A slurry balance shield intelligent decision method, system, device and medium

Abstract

The application discloses a kind of slurry balance shield intelligent decision-making method, system, equipment and medium, it is related to tunnel engineering construction field.The method comprises: according to the initiative control parameter of current driving section shield machine, the geological code in front of tunnel of current driving section and the passive response parameter of current driving section shield machine is predicted by shield response parameter prediction model;According to the geological information code of current driving section, the geometric parameter of current driving section, the technical parameter of current driving section shield machine and surface settlement prediction model, the surface convergence settlement of current driving section is predicted;It is judged whether surface convergence settlement is in set range;If not, the initiative control parameter of current driving section shield machine is regenerated, and the step of predicting the passive response parameter of current driving section shield machine is returned;If yes, further determine the slurry control system parameter of current driving section, and the finally determined parameter is output as suggested driving parameter.The application can realize the decision of shield construction parameter.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of tunnel engineering construction, in particular to a slurry balance shield intelligent decision-making method, system, device and medium. BACKGROUND

[0002] As a convenient and small-impact mechanical construction method, shield method has been widely used in subway tunnel construction. However, when the shield machine is excavating, the disturbance caused by excavation to the soil will inevitably cause additional load to the adjacent stratum and surrounding existing buildings, causing their settlement and deformation. With the gradual complication of shield construction environment, the demand for control of ground settlement has gradually increased. Therefore, the prediction of ground settlement has attracted the attention of many scholars, and traditional methods such as empirical method and theoretical method have been used to predict ground settlement. However, due to the difficulty in obtaining undisturbed soil parameters and the strong regionalism of the method, these methods cannot be widely applied in practical engineering.

[0003] With the rise of artificial intelligence algorithms, more and more artificial intelligence algorithms are being applied to engineering construction. By using neural networks to learn the relationship between shield parameters, geometric parameters and geological parameters, real-time prediction of shield moving track, intelligent prediction of shield machine excavation posture, real-time prediction of excavation settlement, etc. can be achieved. In practical engineering, the settlement caused by tunnel excavation is usually obtained by manual monitoring method, and the operation parameters of the shield machine are adjusted by the operator through experience, but the lag of manual operation cannot provide real-time guidance for the parameter adjustment of the shield machine. In order to ensure the safety and quality of tunnel construction and avoid losses caused by poor information transmission, many intelligent systems have been developed for real-time monitoring of construction settlement and shield machine state.

[0004] However, the current intelligent systems for shield tunnels are mostly for the storage, management and visualization of existing shield machine data. Shield construction operation parameters are collected by sensors on the shield machine and stored and displayed on the system platform. These are data processing and visualization analysis for historical excavation sections, and still need expert system to analyze system data and give corresponding suggestions. This method has obvious characteristics of labor-intensive and time-consuming, and is only suitable for key risk node control and has a delay, which cannot provide real-time suggestions for the whole line. In order to meet the actual needs, some scholars have developed a system with construction risk warning, which analyzes the monitoring data to determine the construction risk, but manual uploading of monitoring data leads to low efficiency of the system.

[0005] At present, there are many digital platforms for shield tunnel construction in China, but these platforms focus on data collection, storage or visualization. Only a small amount of research has carried out simple data analysis and processing on platform data. There is no shield decision platform based on data analysis to give professional engineering recommendations for shield tunnel engineering construction. Data collection, storage and visualization cannot fully play the role of engineering data in guiding engineering construction. SUMMARY

[0006] The purpose of the present application is to provide a slurry balance shield intelligent decision method, system, device and medium to realize the decision of shield construction parameters.

[0007] To achieve the above purpose, the present application provides the following scheme:

[0008] A slurry balance shield intelligent decision method, the method comprising:

[0009] Obtaining geological survey borehole data of a current tunneling section of a shield tunnel and geometric parameters of the current tunneling section; the geometric parameters include: tunnel diameter, tunnel burial depth, segment thickness and groundwater level;

[0010] Determining the geological information code of the current tunneling section according to the geological survey borehole data of the current tunneling section; the geological information code includes: tunnel front geological code and tunnel top geological code;

[0011] Generating shield machine active control parameters of the current tunneling section according to historical tunneling parameters; the shield machine active control parameters include: pushing speed, cutterhead rotating speed and slurry tank pressure;

[0012] Predicting shield machine passive response parameters of the current tunneling section according to the shield machine active control parameters of the current tunneling section, the tunnel front geological code of the current tunneling section and a shield response parameter prediction model; the shield machine passive response parameters include: total thrust and cutterhead torque;

[0013] Predicting surface convergence settlement of the current tunneling section according to the geological information code of the current tunneling section, the geometric parameters of the current tunneling section, shield machine technical parameters of the current tunneling section and a surface settlement prediction model; the shield machine technical parameters of the current tunneling section include: the shield machine active control parameters of the current tunneling section and the shield machine passive response parameters of the current tunneling section;

[0014] Judging whether the surface convergence settlement is within a set surface settlement control target range to obtain a first judgment result;

[0015] If the first determination result is no, an optimization algorithm is adopted to regenerate the shield machine active control parameters of the current tunneling section according to the historical tunneling parameters, and the step of predicting the shield machine passive response parameters of the current tunneling section according to the shield machine active control parameters of the current tunneling section, the geological code in front of the tunnel of the current tunneling section, and a shield response parameter prediction model is returned to.

[0016] If the first determination result is yes, the slurry control system parameters of the current tunneling section are determined according to the set slurry pressure control target and the historical tunneling parameters, and the shield machine technical parameters of the current tunneling section and the slurry control system parameters of the current tunneling section are output as recommended tunneling parameters; the recommended tunneling parameters are used to assist the shield machine driver to drive the shield machine to complete the tunneling work; the slurry control system parameters include slurry tank active control parameters and slurry tank passive response parameters; the slurry tank active control parameters include slurry inflow, slurry outflow, and air tank pressure; the slurry tank passive response parameters include cut pressure; the set slurry pressure control target is determined according to the slurry tank pressure in the shield machine active control parameters of the current tunneling section.

