Method and device for identifying the risk of TBM jamming

Abstract

The present invention relates to the technical field of TBM tunnel excavation, and in particular to a method and device for identifying the risk of a TBM stuck. The method comprises: obtaining the risk level of a physical-driven stuck machine based on a physical-driven stuck machine identification model; obtaining the risk level of an information-driven stuck machine based on an information-driven stuck machine identification model; integrating and fusing the risk level of a physical-driven stuck machine and the risk level of an information-driven stuck machine to construct a physical-information dual-driven TBM stuck machine risk advance identification model, thereby identifying a target stuck machine risk tunnel section and a target normal surrounding rock tunnel section of a target tunnel section, and then generating an excavation method that meets preset safety and high efficiency requirements for the target stuck machine risk tunnel section and the target normal surrounding rock tunnel section according to TBM excavation parameters. Thus, the poor adaptability of a pure data-driven model in the related technology and the low prediction accuracy of a pure physical model, which reduces the efficiency and safety of tunnel excavation, are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of TBM tunnel excavation, and in particular to a method and device for identifying the risk of a TBM (tunnel boring machine) getting stuck. Background Art

[0002] In recent years, as my country's infrastructure construction continues to develop towards the southwest and northwest regions, a large number of highway, railway and water conservancy tunnels (tunnels) are being planned or under construction. Among them, a considerable number are deep-buried and long tunnels (tunnels). Considering the principles of construction efficiency and economy, TBM (Tunnel Boring Machine) construction has become the preferred construction method for these tunnels (tunnels).

[0003] At present, in the process of TBM construction of deep buried long tunnels (holes), it is difficult to implement effective face sketching work because the surrounding rock in front of the face is blocked by the cutterhead; at the same time, due to the influence of factors such as the narrow working space in the TBM tunnel (hole), complex electromagnetic interference and short operating time window, the relevant geophysical exploration equipment is difficult to achieve its ideal detection accuracy; the main driver mainly determines the values ​​of excavation parameters based on his experience during the driving of the TBM, which is often poorly matched with the geological conditions and has a great interference with the formation.

[0004] Therefore, the pure data-driven model in the relevant technology has poor adaptability, and the pure physical model has low prediction accuracy, making it difficult to accurately judge the risk of machine jamming in the surrounding rock ahead, increasing the risk of machine jamming accidents caused by excessive interference of TBM with the stratum during excavation, thereby causing serious economic losses and reducing the efficiency and safety of tunnel excavation, which needs to be solved urgently. Summary of the invention

[0005] The present invention provides a method and device for identifying the risk of a tunnel boring machine (TBM) getting stuck, so as to solve the problems in the related technology that a simple data-driven model has poor adaptability and a simple physical model has low prediction accuracy, making it difficult to accurately judge the risk of a tunnel boring machine getting stuck in the surrounding rock ahead, increasing the risk of a tunnel boring machine getting stuck due to excessive interference of the TBM with the stratum during the tunneling process, thereby causing serious economic losses and reducing the efficiency and safety of tunneling.

[0006] A first aspect of the present invention provides a method for identifying the risk of a tunnel boring machine (TBM) getting stuck, comprising the following steps: obtaining a physical drive getting stuck risk level based on a pre-constructed physical drive getting stuck identification model that meets a first preset condition; obtaining an information drive getting stuck risk level based on a pre-constructed information drive getting stuck identification model that meets a second preset condition; integrating and fusing the physical drive getting stuck risk level and the information drive getting stuck risk level to construct a physical-information dual-drive tunnel boring machine (TBM) getting stuck risk advance identification model; identifying a target getting stuck risk tunnel section and a target normal surrounding rock tunnel section of a target tunnel section based on the physical-information dual-drive TBM getting stuck risk advance identification model, and generating excavation methods that meet preset safety and efficiency requirements for the target getting stuck risk tunnel section and the target normal surrounding rock tunnel section according to TBM excavation parameters.

[0007] Optionally, in one embodiment of the present invention, the physical drive jamming risk level is obtained based on a pre-constructed physical drive jamming identification model that meets the first preset condition, including: constructing a TBM excavation parameter-surrounding rock parameter mapping model, and determining a rock-machine parameter dynamic interaction mechanism based on the TBM excavation parameter-surrounding rock parameter mapping model; inverting current surrounding rock parameters based on the TBM excavation parameter-surrounding rock parameter mapping model, the rock-machine parameter dynamic interaction mechanism and the TBM excavation parameters, and obtaining the current surrounding rock grade based on the current surrounding rock parameters and the target TBM tunnel surrounding rock grading method; obtaining a target safety threshold for each surrounding rock grade, and using the target safety threshold to construct a constraint equation for the TBM surrounding rock disturbance limit, so as to construct the physical drive jamming identification model using the constraint equation, and based on the physical drive jamming identification model, obtain the physical drive jamming risk level.

