TBM card machine risk quantitative prediction method and system

Abstract

The invention relates to the technical field of tunneling, in particular to a TBM card machine risk quantitative prediction method and system, and the method comprises the following steps: determining a TBM card machine prediction index, and collecting TBM card machine prediction index data; constructing a card machine prediction data set according to the TBM card machine prediction index data; performing data supplementation on the card machine prediction data set by using a sample supplementation model to obtain a card machine prediction data complete set; constructing an index prediction model according to the card machine prediction data complete set, and obtaining a TBM card machine prediction index prediction value by using the index prediction model; and according to the predicted value of the TBM card machine prediction index, calculating a card machine index so as to judge the possibility of TBM card machine. The real-time performance of TBM card machine prediction can be improved while the accuracy of TBM card machine prediction is guaranteed, the safety of the tunneling process is guaranteed, meanwhile, the requirement of prediction for professional knowledge can be lowered, and the flexibility of prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel excavation, and in particular to a method and system for quantitatively predicting the risk of a TBM stuck. Background Art

[0002] The Full Face Double Shield Tunnel Boring Machine (TBM) is a large-scale tunnel excavation equipment that integrates mechanical, electrical, hydraulic, sensor, and information technology. It is mainly used for tunnel excavation and construction of railways, highways, water conservancy and hydropower diversion tunnels, subways, and underground engineering projects in rock geological structures. It can achieve continuous excavation and complete operations such as rock breaking, slag removal, and support at the same time, realizing factory-based construction, with a fast excavation speed and high efficiency. However, in the face of complex working environments, TBMs are inevitably stuck, that is, the cutter head or shield of the TBM is squeezed or blocked by the surrounding rock so that it cannot rotate or advance normally, which brings serious safety hazards and huge economic losses. Therefore, it is very necessary to predict the machine jam when the TBM is working, so as to avoid the TBM jam.

[0003] Existing TBM jam predictions are mainly based on geological surveys and geological forecasts, numerical simulations, and machine learning algorithms. However, the accuracy and scope of geological surveys and forecasts are limited, making it difficult to accurately predict TBM jams; although numerical simulations have high prediction accuracy, they require high professional knowledge, are complex and computationally intensive, making it difficult to make real-time predictions of TBM jams; using machine learning algorithms to predict TBM jams is real-time, but due to the scarcity of monitoring samples, the generalization ability of the constructed machine learning algorithms is low, making it difficult to accurately predict TBM jams under different circumstances. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a method and system for quantitatively predicting the risk of TBM jams.

[0005] In order to achieve the above-mentioned purpose, in the first aspect, the present invention provides a method for quantitatively predicting the risk of TBM jams, the method comprising the following steps: determining TBM jam prediction indicators, and then collecting TBM jam prediction indicator data; constructing a jam prediction data set based on the TBM jam prediction indicator data; using a sample supplement model to supplement the jam prediction data set to obtain a complete jam prediction data set; constructing an indicator prediction model based on the complete jam prediction data set, and using the indicator prediction model to obtain TBM jam prediction indicator prediction values; calculating a jam index based on the TBM jam prediction indicator prediction values ​​to determine the possibility of TBM jams. The present invention can improve the real-time nature of TBM jam predictions while ensuring the accuracy of TBM jam predictions, ensure the safety of the tunnel excavation process, and also reduce the requirements for professional knowledge in predictions and improve the flexibility of predictions.

[0006] Optionally, the TBM jam prediction indicators include shield pressure, cutter head torque, penetration and excavation speed.

[0007] Optionally, the step of constructing a card machine prediction data set according to the TBM card machine prediction index data comprises the following steps: Preprocessing the TBM jammer prediction index data, and then cutting the various TBM jammer prediction index data of each construction site into index data sequences of the same length; A card machine prediction dataset is constructed using all the indicator data series.

[0008] Optionally, the method of using a sample supplement model to supplement the card machine prediction data set to obtain a complete card machine prediction data set includes the following steps: Acquire a sample card machine feature map according to the TBM card machine prediction index data, and then acquire a card machine prediction image set according to the card machine prediction data set; Based on the card machine prediction image set, a sample supplement model is constructed using a self-attention generative adversarial neural network; The sample supplement model is used to supplement the card machine prediction data set to obtain the complete card machine prediction data set.

