Shield tunneling machine soil pressure balance control method and system based on multi-parameter dynamic optimization

Abstract

The invention discloses a shield tunneling machine soil pressure balance control method and system based on multi-parameter dynamic optimization, and relates to the technical field of shield tunneling machine control. The method comprises the following steps: calculating a support force required in a shield tunneling process under a current working condition and a theoretical shield soil bin pressure observation value, and establishing a relationship through a linear fitting mode to obtain a soil bin pressure transfer coefficient under a current stratum condition; calculating the theoretical support force of the tunnel face in the tunneling process, and calculating the preset value of the shield soil bin pressure in combination with the soil bin pressure transfer coefficient; and acquiring a real-time shield soil bin pressure observation value, and performing multi-parameter collaborative optimization on an error between the preset value of the shield soil bin pressure and the real-time shield soil bin pressure observation value by using a particle swarm optimization algorithm to obtain an optimized control parameter. According to the method, the pressure of the soil bin can be adjusted more accurately according to actual conditions, so that dynamic balance of an excavation face in the construction process is ensured, and safety and stability of underground tunnel construction are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of tunnel boring machine control technology, and in particular to a method and system for controlling earth pressure balance of tunnel boring machines based on multi-parameter dynamic optimization. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Shield tunneling is a major construction method in urban subway tunnel construction, especially the earth pressure balance (EPB) shield tunneling method, which is widely used. The principle of an EPB shield tunnel is to use a rotating cutterhead to cut and break up the soil at the tunnel face, transporting it to a sealed soil chamber, and then discharging it using a screw conveyor. During the EPB shield tunneling process, the dynamic balance control of the soil chamber pressure is crucial. When the soil pressure inside the chamber is less than the soil pressure at the excavation face, ground settlement will occur; conversely, when the soil pressure inside the chamber is greater than the soil pressure at the excavation face, it will cause surface heave, which in severe cases can lead to major accidents such as tunnel burial, building collapse, and casualties.

[0004] Existing earth pressure balance control methods for tunnel boring machines (TBMs) involve real-time monitoring of the earth chamber pressure during tunneling, comparing it to a set value, and then the TBM operator adjusting the propulsion speed or auger speed based on these changes. However, since the earth chamber pressure is determined by the coupled effects of multiple subsystems, including the cutterhead system, propulsion system, and muck removal system, relying solely on the operator's intuition and experience can easily lead to problems. Furthermore, because multiple factors influence the earth chamber pressure, failing to consider the coupling relationships between these parameters during comprehensive multi-parameter analysis will affect the accuracy of the control scheme and prevent the achievement of optimal pressure balance. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for controlling earth pressure balance of tunnel boring machines based on multi-parameter dynamic optimization. By jointly adjusting multiple parameters of pressure balance, the earth pressure can be adjusted more accurately according to the actual situation to ensure the dynamic balance of the excavation face during construction and to guarantee the safety and stability of underground tunnel construction.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: The first aspect of this invention provides a method for controlling earth pressure balance in a tunnel boring machine based on multi-parameter dynamic optimization, comprising the following steps: Obtain geological survey data for actual projects, determine the parameters of each stratum, and calculate the required support force during shield tunneling under the current working conditions; Obtain the shield machine parameters, and establish a shield soil chamber model based on the shield machine parameters, tunnel depth and stratum parameters to obtain the theoretical shield soil chamber pressure observation value; The required support force during shield tunneling under the current working conditions is established with the observed values ​​of shield soil pressure through linear fitting, and the soil pressure transmission coefficient under the current geological conditions is obtained. Calculate the theoretical support force at the tunnel face during the tunneling process, and calculate the preset value of the shield tunnel pressure in combination with the pressure transmission coefficient of the tunnel. Real-time shield tunnel earth chamber pressure observation values ​​are obtained, and the error between the preset value of the shield tunnel earth chamber pressure and the real-time shield tunnel earth chamber pressure observation value is optimized by multi-parameter collaborative optimization using the particle swarm optimization algorithm to obtain the optimized control parameters.