[0008] Optionally, in one embodiment of the present invention, the information-driven jamming machine identification model based on the pre-constructed information-driven jamming machine identification model that meets the second preset condition is used to obtain the information-driven jamming machine risk level, including: constructing a TBM excavation multi-source information database, and obtaining TBM jamming machine precursor feature information; based on the TBM jamming machine precursor feature information, determining a target jamming machine identification criterion corresponding to the data information of the TBM excavation multi-source information database; using the target jamming machine identification criterion to obtain a TBM jamming machine advance identification criterion, so as to generate the information-driven jamming machine identification model according to the TBM jamming machine advance identification criterion, and based on the information-driven jamming machine identification model, obtaining the information-driven jamming machine risk level.

[0009] Optionally, in one embodiment of the present invention, the TBM excavation multi-source information database includes at least one of the excavation parameter information, surrounding rock parameter information, geophysical parameter information, advance drilling parameter information, slag image information, and cutterhead vibration information generated during the TBM excavation process.

[0010] Optionally, in one embodiment of the present invention, the excavation methods that meet preset safety and efficiency requirements are generated for the target machine-stuck risk tunnel section and the target normal surrounding rock tunnel section respectively according to the TBM excavation parameters, including: determining the target safe excavation interval of the target machine-stuck risk tunnel section according to the surrounding rock disturbance constraint of the TBM excavation parameters, and determining the TBM safe excavation method for the target machine-stuck risk tunnel section using the target safe excavation interval; obtaining the target efficient excavation interval of the target normal surrounding rock tunnel section according to the multi-objective optimization decision result of the TBM excavation parameters, and determining the TBM efficient excavation method for the target normal surrounding rock tunnel section according to the target efficient excavation interval.

[0011] The second aspect of the present invention provides a device for identifying the risk of a tunnel boring machine (TBM) getting stuck, comprising: a first acquisition module, for acquiring the risk level of a physical drive getting stuck based on a pre-constructed physical drive getting stuck identification model that meets a first preset condition; a second acquisition module, for acquiring the risk level of an information drive getting stuck based on a pre-constructed information drive getting stuck identification model that meets a second preset condition; a construction module, for integrating and fusing the physical drive getting stuck risk level and the information drive getting stuck risk level to construct a physical-information dual-drive tunnel boring machine (TBM) getting stuck risk advance identification model; a control module, for identifying a target getting stuck risk tunnel section and a target normal surrounding rock tunnel section of a target tunnel section based on the physical-information dual-drive TBM getting stuck risk advance identification model, and generating excavation methods that meet preset safety and efficiency requirements for the target getting stuck risk tunnel section and the target normal surrounding rock tunnel section according to TBM excavation parameters.

[0012] Optionally, in one embodiment of the present invention, the first acquisition module includes: a first determination unit, used to construct a TBM excavation parameter-surrounding rock parameter mapping model, and determine the rock-machine parameter dynamic interaction mechanism according to the TBM excavation parameter-surrounding rock parameter mapping model; a first acquisition unit, used to invert the current surrounding rock parameters based on the TBM excavation parameter-surrounding rock parameter mapping model, the rock-machine parameter dynamic interaction mechanism and the TBM excavation parameters, and obtain the current surrounding rock grade according to the current surrounding rock parameters and the target TBM tunnel surrounding rock grading method; a second acquisition unit, used to obtain the target safety threshold of each surrounding rock grade, and use the target safety threshold to construct a constraint equation of the TBM surrounding rock disturbance limit, so as to use the constraint equation to construct the physical drive stuck machine identification model, and obtain the physical drive stuck machine risk level based on the physical drive stuck machine identification model.

[0013] Optionally, in one embodiment of the present invention, the second acquisition module includes: a second determination unit, used to construct a TBM excavation multi-source information database and obtain TBM stuck precursor feature information; a third determination unit, used to determine the target stuck machine discrimination criterion corresponding to the data information of the TBM excavation multi-source information database based on the TBM stuck precursor feature information; a third acquisition unit, used to obtain the TBM stuck machine advance identification criterion using the target stuck machine discrimination criterion, so as to generate the information-driven stuck machine identification model according to the TBM stuck machine advance identification criterion, and obtain the information-driven stuck machine risk level based on the information-driven stuck machine identification model.

[0014] Optionally, in one embodiment of the present invention, the TBM excavation multi-source information database includes at least one of the excavation parameter information, surrounding rock parameter information, geophysical parameter information, advance drilling parameter information, slag image information, and cutterhead vibration information generated during the TBM excavation process.

[0015] Optionally, in one embodiment of the present invention, the control module includes: a fourth determination unit, used to determine the target safe excavation interval of the target machine-stuck risk tunnel section according to the surrounding rock disturbance constraints of the TBM excavation parameters, so as to determine the TBM safe excavation mode of the target machine-stuck risk tunnel section using the target safe excavation interval; a fifth determination unit, used to obtain the target efficient excavation interval of the target normal surrounding rock tunnel section according to the multi-objective optimization decision results of the TBM excavation parameters, so as to determine the TBM efficient excavation mode of the target normal surrounding rock tunnel section according to the target efficient excavation interval.

