CN114495433B - Surrounding rock collapse early warning method and device for tunnel boring machine and terminal equipment - Google Patents
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Abstract
The invention is suitable for the technical field of tunneling, and provides a surrounding rock collapse early warning method, device and terminal equipment of a tunneling machine, wherein the method comprises the following steps: calculating a predicted penetration index of the current section based on the historical penetration index, the historical penetration index being determined based on historical operating parameters of the tunneling machine; calculating the predicted tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, wherein the historical tunneling disturbance degree is determined based on the historical operation parameters and the historical surrounding rock parameters of the tunnel boring machine; calculating a first collapse probability based on the predicted penetration index, calculating a second collapse probability based on the predicted tunneling disturbance degree, calculating the current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability, and generating collapse early warning information based on the current collapse probability. The surrounding rock collapse early warning method of the tunnel boring machine can calculate the collapse probability of the surrounding rock based on the running parameters of the surrounding rock parameter tunnel boring machine, avoid collapse accidents and ensure construction safety.
Description
Technical Field
The invention belongs to the technical field of tunneling, and particularly relates to a surrounding rock collapse early warning method and device for a tunneling machine and terminal equipment.
Background
The tunnel boring machine (Tunnel Boring Machine, TBM) is large-scale comprehensive tunnel construction equipment, can integrate drilling, digging and protecting, and effectively realizes the industrial operation of long tunnel construction. Compared with the traditional drilling and blasting method, the tunnel boring machine has the advantages of high construction speed, no blasting, little tunneling and overexcavation, small disturbance to surrounding rock, good working environment, safe construction and the like. In the process of excavating a long and large tunnel, TBM construction becomes a development trend.
However, in the excavation of weak surrounding rock areas, collapse disasters are the most common disaster type with the highest occurrence frequency. On one hand, the disaster caused by collapse of surrounding rock can lead to the fact that tunneling must be performed by adopting measures such as short footage, low rotation speed, low torque and the like, so that the construction process is complex and the tunneling efficiency is low. On the other hand, the surrounding rock collapse also needs to adopt manual cleaning of a large amount of low slag, and the collapse backfill amount is large.
Frequent occurrence of collapse disasters can increase construction cost and reduce construction efficiency. Traditionally, however, the risk of TBM surrounding rock collapse can only be predicted based on experience of constructors, and preventive measures are difficult to take reliably in time.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a surrounding rock collapse early warning method, device and terminal equipment of a tunnel boring machine, which can reliably early warn the surrounding rock collapse condition.
The first aspect of the embodiment of the invention provides a surrounding rock collapse early warning method of a tunnel boring machine, which comprises the following steps:
calculating a predicted penetration index of the current section based on a historical penetration index, the historical penetration index being determined based on historical operating parameters of the tunneling machine;
calculating the predicted tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, wherein the historical tunneling disturbance degree is determined based on the historical operation parameters and the historical surrounding rock parameters of the tunnel boring machine;
calculating a first collapse probability based on the predicted penetration index, and calculating a second collapse probability based on the predicted tunneling disturbance degree;
and calculating the current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability, and generating collapse early warning information based on the current collapse probability.
A second aspect of the embodiment of the present invention provides a surrounding rock collapse early warning device for a tunnel boring machine, including:
the predictive penetration index calculation module is used for calculating a predictive penetration index of the current section based on a historical penetration index, and the historical penetration index is determined based on historical operation parameters of the tunnel boring machine;
the predictive tunneling disturbance degree calculation module is used for calculating the predictive tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, and the historical tunneling disturbance degree is determined based on the historical operation parameters and the historical surrounding rock parameters of the tunnel boring machine operation;
the collapse probability calculation module is used for calculating a first collapse probability based on the predicted penetration index and calculating a second collapse probability based on the predicted tunneling disturbance degree;
and the collapse early warning information generation module is used for calculating the current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability and generating collapse early warning information based on the current collapse probability.
A third aspect of the embodiments of the present invention provides a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described above when executing the computer program.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above.
