Data processing method and device for tunnel boring machine prediction

Abstract

The present application discloses a data processing method and device for predicting tunnel boring machine jams. The method comprises: obtaining tunnel boring construction data; constructing and processing the tunnel boring construction data based on a physical model to obtain first tunnel boring construction model data and second tunnel boring construction model data; performing tunnel boring resistance prediction processing on the first tunnel boring construction model data and the second tunnel boring construction model data to obtain tunnel boring resistance data; performing jam judgment processing on the tunnel boring resistance data based on a preset resistance threshold to obtain jam prediction data. The present application constructs a tunnel boring construction stratum model and a tunnel boring construction equipment model to predict tunnel boring resistance data and jam state prediction processing, thereby solving the problem of low accuracy in the prediction of tunnel boring equipment jams in the prior art during tunnel boring, and achieving the technical effect of improving the accuracy of the prediction of tunnel boring equipment jams during tunnel boring.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a data processing method and device for predicting tunnel boring machine jams. Background Art

[0002] Standardization, mechanization and automation of underground engineering construction are the future development trends. In recent years, the number of tunnel projects constructed using full-section hard rock tunnel boring machines (TBMs) has gradually increased. When TBMs encounter high-stress soft rock or fault fracture zones, after tunnel excavation unloading, the surrounding rock undergoes strong extrusion deformation in the direction of the tunnel. When the displacement of the surrounding rock exceeds the gap reserved for expansion, it will inevitably come into contact with the TBM and get stuck. In the worst case, the TBM will be trapped and the construction period will be delayed. In the worst case, the TBM equipment will be damaged, resulting in significant economic losses and serious delays in the construction period, or even the TBM equipment will be scrapped. During TBM excavation, it is very necessary to predict and analyze the cutter resistance torque in various situations that may occur during excavation and the friction resistance encountered by the TBM during advancement. In the prior art, the resistance encountered by the TBM during advancement is calculated to predict tunnel excavation jams. However, since the prior art lacks consideration of the interaction between excavation construction equipment and surrounding rock during the resistance calculation process, the resistance prediction has a low accuracy rate, which leads to a low accuracy rate in the prediction of tunnel excavation jams.

[0003] Therefore, the existing technology for predicting the jam of tunneling equipment during tunneling has the problem of low accuracy. Summary of the invention

[0004] The main purpose of this application is to provide a data processing method and device for predicting tunnel boring equipment jams, so as to solve the problem of low accuracy in predicting tunnel boring equipment jams in the prior art, and to achieve the technical effect of improving the accuracy of predicting tunnel boring equipment jams in the process of tunnel boring.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application proposes a data processing method for tunnel boring machine prediction, which is characterized by comprising:

[0006] Acquire excavation construction data, wherein the excavation construction data is relevant data used to represent the excavation construction process;

[0007] The excavation construction data is processed based on a physical model to obtain first excavation construction model data and second excavation construction model data, wherein the first excavation construction model data is a physical model data for representing an excavation construction stratum, and the second excavation construction model data is a physical model data for representing an excavation construction equipment;

[0008] Performing tunneling resistance prediction processing on the first tunneling construction model data and the second tunneling construction model data to obtain tunneling resistance data;

[0009] The tunnel boring resistance data is subjected to a jam judgment process based on a preset resistance threshold to obtain jam prediction data.

[0010] Optionally, performing tunneling resistance prediction processing on the first tunneling construction model data and the second tunneling construction model data to obtain tunneling resistance data includes:

[0011] Based on a preset construction interval, the first excavation construction model data and the second excavation construction model data are subjected to model segmentation processing to obtain first excavation construction model data of the preset construction interval and second excavation construction model data of the preset construction interval;

[0012] Preprocessing the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval based on stress balance to obtain the excavation construction model data of the preset construction interval;

[0013] Based on a preset resistance prediction model, excavation resistance prediction processing is performed on the excavation construction model data of the preset construction interval of the process to obtain the tunnel excavation resistance data.

[0014] Optionally, performing tunneling resistance prediction processing on the tunneling construction model data of the preset construction interval of the process based on a preset resistance prediction model to obtain the tunneling resistance data includes:

[0015] Performing identification processing based on surrounding rock characteristics on the excavation construction model data of the preset construction interval of the process to obtain process surrounding rock characteristic data, wherein the process surrounding rock characteristic data is data used to represent the formation surrounding rock characteristics in the preset construction interval;

[0016] Matching construction feature data corresponding to the process surrounding rock feature data in a preset construction database, wherein the construction feature data is data used to represent a relationship between construction parameters of tunneling construction equipment corresponding to the process surrounding rock feature data;

[0017] The construction characteristic data is input into the preset resistance prediction model to obtain the tunnel excavation resistance data.

