Control method for microbial inoculant expansion cultivation and automatic dosing device

Abstract

A control method for a microbial inoculant expansion cultivation and automatic dosing device. The method comprises: acquiring preset parameter values of each characteristic parameter of a microbial inoculant expansion cultivation and automatic dosing device at several preset time points (S102); classifying characteristic parameters in a data repository, and on the basis of a classification result, obtaining actual parameter values of each characteristic parameter of the microbial inoculant expansion cultivation and automatic dosing device at the several preset time point (S106); on the basis of the actual parameter values and preset parameter values of each characteristic parameter at the several preset time points, analyzing each characteristic parameter of the microbial inoculant expansion cultivation and automatic dosing device (S108); and if the operation state of the microbial inoculant expansion cultivation and automatic dosing device is abnormal, performing a control operation on the microbial inoculant expansion cultivation and automatic dosing device (S110). Thus, the real-time control of a microbial inoculant expansion cultivation and automatic dosing device is realized, thereby significantly improving the operating efficiency and stability of the device.

Description

A control method of microbial agent expansion and automatic adding equipment TECHNICAL FIELD

[0001] The present application relates to the technical field of microbial expansion equipment, in particular to a control method of microbial agent expansion and automatic adding equipment. BACKGROUND

[0002] In modern industrial production and agricultural development, microbial agents are widely used in food fermentation, biopharmaceuticals, environmental protection, agricultural planting and other fields due to their unique biological activity and efficient functional characteristics. In recent years, microbial agent expansion and automatic adding equipment has developed rapidly. This kind of equipment integrates advanced automatic control system, precise measuring instrument and intelligent decision algorithm, which can monitor and control various parameters in the expansion process in real time, automatically adjust the adding amount and speed of nutrients, effectively avoid the uncertainty of manual operation, and significantly improve the expansion efficiency and agent quality.

[0003] However, although the existing microbial agent expansion and automatic adding equipment has achieved remarkable results in improving production efficiency, there are still a series of technical and practical challenges in developing and applying microbial agent expansion and automatic adding equipment. First, precise control of microbial growth environment is a complex problem. The growth of microorganisms is affected by many factors, including temperature, pH value, oxygen content, nutrient concentration, etc. The interaction between these factors is complex and difficult to accurately simulate and control. Second, the concentration of microbial agents needs to be accurately adjusted according to the expansion stage and target application, and the precision, stability, response speed of the automatic adding system and the integration with the overall control system are key problems to be solved. The reliability and durability of the system are also important considerations, especially in long-term operation and large-scale application scenarios. In addition, in order to optimize the control strategy, a large amount of operation data needs to be collected and analyzed, including microbial growth parameters, environmental parameters, equipment performance, etc. Efficient processing, pattern recognition, anomaly detection and data-based decision support of these data require powerful computing power and advanced algorithm support. SUMMARY

[0004] The present application overcomes the shortcomings of the prior art and provides a control method of microbial agent expansion and automatic adding equipment.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] The present application discloses a control method of microbial agent expansion and automatic adding equipment, comprising the following steps:

[0007] Obtain the preset control index requirements of the microbial agent amplification and automatic dosing equipment, and determine the preset parameter values ​​of each characteristic parameter in the microbial agent amplification and automatic dosing equipment at several preset time nodes based on the preset control index requirements.

[0008] During the operation of the microbial agent propagation and automatic dosing equipment, the actual parameter values ​​of each characteristic parameter in the microbial agent propagation and automatic dosing equipment are collected sequentially at several preset time nodes, and the collected actual parameter values ​​of the characteristic parameters are stored in the data storage repository.

[0009] After the data collection is completed, the feature parameters in the data storage repository are classified and the classification results are output. Based on the classification results, the actual parameter values ​​of each feature parameter in the microbial agent propagation and automatic dosing equipment at several preset time nodes are obtained.

[0010] Based on the actual parameter values ​​and preset parameter values ​​of each characteristic parameter at several preset time nodes, the working status of the microbial agent expansion and automatic dosing equipment is analyzed.

[0011] If the microbial agent amplification and automatic dosing equipment is in normal working condition, no adjustment or treatment will be performed on the microbial agent amplification and automatic dosing equipment; if the microbial agent amplification and automatic dosing equipment is in abnormal working condition, a recommended control scheme will be generated, and the microbial agent amplification and automatic dosing equipment will be adjusted and treated.

[0012] More specifically, the feature parameters within the data repository are classified, and the classification results are output as follows:

[0013] Construct a grid system and divide the grid system into several sub-grid spaces, obtain the feature parameters in the data repository, and map the feature parameters in the data repository to each sub-grid space respectively;

[0014] Obtain the text features of the feature parameters in each sub-grid space, and calculate the mutual information value between the feature parameters in each sub-grid space based on the text features of the feature parameters in each sub-grid space;

[0015] Feature parameters with mutual information values ​​greater than a preset threshold are marked as strongly correlated feature parameters. The sub-grid spaces to which the feature parameters with mutual information values ​​greater than the preset threshold belong are merged to aggregate all the strongly correlated feature parameters, resulting in several aggregate spaces. Each aggregate space contains feature parameters that share information.

[0016] Calculate the mean of all characteristic parameters in each aggregation space, and determine the aggregation center of each aggregation space based on the calculated mean.

[0017] Calculate the Manhattan distance from all feature parameters in each aggregation space to the aggregation center, square the Manhattan distance from all feature parameters in each aggregation space to the aggregation center, and obtain the contribution value of each feature parameter to the dispersion of its respective aggregation space.

[0018] The total dispersion of each aggregate space is obtained by summing the contribution values ​​of all feature parameters in each aggregate space. The average dispersion of each aggregate space is obtained by dividing the total dispersion of each aggregate space by the total number of all feature parameters in each aggregate space.

[0019] The average dispersion of each aggregate space is compared with a preset dispersion threshold. If the average dispersion of an aggregate space is greater than the preset dispersion threshold, the aggregate space is labeled as a high-variability space. If the average dispersion of an aggregate space is not greater than the preset dispersion threshold, the aggregate space is labeled as a low-variability space.

[0020] The aggregate space marked as a high-variability space is denoised to obtain a denoised aggregate space, and the feature parameters in the denoised aggregate space are sorted based on time series.

[0021] No denoising is performed on the aggregate space that is marked as a low-variability space, and the feature parameters in the aggregate space that has not been denoised are sorted based on time series.