[0007] Furthermore, the specific steps for calculating the required support force during the shield tunneling process under the current working conditions are as follows: Based on the parameters of each stratum and the tunnel diameter, the support force at the current burial depth is calculated using theoretical formulas. Meanwhile, a numerical analysis model is established in the finite element software based on the geological distribution and tunnel diameter, and the required support force during the shield tunneling process under the current working conditions is calculated by iterative method.

[0008] Furthermore, the specific steps for establishing the shield tunnel earth chamber model are as follows: A discrete element numerical model was established based on the tunnel burial depth and geological parameters. A cutterhead model is established based on the parameters of the tunnel boring machine used in actual engineering, and the discrete element method is imported to obtain the tunnel boring machine soil chamber model.

[0009] Furthermore, the pressure changes of the shield and soil chamber in the shield soil chamber model were monitored by measuring the circular pressure to obtain the theoretical shield soil chamber pressure observation value.

[0010] Furthermore, based on the geological information obtained by advanced geological exploration equipment during the tunnel boring process, the geological parameters are updated, and the theoretical support force of the tunnel face during the tunneling process is calculated.

[0011] Furthermore, the specific steps for obtaining real-time shield tunnel earth pressure observation values ​​are as follows: During the tunnel boring machine excavation process, the pressure inside the soil chamber is monitored and output in real time by a soil chamber pressure sensor; The soil pressure values ​​output by the pressure sensors at various locations are weighted according to different weights and then averaged to obtain the observed value of the shield tunnel soil pressure.

[0012] Furthermore, the control parameters include tunneling speed, screw conveyor speed, and cutterhead speed.

[0013] A second aspect of the present invention provides a shield machine earth pressure balance control system based on multi-parameter dynamic optimization, comprising: The preprocessing module is configured to acquire geological survey data from the actual project, determine the parameters of each stratum, calculate the required support force during the shield tunneling process under the current working conditions, acquire shield machine parameters, establish a shield soil chamber model based on the shield machine parameters, tunnel depth, and stratum parameters, and obtain the theoretical shield soil chamber pressure observation value; establish the relationship between the required support force during the shield tunneling process under the current working conditions and the shield soil chamber pressure observation value through linear fitting to obtain the soil chamber pressure transmission coefficient under the current stratum conditions. The earth chamber pressure control module is configured to calculate the theoretical support force of the tunnel face during the tunneling process, and calculate the preset value of the shield earth chamber pressure in combination with the earth chamber pressure transmission coefficient; obtain the real-time shield earth chamber pressure observation value, and use the particle swarm optimization algorithm to perform multi-parameter collaborative optimization on the error between the preset value of the shield earth chamber pressure and the real-time shield earth chamber pressure observation value to obtain the optimized control parameters.

[0014] A third aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps in the shield machine earth pressure balance control method based on multi-parameter dynamic optimization as described in the first aspect of the present invention.

[0015] The fourth aspect of the present invention provides an apparatus including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the shield machine earth pressure balance control method based on multi-parameter dynamic optimization as described in the first aspect of the present invention.

[0016] The above one or more technical solutions have the following beneficial effects: This invention discloses a method and system for controlling earth pressure balance of tunnel boring machines based on multi-parameter dynamic optimization. In the preprocessing stage, the transmission coefficient between the working pressure at the tunnel face and the earth pressure in the earth chamber is studied by using geological modeling based on the actual engineering geological conditions. The preset value of earth pressure in the earth chamber established based on the pressure transmission coefficient is more reasonable and can be applied to various different geological conditions.

[0017] During the soil pressure regulation process, the soil pressure monitoring data is continuously updated and the tunneling parameters are dynamically optimized as the tunnel boring machine advances. Compared with the single-parameter control method for soil pressure regulation, this invention is more accurate, and the real-time data feedback avoids the lag in soil pressure regulation.

