A parameter solving method and system of an escherichia coli fermentation system

Abstract

The application relates to a parameter solving method and system of an Escherichia coli fermentation system, wherein the method comprises the following steps: S1, a dynamic model of an Escherichia coli fermentation process is established, and based on the dynamic model, an output equation of a dilution rate to biomass concentration of an Escherichia coli fermentation system with multiple sensors in the Escherichia coli fermentation is established, the output equation is converted, and an equation with parameters to be solved is obtained; and S2, parameters in the equation with the parameters to be solved are calculated. The application overcomes the influence of external environment and the abnormality of the sensor itself, and the measured biomass concentration output value cannot directly obtain the measurement noise, real-time estimation of the parameters theta at the current time can be ensured when each measurement value is collected, unknown variances at each time are estimated first, then weights are assigned to each sensor to fully utilize all collected measurement data, and the efficient output of the Escherichia coli is ensured.

Description

Technical Field

[0001] This invention relates to the field of Escherichia coli fermentation monitoring technology, and in particular to a method and system for solving parameters of an Escherichia coli fermentation system. Background Technology

[0002] In biomanufacturing, multiple sensors are typically used to obtain measurements of temperature, substrate concentration, product concentration, and dissolved oxygen to ensure sufficient data for accurate modeling of the biomanufacturing process. However, the complexity of biological reactions and the external environment often leads to unknown measurement noise and strong time-varying characteristics, posing a significant challenge to real-time modeling of biomanufacturing processes.

[0003] Escherichia coli fermentation is a complex nonlinear biomanufacturing process that is easily affected by the environment and requires the measurement of a wide variety of variables. Traditional batch modeling requires substituting biomass concentration, substrate concentration, and other data collected at all times into the iterative calculations each time. This not only increases the amount of unnecessary computation but also raises the cost of modeling and makes it impossible to monitor the E. coli fermentation process in a practical manner. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the detection of Escherichia coli fermentation process in the prior art is computationally intensive and cannot be monitored in real time.

[0005] To solve the above-mentioned technical problems, the present invention provides a method for solving the parameters of an Escherichia coli fermentation system, comprising:

[0006] Step S1: Establish a kinetic model of the Escherichia coli fermentation process, and based on the kinetic model, establish an output equation for the dilution rate to biomass concentration of the Escherichia coli fermentation system with multiple sensors in the Escherichia coli fermentation process. Transform the output equation to obtain an equation with parameters to be solved.

[0007] Step S2: Calculate the parameters in the equation with the parameters to be solved.

[0008] In one embodiment of the present invention, step S1 involves establishing a kinetic model of the Escherichia coli fermentation process and, based on the kinetic model, establishing an output equation for the dilution rate to biomass concentration in the Escherichia coli fermentation system with multiple sensors. The method includes:

[0009] A kinetic model of the E. coli fermentation process is established, represented as follows:

[0010]

[0011]

[0012]

[0013] Where P is the growth rate of E. coli, and Y... X / S ω represents cell mass yield, κ represents kinetic parameters, μ represents the growth rate of substrate and product inhibition, and S represents the feed substrate concentration. f Substrate concentration S, biomass concentration X, and dilution rate D are all variables of time t;

[0014] For an E. coli fermentation system with multiple sensors, the process from dilution rate D to biomass concentration X in the kinetic model of E. coli fermentation is divided into several sampling time intervals, and the output equation is established as follows:

[0015] A(q -1 )y k =B(q) -1 )u k +v k (4)

[0016] Where k is the sampling time index, y k The output of the fermentation system is represented by X, which represents the biomass concentration at time k; q -1 The coefficients are constant and satisfy q -1 y k+1 =y k ;u k For the fermentation system input, D represents the dilution rate at time k; v k To measure noise, A(q) -1 ) and B(q -1 ) is a constant coefficient q -1 The polynomial of is expressed as:

[0017]

[0018]

[0019] Where, n a and n b It is a polynomial A(q) -1 ) and B(q -1 The maximum order of ) and and Represents the polynomial A(q) -1 ) and B(q -1 The coefficient corresponding to ).

[0020] In one embodiment of the present invention, the method for transforming the output equation in step S1 to obtain an equation with parameters to be solved is as follows:

[0021] The output equation of dilution rate D versus biomass concentration X in E. coli fermentation is approximated by a second-order ARX model, and its least squares form is:

[0022]

[0023] Where m = 1, 2, 3, ..., M, M is the total number of sensors. Let X be the measured value of biomass concentration X of the m-th sensor at time k; Let be the information vector of the m-th sensor at time k, which includes the input dilution rate and the output biomass concentration; For parameter vectors, To conform to a mean of 0 and satisfy the noise variance as The Gaussian distribution.

