Low-cost efficient culture medium for culturing mycoplasma gallisepticum and preparation method thereof

Abstract

The invention relates to the technical field of biological products, in particular to a preparation method of a culture medium for efficiently culturing mycoplasma gallisepticum at low cost, which comprises the following steps: step 1, dissolving and blending: a, preliminary dissolving: adding NaCl, K2HPO4 and MgSO4. 7H2O into 1000mL of distilled water, heating to 40 DEG C, and slightly stirring until the NaCl, K2HPO4 and MgSO4. 7H2O are completely dissolved to form a basic inorganic salt solution; b, adding a carbon and nitrogen source: adding glucose, soy peptone, tryptone and a yeast extract, and stirring until the materials are completely dissolved; c, pH regulation: regulating the pH value of the solution to 7.4 by using sterile 0.5 M HCl and NaOH; step 2, adding growth factors and nutritional ingredients: a, adding special growth factors: adding L-cysteine and cholesterol under a stirring condition, and continuously stirring until the L-cysteine and the cholesterol are completely dissolved; the cost is low: animal serum and protein raw materials are replaced by using the plant-derived protein hydrolysate, and meanwhile, auxiliary components such as vitamins and growth factors are added, so that the cost of the raw materials is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of biological products, in particular to a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum and a preparation method thereof. Background Art

[0002] Mycoplasma gallisepticum infection is also known as chronic respiratory disease in chickens. It is widely prevalent in all poultry-raising countries in the world. After chicken farms are infected with MG, the rate of weak chicks increases, the egg production rate of laying hens, the weight and feed conversion rate of broilers all decrease, and secondary bacterial or viral infections are prone to occur, resulting in an increase in the mortality rate of chickens, which causes serious harm to the development of the poultry industry. Vaccination is the most important measure to prevent and control this disease. Although purification is difficult, it is the only measure to eradicate this disease.

[0003] At present, there are the following defects in the cultivation process of Mycoplasma gallisepticum:

[0004] 1. High cost: Traditional culture media usually use animal serum (such as horse serum) and high-purity protein as nitrogen and carbon sources, which are expensive, especially in large-scale culture. It is difficult to achieve low cost;

[0005] 2. Complex operation: Traditional culture media require high-temperature sterilization, filtration, and multi-step mixing preparation, which takes a long time to prepare and is prone to contamination.

[0006] Therefore, a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum and a preparation method thereof are proposed. Summary of the invention

[0007] The object of the present invention is to provide a low-cost and efficient culture medium for Mycoplasma gallisepticum and a preparation method thereof, so as to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum, comprising the following weight components:

[0009]

[0010]

[0011] A method for preparing a culture medium for culturing Mycoplasma gallisepticum at low cost and high efficiency comprises the following steps:

[0012] Step 1: Dissolve and prepare:

[0013] a. Preliminary dissolution: Add NaCl, K2HPO4 and MgSO4·7H2O to 1000mL of distilled water, heat to 40°C, and gently stir until completely dissolved to form a basic inorganic salt solution;

[0014] b. Add carbon and nitrogen sources: add glucose, soy peptone, tryptone and yeast extract, and stir until completely dissolved;

[0015] c. pH adjustment: Use sterile 0.5M HCl and NaOH to adjust the pH value of the solution to 7.4;

[0016] Step 2: Add growth factors and nutrients:

[0017] a. Add special growth factors: Add L-cysteine ​​and cholesterol under stirring conditions, and continue stirring until completely dissolved. Since cholesterol is difficult to dissolve, dissolve it in a small amount of sterile ethanol before adding;

[0018] b. Add serum substitute: Add vegetable oil, such as soybean oil or corn oil, according to actual needs, and stir evenly until the oil is evenly dispersed;

[0019] Step 3: Filtration and sterilization:

[0020] a. Filtration sterilization: Filter the prepared culture medium through a 0.22 μm filter membrane to sterilize it to prevent degradation of heat-sensitive components;

[0021] b. Aseptic packaging: The sterilized culture medium is dispensed into sterilized culture bottles in a sterile environment, ready for inoculation;

[0022] Step 4: Storage:

[0023] Storage conditions: The prepared culture medium can be refrigerated at 4°C and used within 1 week to avoid long-term storage that may affect the culture effect.