[0017] Optionally, the step of determining the slurry control system parameters of the current tunneling section according to the set slurry pressure control target and the historical tunneling parameters, and outputting the shield machine technical parameters of the current tunneling section and the slurry control system parameters of the current tunneling section as recommended tunneling parameters specifically includes:

[0018] generating the slurry tank active control parameters of the current tunneling section according to the historical tunneling parameters;

[0019] predicting the slurry tank passive response parameters of the current tunneling section according to the slurry tank active control parameters of the current tunneling section and a slurry response parameter prediction model;

[0020] determining whether the slurry tank passive response parameters of the current tunneling section are within a set slurry pressure control target range, to obtain a second determination result;

[0021] If the second determination result is no, an optimization algorithm is adopted to regenerate the slurry tank active control parameters of the current tunneling section according to the historical tunneling parameters, and the step of predicting the slurry tank passive response parameters of the current tunneling section according to the slurry tank active control parameters of the current tunneling section and the slurry response parameter prediction model is returned to.

[0022] If the second determination result is yes, the shield machine technical parameters of the current tunneling section and the slurry control system parameters of the current tunneling section are output as recommended tunneling parameters.

[0023] Optionally, the method further includes:

[0024] After the current tunneling section is completed, the actual shield machine technical parameters of the current tunneling section, the actual slurry control system parameters of the current tunneling section and the actual ground convergence and settlement of the current tunneling section are obtained and input into the engineering database as historical tunneling parameters; the historical tunneling parameters are used to train a prediction model and determine the shield machine active control parameters and the slurry tank active control parameters of the next tunneling section; the prediction model comprises the shield response parameter prediction model, the ground settlement prediction model and the slurry response parameter prediction model.

[0025] Optionally, the geological information code of the current tunneling section is determined according to the geological survey borehole data of the current tunneling section, and specifically comprises:

[0026] The geological survey borehole data of the current tunneling section is divided into a plurality of soil layer categories according to different soil physical and mechanical parameters; the plurality of soil layer categories include clay, silt, sandy soil, sandstone and rock;

[0027] The height information and thickness information of each soil layer category in the tunnel area in front of the shield machine are encoded to obtain the geological code in front of the tunnel of the current tunneling section;

[0028] The height information and thickness information of each soil layer category above the tunnel in front of the shield machine are encoded to obtain the geological code above the tunnel of the current tunneling section;

[0029] The geological code in front of the tunnel of the current tunneling section and the geological code above the tunnel of the current tunneling section are taken as the geological information code of the current tunneling section.

[0030] Optionally, the passive response parameters of the shield machine of the current tunneling section are predicted according to the active control parameters of the shield machine of the current tunneling section, the geological code in front of the tunnel of the current tunneling section and the shield response parameter prediction model, and specifically comprise:

[0031] The active control parameters of the shield machine of the current tunneling section and the geological code in front of the tunnel of the current tunneling section are input into the shield response parameter prediction model to predict the passive response parameters of the shield machine of the current tunneling section;

[0032] The shield response parameter prediction model is trained by taking the actual shield machine active control parameters of the historical tunneling section and the geological code in front of the tunnel of the historical tunneling section as inputs, taking the predicted passive response parameters of the shield machine of the historical tunneling section as outputs, and taking the error between the predicted passive response parameters of the shield machine of the historical tunneling section and the actual passive response parameters of the shield machine of the historical tunneling section satisfying a set condition as a target.

[0033] Optionally, the surface convergence settlement of the current tunneling section is predicted according to the geological information code of the current tunneling section, the geometric parameters of the current tunneling section, the shield machine technical parameters of the current tunneling section, and a surface settlement prediction model, and specifically includes:

[0034] The geological information code of the current tunneling section, the geometric parameters of the current tunneling section, and the shield machine technical parameters of the current tunneling section are input into the surface settlement prediction model to predict the surface convergence settlement of the current tunneling section.

[0035] The surface settlement prediction model is trained by taking the geological information code of the historical tunneling section, the geometric parameters of the historical tunneling section, and the actual shield machine technical parameters of the historical tunneling section as inputs, taking the predicted surface convergence settlement of the historical tunneling section as output, and taking the error between the predicted surface convergence settlement of the historical tunneling section and the actual surface convergence settlement of the historical tunneling section satisfying a set condition as a target.

[0036] Optionally, the passive response parameters of the slurry cabin of the current tunneling section are predicted according to the active control parameters of the slurry cabin of the current tunneling section and a slurry response parameter prediction model, and specifically include:

[0037] The active control parameters of the slurry cabin of the current tunneling section are input into the slurry response parameter prediction model to predict the passive response parameters of the slurry cabin of the current tunneling section.

[0038] The slurry response parameter prediction model is trained by taking the actual active control parameters of the slurry cabin of the historical tunneling section as inputs, taking the predicted passive response parameters of the slurry cabin of the historical tunneling section as output, and taking the error between the predicted passive response parameters of the slurry cabin of the historical tunneling section and the actual passive response parameters of the slurry cabin of the historical tunneling section satisfying a set condition as a target.

[0039] An intelligent decision system for slurry balance shield, the system comprises:

[0040] A data acquisition module is configured to acquire geological survey borehole data of a current tunneling section of a shield tunnel and geometric parameters of the current tunneling section; the geometric parameters include tunnel diameter, tunnel burial depth, segment thickness, and underground water level.

[0041] A geological information coding module is configured to determine a geological information code of the current tunneling section according to the geological survey borehole data of the current tunneling section; the geological information code includes a geological code in front of the tunnel and a geological code above the tunnel.

[0042] A shield control parameter generation module is configured to generate shield machine active control parameters of the current tunneling section according to historical tunneling parameters; the shield machine active control parameters include propulsion speed, cutterhead rotation speed, and slurry cabin pressure.

[0043] The shield response parameter prediction module is configured to predict a passive response parameter of the shield tunneling machine in the current tunneling section according to the active control parameter of the shield tunneling machine in the current tunneling section, geological coding in front of the tunnel in the current tunneling section, and a shield response parameter prediction model; the passive response parameter of the shield tunneling machine includes total thrust and cutter head torque;

[0044] The ground surface settlement prediction module is configured to predict ground surface convergence settlement in the current tunneling section according to the geological information coding in the current tunneling section, the geometric parameter of the current tunneling section, the technical parameter of the shield tunneling machine in the current tunneling section, and a ground surface settlement prediction model; the technical parameter of the shield tunneling machine in the current tunneling section includes the active control parameter of the shield tunneling machine in the current tunneling section and the passive response parameter of the shield tunneling machine in the current tunneling section;

[0045] The settlement control target judgment module is configured to judge whether the ground surface convergence settlement is within a set ground surface settlement control target range, and obtain a first judgment result;

