. Therefore, by monitoring the stirring speed and aeration rate of the fermentation equipment, the corresponding target oxygen transfer rate per unit volume can be determined, and the target oxygen transfer rate per unit volume accurately reflects the fermentation efficiency and quality of the fermentation equipment. Attached Figure Description

[0028] Figure 1 This is an application environment diagram of the monitoring method for the fermentation equipment in one embodiment;

[0029] Figure 2 This is a flowchart illustrating a monitoring method for a fermentation device in one embodiment;

[0030] Figure 3 This is a flowchart illustrating the training steps of a target oxygen transfer rate monitoring model in one embodiment.

[0031] Figure 4 This is a structural block diagram of the monitoring device of the fermentation equipment in one embodiment;

[0032] Figure 5 This is an internal structural diagram of a computer device in one embodiment;

[0033] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] The monitoring method for fermentation equipment provided in this application can be applied to, for example... Figure 1 In the application environment shown, the fermentation device 102 communicates with the computer device 104 via a network. The fermentation device 102 is a device used for biological fermentation, and the computer device 104 can be implemented using a standalone server, a server cluster consisting of multiple servers, or it can also be a terminal device.

[0036] Specifically, the computer device 104 acquires the current stirring speed and current aeration rate corresponding to the fermentation device 102, determines the target oxygen transfer rate per unit volume corresponding to the fermentation device based on the current stirring speed and current aeration rate, and returns it to the fermentation device 102. The current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume are displayed on the fermentation device 102. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation device.

[0037] In one embodiment, such as Figure 2 As shown, a monitoring method for fermentation equipment is provided, which can be applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0038] Step 202: Monitor the current stirring speed and current aeration rate of the fermentation equipment.

[0039] The fermentation equipment is used for biological fermentation. The current stirring speed is the current stirring rate of the fermentation equipment, while the current aeration rate is the total amount of gas currently being introduced into the fermentation equipment. The fermentation equipment may be equipped with stirring-related devices, through which the current stirring speed of the fermentation equipment can be obtained.

[0040] Specifically, the current stirring speed and aeration rate of the fermentation equipment are monitored and sent to the server.

[0041] Step 204: Determine the target oxygen transfer rate per unit volume for the fermentation equipment based on the current stirring speed and current aeration rate.

[0042] The target oxygen transfer rate per unit volume is the rate of oxygen transfer per unit volume that matches the current stirring speed and aeration rate of the fermentation equipment. It can be calculated using the current stirring speed and aeration rate and is used to reflect the current fermentation efficiency and quality of the fermentation equipment.

[0043] Step 206: Display the current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume on the fermentation equipment. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

[0044] After obtaining the target oxygen transfer rate per unit volume corresponding to the fermentation equipment, the current stirring speed, current aeration rate, and corresponding target oxygen transfer rate per unit volume are displayed on the relevant display device of the fermentation equipment, informing the user of the current fermentation efficiency and quality of the fermentation equipment. That is, the user can determine the current fermentation efficiency and quality of the fermentation equipment by the target oxygen transfer rate per unit volume displayed on the fermentation equipment, and can also determine whether the current stirring speed and aeration rate of the fermentation equipment are appropriate.

[0045] In the aforementioned monitoring method for fermentation equipment, the current stirring speed and current aeration rate of the fermentation equipment are obtained. Based on these parameters, the target oxygen transfer rate per unit volume is determined. The current stirring speed, current aeration rate, and the corresponding target oxygen transfer rate per unit volume are displayed on the fermentation equipment. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment. Therefore, by using the stirring speed and aeration rate of the fermentation equipment, the corresponding target oxygen transfer rate per unit volume can be determined, accurately reflecting the fermentation efficiency and quality of the equipment.

[0046] In one embodiment, determining the target oxygen transfer rate per unit volume corresponding to the fermentation equipment based on the current stirring speed and the current aeration rate includes: acquiring a pre-trained target oxygen transfer rate monitoring model, inputting the current stirring speed and the current aeration rate into the target oxygen transfer rate monitoring model, calculating the current stirring speed and the current aeration rate through the target oxygen transfer rate monitoring model, and outputting the corresponding target oxygen transfer rate per unit volume.

