A method for pre-judging and positioning blockage of a slurry shield tunneling cutter head
By using the PSO-RBF neural network model and temperature resistivity monitoring, the problem of accurate prediction and positioning of mud cake formation on the cutterhead of slurry shield tunneling machines was solved, reducing construction risks and costs and improving construction efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING JIAOTONG UNIV
- Filing Date
- 2022-10-31
- Publication Date
- 2026-07-03
AI Technical Summary
Existing technologies struggle to accurately predict and locate mud cake formation on the cutterhead of slurry shield tunneling machines, leading to high construction risks, project delays, and increased costs.
A conventional tunneling parameter model for shield tunneling was constructed using the Particle Swarm Optimization (PSO) algorithm and Radial Basis Function (RBF) neural network. The model was trained using historical data, and the prediction and location of cutterhead mud cake were achieved by comparing the initial and higher-order parameters and combining temperature sensors and resistivity monitoring.
It enables accurate prediction and positioning of mud cake formation on the cutterhead of slurry shield tunneling machines, providing ample time to take preventative measures, reduce construction risks, and improve construction efficiency.
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Figure CN115758864B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of shield tunnel construction technology, and in particular to a method for predicting and locating blockages in the cutterhead of a slurry shield tunneling machine. Background Technology
[0002] Slurry shield tunneling machines are widely used in water-rich strata, especially in cross-river and cross-sea tunnel construction projects. When the shield encounters strata rich in viscous minerals (such as silty clay layers, silty mudstone, etc.), the excavated soil easily adheres to the cutterhead panel. After being squeezed, dehydrated, and subjected to high temperatures from friction, it forms a hard "mud cake," which blocks the shield cutterhead, reduces tunneling efficiency, and can even trigger secondary disasters such as ground subsidence, instability and collapse of the excavation face, resulting in tragedies of machinery destruction and loss of life. However, the phenomenon of mud cake formation on the shield cutterhead is not instantaneous; it often involves a series of processes: soil adhesion, squeezing and dehydration, high-temperature consolidation, further soil adhesion, and increasing cake area. The mud cake solidified on the cutterhead continuously grows larger and harder. When the mud cake extensively blocks the cutterhead opening of the slurry shield, it is often too late. When the cutterhead opening is blocked by mud cake, manual removal is necessary. Construction workers must enter the tunnel face to break up the solidified mud cake, an operation that is highly dangerous, delays the construction period, and increases construction costs. Therefore, accurate prediction of cutterhead clogging is crucial for timely implementation of measures to prevent cutterhead blockage and remove solidified clogging.
[0003] Currently, most existing research and solutions for predicting cutterhead cake formation in slurry shield tunneling machines (TBMs) focus only on the variation patterns of a few TBM tunneling parameters, such as jacking force, cutterhead torque, tunneling speed, and cutterhead temperature, under the condition of cutterhead cake formation. These individual TBM tunneling parameters often cannot accurately determine and predict the trend of cutterhead cake formation. In actual engineering projects, cutterhead cake formation affects various state parameters of the TBM tunneling machine, and there are certain quantitative relationships between these parameters, allowing for a more accurate reflection of the cutterhead cake formation state.