[0046] The shield control parameter optimization module is configured to, if the first judgment result is no, regenerate the active control parameter of the shield tunneling machine in the current tunneling section according to the historical tunneling parameter by using an optimization algorithm, and return the shield response parameter prediction module;

[0047] The slurry pressure control module is configured to, if the first judgment result is yes, determine a slurry control system parameter in the current tunneling section according to a set slurry pressure control target and the historical tunneling parameter, and output the technical parameter of the shield tunneling machine in the current tunneling section and the slurry control system parameter in the current tunneling section as a recommended tunneling parameter; the recommended tunneling parameter is used to assist a shield tunneling machine driver to drive the shield tunneling machine to complete tunneling work; the slurry control system parameter includes an active control parameter of a slurry tank and a passive response parameter of the slurry tank; the active control parameter of the slurry tank includes slurry inlet flow, slurry outlet flow, and air tank pressure; the passive response parameter of the slurry tank includes incision pressure; the set slurry pressure control target is determined according to slurry tank pressure in the active control parameter of the shield tunneling machine in the current tunneling section.

[0048] An electronic device includes a memory for storing a computer program and a processor for running the computer program to make the electronic device execute the slurry balance shield intelligent decision method.

[0049] A computer readable storage medium stores a computer program, which is executed by a processor to implement the slurry balance shield intelligent decision method.

[0050] According to the specific embodiments of the present application, the following technical effects are provided:

[0051] The mud balance shield intelligent decision-making method provided by the application divides shield machine technical parameters into active control parameters and passive response parameters, uses a shield response parameter prediction model and a ground surface settlement prediction model trained based on a neural network to fully learn actual engineering data in the past, extracts the space-time characteristics of each parameter, and mines the value of multi-source heterogeneous data affecting decision-making, so that shield construction parameters can be predicted by using geological exploration hole data and geometric data of the current tunneling section to assist shield machine drivers to drive the shield machine to complete the tunneling work. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0053] Figure 1 The flow chart of the mud balance shield intelligent decision-making method provided by the present application;

[0054] Figure 2 The module diagram of the mud balance shield intelligent decision-making system provided by the present application;

[0055] Figure 3 The structural schematic diagram of the shield tunnel provided by the embodiment of the present application;

[0056] Figure 4 The structural block diagram of the mud balance shield intelligent decision-making system provided by the embodiment of the present application;

[0057] Figure 5 The working flow chart of the mud balance shield intelligent decision-making system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0059] The purpose of the present application is to provide a mud balance shield intelligent decision-making method, system, device and medium to realize the decision of shield construction parameters.

[0060] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0061] Embodiment One

[0062] The embodiment provides a mud balance shield intelligent decision-making method. As shown in the figure, the method comprises the following steps. Figure 1

[0063] Step 101: acquiring geological survey sounding hole data of a current tunneling section of a shield tunnel and geometric parameters of the current tunneling section; the geometric parameters comprise a tunnel diameter, a tunnel buried depth, a segment thickness and a groundwater level.

[0064] Step 102: determining geological information coding of the current tunneling section according to the geological survey sounding hole data of the current tunneling section; the geological information coding comprises tunnel front geological coding and tunnel top geological coding.

[0065] Step 103: generating shield machine active control parameters of the current tunneling section according to historical tunneling parameters; the shield machine active control parameters comprise a pushing speed, a cutterhead rotating speed and a slurry tank pressure.

[0066] Step 104: predicting shield machine passive response parameters of the current tunneling section according to the shield machine active control parameters of the current tunneling section, the tunnel front geological coding of the current tunneling section and a shield response parameter prediction model; the shield machine passive response parameters comprise total thrust and cutterhead torque. This step specifically comprises the following steps.

[0067] inputting the shield machine active control parameters of the current tunneling section and the tunnel front geological coding of the current tunneling section into the shield response parameter prediction model to predict the shield machine passive response parameters of the current tunneling section. Wherein, the shield response parameter prediction model is trained by taking actual shield machine active control parameters of a historical tunneling section and tunnel front geological coding of the historical tunneling section as inputs, taking predicted shield machine passive response parameters of the historical tunneling section as outputs, and taking errors between the predicted shield machine passive response parameters of the historical tunneling section and actual shield machine passive response parameters of the historical tunneling section satisfying a set condition as a target.

[0068] Step 105: predicting ground surface convergence settlement of the current tunneling section according to the geological information coding of the current tunneling section, the geometric parameters of the current tunneling section, shield machine technical parameters of the current tunneling section and a ground surface settlement prediction model; the shield machine technical parameters of the current tunneling section comprise the shield machine active control parameters of the current tunneling section and the shield machine passive response parameters of the current tunneling section. This step specifically comprises the following steps.

[0069] ​inputting the geological information code of the current tunneling section, the geometric parameters of the current tunneling section and the shield machine technical parameters of the current tunneling section into the ground surface settlement prediction model to predict the ground surface convergence settlement of the current tunneling section; wherein the ground surface settlement prediction model is trained by taking the geological information code of a historical tunneling section, the geometric parameters of the historical tunneling section and the actual shield machine technical parameters of the historical tunneling section as inputs, taking the predicted ground surface convergence settlement of the historical tunneling section as output, and taking the error between the predicted ground surface convergence settlement of the historical tunneling section and the actual ground surface convergence settlement of the historical tunneling section satisfying a set condition as a target.

[0070] Step 106: judging whether the ground surface convergence settlement is within a set ground surface settlement control target range to obtain a first judgment result.

[0071] Step 107: if the first judgment result is no, using an optimization algorithm to regenerate the shield machine active control parameters of the current tunneling section according to the historical tunneling parameters, and returning to the step of predicting the shield machine passive response parameters of the current tunneling section according to the shield machine active control parameters of the current tunneling section, the geological code in front of the tunnel of the current tunneling section and the shield response parameter prediction model.

[0072] Step 108: if the first judgment result is yes, determining the slurry control system parameters of the current tunneling section according to the set slurry pressure control target and the historical tunneling parameters, and outputting the shield machine technical parameters of the current tunneling section and the slurry control system parameters of the current tunneling section as recommended tunneling parameters; the recommended tunneling parameters are used to assist the shield machine driver to drive the shield machine to complete the tunneling work; the slurry control system parameters include: slurry tank active control parameters and slurry tank passive response parameters; the slurry tank active control parameters include: slurry inflow, slurry outflow and air tank pressure; the slurry tank passive response parameters include: incision pressure; the set slurry pressure control target is determined according to the slurry tank pressure in the shield machine active control parameters of the current tunneling section.