[0009] Furthermore, the present invention obtains a complete set of card machine prediction data based on the sample supplement model and the card machine prediction data set to improve the generalization performance of the indicator prediction model, thereby improving the accuracy of TBM card machine risk judgment.

[0010] Optionally, the acquiring a sample card machine feature map according to the TBM card machine prediction index data, and then acquiring a card machine prediction image set according to the card machine prediction data set comprises the following steps: Standardizing the TBM card machine prediction index data in the index data sequence according to the value range of the image grayscale value to obtain a standardized index data sequence; For any construction site, obtain the corresponding sample card machine feature map, and any row of grayscale values ​​in the sample card machine feature map corresponds to a standardized indicator data sequence of the construction site; The sample card machine feature maps of all construction sites are obtained according to the card machine prediction data set, and then a card machine prediction image set is constructed.

[0011] Furthermore, the present invention provides a data basis for the construction of a subsequent sample supplement model by converting the card machine prediction data set into a card machine prediction image set.

[0012] Optionally, the step of constructing a sample supplement model based on the card machine prediction image set using a self-attention generative adversarial neural network comprises the following steps: Dividing the card machine prediction image set into a training set and a verification set to complete the training and verification of the self-attention generative adversarial neural network; After completing the training and verification of the self-attention generative adversarial neural network, the generator in the self-attention generative adversarial neural network is used as the sample supplement model.

[0013] Furthermore, the present invention uses a self-attention generative adversarial neural network to construct a sample supplement model, thereby improving the intelligence level of sample supplementation, thereby reducing the requirements for professional knowledge in sample supplementation when performing TBM card machine prediction.

[0014] Optionally, the using the sample supplement model to supplement the card machine prediction data set to obtain the complete card machine prediction data set includes the following steps: Generate multiple expansion card machine feature maps using the sample supplement model; Determine a standardized index data expansion sequence according to the expansion card machine characteristic graph; Destandardizing the standardized indicator data expansion sequence to obtain an indicator data expansion sequence; The indicator data expansion sequence is supplemented to the card machine prediction data set to obtain the complete card machine prediction data set.

[0015] Furthermore, the present invention uses a sample supplementation model to supplement the card machine prediction data set to obtain a complete set of card machine prediction data, so as to improve the generalization performance of the indicator prediction model, thereby improving the accuracy of TBM card machine risk judgment.

[0016] Optionally, the step of constructing an indicator prediction model based on the complete set of card machine prediction data, and using the indicator prediction model to obtain a TBM card machine prediction indicator prediction value comprises the following steps: Using a long short-term memory neural network to construct an initial indicator prediction model for each of the TBM card machine prediction indicators; Dividing the complete card machine prediction data set into a plurality of complete data subsets according to the TBM card machine prediction indicator types; Using the data complete subset of the TBM card machine prediction index to train and verify the corresponding initial index prediction model to obtain the corresponding index prediction model; For any of the TBM machine jam prediction indicators, the corresponding indicator prediction model is used to predict the TBM machine jam prediction indicator to obtain the corresponding TBM machine jam prediction indicator prediction value.

[0017] Furthermore, the present invention constructs an indicator prediction model based on a complete set of card machine prediction data and a long short-term memory neural network, which can not only improve the accuracy of TBM card machine prediction, but also realize real-time prediction.

[0018] Optionally, the card machine index satisfies the following relationship: in, is the card machine index, n is the number of TBM card machine prediction indicators, is the risk impact weight of the TBM jam prediction indicator of the ith TBM jam, is the TBM jam prediction index prediction value of the i-th TBM jam prediction index, For about function.

[0019] Furthermore, the card machine index of the present invention is simple to calculate and relatively accurate, and can improve the real-time and accuracy of TBM card machine risk judgment.

[0020] In the second aspect, the present invention also provides a TBM card machine risk quantitative prediction system, the TBM card machine risk quantitative prediction system includes: a data acquisition device, a data output device, a processor and a storage, the storage includes a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by the processor, the processor implements a TBM card machine risk quantitative prediction method provided by the present invention.

