A microbial culture management system and method

Abstract

The present invention relates to the field of microbial culture management, and in particular to a microbial culture management system and method. The contents include: based on the number of microorganisms and microbial culture environment parameters collected in real time during the microbial growth process, calculating the growth factor and calculating the predicted value of the number of microorganisms at a future time; based on the predicted value of the number of microorganisms at a future time, detecting the growth state of the microorganism; obtaining the preliminary adjustment amount of the microbial culture environment parameters through a multi-parameter nonlinear optimization algorithm, introducing a synergistic mechanism to obtain the final adjustment amount of the microbial culture environment parameters, and adjusting the microbial culture environment parameters. The problem that the traditional microbial culture environment parameters rely on manual monitoring and experience adjustment, resulting in delayed response and inaccurate adjustment; it is difficult to accurately predict the future growth trend of microorganisms, resulting in delayed culture management response; it is impossible to automatically optimize the microbial culture environment parameters, resulting in an imbalanced microbial culture environment and an inability to achieve optimal management is solved.

Description

Technical Field

[0001] The present invention relates to the field of microbial cultivation management, and in particular to a microbial cultivation management system and method. Background Art

[0002] With the development of biotechnology, the cultivation of microorganisms has gradually expanded from basic research at the laboratory level to the field of industrial production. Microbial cultivation management is a crucial link in modern biotechnology and related industries. Scientific management of microbial cultivation processes can not only improve the yield and quality of microorganisms, but also effectively reduce cultivation costs and improve economic benefits in industrial applications. In this context, the development of microbial cultivation management methods has undergone a huge transformation from traditional empirical operations to automated and intelligent management. In practical applications, the accuracy and stability of microbial cultivation are the focus of technology and management. Therefore, the study of effective microbial cultivation management systems and methods has broad significance.

[0003] Microbial culture management is not only a crucial link in microbial production, but also a core force driving the development of the biotechnology industry. With the advancement of technology and changes in industrial needs, future microbial culture management systems and methods will become intelligent, flexible and adaptable.

[0004] However, the traditional microbial culture management methods have the following technical problems: in the traditional industrial microbial culture process, the microbial culture environment parameters rely on manual monitoring and experience-based adjustment, resulting in delayed response and inaccurate adjustment, especially when facing the needs of industrial-scale production. The errors and untimely responses of manual operations cannot meet the requirements of refined management; it is difficult to accurately predict the future growth trend of microorganisms, resulting in delayed culture management response, waste of resources, and in some scenarios, may cause uncontrolled microbial growth; there is a lack of scientific modeling and prediction methods for the microbial growth process, especially when resources are close to saturation, it is impossible to effectively limit the excessive growth of the number of microorganisms, thus leading to culture failure or reduced yield; it is impossible to automatically optimize the environmental parameters according to the growth state of the microorganisms, resulting in inefficient adjustment of the culture environment; the interaction between the various microbial culture environment parameters is ignored, resulting in an imbalance in the microbial culture environment and an inability to achieve optimal management. Summary of the invention

[0005] The present invention provides a microbial culture management system and method to solve the problems that in the traditional industrial microbial culture process, the microbial culture environment parameters rely on manual monitoring and experience adjustment, resulting in delayed response and inaccurate adjustment, especially when facing the needs of industrial-scale production, the errors and untimely responses of manual operations cannot meet the refined management requirements; it is difficult to accurately predict the future growth trend of microorganisms, resulting in delayed culture management response, waste of resources, and may cause uncontrolled microbial growth in some scenarios; there is a lack of scientific modeling and prediction means for the microbial growth process, especially when resources are close to saturation, it is impossible to effectively limit the excessive growth of the number of microorganisms, thereby causing culture failure or reduced yield; it is impossible to automatically optimize the environmental parameters according to the growth state of the microorganisms, resulting in low efficiency in adjusting the culture environment; the interaction between the various microbial culture environment parameters is ignored, resulting in an imbalance in the microbial culture environment and an inability to achieve optimal management.

