Parameter solving method and system of escherichia coli fermentation system

Abstract

The invention relates to a parameter solving method and system for an Escherichia coli fermentation system, and the method comprises the steps: S1, building a kinetic model of an Escherichia coli fermentation process, and building an output equation from the dilution rate to the biomass concentration of the Escherichia coli fermentation system with multiple sensors in Escherichia coli fermentation based on the kinetic model, converting the output equation to obtain an equation with parameters to be solved; and S2, calculating parameters in the equation with the to-be-solved parameters. According to the method, the defect that measurement noise cannot be directly obtained from the measured biomass concentration output value due to the influence of the external environment and the abnormal movement of the sensor is overcome, it can be guaranteed that when each measurement value is collected, real-time estimation is conducted on the parameter theta at the current moment, and firstly, the unknown variance at each moment is estimated; and then the weight is distributed to each sensor to make full use of all the collected measurement data, so that efficient output of escherichia coli is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of Escherichia coli fermentation monitoring, in particular to a parameter solving method and system for an Escherichia coli fermentation system. Background Art

[0002] In biomanufacturing, multiple sensors are often used to obtain measurements of temperature, substrate concentration, product concentration, and dissolved oxygen to ensure that there are enough measurements to ensure the accuracy of biomanufacturing process modeling. However, due to the complexity of biological reactions and external environments, measurement noise is often unknown and exhibits strong time-varying characteristics, which poses a major challenge to real-time modeling of biomanufacturing processes.

[0003] E. coli fermentation is a complex nonlinear biomanufacturing process that is easily affected by the environment and requires a wide range of variables to be measured. Traditional batch modeling requires that the biomass concentration and substrate concentration collected at all times be substituted into iterative calculations each time, which not only increases unnecessary calculations and modeling costs, but also makes it impossible to monitor the fermentation process of E. coli. Summary of the invention

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

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

[0006] Step S1: establishing a kinetic model of the E. coli fermentation process, and establishing an output equation from the dilution rate to the biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation based on the kinetic model, and converting 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, in step S1, a kinetic model of the E. coli fermentation process is established, and based on the kinetic model, an output equation of the dilution rate to biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation is established, and the method includes:

[0009] The kinetic model of the E. coli fermentation process was established and expressed as:

[0010]

[0011]

[0012]

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

[0014] For the E. coli fermentation system with multiple sensors, the process from the dilution rate D to the 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:

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

[0016] Among them, k is the sampling time index, y k is the output of the fermentation system, representing the biomass concentration X at time k; q -1 is a constant coefficient and satisfies q -1 y k+1 =y k ;u k is the fermentation system input, representing the dilution rate D at time k; v k is the measurement noise, A(q -1 ) and B(q -1 ) is a constant coefficient q -1 The polynomial is expressed as:

[0017]

[0018]

[0019] Among them, n a and n b is the polynomial A(q -1 ) and B(q -1 ), and and Represents the polynomial A(q -1 ) and B(q -1 ) corresponding to the coefficient.

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

[0021] The output equation of the dilution rate D in E. coli fermentation to the biomass concentration X is approximated by the 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, is the measured value of biomass concentration X of the mth sensor at time k; is the information vector of the mth sensor at time k, including the input dilution rate and output biomass concentration; is the parameter vector, The mean is 0 and the noise variance is 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 step includes: constructing an optimization criterion function, specifically:

[0025] In order to make full use of the measurement information from each sensor, a weight is assigned to each sensor. λ m is the weight of the mth sensor, set To store the weighted measurements of M sensors at time k, represents the weighted measurement value of each sensor at time k, λ m,k is the weight of the mth sensor at time k; after obtaining the weighted measurement value of each sensor, the optimization criterion function is constructed according to the maximum likelihood criterion, and the formula is:

[0026]

[0027] Among them, arg max is used to find the parameter that makes the function reach the maximum value, f(,) is the likelihood function), is a set of weighted measurements of M sensors stored at time k, λ k ={λ 1,k ,λ 2,k ,…,λ m,k ,…,λ M,k} is the weight assigned to each sensor at time k.