A fifth aspect of the embodiments of the present invention provides a computer program product for causing an electronic device to carry out the steps of the method according to any one of the first aspects described above when the computer program product is run on a terminal device.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: the embodiment of the invention provides a surrounding rock collapse early warning method of a tunnel boring machine, which comprises the steps of calculating a predicted penetration index of a current section based on a historical penetration index, wherein the historical penetration index is determined based on historical operation parameters of the tunnel boring machine; calculating the predicted tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, wherein the historical tunneling disturbance degree is determined based on the historical operation parameters and the historical surrounding rock parameters of the tunnel boring machine; calculating a first collapse probability based on the predicted penetration index, calculating a second collapse probability based on the predicted tunneling disturbance degree, calculating a current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability, and generating collapse early warning information based on the current collapse probability. The surrounding rock collapse early warning method for the tunnel boring machine provided by the embodiment of the invention can calculate the collapse probability of the surrounding rock based on the running parameters of the surrounding rock parameter tunnel boring machine, avoid collapse accidents and ensure construction safety.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic implementation flow chart of a surrounding rock collapse early warning method of a tunnel boring machine, which is provided by the embodiment of the invention;
fig. 2 is a schematic diagram of an early warning system applied to the surrounding rock collapse early warning method of the tunnel boring machine provided by the embodiment of the invention;
fig. 3 is a schematic structural diagram of a surrounding rock collapse warning device of a tunnel boring machine, which is provided by the embodiment of the invention;
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
In the TBM construction process, different TBM devices adopt different support systems and advanced reinforcement measures. For a certain tunnel construction process, the TBM will not be replaced, i.e. the parameters of the TBM itself will not change during a construction process.
In some embodiments, the main factors responsible for the collapse of the surrounding rock when the TBM is tunneled include the lithology of the surrounding rock and the conditions of the tunnelling disturbance. I.e. the worse the lithology of the surrounding rock, the larger the tunneling disturbance, the more easily collapse.
Fig. 1 shows a schematic implementation flow chart of a surrounding rock collapse early warning method of a tunnel boring machine according to an embodiment of the present invention. Referring to fig. 1, the method for pre-warning surrounding rock collapse of a tunnel boring machine provided by the embodiment of the invention may include steps S101 to S104.
S101: and calculating a predicted penetration index of the current section based on the historical penetration index, wherein the historical penetration index is determined based on historical operating parameters of the tunnel boring machine.
The penetration index FPI may reflect the compressive strength of the surrounding rock.
In some embodiments, the historical operating parameters include average hob thrust and cutterhead penetration. Prior to S101, the method may further include:
and calculating the historical penetration index based on a penetration index formula, the average thrust of the hob and the cutter head penetration. The penetration index formula includes: fpi=f/P; wherein FPI is the penetration index, F is the average thrust of a hob on a cutterhead of the tunnel boring machine, and P is the penetration of the cutterhead of the tunnel boring machine.
In some embodiments, F is the average thrust of a single hob on a TBM cutterhead in kN; p is the penetration of the TBM cutter disc, and the unit is mm/r. Specifically, f=f 0 /n，F 0 ＝0.8F 1 ；F 0 The unit is kN for the thrust of the whole cutter disc of the TBM; n is the total number of hob on TBM cutterhead, F 1 The total propulsion of the TBM is given in kN. The total number n of the hob is determined by TBM design and production, and can be directly obtained; penetration P and total propulsive force F 1 Can be directly read by a data acquisition system of the TBM device. Based on the above data, the penetration index value of the tunnel boring machine can be calculated in real time.
In some embodiments, S101 specifically includes: acquiring a preset number of historical penetration indexes with the smallest time difference from the current moment, and numbering the preset number of the historical penetration indexes according to a time sequence. Fitting the historical penetration indexes of the preset number by using a Lagrange interpolation-based method to obtain a penetration index interpolation polynomial. And calculating the predicted penetration index of the current section based on the penetration index interpolation polynomial.
In some embodiments, a Lagrange interpolation-based method is used to fit a preset number of historical penetration indexes and calculate a predicted penetration index of the current section.
In one specific example, fitting the first 21 historical penetration indices to obtain a fitting equation includes:wherein let x k ＝k。
Obtaining a fitted interpolation polynomial:
substituting x=21 into the fitted difference polynomial, and calculatingCalculating to obtain the predicted penetration index FPI of the current section p 。
S102: and calculating the predicted tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, wherein the historical tunneling disturbance degree is determined based on the historical operation parameters and the historical surrounding rock parameters of the tunnel boring machine.