[0018] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0020] Figure 1 This is a flowchart of the earth pressure balance control method for tunnel boring machines based on multi-parameter dynamic optimization in Embodiment 1 of the present invention. Detailed Implementation

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. Example 1: Embodiment 1 of the present invention provides a method for controlling earth pressure balance of a tunnel boring machine based on multi-parameter dynamic optimization, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain geological survey data for the actual project (including lithology, geological profile, density, compression modulus, internal friction angle, cohesion, Poisson's ratio, permeability coefficient, and burial depth range), determine the parameters of each stratum, and calculate the required support force during the shield tunneling process under the current working conditions.

[0023] Using FLAC software and based on geological survey data, a geological model was established. According to the geological conditions and tunnel diameter, the geological strata were divided into layers. An excavation support model was established, and excavation simulation was carried out. The tunnel face was analyzed to clarify the support force of the face in front of the shield during the shield excavation process, providing data support for determining the soil pressure.

[0024] Step 2: Obtain the tunnel boring machine (TBM) parameters. Based on the TBM parameters, tunnel depth, and geological parameters, establish a TBM soil chamber model to obtain the theoretical TBM soil chamber pressure observation values, specifically: Step 2.1: Establish a discrete element numerical model based on the tunnel depth and geological parameters.

[0025] Step 2.2: Based on the shield machine parameters used in the actual project, including key parameters such as shield machine diameter and cutterhead opening ratio, establish the cutterhead model and import it into the discrete element method to obtain the shield earth chamber model.

[0026] Step 2.3: Use a measuring circle to monitor the changes in pressure on the shield and soil chamber in the shield soil chamber model, and obtain the theoretical shield soil chamber pressure observation value.

[0027] The measuring circle here is a monitoring method in the discrete element method software PFC. It involves evenly arranging many small balls with a certain volume inside the shield tunnel soil chamber, and then obtaining the pressure value of the soil particles contained inside the small balls, thereby obtaining the value of the soil chamber pressure on the rear wall of the entire soil chamber.

[0028] Step 3: Establish the relationship between the required support force during the shield tunneling process under the current working conditions and the observed value of the shield earth chamber pressure through linear fitting, and obtain the earth chamber pressure transmission coefficient under the current geological conditions.

[0029] Data analysis software was used to analyze the required support force P during the shield tunneling process under the current working conditions obtained in steps 1 and 2. L The observed value of the earth pressure P in the shield tunnel soil chamber S After correction and normalization, the shield tunneling face support force P is established through linear fitting. L and the observed value of the earth pressure P in the shield tunnel. S The relationship is as follows: (1) in, This is the soil pressure transmission coefficient under this type of geological condition.

[0030] Step 4: Calculate the theoretical support force of the tunnel face during the tunneling process, and calculate the preset value of the shield tunnel pressure in combination with the pressure transmission coefficient of the earth chamber.

[0031] Step 4.1: Update the geological parameters based on the stratum information obtained by the advanced geological exploration equipment during the tunnel boring machine (TBM) excavation, and calculate the theoretical support force of the tunnel face during the tunneling process. .

[0032] Step 4.2: Based on the earth pressure transmission coefficient obtained in Step 3 Based on the current theoretical support capacity of the working face The preset value of the earth pressure in the shield tunneling chamber is calculated. The calculation formula is as shown in equation (2); (2) Step 5: Obtain the real-time shield tunnel earth chamber pressure observation value, and use the particle swarm optimization algorithm to perform multi-parameter collaborative optimization on the error between the preset value of the shield tunnel earth chamber pressure and the real-time shield tunnel earth chamber pressure observation value to obtain the optimized control parameters.

[0033] Step 5.1: During the tunnel boring machine excavation process, the pressure inside the soil chamber is monitored and output in real time by the soil chamber pressure sensor.

[0034] Specifically, during the tunnel boring process, multiple soil pressure sensors are arranged circumferentially on the rear wall of the soil chamber to monitor and output the pressure inside the soil chamber in real time.

[0035] Step 5.2: The soil pressure values ​​output by the pressure sensors at various locations are weighted according to different weights and then averaged to obtain the observed value P of the shield tunnel soil pressure. T .

