Penetration method and system based on feedback perception of tunnel boring machine and storage medium

Abstract

The invention discloses a penetration method and system based on feedback perception of a tunnel boring machine and a storage medium. The method comprises the steps of model construction, specifically, a discrete element simulation model capable of being used for describing the interaction process of a penetration probe and karst surrounding rock is constructed; simulation and calculation are carried out, step-by-step loading simulation is carried out based on a discrete element method, a penetration probe continuously acts on the surrounding rock model containing the filling karst cave structure, data of the penetration process are recorded, related energy sub-items are calculated, and a risk index is calculated in combination with a fracture index; and optimization control: performing feedback control according to the risk index. The system comprises a construction unit, a penetration simulation unit, a calculation unit and a control optimization unit. A computer program is stored in the storage medium, and the steps of the penetration method are achieved when the program is executed by a processor. According to the invention, the crack evolution and energy release process when the shield cutter penetrates into the filling karst cave area can be simulated. The method can be widely applied to the technical field of tunnel engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel engineering, and in particular to a penetration method and system based on tunnel boring machine feedback sensing and a storage medium. BACKGROUND

[0002] The penetration test is an important in-situ loading method, which is widely used in the rapid evaluation of the structural integrity and local strength of surrounding rock in tunnel engineering, and is particularly suitable for judging the local stability and failure risk of surrounding rock in the area containing filling karst caves. The traditional penetration test relies on the force-displacement curve for macroscopic judgment, lacks microscopic analysis capability for the internal crack evolution, energy dissipation and local instability process of the filling structure, and is difficult to meet the needs of fine transparent modeling and risk control of shield tunneling in karst areas.

[0003] The energy change in the penetration process, such as the bond fracture work, friction dissipation energy and elastic stored energy, is directly related to the failure mode and structural integrity of the filling karst cave area under external load. In the process of shield rock breaking, the penetration behavior essentially corresponds to the rapid cycle of local contact-fracture-unloading of the cutter and rock mass. The distribution of energy items in this process can reveal the failure evolution path of different structural interfaces (such as the filling body-surrounding rock interface and joint weak zone). Fine analysis of energy items not only helps to identify the energy threshold of rock mass failure, but also traces the sensitive area of crack concentration, and assists in delineating the influence range of karst caves and the response area of construction disturbance. However, current penetration simulation research generally lacks systematic modeling of input energy, fracture energy, plastic dissipation and other physical quantities, and has not established a multi-scale coupling framework covering "cutter invasion-structure fracture-energy conversion", making it difficult to effectively extract a response index system that can be used for shield tunneling parameter adjustment or risk warning. SUMMARY

[0004] Therefore, in order to solve the technical problem that the existing penetration simulation method lacks systematic modeling of input energy, fracture energy, plastic dissipation and other physical quantities, and thus has low simulation accuracy, in a first aspect, the present application provides a penetration method based on tunnel boring machine feedback sensing, which comprises the following steps: Modeling, a discrete element simulation model is constructed for describing the interaction process between the penetration probe and the karst surrounding rock; Simulation and calculation, based on the discrete element method, the penetration probe continuously acts on the surrounding rock model containing the filling karst structure, the data of the penetration process are recorded and the related energy items are calculated, and the risk index is calculated in combination with the fracture index; Optimization control, feedback control is performed according to the risk index.

[0005] In some embodiments of the first aspect, the method further comprises drawing according to the data in the penetration simulation process to realize visual recording.

[0006] In a second aspect, the present application further provides a penetration system based on tunnel boring machine feedback sensing, applied to the penetration method based on tunnel boring machine feedback sensing as described above, and the system comprises a model construction unit, a penetration simulation unit, a calculation unit and a control optimization unit.

[0007] In a third aspect, the present application further provides a storage medium having a computer program stored thereon, wherein the computer program is a calibration program, and the calibration program, when executed by a processor, implements the steps of the penetration method based on tunnel boring machine feedback sensing as described above.