[0021] In summary, the present invention has at least the following beneficial effects: 1. The present invention uses a sample supplement model to supplement the card machine prediction data set to obtain a complete card machine prediction data set, which can improve the generalization performance of the indicator prediction model and further improve the accuracy of TBM card machine prediction; 2. The sample supplement model and index prediction model constructed by the present invention are both constructed based on machine learning algorithms, which can not only improve the accuracy and real-time performance of TBM jam prediction, but also reduce the requirements for professional knowledge in TBM jam prediction and improve the flexibility of prediction; 3. Since the present invention has low requirements on professional knowledge and small amount of calculation when predicting TBM jams, it has a wider scope of application.

[0022] 4. The TBM jamming risk quantitative prediction system provided by the present invention has a compact structure, stable operation, and is easy to install. It can also be well combined with the method of the present invention to improve the practicality of the method provided by the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A schematic diagram of a flow chart of a method for quantitatively predicting the risk of a TBM jam according to an embodiment of the present invention; Figure 2 The present invention is a schematic diagram of the framework of a TBM machine risk quantification prediction system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0026] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0027] It should be noted in advance that, in an optional embodiment, except for independent explanations, the same symbols or letters appearing in all formulas have the same meanings and values.

[0028] In an alternative embodiment, see Figure 1 The present invention provides a method for quantitatively predicting the risk of a TBM machine jam, the method comprising the following steps: S1. Determine the TBM jam prediction index, and then collect TBM jam prediction index data.

[0029] Specifically, in this embodiment, the TBM stuck prediction index includes shield pressure, cutter head torque, penetration and excavation speed. The collected TBM stuck prediction index data includes TBM stuck prediction index data used to construct a stuck prediction data set, and real-time TBM stuck prediction index data at the target construction site. The TBM stuck prediction index data used to construct a stuck prediction data set mainly comes from the Internet and journals, while the real-time TBM stuck prediction index data at the target construction site is collected by the TBM's own data acquisition device when the TBM is running. The data acquisition device usually includes a pressure sensor, a torque sensor, and a displacement sensor.

[0030] Further, in other optional embodiments, the TBM stuck prediction index may not be limited to shield pressure, cutter head torque, penetration and tunneling speed. For example, the TBM stuck prediction index may also include cutter head wear degree and tunneling machine power.

[0031] S2. Construct a card machine prediction data set according to the TBM card machine prediction index data.

[0032] Wherein, step S2 specifically includes the following steps: S21, pre-processing the TBM jammer prediction index data, and then cutting the various TBM jammer prediction index data of each construction site into index data sequences with the same length.

[0033] Specifically, in this embodiment, the operations of preprocessing the TBM card machine prediction index data include data discretization, outlier identification and missing value supplementation. In order to facilitate subsequent model training and prediction, various TBM card machine prediction index data need to be cut into index data sequences of the same length.

[0034] More specifically, in this embodiment, the length of each indicator data sequence is 32, and the time interval between two adjacent data in the indicator data sequence is 4 minutes. In other optional embodiments, the length and time interval of the indicator data sequence can be adjusted according to actual needs.

[0035] S22. Use all the indicator data sequences to construct a card machine prediction data set.

[0036] Specifically, in this embodiment, a set of indicator data sequences in the card machine prediction data set includes a shield pressure data sequence, a cutter head torque data sequence, a penetration data sequence and a tunneling speed data sequence, and each data in a set of indicator data sequences comes from the same construction site.

[0037] S3. Use a sample supplement model to supplement the card machine prediction data set to obtain a complete card machine prediction data set.

[0038] This embodiment obtains a complete set of card machine prediction data based on the sample supplement model and the card machine prediction data set, which solves the problem of scarce monitoring samples and is conducive to improving the generalization performance of the indicator prediction model constructed subsequently, thereby improving the accuracy of TBM card machine risk judgment. Step S3 specifically includes the following steps: S31. Acquire a sample card machine feature map according to the TBM card machine prediction index data, and then acquire a card machine prediction image set according to the card machine prediction data set.

[0039] This embodiment converts the card machine prediction data set into a card machine prediction image set, thereby providing a data basis for the construction of a subsequent sample supplement model. Step S31 specifically includes the following steps: S311, standardize the TBM card machine prediction index data in the index data sequence according to the value range of the image grayscale value to obtain a standardized index data sequence.