[0022] After processing, output the classification results.

[0023] More specifically, the aggregate space marked as a high-variability space is denoised to obtain a denoised aggregate space, as follows:

[0024] Obtain all aggregate spaces marked as high-variance spaces, and define them as the aggregate spaces to be denoised;

[0025] The DBSCAN algorithm is introduced, and the neighborhood radius and minimum number of samples are preset; the denoising aggregation space is obtained, and the total number of feature parameters of each feature parameter in the denoising aggregation space within the preset neighborhood radius is calculated;

[0026] Compare the total number of feature parameters within a preset radius with the minimum number of samples.

[0027] If the total number of a certain feature parameter within a preset neighborhood radius is greater than or equal to the minimum number of samples, then the feature parameter is marked as a density-reachable feature parameter of the denoising aggregation space.

[0028] If the total number of feature parameters within a preset neighborhood radius is less than the minimum number of samples, then the feature parameter is marked as a non-density reachable feature parameter in the denoising aggregation space.

[0029] Similarly, the non-density reachable feature parameters of each denoising aggregation space are detected based on the DBSCAN algorithm;

[0030] The non-density reachable feature parameters in each aggregation space to be denoised are screened out to obtain the denoised aggregation space.

[0031] More specifically, based on the actual and preset parameter values ​​of each characteristic parameter at several preset time points, the operating status of the microbial agent propagation and automatic dosing equipment is analyzed to obtain the working status of the microbial agent propagation and automatic dosing equipment.

[0032] Construct several two-dimensional coordinate systems, and in the corresponding two-dimensional coordinate systems, construct the actual parameter value curve and the preset parameter value curve of each feature parameter at several preset time nodes based on the actual parameter value and the preset parameter value of each feature parameter;

[0033] Calculate the overlap rate between the actual parameter value curves and the preset parameter value curves in each two-dimensional coordinate system; and compare the overlap rate between the actual parameter value curves and the preset parameter value curves in each two-dimensional coordinate system with the preset overlap rate threshold.

[0034] If the overlap rate between the actual parameter value curve and the preset parameter value curve in a certain two-dimensional coordinate system is not greater than the preset overlap rate threshold, then the corresponding characteristic parameter in the microbial agent expansion and automatic dosing equipment will be calibrated as the drift characteristic parameter.

[0035] If the overlap rate between the actual parameter value curve and the preset parameter value curve in a certain two-dimensional coordinate system is greater than the preset overlap rate threshold, then the corresponding characteristic parameter in the microbial agent expansion and automatic dosing equipment will be calibrated as a normal characteristic parameter.

[0036] Determine whether drift characteristic parameters have appeared in the microbial agent amplification and automatic dosing equipment; if drift characteristic parameters have appeared, mark the working status of the microbial agent amplification and automatic dosing equipment as abnormal; if no drift characteristic parameters have appeared, mark the working status of the microbial agent amplification and automatic dosing equipment as normal.

[0037] More specifically, if the microbial agent propagation and automatic dosing equipment is in an abnormal operating state, a recommended control scheme will be generated, and the microbial agent propagation and automatic dosing equipment will be controlled accordingly.

[0038] Obtain the assembly drawing information of the microbial agent propagation and automatic dosing equipment, and construct a three-dimensional model of the microbial agent propagation and automatic dosing equipment based on the assembly drawing information;

[0039] If the working status of the microbial agent amplification and automatic dosing equipment is abnormal, obtain the real-time parameter values ​​of each characteristic parameter in the microbial agent amplification and automatic dosing equipment, and use PROE software to integrate the real-time parameter values ​​of each characteristic parameter into the equipment three-dimensional model diagram to establish a real-time simulation model diagram of the microbial agent amplification and automatic dosing equipment.

[0040] If the working status of the microbial agent expansion and automatic dosing equipment is abnormal, the drift characteristic parameters of the microbial agent expansion and automatic dosing equipment are acquired simultaneously.

[0041] Based on the drift characteristic parameters of the microbial agent propagation and automatic dosing equipment, search keywords are constructed, and the shared database is searched according to the search keywords to obtain several control schemes; and the control parameters of each control scheme are obtained.

[0042] Using PROE software and based on the control parameters of each control scheme, the real-time simulation model of the microbial agent propagation and automatic dosing equipment was simulated and controlled. The simulation parameter values ​​of each characteristic parameter in the microbial agent propagation and automatic dosing equipment after simulation control of each control scheme were obtained; and the control time required for simulation control of each control scheme was obtained.

[0043] It also includes the following steps:

[0044] Based on the control time required for simulation control of each control scheme, the predicted working time nodes of the microbial agent expansion and automatic dosing equipment after simulation control of each control scheme are determined.

[0045] Based on the preset control index requirements, the standard parameter range values ​​of each characteristic parameter in the microbial agent propagation and automatic dosing equipment at the predicted working time node are determined;

[0046] Determine whether the simulated parameter values ​​of each characteristic parameter in the microbial agent expansion and automatic dosing equipment after each control scheme simulation are within the corresponding standard parameter range;

[0047] Obtain the control scheme in which all simulation parameter values ​​are within the corresponding standard parameter range, and obtain the energy consumption loss value of the control scheme in which all simulation parameter values ​​are within the corresponding standard parameter range;

[0048] The control scheme with the lowest energy consumption value is selected as the recommended control scheme, and the microbial agent propagation and automatic dosing equipment is controlled according to the recommended control scheme.

[0049] More specifically, the characteristic parameters include the temperature, dissolved oxygen, pH, stirring speed, water volume, and foam volume of the activation tank;

[0050] The characteristic parameters also include the stirring speed of the storage tank and the discharge flow rate of the metering pump;

[0051] The characteristic parameters also include the amount of bacteria added to the dilution tank, the amount of tap water, the stirring speed, and the discharge flow rate of the high-pressure pump.

[0052] The second aspect of the present invention discloses a control system for a microbial agent amplification and automatic dosing equipment. The control system includes a memory and a processor. The memory stores a control method program for the microbial agent amplification and automatic dosing equipment. When the control method program for the microbial agent amplification and automatic dosing equipment is executed by the processor, the steps of the control method for the microbial agent amplification and automatic dosing equipment described in any one of the present invention are implemented.

[0053] The third aspect of the present invention discloses a computer-readable storage medium, the computer-readable storage medium including a control method program for microbial agent amplification and automatic dosing equipment, wherein when the control method program for microbial agent amplification and automatic dosing equipment is executed by a processor, the steps of the control method for microbial agent amplification and automatic dosing equipment as described in any one of the claims are implemented.