[0018] Optionally, inputting the construction characteristic data into the preset resistance prediction model to obtain the tunnel excavation resistance data comprises:

[0019] The construction feature data is subjected to identification processing based on the construction effect to obtain first construction feature data, second construction feature data and third construction feature data, wherein the first construction feature data is data for representing the interaction characteristics between the construction equipment tool and the surrounding rock, the second construction feature data is data for representing the interaction characteristics between the construction equipment cutter head and the surrounding rock, and the third construction feature data is data for representing the interaction characteristics between the construction equipment shield and the surrounding rock;

[0020] The first construction feature data, the second construction feature data and the third construction feature data are respectively input into a preset first resistance prediction model, a second resistance prediction model and a third resistance prediction model to obtain first tunnel excavation resistance data, second tunnel excavation resistance data and third tunnel excavation resistance data, wherein the first tunnel excavation resistance data is data used to represent the excavation resistance experienced by the excavation equipment tool, the second tunnel excavation resistance data is data used to represent the excavation resistance experienced by the excavation equipment cutter head, and the third tunnel excavation resistance data is data used to represent the excavation resistance experienced by the excavation equipment shield;

[0021] The tunnel boring resistance data is determined according to the first tunnel boring resistance data, the second tunnel boring resistance data and the third tunnel boring resistance data.

[0022] Optionally, the tunnel boring resistance data is subjected to a machine jam judgment process based on a preset resistance threshold value to obtain the machine jam prediction data including:

[0023] Determining the preset resistance threshold according to preset excavation power data, wherein the preset excavation power data is power data used to represent excavation equipment;

[0024] Compare the tunneling resistance data with the preset resistance threshold to determine the jam state data of the tunneling equipment,

[0025] If the tunneling resistance data is greater than the preset resistance threshold, first jam prediction data is obtained, wherein the first jam prediction data is data used to indicate a prediction of jamming of the tunneling equipment;

[0026] If the tunnel excavation resistance data is less than the preset resistance threshold, second jam prediction data is obtained, wherein the second jam prediction data is data used to indicate a prediction that the excavation equipment will not jam.

[0027] Optionally, the excavation construction data is processed based on a physical model to obtain the first excavation construction model data and the second excavation construction model data, including:

[0028] Identify the excavation construction data to obtain first excavation construction data and second excavation construction data, wherein the first excavation construction data is data used to represent the excavation construction stratum, and the second excavation construction data is data used to represent the excavation construction equipment;

[0029] Performing construction processing based on a physical model on the first excavation construction data to obtain first excavation construction model data;

[0030] The second excavation construction data is processed based on a physical model to obtain second excavation construction model data.

[0031] According to a second aspect of the present application, there is provided a data processing device for tunnel boring machine prediction, comprising:

[0032] A data acquisition module, used to acquire excavation construction data, wherein the excavation construction data is relevant data used to represent the excavation construction process;

[0033] A model construction module, used for constructing the excavation construction data based on a physical model to obtain first excavation construction model data and second excavation construction model data, wherein the first excavation construction model data is a physical model data for representing an excavation construction stratum, and the second excavation construction model data is a physical model data for representing an excavation construction equipment;

[0034] a resistance prediction module, configured to perform tunneling resistance prediction processing on the first tunneling construction model data and the second tunneling construction model data to obtain tunneling resistance data;

[0035] The stuck machine prediction module is used to perform stuck machine judgment processing on the tunnel excavation resistance data based on a preset resistance threshold value to obtain stuck machine prediction data.

[0036] Optionally, the resistance prediction module includes:

[0037] A model segmentation module, used for performing model segmentation processing on the first excavation construction model data and the second excavation construction model data based on a preset construction interval to obtain first excavation construction model data of the preset construction interval and second excavation construction model data of the preset construction interval;

[0038] A preprocessing module, used for preprocessing the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval based on stress balance to obtain the excavation construction model data of the preset construction interval;

[0039] The prediction model module is used to perform tunneling resistance prediction processing on the tunneling construction model data of the preset construction interval of the process based on a preset resistance prediction model to obtain the tunneling resistance data.