In some embodiments, the historical surrounding rock parameters include rock strength and geologic indicators, and the historical operating parameters include heading speed. Prior to S102, the method may further include: and calculating the historical tunneling disturbance degree based on a tunneling disturbance degree formula, the rock strength, the geological index and the tunneling speed. The tunneling disturbance degree formula comprises:
d=exp (-0.017 UCS-0.007 GSI-0.019rop+1.923). Wherein, D is the tunneling disturbance degree, UCS is the rock strength, GSI is the geological index, and ROP is the tunneling speed.
In some embodiments, during the tunneling process of the tunnel boring machine, the tunneling disturbance is affected by the excavation method, the tunnel dimensions, the rock-soil mass parameters, and the tunneling parameters.
In some embodiments, S103 specifically includes: obtaining a preset number of historical tunneling disturbance degrees with the smallest time difference from the current moment, and numbering the preset number of historical tunneling disturbance degrees according to a time sequence. Fitting the historical tunneling disturbance degrees of the preset number by using a Lagrangian difference value-based method to obtain a tunneling disturbance degree interpolation polynomial. And calculating the predicted tunneling disturbance degree of the current section based on the tunneling disturbance degree interpolation polynomial.
In some embodiments, a Lagrange interpolation-based method is used for fitting a preset number of historical tunneling disturbance degrees and calculating the predicted disturbance degree of the current section.
In a specific example, the first 21 historical tunneling disturbance degrees are fitted to obtain a fitting formula, including:wherein let x k ＝k。
Obtaining a fitted difference polynomial:
substituting x=21 into the fitted difference polynomial, and calculating to obtain the predicted tunneling disturbance degree D of the current section p 。
S103: and calculating a first collapse probability based on the predicted penetration index, and calculating a second collapse probability based on the predicted tunneling disturbance degree.
In some embodiments, S103 comprises: the first collapse probability is calculated based on a first collapse probability formula and the predicted penetration index.
And calculating a second collapse probability based on a second collapse probability formula and the predicted tunneling disturbance degree.
The first collapse probability formula includes:
wherein P (FPI) is the first collapse probability, FPI max FPI is the maximum value of the historical penetration index within a preset period min FPI is the minimum value of the historical penetration index within a preset period p To predict penetration index.
The second collapse probability formula includes:
wherein P (D) is a second collapse probability, D max D, setting the maximum value of the historical tunneling disturbance degree in a preset time period min D is the minimum value of the historical tunneling disturbance degree in a preset period p To predict the extent of the tunneling disturbance.
S104: and calculating the current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability, and generating collapse early warning information based on the current collapse probability.
In some embodiments, S104 comprises: and calculating the current collapse probability of the surrounding rock based on the current collapse probability formula, the first collapse probability and the second collapse probability.
The current collapse probability formula comprises: p=0.538×p (FPI) +0.462×p (D); wherein P is the current collapse probability, P (FPI) is the first collapse probability, and P (D) is the second collapse probability.
In a specific example, collapse early warning information is generated based on the current collapse probability, and when the surrounding rock collapse probability is smaller than 0.1, surrounding rock safety is judged, and an alarm indicator lamp is controlled to be a green light. And when the collapse probability of the surrounding rock is more than or equal to 0.1 and less than or equal to 0.3, judging that the surrounding rock is normal, and controlling the alarm indicator lamp to be a yellow lamp. And when the surrounding rock collapse probability is greater than 0.3, judging that the surrounding rock is in collapse danger, and controlling the alarm indicator lamp to be a red lamp.
According to the surrounding rock collapse early warning method of the tunnel boring machine, provided by the embodiment of the invention, the risk analysis of surrounding rock collapse accidents can be quantitatively carried out on the basis of the demands of TBM tunnel construction process on informatization and data, and the probability of surrounding rock collapse can be accurately obtained by quantitatively analyzing the risk of surrounding rock collapse by adopting a probability statistics and numerical analysis method. On the other hand, the calculation process of the method is easy to operate and realize. The surrounding rock collapse early warning method for the tunnel boring machine provided by the embodiment of the invention can be used for pre-judging the risk of surrounding rock collapse, acquiring the risk in advance and sending out early warning, so that even if staff take measures, the occurrence of collapse accidents is prevented, and the safety control level of the tunneling construction is accurately and intelligently improved.