[0047] The target oxygen transfer rate per unit volume can be detected by a relevant neural network model, such as a target oxygen transfer rate monitoring model. The target oxygen transfer rate detection model can be pre-trained and obtained through supervised training with a large amount of training data. It is used to detect the oxygen transfer rate per unit volume of the fermentation equipment. The input data can be stirring speed and aeration rate, and the output is the matching oxygen transfer rate per unit volume.

[0048] Specifically, after obtaining the current stirring speed and current aeration rate of the fermentation equipment, the current stirring speed and current aeration rate are input into the target oxygen transfer rate monitoring model. The target oxygen transfer rate monitoring model calculates the input current stirring speed and current aeration rate and outputs the matching target oxygen transfer rate per unit volume.

[0049] The network structure of the target oxygen transfer rate monitoring model can include an input layer, a hidden layer, and an output layer. The input layer extracts features from the current stirring speed and current ventilation rate and sends them to the hidden layer. The hidden layer calculates the extracted features, and the output layer outputs the matching target oxygen transfer rate per unit volume.

[0050] In one embodiment, such as Figure 3 As shown, the training steps for the target oxygen transfer rate monitoring model include:

[0051] Step 302: Under cold mold conditions, obtain multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment. Each set of training stirring speeds and training aeration rates is associated with a corresponding standard unit volume oxygen transfer rate.

[0052] Step 304: Obtain the constructed original oxygen transfer rate monitoring model.

[0053] Step 306: Input the training stirring speed and training ventilation of each group into the original oxygen transfer rate monitoring model, calculate the training stirring speed and training ventilation of each group through the original oxygen transfer rate monitoring model, and output the corresponding output oxygen transfer rate per unit volume.

[0054] The training process of the target oxygen transfer rate monitoring model can be carried out under cold model conditions, which are reaction processes without microbial or cellular fermentation. That is, the fermentation equipment is not subject to any microbial or cellular fermentation during the training process. The original oxygen transfer rate monitoring model is trained by different stirring speeds and aeration rates.

[0055] Specifically, under cold model conditions, multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment are obtained. That is, a set of training data includes training stirring speed and training aeration rate, and each set is associated with a corresponding standard unit volume oxygen transfer rate.

[0056] Furthermore, the constructed, untrained, raw oxygen transfer rate monitoring model is obtained. The network structure of the raw oxygen transfer rate monitoring model may include an input layer, a hidden layer, and an output layer, and may also include a loss function, for example, the input layer has 2 elements, the hidden layer has 3 elements, and the output layer has 1 element.

[0057] Furthermore, the training stirring speed and training ventilation volume of each group are input into the original oxygen transfer rate monitoring model for training. The input layer of the original oxygen transfer rate monitoring model extracts features from the input training stirring speed and training ventilation volume of each group and sends them to the hidden layer. The hidden layer calculates the loss function on the extracted features and outputs the output oxygen transfer rate per unit volume corresponding to each group of training stirring speed and training ventilation volume.

[0058] The standard unit volume oxygen transfer rate associated with each training stirring speed and training aeration rate can be calculated using relevant formulas. For example, the control system of the fermentation equipment is notified to introduce nitrogen and oxygen respectively, and then the real-time dissolved oxygen value under the aeration rate and stirring speed is automatically obtained. The corresponding unit volume oxygen transfer rate (kla) is calculated using relevant formulas and recorded. During each introduction of nitrogen and oxygen, the control system will dynamically change the stirring speed and aeration rate within a preset time interval, and then calculate the corresponding unit volume oxygen transfer rate. The accurate standard unit volume oxygen transfer rate for this stage is found using the K-Means algorithm.

[0059] Step 308: Calculate the training loss value based on the oxygen transfer rate per unit volume of each output unit volume and the corresponding standard oxygen transfer rate per unit volume.

[0060] Step 310: Continuously adjust the model parameters of the original oxygen transfer rate monitoring model based on the training loss value until the convergence condition is met, and obtain the trained target oxygen transfer rate monitoring model.

[0061] Specifically, after obtaining the output oxygen transfer rate per unit volume corresponding to the training stirring speed and training ventilation for each group, the training loss value is calculated based on the output oxygen transfer rate per unit volume and the corresponding standard oxygen transfer rate per unit volume. The formula for calculating the training loss value can be shown as follows:

[0062]

[0063] Where CE is the training loss value, and y is the output oxygen transfer rate per unit volume. The oxygen transfer rate is expressed in standard units.