[0024] Preferably, an adaptive nutrition monitoring system is introduced into the preparation process, and the adaptive nutrition monitoring system includes real-time monitoring, intelligent regulation and data analysis modules, which monitor the key indicators of glucose and amino acid concentration in the culture medium in real time, accurately adjust the nutritional balance in the culture medium, and support the efficient proliferation and stable culture of Mycoplasma gallisepticum.

[0025] Preferably, the real-time monitoring module includes a sensor integration unit and a data acquisition unit;

[0026] The sensor integration unit includes multiple sensor modules that simultaneously monitor glucose, amino acids, pH value and dissolved oxygen parameters, and transmit the data to the main control system in real time. The miniaturized sensor is placed in the culture tank. The sensor has an automatic calibration function and performs self-calibration at predetermined time intervals to improve the accuracy and stability of the monitoring data.

[0027] The data acquisition unit transmits the real-time monitoring data to the main control unit, which analyzes the data and makes adaptive control decisions. The data acquisition frequency is dynamically adjusted according to the growth rate of Mycoplasma gallisepticum. In the initial stage, data is collected every 10 minutes, and after entering the stable growth period, it is adjusted to every 30 minutes to ensure the timeliness of the data and the efficiency of the system.

[0028] The adaptive control decision uses a feedback control algorithm to dynamically supplement nutrients, using a proportional-integral-derivative control model based on error adjustment:

[0029]

[0030] Where: C new is the adjusted nutrient concentration, C current is the current concentration, e(t) is the difference between the set value and the current concentration, K p , K i , K d is the control parameter of the differential control model.

[0031] Preferably, the intelligent control module has a built-in carbon-nitrogen ratio control model, through which the supply ratio of the carbon source (such as glucose) and the nitrogen source (such as peptone, yeast extract) is automatically adjusted according to different culture stages;

[0032] The carbon-nitrogen ratio control model automatically adjusts the carbon-nitrogen ratio according to the different growth stages (early, middle and late) of Mycoplasma gallisepticum. The carbon-nitrogen ratio can be set to 3:1 in the early stage; adjusted to 2.5:1 during the peak growth period; and maintained at 2:1 in the late stage;

[0033] Carbon-nitrogen ratio control model:

[0034]

[0035] Where: C / N new is the adjusted carbon-nitrogen ratio, C / N current is the current carbon-nitrogen ratio, Oxygen target and Oxygen current are the target and current dissolved oxygen contents, respectively, and α is the adjustment coefficient;

[0036] Carbon and nitrogen sources are replenished as needed at each stage, and the system automatically performs the feeding operation based on the feedback signal after the carbon-nitrogen ratio is adjusted.

[0037] Preferably, a multi-dimensional monitoring module and an intelligent early warning mechanism are introduced into the preparation process, and data changes during the culture process are controllable by using the multi-dimensional monitoring module and the intelligent early warning mechanism, and abnormal situations are adjusted in time.

[0038] Preferably, the multi-dimensional monitoring module includes multi-dimensional precise data acquisition and dynamic threshold adaptive adjustment;

[0039] Among them, multi-dimensional precise data collection uses fusion sensor technology to collect nutrient concentration, dissolved oxygen, temperature, and pH parameters in real time, calibrating the sensor data with each other to reduce the errors that may be caused by a single sensor;

[0040] In the process of sensor data collection, an adaptive filtering algorithm is introduced to perform real-time smoothing and denoising on the collected data, avoiding measurement deviations caused by external environment or noise and improving data accuracy. The filtering formula is as follows:

[0041] X filtered (t) = α·X(t) + (1-α)·X filtered (t-1)

[0042] Where: X filtered (t) is the filtered value at the current moment, X(t) is the measured value of the sensor at the current moment, and α is the filter coefficient, which is between 0.2 and 0.5.