[0040] Specifically, in this embodiment, in order to convert the indicator data sequence into an image, it is necessary to limit the TBM card machine prediction indicator data in the indicator data sequence to the value range of the image grayscale value, that is, to perform standardization processing on the TBM card machine prediction indicator data in the indicator data sequence according to the value range of the image grayscale value, which is specifically achieved through the following relationship: in, is the standardized TBM jam prediction index data of the i-th TBM jam prediction index, is the TBM jam prediction index data of the i-th TBM jam prediction index, is the maximum value of the prediction index of the i-th TBM jam, is the minimum value of the prediction index of i TBM jams.

[0041] S312. For any construction site, obtain the corresponding sample card machine feature map, where any row of grayscale values ​​in the sample card machine feature map corresponds to a standardized indicator data sequence of the construction site.

[0042] Specifically, in this embodiment, the standardized TBM card machine prediction index data in the standardized index data sequence is used as the grayscale value of the pixel points in the sample card machine feature map to construct the sample card machine feature map. The constructed sample card machine feature map has a size of 64×64, that is, the length and width of the sample card machine feature map are both 64 pixels.

[0043] More specifically, since the size of the sample card machine feature map is 64×64, each row of grayscale values ​​in the sample card machine feature map is determined by the same standardized indicator data sequence, wherein the first 32 grayscale values ​​are respectively the 32 standardized TBM card machine prediction indicator data in the standardized indicator data sequence, and the last 32 grayscale values ​​are also respectively the 32 standardized TBM card machine prediction indicator data in the standardized indicator data sequence; the pixel values ​​of rows 1 to 4 of a sample card machine feature map are determined in sequence by four standardized indicator data sequences in a group of standardized indicator data sequences, and each subsequent four rows of grayscale values ​​are determined in sequence by four standardized indicator data sequences in the group of standardized indicator data sequences.

[0044] Furthermore, a set of standardized indicator data series includes a standardized shield pressure data series, a standardized cutterhead torque data series, a standardized penetration data series, and a standardized excavation speed data series.

[0045] S313. Obtain the sample card machine feature maps of all construction sites according to the card machine prediction data set, and then construct a card machine prediction image set.

[0046] Specifically, in this embodiment, sample card machine feature maps of all construction sites are obtained according to the card machine prediction data set, and then the sample card machine feature maps of all construction sites are used to construct a card machine prediction image set.

[0047] S32. Based on the card machine prediction image set, a sample supplement model is constructed using a self-attention generative adversarial neural network.

[0048] This embodiment uses a self-attention generative adversarial neural network to construct a sample supplement model, which improves the intelligence of sample supplementation and reduces the requirements for professional knowledge in sample supplementation when performing TBM card machine prediction. Step S32 specifically includes the following steps: S321, dividing the card machine prediction image set into a training set and a verification set to complete the training and verification of the self-attention generative adversarial neural network.

[0049] Specifically, in this embodiment, the card machine prediction image set is divided into a training set and a verification set in a ratio of 8:2, wherein the training set is used to complete the training of the self-attention generative adversarial neural network, and the verification set is used to complete the verification of the self-attention generative adversarial neural network.

[0050] More specifically, the self-attention generative adversarial neural network includes a generator and a discriminator. The task of the generator is to generate as realistic false images as possible from random noise, so that it is difficult for the discriminator to determine that these generated false images are generated rather than real. The discriminator takes false images and real images as input, and its task is to determine whether the input image comes from a real data set or an image generated by the generator. The generator and the discriminator improve their image generation quality and discrimination ability in a continuous zero-sum game. In the end, the discriminator cannot tell the difference between false images and real images, that is, the generator at this time can be so real that it can be used to generate false images. Therefore, the trained generator can be used to generate false images to supplement the real images in the real data set. Obviously, the real image in this embodiment is a sample card machine feature map.

[0051] S322. After completing the training and verification of the self-attention generative adversarial neural network, the generator in the self-attention generative adversarial neural network is used as the sample supplement model.

[0052] S33. Use the sample supplement model to supplement the card machine prediction data set to obtain a complete set of card machine prediction data.