[0040] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned data processing method for tunnel boring machine prediction.

[0041] According to the fourth aspect of the present application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the above-mentioned data processing method for tunnel boring machine prediction.

[0042] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0043] In the present application, by acquiring excavation construction data, wherein the excavation construction data is related data used to represent the excavation construction process; constructing and processing the excavation construction data based on a physical model to obtain first excavation construction model data and second excavation construction model data, wherein the first excavation construction model data is physical model data used to represent the excavation construction stratum, and the second excavation construction model data is physical model data used to represent the excavation construction equipment; performing excavation resistance prediction processing on the first excavation construction model data and the second excavation construction model data to obtain tunnel excavation resistance data; performing a jam judgment processing on the tunnel excavation resistance data based on a preset resistance threshold to obtain jam prediction data. The present application constructs an excavation construction stratum model and an excavation construction equipment model to predict the excavation resistance data and jam state prediction processing, thereby improving the accuracy of tunnel excavation resistance prediction, thereby solving the problem of low accuracy in the prediction of jam of excavation equipment in the tunnel excavation process in the prior art, and achieving the technical effect of improving the accuracy of the prediction of jam of excavation equipment in the tunnel excavation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0045] Figure 1 A flow chart of a data processing method for tunnel boring machine prediction provided in this application;

[0046] Figure 2 A flow chart of a data processing method for tunnel boring machine prediction provided in this application;

[0047] Figure 3 A schematic diagram of a data processing device for tunnel boring machine prediction provided by the present application;

[0048] Figure 4 A schematic diagram of another data processing device for tunnel boring machine prediction provided in this application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0052] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0053] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0054] Figure 1 A flowchart of a data processing method for tunnel boring machine prediction provided in this application, such as Figure 1 As shown, the method comprises the following steps:

[0055] S101: Acquiring excavation construction data;

[0056] The excavation construction data is the relevant data used to represent the excavation construction process; the excavation construction data includes the first excavation construction data and the second excavation construction data, which are used to represent the excavation construction stratum data and the excavation construction equipment data respectively. The excavation construction stratum data includes data used to represent the type of stratum surrounding rock, the depth of the construction stratum, etc. The excavation construction stratum data can be obtained through geological exploration data, the exposure of the stratum surrounding rock and the construction site conditions. The excavation construction equipment data is the data used to represent the structure after the excavation construction, the excavation construction working equipment, etc. The structure after the excavation construction can be a tunnel support structure, such as a steel arch frame, shotcrete, anchor rods, etc. The excavation construction working equipment data includes the data of the cutter head, cutter, shield and construction parameters of the TBM equipment.

[0057] S102: Performing construction processing on the excavation construction data based on the physical model to obtain first excavation construction model data and second excavation construction model data;

[0058] The first excavation construction model data is a physical model data for representing the excavation construction stratum, and the second excavation construction model data is a physical model data for representing the excavation construction equipment. By constructing a physical model including the excavation stratum model and the excavation equipment model, the tunnel excavation resistance is predicted by the interaction between the stratum and the equipment during the excavation process, the accuracy of the tunnel excavation resistance prediction is improved, and then the accuracy of the tunnel excavation jam prediction is improved.

[0059] In an optional embodiment of the present application, a data processing method for tunnel boring machine prediction is provided, the method comprising:

[0060] The excavation construction data is identified to obtain first excavation construction data and second excavation construction data, wherein the first excavation construction data is data used to represent the excavation construction stratum, and the second excavation construction data is data used to represent the excavation construction equipment; the first excavation construction data is constructed based on the physical model to obtain first excavation construction model data; the second excavation construction data is constructed based on the physical model to obtain second excavation construction model data.

[0061] The first excavation construction model data is a physical model data used to represent the excavation construction stratum. The physical model of the excavation construction stratum includes the space inside the tunnel generated by TBM excavation and the surrounding rock medium within a certain range. When the tunnel is shallow, it includes the surface, and when the tunnel is deep, it only includes the surrounding rock medium within a certain depth range. The second excavation construction model data is a physical model data used to represent the excavation construction equipment; the physical model of the excavation construction equipment is a model used to represent the structure in which the excavation construction equipment interacts with the surrounding rock of the excavation stratum. When the TBM excavation equipment is excavating, the TBM excavation equipment's cutter, cutterhead, and shield interact with the surrounding rock. The physical model of the excavation construction equipment includes the physical models corresponding to the cutter, cutterhead, and shield, and the geometric parameters of the physical model of the excavation construction equipment (including parameters such as size and position relationship) correspond to the physical model of the excavation construction stratum.