Fig. 2 shows a schematic system structure diagram of an application of the surrounding rock collapse early warning method of the tunnel boring machine according to the embodiment of the invention. Referring to fig. 2, a user can acquire the collapse early warning condition through the TBM tunneling surrounding rock collapse early warning system. The early warning system can comprise a data reading module, a data processing module, a data display module, a graphic display module and an indicator light module.
Specifically, the data reading device is used for collecting dynamic tunneling parameters generated in the TBM tunneling process in real time. The dynamic tunneling parameters comprise the current pile number, the total thrust, the penetration, the tunneling speed, the cutter torque and the cutter rotating speed.
The data processing module is provided with a parameter monitoring module for extracting abnormal change parameters in tunneling parameters, the data processing device can eliminate abrupt abnormal parameters, extract the average value of multiple groups of historical data, and substitute the read dynamic values of the tunneling parameters into the calculation formula to predict the collapse probability of surrounding rock.
The data display module is used for clearly displaying real-time tunneling parameters, penetration index FPI values and surrounding rock collapse probabilities predicted by the data processing module. The graphic display module is used for displaying the penetration index FPI and the change trend of the collapse probability of the surrounding rock, so that constructors can more clearly and definitely pre-judge the collapse risk of the surrounding rock, take measures in time and avoid the collapse risk of the surrounding rock.
The indicator light module can be provided with green, yellow and red indicator lights, and the indicator lights are controlled according to the collapse probability of surrounding rocks. Specifically, when the probability of surrounding rock collapse is greater than 0.3, the red light is on; when the collapse probability of the surrounding rock is less than or equal to 0.3 and more than or equal to 0.1, the yellow lamp is lighted; when the probability of surrounding rock collapse is less than 0.1, the green light is on. It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Fig. 3 shows a schematic structural diagram of a surrounding rock collapse warning device of a tunnel boring machine according to an embodiment of the present invention. Referring to fig. 3, the tunnel boring machine surrounding rock collapse early-warning device 30 provided by the embodiment of the invention may include a predicted penetration index calculation module 310, a predicted tunneling disturbance degree calculation module 320, a collapse probability calculation module 330, and a collapse early-warning information calculation module 340.
The predicted penetration index calculation module 310 is configured to calculate a predicted penetration index of the current section based on a historical penetration index, where the historical penetration index is determined based on historical operating parameters of the tunneling machine.
The predicted tunneling disturbance degree calculation module 320 is configured to calculate a predicted tunneling disturbance degree of the current section based on a historical tunneling disturbance degree, where the historical tunneling disturbance degree is determined based on the historical operation parameter and the historical surrounding rock parameter of the tunnel boring machine.
The collapse probability calculation module 330 is configured to calculate a first collapse probability based on the predicted penetration index, and calculate a second collapse probability based on the predicted tunneling disturbance degree.
The collapse early warning information generating module 340 is configured to calculate a current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability, and generate collapse early warning information based on the current collapse probability.
The surrounding rock collapse early warning device of the tunnel boring machine can calculate the collapse probability of the surrounding rock based on the running parameters of the surrounding rock parameter tunnel boring machine, avoid collapse accidents and ensure construction safety.
In some embodiments, the historical operating parameters include average hob thrust and cutterhead penetration. The surrounding rock collapse early warning device of the tunnel boring machine provided by the embodiment of the invention can further comprise a historical penetration index calculation module, wherein the historical penetration index is calculated based on a penetration index formula, the average pushing force of the hob and the cutter head penetration. The penetration index formula includes: fpi=f/P; wherein FPI is the penetration index, F is the average thrust of a hob on a cutterhead of the tunnel boring machine, and P is the penetration of the cutterhead of the tunnel boring machine.
In some embodiments, the predicted penetration index calculation module 310 is specifically configured to obtain a preset number of historical penetration indexes with the smallest time difference from the current time, and number the preset number of historical penetration indexes according to a time sequence. Fitting the historical penetration indexes of the preset number by using a Lagrange interpolation-based method to obtain a penetration index interpolation polynomial. And calculating the predicted penetration index of the current section based on the penetration index interpolation polynomial.