[0004] Existing technologies disclose a method for predicting the risk level of mud cake formation on the cutterhead of an earth pressure balance shield tunneling machine using the analytic hierarchy process (AHP). However, this method requires identifying numerous influencing factors related to cutterhead mud cake formation, making it difficult to accurately quantify the impact of each factor on the mud cake and preventing real-time prediction of the mud cake state during shield tunneling. Existing technologies also disclose a method for judging the mud cake state based on the cutterhead's rotational inertia. However, a quantitative relationship and determination method between the cutterhead's rotational inertia and the mud cake state have not yet been established, making this method impractical in real-world engineering. Existing technologies also disclose a method for locating specific cutters that experience mud cake formation based on cutter temperature. This method can only determine the mud cake formation status of individual cutters within a localized area of the cutterhead and cannot provide a definitive assessment. Furthermore, existing technologies disclose a device for monitoring mud cake formation on the shield cutterhead using a temperature sensor. However, this device can only assess the state of the cutterhead after mud cake formation and cannot predict cutterhead blockage or mud cake formation in advance. Existing technologies also disclose a method for detecting and judging the state of mud cake based on the temperature change pattern of the cutterhead. However, judging the state of mud cake on the cutterhead based on only one state variable of the tunnel boring machine is not very accurate and is prone to misjudgment. Existing technologies also disclose a method for judging the current state of mud cake on the cutterhead of the tunnel boring machine based on expert experience and characteristics of historical data of the tunnel boring machine. However, it lacks judgment criteria and cannot accurately predict the mud cake on the cutterhead of the tunnel boring machine. Summary of the Invention
[0005] This invention provides a method for predicting and locating blockage of the cutterhead in slurry shield tunneling, so as to accurately judge and predict the condition of mud cake on the cutterhead and locate the position of the solidified mud cake on the cutterhead.
[0006] To achieve the above objectives, the present invention adopts the following technical solution.
[0007] A method for predicting and locating blockages in the cutterhead of a slurry shield tunneling machine includes:
[0008] A conventional shield tunneling parameter model corresponding to the stratum viscosity characteristics was constructed using the particle swarm optimization algorithm PSO-radial basis function RBF neural network. The conventional shield tunneling parameter model was trained using historical slurry shield tunneling project data to obtain a trained conventional shield tunneling parameter model.
[0009] The shield tunneling parameters for the slurry shield tunneling project that needs to be tested are calculated using the trained conventional shield tunneling parameter model.
[0010] The shield tunneling parameters of the slurry shield tunneling project that needs to be detected are sampled and obtained. The sampled shield tunneling parameters are compared with the shield tunneling parameters calculated by the conventional shield tunneling parameter model. Based on the comparison results, the blockage of the cutterhead of the slurry shield tunneling is predicted and located.
[0011] Preferably, the step of constructing a conventional shield tunneling parameter model corresponding to the stratum viscosity characteristics using a particle swarm optimization algorithm (PSO)-radial basis function (RBF) neural network, and training the conventional shield tunneling parameter model using historical slurry shield tunneling project data to obtain a trained conventional shield tunneling parameter model includes:
[0012] The shield tunneling parameters and viscous characteristic data of the strata sampling from the shield tunneling machine information acquisition system are obtained from historical slurry shield tunneling projects. The shield tunneling parameters include shield thrust, cutterhead torque, tunneling speed, cutterhead rotation speed, mud pressure, mud discharge / delivery pipe flow rate, cutterhead rotation speed, mud and strata characteristics, and multi-physics field characteristics of the cutterhead panel. The viscous characteristic data of the strata sampling includes clay content and consistency index.
[0013] A conventional shield tunneling parameter model corresponding to the viscous characteristics of the strata is constructed using a PSO-RBF neural network. The PSO algorithm is used to optimize the parameter center value c, width σ, and connection weight w of the RBF neural network. The viscous characteristic data of the strata are used as the input neurons of the PSO-RBF neural network model, and the shield tunneling parameters are used as the output neurons. The conventional shield tunneling parameter model is trained to obtain a trained conventional shield tunneling parameter model.
[0014] Preferably, the step of calculating the tunneling parameters of the slurry shield tunneling project to be tested using the trained conventional shield tunneling parameter model includes:
[0015] For the slurry shield tunneling project that needs to be inspected, the viscous characteristic data of the strata sample of the slurry shield tunneling project that needs to be inspected is obtained. The viscous characteristic data of the strata sample of the slurry shield tunneling project that needs to be inspected is input into the trained shield tunneling conventional tunneling parameter model to obtain the shield tunneling parameters calculated by the shield tunneling conventional tunneling parameter model.