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

[0029]

[0030] Among them, ∝ means proportional to, Find the partial derivative and set it to 0, and finally substitute it into the above formula to get the optimization equation:

[0031]

[0032] By introducing the Lagrange multiplier method, we get the following formula:

[0033]

[0034] Among them, ξ is the Lagrangian operator, for ξ and λ m,k Taking partial derivatives respectively, we get the following formulas:

[0035]

[0036] like Known, then λ can be obtained by formula (9)-formula (12) m,k , the formula is:

[0037]

[0038] In one embodiment of the present invention, λ is solved by formula (9)-formula (12). m,k , we need to first find The solution methods include:

[0039] Modeling the noise of the E. coli fermentation process: Due to the unknown noise variance in the E. coli fermentation process, the inverse gamma distribution is used to construct the probability density of the noise variance. The formula is:

[0040]

[0041] Among them, α m,k is the hyperparameter of the mth sensor at time k, IG(,) is the inverse gamma score, and Γ() is the gamma function;

[0042] In solving formula (14) Before that, we first solve α m,k , which satisfies the following formula:

[0043]

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

[0045] Noise variance The posterior probability Following the inverse gamma distribution, it satisfies:

[0046]

[0047]

[0048] Among them, U is A collection of for A collection of;

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

[0050]

[0051] The inverse of the posterior probability and the posterior expectation of the logarithm of the noise variance are:

[0052]

[0053]

[0054]

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

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

[0057]

[0058]

[0059]

[0060] Among them, γ k is a random step length;

[0061] Will get As the k-time parameter θ.

[0062] In order to solve the above technical problems, the present invention provides a parameter solving system for an Escherichia coli fermentation system, comprising:

[0063] Construction module: used to establish a kinetic model of the E. coli fermentation process, and to establish an output equation of the dilution rate to biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation based on the kinetic model, and to convert the output equation to obtain an equation with parameters to be solved;

[0064] Calculation module: used for calculating the parameters in the equation with the parameters to be solved.

[0065] To solve the above technical problems, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the parameter solution method for the Escherichia coli fermentation system are implemented.

[0066] In order to solve the above technical problems, the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the parameter solution method for the Escherichia coli fermentation system are implemented.

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

[0068] The parameter solution method of the Escherichia coli fermentation system constructed by the present invention overcomes the problem that the measured biomass concentration output value cannot be directly obtained due to the influence of the external environment and the abnormal movement of the sensor itself. The 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 fully utilize all the collected measurement data, thereby ensuring the efficient output of Escherichia coli. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0070] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

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

[0072] Embodiment 1

[0073] Reference Figure 1 As shown, the present invention relates to a parameter solving method for an Escherichia coli fermentation system, comprising:

[0074] Step S1: establishing a kinetic model of the E. coli fermentation process, and establishing an output equation from the dilution rate to the biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation based on the kinetic model, and converting 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 present embodiment is described in detail below:

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

[0078] The kinetic model of the E. coli fermentation process was established and expressed as:

[0079]

[0080]

[0081]

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

[0083] For the E. coli fermentation system with multiple sensors, the process from the dilution rate D to the 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:

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

[0085] Among them, k is the sampling time index, y k is the output of the fermentation system, representing the biomass concentration X at time k; q -1 is a constant coefficient and satisfies q -1 y k+1 =y k ;u k is the fermentation system input, representing the dilution rate D at time k; v k is the measurement noise, A(q -1) and B(q -1 ) is a constant coefficient q -1 The polynomial is expressed as:

[0086]

[0087]

[0088] Among them, n a and n b is the polynomial A(q -1 ) and B(q -1 ), and and Represents the polynomial A(q -1 ) and B(q -1 ) corresponding to the coefficient.

[0089] The method of converting the output equation in step S1 to obtain an equation with parameters to be solved is:

[0090] The output equation of the dilution rate D in E. coli fermentation to the biomass concentration X is approximated by the 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, is the measured value of biomass concentration X of the mth sensor at time k; is the information vector of the mth sensor at time k, including the input dilution rate and output biomass concentration; is the parameter vector, The mean is 0 and the noise variance is Gaussian distribution.

[0093] Before calculating the parameters in the equation with the parameters to be solved in step S2, the process includes: constructing an optimization criterion function, specifically:

[0094] In order to make full use of the measurement information from each sensor, a weight is assigned to each sensor. λ m is the weight of the mth sensor, set To store the weighted measurements of M sensors at time k, represents the weighted measurement value of each sensor at time k, λ m,kis the weight of the mth sensor at time k; after obtaining the weighted measurement value of each sensor, the optimization criterion function is constructed according to the maximum likelihood criterion, and the formula is:

[0095]

[0096] Among them, arg max is used to find the parameter that makes the function reach the maximum value, even if The value of the variable when it reaches the maximum value; f(,) is the likelihood function, is a set of weighted measurements of M sensors stored at time k, λ k ={λ 1,k ,λ 2,k ,…,λ m,k ,…,λ M,k} is the weight assigned to each sensor at time k.