[0008] Based on the above scheme, the present application provides a penetration method, system and storage medium based on tunnel boring machine feedback sensing, and the penetration damage simulation based on the energy dissipation and fracture statistical coupling mechanism is not only suitable for mechanical response analysis under experimental scale, but also can be extended to simulate the crack evolution and energy release process when the shield cutter penetrates into the filling cave area, thereby providing core data support for three-dimensional transparent modeling of caves, grade discrimination of surrounding rocks and construction disturbance risk warning. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 is a step flow chart of a penetration method based on tunnel boring machine feedback sensing of the present application; Figure 2 is a schematic diagram of a penetration test model; Figure 3 is a curve diagram of cumulative dissipated energy-penetration depth of a specific embodiment of the present application; Figure 4 is a curve diagram of bond breakage number-penetration depth of a specific embodiment of the present application; Figure 5 is a particle model stress nephogram of a specific embodiment of the present application; Figure 6 is a particle model strain nephogram of a specific embodiment of the present application; Figure 7 is a curve diagram of fracture index-penetration depth of a specific embodiment of the present application; Figure 8 is a curve diagram of risk index-penetration depth of a specific embodiment of the present application. DETAILED DESCRIPTION

[0010] The discrete element method (DEM) is naturally suitable for complex rock mass with developed fissures and discontinuous structure, and has advantages in simulating behaviors such as crack propagation, slip failure and particle rearrangement in the process of filling cave damage. Existing researches have attempted to use DEM for numerical simulation of the penetration process, but most of them are still limited to the reproduction of force-depth response, and lack of systematic analysis of energy transmission, fracture evolution and local instability mechanism induced by penetration.

[0011] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0012] It should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0013] It should be understood that the "system", "device", "unit" and / or "module" used in the present application is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0014] As shown in the present application and claims, unless the context clearly indicates otherwise, "one", "a", "an" and / or "the" do not refer to the singular, but also include the plural. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements. The element defined by the statement "comprises a" does not exclude the presence of additional same elements in the process, method, product or device comprising the element.

[0015] In the description of the embodiments of the present application, "a plurality of" means two or more than two. The following terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features.

[0016] In addition, flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. Meanwhile, other operations can be added to these processes, or one or more steps of operation can be removed from these processes.

[0017] Reference Figure 1 A flowchart of an optional example of a penetration method based on tunnel boring machine feedback sensing is provided for the present application. The method can be applied to a computer device. The penetration method provided in the present embodiment can include but is not limited to the following steps: Step S1, constructing a penetration simulation model based on the discrete element method; Step S2, loading simulation based on the penetration simulation model and calculating energy; The energy in the penetration simulation process includes input work, elastic energy, fracture energy, elastic stored energy, dissipated energy, and kinetic energy. Step S3, constructing a fracture index and combining energy to calculate a risk index; Step S4, controlling and optimizing according to the risk index.

[0018] In some possible embodiments, step S1 specifically includes: A discrete element simulation model is constructed, which can be used to describe the interaction process between the penetration probe and the karst surrounding rock. The model can be internally set with irregular geometric filling cave structure, weakly cemented zone, and joint fracture, etc. The contact model and mechanical parameters between particles are set, and the simulation penetration loading path (optional: vertical penetration, shield rotation propulsion, etc.) is initialized.

[0019] In this embodiment, the input parameters include but are not limited to particle size, normal stiffness, tangential stiffness, friction coefficient, normal and tangential bond strength, damping coefficient, density, key type (such as linear contact or parallel bond), and loading mode of the penetration probe (force control or displacement control).

[0020] The initial coordinates of the discrete particles are determined. The particles are generated in the given boundary using the random close packing algorithm to form a stable initial configuration. The position coordinates of each particle are calibrated according to the selected coordinate system.

[0021] The discrete particles are numbered and the corresponding data information is input, including but not limited to: the number, position coordinates, radius, mass, current speed and acceleration, force state (normal force, tangential force, friction force, damping force), key connection state, contact neighbor number and number, etc.

[0022] Initial boundary conditions are applied and the time step is determined. In this embodiment, the bottom boundary of the model is fixed, the rest of the sides are set as frictional or elastic boundaries, and a rigid cylindrical probe is arranged at the top for loading. The time step is determined according to the maximum stiffness and minimum mass according to the stability criterion, so as to balance the calculation accuracy and efficiency.

