CN109630154A - A kind of development machine people and remote mobile terminal command system for tunnel piercing - Google Patents
A kind of development machine people and remote mobile terminal command system for tunnel piercing Download PDFInfo
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Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/10—Making by using boring or cutting machines
- E21D9/108—Remote control specially adapted for machines for driving tunnels or galleries
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/10—Making by using boring or cutting machines
- E21D9/11—Making by using boring or cutting machines with a rotary drilling-head cutting simultaneously the whole cross-section, i.e. full-face machines
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a kind of development machine people for tunnel piercing and remote mobile terminal command systems, wherein, the robot includes: this tunneling machine body of development machine people, the sension unit being arranged on development machine, intelligent decision unit and control unit, sension unit and intelligent decision unit communicate to connect, and intelligent decision unit and control unit communicate to connect；Sension unit real-time perception carries out excavation operation data when excavation operation according to setting boring parameter information in current period, development machine；Intelligent decision unit receives the excavation operation data that sension unit is sent, and the setting boring parameter information in next period is generated according to default prediction algorithm, excavation operation data and expectation tunneling effect prediction；Control unit receives and carries out the excavation operation of next cycle according to setting boring parameter information control development machine.The robot can monitor tunneling process on-line, feedback control of making decisions on one's own and unmanned driving operate, and realization development machine is unattended or long-range control.
Description
Technical Field
The invention relates to the technical field of intelligent robots, in particular to a tunneling robot for tunneling and a remote mobile terminal command system.
Background
At present, China is the second economic entity in the world, and the demand for water resource cross-domain allocation, hydropower development and traffic construction is large. The major infrastructures of water conservancy and hydropower engineering, railway, highway traffic engineering and the like are constructed in succession, and the tunnel is a closed construction control engineering of the water conservancy and hydropower engineering and the traffic construction.
The demand of China's water conservancy and hydropower construction on the development machines will increase explosively, such as 264km total length of deep-buried long tunnels, 73km of longest tunnel sections, 1150m maximum buried depth of south-to-north water diversion west line first-stage projects in the planning, and the quantity of development machines required is nearly 20; the total length of the water diversion project in the Yunnan exceeds 650km, the project consists of more than 60 tunnels, and 2 heading machines are adopted for heading; the total length of the started diversion tunnel for Hanjiwei is 98.30km, the maximum buried depth exceeds 2000m, the tunnel passes through the main ridge section of Qinling mountain for 39.08km, and 2 development machines are adopted; the elegance development of the downstream water and electricity of the Yaluzang Bujiang reaches 6000 ten thousand kW, and the Yusong reservoir power station has 6 diversion tunnels, and the length of a single tunnel is about 30km, and 6 heading machines are estimated to be needed.
The construction focus of traffic engineering will shift to the west mountain area with complex terrain and geology and the east strait area blocked by water, and a large number of tunnel in hard mountain area and tunnel across river and sea will be built in future. According to statistics, 4.5 kilometers of high-speed rails are built in China by 2030, ten thousand kilometers of highway and railway tunnels are built in China in the next 10 years, and the extra-long tunnels exceed 2000 kilometers. The deep and long traffic tunnel engineering to be built is increasingly constructed by adopting heading machines, the demonstrated Bohai sea channel crossing sea tunnel is taken as an example, the overall length is planned to be about 120 kilometers, and the Chinese engineering institute proves that the best scheme is that the heading machines are mainly used, the drilling and blasting method is assisted, and about 15 heading machines are required in total.
Compared with a conventional drilling and blasting method, the adaptability of the construction of the heading machine to a bad geological tunnel section is poor, the construction of the heading machine is easily influenced by the bad geological tunnel section such as a fault fracture zone, a weak stratum, sudden water burst, high ground stress and the like in the construction of a long tunnel with large burial depth, the construction risks such as abnormal damage of a cutter head of a main bearing of the heading machine, low rock breaking efficiency, low heading speed, even machine sticking, trapping and the like exist, the loss is tens of millions or even hundreds of millions of yuan, and even the disastrous results of complete machine scrapping and casualties are caused. In addition, in the traditional construction mode, a master control room operator evaluates the surrounding rock state parameters through a slow trial tunneling process, and repeatedly adjusts the tunneling parameters until the tunneling parameters are stable, and finally, the master control room operator gives instructions to the execution mechanism of the tunneling machine to perform actions. On one hand, the operation mode can cause the cost of a large amount of construction time, on the other hand, when the situation of the surrounding rock is changed greatly, the actual parameter situation of the current surrounding rock cannot be effectively sensed in real time, so that the tunneling parameters cannot adapt to the current tunneling environment, the abnormal abrasion of a cutter and the reduction of the rock breaking performance of a cutter head system are caused, the key parts of the hard rock tunneling machine are damaged and shut down under severe conditions, the normal service life of the hard rock tunneling machine is influenced, errors and even errors caused by manually controlling the tunneling machine executing mechanism to perform tunneling actions are quite unfavorable for the fine control of the tunneling machine, the tunneling construction quality cannot be expected under light conditions, and serious construction accidents of machine damage and people death are caused under severe conditions.