[0036] Step 5.3: Employ the Particle Swarm Optimization (PSO) algorithm to... As the objective function, a group of particles is randomly generated based on a combination of various control parameters. These control parameters include the tunneling speed, the screw conveyor speed, and the cutterhead speed.

[0037] Step 5.4: Calculate the objective function value and compare it with the individual optimal solution and the global optimal solution.

[0038] Step 5.5: Update the three tunneling parameters according to the optimization strategy in Step 5.3; repeat the evaluation and update steps until the set termination condition is met (the objective function converges to a certain threshold), and take the parameter value when the objective function is minimized as the optimal parameter value.

[0039] Step 5.6: The adjusted tunneling speed, screw conveyor speed and cutterhead speed are fed back to the tunnel boring machine control system. The tunnel boring machine continues to tunnel according to the adjusted tunneling parameters to achieve dynamic control of earth pressure balance.

[0040] Step 5.7: As the tunnel boring machine advances, the pressure sensor in the soil chamber continues to output the observed pressure value, and then the optimization and update steps are repeated.

[0041] The following section provides a detailed explanation of the Particle Swarm Optimization (PSO) algorithm and PSO-based multi-parameter collaborative optimization: Particle Swarm Optimization (PSO) is a swarm intelligence-based optimization algorithm. Its basic idea is to find the optimal solution by simulating the movement of individual particles in the search space. Each particle has only two attributes: position and velocity. Velocity represents the speed of movement, and position represents the direction of movement. Each particle represents a solution vector for the problem, and the quality of a particle is determined by feeding the solution vector into a fitness function and calculating the fitness value. Each particle in the swarm operates within the given solution range and adjusts its position based on its individual best position and the swarm's overall best position. By continuously adjusting its velocity and position, the particle swarm gradually evolves towards the optimal solution.

[0042] The initialization phase of Particle Swarm Optimization (PSO) involves creating a swarm of particles with randomized solutions. Subsequently, through an iterative process, each particle updates its velocity and position by tracking two optimal solutions: the individual best (pbest) and the global best (gbest). After obtaining these two optimal values, the particle updates its velocity and position using the following mathematical formula: (3) (4) in, For the first The velocity of each particle at the current iteration number; For the first The position of each particle at the current iteration number; It is the inertial weight, which is responsible for adjusting the degree of influence of the previous velocity on the current velocity. The larger this value is, the less likely the particle is to change its previous trajectory. It is an individual learning factor. These are social learning factors; both numbers are... The acceleration constants between these constants are used to control the learning step size; and It is distributed in Random numbers; It is the th iteration in the current iteration number. The individual optimal position that each particle has ever experienced; It is the global optimum, representing the best option among all the optimal positions that the particles have experienced in the current iteration.

[0043] In the PSO algorithm update formula, formula (3) consists of three parts. The first part is the memory term, which represents the influence of the previous velocity on the particle's current state. The second part is the self-cognition term, which represents the vector from the current position to the particle's own best position, reflecting the particle's ability to adjust and update based on its own experience. The third part is the group cognition term, which represents the vector from the current position to the best position in the entire population, reflecting the particle's ability to adjust and update based on the experience of other particles in the population. The particle determines the next update state based on its own experience and the best experience among its peers.

[0044] In the earth pressure balance shield tunneling process of this embodiment, the pressure balance within the earth chamber is mainly maintained by adjusting the advance speed and the screw conveyor speed. The objective function is to minimize the deviation between the predicted and observed pressure values ​​within the earth chamber. A particle swarm optimization algorithm is used to optimize these two control parameters to obtain optimal values. Subsequently, these optimized parameter values ​​are returned to the shield's control panel, and timely regulation of the pressure within the shield's earth chamber is achieved through coordinated control of the soil ingress and egress.

[0045] The objective function expression for the entire soil pressure control process is as follows: (5) in, This is the preset value for the earthwork pressure. The observed value of the earth pressure chamber; and These are the minimum and maximum propulsion speeds, respectively. and These are the minimum and maximum speeds of the screw conveyor, respectively.