[0006] A microbial culture management system and method of the present invention specifically include the following technical solutions:

[0007] A microbial culture management method comprises the following steps:

[0008] S1: Real-time collection of the number of microorganisms and microorganism culture environment parameters during the microbial growth process; based on the microbial culture environment parameters, the growth factor is calculated by a microbial growth factor generation algorithm;

[0009] S2: Based on the number and growth factors of microorganisms, the predicted value of the number of microorganisms at a future time is calculated by a microbial growth prediction algorithm; based on the predicted value of the number of microorganisms at a future time, the growth status of microorganisms is detected;

[0010] S3: Based on the predicted value of the number of microorganisms at a future time and the growth status of microorganisms, the preliminary adjustment amount of the microorganism culture environment parameters is calculated by a multi-parameter nonlinear optimization algorithm;

[0011] S4: Based on the preliminary adjustment amounts of the microbial culture environment parameters, the final adjustment amounts of the microbial culture environment parameters are calculated by a multi-parameter collaborative optimization algorithm; based on the final adjustment amounts of the microbial culture environment parameters, the microbial culture environment parameters are adjusted.

[0012] Preferably, the S1 specifically includes:

[0013] Microbial culture environmental parameters include nutrient concentration, ambient temperature, pH and dissolved oxygen concentration.

[0014] Preferably, the S1 specifically includes:

[0015] The specific implementation formula of the microbial growth factor generation algorithm is:

[0016]

[0017] in, Indicates at time Growth factors; Indicates at time The concentration of nutrients in the microbial culture environment parameters; Used to prevent the denominator from being zero; represents the hyperbolic cosine function; Indicates at time The ambient temperature in the microbial culture environment parameters; It indicates the optimum temperature for the growth of microorganisms; Indicates the standard deviation of ambient temperature; Indicates at time pH value in the microbial culture environment parameters; Indicates the optimum pH for microbial growth; Indicates the standard deviation of pH; Indicates at time Dissolved oxygen concentration in the microbial culture environment parameters.

[0018] Preferably, the S2 specifically includes:

[0019] The microbial growth prediction algorithm is based on the number and growth factor of microorganisms, introduces the time correction factor and resource saturation factor, predicts the number of microorganisms at future times, and obtains the predicted value of the number of microorganisms at future times. The specific calculation formula is:

[0020]

[0021] in, Indicates at time The predicted value of the number of microorganisms; Indicates at time The number of microorganisms; represents the time correction factor; represents the correction factor; Indicates the time point when microbial growth transitions from exponential growth to saturation stage; Indicates the maximum number of organisms that the microbial culture management system can support; represents the resource saturation factor; Indicates the resource saturation coefficient.

[0022] Preferably, the S2 specifically includes:

[0023] The set minimum safety threshold and maximum safety threshold of the number of microorganisms are compared with the predicted value of the number of microorganisms at a future moment to detect the growth status of the microorganisms.

[0024] Preferably, the S2 specifically includes:

[0025] When the predicted value of the number of microorganisms at a future moment is greater than or equal to the minimum safety threshold of the number of microorganisms and less than or equal to the maximum safety threshold of the number of microorganisms, the microbial growth state is normal; when the predicted value of the number of microorganisms at a future moment is less than the minimum safety threshold of the number of microorganisms or greater than the maximum safety threshold of the number of microorganisms, the microbial growth state is abnormal; when the microbial growth state is abnormal, adjust the microbial culture environment parameters.

[0026] Preferably, the S3 specifically includes:

[0027] In the process of implementing the multi-parameter nonlinear optimization algorithm, the standard microbial number is obtained based on the minimum safety threshold and the maximum safety threshold of the microbial number; the error value is calculated based on the predicted value of the microbial number at a future moment and the standard microbial number.

[0028] Preferably, the S3 specifically includes:

[0029] In the implementation process of the multi-parameter nonlinear optimization algorithm, the sensitivity factor is introduced to control the response intensity of the microbial culture environment parameters to the error change. At the same time, the preliminary adjustment amount of the microbial culture environment parameters is calculated based on the error value.

[0030] Preferably, the S4 specifically includes:

[0031] The multi-parameter collaborative optimization algorithm introduces the synergistic mechanism among the microbial culture environment parameters and obtains the final adjustment amount of the microbial culture environment parameters based on the preliminary adjustment amount of the microbial culture environment parameters. The specific calculation formula is:

[0032]

[0033] in, Indicates Microbial culture environment parameters in time The final adjustment amount; Indicates Microbial culture environment parameters in time The initial adjustment amount; Indicates Microbial culture environment parameters and Nonlinear time dynamic influence coefficient between microbial culture environment parameters; represents the hyperbolic tangent function.