[0073] Further, step 102 specifically includes:

[0074] Step 102.1: dividing the geological exploration borehole data of the current tunneling section into multiple soil layer categories according to different soil physical and mechanical parameters; the multiple soil layer categories include: cohesive soil, silt, sandy soil, sandstone and rock.

[0075] Step 102.2: encoding the height information and thickness information of each soil layer category in the tunnel area in front of the shield machine to obtain the geological code in front of the tunnel of the current tunneling section.

[0076] Step 102.3: encoding the height information and the thickness information of each of the soil layers above the tunnel in front of the tunnel boring machine to obtain a geological code of the tunnel above the current tunneling section.

[0077] Step 102.4: encoding the geological code in front of the tunnel of the current tunneling section and the geological code above the tunnel of the current tunneling section as the geological information of the current tunneling section.

[0078] Further, in step 108, the mud control system parameters of the current tunneling section are determined according to the set mud pressure control target and the historical tunneling parameters, and the shield tunneling machine technical parameters of the current tunneling section and the mud control system parameters of the current tunneling section are output as the recommended tunneling parameters, specifically including:

[0079] Step 108.1: generating the mud tank active control parameters of the current tunneling section according to the historical tunneling parameters.

[0080] Step 108.2: predicting the mud tank passive response parameters of the current tunneling section according to the mud tank active control parameters of the current tunneling section and a mud response parameter prediction model. This step specifically includes:

[0081] inputting the mud tank active control parameters of the current tunneling section into the mud response parameter prediction model to predict the mud tank passive response parameters of the current tunneling section; wherein the mud response parameter prediction model is trained to have the actual mud tank active control parameters of the historical tunneling section as the input, the predicted mud tank passive response parameters of the historical tunneling section as the output, and the error between the predicted mud tank passive response parameters of the historical tunneling section and the actual mud tank passive response parameters of the historical tunneling section satisfying a set condition as the target. The set condition is preferably that the total error of all training samples within a set number of training rounds is minimum or the total error of all training samples is less than a set threshold.

[0082] Step 108.3: determining whether the mud tank passive response parameters of the current tunneling section are within the set mud pressure control target range to obtain a second determination result.

[0083] Step 108.4: if the second determination result is no, using an optimization algorithm to regenerate the mud tank active control parameters of the current tunneling section according to the historical tunneling parameters, and returning to step 108.2.

[0084] Step 108.5: if the second determination result is yes, outputting the shield tunneling machine technical parameters of the current tunneling section and the mud control system parameters of the current tunneling section as the recommended tunneling parameters.

[0085] Preferably, the method further comprises: after the current tunneling section completes tunneling, acquiring actual shield machine technical parameters of the current tunneling section, actual slurry control system parameters of the current tunneling section, and actual ground surface convergence and settlement of the current tunneling section, and inputting the actual shield machine technical parameters, the actual slurry control system parameters, and the actual ground surface convergence and settlement into the engineering database as historical tunneling parameters; the historical tunneling parameters are used for training a prediction model and determining shield machine active control parameters and slurry tank active control parameters of a next tunneling section; the prediction model comprises: the shield response parameter prediction model, the ground surface settlement prediction model, and the slurry response parameter prediction model.

[0086] In order to perform the method corresponding to the above-mentioned embodiment one, to realize the corresponding functions and technical effects, this embodiment also provides a slurry balance shield intelligent decision system. As shown in Figure 2 The system comprises:

[0087] A data acquisition module 201 is configured to acquire geological survey borehole data of a current tunneling section of a shield tunnel and geometric parameters of the current tunneling section; the geometric parameters comprise: a tunnel diameter, a tunnel burial depth, a segment thickness, and a groundwater level.

[0088] A geological information coding module 202 is configured to determine geological information coding of the current tunneling section according to the geological survey borehole data of the current tunneling section; the geological information coding comprises: tunnel front geological coding and tunnel top geological coding.

[0089] A shield control parameter generation module 203 is configured to generate shield machine active control parameters of the current tunneling section according to historical tunneling parameters; the shield machine active control parameters comprise: a pushing speed, a cutterhead rotating speed, and a slurry tank pressure.

[0090] A shield response parameter prediction module 204 is configured to predict shield machine passive response parameters of the current tunneling section according to the shield machine active control parameters of the current tunneling section, tunnel front geological coding of the current tunneling section, and a shield response parameter prediction model; the shield machine passive response parameters comprise: total thrust and cutterhead torque.

[0091] A ground surface settlement prediction module 205 is configured to predict ground surface convergence and settlement of the current tunneling section according to the geological information coding of the current tunneling section, the geometric parameters of the current tunneling section, shield machine technical parameters of the current tunneling section, and a ground surface settlement prediction model; the shield machine technical parameters of the current tunneling section comprise: the shield machine active control parameters of the current tunneling section and the shield machine passive response parameters of the current tunneling section.

[0092] A settlement control target judgment module 206 is configured to judge whether the ground surface convergence and settlement is within a set ground surface settlement control target range, to obtain a first judgment result.

[0093] The shield control parameter optimization module 207 is configured to, if the first determination result is no, generate shield machine active control parameters of the current tunneling section according to the historical tunneling parameters by using an optimization algorithm, and return the shield response parameter prediction module 204.

[0094] The slurry pressure control module 208 is configured to, if the first determination result is yes, determine slurry control system parameters of the current tunneling section according to a set slurry pressure control target and the historical tunneling parameters, and output the shield machine technical parameters of the current tunneling section and the slurry control system parameters of the current tunneling section as recommended tunneling parameters; the recommended tunneling parameters are used to assist a shield machine driver to drive the shield machine to complete tunneling work; the slurry control system parameters include slurry tank active control parameters and slurry tank passive response parameters; the slurry tank active control parameters include slurry inflow, slurry outflow and air tank pressure; the slurry tank passive response parameters include cut pressure; the set slurry pressure control target is determined according to slurry tank pressure in the shield machine active control parameters of the current tunneling section.

[0095] Further, the system further comprises:

[0096] The engineering data processing module is configured to, after the current tunneling section is completed, acquire actual shield machine technical parameters of the current tunneling section, actual slurry control system parameters of the current tunneling section and actual ground surface convergence and settlement of the current tunneling section, and input the actual shield machine technical parameters, the actual slurry control system parameters and the actual ground surface convergence and settlement of the current tunneling section into the engineering database as historical tunneling parameters; the historical tunneling parameters are used to train a prediction model and determine shield machine active control parameters and slurry tank active control parameters of a next tunneling section; the prediction model includes the shield response parameter prediction model, the ground surface settlement prediction model and the slurry response parameter prediction model.