In some embodiments, the historical surrounding rock parameters include rock strength and geologic indicators, and the historical operating parameters include heading speed. The surrounding rock collapse early warning device of the tunnel boring machine provided by the embodiment of the invention can further comprise a historical tunneling disturbance degree calculation module, wherein the method further comprises the step of before the historical tunneling disturbance degree-based calculation of the predicted tunneling disturbance degree of the current section. And calculating the historical tunneling disturbance degree based on a tunneling disturbance degree formula, the rock strength, the geological index and the tunneling speed. The tunneling disturbance degree formula comprises: d=exp (-0.017 UCS-0.007 GSI-0.019rop+1.923). Wherein, D is the tunneling disturbance degree, UCS is the rock strength, GSI is the geological index, and ROP is the tunneling speed.
In some embodiments, the predicted tunneling disturbance degree calculation module 320 is specifically configured to obtain a preset number of historical tunneling disturbance degrees with the smallest time difference from the current time, and number the preset number of historical tunneling disturbance degrees according to a time sequence. Fitting the historical tunneling disturbance degrees of the preset number by using a Lagrangian difference value-based method to obtain a tunneling disturbance degree interpolation polynomial. And calculating the predicted tunneling disturbance degree of the current section based on the tunneling disturbance degree interpolation polynomial.
In some embodiments, collapse probability calculation module 330 is specifically configured to: the first collapse probability is calculated based on a first collapse probability formula and the predicted penetration index.
And calculating a second collapse probability based on a second collapse probability formula and the predicted tunneling disturbance degree.
The first collapse probability formula includes:
wherein P (FPI) is the first collapse probability, FPI max FPI is the maximum value of the historical penetration index within a preset period min FPI is the minimum value of the historical penetration index within a preset period p To predict penetration index.
The second collapse probability formula includes:
wherein P (D) is a second collapse probability, D max D, setting the maximum value of the historical tunneling disturbance degree in a preset time period min D is the minimum value of the historical tunneling disturbance degree in a preset period p To predict the extent of the tunneling disturbance.
In some embodiments, the collapse warning information generating module 340 is specifically configured to: and calculating the current collapse probability of the surrounding rock based on the current collapse probability formula, the first collapse probability and the second collapse probability.
The current collapse probability formula comprises: p=0.538×p (FPI) +0.462×p (D); wherein P is the current collapse probability, P (FPI) is the first collapse probability, and P (D) is the second collapse probability.
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 40 of this embodiment includes: a processor 400, a memory 410, and a computer program 420 stored in the memory 410 and executable on the processor 400, such as a tunnelling machine surrounding rock collapse warning program. The steps of the embodiments of the method for pre-warning the collapse of surrounding rock of the tunneling machine described above, such as steps S101 to S104 shown in fig. 1, are implemented by the processor 40 when executing the computer program 420. Alternatively, the processor 400, when executing the computer program 420, performs the functions of the modules/units of the apparatus embodiments described above, e.g., the functions of the modules 310 to 340 shown in fig. 3.
Illustratively, the computer program 420 may be partitioned into one or more modules/units that are stored in the memory 410 and executed by the processor 400 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program 420 in the terminal device 40. For example, the computer program 420 may be partitioned into a predicted penetration index calculation module, a predicted tunneling disturbance degree calculation module, a collapse probability calculation module, and a collapse warning information calculation module.
The terminal device 40 may be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 400, a memory 410. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the terminal device 40 and is not limiting of the terminal device 40, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The processor 400 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 410 may be an internal storage unit of the terminal device 40, such as a hard disk or a memory of the terminal device 40. The memory 410 may also be an external storage device of the terminal device 40, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device 40. Further, the memory 410 may also include both an internal storage unit and an external storage device of the terminal device 40. The memory 410 is used for storing the computer program and other programs and data required by the terminal device. The memory 410 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. . Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. The utility model provides a tunnel boring machine country rock collapse early warning method which is characterized in that the method comprises the following steps:
calculating a predicted penetration index of the current section based on a historical penetration index, the historical penetration index being determined based on historical operating parameters of the tunneling machine;
calculating the predicted tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, wherein the historical tunneling disturbance degree is determined based on the historical operation parameters and the historical surrounding rock parameters of the tunnel boring machine;
calculating a first collapse probability based on the predicted penetration index, and calculating a second collapse probability based on the predicted tunneling disturbance degree;
and calculating the current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability, and generating collapse early warning information based on the current collapse probability.