[0023] In some possible embodiments, step S2 specifically includes: Step S2, loading simulation based on the penetration simulation model and calculating energy;

[0024] During the whole simulation process, the input work, elastic energy, fracture energy, elastic stored energy, dissipated energy, and kinetic energy are calculated in real time. The calculation method of input work is: in, Indicates the initial depth to Deep input capabilities, Indicates the first The thrust of the step, Indicates the initial depth to Number of sampling steps for depth, Indicates the first The increment of the penetration displacement of the step. Indicates the first The torque of the step, Indicates the first The step's rotation increment.

[0025] The calculation methods for elastic energy, fracture energy, and elastic stored energy are as follows: in, This represents the elastic energy of the j-th bond. Indicates the first The normal stiffness of the contact, Indicates the first The tangential stiffness of the contact, Indicates the first The normal relative displacement of each contact Indicates the first The tangential relative displacement of the contact; Indicates from the initial time to The cumulative fracture energy at time t. Indicates from the initial time to The set of broken bonds at any given moment. Indicates the moment of fracture; Indicates that the j-th bond is in Elastic energy at time t; Indicates time from the initial time to time 2. Flexible energy storage, Indicates from the initial time to The set of unbroken contacts at any given moment. Indicates that the j-th bond is in Elastic energy at a given time.

[0026] Dissipated energy and energy balance: wherein, represents the frictional dissipation energy from the initial time to the time represents the contact set of slip state from the initial time to the time represents the initial time, represents the tangential stress, represents the tangential velocity, represents the instantaneous time, represents the contact set of actual contact and force transmission from the initial time to the time represents the viscous dissipation energy from the initial time to the time represents the normal damping, represents the normal velocity, represents the tangential damping, represents the total dissipation energy from the initial time to the time represents the kinetic energy at the time represents the mass of the th particle; represents the translational velocity of the th particle; represents the moment of inertia of the th particle; represents the angular velocity of the th particle.

[0027] In some possible embodiments, the fracture index of step S3 specifically comprises: wherein, represents the unit depth energy dissipation rate within the th depth interval, represents the th sampled depth interval, represents the depth increment of the th sampled interval, represents the total dissipation energy of the th depth interval, represents the unit depth newly added fracture rate within the th depth interval, is a localization index, represents the​​​​​​ The second invariant of shear strain within the depth range Indicates in The mean of the window within the depth interval.

[0028] In some feasible embodiments, step S3 further includes: The formula for calculating the joint inflection point is as follows: like Then this point is determined to be the critical depth. .

[0029] in, Indicates in Energy dissipation rate per unit depth within the depth range This is the threshold coefficient; , Used as a reference scale (window mean / historical quantile); This is the critical depth.

[0030] In some feasible embodiments, the risk index of step S3 specifically includes: in, For localized reference scale, These are weighting coefficients. Indicates in Risk index within the depth range.

[0031] In some feasible embodiments, step S4 specifically includes: when Trigger an alert and output the feed / speed / pressure and tool strategy. This is the warning threshold.

[0032] In this embodiment, .

[0033] Based on the overall process of the above method embodiment, after the calculation is completed, the evolution curves of the penetration depth-energy and the penetration depth-fracture number are derived, and the fracture space distribution map, the energy density cloud map, etc. are drawn. The analysis results show that there is an obvious energy threshold point in the penetration process, which is highly consistent with the "jump section" of the number of cohesive fractures, revealing the energy triggering characteristics of structure failure. The threshold can be used as a quantitative criterion for identifying local instability area, which can be further embedded in the transparent modeling system of tunnel surrounding rock to divide the risk area and make optimization decisions for support in the disturbance sensitive section.

[0034] In the implementation process of the present application, the discrete element numerical simulation method is used to analyze the energy and structure failure evolution of the rock penetration test, so as to explore the failure mechanism of the rock mass inside under the action of external load and the coupling relationship between the failure mechanism and the energy input. Through the simulation of the rock penetration test, the cumulative dissipated energy and the number of cohesive fractures are calculated. As shown in Figure 2 The particle model used in the discrete element simulation is a cuboid rock sample with a size of 100mm×100mm×50mm. According to the fracture distribution of the rock, it is divided into a geological strength index GSI of 100, and the specific value is based on the GSI value method. When there are not more than two obvious penetrating fractures on the surface of the rock, the value is GSI=100.