[0097] Since the slurry balance shield intelligent decision system corresponds to the slurry balance shield intelligent decision method described above, the same or corresponding contents as the method part will not be described here.

[0098] Embodiment two

[0099] This embodiment combines the slurry balance shield intelligent decision method and system provided in embodiment one to specifically discuss the functions and work processes of main modules in the slurry balance shield intelligent decision system.

[0100] First, for the convenience of understanding, some professional terms in the field of tunnel construction engineering are explained as follows:

[0101] Shield construction: in the construction of a subway tunnel, a shield machine is used to construct a subway tunnel in a section mainly composed of soil.

[0102] Shield construction settlement: Shield construction will inevitably cause ground disturbance, and ground disturbance will further develop to the ground surface, resulting in ground settlement.

[0103] Shield machine active control parameters: In the process of slurry balance shield machine construction, shield machine driving is mainly to control the thrust speed and cutterhead speed of the shield machine to be stable within a certain value range to control the shield machine parameters, so the slurry tank pressure, cutterhead speed and thrust speed of the shield machine can be used as the active control parameters of the shield machine control.

[0104] Shield machine passive response parameters: When the shield machine controls the thrust speed, cutterhead speed and slurry tank pressure to excavate in different strata, the cutterhead torque and total thrust of the shield machine will be different due to different strata, which are the passive response parameters of the shield machine.

[0105] In order to realize the normal excavation of the shield machine and ensure the construction quality, the shield machine operator needs to set reasonable active parameters of the shield machine. However, there is no intelligent system for shield machine excavation parameter decision-making at present, because the relationship between shield machine active control parameters and engineering quality is complex, and there is a lack of control standard for shield machine parameters. In order to realize intelligent decision-making of shield machine parameters, it is necessary to understand the relationship between shield machine parameters and engineering quality and establish appropriate control standards.

[0106] As shown in Figure 3 , the embodiment discusses six main modules in the intelligent decision-making system, which are geological information coding module, shield response parameter prediction module, ground settlement prediction module, shield control parameter optimization module, slurry pressure control module and engineering data processing module. The above six modules include traditional data collection module and data storage module, and also include different types of intelligent prediction modules based on collected data, and can also find the optimal shield engineering construction parameters according to the prediction model, give suggestions to engineering construction personnel, and ensure the safety and efficiency of engineering construction.

[0107] (1) Geological information coding module

[0108] The geological information coding module is a module that forms the geological information coding of the upper and front of the shield tunnel based on the sounding data in the geological survey. The sounding data in the geological survey first needs to be divided into five categories according to the soil physical and mechanical parameters such as friction angle, cohesion, compression modulus or standard penetration, etc. The five categories include cohesive soil, silt, sandy soil, sandstone and rock. Then the height and thickness information of each soil layer are coded to form height information array and thickness information array respectively. The structure of the shield tunnel is shown in Figure 4Since the mechanical response of the stratum above the tunnel is different from that of the stratum in the tunnel area, the geological information above the tunnel and the geological information at the tunnel need to be input separately to ensure that the input information does not cover each other. According to the linear interpolation of the sounding information, the stratum information codes above and in front of each ring of the shield tunnel are obtained. The geological information code will be input to the shield response parameter prediction module and the surface settlement prediction module.

[0109] Specifically, the input of the geological information coding module is the geological exploration sounding data, and the output is the geological information code above the tunnel and the geological information code in front of the tunnel.

[0110] (2) Shield response parameter prediction module

[0111] In the process of shield tunneling, there are five kinds of shield machine technical parameters that have an interaction relationship with the soil layer, some of which are active control parameters, which are actively controlled by the shield machine driver during tunneling, generally including the propulsion speed, cutter head speed and mud tank pressure; the remaining parameters are passive response parameters, which are the response of the shield machine active control parameters to the stratum, determined by the active control parameters and the stratum properties, generally including total thrust and cutter head torque. In the process of shield tunneling, the shield machine driver controls the active control parameters, and the data acquisition system of the shield machine acquires all parameters of the shield machine, including active control parameters and passive response parameters. For the area that has been excavated, all technical parameters of the shield machine are collected by the shield system. However, all shield machine parameters in front of the cutter head of the shield machine are unknown, among which the active control parameters can be actively controlled by the driver, and the passive response parameters are predicted by the system, together with the active control parameters controlled by the shield machine driver to form the shield machine technical parameters of the unexcavated section. And according to the different tunneling methods, the active control and passive response parameters of the shield machine will be different.

[0112] The input of the shield response parameter prediction module is the geological code in front of the tunnel and the assumed shield machine active control parameters (randomly generated according to historical tunneling parameters), and the output is the shield machine technical parameters of the unexcavated section (i.e. the current excavation section).

[0113] (3) Surface settlement prediction module

[0114] The ground settlement prediction module predicts the ground convergence settlement of the measuring point in front of the shield tunnel according to the geometric parameters, geological parameters and technical parameters of the shield machine. Preferably, all the above-mentioned parameters include all the parameters of the five rings in front and behind the prediction section. The shield geometric parameters include the tunnel diameter, the tunnel axis depth, the tunnel segment thickness and the underground water level. The geological parameters include the geological codes above the tunnel and in front of the tunnel obtained by the geological information coding module. The technical parameters of the shield machine include the technical parameters of the shield machine obtained by the system and the technical parameters of the unexcavated section of the shield machine obtained by the shield response parameter prediction module.

[0115] The input of the ground settlement prediction module is the geometric parameters of the shield project, the geological information codes above the tunnel and in front of the tunnel and the complete technical parameters of the shield machine, and the output is the convergence settlement value of the ground.

[0116] (4) Shield control parameter optimization module

[0117] The shield control parameter optimization module is based on the shield response parameter prediction module and the ground settlement prediction module, and realizes the optimal selection of the active control parameters of the shield machine. Preferably, first, the module takes the active control parameters of the nearest excavated section as the input of the shield response parameter prediction module, and obtains the complete technical parameters of the complete shield in front of the cutter head. Then the parameters are substituted into the ground settlement prediction module to obtain the predicted ground convergence settlement. If the predicted ground convergence settlement meets the settlement control requirement, the active control parameter is the target of the optimal active control parameter. If the predicted ground settlement does not meet the settlement control requirement, the active control parameter needs to be changed based on the optimization algorithm on the basis of the original active control parameter, and is re-input into the shield response parameter prediction module, which is repeated until the predicted settlement meets the ground settlement control requirement.