[0064] Furthermore, the pre-set convergence conditions can be set according to actual business needs, actual product needs, and actual application scenarios. For example, the convergence condition is determined when the training loss value reaches the preset loss value, or when the training loss value no longer changes, or when the training number reaches the preset number of training sessions.

[0065] Finally, determine whether the training loss value meets the convergence condition. If not, continuously adjust the model parameters of the original oxygen transfer rate monitoring model until the training loss value reaches the convergence condition, and obtain the trained target oxygen transfer rate monitoring model.

[0066] In one embodiment, the model parameters of the original oxygen transfer rate monitoring model are continuously adjusted according to the training loss value until the convergence condition is met, and a trained target oxygen transfer rate monitoring model is obtained. This includes: when the training loss value does not meet the convergence condition, adjusting the number of hidden layers of the original oxygen transfer rate monitoring model, returning to the step of obtaining the constructed original oxygen transfer rate monitoring model, until the training loss value meets the convergence condition, and a trained target oxygen transfer rate monitoring model is obtained.

[0067] Specifically, the pre-set convergence conditions are obtained, and it is determined whether the training loss value meets the convergence conditions. If it does not meet the conditions, it means that the original oxygen transfer rate monitoring model has not achieved the training objective. In this case, the number of hidden layers in the original oxygen transfer rate monitoring model can be adjusted. For example, the number of hidden layers in the original oxygen transfer rate monitoring model can be 3. If the convergence condition is not met, the number of hidden layers in the original oxygen transfer rate monitoring model can be adjusted to 4. Then, the process returns to the step of obtaining the constructed original oxygen transfer rate monitoring model and training is performed again. This process is repeated until the training loss value meets the convergence condition, and the trained target oxygen transfer rate monitoring model is obtained.

[0068] In this process, the number of hidden layers in the original oxygen transfer rate monitoring model is increased, as it affects the prediction accuracy. The original oxygen transfer rate monitoring model is then reconstructed, for example, by increasing the number of hidden layers from 3 to 4. The current training loss value is recorded, and it is determined whether the convergence condition has been met. For example, when the maximum number of hidden layers reaches 10, the convergence condition is met, and the trained target oxygen transfer rate monitoring model is obtained.

[0069] In one embodiment, after obtaining multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment, and after each set of training stirring speeds and training aeration rates is associated with a corresponding standard unit volume oxygen transfer rate, the method further includes: performing cluster calculations based on the standard unit volume oxygen transfer rates associated with each set of training stirring speeds and training aeration rates to obtain abnormal standard unit volume oxygen transfer rates, removing the training stirring speeds and training aeration rates corresponding to the abnormal standard unit volume oxygen transfer rates, and obtaining cleaned sets of training stirring speeds and training aeration rates.

[0070] Specifically, clustering can be performed based on the standard unit volume oxygen transfer rate associated with each group of training stirring speeds and training ventilation volumes. Clustering calculations, such as the k-means algorithm, can be used to identify deviations from the standard unit volume oxygen transfer rate. These deviations are identified as anomalous and removed, resulting in cleaned sets of training stirring speeds and training ventilation volumes. Finally, these cleaned sets of training stirring speeds and training ventilation volumes are used as training data for the original oxygen transfer rate monitoring model. This cleaning of the large amount of collected training data removes anomalous data, improves data accuracy, and ultimately enhances the training accuracy of the target oxygen transfer rate monitoring model.

[0071] In one embodiment, the method further includes: obtaining a preset trend graph, which includes a mapping relationship between different stirring speeds, aeration rates and corresponding oxygen transfer rates per unit volume, and adjusting the corresponding operating parameters of the fermentation equipment according to the mapping relationship in the preset trend graph and the target oxygen transfer rate per unit volume.