[0035] The test uses a wedge-shaped cutter shape. The cutter inclination angle α=30°, the inter-cone width α0=0.5mm, the loading rate is set to 0.5mm / min, the test is started until the sample is completely penetrated, the input energy, fracture energy and elastic deformation energy of the model are monitored, and the cumulative dissipated energy of the simulation is calculated, and the number of cohesive fractures is monitored. Finally, the simulation will output the cumulative dissipated energy-penetration depth relationship graph and the cohesive fracture number-penetration depth relationship graph.

[0036] Through the comprehensive analysis of Figure 3 and Figure 4 , it can be determined that the work done by the penetration force is mainly converted into two parts: one part is the elastic deformation energy storage in the rock mass, and the other part is released in the form of crack formation and expansion, i.e. fracture energy.

[0037] As can be seen from Figure 3 , the input energy continuously accumulates with the penetration depth, and the dissipated energy also continuously increases, showing a overall monotonous upward trend, and the energy dissipation rate also gradually increases. This characteristic of accelerated energy release usually occurs at the position where the internal structure of the rock sample is significantly damaged or the strength is suddenly changed, indicating that the mechanical energy of external loading is efficiently converted into breaking work, thereby promoting the initiation and expansion of cracks. At the same time, Figure 4 shows that the number of cohesive fractures grows slowly at the beginning of penetration, but rises rapidly at a certain depth section, forming a clear "jump" section, and this jump is just Figure 3The corresponding energy growth rate accelerates, indicating that the rock mass has undergone a damage evolution process from micro-crack accumulation to macroscopic penetration in this depth range.

[0038] The synchronous growth of energy conversion and the number of broken bonds in the penetration process not only verifies the effective tracking ability of the method for micro-crack evolution, but also reveals the clear energy threshold characteristics of the damage behavior. That is, before a certain energy input, the rock mass is mainly in the elastic energy storage state, and the bonding bond remains intact. Once the energy accumulation exceeds a certain critical point, the crack will concentrate and expand, leading to a sharp increase in the number of broken bonds, indicating that the rock sample has entered the rapid damage stage. The energy threshold and its corresponding depth can be used as an important quantitative criterion for the instability risk of surrounding rock, and it is suitable for identifying the damage initiation point and analyzing the construction risk sensitivity in the penetration test.

[0039] As shown in Figure 5 , from the stress cloud map, it can be observed that a clear high stress concentration zone is formed between the particles around the penetration probe, showing a vertical distribution characteristic of step-by-step expansion from top to bottom, indicating that the vertical crack zone is induced directly below the probe under the probe loading; the contact force between particles in this area increases sharply, which is the main control channel for crack initiation and expansion. Corresponding to this, as shown in Figure 6 , the shear band and local deformation nucleus in the strain cloud map extend downward from the penetration point, and these high strain zones generally develop along the diagonal or skew angle direction, forming a divergent damage structure with the penetration axis as the core. The stress concentration and strain anomaly area overlap, reflecting the evolution path of the micro-damage process under the local stress-strain coupling; these areas are the key spatial distribution zones for the rapid accumulation of broken bond numbers and the significant increase of energy dissipation in the subsequent simulation. The stress-strain response spectrum output by the simulation can not only accurately locate the local crack development and energy release area, but also serve as the basis for the sensitivity weighting of the structure around the karst cave in transparent geological modeling, assisting in the risk zoning of the construction disturbance path and the dynamic optimization of the excavation parameters.

[0040] In summary, based on the discrete element simulation method, combined with energy and broken bond data, stress cloud map and strain cloud map, the damage evolution mechanism in the penetration process is revealed. The stress concentration zone and the deformation zone are highly consistent, forming a crack network that extends downward along the penetration path. The input energy gradually transforms into broken bond energy and dissipation energy with the increase of depth, and the number of broken bonds and the dissipation energy growth show a synchronous trend, indicating that energy is mainly used for the damage process. This method not only improves the identification accuracy of the penetration damage initiation characteristics and evolution path, but also provides quantifiable simulation support for three-dimensional transparent modeling, risk sensitivity section identification and excavation parameter optimization in karst tunnel engineering.