[0118] The input of the shield control parameter optimization module is the initial active control parameter and the ground settlement control target, and the output is the optimal active control parameter of the shield machine.

[0119] (5) Mud pressure control module

[0120] The mud pressure is the main feature of the slurry balance shield machine for tunnel excavation, which is different from other construction methods. The control of the mud pressure is particularly complex, and requires the cooperation of multiple control systems. This module takes the optimized mud chamber pressure (usually reflected by the incision pressure) of the shield control parameter optimization module as the control target, and takes the air chamber pressure (i.e. the gas chamber pressure), the slurry inflow and the slurry outflow as the control means. By adjusting the air chamber pressure, the slurry inflow and the slurry outflow, the mud chamber pressure is ensured to reach the target value, so as to ensure the ground settlement.

[0121] The input of the slurry pressure control module is the slurry pressure optimization value (i.e., the slurry pressure control target), and the output is the air chamber pressure, the slurry inlet flow rate and the slurry outlet flow rate when the slurry chamber pressure meets the control target requirement.

[0122] (6) Engineering data processing module

[0123] For each ring of shield tunneling, the collected monitoring data and the shield machine system recorded shield machine technical parameters need to be input into the system, and the data needs to be processed for further training of the model of the ground settlement prediction module and the model of the shield response parameter prediction module. Specifically, for the ground settlement monitoring data, the convergence settlement of the measuring point needs to be determined. For the shield machine technical parameters, data division, debugging, noise reduction and weighted average, etc. are needed to obtain the weighted average of each ring to represent the state of each ring of shield machine. The ground convergence settlement is mainly used for the ground settlement prediction module, while the processed shield machine technical parameters are used for both the shield response parameter prediction module and the ground settlement prediction module.

[0124] In addition, for the slurry pressure control module, if passive response parameters in the slurry control system parameters need to be predicted, the slurry control system parameters also need to be collected and input into the system, and the data needs to be processed for further training of the slurry response parameter prediction model. The data processing method is similar to that of the shield machine technical parameters, which will not be repeated here.

[0125] The input of the engineering data processing module is the original ground monitoring data and the full amount of data collected by the shield machine system, and the output is the ground convergence settlement and the shield machine technical parameters of each ring.

[0126] The workflow of the intelligent decision-making system is shown in Figure 5 First, before the system model operation, the digital twin model engineering database of the shield project will collect enough data before the project starts. The content includes the engineering geological exploration hole data (as the input of the geological information coding module), the geometric data of the shield project (as the input of the ground settlement prediction module), the shield machine technical parameters of all ring numbers of the project, including the slurry chamber pressure, the propulsion speed, the total thrust, the cutter head torque and the cutter head speed. Among them, the geometric parameters are specifically referred to Table 1, and the shield machine technical parameters are specifically referred to Table 2.

[0127] First, the geological information coding module is used to obtain the geological code of the front and the top of the tunnel.

[0128] The shield control parameter optimization module randomly generates active control parameters according to previous tunneling parameters. The active control parameters and the geological code of the front of the tunnel are jointly input into the shield response parameter prediction module to predict the passive response parameters of the shield corresponding to the active control parameters of the shield this time. The predicted passive response parameters of the shield and the active control parameters of the shield this time jointly form complete shield technical parameters.

[0129] The complete shield technical parameters, the geological information code above the tunnel, the geological information code in front of the tunnel, and the engineering geometric parameters are jointly input into the ground surface prediction module to output the predicted ground surface convergence settlement.

[0130] Then, it is judged whether the settlement meets the input of the shield control parameter optimization module, i.e., the settlement control standard, i.e., the set ground surface settlement control target. If the predicted settlement is greater than the settlement control standard, the control parameter optimization module will continue to generate different active control parameters from before and continue the prediction process. If the predicted settlement is less than the settlement control standard, the active control parameters at this time will meet the settlement control standard, and the active control parameters at this time will be taken as the optimized active control parameters, which will be taken as the output of the shield control parameter optimization module.

[0131] The slurry tank pressure in the optimized active control parameters will be taken as the input value of the slurry pressure control module to output the indirect control parameters of the slurry pressure (i.e., the slurry inflow rate, the slurry outflow rate, and the air tank pressure). In this way, the optimized shield technical parameters (especially the active control parameters of the shield therein) and the slurry control system parameters (especially the active control parameters of the slurry tank therein) will be taken as the recommended tunneling parameters for shield construction, and the shield driver will reasonably select the actual parameters according to the engineering experience and the recommended tunneling parameters.

[0132] After the shield tunneling is completed, the obtained ground surface settlement monitoring data and the shield technical parameters collected by the shield system are input into the engineering data processing module to obtain processed data, i.e., the ground surface convergence settlement and the shield technical parameters of each ring. The processed data and the collected data will be put into the digital twin model engineering for model training and next-stage shield tunneling.

[0133] Table 1: Geometric parameter details of the digital twin model engineering database

[0134]

[0135] Table 2: Shield technical parameter details

[0136]

[0137]

[0138] Further, for the indirect control type slurry balance shield machine, the working principle is to control the slurry pressure of the slurry tank by adjusting the slurry amount of the slurry inlet and outlet pipe and the air chamber pressure, so that the slurry pressure of the shield machine is leveled with the water and soil pressure of the front stratum to maintain the stability of the excavation face.

[0139] As a specific embodiment, for the slurry pressure control module, the input is the optimized slurry pressure (i.e. the set slurry pressure control target), and the output is the slurry control system parameter. In the slurry pressure control system, the active control parameters are the air chamber pressure and the slurry amount of the slurry inlet and outlet pipe, which control the slurry pressure of the shield machine. The slurry pressure of the shield machine is reflected by the incision pressure, so the passive response parameter is the incision pressure. The working process of this module is similar to that of the shield response parameter prediction module, the ground surface settlement prediction module and the shield control parameter optimization module. The input optimized slurry pressure is the control target, and the module continuously optimizes the active control parameters. If the incision pressure corresponding to the current active control parameter meets the control target requirement, the module is calculated, and the current slurry control system parameter is output, otherwise the active control parameter is continuously optimized. The slurry control system parameter is shown in Table 3.