[0072] The preset trend chart reflects the mapping relationship between different stirring speeds, aeration rates, and corresponding oxygen transfer rates per unit volume. The preset trend chart reveals the oxygen transfer rates per unit volume corresponding to different stirring speeds and aeration rates. Therefore, after obtaining the target oxygen transfer rate per unit volume, the operating parameters of the fermentation equipment are adjusted based on the target stirring speed corresponding to that target oxygen transfer rate in the preset trend chart. This can be achieved by adjusting the current stirring speed and aeration rate of the fermentation equipment to obtain the target stirring speed, or by determining the corresponding current oxygen transfer rate per unit volume based on the current stirring speed and aeration rate in the preset trend chart, comparing the difference between the target oxygen transfer rate per unit volume and the current oxygen transfer rate per unit volume, and then adjusting the current stirring speed and / or current aeration rate of the fermentation equipment accordingly.

[0073] In one embodiment, the step of generating the preset trend chart includes: obtaining multiple sets of different verification ventilation rates and verification stirring speeds; determining the corresponding verification unit volume oxygen transfer rate based on each set of verification stirring speeds and verification ventilation rates; establishing a mapping relationship between each set of verification stirring speeds, verification ventilation rates and the corresponding verification unit volume oxygen transfer rate; and obtaining the preset trend chart.

[0074] After obtaining the trained target oxygen transfer rate monitoring model, the target oxygen transfer rate monitoring model can be verified by multiple sets of different verification stirring speeds and aeration rates to obtain the corresponding verification unit volume oxygen transfer rate, establish a mapping relationship, and obtain a preset trend chart. The preset trend chart can be used to adjust the corresponding operating parameters of the fermentation equipment.

[0075] Specifically, multiple sets of different verification aeration rates and verification stirring speeds are obtained, meaning that each set of verification aeration rates and verification stirring speeds is different. The corresponding verification unit volume oxygen transfer rate is calculated based on each set of verification stirring speeds and verification aeration rates. For example, each set of verification stirring speeds and verification aeration rates is input into the target oxygen transfer rate monitoring model. The target oxygen transfer rate monitoring model calculates each set of verification stirring speeds and aeration rates, outputs the corresponding verification unit volume oxygen transfer rate, establishes the mapping relationship between each set of verification stirring speeds and aeration rates and the corresponding verification unit volume oxygen transfer rate, and obtains a preset trend chart, which can be used to characterize the relationship between the verification unit volume oxygen transfer rate and the corresponding verification stirring speed and aeration rate of the fermentation equipment under cold model conditions.

[0076] For example, 1000 sets of data were measured in the same tank, and different verification aeration rates and different verification stirring speeds were extracted for calculation to obtain the corresponding verification oxygen transfer rate per unit volume. The mapping relationship between different verification stirring speeds, verification aeration rates and the corresponding verification oxygen transfer rates per unit volume was established.

[0077] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0078] In one embodiment, such as Figure 4As shown, a monitoring device 400 for a fermentation apparatus is provided, comprising: a monitoring module 402, a determination module 404, and a display module 406, wherein:

[0079] The monitoring module 402 is used to monitor the current stirring speed and current aeration rate of the fermentation equipment.

[0080] The determination module 406 is used to determine the target oxygen transfer rate per unit volume corresponding to the fermentation equipment based on the current stirring speed and the current aeration rate.

[0081] Display module 408 is used to display the current stirring speed, current aeration rate and corresponding target oxygen transfer rate per unit volume on the fermentation equipment. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

[0082] In one embodiment, the determining module 406 acquires a pre-trained target oxygen transfer rate monitoring model, inputs the current stirring speed and current aeration rate into the target oxygen transfer rate monitoring model, calculates the current stirring speed and current aeration rate through the target oxygen transfer rate monitoring model, and outputs the corresponding target oxygen transfer rate per unit volume.

[0083] In one embodiment, the determining module 406, under cold model conditions, acquires multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment. Each set of training stirring speeds and training aeration rates is associated with a corresponding standard unit volume oxygen transfer rate. The module acquires the constructed original oxygen transfer rate monitoring model, inputs each set of training stirring speeds and training aeration rates into the original oxygen transfer rate monitoring model, calculates each set of training stirring speeds and training aeration rates through the original oxygen transfer rate monitoring model, outputs the corresponding output unit volume oxygen transfer rate, calculates the training loss value based on each output unit volume oxygen transfer rate and the corresponding standard unit volume oxygen transfer rate, and continuously adjusts the model parameters of the original oxygen transfer rate monitoring model based on the training loss value until the convergence condition is met, thus obtaining the trained target oxygen transfer rate monitoring model.