[0054] This invention addresses the technical deficiencies in the prior art and offers the following advantages: It acquires preset parameter values ​​for various characteristic parameters in a microbial agent amplification and automatic dosing equipment at several preset time points; it sequentially collects the actual parameter values ​​of each characteristic parameter in the equipment at several preset time points and classifies the characteristic parameters in the data repository; it obtains the actual parameter values ​​of each characteristic parameter in the equipment at several preset time points based on the classification results; it analyzes each characteristic parameter in the equipment based on the actual parameter values ​​and preset parameter values ​​at several preset time points; if the equipment's operating state is abnormal, it generates a recommended control scheme and adjusts the equipment accordingly. The entire control process, through automation, achieves refined management and real-time monitoring of the equipment, significantly improving its operating efficiency and stability. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0056] Figure 1 is a flowchart of the overall method for controlling the microbial agent propagation and automatic dosing equipment.

[0057] Figure 2 is a partial flowchart of the control method for microbial agent propagation and automatic dosing equipment.

[0058] Figure 3 is a system block diagram of the control system for the microbial agent propagation and automatic dosing equipment;

[0059] Figure 4 is a process flow diagram of the microbial agent propagation and automatic dosing equipment. Detailed Implementation

[0060] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0061] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0062] As shown in Figure 1, the first aspect of the present invention discloses a control method for a microbial agent propagation and automatic dosing equipment, comprising the following steps:

[0063] S102: Obtain the preset control index requirements of the microbial agent expansion and automatic dosing equipment, and determine the preset parameter values ​​of each characteristic parameter in the microbial agent expansion and automatic dosing equipment at several preset time nodes according to the preset control index requirements.

[0064] S104: During the operation of the microbial agent expansion and automatic dosing equipment, the actual parameter values ​​of each characteristic parameter in the microbial agent expansion and automatic dosing equipment are collected sequentially at several preset time nodes, and the collected actual parameter values ​​of the characteristic parameters are stored in the data storage repository.

[0065] S106: After the data collection is completed, the feature parameters in the data storage repository are classified and processed, and the classification results are output. Based on the classification results, the actual parameter values ​​of each feature parameter in the microbial agent propagation and automatic dosing equipment at several preset time nodes are obtained.

[0066] S108: Analyze the characteristic parameters of the microbial agent expansion and automatic dosing equipment based on the actual parameter values ​​and preset parameter values ​​of each characteristic parameter at several preset time nodes, and obtain the working status of the microbial agent expansion and automatic dosing equipment.

[0067] S110: If the microbial agent amplification and automatic dosing equipment is in normal working condition, no adjustment or treatment will be performed on the microbial agent amplification and automatic dosing equipment; if the microbial agent amplification and automatic dosing equipment is in abnormal working condition, a recommended control scheme will be generated, and the microbial agent amplification and automatic dosing equipment will be adjusted or treated.

[0068] The characteristic parameters include the temperature, dissolved oxygen, pH, stirring speed, water volume, and foam volume of the activation tank; the stirring speed and metering pump discharge flow rate of the storage tank; and the bacterial addition, tap water volume, stirring speed, and high-pressure pump discharge flow rate of the dilution tank.

[0069] Among them, the preset control index requirements for microbial agent propagation and automatic dosing equipment refer to a series of key parameter standards and targets set in advance during the production process of microbial agents to ensure the quality and efficiency of propagation and dosing. These indicators usually cover multiple aspects such as temperature, pH value, dissolved oxygen concentration, nutrient supply rate, and agent concentration, which together constitute the ideal environmental conditions for microbial growth and reproduction.

[0070] It should be noted that, firstly, by setting preset control indicators, the ideal parameter values ​​that the microbial agent propagation and automatic dosing equipment should achieve at each key time node are determined. This step ensures the target-oriented and standardized operation of the equipment and provides a clear reference benchmark for subsequent data collection and analysis. Next, during equipment operation, the actual parameter values ​​of each characteristic parameter are collected sequentially at preset time nodes and stored in the data repository, enabling real-time monitoring of the equipment's operating status. This data accumulation provides rich and continuous evidence for subsequent status assessment. After classification processing, the parameter data in the data repository outputs the actual values ​​of various parameters. By comparing the preset values ​​with the actual values, the operating status of the equipment at each preset time node can be intuitively assessed, thus determining the overall operating status of the equipment. Based on the analysis of the actual parameter values, it can be determined whether the equipment is in a normal or abnormal state. If the state is normal, no additional control processing is required to avoid unnecessary intervention. However, once an abnormal state is detected, the system will generate a recommended control plan and immediately initiate corresponding control measures to correct deviations from the preset targets, ensuring that the equipment operates in an optimal state. The entire control process, through automation, achieves refined management and real-time monitoring of the microbial agent propagation and automatic dosing equipment, significantly improving the equipment's operational efficiency and stability. Simultaneously, through intelligent analysis and rapid response mechanisms, potential operational problems are effectively prevented, reducing losses caused by equipment malfunctions. This provides strong technical support for the efficient production and quality control of microbial agents, realizing comprehensive monitoring and management of the microbial agent propagation and automatic dosing equipment, aiming to ensure the equipment operates efficiently and stably during operation.

[0071] More specifically, the feature parameters within the data repository are classified, and the classification results are output as follows:

[0072] Construct a grid system and divide the grid system into several sub-grid spaces, obtain the feature parameters in the data repository, and map the feature parameters in the data repository to each sub-grid space respectively;

[0073] Obtain the text features of the feature parameters in each sub-grid space, and calculate the mutual information value between the feature parameters in each sub-grid space based on the text features of the feature parameters in each sub-grid space;

[0074] Feature parameters with mutual information values ​​greater than a preset threshold are marked as strongly correlated feature parameters. The sub-grid spaces to which the feature parameters with mutual information values ​​greater than the preset threshold belong are merged to aggregate all the strongly correlated feature parameters, resulting in several aggregate spaces. Each aggregate space contains feature parameters that share information.

[0075] Calculate the mean of all characteristic parameters in each aggregation space, and determine the aggregation center of each aggregation space based on the calculated mean.

[0076] Calculate the Manhattan distance from all feature parameters in each aggregation space to the aggregation center, square the Manhattan distance from all feature parameters in each aggregation space to the aggregation center, and obtain the contribution value of each feature parameter to the dispersion of its respective aggregation space.