[0034] A microbial culture management system, comprising the following parts:

[0035] Data acquisition module, microbial growth factor generation module, microbial growth prediction module, microbial culture detection module, multi-parameter nonlinear optimization module, multi-parameter collaborative optimization module;

[0036] Data acquisition module: collects the number of microorganisms and microorganism culture environment parameters in real time during the microbial growth process, outputs the microorganism culture environment parameters to the microbial growth factor generation module; outputs the number of microorganisms to the microbial growth prediction module;

[0037] Microbial growth factor generation module: Based on the microbial culture environment parameters, the growth factor is calculated using the microbial growth factor generation algorithm; the growth factor is output to the microbial growth prediction module;

[0038] Microbial growth prediction module: Based on the growth factors and the number of microorganisms, the predicted value of the number of microorganisms at a future time is calculated by a microbial growth prediction algorithm; the predicted value of the number of microorganisms at a future time is output to the microbial culture detection module and the multi-parameter nonlinear optimization module;

[0039] Microbial culture detection module: compares the predicted value of the number of microorganisms at a future moment with the set minimum safety threshold of the number of microorganisms and the maximum safety threshold of the number of microorganisms to detect the growth status of microorganisms; outputs the minimum safety threshold of the number of microorganisms, the maximum safety threshold of the number of microorganisms and the growth status of microorganisms to the multi-parameter nonlinear optimization module;

[0040] Multi-parameter nonlinear optimization module: Based on the predicted value of the number of microorganisms at a future time, the minimum safety threshold of the number of microorganisms, the maximum safety threshold of the number of microorganisms and the growth status of microorganisms, the preliminary adjustment amount of the microorganism culture environment parameters is calculated through a multi-parameter nonlinear optimization algorithm; the preliminary adjustment amount of the microorganism culture environment parameters is output to the multi-parameter collaborative optimization module;

[0041] Multi-parameter collaborative optimization module: Based on the preliminary adjustment of microbial culture environment parameters, the final adjustment of microbial culture environment parameters is calculated through a multi-parameter collaborative optimization algorithm.

[0042] The beneficial effects of the technical solution of the present invention are:

[0043] 1. Through the sensor network, the microbial culture environment parameters (such as nutrient concentration, temperature, pH, dissolved oxygen concentration) during the microbial growth process can be collected in real time, and accurate microbial culture environment data can be obtained in time. Compared with the traditional method that relies on manual monitoring, it overcomes the problem of delayed response and can quickly and accurately grasp the microbial culture environment conditions, thereby improving the accuracy and timeliness of management, helping to reduce manual errors and improve microbial culture efficiency.

[0044] 2. By processing different microbial culture environment parameters through the microbial growth factor generation algorithm, the excessive impact of extreme conditions on microbial growth is avoided, thereby providing an accurate description of the microbial growth status and optimizing management decisions.

[0045] 3. Using the microbial growth prediction algorithm, the number of microorganisms at future times can be accurately predicted through growth factors, time correction factors and resource saturation factors. The characteristics of the S-curve are introduced to simulate the changing trends of microorganisms in different growth stages. The impact of resource depletion on the growth rate is also considered. The changes in the number of microorganisms can be foreseen, so that management measures can be taken in advance, the management of the microbial culture process can be optimized, unnecessary waste of resources can be reduced, and prediction accuracy and management efficiency can be improved.

[0046] 4. The use of multi-parameter nonlinear optimization algorithm can effectively adjust the microbial culture environment parameters, especially when the error between the predicted value of the microbial number in the future and the standard microbial number is large, the adjustment force can be increased, and at the same time, the excessive oscillation of the microbial culture environment parameters during the adjustment process can be prevented. The introduction of sensitivity factors ensures the response intensity of each microbial culture environment parameter to the error change, and can make reasonable adjustments based on the deviation between the predicted value of the microbial number in the future and the standard microbial number. It has intelligent and dynamic parameter adjustment effects, can correct deviations in the microbial growth process, and ensure the optimal growth state of microorganisms.

[0047] 5. Taking into account the mutual influence between various microbial culture environment parameters, a synergistic mechanism can be established between different microbial culture environment parameters through a multi-parameter collaborative optimization algorithm, so that the final microbial culture environment parameter adjustment is not limited to the independent adjustment of a single parameter, but can also integrate the coupling effect between various parameters, thereby improving the overall management effect of the microbial culture environment and enhancing the culture efficiency.