[0062] Optionally, due to the symmetry of the constructed physical model, 1 / 2 of the overall model can be used as the numerical simulation model, and the model size can be 50m×50m×100m. When meshing the model, in order to increase the calculation accuracy and improve the calculation efficiency, the mesh near the tunnel is finer, and the mesh far away from the tunnel is relatively coarse. By constructing a partial model, the prediction efficiency can be improved in the process of predicting tunnel boring machine jams.

[0063] S103: performing tunneling resistance prediction processing on the first tunneling construction model data and the second tunneling construction model data to obtain tunneling resistance data;

[0064] Figure 2 A flowchart of a data processing method for tunnel boring machine prediction provided in this application, such as Figure 2 As shown, the method comprises the following steps:

[0065] S201: performing model segmentation processing on the first excavation construction model data and the second excavation construction model data based on the preset construction interval to obtain the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval;

[0066] During the excavation construction process, due to the different construction location parameters, the formation surrounding rock at different locations is different. Under different formation surrounding rock environments, the interaction relationship between the construction equipment and the formation surrounding rock is different. Therefore, according to the formation surrounding rock conditions corresponding to different construction intervals, the first excavation construction model data and the second excavation construction model data are segmented to obtain the formation surrounding rock physical model and the construction equipment surrounding rock model in the current construction interval. Optionally, according to the surrounding rock conditions corresponding to different construction intervals, the formation surrounding rock physical model and the construction equipment physical model in the current construction interval can be constructed according to the construction interval.

[0067] S202: Preprocessing the first excavation construction model data of the preset construction section and the second excavation construction model data of the preset construction section based on stress balance to obtain the excavation construction model data of the preset construction section;

[0068] The first stress balance processing is performed on the stratum surrounding rock model in the current construction section to avoid deformation of the stratum model under the action of gravity; the stratum surrounding rock model stratum that has undergone the first stress balance is removed and the construction equipment model is applied, and the secondary stress balance is performed on the physical model of the current construction section after the construction equipment model is applied to avoid deformation of the physical model in the current construction section due to stress, which will cause the failure of the jam prediction process and reduce the efficiency of the jam prediction.

[0069] S203: Based on a preset resistance prediction model, tunneling resistance prediction processing is performed on the tunneling construction model data of the preset construction section of the process to obtain tunneling resistance data.

[0070] The excavation construction model data of the preset construction section of the process is processed based on the surrounding rock characteristics to obtain the process surrounding rock characteristic data, wherein the process surrounding rock characteristic data is data used to represent the formation surrounding rock characteristics in the preset construction section; the construction characteristic data corresponding to the process surrounding rock characteristic data is matched in the preset construction database, wherein the construction characteristic data is data used to represent the relationship between the construction parameters of the excavation construction equipment corresponding to the process surrounding rock characteristic data; the construction characteristic data is input into the preset resistance prediction model to obtain the tunnel excavation resistance data.

[0071] According to the surrounding rock characteristics in the current construction area, the stress-strain relationship corresponding to the current surrounding rock characteristics and the friction coefficient between the surrounding rock and the cutterhead and shield are determined; according to the historical data of TBM excavation parameters, the thrust and penetration of the TBM under the corresponding surrounding rock characteristics are determined, and the cutter height, reserved deformation, friction coefficient, surrounding rock stress-strain relationship, TBM thrust and penetration corresponding to the current surrounding rock characteristics are obtained; the cutter height, reserved deformation, friction coefficient, surrounding rock stress-strain relationship, TBM thrust and penetration corresponding to the current surrounding rock characteristics are input into the preset resistance prediction model to obtain the tunnel excavation resistance data.