[0024] In one embodiment of the present invention, before calculating the parameters in the equation with the parameters to be solved in step S2, the method includes: constructing an optimization criterion function, specifically:

[0025] To fully utilize the measurement information from each sensor, weights are assigned to each sensor. λ m Set the weights for the m-th sensor. To store the weighted measurements of M sensors at time k, where λ represents the weighted measurement value of each sensor at time k. m,k Let be the weight of the m-th sensor at time k; after obtaining the weighted measurement value of each sensor, construct the optimization criterion function according to the maximum likelihood criterion, the formula is:

[0026]

[0027] Where arg max is the parameter used to find the maximum value of the function, and f(,) is the likelihood function. To store the set of weighted measurements from M sensors at time k, λ k ={λ 1,k ,λ 2,k ,…,λ m,k ,…,λ M,k} represents the weights assigned to each sensor at time k.

[0028] In one embodiment of the present invention, the method for calculating the parameters in the equation having parameters to be solved in step S2 includes: to solve θ in formula (8), first solving λ in formula (8). m,k Expanding formula (8) yields:

[0029]

[0030] Where ∝ represents proportional to, with respect to Take the partial derivative and set it to 0, then substitute it back into the above equation to obtain the optimized equation:

[0031]

[0032] By introducing the Lagrange multiplier method, the following formula is obtained:

[0033]

[0034] Where ξ is a Lagrange operator, for ξ and λ m,k Taking the partial derivatives, we get the following formula:

[0035]

[0036] like Given that λ is given, we can calculate it using formulas (9) and (12). m,k The formula is:

[0037]

[0038] In one embodiment of the present invention, λ is solved by formulas (9)-(12). m,k In this case, it is necessary to first determine The methods for solving this problem include:

[0039] Noise modeling in the E. coli fermentation process: Due to the unknown variance of noise during E. coli fermentation, an inverse gamma distribution is used to construct the probability density of the noise variance, as shown in the formula:

[0040]

[0041] Where, α m,k Let be the hyperparameter of the m-th sensor at time k, IG(,) be the inverse gamma fraction, and Γ() be the gamma function;

[0042] In solving formula (14) First, solve for α. m,k It satisfies the following formula:

[0043]

[0044] Where ψ() is the Psi function, set

[0045] noise variance posterior probability It follows an inverse gamma distribution and satisfies:

[0046]

[0047]

[0048] Where U is The set, for A set;

[0049] The expected value corresponding to the posterior probability of the noise variance is:

[0050]

[0051] The posterior expected values ​​of the reciprocal and logarithm of the posterior probability of the noise variance are respectively:

[0052]

[0053]

[0054]

[0055] in, For the calculation of the expected value, Θ k-1 θ to be found at time k-1 k-1 and α m,k-1 The set representation of .

[0056] In one embodiment of the present invention, step S2 involves calculating the parameters in the equation having parameters to be solved, including λ obtained at time k-1. m,k-1 and The parameter θ at time k is obtained recursively, using the following formula:

[0057]

[0058]

[0059]

[0060] Where, γ k The step size is random.

[0061] The result θ is the parameter at time k.

[0062] To solve the above-mentioned technical problems, the present invention provides a parameter solving system for an Escherichia coli fermentation system, comprising:

[0063] The construction module is used to establish a kinetic model of the Escherichia coli fermentation process, and based on the kinetic model, to establish an output equation from the dilution rate to the biomass concentration in the Escherichia coli fermentation system with multiple sensors. The output equation is then transformed to obtain an equation with parameters to be solved.

[0064] Calculation module: Used to calculate the parameters in the equation with parameters to be solved.

[0065] To solve the above-mentioned technical problems, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the parameter solving method for the Escherichia coli fermentation system described above.

[0066] To solve the above-mentioned technical problems, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the parameter solving method for the Escherichia coli fermentation system described above.