[0053] This embodiment uses a sample supplement model to supplement the card machine prediction data set to obtain a complete card machine prediction data set, so as to obtain enough data for model training, thereby improving the accuracy of TBM card machine risk judgment. Step S33 specifically includes the following steps: S331. Generate multiple expansion card machine feature maps using the sample supplement model.

[0054] Specifically, in this embodiment, the random noise input to the sample supplement model is adjusted to generate a plurality of expansion card machine characteristic graphs.

[0055] S332. Determine a standardized indicator data expansion sequence according to the expansion card machine characteristic diagram.

[0056] Specifically, in this embodiment, according to the description of step S312, for an expansion card machine characteristic map, a set of standardized indicator data expansion sequences can be determined based on only the first 32 pixel values ​​of each row of its 1 to 4 rows of pixel values.

[0057] S333. Destandardize the standardized indicator data expansion sequence to obtain an indicator data expansion sequence.

[0058] Specifically, in this embodiment, the data in the standardized indicator data expansion sequence is standardized data and cannot be directly used as TBM card machine prediction indicator data to supplement the card machine prediction data set. Therefore, it is necessary to destandardize the data in the standardized indicator data expansion sequence to obtain the indicator data expansion sequence.

[0059] More specifically, the process of destandardizing the standardized indicator data expansion sequence is actually known To calculate For the sake of simplicity, the process will not be described in detail here.

[0060] S334. Supplement the indicator data expansion sequence to the card machine prediction data set to obtain the complete set of card machine prediction data.

[0061] S4. Construct an indicator prediction model based on the complete set of card machine prediction data, and use the indicator prediction model to obtain the TBM card machine prediction indicator prediction value.

[0062] This embodiment builds an index prediction model based on the complete set of card machine prediction data and the long short-term memory neural network, which can not only improve the accuracy of TBM card machine prediction, but also realize real-time prediction. Step S4 specifically includes the following steps: S41. Use a long short-term memory neural network to construct an initial indicator prediction model for each of the TBM card machine prediction indicators.

[0063] Specifically, in this embodiment, a long short-term memory neural network, i.e., LSTM, is used to construct initial indicator prediction models for various TBM jam prediction indicators, specifically, an initial prediction model for shield pressure, an initial prediction model for cutterhead torque, an initial prediction model for penetration, and an initial prediction model for excavation speed.

[0064] S42, dividing the complete card machine prediction data set into multiple complete data subsets according to the TBM card machine prediction indicator types.

[0065] Specifically, in this embodiment, since different TBM card machine prediction indicators correspond to an initial indicator prediction model respectively, it is necessary to divide the card machine prediction data complete set into multiple data complete subsets according to the type of TBM card machine prediction indicators, so as to train and verify each initial indicator prediction model.

[0066] More specifically, the data complete subsets of this embodiment include a shield pressure data complete subset, a cutterhead torque data complete subset, a penetration data complete subset and a tunneling speed data complete subset.

[0067] S43, using the data complete subset of the TBM card machine prediction indicators to train and verify the corresponding initial indicator prediction model to obtain the corresponding indicator prediction model.

[0068] Specifically, in this embodiment, the indicator prediction model includes a shield pressure prediction model, a cutterhead torque prediction model, a penetration prediction model, and a tunneling speed prediction model. The shield pressure initial prediction model is trained and verified using the complete subset of shield pressure data to obtain the shield pressure prediction model; the cutterhead torque data complete subset is used to train and verify the shield pressure initial prediction model to obtain the cutterhead torque prediction model; the penetration data complete subset is used to train and verify the shield pressure initial prediction model to obtain the penetration prediction model; the tunneling speed data complete subset is used to train and verify the shield pressure initial prediction model to obtain the tunneling speed prediction model.

[0069] S44. For any of the TBM machine jam prediction indicators, use the corresponding indicator prediction model to predict the TBM machine jam prediction indicator to obtain the corresponding TBM machine jam prediction indicator prediction value.

[0070] Specifically, in this embodiment, a corresponding indicator data sequence is obtained based on the data of each TBM stuck prediction indicator collected in the target construction site, and the obtained indicator data sequence is input into the corresponding indicator prediction model to predict the future TBM stuck prediction indicator data, so as to obtain the TBM stuck prediction indicator prediction value of each TBM stuck prediction indicator in the target construction site.