[0043] Automatic calibration mechanism: Set the automatic calibration function to enable the sensor to automatically calibrate at predetermined time intervals to improve the accuracy and consistency of data collection.

[0044] Preferably, the dynamic threshold is adaptively adjusted, and the system automatically adjusts the threshold range of the multidimensional parameters according to the different stages of bacterial growth in the culture medium (such as early, middle, and late stages), avoiding excessive warnings or delayed responses, and introducing a stage-by-stage adaptive threshold adjustment algorithm to dynamically adjust the threshold of each parameter according to the data characteristics of different growth stages. For example:

[0045] Initial stage (adaptation period): the nutrient concentration threshold is loose and the dissolved oxygen is high.

[0046] Mid-term (growth period): dissolved oxygen is maintained at a moderate level and the threshold of nutrient concentration is tightened.

[0047] Late stage (stable period): dissolved oxygen is controlled at a lower level and the nutrient concentration threshold is moderately relaxed.

[0048] Adaptive adjustment formula:

[0049] Threshold new =Threshold base ×(1+γ·f stage )

[0050] Where: Threshold new is the dynamically adjusted threshold, Threshold base is the basic threshold, γ is the adjustment coefficient, fstage is the growth stage coefficient (it takes different values ​​according to the stage).

[0051] Preferably, the intelligent early warning mechanism includes intelligent anomaly detection and multi-dimensional early warning response, and uses deep learning algorithms (such as neural networks, decision trees, etc.) to perform anomaly detection on multi-dimensional data, judge data fluctuations and anomalies, and quickly issue early warning responses.

[0052] Multi-parameter comprehensive analysis model: Build a comprehensive analysis model based on multiple parameters, take nutrient concentration, pH value, temperature, DO and other parameters as input variables, use neural network model to classify abnormal conditions, and conduct comprehensive analysis.

[0053] Y=σ(W·X+b)

[0054] Among them: Y is the warning output, W is the weight matrix, X is the input variable (multidimensional parameter), b is the bias term, and σ is the activation function.

[0055] Multi-level warning response mechanism: set three levels of warning according to the degree of abnormality:

[0056] Level 1 warning: Slight deviation from the preset range, the system makes slight adjustments and records the data.

[0057] Level 2 warning: moderate deviation, the system automatically makes real-time adjustments and notifies the operator.

[0058] Level 3 warning: serious deviation, the system suspends the cultivation process and conducts in-depth inspection, and the operator needs to intervene.

[0059] Abnormal response algorithm: When the system detects that the deviation value e(t) exceeds the set range, abnormal feedback control is performed using the following formula:

[0060]

[0061] Where: u(t) is the regulation signal of feedback control; K p , K i , K d is the feedback parameter.

[0062] Preferably, the multi-dimensional monitoring module and the intelligent early warning mechanism module have built-in automatic parameter optimization and self-learning feedback mechanisms;

[0063] The system dynamically adjusts parameters based on monitoring data and optimizes itself through machine learning models that accumulate historical data, gradually improving the system's adaptive capabilities.

[0064] Data-driven automated parameter optimization: The system establishes the optimal range of multi-dimensional parameters such as nutrient consumption, temperature, DO, etc. based on the culture data of each batch, and dynamically adjusts parameters such as nutrient supply and gas supplementation for each batch through machine learning.

[0065] Self-learning feedback system: The system continuously optimizes parameter configuration through training of neural networks or random forest models. For example, the system can automatically learn the growth rate of bacteria under different DO and pH values, and adjust these parameters to improve culture efficiency.

[0066] Adaptive optimization formula:

[0067]

[0068] Where: Δθ is the parameter to be optimized; α is the learning rate; L is the loss function, which is used to evaluate the deviation between the monitoring data and the target value.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] 1. Low cost: By using plant-derived protein hydrolysates to replace animal serum and protein raw materials, and adding auxiliary ingredients such as vitamins and growth factors, the cost of raw materials is reduced.