[0016] The third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for identifying the risk of a tunnel boring machine (TBM) getting stuck as described in the above embodiment.

[0017] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for identifying the risk of a tunnel boring machine (TBM) getting stuck.

[0018] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for identifying the risk of a tunnel boring machine (TBM) getting stuck.

[0019] The embodiment of the present invention can obtain the physical drive jam risk level according to the physical drive jam identification model, and obtain the information drive jam risk level according to the information drive jam identification model, so as to integrate and fuse the physical drive jam risk level and the information drive jam risk level, so as to construct a physical-information dual-driven tunnel boring machine TBM jam risk advance identification model, and then identify the target jam risk tunnel section and the target normal surrounding rock tunnel section of the target tunnel section, and then generate the excavation mode that meets the preset safety and efficiency requirements for the target jam risk tunnel section and the target normal surrounding rock tunnel section according to the TBM excavation parameters, effectively improve the accuracy of the prediction of the front surrounding rock jam risk, and improve the efficiency and safety of tunnel construction. Therefore, the poor adaptability of the pure data-driven model in the related technology and the low prediction accuracy of the pure physical model are solved, which makes it difficult to accurately judge the risk of the front surrounding rock jam, increase the jam accidents caused by the excessive interference of the TBM to the stratum during the excavation process, thereby causing serious economic losses and reducing the efficiency and safety of tunnel excavation.

[0020] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A schematic diagram of the structure of a system for identifying the risk of a tunnel boring machine (TBM) jamming provided according to an embodiment of the present invention;

[0023] Figure 2 A flowchart of a method for identifying a risk of a tunnel boring machine (TBM) jamming provided according to an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of the structure of a device for identifying the risk of a tunnel boring machine (TBM) jamming provided according to an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention.

[0026] Among them, 10 is a device for identifying the risk of a tunnel boring machine (TBM) getting stuck; 100 is a first acquisition module, 200 is a second acquisition module, 300 is a construction module and 400 is a control module; 401 is a memory, 402 is a processor and 403 is a communication interface. DETAILED DESCRIPTION

[0027] 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 throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0028] The following describes, with reference to the accompanying drawings, a method, device, equipment and medium for identifying the risk of a tunnel boring machine (TBM) getting stuck according to an embodiment of the present invention. Aiming at the problems that the simple data-driven model in the related technology mentioned in the above background technology has poor adaptability and the simple physical model has low prediction accuracy, it is difficult to accurately judge the risk of machine jamming in the surrounding rock ahead, and the machine jamming accidents caused by excessive interference of TBM to the stratum during the excavation process are increased, thereby causing serious economic losses and reducing the efficiency and safety of tunnel excavation. The present invention provides a method for identifying the risk of machine jamming of a tunnel boring machine (TBM). In the method, the physical drive machine jamming risk level can be obtained according to the physical drive machine jamming identification model, and the information drive machine jamming risk level can be obtained according to the information drive machine jamming identification model, so that the physical drive machine jamming risk level and the information drive machine jamming risk level are integrated and fused to construct a physical-information dual-driven tunnel boring machine (TBM) machine jamming risk advance identification model, and then the target machine jamming risk tunnel section and the target normal surrounding rock tunnel section of the target tunnel section are identified, and then the excavation mode that meets the preset safety and efficiency requirements is generated for the target machine jamming risk tunnel section and the target normal surrounding rock tunnel section according to the TBM excavation parameters, so as to effectively improve the accuracy of the prediction of the risk of machine jamming in the surrounding rock ahead, and improve the efficiency and safety of tunnel construction. This solves the problems of poor adaptability of pure data-driven models in related technologies and low prediction accuracy of pure physical models, which make it difficult to accurately judge the risk of machine jamming in the surrounding rock ahead, increase the risk of machine jamming accidents caused by excessive interference of TBM with the stratum during excavation, thus causing serious economic losses and reducing the efficiency and safety of tunnel excavation.

[0029] In the embodiment of the present invention, a system for identifying the risk of a tunnel boring machine (TBM) jam is established. Figure 1 As shown, the tunneling system consists of a physical drive module, an information drive module, an integrated fusion module, a safe tunneling module and an efficient tunneling module.

[0030] Among them, the physical drive module is used to form a physical drive jam identification model to obtain the physical drive jam risk level; the information drive module is used to form an information drive jam identification model to obtain the information drive jam risk level; the integrated fusion module is used to integrate the physical drive jam risk level and the information drive jam risk level using the Stacking algorithm to form a physical-information dual-driven TBM jam risk advance identification model; the safe excavation module is used to determine the target value range of the TBM excavation parameters based on the jam risk tunnel section identified by the physical-information dual-driven TBM jam risk advance identification model according to the surrounding rock disturbance constraints of the TBM excavation parameters, so as to achieve safe TBM excavation in the jam risk section; the efficient excavation module is used to achieve the target surrounding rock tunnel section identified by the dual-drive model according to the multi-objective optimization decision-making results of the TBM excavation parameters; therefore, the excavation system can also be called a TBM jam risk advance identification and safe and efficient excavation system based on physical-information dual-driven, and the present invention will be described in detail by taking the excavation system as an example.