[0071] More specifically, for an indicator data sequence of a certain TBM stuck prediction indicator in a target construction site, the last data in the sequence is the current value of the TBM stuck prediction indicator.

[0072] S5. Calculate a jam index according to the predicted value of the TBM jam prediction index to determine the possibility of TBM jam.

[0073] Specifically, in this embodiment, the card machine index satisfies the following relationship: in, is the machine jam index, n is the number of TBM machine jam prediction indicators, is the risk impact weight of the i-th TBM jam prediction indicator, is the TBM jam prediction index prediction value of the i-th TBM jam prediction index, For about function.

[0074] More specifically, It was obtained using the expert evaluation method. It is a function that reflects the degree of change of TBM jam prediction index within a certain period of time. If we use , , and represents shield pressure, cutterhead torque, penetration and tunneling speed, then Specifically, the following relationship is satisfied: in, is the current value of the prediction index of the i-th TBM stuck at the target construction site, and t is the time interval described in step S21.

[0075] Furthermore, a card machine judgment threshold is set, and when the calculated card machine index is greater than the card machine judgment threshold, it is determined that a TBM card machine may occur. In this embodiment, the card machine judgment threshold is set to 0.5.

[0076] It should be noted that, in some cases, the actions described in the specification can be performed in a different order and still achieve the desired results. In this embodiment, the order of steps given is only to make the embodiment appear clearer and easier to explain, rather than to limit it.

[0077] In an alternative embodiment, see Figure 2 The present embodiment provides a TBM card machine risk quantitative prediction system, the TBM card machine risk quantitative prediction system comprises: a data acquisition device 1, a data output device 2, a processor 3 and a storage 4, the storage 4 comprises a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program comprises program instructions, and the program instructions, when executed by the processor 3, enable the processor 3 to implement a TBM card machine risk quantitative prediction method of the present embodiment. The TBM card machine risk quantitative prediction system provided in the present embodiment has a compact structure, stable operation, and is easy to install, and can be well combined with the method of the present invention, which makes the present invention more practical and commercially valuable.

[0078] Specifically, in the embodiment, the data acquisition device 1, the data output device 2, the processor 3 and the storage 4 are electrically connected to each other. The data acquisition device 1 includes sensing devices such as a pressure sensor, a torque sensor and a displacement sensor installed on the TBM, and also includes a first digital display screen for manually inputting TBM card machine prediction index data. The data output device 2 includes a second digital display screen, and can display the card machine index through the second digital display screen.

[0079] In summary, firstly, the method provided by the present invention uses a sample supplement model to supplement the card machine prediction data set to obtain a complete card machine prediction data set, which can improve the generalization performance of the indicator prediction model, and thus improve the accuracy of TBM card machine prediction; secondly, the sample supplement model and the indicator prediction model constructed by the method provided by the present invention are both based on machine learning algorithms, which can not only improve the accuracy and real-time performance of TBM card machine prediction, but also reduce the requirements of TBM card machine prediction for professional knowledge and improve the flexibility of prediction; finally, because the method provided by the present invention has low requirements for professional knowledge and small amount of calculation when predicting TBM card machines, it has a wider scope of application. In addition, the TBM card machine risk quantitative prediction system provided by the present invention is a system compatible with the method provided by the present invention, which has a compact structure, stable operation, easy installation, and can improve the practicality of the method provided by the present invention.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for quantitatively predicting the risk of a TBM jam, characterized in that: The steps include: Determine the TBM jam prediction index and then collect TBM jam prediction index data; Constructing a card machine prediction data set according to the TBM card machine prediction indicator data; Use a sample supplement model to supplement the card machine prediction data set to obtain a complete card machine prediction data set; Building an indicator prediction model based on the complete set of card machine prediction data, and using the indicator prediction model to obtain TBM card machine prediction indicator prediction values; The jam index is calculated based on the predicted value of the TBM jam prediction index to determine the possibility of TBM jam.

2. A method for quantitatively predicting the risk of a TBM jam according to claim 1, characterized in that: The TBM jam prediction indicators include shield pressure, cutter head torque, penetration and excavation speed.