[0071] 2. Simplify the preparation process: The preparation method of the culture medium adopts autolysis and low-temperature filtration, which greatly simplifies the operation process, reduces the sterilization and high-temperature treatment steps, and reduces the risk of contamination.

[0072] 3. High stability: The use of alternative ingredients makes the culture medium formula more stable, and the nutrients are not easy to fluctuate, ensuring the consistent growth effect of Mycoplasma gallisepticum.

[0073] 4. Efficient growth: The optimized nutritional ratio and auxiliary ingredients can effectively promote the proliferation of Mycoplasma gallisepticum, increase the growth rate and shorten the culture time.

[0074] 6. More accurate nutrient supply: Through real-time sensor monitoring and PID feedback algorithm, the system can accurately control the supply of nutrients, avoid insufficient or excessive nutrition, and increase the proliferation rate of bacteria.

[0075] 7. Intelligent adjustment of carbon-nitrogen ratio: Automatically adjust the carbon-nitrogen ratio according to the growth stage of Mycoplasma gallisepticum, achieve the best nutritional balance, effectively shorten the culture time and increase the yield of bacteria.

[0076] 8. Real-time early warning and dynamic adjustment: The intelligent early warning system monitors and provides feedback in real time to ensure the stability of culture conditions and quickly respond and adjust parameters when abnormal situations occur.

[0077] 9. Data accumulation and optimization: Through database and big data analysis models, historical data is continuously accumulated and nutritional ratios are optimized, which improves the system's adaptability and intelligence.

[0078] 10. Reduce the cost of cultivation: Through the automated and intelligent monitoring system, the waste of nutrient supplements is reduced, the cost of cultivation is reduced, and the low-cost and high-efficiency culture medium is more suitable for large-scale Mycoplasma gallisepticum cultivation. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 The composition table of the Mycoplasma gallisepticum culture medium of the present invention is as follows;

[0081] Figure 2 This is a flow chart of the method for preparing a culture medium for culturing Mycoplasma gallisepticum at low cost and high efficiency according to the present invention;

[0082] Figure 3 This is a block diagram of the adaptive nutrition monitoring system of the present invention. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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.

[0084] See also Figures 1 to 3 , the present invention provides a technical solution:

[0085] A low-cost and high-efficiency culture medium for Mycoplasma gallisepticum includes the following weight components:

[0086]

[0087]

[0088] A method for preparing a culture medium for culturing Mycoplasma gallisepticum at low cost and high efficiency comprises the following steps:

[0089] Step 1: Dissolve and prepare:

[0090] a. Preliminary dissolution: Add NaCl, K2HPO4 and MgSO4·7H2O to 1000mL of distilled water, heat to 40°C, and gently stir until completely dissolved to form a basic inorganic salt solution;

[0091] b. Add carbon and nitrogen sources: add glucose, soy peptone, tryptone and yeast extract, and stir until completely dissolved;

[0092] c. pH adjustment: Use sterile 0.5M HCl and NaOH to adjust the pH value of the solution to 7.4;

[0093] Step 2: Add growth factors and nutrients:

[0094] a. Add special growth factors: Add L-cysteine ​​and cholesterol under stirring conditions, and continue stirring until completely dissolved. Since cholesterol is difficult to dissolve, dissolve it in a small amount of sterile ethanol before adding;

[0095] b. Add serum substitute: Add vegetable oil, such as soybean oil or corn oil, according to actual needs, and stir evenly until the oil is evenly dispersed;

[0096] Step 3: Filtration and sterilization:

[0097] a. Filtration sterilization: Filter the prepared culture medium through a 0.22 μm filter membrane to sterilize it to prevent degradation of heat-sensitive components;

[0098] b. Aseptic packaging: The sterilized culture medium is dispensed into sterilized culture bottles in a sterile environment, ready for inoculation;

[0099] Step 4: Storage:

[0100] Storage conditions: The prepared culture medium can be refrigerated at 4°C and used within 1 week to avoid long-term storage that may affect the culture effect.