[0077] The total dispersion of each aggregate space is obtained by summing the contribution values ​​of all feature parameters in each aggregate space. The average dispersion of each aggregate space is obtained by dividing the total dispersion of each aggregate space by the total number of all feature parameters in each aggregate space.

[0078] The average dispersion of each aggregate space is compared with a preset dispersion threshold. If the average dispersion of an aggregate space is greater than the preset dispersion threshold, the aggregate space is labeled as a high-variability space. If the average dispersion of an aggregate space is not greater than the preset dispersion threshold, the aggregate space is labeled as a low-variability space.

[0079] The aggregated space marked as a high-variability space is denoised to obtain a denoised aggregated space, and the feature parameters in the denoised aggregated space are sorted based on time series.

[0080] No denoising is performed on the aggregate space that is marked as a low-variability space, and the feature parameters in the aggregate space that has not been denoised are sorted based on time series.

[0081] After processing, output the classification results.

[0082] The mutual information value ranges from 0 to infinity, with a larger value indicating a stronger correlation between the two feature parameters. If the mutual information is 0, it means that the two feature parameters do not share information, i.e., they are independent; if the mutual information is close to infinity, it indicates that the two feature parameters are strongly correlated.

[0083] It's important to note that, firstly, by constructing a grid system and dividing it into sub-grid spaces, the complex dataset is broken down into more easily understood and analyzed parts. This structured approach helps capture the interdependencies between different parameters. Next, by calculating the mutual information values ​​between feature parameters within each sub-grid space, highly correlated feature parameters are identified and then aggregated to form an information-sharing aggregate space. This method helps reveal the intrinsic connections between parameters, providing a foundation for subsequent analysis. Furthermore, by calculating the mean of the feature parameters within the aggregate space and the Manhattan distance to the aggregation center, the concentration and distribution of the parameters are quantified, and the average dispersion of each aggregate space is calculated. This step helps distinguish between spaces with high variability (i.e., high-variability spaces) and spaces with low variability (i.e., low-variability spaces). For high-variability spaces, noise is removed through denoising to improve data purity and reliability; while for low-variability spaces, sorting is performed directly. Finally, through this series of analyses and processes, the classification results of the feature parameters within the database repository can be obtained, i.e., the division between high-variability and low-variability spaces. This not only helps in understanding the stability of equipment operation and potential problem areas, but also provides a scientific basis for optimizing equipment operating parameters, improving production efficiency and product quality.

[0084] Overall, this method classifies the feature parameters in the data repository, such as classifying the collected stirring speed of the storage tank into a dataset and sorting it based on the time characteristics of the collection time. This allows us to obtain the actual parameter values ​​of each feature parameter in the microbial agent propagation and automatic dosing equipment at several preset time nodes. This improves the monitoring accuracy of the operating status of the microbial agent propagation and automatic dosing equipment, enhances the ability to predict equipment anomalies and the response speed, and helps to achieve stable operation and optimized control of the equipment.

[0085] More specifically, the aggregate space marked as a high-variability space is denoised to obtain a denoised aggregate space, as follows:

[0086] Obtain all aggregate spaces marked as high-variance spaces, and define them as the aggregate spaces to be denoised;

[0087] The DBSCAN algorithm is introduced, and the neighborhood radius and minimum number of samples are preset; the denoising aggregation space is obtained, and the total number of feature parameters of each feature parameter in the denoising aggregation space within the preset neighborhood radius is calculated;

[0088] Compare the total number of feature parameters within a preset radius with the minimum number of samples.

[0089] If the total number of a certain feature parameter within a preset neighborhood radius is greater than or equal to the minimum number of samples, then the feature parameter is marked as a density-reachable feature parameter of the denoising aggregation space.

[0090] If the total number of feature parameters within a preset neighborhood radius is less than the minimum number of samples, then the feature parameter is marked as a non-density reachable feature parameter in the denoising aggregation space.

[0091] Similarly, the non-density reachable feature parameters of each denoising aggregation space are detected based on the DBSCAN algorithm;

[0092] The non-density reachable feature parameters in each aggregation space to be denoised are screened out to obtain the denoised aggregation space.

[0093] It should be noted that, firstly, from all aggregate spaces marked as high-variability spaces, the aggregate spaces requiring denoising are selected and defined as the "aggregate space to be denoised". Then, the DBSCAN algorithm is introduced, with the neighborhood radius and minimum sample number preset as key parameters; this step lays the foundation for subsequent denoising. Next, the total number of feature parameters within the preset neighborhood radius for each feature parameter is calculated and compared with the minimum sample number. If the total number of feature parameters within the preset neighborhood radius for a certain feature parameter is greater than or equal to the minimum sample number, then that feature parameter is considered sufficiently representative and is marked as a "density-reachable feature parameter of the aggregate space to be denoised". Conversely, if the total number of feature parameters within the preset neighborhood radius is less than the minimum sample number, it is marked as a "non-density-reachable feature parameter of the aggregate space to be denoised". In this way, feature parameters with insufficient sample numbers within a specific neighborhood can be systematically identified and removed from the aggregate space, resulting in the denoised aggregate space. This process helps remove noise interference, enhances the representativeness and accuracy of the data, and achieves refined denoising of feature parameters in a highly variable space. It not only improves the quality, purity and reliability of the data, but also provides a more reliable foundation for subsequent analysis and decision-making.

[0094] More specifically, as shown in Figure 2, the actual and preset parameter values ​​of each characteristic parameter at several preset time points are analyzed to determine the working status of the microbial agent propagation and automatic dosing equipment.

[0095] S202: Construct several two-dimensional coordinate systems, and in the corresponding two-dimensional coordinate systems, construct the actual parameter value curve and the preset parameter value curve of each feature parameter at several preset time nodes based on the actual parameter value and the preset parameter value of each feature parameter;

[0096] S204: Calculate the overlap rate between the actual parameter value curve and the preset parameter value curve in each two-dimensional coordinate system; and compare the overlap rate between the actual parameter value curve and the preset parameter value curve in each two-dimensional coordinate system with the preset overlap rate threshold.

[0097] S206: If the overlap rate between the actual parameter value curve and the preset parameter value curve in a certain two-dimensional coordinate system is not greater than the preset overlap rate threshold, then the corresponding characteristic parameter in the microbial agent expansion and automatic dosing equipment will be calibrated as the drift characteristic parameter.