[0048] 6. Through real-time monitoring, calculation prediction and parameter adjustment, the microbial culture environment is ensured to always remain in the best state, which can realize intelligent and refined management of the microbial culture environment, improve the accuracy of microbial quantity prediction and the efficiency of the culture process, reduce the need for manual intervention, and thus improve the automation and stability of the entire microbial culture management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a structural diagram of a microbial culture management system according to the present invention;

[0050] Figure 2 The present invention is a flow chart of a microbial culture management method. DETAILED DESCRIPTION

[0051] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0053] The specific scheme of a microorganism culture management system and method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0054] See attached Figure 1 , which shows a structural diagram of a microorganism cultivation management system provided by an embodiment of the present invention, the system includes the following parts:

[0055] Data acquisition module, microbial growth factor generation module, microbial growth prediction module, microbial culture detection module, multi-parameter nonlinear optimization module, multi-parameter collaborative optimization module;

[0056] Data acquisition module: collects the number of microorganisms and microorganism culture environment parameters in the process of microorganism growth in real time through the sensor network, such as nutrient concentration, ambient temperature, pH, dissolved oxygen concentration; outputs the microorganism culture environment parameters to the microorganism growth factor generation module; outputs the number of microorganisms to the microorganism growth prediction module;

[0057] Microbial growth factor generation module: Based on the microbial culture environment parameters from the data acquisition module, the microbial growth factor generation algorithm is used to calculate the growth factor; the growth factor is output to the microbial growth prediction module;

[0058] Microbial growth prediction module: Based on the growth factors from the microbial growth factor generation module and the number of microorganisms from the data acquisition module, the predicted value of the number of microorganisms at a future time is calculated by a microbial growth prediction algorithm; the predicted value of the number of microorganisms at a future time is output to the microbial culture detection module and the multi-parameter nonlinear optimization module;

[0059] Microbial culture detection module: compares the predicted value of the number of microorganisms at a future moment from the microbial growth prediction module with the set minimum safety threshold of the number of microorganisms and the maximum safety threshold of the number of microorganisms, and automatically detects the growth status of microorganisms; outputs the minimum safety threshold of the number of microorganisms, the maximum safety threshold of the number of microorganisms and the growth status of microorganisms to the multi-parameter nonlinear optimization module;

[0060] Multi-parameter nonlinear optimization module: Based on the predicted value of the number of microorganisms at a future moment from the microbial growth prediction module and the minimum safety threshold of the number of microorganisms, the maximum safety threshold of the number of microorganisms and the microbial growth state from the microbial culture detection module, the preliminary adjustment amount of the microbial culture environment parameters is calculated through a multi-parameter nonlinear optimization algorithm; the preliminary adjustment amount of the microbial culture environment parameters is output to the multi-parameter collaborative optimization module;

[0061] Multi-parameter collaborative optimization module: Based on the preliminary adjustment of the microbial culture environment parameters from the multi-parameter nonlinear optimization module, the final adjustment of the microbial culture environment parameters is calculated by the multi-parameter collaborative optimization algorithm.

[0062] See attached Figure 2 , which shows a flow chart of a microbial culture management method provided by an embodiment of the present invention, the method comprises the following steps:

[0063] S1: Real-time collection of the number of microorganisms and microorganism culture environment parameters during the microbial growth process; based on the microbial culture environment parameters, the growth factor is calculated by a microbial growth factor generation algorithm;

[0064] In the process of industrial microbial cultivation, the growth of microorganisms is affected by microbial cultivation environment parameters, such as nutrient concentration, ambient temperature, pH and dissolved oxygen concentration. In view of the fact that traditional microbial cultivation management relies on manual monitoring and experience adjustment, there are problems of delayed response and insufficient precision, which makes it difficult to meet the demand for precise control in modern industry, the present invention introduces a microbial growth factor generation algorithm.