[0046] The PSO algorithm iteratively optimizes the parameters within a specified range, continuously updating its position and velocity until it finds the optimal parameter values ​​that satisfy the objective function. The specific process of using the PSO algorithm to optimize tunneling parameters for the problem of pressure control within the shield tunnel's soil chamber is as follows: (1) The algorithm starts by initializing the parameters of the PSO algorithm, including population size, number of iterations, learning factor, inertia weight, initial position and velocity of particles, etc. (2) The predicted value of the soil pressure was calculated using the SSA-RF shield soil pressure prediction model; (3) In each iteration, calculate the fitness function value of each particle according to the objective function of the optimization control; (4) Fitness values ​​of each particle and the best positions it has experienced The fitness value is compared, and if it is better, it is taken as the current optimal position of the individual; (5) The fitness values ​​of each particle and the best positions experienced globally. The fitness values ​​are compared, and if the fitness value is better, it is taken as the optimal position of the current population. (6) Based on the velocity and position formulas, continuously update the position and velocity of the particles, determine whether the condition for terminating the optimization has been met, and if so, output the optimal solution. and Otherwise, return to (3) and start a new round of search.

[0047] After optimizing and adjusting these two tunneling parameters, the optimized parameter values ​​are returned to the shield's control panel. Through coordinated control of soil entry and exit, the pressure inside the shield's soil chamber can be adjusted in a timely manner.

[0048] Example 2: Embodiment 2 of the present invention provides a shield machine earth pressure balance control system based on multi-parameter dynamic optimization, comprising: The preprocessing module is configured to acquire geological survey data from the actual project, determine the parameters of each stratum, calculate the required support force during the shield tunneling process under the current working conditions, acquire shield machine parameters, establish a shield soil chamber model based on the shield machine parameters, tunnel depth, and stratum parameters, and obtain the theoretical shield soil chamber pressure observation value; establish the relationship between the required support force during the shield tunneling process under the current working conditions and the shield soil chamber pressure observation value through linear fitting to obtain the soil chamber pressure transmission coefficient under the current stratum conditions. The earth chamber pressure control module is configured to calculate the theoretical support force of the tunnel face during the tunneling process, and calculate the preset value of the shield earth chamber pressure in combination with the earth chamber pressure transmission coefficient; obtain the real-time shield earth chamber pressure observation value, and use the particle swarm optimization algorithm to perform multi-parameter collaborative optimization on the error between the preset value of the shield earth chamber pressure and the real-time shield earth chamber pressure observation value to obtain the optimized control parameters.

[0049] Example 3: Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, it implements the steps in the shield machine earth pressure balance control method based on multi-parameter dynamic optimization as described in Embodiment 1 of the present invention.

[0050] Example 4: Embodiment 4 of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the shield machine earth pressure balance control method based on multi-parameter dynamic optimization as described in Embodiment 1 of the present invention.

[0051] The steps and methods involved in Examples 2, 3 and 4 above correspond to those in Example 1. For specific implementation details, please refer to the relevant description section of Example 1.

[0052] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0053] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for controlling earth pressure balance in a tunnel boring machine based on multi-parameter dynamic optimization, characterized in that, Includes the following steps: Obtain geological survey data for actual projects, determine the parameters of each stratum, and calculate the required support force during the shield tunneling process under the current working conditions; Obtain the shield machine parameters, and establish a shield soil chamber model based on the shield machine parameters, tunnel depth and stratum parameters to obtain the theoretical shield soil chamber pressure observation value; The required support force during shield tunneling under the current working conditions is established with the observed values ​​of shield soil pressure through linear fitting, thereby obtaining the soil pressure transmission coefficient under the current geological conditions. Calculate the theoretical support force at the tunnel face during the tunneling process, and calculate the preset value of the shield tunnel pressure in combination with the pressure transmission coefficient of the tunnel. Real-time shield tunnel earth chamber pressure observation values ​​are obtained, and the error between the preset value of the shield tunnel earth chamber pressure and the real-time shield tunnel earth chamber pressure observation value is optimized by multi-parameter collaborative optimization using the particle swarm optimization algorithm to obtain the optimized control parameters.