[0140] Table 3 Slurry control system parameter detail table

[0141]

[0142] It should be noted that the shield machine technical parameters of the shield response parameter prediction module are basically unchanged within the scope of each ring of the shield machine tunneling, and need to be adjusted before tunneling the next ring after tunneling the ring. However, the slurry pressure needs to be changed at any time to balance with the front water and soil pressure to ensure the stability of the excavation face, and the slurry pressure control module outputs the slurry control system parameters which change at any time after inputting the slurry pressure optimization value in the ring. Therefore, in order to reduce the calculation amount of the system, the prediction and optimization of the slurry control system parameters are divided into separate slurry pressure control modules for work.

[0143] Example three

[0144] The embodiment of the present application also provides an electronic device, which comprises a memory for storing a computer program and a processor for running the computer program to make the electronic device execute the slurry balance shield intelligent decision-making method in the embodiment one. The electronic device can be a server.

[0145] In addition, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the slurry balance shield intelligent decision-making method in the embodiment one.

[0146] The application provides a slurry balance shield intelligent decision-making method, system, device and medium, utilizes intelligent deep learning technology and professional knowledge, constructs multiple modules, and the modules are connected with each other by parameters. Finally, the system can assist the shield machine operator to drive the shield machine. The system can utilize the Internet of Things technology to obtain the construction parameters and monitoring data in real time, and utilize the neural network to analyze the data. The neural network learns the previous actual engineering data, utilizes the current geological data and geometric data to predict the shield parameters, inputs the predicted shield parameters and geometric and geological data into the network to predict the settlement data, and adjusts the shield parameters through the settlement control value and the predicted value to realize the control of the shield parameters and the surface settlement. Compared with the prior art, the application has the following advantages:

[0147] The application divides the technical parameters of the shield machine into active control parameters and passive response parameters, extracts the space-time characteristics of the parameters by utilizing the neural network model, fully excavates the value of the multi-source heterogeneous data affecting the decision, establishes six function modules of the system based on the soil mechanics response, realizes the decision of the shield parameters, and builds the shield parameter intelligent decision-making system. The application realizes the decision of the shield machine control parameters based on the modular structure, in combination with the intelligent technologies such as deep learning and the traditional theoretical knowledge. Compared with the shield machine parameter prediction using only the intelligent technology, the application has higher credibility.

[0148] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the system disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0149] The principles and implementation modes of the application are described by applying specific examples in the present application, and the above embodiment description is only used to help understand the core idea of the application; meanwhile, for the general technical personnel in the art, according to the idea of the application, the specific implementation mode and application range will be changed. In conclusion, the content of the specification should not be understood as the limitation of the application.

Claims

1. A slurry shield intelligent decision-making method, characterized in that: The intelligent decision-making method for slurry shield includes: Acquiring geological survey borehole data and geometric parameters of the current excavation section of the shield tunnel; the geometric parameters of the current excavation section include: tunnel diameter, tunnel burial depth, segment thickness and groundwater level; Determining a geological information code of the current excavation section based on geological survey borehole data of the current excavation section; the geological information code of the current excavation section includes: a geological code in front of the tunnel and a geological code above the tunnel; Generating active control parameters of the shield machine for the current tunneling section based on historical tunneling parameters; the active control parameters of the shield machine include: propulsion speed, cutterhead speed and slurry chamber pressure; Predicting the passive response parameters of the shield machine in the current tunneling section based on the active control parameters of the shield machine in the current tunneling section, the geological code of the tunnel ahead of the current tunneling section, and the shield response parameter prediction model; the passive response parameters of the shield machine include: total thrust and cutterhead torque; Predicting the surface convergence settlement of the current tunneling section based on the geological information code of the current tunneling section, the geometric parameters of the current tunneling section, the technical parameters of the shield machine of the current tunneling section, and a surface settlement prediction model; the technical parameters of the shield machine of the current tunneling section include: active control parameters of the shield machine of the current tunneling section and passive response parameters of the shield machine of the current tunneling section; Determining whether the surface convergence settlement is within a set surface settlement control target range to obtain a first determination result; If the first judgment result is no, an optimization algorithm is used to regenerate the shield machine active control parameters of the current excavation section based on the historical excavation parameters, and the process returns to the step of "predicting the shield machine passive response parameters of the current excavation section based on the shield machine active control parameters of the current excavation section, the geological code of the tunnel ahead of the current excavation section, and the shield machine response parameter prediction model." If the first judgment result is yes, the mud and water control system parameters of the current excavation section are determined according to the set mud and water pressure control target and the historical excavation parameters, and the shield machine technical parameters of the current excavation section and the mud and water control system parameters of the current excavation section are output as recommended excavation parameters; the recommended excavation parameters are used to assist the shield machine driver in driving the shield machine to complete the excavation work; the mud and water control system parameters include: mud and water compartment active control parameters and mud and water compartment passive response parameters; the mud and water compartment active control parameters include: slurry inlet flow rate, slurry outlet flow rate and air chamber pressure; the mud and water compartment passive response parameters include: incision pressure; the set mud and water pressure control target is determined according to the mud and water compartment pressure in the shield machine active control parameters of the current excavation section; After the excavation of the current excavation section is completed, the actual shield machine technical parameters of the current excavation section, the actual mud and water control system parameters of the current excavation section, and the actual surface convergence and settlement of the current excavation section are obtained and input into the engineering database as historical excavation parameters; the historical excavation parameters are used to train the prediction model and determine the shield machine active control parameters and mud and water chamber active control parameters of the next excavation section; the prediction model includes: the shield response parameter prediction model, the surface settlement prediction model, and the mud and water response parameter prediction model.

2. The intelligent decision-making method for slurry shield according to claim 1 is characterized in that: The method of determining the mud water control system parameters of the current excavation section according to the set mud water pressure control target and the historical excavation parameters, and outputting the shield machine technical parameters of the current excavation section and the mud water control system parameters of the current excavation section as recommended excavation parameters, specifically includes: generating active control parameters of the mud and water compartment of the current excavation section according to the historical excavation parameters; Predicting the passive response parameters of the mud water compartment of the current excavation section according to the active control parameters of the mud water compartment of the current excavation section and the mud water response parameter prediction model; Determining whether the passive response parameter of the mud water chamber of the current excavation section is within a set mud water pressure control target range to obtain a second determination result; If the second judgment result is no, an optimization algorithm is used to regenerate the active control parameters of the mud water compartment of the current excavation section based on the historical excavation parameters, and the process returns to the step of "predicting the passive response parameters of the mud water compartment of the current excavation section based on the active control parameters of the mud water compartment of the current excavation section and the mud water response parameter prediction model"; If the second judgment result is yes, the shield machine technical parameters of the current excavation section and the mud and water control system parameters of the current excavation section are output as recommended excavation parameters.