[0016] Preferably, the sampling process involves acquiring the tunneling parameters of the slurry shield tunneling project that needs to be detected, comparing the sampled tunneling parameters with the tunneling parameters calculated by the conventional tunneling parameter model, and predicting and locating cutterhead blockages in the slurry shield tunneling project based on the comparison results, including:
[0017] The tunneling parameters of the slurry shield tunneling project that needs to be detected are obtained by sampling, and the tunneling parameters are smoothed using Formula 1.
[0018]
[0019] In the formula, F i Let F be the shield tunneling parameters at time i. t The average value of the shield tunneling parameters recorded at time t and the four times preceding it;
[0020] The smoothed shield tunneling parameters are compared with the shield tunneling parameters calculated by the conventional shield tunneling parameter model. The data differences corresponding to each shield tunneling parameter are calculated. If the difference of three of the shield thrust, cutterhead torque, tunneling speed and cutterhead rotation speed exceeds 20%, it is determined that the shield is in the mud cake warning state.
[0021] Preferably, the method for sampling and acquiring the tunneling parameters of the slurry shield tunneling project that needs to be detected, comparing the sampled tunneling parameters with the tunneling parameters calculated by the conventional tunneling parameter model, and predicting and locating cutterhead blockage in the slurry shield tunneling project based on the comparison results, further includes:
[0022] Obtain real-time data on the slurry flow rate, slurry pressure, and cutter rotation speed of the slurry shield tunneling project that needs to be tested. Calculate the first shield torque index using Formula 2, the shield flow resistance index using Formula 3, the viscosity index using Formula 4, and the second shield torque index using the shield tunneling parameters obtained from the conventional shield tunneling parameter model.
[0023]
[0024]
[0025]
[0026] In the formula, T s T is the shield tunneling torque index; t The torque of the cutter head at time t; N t P is the shield thrust at time t; s P is the mud flow resistance index at the cutterhead of the tunnel boring machine; in-t P is the mud pressure in the mud chamber at time t. out-t η is the mud pressure at the excavation face at time t; out To reduce the viscosity of the discharged mud; η in Input mud viscosity; η is the average mud viscosity, obtained by (η... out +η in ) / 2 is calculated to obtain;
[0027] Perform the following three judgments:
[0028] Judgment 1: Determine whether the data difference between the first shield torque index and the second shield torque index exceeds 10%;
[0029] Judgment 2: Determine whether the shield tunneling flow resistance index exceeds 10% of the flow resistance index under the condition that the cutterhead is not blocked;
[0030] Judgment 3: Determine whether the viscosity index is greater than 10%;
[0031] If at least two of the three judgments are true, then it is determined that there is mud cake formation on the cutterhead in the slurry shield tunneling project that needs to be detected.
[0032] Preferably, the method for sampling and acquiring the tunneling parameters of the slurry shield tunneling project that needs to be detected, comparing the sampled tunneling parameters with the tunneling parameters calculated by the conventional tunneling parameter model, and predicting and locating cutterhead blockage in the slurry shield tunneling project based on the comparison results, further includes:
[0033] Temperature sensors are used to measure the temperature of the cutter head panel and the resistivity of the cutter head interface. When the temperature and resistivity at a certain location on the cutter head panel reach a peak, it is determined that there is a cake formation phenomenon at that location on the cutter head.
[0034] As can be seen from the technical solutions provided by the embodiments of the present invention above, the method of the present invention can make a preliminary judgment on the condition of mud cake on the cutterhead during slurry shield tunneling by analyzing and comparing the initial parameters. It can accurately judge and predict the condition of mud cake on the cutterhead, providing sufficient time for the slurry shield to take measures to reduce adhesion and remove cake. Finally, by using the cutterhead temperature and interface resistivity data, the location of the solidified mud cake on the cutterhead can be accurately located, which facilitates the construction personnel to remove the solidified mud cake in a targeted and rapid manner.