[0065] The number of microorganisms and microorganism culture environment parameters in the process of microorganism growth are collected in real time through a sensor network; based on the microorganism culture environment parameters collected in real time during the process of microorganism growth, a microorganism growth factor generation algorithm is used to calculate the growth factor;

[0066] The microbial growth factor generation algorithm obtains the nutrient concentration from the microbial culture environment parameters collected in real time during the microbial growth process, and processes the nutrient concentration by applying a logarithmic function to ensure that the influence of the nutrient is amplified at low concentrations, and the influence of the nutrient concentration gradually tends to saturation at high concentrations, thereby preventing excessive nutrient concentrations from causing excessive increases in growth factors, thereby affecting the accuracy of prediction;

[0067] The microbial growth factor generation algorithm uses a hyperbolic cosine function to describe the nonlinear effect of ambient temperature in the microbial culture environment parameters. The characteristics of the hyperbolic cosine function can ensure that when the ambient temperature is close to the optimal temperature for microbial growth, the growth promotion effect is the greatest, and once the ambient temperature deviates, it decreases rapidly, reflecting the inhibitory effect of ambient temperature on growth;

[0068] The microbial growth factor generation algorithm introduces a logic function ( Function) is used to deal with the effect of pH changes in microbial culture environment parameters on growth. The logic function can simulate the gradual effect of pH on growth. When the pH is close to the optimal pH for microbial growth, the growth factor changes steadily. When the pH deviates from the optimal pH for microbial growth, the growth factor changes rapidly, thus reflecting the nonlinear effect of pH on microbial growth.

[0069] The microbial growth factor generation algorithm uses a square root function to describe the influence of dissolved oxygen concentration in the microbial culture environment parameters. Dissolved oxygen is a vital resource in the microbial metabolism process. The use of the square root function can ensure that when the dissolved oxygen concentration is low, the microbial growth is significantly inhibited, and when the concentration gradually increases, the growth promotion effect gradually decreases, simulating the saturation effect of dissolved oxygen concentration on microbial growth;

[0070] The growth factor calculation formula is:

[0071]

[0072] in, Indicates at time Growth factors are used to describe the overall effect of microbial growth on the microbial culture environment parameters; Indicates at time The concentration of nutrients in the microbial culture environment parameters; Represents the natural logarithm, which is used to smooth the effect of nutrient concentration, ensuring that the effect of nutrients on growth factors slows down as the concentration increases, and avoiding unrealistic linear growth at very high concentrations. Represents a very small positive number used to prevent the denominator from being zero; Indicates at time The ambient temperature in the microbial culture environment parameters; It represents the optimum temperature for microbial growth, which is derived from biological literature and used as a reference; It represents the standard deviation of ambient temperature, reflecting the sensitivity of ambient temperature to microbial growth; represents the hyperbolic cosine function, which is used to make the ambient temperature effect nonlinear; Indicates at time pH value in the microbial culture environment parameters; It indicates the optimum pH for microbial growth, which is derived from biological literature and used as a reference; It represents the standard deviation of pH, reflecting the sensitivity of pH to microbial growth; represents the logical function ( function), so that the influence of pH presents an S-shaped curve, ensuring that the influence is large when the pH is close to the optimal pH, and gradually weakens when it deviates from the optimal pH; Indicates at time Dissolved oxygen concentration in the microbial culture environment parameters; represents the effect of dissolved oxygen concentration on growth factor, and uses the square root function to correct the effect of dissolved oxygen concentration. Represents a very small positive number used to prevent the denominator from being zero;

[0073] The calculation of growth factors provides an accurate description of the microbial growth status, thereby helping to optimize the management of the microbial culture process.

[0074] S2: Based on the number and growth factors of microorganisms, the predicted value of the number of microorganisms at a future time is calculated by a microbial growth prediction algorithm; based on the predicted value of the number of microorganisms at a future time, the growth status of microorganisms is detected;

[0075] Based on the number and growth factors of microorganisms, the predicted value of the number of microorganisms at a future time is calculated through the microbial growth prediction algorithm, which is convenient for making microbial cultivation management decisions in advance;

[0076] The microbial growth prediction algorithm introduces a time correction factor based on the number of microorganisms collected in real time during the microbial growth process and the calculated growth factor, simulates the change trend of microorganisms in the early and late growth stages, and ensures that the growth curve conforms to the actual S-shaped growth model. Specifically, the growth rate is adjusted according to the difference between the current time point and the inflection point time of the growth curve to make the growth curve conform to the actual situation. The resource saturation factor is further introduced to describe the impact of resource limitations on the microbial growth rate, simulate the gradual slowdown of microbial growth when resources are close to exhaustion, and limit the exponential growth of the number of microorganisms. Specifically, the impact of resources close to exhaustion on the growth rate is calculated based on the ratio of the number of microorganisms at the current moment to the available resources in the culture environment. As resources decrease, the growth rate of the microorganism will gradually decrease until it reaches a stable state.