[0041] The present invention also provides another embodiment, an EPB / slurry / variable density shield tunneling machine for karst-soft-hard interbedded strata, the difference being in steps S1 and S4, wherein the data of the simulation model involved includes: Thrust F, cutterhead torque M, rotational speed ω, penetration depth / penetration rate PI, face / mud pressure Pc, screw press current Is, screw press rotational speed ns, slag sample moisture content Wm, feed speed VP, cutter temperature / wear rate Tc.

[0042] The data preprocessing steps include: (1) time alignment and unit unification; (2) mapping the time axis to the penetration depth sequence h and resampling (Δh=0.005~0.02m, 0.01m recommended); (3) smoothing: using a window of m=5 to perform moving averages on M, ω, and Pc; (4) outlier removal: local z-score |z|>3; (5) shield verification amount (discrete gradient of silo pressure with depth): in, Indicates the first Each sampling depth, Indicates the first Each sampling depth, Indicates the first Working face / mud pressure within each sampling depth, Indicates the first Working face / mud pressure within each sampling depth.

[0043] A DEM was established in the near field at the working face to explicitly embed and fill cavities, weakly cemented zones, and joints and fissures. Contact / adhesion parameter set: in is the normal / tangential stiffness (N / m); Q is the coefficient of friction; The tangential bond strength is expressed in Pa.

[0044] Assimilation objective: using the weight matrix Minimize the cost function of weighted residuals plus regularization; Among the observations Operating conditions , The regularization coefficient is... For difference / unit operator, As a priori. Convergence: Or the relative improvement between adjacent iterations is <1%. Indicates by parameters The model under operating conditions input The predicted observations generated below.

[0045] The simulation of the fracture index and the penetration depth in this embodiment is shown in the figure Figure 7 .

[0046] Shield recommendation weight .

[0047] Figure 8 The risk index is given The change of the penetration depth is shown in the figure, and the threshold line marked only shows a typical early warning interval. In actual application, three threshold values , , , the normalized risk index is divided into four levels: Normal; Early warning (I); Early warning (II); : High risk (III).

[0048] Target silo pressure control law: Wherein is the target silo pressure, is the baseline silo pressure, .

[0049] Triggering strategy: When or close to , increase Pc to the target zone and adjust the screw machine speed ns and the cutter head speed ω.

[0050] The present application also gives another embodiment, hard rock TBM (open / single shield / double shield), suitable for hard rock (such as limestone / basalt) and joint fissure development stratum. The difference between the steps is also S1 and S4, which involves data acquisition including: Thrust F, cutter head torque M, speed ω, penetration amount / penetration rate PI, propulsion speed V P , cutter temperature / wear proxy T c , etc.; as in the above embodiment, emphasize the smoothing and alignment of PI, M, ω, and generate cutter / depth partition index.

[0051] Generate block network and joint set, local containing filling body and weak cementation zone; according to formula (19) to establish parameters and "cutter action area-depth partition" mapping; according to formula (20) to minimize the estimated parameters .

[0052] Along the "penetration + rotation" path, the depth and torque fluctuation are determined by PI and ω.

[0053] Triggering strategy: At that time, prioritize reducing ω (reducing the depth of cut / reducing the pulsating torque), and then slightly reduce V. P If T c Exceeding limits triggers maintenance / tool ​​replacement.

[0054] The optimization uses a soft upper limit on torque and a risk threshold as dual objectives, and performs online calculations within the permissible range: in, As a soft upper limit for torque, These are weighting coefficients. , This is the allowed range.

[0055] A tunnel boring machine-based feedback sensing penetration system includes: The model building unit is used to execute step S1; The simulation unit is inserted to execute step S2; The calculation unit is used to execute step S3; The control optimization unit is used to execute step S4.

[0056] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0057] A storage medium storing a computer program thereon, the computer program being a calibration program, which, when executed by a processor, implements the steps of the penetration method based on feedback sensing of a tunnel boring machine as described above.