[0035] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description
[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0037] Figure 1 A flowchart illustrating a method for predicting and locating blockages in a slurry shield tunneling cutterhead, provided by an embodiment of the present invention;
[0038] Figure 2 A schematic diagram of multi-level parameters for the cake formation state of a shield tunneling cutterhead provided in an embodiment of the present invention;
[0039] Figure 3 This invention provides a resistivity testing local mud cake device and its arrangement. Detailed Implementation
[0040] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0041] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.
[0042] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.
[0043] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.
[0044] Figure 1 The processing flow of a method for predicting and locating blockage of the cutterhead in a slurry shield tunneling machine, provided by an embodiment of the present invention, is as follows: Figure 1 As shown, the processing steps include the following:
[0045] Step S1: Obtain shield tunneling parameters and stratum sampling viscosity characteristics data from the shield machine information acquisition system for historical slurry shield tunneling projects.
[0046] Figure 2This invention provides a schematic diagram of multi-dimensional, multi-level parameters for the cake formation state of a tunnel boring machine (TBM) cutterhead. By acquiring TBM tunneling parameters, this invention enables quantitative judgment of the degree and location of cutterhead cake formation and accurate prediction of cutterhead blockage trends. These TBM tunneling parameters include multiple factors such as TBM thrust, cutterhead torque, tunneling speed, cutterhead rotation speed, mud pressure, mud discharge / delivery pipe flow rate, cutterhead rotation speed, mud and formation characteristics, and multi-physical field characteristics of the cutterhead panel. The formation sampling viscosity characteristics data include clay content and consistency index.
[0047] Step S2: Construct a conventional shield tunneling parameter model corresponding to the stratum viscosity characteristics using a PSO (Particle Swarm Optimization)-RBF (Radial Basis Function) neural network. First, optimize the main parameters of the RBF neural network, including the center value c, width σ, and connection weights w, using the PSO algorithm. Then, use the stratum sampling viscosity characteristic data (clay content, consistency index, etc.) as input neurons of the PSO-RBF neural network model, and the shield tunneling parameters (thrust, cutterhead torque, tunneling speed, and cutterhead rotation speed, etc.) as output neurons. Train the above conventional shield tunneling parameter model to obtain a trained conventional shield tunneling parameter model.
[0048] Step S3: For the slurry shield tunneling project that needs to be inspected, sample and obtain the viscous characteristic data of the strata of the slurry shield tunneling project that needs to be inspected. Input the viscous characteristic data of the strata of the slurry shield tunneling project that needs to be inspected into the above-trained conventional shield tunneling parameter model to obtain the shield tunneling parameters calculated by the conventional shield tunneling parameter model.
[0049] The shield tunneling parameters of the slurry shield tunneling project to be detected are obtained by sampling. The shield tunneling parameters are smoothed by the moving average method (Equation 1). The smoothed shield tunneling parameters are compared with the shield tunneling parameters calculated by the above-mentioned conventional shield tunneling parameter model. The data difference corresponding to each shield tunneling parameter is calculated. If the difference of three data points of shield thrust, cutterhead torque, tunneling speed and cutterhead rotation speed exceeds 20%, the shield enters the mud cake warning state, and further judgment is made on the higher-order parameters.
[0050]
[0051] In the formula, F i Let i be the shield tunneling parameters at time i; The average value of the shield tunneling parameters recorded at time t and the four times preceding it.
[0052] Step S4: Further acquire real-time data such as mud flow rate, mud pressure, and cutter speed through the tunnel boring machine information acquisition system, and calculate three higher-order parameters: shield torque index (Equation 2), flow resistance index (Equation 3), and viscosity index (Equation 4). If the torque index and its rate of change with the tunnel boring machine mileage exceed the corresponding value in the conventional tunneling parameter model of the shield, the flow resistance index exceeds the flow resistance index under the condition that the cutterhead is not blocked by 10%, and the viscosity index is greater than 10%, then it is determined that the shield cutterhead has started to form mud cake.