[0031] Specifically, Figure 2 A schematic flow chart of a method for identifying the risk of a tunnel boring machine (TBM) getting stuck provided in an embodiment of the present invention.

[0032] like Figure 2 As shown, the method for identifying the risk of a tunnel boring machine (TBM) jamming includes the following steps:

[0033] In step S201, based on a pre-built physical driver jamming machine identification model that meets a first preset condition, a physical driver jamming machine risk level is obtained.

[0034] In the embodiment of the present invention, the first preset condition is a condition for constructing a physical drive card machine identification model.

[0035] It can be understood that the embodiment of the present invention can be based on the pre-constructed physical drive stuck machine identification model that meets the first preset condition in the following steps. For example, the embodiment of the present invention can use the physical drive module in the above-mentioned tunneling system to form a physical drive stuck machine identification model that meets certain conditions to obtain the physical drive stuck machine risk level, thereby effectively improving the accuracy of TBM stuck machine risk prediction.

[0036] Among them, in one embodiment of the present invention, based on a pre-constructed physical drive stuck identification model that meets the first preset condition, the physical drive stuck risk level is obtained, including: constructing a TBM excavation parameter-surrounding rock parameter mapping model, and determining the rock-machine parameter dynamic interaction mechanism according to the TBM excavation parameter-surrounding rock parameter mapping model; inverting the current surrounding rock parameters based on the TBM excavation parameter-surrounding rock parameter mapping model, the rock-machine parameter dynamic interaction mechanism and the TBM excavation parameter, and obtaining the current surrounding rock grade according to the current surrounding rock parameters and the target TBM tunnel surrounding rock grading method; obtaining the target safety threshold of each surrounding rock grade, and constructing the constraint equation of the TBM surrounding rock disturbance limit using the target safety threshold, so as to construct the physical drive stuck identification model using the constraint equation, and obtaining the physical drive stuck risk level based on the physical drive stuck identification model.

[0037] In the actual implementation process, Figure 1 As shown, the embodiment of the present invention can perform a force analysis on the interaction relationship between the TBM cutter disc and the surrounding rock according to the physical drive module in the above-mentioned tunneling system, and collect on-site surrounding rock physical and mechanical parameters and TBM tunneling parameters through on-site tests, wherein the surrounding rock physical and mechanical parameters may include uniaxial compressive strength, tensile strength, rock integrity coefficient, joint inclination, and degree of joint fissure development, thereby constructing a mapping model between TBM tunneling parameters and surrounding rock parameters to reveal the dynamic interaction mechanism of rock-machine parameters.

[0038] Next, the embodiment of the present invention can invert the current surrounding rock parameters through the TBM excavation parameter-surrounding rock parameter mapping model, the rock-machine parameter dynamic interaction mechanism and the TBM real-time excavation parameter inversion, and obtain the current surrounding rock grade according to the current surrounding rock parameters and the pre-set TBM tunnel surrounding rock classification method, wherein the TBM tunnel surrounding rock classification method is specifically set by those skilled in the art and is not specifically limited here.

[0039] Secondly, the embodiment of the present invention can collect excavation data corresponding to different surrounding rock grades of TBM excavation, extract excavation parameters and surrounding rock parameters of different degrees of jamming, such as no jamming, slight jamming, moderate jamming, and severe jamming, and obtain the safety thresholds of TBM excavation parameters corresponding to different surrounding rock grades relative to the current surrounding rock parameters, so as to construct the TBM surrounding rock disturbance limit constraint equation using the safety threshold, wherein the safety threshold is specifically set by technical personnel in this field and is not specifically limited here, so that the constraint equation can be used to construct a physical drive jam identification model, and based on the physical drive jam identification model, the physical drive jam risk level can be obtained, thereby effectively improving the accuracy of TBM jam risk prediction.

[0040] In step S202, based on a pre-constructed information-driven card machine identification model that meets a second preset condition, a risk level of the information-driven card machine is obtained.

[0041] In the embodiment of the present invention, the second preset condition is a condition for constructing an information-driven card machine identification model.

[0042] It can be understood that the embodiment of the present invention can be based on the information-driven jamming machine identification model that meets the second preset condition pre-constructed in the following steps. For example, the embodiment of the present invention can use the information-driven jamming machine identification model in the above-mentioned excavation system to form an information-driven jamming machine identification model that meets certain conditions to obtain the information-driven jamming machine risk level, which effectively improves the accuracy of TBM jamming machine risk prediction and improves the adaptability of tunnel excavation.