[0077] The calculation formula for the predicted value of the number of microorganisms at a future time is:

[0078]

[0079] in, Indicates at time The predicted value of the number of microorganisms; Indicates at time The number of microorganisms; Indicates at time Growth factors; It means that under the action of growth factors, the number of microorganisms increases exponentially over time; It represents the time correction factor, which is used to simulate the changing trend of microorganisms in the early and late growth stages to ensure that the growth curve conforms to the actual S-shaped growth pattern; represents the correction factor, which is used to control the steepness of the growth curve; It indicates the inflection point time of the growth curve, that is, the time point when the microbial growth transitions from exponential growth to the saturation stage; Indicates the maximum number of organisms that the microbial culture management system can support under specific resource conditions. It can be set according to the specific implementation scenario and is not limited here; represents the resource saturation factor, which describes the effect of resource limitation on the growth rate of microorganisms, simulates the gradual slowing down of microbial growth when resources are close to exhaustion, and limits the exponential growth of the number of microorganisms; It represents the resource saturation coefficient, which reflects the sensitivity of microorganisms to resource saturation;

[0080] Set the minimum and maximum safety thresholds for the number of microorganisms, and compare them with the predicted value of the number of microorganisms at a future time to automatically detect the growth status of microorganisms;

[0081] like , indicating that the predicted value of the number of microorganisms at the future moment is within the safe range, and the growth state of microorganisms is normal. ;

[0082] like , indicating that the predicted value of the number of microorganisms at the future moment is lower than the minimum safety threshold of the number of microorganisms, and the growth state of microorganisms is abnormal. ;

[0083] like , indicating that the predicted value of the number of microorganisms at the future moment is higher than the highest safety threshold of the number of microorganisms, and the growth state of microorganisms is abnormal. ;

[0084] The formula is as follows:

[0085]

[0086] in, Indicates at time The growth status of microorganisms; Indicates the minimum safety threshold of the number of microorganisms, which can be set according to the specific implementation scenario and is not limited here; Indicates the highest safety threshold of the number of microorganisms, which can be set according to the specific implementation scenario and is not limited here;

[0087] When detected or When the microbial growth state is abnormal, the microbial culture environment parameters need to be further adjusted.

[0088] S3: Based on the predicted value of the number of microorganisms at a future time and the growth status of microorganisms, the preliminary adjustment amount of the microorganism culture environment parameters is calculated by a multi-parameter nonlinear optimization algorithm;

[0089] Based on the predicted value of the number of microorganisms at a future time, a multi-parameter nonlinear optimization algorithm is used to calculate the initial adjustment of the microbial culture environment parameters, correct the deviation of microbial growth, and enhance process control;

[0090] The multi-parameter nonlinear optimization algorithm compares the predicted value of the number of microorganisms at a future moment with the standard number of microorganisms, calculates the error value, and reflects the deviation between the predicted state and the ideal state. In order to ensure the efficiency of error processing, the error is processed in a nonlinear manner, and the size of the error value is amplified in an exponential form. In particular, when the error value is extremely large, the exponential change will significantly increase the intensity of the adjustment of the microbial culture environment parameters. The absolute value change of the error is further smoothed by a square root function to prevent large oscillations during the adjustment process, and a sensitivity factor is introduced. The sensitivity factor determines the response intensity of the microbial culture environment parameters to the error change;

[0091] The standard microbial count is an average value obtained based on the set minimum safety threshold and maximum safety threshold of the microbial count, which can ensure that the microbial culture environment operates under optimal conditions;

[0092] The calculation formula for the initial adjustment of microbial culture environment parameters is:

[0093]

[0094] in, Indicates Microbial culture environment parameters in time The initial adjustment amount is used to correct the microbial culture environment parameters; Indicates Sensitivity factors of microbial culture environment parameters; Indicates the number of standard microorganisms, used as a reference; Indicates at time The predicted value of the number of microorganisms; Represents the standard microbial count and time The difference between the predicted values ​​of the number of microorganisms and the error value; An exponential function representing the error, used to increase the adjustment force when the error value is extremely large; Used to balance the exponential function so that when the error value approaches zero, the adjustment amount also approaches zero, avoiding unnecessary adjustments; It means that the square root of the absolute value of the error is processed so that the initial adjustment amount of the microbial culture environment parameter changes smoothly with the error value;

[0095] Preliminary adjustments to microbial culture environment parameters provide initial corrective measures for readjusting microbial growth to optimal conditions.