[0084] In one embodiment, when the training loss value does not meet the convergence condition, the determining module 406 adjusts the number of hidden layers in the original oxygen transfer rate monitoring model and returns to the step of obtaining the constructed original oxygen transfer rate monitoring model until the training loss value meets the convergence condition, thus obtaining the trained target oxygen transfer rate monitoring model.

[0085] In one embodiment, the determining module 406 performs clustering calculations based on the standard unit volume oxygen transfer rate associated with each group of training stirring speeds and training ventilation rates to obtain abnormal standard unit volume oxygen transfer rates. The training stirring speeds and training ventilation rates corresponding to the abnormal standard unit volume oxygen transfer rates are then removed to obtain cleaned sets of training stirring speeds and training ventilation rates.

[0086] In one embodiment, the monitoring device 400 of the fermentation equipment acquires a preset trend graph, which includes the mapping relationship between different stirring speeds, aeration rates and corresponding oxygen transfer rates per unit volume. The operating parameters of the fermentation equipment are adjusted according to the mapping relationship in the preset trend graph and the target oxygen transfer rate per unit volume.

[0087] In one embodiment, the monitoring device 400 of the fermentation equipment acquires multiple sets of different verification aeration rates and verification stirring speeds, determines the corresponding verification unit volume oxygen transfer rate based on each set of verification stirring speeds and verification aeration rates, establishes a mapping relationship between each set of verification stirring speeds, verification aeration rates and the corresponding verification unit volume oxygen transfer rates, and obtains a preset trend chart.

[0088] Specific limitations regarding the monitoring devices for fermentation equipment can be found in the above description of the monitoring methods for fermentation equipment, and will not be repeated here. Each module in the aforementioned monitoring device for fermentation equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0089] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the target oxygen transfer rate per unit volume. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a monitoring method for a fermentation apparatus.

[0090] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a monitoring method for a fermentation device. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0091] Those skilled in the art will understand that Figure 5 or Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0092] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: obtaining the current stirring speed and current aeration rate corresponding to the fermentation device; determining the target oxygen transfer rate per unit volume corresponding to the fermentation device based on the current stirring speed and current aeration rate; and displaying the current stirring speed, current aeration rate, and the corresponding target oxygen transfer rate per unit volume on the fermentation device. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation device.

[0093] In one embodiment, when the processor executes the computer program, it also performs the following steps: acquiring a pre-trained target oxygen transfer rate monitoring model, inputting the current stirring speed and current aeration rate into the target oxygen transfer rate monitoring model, calculating the current stirring speed and current aeration rate through the target oxygen transfer rate monitoring model, and outputting the corresponding target oxygen transfer rate per unit volume.

[0094] In one embodiment, when the processor executes the computer program, it further performs the following steps: under cold model conditions, it acquires multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment, each set of training stirring speeds and training aeration rates being associated with a corresponding standard unit volume oxygen transfer rate; it acquires the constructed original oxygen transfer rate monitoring model; it inputs each set of training stirring speeds and training aeration rates into the original oxygen transfer rate monitoring model; it calculates each set of training stirring speeds and training aeration rates through the original oxygen transfer rate monitoring model, outputs the corresponding output unit volume oxygen transfer rate; it calculates the training loss value based on each output unit volume oxygen transfer rate and the corresponding standard unit volume oxygen transfer rate; it continuously adjusts the model parameters of the original oxygen transfer rate monitoring model based on the training loss value until the convergence condition is met, thus obtaining the trained target oxygen transfer rate monitoring model.

[0095] In one embodiment, when the processor executes the computer program, it further implements the following steps: when the training loss value does not meet the convergence condition, the number of hidden layers of the original oxygen transfer rate monitoring model is adjusted, and the process returns to the step of obtaining the constructed original oxygen transfer rate monitoring model until the training loss value meets the convergence condition, thereby obtaining the trained target oxygen transfer rate monitoring model.

[0096] In one embodiment, when the processor executes the computer program, it further performs the following steps: performing cluster calculations based on the standard unit volume oxygen transfer rate associated with each group of training stirring speeds and training ventilation rates to obtain abnormal standard unit volume oxygen transfer rates, removing the training stirring speeds and training ventilation rates corresponding to the abnormal standard unit volume oxygen transfer rates, and obtaining multiple cleaned groups of training stirring speeds and training ventilation rates.