[0053]
[0054]
[0055]
[0056] In the formula, T s T is the shield tunneling torque index; t The torque of the cutter head at time t; N t P is the shield thrust at time t; s P is the mud flow resistance index at the cutterhead of the tunnel boring machine; in-t P is the mud pressure in the mud chamber at time t. out-t η is the mud pressure at the excavation face at time t; out To reduce the viscosity of the discharged mud; η in Input mud viscosity; η is the average mud viscosity, which can be obtained by (η... out +η in The result is calculated as ) / 2.
[0057] Step S5: Use the temperature of the cutterhead panel and the resistivity of the cutterhead interface to make a comprehensive judgment on the location of the mud cake on the cutterhead. Use a temperature sensor to measure the temperature of the cutterhead panel and the resistivity of the cutterhead interface. When the temperature and resistivity at a certain position on the cutterhead panel reach a peak, it is determined that local soil consolidation into cake has occurred at that position on the cutterhead.
[0058] The temperature of the cutter head panel is monitored using a temperature sensor; the interface resistivity of the cutter head and its arrangement scheme are as follows: Figure 3 As shown. In Figure 3 In this process, a cyclic switching energizing method is used to energize any two adjacent electrodes. Figure 3 (a) Monitor and record the voltage and current after energization, and calculate the apparent resistivity between the two energized electrodes. If the resistivity suddenly increases, it indicates that a solidified mud cake has formed between the electrodes. Figure 3 (b)), and the larger the resistivity value, the greater the degree of mud cake solidification, and an alarm message is sent to the system, and then the next pair of adjacent electrodes is switched.
[0059] In summary, this invention, through the analysis and comparison of initial parameters, makes a preliminary judgment on the mud cake condition on the cutterhead during slurry shield tunneling; further, by utilizing higher-order parameters, it accurately judges and predicts the mud cake condition on the cutterhead, providing ample time for the slurry shield to take measures to reduce adhesion and remove the mud cake; finally, by using cutterhead temperature and interface resistivity data, it accurately locates the position of the solidified mud cake on the cutterhead, facilitating targeted and rapid removal of the solidified mud cake by construction personnel.
[0060] Compared with other related technologies, this invention collects richer data related to shield tunneling, which can more comprehensively reflect the state of the shield cutterhead cake formation. The two-stage method can make the judgment and prediction of cutterhead cake formation more accurate, and the method can also locate the solidification position of the cake on the cutterhead by simultaneously using temperature and resistivity, which is also more precise.
[0061] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.
[0062] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.
[0063] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.
[0064] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for predicting and locating blockage of the cutterhead in a slurry shield tunneling machine, characterized in that, include: A conventional shield tunneling parameter model corresponding to the stratum viscosity characteristics was constructed using the particle swarm optimization algorithm PSO-radial basis function RBF neural network. The conventional shield tunneling parameter model was trained using historical slurry shield tunneling project data to obtain a trained conventional shield tunneling parameter model. The shield tunneling parameters for the slurry shield tunneling project that needs to be tested are calculated using the trained conventional shield tunneling parameter model. The method described above utilizes a particle swarm optimization algorithm (PSO)-radial basis function (RBF) neural network to construct a conventional shield tunneling parameter model corresponding to the stratum viscosity characteristics. This model is then trained using historical slurry shield tunneling project data to obtain a trained conventional shield tunneling parameter model, which includes: The shield tunneling parameters and viscous characteristic data of the strata sampling from the shield tunneling machine information acquisition system are obtained from historical slurry shield tunneling projects. The shield tunneling parameters include shield thrust, cutterhead torque, tunneling speed, cutterhead rotation speed, mud pressure, mud discharge / delivery pipe flow rate, cutterhead rotation speed, mud and strata characteristics, and multi-physics field characteristics of the cutterhead panel. The viscous characteristic data of the strata sampling includes clay content and consistency index. A conventional shield tunneling parameter model corresponding to the viscous characteristics of the stratum is constructed