[0097] The ultimate goal of this embodiment is to solve 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 θ in formula (8), first solve λ in formula (8) m,k , expand formula (8) to obtain:

[0099]

[0100] Among them, ∝ means proportional to, Find the partial derivative and set it to 0, and finally substitute it into the above formula to get the optimization equation:

[0101]

[0102] By introducing the Lagrange multiplier method, we get the following formula:

[0103]

[0104] Among them, ξ is the Lagrangian operator, for ξ and λ m,k Taking partial derivatives respectively, we get the following formulas:

[0105]

[0106] like Known, then λ can be obtained by formula (9)-formula (12) m,k , the formula is:

[0107]

[0108] Solve λ by using formula (9)-formula (12) m,k , we need to first find The solution methods include:

[0109] Modeling the noise of the E. coli fermentation process: Due to the unknown noise variance in the E. coli fermentation process, the inverse gamma distribution is used to construct the probability density of the noise variance. The formula is:

[0110]

[0111] Among them, α m,k is the hyperparameter of the mth sensor at time k, IG(,) is the inverse gamma distribution, and Γ() is the gamma function;

[0112] In solving formula (14) Before that, we first solve α m,k , which satisfies the following formula:

[0113]

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

[0115] In this embodiment, each time calculation is performed, The parameters in are all from the parameters of the previous time, and then we can find

[0116] Noise variance The posterior probability Following the inverse gamma distribution, it satisfies:

[0117]

[0118]

[0119] Among them, U is A collection of for A collection of;

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

[0121]

[0122] The inverse of the posterior probability and the posterior expectation of the logarithm of the noise variance are:

[0123]

[0124]

[0125]

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

[0127] Step S2 calculates the parameters in the equation with the parameters to be solved, including the α obtained at time k-1. m,k and The k-time parameter θ is obtained recursively, and the formula is:

[0128]

[0129]

[0130]

[0131] Among them, γ k is a random step length;

[0132] The formula (20) is obtained As the k-time parameter θ.

[0133] This embodiment also includes an index evaluation of the identification result, and the calculation formula is:

[0134]

[0135] Where d is the total dimension of the parameter vector θ, θ real is the true value of the parameter vector.

[0136] It should be noted that the present embodiment will initialize the parameters, for example, initialization (θ 0 ) den 、(θ 0 ) num , α m,0 , m,0 , value for ease of ground calculation.

[0137] In summary, multi-sensor measurement fusion can greatly improve the parameter estimation accuracy and robustness of recursive identification in Escherichia coli fermentation. A single sensor is greatly affected by noise and has limited information, making it difficult to accurately identify dynamic parameters. When multiple sensor data such as dissolved oxygen, substrate concentration, product concentration, and biomass are used simultaneously, the identification algorithm can converge quickly and reduce parameter uncertainty. Therefore, the present invention is still stable under non-ideal conditions, and the optimization and control strategies are more flexible and efficient, which improves fermentation yield and quality and provides strong support for industrial production.

[0138] Embodiment 2

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

[0140] Construction module: used to establish a kinetic model of the E. coli fermentation process, and to establish an output equation of the dilution rate to biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation based on the kinetic model, and to convert the output equation to obtain an equation with parameters to be solved;

[0141] Calculation module: used for calculating the parameters in the equation with the parameters to be solved.

[0142] Embodiment 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, the steps of the parameter solution method for the Escherichia coli fermentation system described in Embodiment 1 are implemented.

[0144] Embodiment 4

[0145] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the parameter solution method for the Escherichia coli fermentation system described in the first embodiment are implemented.

[0146] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt 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.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0150] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0151] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A method for solving parameters of an Escherichia coli fermentation system, characterized in that: include: Step S1: establishing a kinetic model of the E. coli fermentation process, and establishing an output equation of the dilution rate to biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation based on the kinetic model, and converting the output equation to obtain an equation with parameters to be solved; Step S2: Calculate the parameters in the equation with the parameters to be solved.

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 of the dilution rate to biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation is established, and the method includes: The kinetic model of the E. coli fermentation process was established and expressed as: Where, P is the growth rate of E. coli, Y X / S is the cell mass yield, ω and κ are kinetic parameters, μ is defined as the growth rate due to expression substrate and product inhibition, and the feed substrate concentration S f , substrate concentration S, biomass concentration X, and dilution rate D are all variables of time t; For the E. coli fermentation system with multiple sensors, the process from the dilution rate D to the 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: A(q -1 )y k =B(q -1 )u k +v k (4) Among them, k is the sampling time index, y k is the output of the fermentation system, representing the biomass concentration X at time k; q -1 is a constant coefficient and satisfies q -1 y k+1 =y k ;u k is the fermentation system input, representing the dilution rate D at time k; v k is the measurement noise, A(q -1 ) and B(q -1 ) is a constant coefficient q -1 The polynomial is expressed as: Among them, n a and n b is the polynomial A(q -1 ) and B(q -1 ), and and Represents the polynomial A(q -1 ) and B(q -1 ) corresponding to the coefficient.