[0098] S208: If the overlap rate between the actual parameter value curve and the preset parameter value curve in a certain two-dimensional coordinate system is greater than the preset overlap rate threshold, then the corresponding characteristic parameter in the microbial agent expansion and automatic dosing equipment will be calibrated as a normal characteristic parameter.

[0099] S210: Determine whether drift characteristic parameters have appeared in the microbial agent amplification and automatic dosing equipment; if drift characteristic parameters have appeared, mark the working status of the microbial agent amplification and automatic dosing equipment as abnormal; if no drift characteristic parameters have appeared, mark the working status of the microbial agent amplification and automatic dosing equipment as normal.

[0100] It should be noted that by constructing several two-dimensional coordinate systems, each feature parameter is assigned an independent coordinate system. Then, within each coordinate system, a curve showing the actual parameter value and a curve showing the preset parameter value are plotted based on the actual parameter value and the preset parameter value at a preset time point. This step provides an intuitive visual reference for subsequent drift detection. Next, the overlap rate between the actual parameter value curve and the preset parameter value curve in each two-dimensional coordinate system is calculated; that is, the proportion of the area covered by the two curves. This is a quantitative indicator of whether the feature parameter deviates from the preset value. Then, the calculated overlap rate is compared with a preset overlap rate threshold. If the overlap rate is lower than the threshold, it indicates that the feature parameter fluctuates significantly and deviates greatly from the preset value; therefore, it is marked as a "drift feature parameter." Conversely, if the overlap rate is higher than the threshold, it indicates that the feature parameter fluctuates less and is more consistent with the preset value; therefore, it is marked as a "normal feature parameter." Finally, by detecting all feature parameters, it is determined whether a drift feature parameter exists. If the presence of a drift feature parameter is detected, it indicates that the working state of the microbial agent propagation and automatic dosing equipment is abnormal and should be marked as an "abnormal state." Conversely, if no drift characteristic parameters are detected, it is marked as "normal state". For the identified drift characteristic parameters, the preset parameter values ​​can be adjusted according to the actual situation to optimize the control strategy, improve the stability and efficiency of equipment operation, and promptly handle problems in equipment operation. This can effectively reduce production interruptions, improve production efficiency, and ensure the stability of product quality.

[0101] More specifically, if the microbial agent propagation and automatic dosing equipment is in an abnormal operating state, a recommended control scheme will be generated, and the microbial agent propagation and automatic dosing equipment will be controlled accordingly.

[0102] Obtain the assembly drawing information of the microbial agent propagation and automatic dosing equipment, and construct a three-dimensional model of the microbial agent propagation and automatic dosing equipment based on the assembly drawing information;

[0103] If the working status of the microbial agent amplification and automatic dosing equipment is abnormal, obtain the real-time parameter values ​​of each characteristic parameter in the microbial agent amplification and automatic dosing equipment, and use PROE software to integrate the real-time parameter values ​​of each characteristic parameter into the equipment three-dimensional model diagram to establish a real-time simulation model diagram of the microbial agent amplification and automatic dosing equipment.

[0104] If the working status of the microbial agent expansion and automatic dosing equipment is abnormal, the drift characteristic parameters of the microbial agent expansion and automatic dosing equipment are acquired simultaneously.

[0105] Based on the drift characteristic parameters of the microbial agent propagation and automatic dosing equipment, search keywords are constructed, and the shared database is searched according to the search keywords to obtain several control schemes; and the control parameters of each control scheme are obtained.

[0106] Using PROE software and based on the control parameters of each control scheme, the real-time simulation model of the microbial agent propagation and automatic dosing equipment was simulated and controlled. The simulation parameter values ​​of each characteristic parameter in the microbial agent propagation and automatic dosing equipment after simulation control of each control scheme were obtained; and the control time required for simulation control of each control scheme was obtained.

[0107] It also includes the following steps:

[0108] Based on the control time required for simulation control of each control scheme, the predicted working time nodes of the microbial agent expansion and automatic dosing equipment after simulation control of each control scheme are determined.

[0109] Based on the preset control index requirements, the standard parameter range values ​​of each characteristic parameter in the microbial agent propagation and automatic dosing equipment at the predicted working time node are determined;

[0110] Determine whether the simulated parameter values ​​of each characteristic parameter in the microbial agent expansion and automatic dosing equipment after each control scheme simulation are within the corresponding standard parameter range;

[0111] Obtain the control scheme in which all simulation parameter values ​​are within the corresponding standard parameter range, and obtain the energy consumption loss value of the control scheme in which all simulation parameter values ​​are within the corresponding standard parameter range;

[0112] The control scheme with the lowest energy consumption value is selected as the recommended control scheme, and the microbial agent propagation and automatic dosing equipment is controlled according to the recommended control scheme.

[0113] The shared database refers to a comprehensive database system that integrates a large amount of information on the operating status, control strategies, performance optimization schemes, and related equipment parameters of microbial agent propagation and automatic dosing equipment. This database not only includes various parameter settings, operating guidelines, and historical operating data during normal equipment operation, but also a large number of abnormal equipment situations and their corresponding solutions, control parameters, execution steps, expected effects, and energy consumption analysis. The shared database provides a comprehensive and dynamic information resource platform for equipment managers, maintenance personnel, and technicians to query, reference, and learn when encountering equipment operation problems. By searching keywords, users can quickly find control solutions related to the current problem in the database, saving time and improving the efficiency and accuracy of problem solving. At the same time, the historical data and case analyses in the database provide valuable experience and theoretical basis for optimizing equipment operation strategies, predicting potential problems, and developing preventive measures, thereby helping to improve equipment stability and production efficiency, reduce operating costs, and achieve energy conservation, emission reduction, and sustainable development.