2. The tunnel boring machine surrounding rock collapse early warning method according to claim 1, wherein the historical operating parameters comprise average thrust of a hob and penetration of a cutterhead;
before the calculating the predicted penetration index of the current section based on the historical penetration index, the method further comprises:
calculating the historical penetration index based on a penetration index formula, the average thrust of the hob and the cutter head penetration;
the penetration index formula includes: fpi=f/P; wherein FPI is the penetration index, F is the average thrust of a hob on a cutterhead of the tunnel boring machine, and P is the penetration of the cutterhead of the tunnel boring machine.
3. The method for pre-warning the collapse of surrounding rock of a tunnel boring machine according to claim 1, wherein the calculating the predicted penetration index of the current section based on the historical penetration index comprises:
acquiring a preset number of historical penetration indexes with the smallest time difference from the current moment, and numbering the preset number of historical penetration indexes according to a time sequence;
fitting the historical penetration indexes of the preset number based on a Lagrangian interpolation method to obtain a penetration index interpolation polynomial;
and calculating the predicted penetration index of the current section based on the penetration index interpolation polynomial.
4. The method for pre-warning surrounding rock collapse of a tunnel boring machine according to claim 1, wherein the historical surrounding rock parameters comprise rock strength and geological indexes, and the historical operating parameters comprise tunneling speed;
before calculating the predicted tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, the method further comprises the following steps:
calculating the historical tunneling disturbance degree based on a tunneling disturbance degree formula, the rock strength, the geological index and the tunneling speed;
the tunneling disturbance degree formula comprises: d=exp (-0.017 UCS-0.007 GSI-0.019rop+1.923), where D is the tunneling disturbance degree, UCS is the rock strength, GSI is the geological index, ROP is the tunneling speed.
5. The method for pre-warning surrounding rock collapse of a tunnel boring machine according to claim 1, wherein the calculating the predicted boring disturbance degree of the current section based on the historic boring disturbance degree comprises:
acquiring a preset number of historical tunneling disturbance degrees with the smallest time difference from the current moment, and numbering the preset number of historical tunneling disturbance degrees according to a time sequence;
fitting the historical tunneling disturbance degrees of the preset number by using a Lagrangian difference value-based method to obtain a tunneling disturbance degree interpolation polynomial;
and calculating the predicted tunneling disturbance degree of the current section based on the tunneling disturbance degree interpolation polynomial.
6. The tunneling machine surrounding rock collapse warning method according to any one of claims 1-5, wherein the calculating a first collapse probability based on the predicted penetration index and a second collapse probability based on the predicted tunneling disturbance degree comprises:
calculating a first collapse probability based on a first collapse probability formula and the predicted penetration index;
calculating a second collapse probability based on a second collapse probability formula and the predicted tunneling disturbance degree;
the first collapse probability formula includes:
wherein P (FPI) is the first collapse probability, FPI max FPI is the maximum value of the historical penetration index within a preset period min FPI is the minimum value of the historical penetration index within a preset period p To predict penetration index;
the second collapse probability formula includes:
wherein P (D) is a second collapse probability, D max The most historic tunneling disturbance degree in a preset time periodLarge value, D min D is the minimum value of the historical tunneling disturbance degree in a preset period p To predict the extent of the tunneling disturbance.
7. The tunneling machine surrounding rock collapse warning method according to any one of claims 1-5, wherein the calculating the current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability comprises:
calculating the current collapse probability of the surrounding rock based on the current collapse probability formula, the first collapse probability and the second collapse probability;
the current collapse probability formula comprises: p=0.538×p (FPI) +0.462×p (D); wherein P is the current collapse probability, P (FPI) is the first collapse probability, and P (D) is the second collapse probability.
8. Tunnel boring machine country rock early warning device that collapses, its characterized in that includes:
the predictive penetration index calculation module is used for calculating a predictive penetration index of the current section based on a historical penetration index, and the historical penetration index is determined based on historical operation parameters of the tunnel boring machine;
the predictive tunneling disturbance degree calculation module is used for calculating the predictive tunneling disturbance degree of the current section based on the historical tunneling disturbance degree, and the historical tunneling disturbance degree is determined based on the historical operation parameters and the historical surrounding rock parameters of the tunnel boring machine operation;
the collapse probability calculation module is used for calculating a first collapse probability based on the predicted penetration index and calculating a second collapse probability based on the predicted tunneling disturbance degree;
and the collapse early warning information generation module is used for calculating the current collapse probability of the surrounding rock based on the first collapse probability and the second collapse probability and generating collapse early warning information based on the current collapse probability.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.
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