3. The method for quantitatively predicting the risk of TBM jamming according to claim 1 is characterized in that: The step of constructing a card machine prediction data set according to the TBM card machine prediction index data comprises the following steps: Preprocessing the TBM jammer prediction index data, and then cutting the various TBM jammer prediction index data of each construction site into index data sequences of the same length; A card machine prediction dataset is constructed using all the indicator data series.

4. A method for quantitatively predicting the risk of TBM jamming according to claim 3, characterized in that: The method of using the sample supplement model to supplement the card machine prediction data set to obtain a complete card machine prediction data set includes the following steps: Acquire a sample card machine feature map according to the TBM card machine prediction index data, and then acquire a card machine prediction image set according to the card machine prediction data set; Based on the card machine prediction image set, a sample supplement model is constructed using a self-attention generative adversarial neural network; The sample supplement model is used to supplement the card machine prediction data set to obtain the complete card machine prediction data set.

5. A method for quantitatively predicting the risk of TBM jamming according to claim 4, characterized in that: The step of obtaining a sample card machine feature map according to the TBM card machine prediction index data, and then obtaining a card machine prediction image set according to the card machine prediction data set comprises the following steps: Standardizing the TBM card machine prediction index data in the index data sequence according to the value range of the image grayscale value to obtain a standardized index data sequence; For any construction site, obtain the corresponding sample card machine feature map, and any row of grayscale values ​​in the sample card machine feature map corresponds to a standardized indicator data sequence of the construction site; The sample card machine feature maps of all construction sites are obtained according to the card machine prediction data set, and then a card machine prediction image set is constructed.

6. A method for quantitatively predicting the risk of TBM jamming according to claim 4, characterized in that: The method of constructing a sample supplement model based on the card machine prediction image set using a self-attention generative adversarial neural network comprises the following steps: Dividing the card machine prediction image set into a training set and a verification set to complete the training and verification of the self-attention generative adversarial neural network; After completing the training and verification of the self-attention generative adversarial neural network, the generator in the self-attention generative adversarial neural network is used as the sample supplement model.

7. A method for quantitatively predicting the risk of a TBM jam according to claim 4, characterized in that: The method of using the sample supplement model to supplement the card machine prediction data set to obtain the complete card machine prediction data set comprises the following steps: Generate multiple expansion card machine feature maps using the sample supplement model; Determine a standardized index data expansion sequence according to the expansion card machine characteristic graph; Destandardizing the standardized indicator data expansion sequence to obtain an indicator data expansion sequence; The indicator data expansion sequence is supplemented to the card machine prediction data set to obtain the complete card machine prediction data set.

8. The method for quantitatively predicting the risk of TBM jamming according to claim 1 is characterized in that: The steps of constructing an indicator prediction model based on the complete set of card machine prediction data and using the indicator prediction model to obtain the TBM card machine prediction indicator prediction value include the following steps: Using a long short-term memory neural network to construct an initial indicator prediction model for each of the TBM card machine prediction indicators; Dividing the complete card machine prediction data set into a plurality of complete data subsets according to the TBM card machine prediction indicator types; Using the data complete subset of the TBM card machine prediction index to train and verify the corresponding initial index prediction model to obtain the corresponding index prediction model; For any of the TBM machine jam prediction indicators, the corresponding indicator prediction model is used to predict the TBM machine jam prediction indicator to obtain the corresponding TBM machine jam prediction indicator prediction value.

9. The method for quantitatively predicting the risk of TBM jamming according to claim 1, characterized in that: The card machine index satisfies the following relationship: ， in, is the card machine index, n is the number of TBM card machine prediction indicators, is the risk impact weight of the TBM jam prediction indicator of the ith TBM jam, is the TBM jam prediction index prediction value of the i-th TBM jam prediction index, About function.

10. A TBM machine jam risk quantitative prediction system, characterized in that: The TBM card machine risk quantitative prediction system described herein comprises: a data acquisition device, a data output device, a processor and a storage device, wherein the storage device comprises a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program comprises program instructions, and when the program instructions are executed by the processor, the processor implements a TBM card machine risk quantitative prediction method as described in any one of claims 1 to 9.

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