3. The method for solving the parameters of the Escherichia coli fermentation system according to claim 2, characterized in that: The method of converting the output equation in step S1 to obtain an equation with parameters to be solved is: The output equation of the dilution rate D in E. coli fermentation to the biomass concentration X is approximated by the second-order ARX model, and its least squares form is: Where m = 1, 2, 3, ..., M, M is the total number of sensors, is the measured value of biomass concentration X of the mth sensor at time k; is the information vector of the mth sensor at time k, including the input dilution rate and output biomass concentration; is the parameter vector, The mean is 0 and the noise variance is Gaussian distribution.

4. The method for solving the parameters of the Escherichia coli fermentation system according to claim 3, characterized in that: Before calculating the parameters in the equation with the parameters to be solved in step S2, the process includes: constructing an optimization criterion function, specifically: In order to make full use of the measurement information from each sensor, a weight is assigned to each sensor. λ m is the weight of the mth sensor, set To store the weighted measurements of M sensors at time k, represents the weighted measurement value of each sensor at time k, λ m,k is the weight of the mth sensor at time k; after obtaining the weighted measurement value of each sensor, the optimization criterion function is constructed according to the maximum likelihood criterion, and the formula is: Among them, argmax is used to find the parameter that makes the function reach the maximum value, f(,) is the likelihood function, is a set of weighted measurements of M sensors stored at time k, λ k ={λ 1,k ,λ 2, k,…,λ m,k ,…,λ M,k } is the weight assigned to each sensor at time k.

5. The method for solving the parameters of the Escherichia coli fermentation system according to claim 4, characterized in that: The method for calculating the parameters in the equation with the parameters to be solved in step S2 includes: to solve θ in formula (8), first solve λ in formula (8) m,k , expand formula (8) to obtain: Among them, ∝ means proportional to, Find the partial derivative and set it to 0, and finally substitute it into formula (8) to obtain the optimization equation: By introducing the Lagrange multiplier method, we get the following formula: Among them, ξ is the Lagrangian operator, for ξ and λ m,k Taking partial derivatives respectively, we get the following formulas: like Known, then λ can be obtained by formula (9)-formula (12) m,k , the formula is:

6. The method for solving the parameters of the E. coli fermentation system according to claim 5, characterized in that: Solve λ by using formula (9)-formula (12) m,k , we need to first find The solution methods include: Modeling the noise of the E. coli fermentation process: Due to the unknown noise variance in the E. coli fermentation process, the inverse gamma distribution is used to construct the probability density of the noise variance. The formula is: Among them, α m,k is the hyperparameter of the mth sensor at time k, IG(,) is the inverse gamma distribution, and Γ() is the gamma function; In solving formula (14) Before that, we first solve α m,k , which satisfies the following formula: Among them, ψ() is the Psi function, set Noise variance The posterior probability U,Θ k-1 Following the inverse gamma distribution, it satisfies: Among them, U is A collection of for A collection of; The expectation corresponding to the posterior probability of the noise variance is: The inverse of the posterior probability and the posterior expectation of the logarithm of the noise variance are: in, is the expected value calculation, Θ k-1 is the θ to be determined at time k-1 k-1 and α m,k-1 The collection representation of .

7. The method for solving the parameters of the E. coli fermentation system according to claim 6, characterized in that: The step S2 calculates the parameters in the equation with the parameters to be solved, including the λ obtained at the k-1 time. m,k-1 and The k-time parameter θ is obtained recursively, and the formula is: Among them, γ k is a random step length; Will get As the k-time parameter θ.

8. A parameter solving system for an E. coli fermentation system, characterized in that: include: Construction module: used to establish a kinetic model of the E. coli fermentation process, and to establish an output equation of the dilution rate to biomass concentration of the E. coli fermentation system with multiple sensors in the E. coli fermentation based on the kinetic model, and to convert the output equation to obtain an equation with parameters to be solved; Calculation module: used for calculating the parameters in the equation with the parameters to be solved.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the parameter solving method for the E. coli fermentation system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the parameter solving method for the E. coli fermentation system according to any one of claims 1 to 7 are implemented.

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