3. The intelligent decision-making method for slurry shield according to claim 1 is characterized in that: Determining the geological information code of the current excavation section based on the geological survey borehole data of the current excavation section specifically includes: Dividing the geological survey borehole data of the current excavation section into multiple soil layer categories according to different soil physical and mechanical parameters; the multiple soil layer categories include: clay, silt, sand, gravel and rock; Encoding the height and thickness information of each soil layer type in the tunnel area in front of the shield machine to obtain a geological code in front of the tunnel in the current excavation section; Encoding the height and thickness information of each soil layer above the tunnel in front of the shield machine to obtain a geological code above the tunnel in the current excavation section; The geological code in front of the tunnel of the current excavation section and the geological code above the tunnel of the current excavation section are used as the geological information code of the current excavation section.

4. The intelligent decision-making method for slurry shield according to claim 1 is characterized in that: The method of predicting the passive response parameters of the shield machine in the current tunneling section according to the active control parameters of the shield machine in the current tunneling section, the geological code of the tunnel ahead of the current tunneling section, and the shield response parameter prediction model specifically includes: Inputting the active control parameters of the shield machine of the current excavation section and the geological code of the tunnel ahead of the current excavation section into the shield response parameter prediction model to predict the passive response parameters of the shield machine of the current excavation section; The shield machine response parameter prediction model is trained with the actual shield machine active control parameters of the historical excavation section and the geological code in front of the tunnel in the historical excavation section as input, the predicted shield machine passive response parameters of the historical excavation section as output, and the error between the predicted shield machine passive response parameters of the historical excavation section and the actual shield machine passive response parameters of the historical excavation section meeting the set conditions.

5. The intelligent decision-making method for slurry shield according to claim 1 is characterized in that: The predicting of the surface convergence settlement of the current excavation section according to the geological information code of the current excavation section, the geometric parameters of the current excavation section, the technical parameters of the shield machine of the current excavation section, and the surface settlement prediction model specifically includes: Inputting the geological information code of the current excavation section, the geometric parameters of the current excavation section, and the technical parameters of the shield machine of the current excavation section into the surface settlement prediction model to predict the surface convergence settlement of the current excavation section; The surface settlement prediction model is obtained by taking the geological information coding of the historical excavation section, the geometric parameters of the historical excavation section and the actual shield machine technical parameters of the historical excavation section as input, the predicted surface convergence settlement of the historical excavation section as output, and the error between the predicted surface convergence settlement of the historical excavation section and the actual surface convergence settlement of the historical excavation section meeting the set conditions as the goal.

6. The intelligent decision-making method for slurry shield according to claim 2 is characterized in that: The predicting of the passive response parameters of the mud water compartment of the current excavation section according to the active control parameters of the mud water compartment of the current excavation section and the mud water response parameter prediction model specifically includes: Inputting the active control parameters of the mud water compartment of the current excavation section into the mud water response parameter prediction model to predict the passive response parameters of the mud water compartment of the current excavation section; The mud water response parameter prediction model is obtained by taking the actual mud water compartment active control parameters of the historical excavation section as input and the predicted mud water compartment passive response parameters of the historical excavation section as output, and is trained with the goal of ensuring that the error between the predicted mud water compartment passive response parameters of the historical excavation section and the actual mud water compartment passive response parameters of the historical excavation section meets the set conditions.

7. A slurry shield intelligent decision-making system, applied to the slurry shield intelligent decision-making method according to any one of claims 1 to 6, characterized in that: The slurry shield intelligent decision-making system includes: A data acquisition module is used to acquire geological survey borehole data and geometric parameters of the current excavation section of the shield tunnel; the geometric parameters of the current excavation section include: tunnel diameter, tunnel burial depth, segment thickness and groundwater level; A geological information coding module is used to determine the geological information coding of the current excavation section based on the geological survey and exploration data of the current excavation section; the geological information coding of the current excavation section includes: a geological code in front of the tunnel and a geological code above the tunnel; A shield machine control parameter generation module is used to generate active control parameters of the shield machine for the current tunneling section based on historical tunneling parameters; the active control parameters of the shield machine include: propulsion speed, cutterhead speed and slurry chamber pressure; a shield machine response parameter prediction module, configured to predict the shield machine passive response parameters of the current tunneling section based on the shield machine active control parameters of the current tunneling section, the geological code of the tunnel ahead of the current tunneling section, and the shield machine response parameter prediction model; the shield machine passive response parameters include: total thrust and cutterhead torque; a surface settlement prediction module, configured to predict the surface convergence settlement of the current tunneling section based on the geological information code of the current tunneling section, the geometric parameters of the current tunneling section, the technical parameters of the shield machine of the current tunneling section, and a surface settlement prediction model; the technical parameters of the shield machine of the current tunneling section include: active control parameters of the shield machine of the current tunneling section and passive response parameters of the shield machine of the current tunneling section; a settlement control target judgment module, configured to judge whether the surface convergence settlement is within a set surface settlement control target range, and obtain a first judgment result; a shield control parameter optimization module, configured to, if the first judgment result is negative, use an optimization algorithm to regenerate the shield machine active control parameters for the current tunneling section based on the historical tunneling parameters, and return the parameters to the shield response parameter prediction module; The mud and water pressure control module is used to determine the mud and water control system parameters of the current excavation section according to the set mud and water pressure control target and the historical excavation parameters if the first judgment result is yes, and output the shield machine technical parameters of the current excavation section and the mud and water control system parameters of the current excavation section as recommended excavation parameters; the recommended excavation parameters are used to assist the shield machine driver in driving the shield machine to complete the excavation work; the mud and water control system parameters include: mud and water tank active control parameters and mud and water tank passive response parameters; the mud and water tank active control parameters include: slurry inlet flow, slurry outlet flow and air cabin pressure; the mud and water tank passive response parameters include: incision pressure; the set mud and water pressure control target is determined based on the mud and water tank pressure in the shield machine active control parameters of the current excavation section.

8. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the slurry shield intelligent decision-making method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the slurry shield intelligent decision-making method as described in any one of claims 1 to 6.

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