[0072] In another optional embodiment of the present application, a data processing method for tunnel boring machine prediction is provided, the method comprising:

[0073] The construction feature data is processed based on the identification of the construction effect to obtain the first construction feature data, the second construction feature data and the third construction feature data, wherein the first construction feature data is data for representing the interaction characteristics between the construction equipment tool and the surrounding rock, the second construction feature data is data for representing the interaction characteristics between the construction equipment cutter and the surrounding rock, and the third construction feature data is data for representing the interaction characteristics between the construction equipment shield and the surrounding rock; the first construction feature data, the second construction feature data and the third construction feature data are respectively input into the preset first resistance prediction model, the second resistance prediction model and the third resistance prediction model to obtain the first tunnel excavation resistance data, the second tunnel excavation resistance data and the third tunnel excavation resistance data, wherein the first tunnel excavation resistance data is data for representing the excavation resistance suffered by the excavation equipment tool, the second tunnel excavation resistance data is data for representing the excavation resistance suffered by the excavation equipment cutter, and the third tunnel excavation resistance data is data for representing the excavation resistance suffered by the excavation equipment shield; the tunnel excavation resistance data is determined according to the first tunnel excavation resistance data, the second tunnel excavation resistance data and the third tunnel excavation resistance data.

[0074] When TBM equipment is carrying out excavation construction, there is interaction between the TBM equipment and the surrounding rock in terms of cutter, cutterhead and shield. Resistance prediction calculations are performed for the resistance between cutter-surrounding rock, cutterhead-surrounding rock, and shield-surrounding rock respectively.

[0075] The tool-rock cutting action model is expressed as follows:

[0076]

[0077] Among them, T C is the tunneling resistance torque caused by the cutting action between tool and surrounding rock, M C i is the resistance moment of the i-th single tool in cutting surrounding rock, F NP i is the passive normal force borne by the i-th single tool on its contact surface with the surrounding rock due to the deformation of the surrounding rock at the tunnel face, F NT i is the active normal force caused by the TBM thrust on the contact surface between the i-th single tool and the surrounding rock, L C i is the distance between the i-th tool center and the TBM cutterhead rotation axis, i.e., the tool cutting arm, κ i is the cutting coefficient of the i-th tool.

[0078] The normal displacement-reaction relationship at any point on the cutterhead surface in the tunneling construction equipment model is calculated according to the following formula:

[0079]

[0080] P HP =-k H u HN

[0081] ε F =hp

[0082] Among them, u HN is the normal displacement at any point on the cutterhead surface, with the direction pointing to the tunnel clearance as positive, u F is the normal displacement of the projection point of the point on the tunnel face, and its sign is the same as u HN , P HT is the reaction force applied by the cutterhead to the tunnel face at this point, with pressure as negative and constant, k H is the elastic reaction coefficient of the cutter head, ε F is the gap between the cutter head and the palm face, h is the cutter height, and p is the penetration rate.

[0083] The cutterhead-surrounding rock contact model is expressed as follows:

[0084]

[0085] Among them, T H is the tunneling resistance torque caused by the contact between cutterhead and surrounding rock, f cH is the dynamic friction coefficient between the cutterhead and the surrounding rock, P HP (A) is the passive normal stress borne by the cutterhead-surrounding rock contact surface microelement A due to the deformation of the tunnel face surrounding rock, P HT is the active normal stress on the cutterhead-surrounding rock contact surface caused by the TBM thrust, L H (A) is the distance between the centroid of the cutterhead-surrounding rock contact surface and the rotation axis of the TBM cutterhead. H is the cutterhead-surrounding rock contact area.

[0086] The discrete cutterhead-surrounding rock contact model is expressed as follows:

[0087]

[0088] Where P HP i is the passive normal stress borne by the i-th cutterhead-surrounding rock contact surface unit due to the deformation of the tunnel face surrounding rock, L H i A is the distance between the centroid of the i-th cutterhead-surrounding rock contact surface unit and the TBM cutterhead rotation axis, H iis the unit area of ​​the i-th cutterhead-surrounding rock contact surface.

[0089] In the tunneling equipment model, the normal displacement-reaction relationship at any point on the shield surface is calculated according to the following formula:

[0090]

[0091] P S =-k S u SN

[0092]

[0093] In the formula, u SN is the normal displacement at any point on the shield surface, with the direction pointing to the tunnel clearance as positive, u B is the normal displacement of the projection point of the line connecting this point to the TBM rotation axis on the excavation surface, and the symbol is the same as u SN , P S is the reaction force applied by the shield to the excavation surface at this point, with pressure as negative and constant, k S is the elastic reaction coefficient of the shield, ε B is the gap distance between the shield and the excavation surface, θ B It is the angle between the calculation point and the TBM plumb symmetry plane, with the tunnel crown as 0°.