[0058] The content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0059] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A penetration method based on tunneling machine feedback awareness, characterized by, The method comprises the following steps: constructing a penetration simulation model based on a discrete element method; loading simulation based on the penetration simulation model and calculating energy, which comprises input work, elastic energy, fracture energy, elastic storage energy, dissipation energy and kinetic energy; constructing a fracture index and calculating a risk index in combination with the energy; controlling optimization according to the risk index.

2. The penetration method based on tunneling machine feedback awareness according to claim 1, characterized in that, Further comprising: joint inflection point calculation according to the fracture index.

3. The penetration method based on tunneling machine feedback awareness according to claim 2, characterized in that, Further comprising: penetration visualization recording according to the energy, fracture index and joint inflection point.

4. The penetration method based on tunneling machine feedback awareness according to claim 2, wherein, The calculation formula of the input work is as follows: wherein, represents the initial depth to the input power at depth, represents the thrust at step , represents the number of sampling steps from the initial depth to depth, represents the penetration displacement increment at step , represents the torque at step , represents the angle increment at step .

5. The penetration method based on tunneling machine feedback awareness as claimed in claim 2, wherein, Wherein: The calculation formula of the elastic energy is as follows: wherein, represents the elastic energy of the th bond, represents the normal stiffness of the th contact, represents the tangential stiffness of the th contact, represents the normal relative displacement of the th contact, represents the tangential relative displacement of the th contact; The calculation formula of the fracture energy is as follows: wherein, represents the fracture energy cumulative value from the initial time to the time represents the set of broken bonds from the initial time to the time represents the fracture energy cumulative value from the initial time to the time represents the set of broken bonds from the initial time to the time represents the time of fracture; represents the elastic energy of the th bond at the time th bond at the time The calculation formula of the elastic storage energy is as follows: wherein, represents the elastic stored energy from the initial time to the time t, represents the set of unbroken contacts from the initial time to the time t, represents the elastic energy of the th bond at the time t. The calculation formula of the dissipation energy is as follows: wherein, represents the frictional dissipation energy from the initial time to the time represents the contact set of slip state from the initial time to the time represents the initial time, represents the tangential stress, represents the tangential velocity, represents the instantaneous time, represents the initial time to the time represents the contact set of actual contact and force transmission from the initial time to the time represents the normal damping, represents the normal velocity, represents the tangential damping, represents the total dissipation energy from the initial time to the time ;​​​​ The calculation formula of the kinetic energy is as follows: wherein represents the kinetic energy at the time t, represents the mass of the th particle; represents the translational velocity of the th particle; represents the moment of inertia of the th particle; represents the angular velocity of the th particle.

6. The penetration method based on tunneling machine feedback awareness as claimed in claim 4, wherein, The formula of the fracture index is as follows: in, Indicates in Energy dissipation rate per unit depth within the depth range Indicates the first Each sampling depth interval, then Indicates the first The depth increment of each sampled depth interval. express Total energy dissipation within the depth range Indicates in The rate of increase in the number of fractures per unit depth within the depth range. As a localized indicator, Indicates in The second invariant of shear strain within the depth range Indicates in The mean of the window within the depth interval.

7. The penetration method based on tunneling machine feedback awareness as claimed in claim 5, wherein, The calculation formula of the risk index is as follows: in, Indicates in Risk index within the depth range This represents the corresponding weighting coefficient. This indicates the corresponding reference scale.

8. A penetration method system based on tunneling machine feedback awareness, characterized by, Comprise: a model construction unit for constructing a penetration simulation model based on a discrete element method; a penetration simulation unit for loading simulation based on the penetration simulation model; a calculation unit for calculating energy in the process of penetration, which comprises input work, elastic energy, fracture energy, elastic storage energy, dissipation energy and kinetic energy; constructing a fracture index and calculating a risk index in combination with the energy; a control optimization unit for controlling optimization according to the risk index.

9. A storage medium having stored thereon a computer program, the computer program being a calibration program, characterized in that The calibration program, when executed by a processor, implements the steps of the method according to any one of claims 1-7.

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