[0067] The technical solution of the present invention has the following advantages compared with the prior art:

[0068] The parameter solving method of the Escherichia coli fermentation system constructed in this invention overcomes the measurement noise caused by the inability to directly obtain the output value of biomass concentration due to the influence of the external environment and the anomaly of the sensor itself. This method can ensure that the parameter θ at the current moment is estimated in real time when each measurement value is collected. First, the unknown variance at each moment is estimated, and then weights are assigned to each sensor to make full use of all the collected measurement data, thus ensuring the efficient production of Escherichia coli. Attached Figure Description

[0069] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0070] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0071] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0072] Example 1

[0073] Reference Figure 1 As shown, this invention relates to a method for solving parameters in an Escherichia coli fermentation system, comprising:

[0074] Step S1: Establish a kinetic model of the Escherichia coli fermentation process, and based on the kinetic model, establish an output equation for the dilution rate to biomass concentration of the Escherichia coli fermentation system with multiple sensors in the Escherichia coli fermentation process. Transform the output equation to obtain an equation with parameters to be solved.

[0075] Step S2: Calculate the parameters in the equation with the parameters to be solved.

[0076] The following is a detailed description of this embodiment:

[0077] In step S1, a kinetic model of the E. coli fermentation process is established, and based on the kinetic model, an output equation for the dilution rate to biomass concentration in the E. coli fermentation system with multiple sensors is established. The method includes:

[0078] A kinetic model of the E. coli fermentation process is established, represented as follows:

[0079]

[0080]

[0081]

[0082] Where P is the growth rate of E. coli, and Y... X / S ω represents cell mass yield, κ represents kinetic parameters, μ represents the growth rate of substrate and product inhibition, and S represents the feed substrate concentration. f Substrate concentration S, biomass concentration X, and dilution rate D are all variables of time t;

[0083] For an E. coli fermentation system with multiple sensors, the process from dilution rate D to biomass concentration X in the kinetic model of E. coli fermentation is divided into several sampling time intervals, and the output equation is established as follows:

[0084] A(q -1 )y k =B(q) -1 )u k +v k (4)

[0085] Where k is the sampling time index, y k The output of the fermentation system is represented by X, which represents the biomass concentration at time k; q -1 The coefficients are constant and satisfy q -1 y k+1 =y k ;u k For the fermentation system input, D represents the dilution rate at time k; v k To measure noise, A(q) -1) and B(q -1 ) is a constant coefficient q -1 The polynomial of is expressed as:

[0086]

[0087]

[0088] Where, n a and n b It is a polynomial A(q) -1 ) and B(q -1 The maximum order of ) and and Represents the polynomial A(q) -1 ) and B(q -1 The coefficient corresponding to ).

[0089] The method for transforming the output equation in step S1 to obtain the equation with the parameters to be solved is as follows:

[0090] The output equation of dilution rate D versus biomass concentration X in E. coli fermentation is approximated by a second-order ARX model, and its least squares form is:

[0091]

[0092] Where m = 1, 2, 3, ..., M, M is the total number of sensors. Let X be the measured value of biomass concentration X of the m-th sensor at time k; Let be the information vector of the m-th sensor at time k, which includes the input dilution rate and the output biomass concentration; For parameter vectors, To conform to a mean of 0 and satisfy the noise variance as The Gaussian distribution.

[0093] Before calculating the parameters in the equation with the parameters to be solved in step S2, the following steps are included: constructing the optimization criterion function, specifically:

[0094] To fully utilize the measurement information from each sensor, weights are assigned to each sensor. λ m Set the weights for the m-th sensor. To store the weighted measurements of M sensors at time k, where λ represents the weighted measurement value of each sensor at time k. m,kLet be the weight of the m-th sensor at time k; after obtaining the weighted measurement value of each sensor, construct the optimization criterion function according to the maximum likelihood criterion, the formula is:

[0095]

[0096] Where arg max is the parameter used to find the maximum value of the function, even if The value of the variable when it reaches its maximum value; f(,) is the likelihood function. To store the set of weighted measurements from M sensors at time k, λ k ={λ 1,k ,λ 2,k ,…,λ m,k ,…,λ M,k} represents the weights assigned to each sensor at time k.

[0097] The ultimate goal of this embodiment is to solve for θ in formula (8).