2. The shield machine earth pressure balance control method based on multi-parameter dynamic optimization as described in claim 1, characterized in that, The specific steps for calculating the required support force during the shield tunneling process under the current working conditions are as follows: Based on the parameters of each stratum and the tunnel diameter, the support force at the current burial depth is calculated using theoretical formulas. Meanwhile, a numerical analysis model is established in the finite element software based on the geological distribution and tunnel diameter, and the required support force during the shield tunneling process under the current working conditions is calculated by iterative method.

3. The earth pressure balance control method for tunnel boring machines based on multi-parameter dynamic optimization as described in claim 1, characterized in that, The specific steps for establishing the shield tunnel earth chamber model are as follows: A discrete element numerical model was established based on the tunnel burial depth and geological parameters. A cutterhead model is established based on the parameters of the tunnel boring machine used in actual engineering, and the discrete element method is imported to obtain the tunnel boring machine soil chamber model.

4. The shield machine earth pressure balance control method based on multi-parameter dynamic optimization as described in claim 3, characterized in that, The pressure changes of the shield and soil chamber in the shield soil chamber model were monitored by measuring the circular pressure to obtain the theoretical shield soil chamber pressure observation value.

5. The earth pressure balance control method for tunnel boring machines based on multi-parameter dynamic optimization as described in claim 1, characterized in that, Based on the geological information obtained by advanced geological exploration equipment during the tunnel boring process, the geological parameters are updated, and the theoretical support force of the tunnel face during the tunneling process is calculated.

6. The earth pressure balance control method for tunnel boring machines based on multi-parameter dynamic optimization as described in claim 1, characterized in that, The specific steps for obtaining real-time shield tunnel earth pressure observation values ​​are as follows: During the tunnel boring machine (TBM) excavation process, the pressure inside the soil chamber is monitored and output in real time by a soil chamber pressure sensor; The soil pressure values ​​output by the pressure sensors at various locations are weighted according to different weights and then averaged to obtain the observed value of the shield tunnel soil pressure.

7. The earth pressure balance control method for tunnel boring machines based on multi-parameter dynamic optimization as described in claim 1, characterized in that, The control parameters include tunneling speed, screw conveyor speed, and cutterhead speed.

8. A shield tunneling machine earth pressure balance control system based on multi-parameter dynamic optimization, characterized in that, include: The preprocessing module is configured to acquire geological survey data of the actual project, determine the parameters of each stratum, and calculate the support force required during the shield tunneling process under the current working conditions. Obtain the shield machine parameters, establish a shield soil chamber model based on the shield machine parameters, tunnel depth and stratum parameters, and obtain the theoretical shield soil chamber pressure observation value; establish the relationship between the required support force during shield tunneling under the current working conditions and the shield soil chamber pressure observation value through linear fitting, and obtain the soil chamber pressure transmission coefficient under the current stratum conditions. The earth chamber pressure control module is configured to calculate the theoretical support force of the tunnel face during the tunneling process, and calculate the preset value of the shield earth chamber pressure in combination with the earth chamber pressure transmission coefficient; obtain the real-time shield earth chamber pressure observation value, and use the particle swarm optimization algorithm to perform multi-parameter collaborative optimization on the error between the preset value of the shield earth chamber pressure and the real-time shield earth chamber pressure observation value to obtain the optimized control parameters.

9. A computer-readable storage medium, characterized in that, It stores multiple instructions, which are adapted to be loaded and executed by the processor of the terminal device according to any one of claims 1-7, the earth pressure balance control method for tunnel boring machines based on multi-parameter dynamic optimization.

10. A terminal device, characterized in that, The device includes a processor and a computer-readable storage medium, wherein the processor implements various instructions; and the computer-readable storage medium stores multiple instructions adapted to be loaded and executed by the processor as described in any one of claims 1-7, which is a method for controlling earth pressure balance of a tunnel boring machine based on multi-parameter dynamic optimization.

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