[0096] S4: Based on the preliminary adjustment amounts of the microbial culture environment parameters, the final adjustment amounts of the microbial culture environment parameters are calculated by a multi-parameter collaborative optimization algorithm; based on the final adjustment amounts of the microbial culture environment parameters, the microbial culture environment parameters are adjusted.

[0097] Considering the mutual influence between microbial culture environment parameters, based on the preliminary adjustment of microbial culture environment parameters, the final adjustment of microbial culture environment parameters is calculated using a multi-parameter collaborative optimization algorithm;

[0098] The multi-parameter collaborative optimization algorithm introduces a synergistic mechanism between various microbial culture environment parameters to ensure that the adjustment of different microbial culture environment parameters is not limited to independent processing, but also takes into account the influence of related microbial culture environment parameters;

[0099] The calculation formula for the final adjustment of microbial culture environment parameters is:

[0100]

[0101] in, Indicates Microbial culture environment parameters in time The final adjustment amount; Indicates that for all Different microbial culture environment parameters The sum operation of Indicates Microbial culture environment parameters and The nonlinear time dynamic influence coefficient between the microbial culture environment parameters is used to describe the correlation strength between the microbial culture environment parameters, reflecting the coupling relationship between the two microbial culture environment parameters. It can be set according to the specific implementation scenario and is not limited here; Indicates Microbial culture environment parameters in time The square of the absolute value of the preliminary adjustment amount is used to amplify or suppress the adjustment amount through the square term to ensure that the impact introduced when the adjustment amplitude is extremely large will be weakened to avoid over-adjustment. The absolute value sign ensures that the sign direction of the microbial culture environment parameter will not affect the amplitude of the adjustment, and only the size is concerned. represents the hyperbolic tangent function, which is used for smooth adjustment;

[0102] Using a multi-parameter collaborative optimization algorithm, the final adjustment amount of the microbial culture environment parameters is obtained, so as to intelligently adjust the microbial culture environment parameters, realize accurate monitoring and management of the microbial growth status, improve the culture efficiency and prediction accuracy, ensure that the microbial culture environment is in the best state, and realize refined management of the culture process.

[0103] In summary, a microbial culture management system and method are completed.

[0104] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for managing microbial cultivation, characterized in that: The following steps are involved: S1: Real-time collection of the number of microorganisms and microorganism culture environment parameters during the microorganism growth process; the microorganism culture environment parameters include nutrient concentration, ambient temperature, pH and dissolved oxygen concentration; based on the microorganism culture environment parameters, the growth factor is calculated by a microorganism growth factor generation algorithm; the specific implementation formula of the microorganism growth factor generation algorithm is: in, Indicates at time Growth factors; Indicates at time The concentration of nutrients in the microbial culture environment parameters; Used to prevent the denominator from being zero; represents the hyperbolic cosine function; Indicates at time The ambient temperature in the microbial culture environment parameters; It indicates the optimum temperature for the growth of microorganisms; Indicates the standard deviation of ambient temperature; Indicates at time pH value in the microbial culture environment parameters; Indicates the optimum pH for microbial growth; Indicates the standard deviation of pH; Indicates at time Dissolved oxygen concentration in the microbial culture environment parameters; S2: Based on the number and growth factor of microorganisms, the microbial growth prediction algorithm is used to introduce the time correction factor and resource saturation factor to predict the number of microorganisms at the future time, and the predicted value of the number of microorganisms at the future time is calculated; based on the predicted value of the number of microorganisms at the future time, the microbial growth state is detected; the specific calculation formula of the microbial growth prediction algorithm is: in, Indicates at time The predicted value of the number of microorganisms; Indicates at time The number of microorganisms; represents the time correction factor; represents the correction factor; Indicates the time point when microbial growth transitions from exponential growth to saturation stage; Indicates the maximum number of organisms that the microbial culture management system can support; represents the resource saturation factor; represents the resource saturation coefficient; S3: Based on the predicted value of the number of microorganisms at a future time and the growth status of microorganisms, the preliminary adjustment amount of the microorganism culture environment parameters is calculated by a multi-parameter nonlinear optimization algorithm; S4: Based on the preliminary adjustment of the microbial culture environment parameters, the synergistic mechanism between the microbial culture environment parameters is introduced through the multi-parameter collaborative optimization algorithm to calculate the final adjustment of the microbial culture environment parameters; based on the final adjustment of the microbial culture environment parameters, the microbial culture environment parameters are adjusted; the specific calculation formula of the multi-parameter collaborative optimization algorithm is: in, Indicates Microbial culture environment parameters in time The final adjustment amount; Indicates Microbial culture environment parameters in time The initial adjustment amount; Indicates Microbial culture environment parameters in time The initial adjustment amount; Indicates Microbial culture environment parameters and Nonlinear time dynamic influence coefficient between microbial culture environment parameters; represents the hyperbolic tangent function.