[0114] It should be noted that by acquiring assembly drawing information and constructing a 3D model, visualized management of the equipment is achieved. This step provides a basic framework for subsequent fault diagnosis, status monitoring, and performance optimization. When the equipment's operating status is abnormal, the system can respond quickly, integrating real-time parameter values ​​into the 3D model to construct a real-time simulation model. This process not only visually displays the current operating status of the equipment but also quickly identifies abnormal parameters by comparing them with preset parameter values, providing direct evidence for subsequent fault location and problem analysis. After identifying drift characteristic parameters, the system uses search keywords to find relevant control schemes in the shared database. These schemes contain specific control parameters, and the real-time simulation model is simulated and controlled using PROE software to simulate the effects of various control strategies. This process not only verifies the feasibility of different control schemes but also quantifies the control time required for each scheme, providing time-dimensional information for subsequent decision-making. The system further predicts the equipment's operating time node after control based on the control time and, combined with preset control index requirements, determines the standard parameter range of each characteristic parameter at the predicted operating time node. By comparing the simulated parameter values ​​with the standard parameter range, control schemes where all parameter values ​​are within the specified range are selected, and their energy consumption loss values ​​are calculated. Ultimately, the scheme with the lowest energy consumption was selected as the recommended solution for actual control. This method achieves end-to-end management from equipment status monitoring, fault diagnosis, scheme optimization to actual control. Through virtual simulation, the complexity and risk of actual operation are reduced, improving the scientific rigor and efficiency of decision-making. Simultaneously, energy consumption optimization promotes green and sustainable production, aligning with the trends of modern industrial development.

[0115] In addition, this control method also includes the following steps:

[0116] Based on big data network retrieval, the target microorganisms are shown in the images of their pathological features during various lesion infections; a knowledge graph is constructed based on the image information of their pathological features after various bacterial infections; and the knowledge graph is updated regularly.

[0117] The culture area of ​​the microbial agent propagation and automatic dosing equipment is disinfected by a disinfection mechanism. After disinfection, a preset amount of microbial agent is placed in the culture area for pre-culture.

[0118] After pre-culture, microbial agent samples were obtained from the culture area, and the actual characteristic image information of the target microorganisms in the microbial agent samples was obtained based on optical microscopy.

[0119] Extract the image information of each lesion feature from the knowledge graph, and calculate the hash value between the actual feature image information and each lesion feature image information based on the perceptual hash algorithm to obtain several hash values;

[0120] Each hash value is compared with the preset hash value; if each hash value is not greater than the preset hash value, it means that the disinfection of pathogens in the culture area meets the requirements, and the microbial agent expansion and automatic dosing equipment is put into operation.

[0121] If at least one hash value is greater than the preset hash value, the microbial agent amplification and automatic dosing equipment will stop working; and the disinfection log of the disinfection mechanism disinfecting the culture area in the microbial agent amplification and automatic dosing equipment will be obtained.

[0122] The actual disinfection parameters for each sub-region of the culture area are obtained from the disinfection log; the actual disinfection parameters for each sub-region are compared with the preset disinfection parameters to obtain the disinfection parameter difference between the actual disinfection parameters for each sub-region and the preset disinfection parameters.

[0123] Sub-regions where the difference in disinfection parameters exceeds a preset threshold are defined as abnormal disinfection regions. The disinfection mechanism is then corrected based on these abnormal regions to improve its disinfection performance in the culture area.

[0124] It should be noted that, through big data network retrieval and knowledge graph construction, the system can collect and integrate characteristic image information of microbial agents under different lesion infection states, constructing a comprehensive lesion feature database. Regular updates to the knowledge graph ensure the timeliness and accuracy of information, providing a solid foundation for subsequent image analysis. Secondly, disinfection is a crucial step in ensuring a clean and harmless growth environment for microbial agents. Regular disinfection of the cultivation area by a disinfection facility ensures that the growth environment of the microbial agents is free from pathogen contamination. After disinfection, the pre-cultivation stage simulates the real growth environment, providing a reliable starting point for subsequent agent growth. The actual characteristic image information of the microbial agent samples obtained through optical microscopy is compared and analyzed with the lesion feature image information in the knowledge graph. A perceptual hash algorithm is used to calculate the hash value, enabling rapid and accurate judgment of the agent's growth status. The hash value comparison result is directly related to the pathogen elimination status of the cultivation area, thus determining whether the microbial agent propagation and automatic dosing equipment can be officially put into operation. Once at least one hash value greater than the preset hash value is detected, indicating a potential risk of pathogen residue, the equipment will automatically stop working to avoid possible contamination. At this point, the system records a disinfection log, tracking the specific operating parameters of the disinfection facility, including disinfection area, dosage, and time, and compares them with preset disinfection parameters to analyze the disinfection effect. For discrepancies between the actual disinfection parameters and the preset parameters, especially sub-regions where the difference exceeds a preset threshold, the system defines them as abnormal disinfection areas and accordingly corrects the disinfection facility to optimize the disinfection effect, ensuring comprehensive cleanliness and hygiene of the cultivation area, thereby improving the efficiency and safety of the entire production process. In summary, this method not only achieves real-time monitoring and management of the microbial agent growth environment but also improves the autonomy and intelligence of the equipment through technological innovation, effectively guaranteeing the production quality of the microbial agent while enhancing the sustainability and environmental friendliness of the production process.

[0125] In addition, this control method also includes the following steps:

[0126] To obtain the actual activity of the target microorganisms after microbial agent amplification and automatic dosing equipment;

[0127] Obtain the preset control index requirements of the microbial agent expansion and automatic dosing equipment, and determine the preset parameter range values ​​of each characteristic parameter in the microbial agent expansion and automatic dosing equipment according to the preset control index requirements;

[0128] The linear relationship between the activity of the target microorganism and each characteristic parameter was analyzed and obtained using grey relational analysis. A linear regression analysis model was then constructed based on the linear relationship between the activity of the target microorganism and each characteristic parameter.

[0129] The preset parameter range values ​​of each characteristic parameter in the microbial agent amplification and automatic dosing equipment are imported into the linear regression analysis model for analysis, and the predicted activity of the target microorganism after amplification and dosing by the microbial agent amplification and automatic dosing equipment is obtained.

[0130] If the actual activity of the target microorganism after amplification and addition by the microbial agent and automatic dosing equipment is less than the predicted activity, then the working status of the microbial agent amplification and automatic dosing equipment should be analyzed.

[0131] If the microbial agent propagation and automatic dosing equipment is in an abnormal state, a recommended control scheme will be generated, and the microbial agent propagation and automatic dosing equipment will be controlled accordingly.

[0132] If the microbial agent propagation and automatic dosing equipment is in normal working condition, obtain the disinfection log of the disinfection agency disinfecting the culture area in the microbial agent propagation and automatic dosing equipment;

[0133] The actual disinfection parameters for each sub-region of the culture area are obtained from the disinfection log; the actual disinfection parameters for each sub-region are compared with the preset disinfection parameters to obtain the disinfection parameter difference between the actual disinfection parameters for each sub-region and the preset disinfection parameters.