[0043] Among them, in one embodiment of the present invention, based on a pre-constructed information-driven stuck machine identification model that meets the second preset condition, obtaining the information-driven stuck machine risk level includes: constructing a TBM excavation multi-source information database, and obtaining TBM stuck machine precursor feature information; based on the TBM stuck machine precursor feature information, determining the target stuck machine identification criterion corresponding to the data information of the TBM excavation multi-source information database; using the target stuck machine identification criterion to obtain the TBM stuck machine advance identification criterion, so as to generate an information-driven stuck machine identification model according to the TBM stuck machine advance identification criterion, and based on the information-driven stuck machine identification model, obtaining the information-driven stuck machine risk level.

[0044] As a possible way to achieve this, Figure 1 As shown, the embodiment of the present invention can utilize the information driving module in the above-mentioned tunneling system to construct a TBM tunneling multi-source information database, wherein the TBM tunneling multi-source information database may include at least one of the tunneling parameter information, surrounding rock parameter information, geophysical parameter information, advance drilling parameter information, slag image information, and cutterhead vibration information generated during the TBM tunneling process, thereby effectively improving the comprehensiveness of the TBM tunneling information.

[0045] Next, the embodiment of the present invention can collect multi-source information of the TBM in the normal excavation section, the section near a stuck machine, the stuck section, and after passing through the stuck section, and analyze the changing trend of the multi-source information of the whole process of TBM stuck compared with the multi-source information of the normal excavation section, so as to obtain the precursor feature information of TBM stuck and effectively improve the accuracy of TBM stuck prediction.

[0046] Secondly, the embodiment of the present invention can summarize and extract the jamming identification criteria corresponding to the excavation parameter information, surrounding rock parameter information, geophysical parameter information, advance drilling parameter information, slag image information, and cutterhead vibration information in the TBM excavation multi-source information database according to the TBM jam precursor feature information, thereby forming a TBM jam advance identification criterion, which effectively improves the accuracy of TBM jam advance identification.

[0047] In step S203, the physical drive jam risk level and the information drive jam risk level are integrated and fused to construct a physical-information dual-drive tunnel boring machine (TBM) jam risk advance identification model.

[0048] It is understandable that the embodiment of the present invention can integrate and merge the risk level of the physical drive card machine and the risk level of the information drive card machine, for example, Figure 1 As shown, the embodiment of the present invention can utilize the integrated fusion module in the above-mentioned tunneling system to integrate the physical drive jam risk level and the information drive jam risk level through the Stacking algorithm to construct a physical-information dual-driven TBM jam risk advance identification model to achieve adaptability to different TBM tunnel working conditions and effectively improve the prediction accuracy of the model.

[0049] In step S204, based on the physical-information dual-driven TBM stuck risk advance identification model, the target stuck risk tunnel section and the target normal surrounding rock tunnel section of the target tunnel section are identified, and according to the TBM excavation parameters, excavation methods that meet the preset safety and efficiency requirements are generated for the target stuck risk tunnel section and the target normal surrounding rock tunnel section respectively.

[0050] In the embodiment of the present invention, the excavation method with preset safety and high efficiency requirements is a safe excavation method for tunnel sections with machine jam risk and an efficient excavation method for tunnel sections with normal surrounding rock.

[0051] It can be understood that the embodiment of the present invention can identify the target tunnel section with stuck risk and the target tunnel section with normal surrounding rock based on the physical-information dual-driven TBM stuck risk advance identification model. For example, the physical-information dual-driven TBM stuck risk advance identification model in the embodiment of the present invention can identify the tunnel section with stuck risk and the tunnel section with normal surrounding rock of the current tunnel section by inputting the current TBM excavation multi-source information, and generate excavation methods that meet the preset safety and efficiency requirements for the target tunnel section with stuck risk and the target tunnel section with normal surrounding rock according to the TBM excavation parameters in the following steps, thereby effectively improving the accuracy of the prediction of the surrounding rock stuck risk ahead and improving the efficiency and safety of tunnel construction.

[0052] Optionally, in one embodiment of the present invention, excavation methods that meet preset safety and efficiency requirements are generated for the target tunnel section with stuck machine risk and the target tunnel section with normal surrounding rock according to the TBM excavation parameters, including: determining the target safe excavation interval of the target tunnel section with stuck machine risk according to the surrounding rock disturbance constraint of the TBM excavation parameters, so as to determine the TBM safe excavation method for the target tunnel section with stuck machine risk by using the target safe excavation interval; obtaining the target efficient excavation interval of the target normal surrounding rock tunnel section according to the multi-objective optimization decision result of the TBM excavation parameters, so as to determine the TBM efficient excavation method for the target normal surrounding rock tunnel section according to the target efficient excavation interval.

[0053] For example, Figure 1 As shown, the safe excavation module in the above-mentioned excavation system can determine the jam risk tunnel section of the current tunnel section based on the dual-drive TBM jam risk advance identification model, and determine the jam risk level of the current tunnel section, and determine the TBM safe excavation parameter range of the current jam risk tunnel section in combination with the surrounding rock level of the current tunnel section and the TBM surrounding rock disturbance limit constraint equation, thereby determining the TBM safe excavation method of the jam risk tunnel section to achieve TBM safe excavation of the current jam risk tunnel section.