[0097] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a preset trend graph, which includes the mapping relationship between different stirring speeds, aeration rates and corresponding oxygen transfer rates per unit volume, and adjusting the corresponding operating parameters of the fermentation equipment according to the mapping relationship in the preset trend graph and the target oxygen transfer rate per unit volume.

[0098] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring multiple sets of different verification aeration rates and verification stirring speeds, determining the corresponding verification unit volume oxygen transfer rate based on each set of verification stirring speeds and verification aeration rates, establishing a mapping relationship between each set of verification stirring speeds, verification aeration rates and the corresponding verification unit volume oxygen transfer rate, and obtaining a preset trend chart.

[0099] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon. When the computer program is executed by a processor, it performs the following steps: obtaining the current stirring speed and current aeration rate of the fermentation equipment; determining the target oxygen transfer rate per unit volume of the fermentation equipment based on the current stirring speed and current aeration rate; displaying the current stirring speed, current aeration rate, and the corresponding target oxygen transfer rate per unit volume on the fermentation equipment; wherein the target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

[0100] In one embodiment, when the processor executes the computer program, it also performs the following steps: acquiring a pre-trained target oxygen transfer rate monitoring model, inputting the current stirring speed and current aeration rate into the target oxygen transfer rate monitoring model, calculating the current stirring speed and current aeration rate through the target oxygen transfer rate monitoring model, and outputting the corresponding target oxygen transfer rate per unit volume.

[0101] In one embodiment, when the processor executes the computer program, it further performs the following steps: under cold model conditions, it acquires multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment, each set of training stirring speeds and training aeration rates being associated with a corresponding standard unit volume oxygen transfer rate; it acquires the constructed original oxygen transfer rate monitoring model; it inputs each set of training stirring speeds and training aeration rates into the original oxygen transfer rate monitoring model; it calculates each set of training stirring speeds and training aeration rates through the original oxygen transfer rate monitoring model, outputs the corresponding output unit volume oxygen transfer rate; it calculates the training loss value based on each output unit volume oxygen transfer rate and the corresponding standard unit volume oxygen transfer rate; it continuously adjusts the model parameters of the original oxygen transfer rate monitoring model based on the training loss value until the convergence condition is met, thus obtaining the trained target oxygen transfer rate monitoring model.

[0102] In one embodiment, when the processor executes the computer program, it further implements the following steps: when the training loss value does not meet the convergence condition, the number of hidden layers of the original oxygen transfer rate monitoring model is adjusted, and the process returns to the step of obtaining the constructed original oxygen transfer rate monitoring model until the training loss value meets the convergence condition, thereby obtaining the trained target oxygen transfer rate monitoring model.

[0103] In one embodiment, when the processor executes the computer program, it further performs the following steps: performing cluster calculations based on the standard unit volume oxygen transfer rate associated with each group of training stirring speeds and training ventilation rates to obtain abnormal standard unit volume oxygen transfer rates, removing the training stirring speeds and training ventilation rates corresponding to the abnormal standard unit volume oxygen transfer rates, and obtaining multiple cleaned groups of training stirring speeds and training ventilation rates.

[0104] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a preset trend graph, which includes the mapping relationship between different stirring speeds, aeration rates and corresponding oxygen transfer rates per unit volume, and adjusting the corresponding operating parameters of the fermentation equipment according to the mapping relationship in the preset trend graph and the target oxygen transfer rate per unit volume.

[0105] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring multiple sets of different verification aeration rates and verification stirring speeds, determining the corresponding verification unit volume oxygen transfer rate based on each set of verification stirring speeds and verification aeration rates, establishing a mapping relationship between each set of verification stirring speeds, verification aeration rates and the corresponding verification unit volume oxygen transfer rate, and obtaining a preset trend chart.