[0134] Sub-regions where the difference in disinfection parameters exceeds a preset threshold are defined as abnormal disinfection regions, and the disinfection mechanism is corrected based on these abnormal regions.

[0135] It is important to note that, firstly, the actual activity of the target microorganism and the preset control index requirements of the equipment are obtained, and the preset parameter ranges for each characteristic parameter are determined. Then, a linear regression analysis model is constructed using grey relational analysis to predict the activity of the target microorganism after equipment expansion and dosing. If the actual activity is less than the predicted activity, the operating status of the equipment needs to be analyzed. If the operating status is abnormal, a recommended control plan is generated and implemented; if the operating status is normal, the disinfection logs of the disinfection mechanism are obtained, and the actual disinfection parameters are compared with the preset disinfection parameters. Sub-regions with differences greater than a threshold are defined as abnormal disinfection areas, and the disinfection mechanism is corrected. This is because if the microbial agent expansion and automatic dosing equipment are operating normally, the low activity of the target microorganism is highly likely due to bacterial infection. Therefore, it is necessary to further trace the abnormal disinfection areas to help identify and resolve incomplete disinfection issues in a timely manner, ensuring the hygiene and safety of the cultivation area. Correcting the disinfection mechanism can improve the disinfection effect and reliability, and guarantee the quality and safety of the microbial agent. The entire process aims to ensure the effectiveness of microbial agent expansion and dosing, as well as the normal operation of the equipment and the disinfection effect.

[0136] As shown in Figure 2, the second aspect of the present invention discloses a control system for a microbial agent amplification and automatic dosing equipment. The control system includes a memory 41 and a processor 62. The memory 41 stores a control method program for the microbial agent amplification and automatic dosing equipment. When the control method program for the microbial agent amplification and automatic dosing equipment is executed by the processor 62, the steps of the control method for the microbial agent amplification and automatic dosing equipment described in any one of the present invention are implemented.

[0137] The third aspect of the present invention discloses a computer-readable storage medium, the computer-readable storage medium including a control method program for microbial agent amplification and automatic dosing equipment, wherein when the control method program for microbial agent amplification and automatic dosing equipment is executed by a processor, the steps of the control method for microbial agent amplification and automatic dosing equipment as described in any one of the present invention are implemented.

[0138] In addition, to better understand the control process of this microbial agent propagation and automatic dosing equipment, the workflow of the microbial agent propagation and automatic dosing equipment is briefly described, as shown in Figure 4. The microbial agent propagation and automatic dosing equipment consists of two paths:

[0139] (1) First route: The bacterial solution is pumped from the activation tank to the storage tank, and then pumped to the subsequent unit by the metering pump (flow rate 1 to 2 L / min, head 15 m, flow rate controllable).

[0140] (2) Second path: The bacterial solution is pumped from the activation tank to the storage tank, and then pumped to the dilution tank by the metering pump. Tap water is added to the dilution tank in a quantitative manner to dilute the bacterial agent. After quantitative dilution, it is pumped to the subsequent unit by the high pressure pump (20MPa, 50L / min, with frequency conversion, flow and pressure controllable).

[0141] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for a microbial agent propagation and automatic dosing equipment, characterized in that, Includes the following steps: Obtain the preset control index requirements of the microbial agent amplification and automatic dosing equipment, and determine the preset parameter values ​​of each characteristic parameter in the microbial agent amplification and automatic dosing equipment at several preset time nodes based on the preset control index requirements. During the operation of the microbial agent propagation and automatic dosing equipment, the actual parameter values ​​of each characteristic parameter in the microbial agent propagation and automatic dosing equipment are collected sequentially at several preset time nodes, and the collected actual parameter values ​​of the characteristic parameters are stored in the data storage repository. After data collection, the feature parameters in the data repository are classified and the classification results are output. Based on the classification results, the actual parameter values ​​of each feature parameter in the microbial agent propagation and automatic dosing equipment at several preset time points are obtained. Based on the actual and preset parameter values ​​of each characteristic parameter at several preset time points, the working status of the microbial agent expansion and automatic dosing equipment is analyzed. If the microbial agent propagation and automatic dosing equipment is in normal working condition, no adjustment or treatment will be performed on the microbial agent propagation and automatic dosing equipment; if the microbial agent propagation and automatic dosing equipment is in abnormal working condition, a recommended control scheme will be generated and the microbial agent propagation and automatic dosing equipment will be adjusted or treated. Specifically, the feature parameters within the data repository are classified, and the classification results are output as follows: Construct a grid system and divide the grid system into several sub-grid spaces, obtain the feature parameters in the data repository, and map the feature parameters in the data repository to each sub-grid space respectively; Obtain the text features of the feature parameters in each sub-grid space, and calculate the mutual information value between the feature parameters in each sub-grid space based on the text features of the feature parameters in each sub-grid space; Feature parameters with mutual information values ​​greater than a preset threshold are marked as strongly correlated feature parameters. The sub-grid spaces to which the feature parameters with mutual information values ​​greater than the preset threshold belong are merged to aggregate all the strongly correlated feature parameters, resulting in several aggregate spaces. Each aggregate space contains feature parameters that share information. Calculate the mean of all characteristic parameters in each aggregation space, and determine the aggregation center of each aggregation space based on the calculated mean. Calculate the Manhattan distance from all feature parameters in each aggregation space to the aggregation center, square the Manhattan distance from all feature parameters in each aggregation space to the aggregation center, and obtain the contribution value of each feature parameter to the dispersion of its respective aggregation space. The total dispersion of each aggregate space is obtained by summing the contribution values ​​of all feature parameters in each aggregate space. The average dispersion of each aggregate space is obtained by dividing the total dispersion of each aggregate space by the total number of all feature parameters in each aggregate space. The average dispersion of each aggregate space is compared with a preset dispersion threshold. If the average dispersion of an aggregate space is greater than the preset dispersion threshold, the aggregate space is labeled as a high-variability space. If the average dispersion of an aggregate space is not greater than the preset dispersion threshold, the aggregate space is labeled as a low-variability space. The aggregated space marked as a high-variability space is denoised to obtain a denoised aggregated space, and the feature parameters in the denoised aggregated space are sorted based on time series. No denoising is performed on the aggregate space that is marked as a low-variability space, and the feature parameters in the aggregate space that has not been denoised are sorted based on time series. After processing, output the classification results.