[0101] Specifically, the preparation process introduces an adaptive nutrition monitoring system, which includes real-time monitoring, intelligent regulation and data analysis modules, which monitor the key indicators of glucose and amino acid concentration in the culture medium in real time, accurately adjust the nutritional balance in the culture medium, and support the efficient proliferation and stable culture of Mycoplasma gallisepticum.

[0102] Specifically, the real-time monitoring module includes a sensor integration unit and a data acquisition unit;

[0103] The sensor integration unit includes multiple sensor modules that simultaneously monitor glucose, amino acids, pH value and dissolved oxygen parameters, and transmit the data to the main control system in real time. The miniaturized sensor is placed in the culture tank. The sensor has an automatic calibration function and performs self-calibration at predetermined time intervals to improve the accuracy and stability of the monitoring data.

[0104] The data acquisition unit transmits the real-time monitoring data to the main control unit, which analyzes the data and makes adaptive control decisions. The data acquisition frequency is dynamically adjusted according to the growth rate of Mycoplasma gallisepticum. In the initial stage, data is collected every 10 minutes, and after entering the stable growth period, it is adjusted to every 30 minutes to ensure the timeliness of the data and the efficiency of the system.

[0105] The adaptive control decision uses a feedback control algorithm to dynamically supplement nutrients, using a proportional-integral-derivative control model based on error adjustment:

[0106]

[0107] Where: C new is the adjusted nutrient concentration, C current is the current concentration, e(t) is the difference between the set value and the current concentration, K p , K i , K d is the control parameter of the differential control model.

[0108] Specifically, the intelligent control module has a built-in carbon-nitrogen ratio control model, through which the supply ratio of the carbon source (such as glucose) and the nitrogen source (such as peptone, yeast extract) is automatically adjusted according to different culture stages;

[0109] The carbon-nitrogen ratio control model automatically adjusts the carbon-nitrogen ratio according to the different growth stages (early, middle and late) of Mycoplasma gallisepticum. The carbon-nitrogen ratio can be set to 3:1 in the early stage; adjusted to 2.5:1 during the peak growth period; and maintained at 2:1 in the late stage;

[0110] Carbon-nitrogen ratio control model:

[0111]

[0112] Where: C / N new is the adjusted carbon-nitrogen ratio, C / N current is the current carbon-nitrogen ratio, Oxygen target and Oxygen current are the target and current dissolved oxygen contents, respectively, and α is the adjustment coefficient;

[0113] Carbon and nitrogen sources are replenished as needed at each stage, and the system automatically performs the feeding operation based on the feedback signal after the carbon-nitrogen ratio is adjusted.

[0114] Specifically, a multi-dimensional monitoring module and an intelligent early warning mechanism are introduced into the preparation process, and data changes during the cultivation process can be controlled by using the multi-dimensional monitoring module and the intelligent early warning mechanism, and abnormal situations can be adjusted in time.

[0115] Specifically, the multi-dimensional monitoring module includes multi-dimensional precise data acquisition and dynamic threshold adaptive adjustment;

[0116] Among them, multi-dimensional precise data collection uses fusion sensor technology to collect nutrient concentration, dissolved oxygen, temperature, and pH parameters in real time, calibrating the sensor data with each other to reduce the errors that may be caused by a single sensor;

[0117] In the process of sensor data collection, an adaptive filtering algorithm is introduced to perform real-time smoothing and denoising on the collected data, avoiding measurement deviations caused by external environment or noise and improving data accuracy. The filtering formula is as follows:

[0118] X filtered (t) = α·X(t) + (1-α)·X filtered (t-1)

[0119] Where: X filtered (t) is the filtered value at the current moment, X(t) is the measured value of the sensor at the current moment, and α is the filter coefficient, which is between 0.2 and 0.5.