using a PSO-RBF neural network. The PSO algorithm is used to optimize the parameter center value c, width σ, and connection weight w of the RBF neural network. The viscous characteristic data of the stratum is used as the input neurons of the PSO-RBF neural network model, and the shield tunneling parameters are used as the output neurons. The conventional shield tunneling parameter model is trained to obtain a trained conventional shield tunneling parameter model. The calculation of shield tunneling parameters for the slurry shield tunneling project that needs to be detected using the trained conventional shield tunneling parameter model includes: For the slurry shield tunneling project that needs to be inspected, the viscous characteristic data of the strata sampling of the slurry shield tunneling project that needs to be inspected is obtained by sampling. The viscous characteristic data of the strata sampling of the slurry shield tunneling project that needs to be inspected is input into the trained shield tunneling conventional tunneling parameter model to obtain the shield tunneling parameters calculated by the shield tunneling conventional tunneling parameter model. The sampling process acquires the tunneling parameters of the slurry shield tunneling project that needs to be detected. The sampled tunneling parameters are compared with the tunneling parameters calculated by the conventional tunneling parameter model. Based on the comparison results, the blockage of the slurry shield tunneling cutterhead is predicted and located, including: The tunneling parameters of the slurry shield tunneling project that needs to be detected are obtained by sampling, and the tunneling parameters are smoothed using Formula 1. In the formula, F i Let F be the shield tunneling parameters at time i. t The average value of the shield tunneling parameters recorded at time t and the four times preceding it; The smoothed shield tunneling parameters are compared with the shield tunneling parameters calculated by the conventional shield tunneling parameter model. The data difference corresponding to each shield tunneling parameter is calculated. If the difference of three of the shield thrust, cutterhead torque, tunneling speed and cutterhead rotation speed exceeds 20%, it is determined that the shield tunneling mud cake warning state has been entered. The process involves sampling and acquiring the tunneling parameters of the slurry shield tunneling project that needs to be detected. These parameters are then compared with those calculated from the conventional tunneling parameter model. Based on the comparison results, the blockage of the slurry shield tunneling cutterhead is predicted and located. Specifically, this includes: Obtain real-time data on the slurry flow rate, slurry pressure, and cutter rotation speed of the slurry shield tunneling project that needs to be tested. Calculate the first shield torque index using Formula 2, the shield flow resistance index using Formula 3, the viscosity index using Formula 4, and the second shield torque index using the shield tunneling parameters obtained from the conventional shield tunneling parameter model. In the formula, T s T is the shield tunneling torque index; t The torque of the cutter head at time t; N t P is the shield thrust at time t; s P is the mud flow resistance index at the cutterhead of the tunnel boring machine; in-t P represents the mud pressure in the mud chamber at time t. out-t η is the mud pressure at the excavation face at time t; out To reduce the viscosity of the discharged mud; η in Input mud viscosity; η is the average mud viscosity, obtained by (η... out +η in ) / 2 is calculated to obtain; Perform the following three judgments: Judgment 1: Determine whether the data difference between the first shield torque index and the second shield torque index exceeds 10%; Judgment 2: Determine whether the shield tunneling flow resistance index exceeds 10% of the flow resistance index under the condition that the cutterhead is not blocked; Judgment 3: Determine whether the viscosity index is greater than 10%; If at least two of the three judgments are true, then it is determined that there is mud cake formation on the cutterhead in the slurry shield tunneling project that needs to be detected.
2. The method according to claim 1, characterized in that, The method for sampling and acquiring tunneling parameters of the slurry shield tunneling project that needs to be detected, comparing the sampled tunneling parameters with the tunneling parameters calculated by the conventional tunneling parameter model, and predicting and locating cutterhead blockage in slurry shield tunneling based on the comparison results, further includes: Temperature sensors are used to measure the temperature of the cutter head panel and the resistivity of the cutter head interface. When the temperature and resistivity at a certain location on the cutter head panel reach a peak, it is determined that there is a cake formation phenomenon at that location on the cutter head.