[0106] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for monitoring a fermentation device, the method comprising: Monitor the current stirring speed and current aeration rate of the fermentation equipment; The target oxygen transfer rate per unit volume corresponding to the fermentation equipment is determined based on the current stirring speed and the current aeration rate. The fermentation equipment displays the current stirring speed, the current aeration rate, and the corresponding target oxygen transfer rate per unit volume, wherein the target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment. The step of determining the target oxygen transfer rate per unit volume for the fermentation equipment based on the current stirring speed and the current aeration rate includes: Obtain a pre-trained target oxygen transfer rate monitoring model; The current stirring speed and the current aeration rate are input into the target oxygen transfer rate monitoring model. The target oxygen transfer rate monitoring model calculates the current stirring speed and the current aeration rate and outputs the corresponding target oxygen transfer rate per unit volume.

2. The monitoring method according to claim 1, characterized in that, The training steps of the target oxygen transfer rate monitoring model include: Under cold conditions, multiple sets of training stirring speeds and training aeration rates are obtained for the fermentation equipment, and each set of training stirring speeds and training aeration rates is associated with a corresponding standard unit volume oxygen transfer rate. Obtain the constructed original oxygen transfer rate monitoring model; The training stirring speed and training ventilation volume of each group are input into the original oxygen transfer rate monitoring model. The original oxygen transfer rate monitoring model calculates the training stirring speed and training ventilation volume of each group and outputs the corresponding output oxygen transfer rate per unit volume. The training loss value is calculated based on the output oxygen transfer rate per unit volume and the corresponding standard oxygen transfer rate per unit volume. The model parameters of the original oxygen transfer rate monitoring model are continuously adjusted based on the training loss value until the convergence condition is met, thus obtaining the trained target oxygen transfer rate monitoring model.

3. The monitoring method according to claim 2, characterized in that, The step of continuously adjusting the model parameters of the original oxygen transfer rate monitoring model based on the training loss value until the convergence condition is met, thereby obtaining the trained target oxygen transfer rate monitoring model, includes: If the training loss value does not meet the convergence condition, the number of hidden layers in the original oxygen transfer rate monitoring model is adjusted, and the process returns to the step of obtaining the constructed original oxygen transfer rate monitoring model until the training loss value meets the convergence condition, thus obtaining the trained target oxygen transfer rate monitoring model.

4. The monitoring method according to claim 2, characterized in that, After obtaining multiple sets of training stirring speeds and training aeration rates corresponding to the fermentation equipment, and after each set of training stirring speeds and training aeration rates is associated with a corresponding standard unit volume oxygen transfer rate, the method further includes: Clustering calculations were performed based on the standard unit volume oxygen transfer rate associated with the training stirring speed and training ventilation rate described in each group to obtain the abnormal standard unit volume oxygen transfer rate. The training stirring speed and training ventilation corresponding to the abnormal standard unit volume oxygen transfer rate are removed to obtain multiple sets of cleaned training stirring speed and training ventilation.

5. The monitoring method according to claim 1, characterized in that, The method further includes: Obtain a preset trend graph, which includes the mapping relationship between different stirring speeds, ventilation rates and corresponding oxygen transfer rates per unit volume. The operating parameters of the fermentation equipment are adjusted according to the mapping relationship in the preset trend chart and the target oxygen transfer rate per unit volume.

6. The monitoring method according to claim 5, characterized in that, The steps for generating the preset trend chart include: Obtain multiple sets of different verification ventilation rates and verification stirring speeds; The corresponding oxygen transfer rate per unit volume is determined based on the verification stirring speed and verification aeration rate described in each group. Establish a mapping relationship between the verification stirring speed, the verification aeration rate and the corresponding verification oxygen transfer rate per unit volume for each group, and obtain a preset trend chart.

7. A monitoring device for fermentation equipment, characterized in that, The device includes: The monitoring module is used to monitor the current stirring speed and current aeration rate of the fermentation equipment. A determination module is used to determine the target oxygen transfer rate per unit volume corresponding to the fermentation equipment based on the current stirring speed and the current aeration rate; wherein, the determination module acquires a pre-trained target oxygen transfer rate monitoring model, inputs the current stirring speed and the current aeration rate into the target oxygen transfer rate monitoring model, calculates the current stirring speed and the current aeration rate through the target oxygen transfer rate monitoring model, and outputs the corresponding target oxygen transfer rate per unit volume; The display module is used to display the current stirring speed, the current aeration rate and the corresponding target oxygen transfer rate per unit volume on the fermentation equipment. The target oxygen transfer rate per unit volume reflects the current fermentation efficiency and quality of the fermentation equipment.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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