[0054] For example, Figure 1 As shown, the efficient excavation module in the above-mentioned excavation system can be based on the normal surrounding rock tunnel section of the current tunnel section identified by the dual-drive TBM jammer risk advance identification model, with the highest TBM excavation efficiency, the lowest excavation specific energy and the highest utilization rate as the target optimization function, and the NSGA-III (Non-dominated Sorting Genetic Algorithms-III) multi-objective optimization method is used to optimize the excavation parameters of thrust, speed, torque and penetration, so as to determine the optimal excavation parameter range of the normal surrounding rock tunnel section according to the multi-objective optimization decision results of the TBM excavation parameters, that is, the TBM efficient excavation parameter range, so as to determine the TBM efficient excavation method of the normal surrounding rock tunnel section, so as to achieve TBM efficient excavation of the normal surrounding rock tunnel section.

[0055] Therefore, the embodiment of the present invention can divide the current tunnel section into a tunnel section with a risk of a TBM getting stuck and a tunnel section with normal surrounding rock through a physical-information dual-driven TBM stuck risk advance identification model, and determine the safe excavation parameter range for the tunnel section with a risk of a TBM getting stuck, thereby reducing the disturbance of the TBM excavation to the stratum; for the tunnel section with a normal surrounding rock, a multi-objective optimization strategy is used to obtain the optimal TBM excavation parameters, thereby improving the TBM excavation efficiency, and ultimately achieving safe and efficient excavation of the TBM through complex strata.

[0056] According to the identification method of the risk of a TBM stuck in a tunnel, the physical drive stuck risk level can be obtained according to the physical drive stuck identification model, and the information drive stuck risk level can be obtained according to the information drive stuck identification model, so as to integrate and fuse the physical drive stuck risk level and the information drive stuck risk level, so as to construct a physical-information dual-driven TBM stuck risk advance identification model, and then identify the target stuck risk tunnel section and the target normal surrounding rock tunnel section of the target tunnel section, and then generate a tunneling method that meets the preset safety and efficiency requirements for the target stuck risk tunnel section and the target normal surrounding rock tunnel section according to the TBM tunneling parameters, effectively improving the accuracy of the prediction of the risk of the surrounding rock stuck in the front, and improving the efficiency and safety of tunnel construction. Therefore, the poor adaptability of the pure data-driven model in the related technology and the low prediction accuracy of the pure physical model are solved, which makes it difficult to accurately judge the risk of the surrounding rock stuck in the front, and increases the jamming accidents caused by the excessive interference of the TBM to the stratum during the tunneling process, thereby causing serious economic losses and reducing the efficiency and safety of tunneling.

[0057] Next, a device for identifying the risk of a tunnel boring machine (TBM) getting stuck according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0058] Figure 3 It is a block diagram of a device for identifying the risk of a tunnel boring machine (TBM) jamming according to an embodiment of the present invention.

[0059] like Figure 3 As shown, the device 10 for identifying the risk of a tunnel boring machine (TBM) getting stuck includes: a first acquisition module 100 , a second acquisition module 200 , a construction module 300 and a control module 400 .

[0060] Specifically, the first acquisition module 100 is used to acquire the risk level of a physical drive jam machine based on a pre-built physical drive jam machine identification model that meets a first preset condition.

[0061] The second acquisition module 200 is used to acquire the risk level of the information-driven card machine based on a pre-built information-driven card machine identification model that meets the second preset condition.

[0062] The construction module 300 is used to integrate and fuse the physical drive jam risk level and the information drive jam risk level to construct a physical-information dual-drive tunnel boring machine (TBM) jam risk advance identification model.

[0063] The control module 400 is used to identify the target tunnel section with a risk of TBM jamming and the target tunnel section with normal surrounding rock based on the physical-information dual-driven TBM jamming risk advance identification model, and generate excavation methods that meet the preset safety and efficiency requirements for the target tunnel section with a risk of TBM jamming and the target tunnel section with normal surrounding rock according to the TBM excavation parameters.

[0064] Optionally, in one embodiment of the present invention, the first acquisition module 100 includes: a first determination unit, a first acquisition unit and a second acquisition unit.

[0065] Among them, the first determination unit is used to construct a TBM excavation parameter-surrounding rock parameter mapping model, and determine the rock-machine parameter dynamic interaction mechanism according to the TBM excavation parameter-surrounding rock parameter mapping model.

[0066] The first acquisition unit is used to invert the current surrounding rock parameters based on the TBM excavation parameter-surrounding rock parameter mapping model, the rock-machine parameter dynamic interaction mechanism and the TBM excavation parameter, and obtain the current surrounding rock grade according to the current surrounding rock parameters and the target TBM tunnel surrounding rock classification method.

[0067] The second acquisition unit is used to obtain the target safety threshold of each surrounding rock level, and use the target safety threshold to construct the constraint equation of the TBM surrounding rock disturbance limit, so as to use the constraint equation to construct a physical drive jam identification model, and based on the physical drive jam identification model, obtain the physical drive jam risk level.