[0094] The shield-surrounding rock contact model is expressed as follows:

[0095]

[0096] Among them, F S is the friction resistance caused by the contact between shield and surrounding rock, f cS is the dynamic friction coefficient between the shield and the surrounding rock, P S (A) is the passive normal force borne by the shield-rock contact surface microelement A due to the convergence of the excavation surface surrounding rock, A S is the shield-rock contact area.

[0097] The discrete shield-rock contact model is expressed as follows:

[0098]

[0099] Where P S i is the passive normal force borne by the ith shield-rock interface unit due to the convergence of the excavation face surrounding rock, A S i is the unit area of ​​the i-th shield-surrounding rock contact surface.

[0100] The calculation of active normal force on the contact surface between tool and surrounding rock and the contact surface between cutter head and surrounding rock is as follows:

[0101]

[0102]

[0103] Where Th is the TBM thrust, B i is the blade width of the i-th tool, R i is the radius of the i-th tool.

[0104] Through the interaction between different positions of the tunneling equipment and the surrounding rock of the stratum, the tunneling resistance of the tunneling equipment is predicted and calculated respectively, so as to improve the accuracy of the tunneling resistance prediction and thus the accuracy of the tunneling jam prediction.

[0105] S104: Performing a jam determination process on the tunnel boring resistance data based on a preset resistance threshold to obtain jam prediction data.

[0106] A preset resistance threshold is determined according to preset excavation power data, wherein the preset excavation power data is power data for indicating the excavation equipment; the tunnel excavation resistance data is compared with the preset resistance threshold to determine the stuck state data of the excavation equipment; if the tunnel excavation resistance data is greater than the preset resistance threshold, first stuck prediction data is obtained, wherein the first stuck prediction data is data for indicating a prediction that the excavation equipment is stuck; if the tunnel excavation resistance data is less than the preset resistance threshold, second stuck prediction data is obtained, wherein the second stuck prediction data is data for indicating a prediction that the excavation equipment is not stuck.

[0107] During excavation in strata with good geological conditions, the torque is mainly the resistance torque required for rolling cutting of the cutter; when the geological conditions of the surrounding rock are poor, some broken rock masses or areas with large deformation will contact the cutterhead, causing the cutterhead to be subjected to a certain extrusion force, generating a resistance torque on the rotation of the cutterhead; as the surrounding rock grade increases, the degree of surrounding rock fragmentation increases and the deformation increases, the surrounding rock deformation value exceeds the excavation gap value, generating a certain extrusion pressure on the shield, resulting in an increase in the shield friction resistance.

[0108] The jam status of the tunneling equipment is determined by the following formula:

[0109] F r +F b >F I , card machine

[0110] F r +F b <F I , no jamming

[0111] Among them, F rThe thrust required to overcome the frictional resistance of the shield, F b F is the excavation thrust required for normal and continuous excavation of the TBM machine; I Rated thrust of TBM.

[0112] T1+T2+T3+T τ >T I , card machine

[0113] T1+T2+T3+T τ <T I , no jamming

[0114] Among them, T1 is the rolling resistance torque of the cutter head, T2 is the friction torque on the circumferential surface of the cutter head, T3 is the torque required to overcome the weight of the cutter head, and T τ is the torque generated by the cutter head driving the soil to rotate, T I The breakaway torque that the TBM cutterhead system can provide.

[0115] In another optional embodiment of the present application, a data processing device for tunnel boring machine prediction is provided. Figure 3 A schematic diagram of a data processing device for tunnel boring machine prediction provided in this application, such as Figure 3 As shown, the device comprises:

[0116] The data acquisition module 31 is used to acquire excavation construction data, wherein the excavation construction data is relevant data used to represent the excavation construction process;

[0117] The model building module 32 is used to build the excavation construction data based on the physical model to obtain the first excavation construction model data and the second excavation construction model data, wherein the first excavation construction model data is the physical model data used to represent the excavation construction stratum, and the second excavation construction model data is the physical model data used to represent the excavation construction equipment;

[0118] The resistance prediction module 33 is used to perform tunneling resistance prediction processing on the first tunneling construction model data and the second tunneling construction model data to obtain tunneling resistance data;

[0119] The stuck machine prediction module 34 is used to perform stuck machine judgment processing on the tunnel excavation resistance data based on a preset resistance threshold value to obtain stuck machine prediction data.