2. The control method for a microbial agent propagation and automatic dosing equipment according to claim 1, characterized in that, The aggregate space marked as a high-variability space is denoised to obtain the denoised aggregate space, specifically: Obtain all aggregate spaces marked as high-variance spaces, and define them as the aggregate spaces to be denoised; The DBSCAN algorithm is introduced, and the neighborhood radius and minimum number of samples are preset; the denoising aggregation space is obtained, and the total number of feature parameters of each feature parameter in the denoising aggregation space within the preset neighborhood radius is calculated; Compare the total number of feature parameters within a preset radius with the minimum number of samples. If the total number of a certain feature parameter within a preset neighborhood radius is greater than or equal to the minimum number of samples, then the feature parameter is marked as a density-reachable feature parameter of the denoising aggregation space. If the total number of feature parameters within a preset neighborhood radius is less than the minimum number of samples, then the feature parameter is marked as a non-density-reachable feature parameter of the denoising aggregation space. Similarly, the non-density reachable feature parameters of each denoising aggregation space are detected based on the DBSCAN algorithm; The non-density reachable feature parameters in each aggregation space to be denoised are screened out to obtain the denoised aggregation space.

3. The control method for a microbial agent propagation and automatic dosing equipment according to claim 1, characterized in that, The microbial agent propagation and automatic dosing equipment was analyzed based on the actual and preset parameter values ​​at several preset time points. The analysis yielded the operating status of the equipment, specifically: Construct several two-dimensional coordinate systems, and in the corresponding two-dimensional coordinate systems, construct the actual parameter value curve and the preset parameter value curve of each feature parameter at several preset time nodes based on the actual parameter value and the preset parameter value of each feature parameter; Calculate the overlap rate between the actual parameter value curves and the preset parameter value curves in each two-dimensional coordinate system; and compare the overlap rate between the actual parameter value curves and the preset parameter value curves in each two-dimensional coordinate system with the preset overlap rate threshold. If the overlap rate between the actual parameter value curve and the preset parameter value curve in a certain two-dimensional coordinate system is not greater than the preset overlap rate threshold, then the corresponding characteristic parameter in the microbial agent expansion and automatic dosing equipment will be calibrated as the drift characteristic parameter. If the overlap rate between the actual parameter value curve and the preset parameter value curve in a certain two-dimensional coordinate system is greater than the preset overlap rate threshold, then the corresponding characteristic parameter in the microbial agent expansion and automatic dosing equipment will be calibrated as a normal characteristic parameter. Determine whether drift characteristic parameters have appeared in the microbial agent amplification and automatic dosing equipment; if drift characteristic parameters have appeared, mark the working status of the microbial agent amplification and automatic dosing equipment as abnormal; if no drift characteristic parameters have appeared, mark the working status of the microbial agent amplification and automatic dosing equipment as normal.

4. The control method for a microbial agent propagation and automatic dosing equipment according to claim 1, characterized in that, If the microbial agent propagation and automatic dosing equipment is in an abnormal state, a recommended control plan will be generated, and the microbial agent propagation and automatic dosing equipment will be controlled accordingly. Specifically: Obtain the assembly drawing information of the microbial agent propagation and automatic dosing equipment, and construct a three-dimensional model of the microbial agent propagation and automatic dosing equipment based on the assembly drawing information; If the working status of the microbial agent amplification and automatic dosing equipment is abnormal, obtain the real-time parameter values ​​of each characteristic parameter in the microbial agent amplification and automatic dosing equipment, and use PROE software to integrate the real-time parameter values ​​of each characteristic parameter into the equipment three-dimensional model diagram to establish a real-time simulation model diagram of the microbial agent amplification and automatic dosing equipment. If the working status of the microbial agent propagation and automatic dosing equipment is abnormal, the drift characteristic parameters of the microbial agent propagation and automatic dosing equipment are acquired simultaneously. Based on the drift characteristic parameters of the microbial agent propagation and automatic dosing equipment, search keywords are constructed, and the shared database is searched according to the search keywords to obtain several control schemes; and the control parameters of each control scheme are obtained. Using PROE software and based on the control parameters of each control scheme, the real-time simulation model of the microbial agent expansion and automatic dosing equipment was simulated and controlled to obtain the simulation parameter values ​​of each characteristic parameter in the microbial agent expansion and automatic dosing equipment after each control scheme was simulated and controlled. And obtain the control time required for simulation control of each control scheme.

5. The control method for a microbial agent propagation and automatic dosing equipment according to claim 4, characterized in that, It also includes the following steps: Based on the control time required for simulation control of each control scheme, the predicted working time nodes of the microbial agent expansion and automatic dosing equipment after simulation control of each control scheme are determined. Based on the preset control index requirements, the standard parameter range values ​​of each characteristic parameter in the microbial agent propagation and automatic dosing equipment at the predicted working time node are determined; Determine whether the simulated parameter values ​​of each characteristic parameter in the microbial agent expansion and automatic dosing equipment after each control scheme simulation are within the corresponding standard parameter range; Obtain the control scheme in which all simulation parameter values ​​are within the corresponding standard parameter range, and obtain the energy consumption loss value of the control scheme in which all simulation parameter values ​​are within the corresponding standard parameter range; The control scheme with the lowest energy consumption value is selected as the recommended control scheme, and the microbial agent expansion and automatic dosing equipment is controlled according to the recommended control scheme.

6. The control method for a microbial agent propagation and automatic dosing equipment according to claim 1, characterized in that: The characteristic parameters include the temperature, dissolved oxygen, pH, stirring speed, water volume, and foam volume of the activation tank; The characteristic parameters also include the stirring speed of the storage tank and the discharge flow rate of the metering pump; The characteristic parameters also include the amount of bacteria added to the dilution tank, the amount of tap water, the stirring speed, and the discharge flow rate of the high-pressure pump.

7. A control system for a microbial agent propagation and automatic dosing equipment, characterized in that, The control system includes a memory and a processor. The memory stores a control method program for the microbial agent amplification and automatic dosing equipment. When the control method program for the microbial agent amplification and automatic dosing equipment is executed by the processor, the steps of the control method for the microbial agent amplification and automatic dosing equipment as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a control method program for microbial agent amplification and automatic dosing equipment. When the control method program for microbial agent amplification and automatic dosing equipment is executed by a processor, it implements the steps of the control method for microbial agent amplification and automatic dosing equipment as described in any one of claims 1 to 6.

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