[0068] Optionally, in one embodiment of the present invention, the second acquisition module 200 includes: a second determination unit, a third determination unit and a third acquisition unit.

[0069] Among them, the second determination unit is used to build a multi-source information database for TBM excavation and obtain precursor feature information of TBM jam.

[0070] The third determination unit is used to determine the target jamming machine discrimination criterion corresponding to the data information of the TBM excavation multi-source information database based on the TBM jamming machine precursor feature information.

[0071] The third acquisition unit is used to obtain the TBM card machine advance identification criterion by using the target card machine identification criterion, so as to generate an information-driven card machine identification model according to the TBM card machine advance identification criterion, and obtain the information-driven card machine risk level based on the information-driven card machine identification model.

[0072] Optionally, in one embodiment of the present invention, the TBM excavation multi-source information database includes at least one of the excavation parameter information, surrounding rock parameter information, geophysical parameter information, advance drilling parameter information, slag image information, and cutterhead vibration information generated during the TBM excavation process.

[0073] Optionally, in one embodiment of the present invention, the control module 400 includes: a fourth determining unit and a fifth determining unit.

[0074] Among them, the fourth determination unit is used to determine the target safe excavation interval of the target tunnel section with machine jam risk according to the surrounding rock disturbance constraint of the TBM excavation parameters, so as to determine the TBM safe excavation mode of the target tunnel section with machine jam risk by using the target safe excavation interval.

[0075] The fifth determination unit is used to obtain the target efficient excavation interval of the target normal surrounding rock tunnel section according to the multi-objective optimization decision result of the TBM excavation parameters, so as to determine the TBM efficient excavation mode of the target normal surrounding rock tunnel section according to the target efficient excavation interval.

[0076] It should be noted that the aforementioned explanation of the embodiment of the method for identifying the risk of a tunnel boring machine (TBM) getting stuck is also applicable to the device for identifying the risk of a tunnel boring machine (TBM) getting stuck in this embodiment, and will not be repeated here.

[0077] According to the identification device of the risk of a TBM stuck in a tunnel, the physical drive stuck risk level can be obtained according to the physical drive stuck identification model, and the information drive stuck risk level can be obtained according to the information drive stuck identification model, so as to integrate and fuse the physical drive stuck risk level and the information drive stuck risk level, so as to construct a physical-information dual-driven TBM stuck risk advance identification model, and then identify the target stuck risk tunnel section and the target normal surrounding rock tunnel section of the target tunnel section, and then generate the excavation mode that meets the preset safety and efficiency requirements for the target stuck risk tunnel section and the target normal surrounding rock tunnel section according to the TBM excavation parameters, effectively improving the accuracy of the prediction of the risk of the surrounding rock stuck in the front, and improving the efficiency and safety of tunnel construction. Therefore, the problems of poor adaptability of the pure data driven model in the related technology and low prediction accuracy of the pure physical model are solved, which make it difficult to accurately judge the risk of the surrounding rock stuck in the front, increase the jamming accidents caused by the excessive interference of the TBM to the stratum during the excavation process, thereby causing serious economic losses and reducing the efficiency and safety of tunnel excavation.

[0078] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0079] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0080] When the processor 402 executes the program, the method for identifying the risk of a tunnel boring machine (TBM) jam provided in the above embodiment is implemented.

[0081] Furthermore, the electronic device further comprises:

[0082] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0083] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0084] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0085] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0086] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0087] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0088] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for identifying the risk of a tunnel boring machine (TBM) getting stuck.

[0089] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for identifying the risk of a tunnel boring machine (TBM) getting stuck.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0091] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0092] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0094] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0095] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0096] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0097] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for identifying the risk of a tunnel boring machine (TBM) jam, characterized in that: The following steps are involved: Based on a pre-constructed physical drive jam identification model that meets the first preset condition, the physical drive jam risk level is obtained, wherein the physical drive jam risk level based on the pre-constructed physical drive jam identification model that meets the first preset condition includes: constructing a TBM excavation parameter-surrounding rock parameter mapping model, and determining a rock-machine parameter dynamic interaction mechanism according to the TBM excavation parameter-surrounding rock parameter mapping model; inverting current surrounding rock parameters based on the TBM excavation parameter-surrounding rock parameter mapping model, the rock-machine parameter dynamic interaction mechanism and the TBM excavation parameter, and obtaining the current surrounding rock grade according to the current surrounding rock parameters and the target TBM tunnel surrounding rock classification method; obtaining a target safety threshold for each surrounding rock grade, and using the target safety threshold to construct a constraint equation for the TBM surrounding rock disturbance limit, so as to construct the physical drive jam identification model using the constraint equation, and based on the physical drive jam identification model, obtaining the physical drive jam risk level; Based on a pre-constructed information-driven stuck machine identification model that meets the second preset condition, the information-driven stuck machine risk level is obtained, wherein the information-driven stuck machine risk level is obtained based on the pre-constructed information-driven stuck machine identification model that meets the second preset condition, including: constructing a TBM excavation multi-source information database, and obtaining TBM stuck machine precursor feature information; based on the TBM stuck machine precursor feature information, determining a target stuck machine identification criterion corresponding to the data information of the TBM excavation multi-source information database; using the target stuck machine identification criterion to obtain a TBM stuck machine advance identification criterion, so as to generate the information-driven stuck machine identification model according to the TBM stuck machine advance identification criterion, and based on the information-driven stuck machine identification model, obtaining the information-driven stuck machine risk level; The physical drive jam risk level and the information drive jam risk level are integrated and fused to construct a physical-information dual-drive tunnel boring machine (TBM) jam risk advance identification model; Based on the physical-information dual-driven TBM stuck risk advance identification model, the target stuck risk tunnel section and the target normal surrounding rock tunnel section of the target tunnel section are identified, and according to the TBM excavation parameters, excavation methods that meet the preset safety and high efficiency requirements are generated for the target stuck risk tunnel section and the target normal surrounding rock tunnel section respectively.