2. A microbial culture management method according to claim 1, characterized in that: The S2 specifically includes: The set minimum safety threshold and maximum safety threshold of the number of microorganisms are compared with the predicted value of the number of microorganisms at a future moment to detect the growth status of the microorganisms.

3. A microbial cultivation management method according to claim 2, characterized in that: In the S2, it also includes: When the predicted value of the number of microorganisms at a future moment is greater than or equal to the minimum safety threshold of the number of microorganisms and less than or equal to the maximum safety threshold of the number of microorganisms, the microbial growth state is normal; when the predicted value of the number of microorganisms at a future moment is less than the minimum safety threshold of the number of microorganisms or greater than the maximum safety threshold of the number of microorganisms, the microbial growth state is abnormal; when the microbial growth state is abnormal, adjust the microbial culture environment parameters.

4. A microbial culture management method according to claim 2, characterized in that: The S3 specifically includes: In the process of implementing the multi-parameter nonlinear optimization algorithm, the standard microbial number is obtained based on the minimum safety threshold and the maximum safety threshold of the microbial number; the error value is calculated based on the predicted value of the microbial number at the future moment and the standard microbial number.

5. A microbial culture management method according to claim 4, characterized in that: In the S3, it also includes: In the implementation process of the multi-parameter nonlinear optimization algorithm, the sensitivity factor is introduced to control the response intensity of the microbial culture environment parameters to the error change. At the same time, the preliminary adjustment amount of the microbial culture environment parameters is calculated based on the error value.

6. A microorganism culture management system, applied to a microorganism culture management method as claimed in claim 1, characterized in that: Includes the following parts: Data acquisition module, microbial growth factor generation module, microbial growth prediction module, microbial culture detection module, multi-parameter nonlinear optimization module, multi-parameter collaborative optimization module; Data acquisition module: collects the number of microorganisms and microorganism culture environment parameters in real time during the microbial growth process, outputs the microbial culture environment parameters to the microbial growth factor generation module; outputs the number of microorganisms to the microbial growth prediction module; Microbial growth factor generation module: Based on the microbial culture environment parameters, the growth factor is calculated using the microbial growth factor generation algorithm; the growth factor is output to the microbial growth prediction module; Microbial growth prediction module: Based on the growth factors and the number of microorganisms, the predicted value of the number of microorganisms at a future time is calculated by a microbial growth prediction algorithm; the predicted value of the number of microorganisms at a future time is output to the microbial culture detection module and the multi-parameter nonlinear optimization module; Microbial culture detection module: compares the predicted value of the number of microorganisms at a future moment with the set minimum safety threshold of the number of microorganisms and the maximum safety threshold of the number of microorganisms to detect the growth status of microorganisms; outputs the minimum safety threshold of the number of microorganisms, the maximum safety threshold of the number of microorganisms and the growth status of microorganisms to the multi-parameter nonlinear optimization module; Multi-parameter nonlinear optimization module: Based on the predicted value of the number of microorganisms at a future time, the minimum safety threshold of the number of microorganisms, the maximum safety threshold of the number of microorganisms and the growth status of microorganisms, the preliminary adjustment amount of the microorganism culture environment parameters is calculated through a multi-parameter nonlinear optimization algorithm; the preliminary adjustment amount of the microorganism culture environment parameters is output to the multi-parameter collaborative optimization module; Multi-parameter collaborative optimization module: Based on the preliminary adjustment of microbial culture environment parameters, the final adjustment of microbial culture environment parameters is calculated through a multi-parameter collaborative optimization algorithm.

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