[0098] The method for calculating the parameters in the equation with the parameters to be solved in step S2 includes: to solve for θ in formula (8), first solve for λ in formula (8). m,k Expanding formula (8) yields:

[0099]

[0100] Where ∝ represents proportional to, with respect to Take the partial derivative and set it to 0, then substitute it back into the above equation to obtain the optimized equation:

[0101]

[0102] By introducing the Lagrange multiplier method, the following formula is obtained:

[0103]

[0104] Where ξ is a Lagrange operator, for ξ and λ m,k Taking the partial derivatives, we get the following formula:

[0105]

[0106] like Given that λ is given, we can calculate it using formulas (9) and (12). m,k The formula is:

[0107]

[0108] Solve for λ using formulas (9)-(12) m,k In this case, it is necessary to first determine The methods for solving this problem include:

[0109] Noise modeling in the E. coli fermentation process: Due to the unknown variance of noise during E. coli fermentation, an inverse gamma distribution is used to construct the probability density of the noise variance, as shown in the formula:

[0110]

[0111] Where, α m,k Let be the hyperparameters of the m-th sensor at time k, IG(,) be the inverse gamma distribution, and Γ() be the gamma function;

[0112] In solving formula (14) First, solve for α. m,k It satisfies the following formula:

[0113]

[0114] Where ψ() is the Psi function, set

[0115] In this embodiment, each calculation... The parameters in the table are all derived from the parameters in the previous time step, thus enabling the calculation of...

[0116] noise variance posterior probability It follows an inverse gamma distribution and satisfies:

[0117]

[0118]

[0119] Where U is The set, for A set;

[0120] The expected value corresponding to the posterior probability of the noise variance is:

[0121]

[0122] The posterior expected values ​​of the reciprocal and logarithm of the posterior probability of the noise variance are respectively:

[0123]

[0124]

[0125]

[0126] in, For the calculation of the expected value, Θ k-1 θ to be found at time k-1 k-1 and α m,k-1 The set representation of .

[0127] In step S2, the parameters in the equation with the parameters to be solved are calculated, including α obtained from time k-1. m,k and The parameter θ at time k is obtained recursively, using the following formula:

[0128]

[0129]

[0130]

[0131] Where, γ k The step size is random.

[0132] The formula (20) yields θ is the parameter at time k.

[0133] This embodiment also includes an evaluation index for the identification results, calculated using the following formula:

[0134]

[0135] Where d is the total dimension of the parameter vector θ, θ real These are the actual values ​​of the parameter vector.

[0136] It should be noted that this embodiment will initialize the parameters, for example, initialize (θ) 0 ) den 、(θ 0 ) num α m,0 , λ m,0 , The value is used to facilitate ground-based calculations.

[0137] In summary, multi-sensor measurement fusion can significantly improve the accuracy and robustness of parameter estimation in recursive identification during E. coli fermentation. Single sensors are highly susceptible to noise and provide limited information, making it difficult to accurately identify dynamic parameters. When data from multiple sensors, such as dissolved oxygen, substrate concentration, product concentration, and biomass, are used simultaneously, the identification algorithm can converge quickly and reduce parameter uncertainty. Therefore, this invention maintains stability even under non-ideal conditions, offers more flexible and efficient optimization and control strategies, improves fermentation yield and quality, and provides strong support for industrial production.

[0138] Example 2

[0139] This embodiment provides a parameter solving system for an Escherichia coli fermentation system, including:

[0140] The construction module is used to establish a kinetic model of the Escherichia coli fermentation process, and based on the kinetic model, to establish an output equation from the dilution rate to the biomass concentration in the Escherichia coli fermentation system with multiple sensors. The output equation is then transformed to obtain an equation with parameters to be solved.

[0141] Calculation module: Used to calculate the parameters in the equation with parameters to be solved.

[0142] Example 3

[0143] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the parameter solving method for the Escherichia coli fermentation system described in Embodiment 1.

[0144] Example 4

[0145] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the parameter solving method for the Escherichia coli fermentation system described in Embodiment 1.