[0120] Automatic calibration mechanism: Set the automatic calibration function to enable the sensor to automatically calibrate at predetermined time intervals to improve the accuracy and consistency of data collection.

[0121] Specifically, the dynamic threshold is adaptively adjusted. According to the different stages of bacterial growth in the culture medium (such as early, middle, and late stages), the system automatically adjusts the threshold range of the multidimensional parameters to avoid excessive warnings or delayed responses. A phased adaptive threshold adjustment algorithm is introduced to dynamically adjust the threshold of each parameter according to the data characteristics of different growth stages. For example:

[0122] Initial stage (adaptation period): the nutrient concentration threshold is loose and the dissolved oxygen is high.

[0123] Mid-term (growth period): dissolved oxygen is maintained at a moderate level and the threshold of nutrient concentration is tightened.

[0124] Late stage (stable period): dissolved oxygen is controlled at a lower level and the nutrient concentration threshold is moderately relaxed.

[0125] Adaptive adjustment formula:

[0126] Threshold new =Threshold base ×(1+γ·f stage )

[0127] Where: Threshold new is the dynamically adjusted threshold, Threshold base is the basic threshold, γ is the adjustment coefficient, fstage is the growth stage coefficient (it takes different values ​​according to the stage).

[0128] Specifically, the intelligent early warning mechanism includes intelligent anomaly detection and multi-dimensional early warning response, which uses deep learning algorithms (such as neural networks, decision trees, etc.) to perform anomaly detection on multi-dimensional data, judge data fluctuations and anomalies, and quickly issue early warning responses.

[0129] Multi-parameter comprehensive analysis model: Build a comprehensive analysis model based on multiple parameters, take nutrient concentration, pH value, temperature, DO and other parameters as input variables, use neural network model to classify abnormal conditions, and conduct comprehensive analysis.

[0130] Y=σ(W·X+b)

[0131] Among them: Y is the warning output, W is the weight matrix, X is the input variable (multidimensional parameter), b is the bias term, and σ is the activation function.

[0132] Multi-level warning response mechanism: set three levels of warning according to the degree of abnormality:

[0133] Level 1 warning: Slight deviation from the preset range, the system makes slight adjustments and records the data.

[0134] Level 2 warning: moderate deviation, the system automatically makes real-time adjustments and notifies the operator.

[0135] Level 3 warning: serious deviation, the system suspends the cultivation process and conducts in-depth inspection, and the operator needs to intervene.

[0136] Abnormal response algorithm: When the system detects that the deviation value e(t) exceeds the set range, abnormal feedback control is performed using the following formula:

[0137]

[0138] Where: u(t) is the regulation signal of feedback control; K p , K i , K d is the feedback parameter.

[0139] Specifically, the multi-dimensional monitoring module and the intelligent early warning mechanism module have built-in automatic parameter optimization and self-learning feedback mechanisms;

[0140] The system dynamically adjusts parameters based on monitoring data and optimizes itself through machine learning models that accumulate historical data, gradually improving the system's adaptive capabilities.

[0141] Data-driven automated parameter optimization: The system establishes the optimal range of multi-dimensional parameters such as nutrient consumption, temperature, DO, etc. based on the culture data of each batch, and dynamically adjusts parameters such as nutrient supply and gas supplementation for each batch through machine learning.

[0142] Self-learning feedback system: The system continuously optimizes parameter configuration through training of neural networks or random forest models. For example, the system can automatically learn the growth rate of bacteria under different DO and pH values, and adjust these parameters to improve culture efficiency.