[0120] Figure 4 A schematic diagram of another data processing device for tunnel boring machine prediction provided by this application, such as Figure 4 As shown, the device comprises:

[0121] The model segmentation module 41 is used to perform model segmentation processing on the first excavation construction model data and the second excavation construction model data based on the preset construction interval to obtain the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval;

[0122] A preprocessing module 42 is used to preprocess the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval based on stress balance to obtain the excavation construction model data of the preset construction interval;

[0123] The prediction model module 43 is used to perform tunneling resistance prediction processing on the tunneling construction model data of the preset construction section of the process based on the preset resistance prediction model to obtain tunneling resistance data.

[0124] The specific manner of executing the operation of each unit in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0125] In summary, in the present application, by acquiring excavation construction data, wherein the excavation construction data is relevant data for representing the excavation construction process; constructing and processing the excavation construction data based on a physical model to obtain first excavation construction model data and second excavation construction model data, wherein the first excavation construction model data is physical model data for representing the excavation construction stratum, and the second excavation construction model data is physical model data for representing the excavation construction equipment; performing excavation resistance prediction processing on the first excavation construction model data and the second excavation construction model data to obtain tunnel excavation resistance data; performing a jam judgment processing on the tunnel excavation resistance data based on a preset resistance threshold to obtain jam prediction data. The present application constructs an excavation construction stratum model and an excavation construction equipment model to predict the excavation resistance data and jam state prediction processing, thereby improving the accuracy of tunnel excavation resistance prediction, thereby solving the problem of low accuracy in the prediction of jam of excavation equipment during tunnel excavation in the prior art, and achieving the technical effect of improving the accuracy of the prediction of jam of excavation equipment during tunnel excavation.

[0126] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0127] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0128] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method for predicting tunnel boring machine jams, characterized in that: include: Acquire excavation construction data, wherein the excavation construction data is relevant data used to represent the excavation construction process; The excavation construction data is processed based on a physical model to obtain first excavation construction model data and second excavation construction model data, wherein the first excavation construction model data is a physical model data for representing an excavation construction stratum, and the second excavation construction model data is a physical model data for representing an excavation construction equipment; The first excavation construction model data and the second excavation construction model data are subjected to excavation resistance prediction processing to obtain tunnel excavation resistance data, including: Based on a preset construction interval, the first excavation construction model data and the second excavation construction model data are subjected to model segmentation processing to obtain first excavation construction model data of the preset construction interval and second excavation construction model data of the preset construction interval; the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval are subjected to stress balance preprocessing to obtain excavation construction model data of the preset construction interval; based on a preset resistance prediction model, the excavation resistance prediction processing is performed on the excavation construction model data of the preset construction interval to obtain the tunnel excavation resistance data; Performing identification processing based on surrounding rock characteristics on the tunneling construction model data of the preset construction interval of the process to obtain process surrounding rock characteristic data, wherein the process surrounding rock characteristic data is data used to represent the formation surrounding rock characteristics in the preset construction interval; matching the construction characteristic data corresponding to the process surrounding rock characteristic data in the preset construction database, wherein the construction characteristic data is data used to represent the relationship between the construction parameters of the tunneling construction equipment corresponding to the process surrounding rock characteristic data; inputting the construction characteristic data into the preset resistance prediction model to obtain the tunneling resistance data; The construction feature data is processed based on the construction effect to obtain first construction feature data, second construction feature data and third construction feature data, wherein the first construction feature data is data for indicating the interaction feature between the construction equipment tool and the surrounding rock, the second construction feature data is data for indicating the interaction feature between the construction equipment cutter and the surrounding rock, and the third construction feature data is data for indicating the interaction feature between the construction equipment shield and the surrounding rock; the first construction feature data, the second construction feature data and the third construction feature data are respectively input into a preset first resistance prediction model, a second resistance prediction model and a third resistance prediction model to obtain first tunneling resistance data, second tunneling resistance data and third tunneling resistance data, wherein the first tunneling resistance data is data for indicating the tunneling resistance suffered by the tunneling equipment tool, the second tunneling resistance data is data for indicating the tunneling resistance suffered by the tunneling equipment cutter, and the third tunneling resistance data is data for indicating the tunneling resistance suffered by the tunneling equipment shield; the tunneling resistance data is determined according to the first tunneling resistance data, the second tunneling resistance data and the third tunneling resistance data; The tunnel boring resistance data is subjected to a jam judgment process based on a preset resistance threshold to obtain jam prediction data.