2. The method for identifying the risk of a tunnel boring machine (TBM) jam according to claim 1, characterized in that: The TBM excavation multi-source information database includes at least one of the excavation parameter information, surrounding rock parameter information, geophysical exploration parameter information, advance drilling parameter information, slag image information, and cutterhead vibration information generated during the TBM excavation process.

3. The method for identifying the risk of a tunnel boring machine (TBM) jam according to claim 1, characterized in that: The generating of excavation methods that meet preset safety and high efficiency requirements for the target tunnel section with machine jam risk and the target tunnel section with normal surrounding rock according to the TBM excavation parameters respectively includes: Determine a target safe excavation interval of the target tunnel section with a risk of machine jamming according to the surrounding rock disturbance constraint of the TBM excavation parameters, and determine a safe TBM excavation mode of the target tunnel section with a risk of machine jamming by using the target safe excavation interval; The target efficient excavation interval of the target normal surrounding rock tunnel section is obtained according to the multi-objective optimization decision result of the TBM excavation parameters, so as to determine the TBM efficient excavation mode of the target normal surrounding rock tunnel section according to the target efficient excavation interval.

4. A device for identifying the risk of a tunnel boring machine (TBM) jam, characterized in that: include: A first acquisition module is used to acquire the risk level of a physical drive jam based on a pre-constructed physical drive jam identification model that meets the first preset condition, wherein the acquisition of the risk level of a physical drive jam based on the pre-constructed physical drive jam identification model that meets the first preset condition includes: constructing a TBM excavation parameter-surrounding rock parameter mapping model, and determining a rock-machine parameter dynamic interaction mechanism according to the TBM excavation parameter-surrounding rock parameter mapping model; inverting current surrounding rock parameters based on the TBM excavation parameter-surrounding rock parameter mapping model, the rock-machine parameter dynamic interaction mechanism and the TBM excavation parameter, and acquiring the current surrounding rock grade according to the current surrounding rock parameters and a target TBM tunnel surrounding rock classification method; acquiring a target safety threshold for each surrounding rock grade, and constructing a constraint equation of a TBM surrounding rock disturbance limit using the target safety threshold, so as to construct the physical drive jam identification model using the constraint equation, and acquiring the risk level of the physical drive jam based on the physical drive jam identification model; A second acquisition module is used to acquire the risk level of an information-driven stuck machine based on a pre-constructed information-driven stuck machine identification model that meets the second preset condition, wherein the acquisition of the risk level of an information-driven stuck machine based on the pre-constructed information-driven stuck machine identification model that meets the second preset condition includes: constructing a TBM excavation multi-source information database and acquiring TBM stuck machine precursor feature information; determining a target stuck machine identification criterion corresponding to data information in the TBM excavation multi-source information database based on the TBM stuck machine precursor feature information; obtaining a TBM stuck machine advance identification criterion using the target stuck machine identification criterion, so as to generate the information-driven stuck machine identification model according to the TBM stuck machine advance identification criterion, and acquiring the information-driven stuck machine risk level based on the information-driven stuck machine identification model; A construction module is used to integrate and fuse the physical drive jam risk level and the information drive jam risk level to construct a physical-information dual-drive tunnel boring machine (TBM) jam risk advance identification model; The control module is used to identify the target tunnel section with a risk of TBM jamming and the target tunnel section with normal surrounding rock of the target tunnel section based on the physical-information dual-driven TBM jamming risk advance identification model, and generate excavation methods that meet preset safety and high efficiency requirements for the target tunnel section with a risk of TBM jamming and the target tunnel section with normal surrounding rock according to TBM excavation parameters.

5. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for identifying the risk of a tunnel boring machine (TBM) jam as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for identifying the risk of a tunnel boring machine (TBM) jam as described in any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the method for identifying the risk of a tunnel boring machine (TBM) jam as described in any one of claims 1 to 3.

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