[0143] Adaptive optimization formula:

[0144]

[0145] Where: Δθ is the parameter to be optimized; α is the learning rate; L is the loss function, which is used to evaluate the deviation between the monitoring data and the target value.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-cost and high-efficiency culture medium for Mycoplasma gallisepticum, characterized in that: It includes the following weight components:

2. A method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 1, characterized in that: The steps include: Step 1: Dissolve and prepare: a. Preliminary dissolution: Add NaCl, K2HPO4 and MgSO4·7H2O to 1000mL of distilled water, heat to 40°C, and gently stir until completely dissolved to form a basic inorganic salt solution; b. Add carbon and nitrogen sources: add glucose, soy peptone, tryptone and yeast extract, and stir until completely dissolved; c. pH adjustment: Use sterile 0.5M HCl and NaOH to adjust the pH of the solution to 7.4; Step 2: Add growth factors and nutrients: a. Add special growth factors: Add L-cysteine ​​and cholesterol under stirring conditions, and continue stirring until completely dissolved. Since cholesterol is difficult to dissolve, dissolve it in a small amount of sterile ethanol before adding; b. Add serum substitute: Add vegetable oil, such as soybean oil or corn oil, according to actual needs, and stir evenly until the oil is evenly dispersed; Step 3: Filtration and sterilization: a. Filtration sterilization: Filter the prepared culture medium through a 0.22 μm filter membrane to sterilize it to prevent degradation of heat-sensitive components; b. Aseptic packaging: The sterilized culture medium is dispensed into sterilized culture bottles in a sterile environment, ready for inoculation; Step 4: Storage: Storage conditions: The prepared culture medium can be refrigerated at 4°C and used within 1 week to avoid long-term storage that may affect the culture effect.

3. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 1, characterized in that: An adaptive nutrition monitoring system is introduced into the preparation process. The adaptive nutrition monitoring system includes real-time monitoring, intelligent regulation and data analysis modules, which monitor the key indicators of glucose and amino acid concentration in the culture medium in real time, accurately adjust the nutritional balance in the culture medium, and support the efficient proliferation and stable culture of Mycoplasma gallisepticum.

4. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 3, characterized in that: The real-time monitoring module includes a sensor integration unit and a data acquisition unit; The sensor integration unit includes multiple sensor modules that simultaneously monitor glucose, amino acids, pH value and dissolved oxygen parameters, and transmit the data to the main control system in real time. The miniaturized sensor is placed in the culture tank. The sensor has an automatic calibration function and performs self-calibration at predetermined time intervals. The data acquisition unit transmits the real-time monitoring data to the main control unit, which performs data analysis and makes adaptive control decisions. The data acquisition frequency is dynamically adjusted according to the growth rate of Mycoplasma gallisepticum. The data is collected every 10 minutes in the initial stage and adjusted to every 30 minutes after entering the stable growth period. The adaptive control decision uses a feedback control algorithm to dynamically supplement nutrients, using a proportional-integral-derivative control model based on error adjustment: Where: C new is the adjusted nutrient concentration, C current is the current concentration, e(t) is the difference between the set value and the current concentration, K p , K i , K d is the control parameter of the differential control model.

5. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 4, characterized in that: The intelligent control module has a built-in carbon-nitrogen ratio control model, through which the supply ratio of the carbon source to the nitrogen source is automatically adjusted according to different cultivation stages; The carbon-nitrogen ratio control model automatically adjusts the carbon-nitrogen ratio according to the different growth stages of Mycoplasma gallisepticum, namely the early, middle and late stages. The carbon-nitrogen ratio can be set to 3:1 in the early stage; adjusted to 2.5:1 during the peak growth period; and maintained at 2:1 in the late stage. Carbon-nitrogen ratio control model: Where: C / N new is the adjusted carbon-nitrogen ratio, C / N current is the current carbon-nitrogen ratio, Oxygen target and Oxygen current are the target and current dissolved oxygen contents, respectively, and α is the adjustment coefficient; Carbon and nitrogen sources are replenished as needed at each stage, and the system automatically performs the feeding operation based on the feedback signal after the carbon-nitrogen ratio is adjusted.

6. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 1, characterized in that: A multi-dimensional monitoring module and an intelligent early warning mechanism are introduced into the preparation process. The data changes during the cultivation process can be controlled by using the multi-dimensional monitoring module and the intelligent early warning mechanism, and abnormal situations can be adjusted in time.

7. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 6, characterized in that: The multi-dimensional monitoring module includes multi-dimensional precise data acquisition and dynamic threshold adaptive adjustment; Among them, multi-dimensional precise data collection uses fusion sensor technology to collect nutrient concentration, dissolved oxygen, temperature, and pH parameters in real time, calibrating the sensor data with each other to reduce the errors that may be caused by a single sensor; An adaptive filtering algorithm is introduced during the sensor data collection process to perform real-time smoothing and denoising on the collected data. The filtering formula is as follows: X filtered (t)=α·X(t)+(1-α)·X filtered (t-1) Where: X filtered (t) is the filtered value at the current moment, X(t) is the measured value of the sensor at the current moment, and α is the filter coefficient, which is between 0.2 and 0.

5.

8. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 7, characterized in that: The dynamic threshold adaptive adjustment, according to the different stages of bacterial growth in the culture medium, such as the early, middle and late stages, the system automatically adjusts the threshold range of the multidimensional parameters, introduces a stage-by-stage adaptive threshold adjustment algorithm, and dynamically adjusts the threshold of each parameter according to the data characteristics of different growth stages; Initial stage (adaptation period): nutrient concentration threshold is loose and dissolved oxygen is high; Mid-term (growth period): dissolved oxygen is maintained at a medium level, and the threshold of nutrient concentration is tightened; Late stage (stable stage): dissolved oxygen is controlled at a lower level, and the nutrient concentration threshold is moderately relaxed; Adaptive adjustment formula: Threshold new =Threshold base ×(1+γ·f stage ) Where: Threshold new is the dynamically adjusted threshold, Threshold base is the basic threshold, γ is the adjustment coefficient, f stage is the growth stage coefficient, which takes different values ​​according to the stage.

9. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 8, characterized in that: The intelligent early warning mechanism includes intelligent anomaly detection and multi-dimensional early warning response, which uses deep learning algorithms to detect anomalies in multi-dimensional data, judge data fluctuations and anomalies, and quickly issue early warning responses; Multi-parameter comprehensive analysis model: Build a comprehensive analysis model based on multiple parameters, take nutrient concentration, pH value, temperature, and DO parameters as input variables, use a neural network model to classify abnormal conditions, and conduct a comprehensive analysis; Analytical Model: Y=σ(W·X+b) Among them: Y is the warning output, W is the weight matrix, X is the multidimensional parameter input variable, b is the bias term, and σ is the activation function; Multi-level warning response mechanism: set three levels of warning according to the degree of abnormality: Level 1 warning: If the system deviates slightly from the preset range, it will make slight adjustments and record the data. Level 2 warning: Moderate deviation, the system automatically makes real-time adjustments and notifies the operator; Level 3 warning: serious deviation, the system suspends the cultivation process and conducts in-depth inspection, and the operator needs to intervene; Abnormal response algorithm: When the system detects that the deviation value e(t) exceeds the set range, abnormal feedback control is performed using the following formula: Where: u(t) is the regulation signal of feedback control, K p , K i , K d is the feedback parameter.

10. The method for preparing a low-cost and high-efficiency culture medium for Mycoplasma gallisepticum according to claim 6, characterized in that: The multi-dimensional monitoring module and intelligent early warning mechanism module have built-in automatic parameter optimization and self-learning feedback mechanisms; The system dynamically adjusts parameters based on monitoring data and optimizes itself through machine learning models that accumulate historical data; Data-driven automated parameter optimization: The system establishes the optimal range of nutrient consumption, temperature, and DO multidimensional parameters based on the culture data of each batch, and dynamically adjusts the nutrient supply and gas supplement parameters of each batch through machine learning; Self-learning feedback system: The system continuously optimizes parameter configuration through training of neural network or random forest model. The system automatically learns the growth rate of bacteria under different DO and pH values ​​and adjusts these parameters to improve culture efficiency. Adaptive optimization formula: Where: Δθ is the optimized adjustment parameter, α is the learning rate, and L is the loss function, which is used to evaluate the deviation between the monitoring data and the target value.

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