2. The data processing method according to claim 1, characterized in that: The tunnel boring resistance data is subjected to a jam judgment process based on a preset resistance threshold, and the jam prediction data obtained includes: Determining the preset resistance threshold according to preset excavation power data, wherein the preset excavation power data is power data used to represent excavation equipment; Compare the tunneling resistance data with the preset resistance threshold to determine the jam state data of the tunneling equipment, If the tunneling resistance data is greater than the preset resistance threshold, first jam prediction data is obtained, wherein the first jam prediction data is data used to indicate a prediction of jamming of the tunneling equipment; If the tunnel excavation resistance data is less than the preset resistance threshold, second jam prediction data is obtained, wherein the second jam prediction data is data used to indicate a prediction that the excavation equipment will not jam.

3. The data processing method according to claim 1, characterized in that: The excavation construction data is processed based on a physical model to obtain first excavation construction model data and second excavation construction model data, including: Identify the excavation construction data to obtain first excavation construction data and second excavation construction data, wherein the first excavation construction data is data used to represent the excavation construction stratum, and the second excavation construction data is data used to represent the excavation construction equipment; Performing construction processing based on a physical model on the first excavation construction data to obtain first excavation construction model data; The second excavation construction data is processed based on a physical model to obtain second excavation construction model data.

4. A data processing device for predicting tunnel boring machine jams, characterized in that: include: A data acquisition module, used to acquire excavation construction data, wherein the excavation construction data is relevant data used to represent the excavation construction process; A model construction module, used for constructing the excavation construction data based on a physical model to obtain first excavation construction model data and second excavation construction model data, wherein the first excavation construction model data is a physical model data for representing an excavation construction stratum, and the second excavation construction model data is a physical model data for representing an excavation construction equipment; a resistance prediction module, configured to perform tunneling resistance prediction processing on the first tunneling construction model data and the second tunneling construction model data to obtain tunneling resistance data; The resistance prediction module includes: a model segmentation module, which is used to perform model segmentation processing on the first excavation construction model data and the second excavation construction model data based on a preset construction interval to obtain the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval; a preprocessing module, which is used to perform stress balance preprocessing on the first excavation construction model data of the preset construction interval and the second excavation construction model data of the preset construction interval to obtain the excavation construction model data of the preset construction interval; a prediction model module, which is used to perform excavation resistance prediction processing on the excavation construction model data of the preset construction interval based on a preset resistance prediction model to obtain the tunnel excavation resistance data, including: Performing identification processing based on surrounding rock characteristics on the tunneling construction model data of the preset construction interval of the process to obtain process surrounding rock characteristic data, wherein the process surrounding rock characteristic data is data used to represent the formation surrounding rock characteristics in the preset construction interval; matching the construction characteristic data corresponding to the process surrounding rock characteristic data in the preset construction database, wherein the construction characteristic data is data used to represent the relationship between the construction parameters of the tunneling construction equipment corresponding to the process surrounding rock characteristic data; inputting the construction characteristic data into the preset resistance prediction model to obtain the tunneling resistance data; The construction feature data is processed based on the construction effect to obtain first construction feature data, second construction feature data and third construction feature data, wherein the first construction feature data is data for indicating the interaction feature between the construction equipment tool and the surrounding rock, the second construction feature data is data for indicating the interaction feature between the construction equipment cutter and the surrounding rock, and the third construction feature data is data for indicating the interaction feature between the construction equipment shield and the surrounding rock; the first construction feature data, the second construction feature data and the third construction feature data are respectively input into a preset first resistance prediction model, a second resistance prediction model and a third resistance prediction model to obtain first tunneling resistance data, second tunneling resistance data and third tunneling resistance data, wherein the first tunneling resistance data is data for indicating the tunneling resistance suffered by the tunneling equipment tool, the second tunneling resistance data is data for indicating the tunneling resistance suffered by the tunneling equipment cutter, and the third tunneling resistance data is data for indicating the tunneling resistance suffered by the tunneling equipment shield; the tunneling resistance data is determined according to the first tunneling resistance data, the second tunneling resistance data and the third tunneling resistance data; The stuck machine prediction module is used to perform stuck machine judgment processing on the tunnel excavation resistance data based on a preset resistance threshold value to obtain stuck machine prediction data.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the data processing method for tunnel boring machine prediction according to any one of claims 1 to 3.

6. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the data processing method for tunnel boring machine prediction as described in any one of claims 1-3.

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