[0146] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0147] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0151] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for solving parameters of an Escherichia coli fermentation system, characterized in that: include: Step S1: Establish a kinetic model of the Escherichia coli fermentation process, and based on the kinetic model, establish an output equation for the dilution rate to biomass concentration of the Escherichia coli fermentation system with multiple sensors in the Escherichia coli fermentation process. Transform the output equation to obtain an equation with parameters to be solved. The method for transforming the output equation in step S1 to obtain the equation with the parameters to be solved is as follows: The output equation of dilution rate D versus biomass concentration X in E. coli fermentation is approximated by a second-order ARX model, and its least squares form is: （7）； in, , The total number of sensors, For the first One sensor in The measured value of biomass concentration X at time t; For the first One sensor in The information vector at time step includes the input dilution rate and the output biomass concentration; For parameter vectors, To conform to a mean of 0 and satisfy the noise variance as Gaussian distribution; Step S2: Calculate the parameters in the equation with the parameters to be solved; Before calculating the parameters in the equation with the parameters to be solved in step S2, the following steps are included: constructing an optimization criterion function, specifically: To fully utilize the measurement information from each sensor, weights are assigned to each sensor. , For the first The weights of each sensor are set. To store time k The weighted measurements of the sensors, of which This represents the weighted measurement value of each sensor at time k. For time k, the first The weights of each sensor are determined; after obtaining the weighted measurement value of each sensor, an optimization criterion function is constructed based on the maximum likelihood criterion, with the following formula: （8）； in, To find the parameters that make the function reach its maximum value, Let be the likelihood function. To store time k A set of weighted measurements from multiple sensors. The weights assigned to each sensor at time k; The method for calculating the parameters in the equation with parameters to be solved in step S2 includes: [The method is missing here, likely due to an error in the original text]. First solve formula (8) Expanding formula (8) yields: （9）； in, Indicates proportional to, with Find the partial derivative and set it to 0, then substitute it into formula (8) to obtain the optimized equation: （10）； By introducing the Lagrange multiplier method, the following formula is obtained: （11）； in, For Lagrange operators, for and Taking the partial derivatives, we get the following formula: （12）； like Given that, we can find the answer using formulas (9) and (12). The formula is: （13）； Solve using formulas (9)-(12) In this case, it is necessary to first determine , The methods for solving this problem include: Noise modeling in the E. coli fermentation process: Due to the unknown variance of noise during E. coli fermentation, an inverse gamma distribution is used to construct the probability density of the noise variance, as shown in the formula: （14）； in, For the first The hyperparameters of a sensor at time k. It has an inverse gamma distribution. It is a gamma function; In solving formula (14) First, solve the problem. , Satisfy the following formula: （15）； in, For the Psi function, set ; noise variance posterior probability It follows an inverse gamma distribution and satisfies: （16）； Where U is The set, for A set; The expected value corresponding to the posterior probability of the noise variance is: （17）； The posterior expected values ​​of the reciprocal and logarithm of the posterior probability of the noise variance are respectively: （18）； （19）； in, For the calculation of the expected value, for Always ready to be sought and The set representation of .

2. The method for solving the parameters of the Escherichia coli fermentation system according to claim 1, characterized in that: In step S1, a kinetic model of the E. coli fermentation process is established, and based on the kinetic model, an output equation for the dilution rate to biomass concentration in the E. coli fermentation system with multiple sensors is established. The method includes: A kinetic model of the E. coli fermentation process is established, represented as follows: （1）； （2）； （3）； in, The growth rate of Escherichia coli. For cell quality yield, and For dynamic parameters, Defined as the growth rate expressing the inhibitory effects of the substrate and product, and the feed substrate concentration. substrate concentration Biomass concentration Both the dilution rate D and the time are time. Variables; For an E. coli fermentation system with multiple sensors, the process from dilution rate D to biomass concentration X in the kinetic model of E. coli fermentation is divided into several sampling time intervals, and the output equation is established as follows: （4）； in, For sampling time indicators, The output of the fermentation system represents the biomass concentration X at time k. The coefficients are constant and satisfy the following conditions: ; The input to the fermentation system is D, representing the dilution rate at time k. To measure noise, and constant coefficients The polynomial of is expressed as: （5）； （6）； in, and It is a polynomial and The maximum order, and ~ and Representing a polynomial and The corresponding coefficient.

3. The parameter solving method for the Escherichia coli fermentation system according to claim 1, characterized in that: In step S2, the parameters in the equation with the parameters to be solved are calculated, including those obtained from time k-1. and The parameters at time k are obtained by recursion. The formula is: （20）； （21）； （22）； in, The step size is random. The result As a parameter at time k .

4. A parameter solving system for an Escherichia coli fermentation system, used to implement the parameter solving method for the Escherichia coli fermentation system as described in any one of claims 1 to 3, characterized in that: include: The construction module is used to establish a kinetic model of the Escherichia coli fermentation process, and based on the kinetic model, to establish an output equation from the dilution rate to the biomass concentration in the Escherichia coli fermentation system with multiple sensors. The output equation is then transformed to obtain an equation with parameters to be solved. Calculation module: Used to calculate the parameters in the equation with parameters to be solved.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the parameter solving method for the Escherichia coli fermentation system as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the parameter solving method for the Escherichia coli fermentation system as described in any one of claims 1 to 3.

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