In order to solve the construction problems of large faults, broken zones, high ground stress, rock burst, sudden water burst and other unfavorable geological tunnel sections under complex geological conditions faced by tunnel construction and other problems, an intelligent Tunnel Boring Robot (TBR) is developed, a tunneling parameter intelligent decision theory and an optimization control method of a tunneling machine are explored, and finally a set of basic theory, a practical method, supporting software and a key technology for real-time advanced detection and intelligent decision of tunneling of the unfavorable geology of the tunneling machine construction are formed, so that major disaster accidents of tunneling of the tunneling machine are avoided and prevented, the safety, the efficiency and the high-quality tunneling of the tunneling machine are guaranteed, and the tunnel boring Robot has extremely important significance for improving the core competitiveness of intelligent tunnel construction and tunneling machine manufacturing in China.
Aiming at the construction problems of large faults, broken zones, high ground stress, rock burst, burst water and other unfavorable geological tunnel sections under complex geological conditions faced by tunnel construction at present, the state parameters of surrounding rocks can be guessed only by the artificial experience of operators in a master control room in related operation modes, and the problems of time and labor consumption, abnormal wear and even damage of cutters of the tunneling machine and the cutter head, damage of other key parts of the tunneling machine and the like caused by continuously trying different tunneling parameters are solved, and the tunneling construction quality caused by manually controlling an executing mechanism of the tunneling machine to carry out tunneling action cannot be expected or even causes major construction accidents.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
To this end, an object of the present invention is to provide a tunneling robot for tunneling, which can perform on-line monitoring, autonomous decision feedback control, and unmanned tunneling operation of a tunneling machine, and realize unmanned operation or remote control of the tunneling machine.
Another object of the present invention is to propose a remote mobile terminal command system for tunnelling.
In order to achieve the above object, an embodiment of the invention provides a tunneling robot for tunneling, including: the system comprises a main body excavator of the tunneling robot, a sensing unit, an intelligent decision unit and a control unit, wherein the sensing unit is in communication connection with the intelligent decision unit, the intelligent decision unit is in communication connection with the control unit, the sensing unit is used for sensing tunneling operation data in a current period in real time when the tunneling robot performs tunneling operation according to set tunneling parameter information, and the tunneling operation data comprises tunnel surrounding rock state information, tunnel geological forecast information, fine exploration information, accurate lining information, a real-time tunneling effect information index and real-time tunneling parameter information; the intelligent decision unit is used for receiving the tunneling operation data sent by the sensing unit and generating the set tunneling parameter information of the next period according to a preset prediction algorithm, the tunneling operation data and the expected tunneling effect prediction; and the control unit is used for receiving the set tunneling parameter information and controlling the tunneling machine to carry out tunneling operation of the next period according to the set tunneling parameter information.
The tunneling robot for tunneling can perform on-line monitoring, autonomous decision feedback control and unmanned tunneling operation on a tunneling process, realize unmanned operation or remote control of the tunneling machine, is particularly suitable for tunneling construction scenes of rock tunnels and underground passages in all weather, dangerous areas, unmanned areas or extreme conditions, and can be autonomously adapted to tunneling machines with different surrounding rocks, different diameters and different performances and the use of the same tunneling machine in different stages of the whole life cycle so as to improve the tunneling construction quality and the construction efficiency and realize the fine management of engineering construction.
In addition, the tunneling robot for tunneling according to the above embodiment of the present invention may further have the following additional technical features:
further, in one embodiment of the invention, the tunnel surrounding rock state information comprises rock uniaxial saturation compressive strength Rc(x, y, z) and the number of joints J per volume of rock massv(x, y, z), surrounding rock grade W (x, y, z), tunneling parameter information including single-blade thrust Ft(x, y, z), single blade torque Tn(x, y, z), cutterhead speed N (x, y, z), propulsion speed V (x, y, z), and heading guide D (x, y, z).
Further, in an embodiment of the present invention, the sensing unit includes a leading geology detection subunit, and the leading geology detection subunit is configured to obtain the tunnel geology forecast information by detecting with a horizontal ultra-long drilling technology.
Further, in an embodiment of the present invention, the preset prediction algorithm includes a tunneling parameter prediction algorithm with a neural network structure as a core; and the intelligent decision unit is also used for training and generating the tunneling parameter prediction algorithm according to historical tunneling operation data.
Further, in one embodiment of the present invention, the control unit comprises an electrical control subunit, wherein the electrical control subunit is established by a plurality of PLCs communicatively connected to each other by a network communication interface; and the electrical control subunit is used for controlling corresponding tunneling devices in the tunneling machine to execute tunneling actions according to the set tunneling parameter information through the plurality of PLCs.
Further, in an embodiment of the present invention, the electrical control subunit includes an a/D conversion function module, a PLC control program module, and a D/a conversion function module, where the a/D conversion function module and the D/a conversion function module are both in communication connection with the PLC control program module, and when the set tunneling parameter information includes a single-blade thrust, the a/D conversion function module is configured to output a target thrust value corresponding to a voltage value corresponding to the single-blade thrust according to the voltage value corresponding to the single-blade thrust; the PLC control program module is used for calculating the quantity value and the current thrust value of the thrust gear of the operating panel, calculating the thrust difference value between the target thrust value and the current thrust value, and obtaining a thrust control digital value according to a preset algorithm and the thrust difference value; the D/A conversion function module is used for converting the thrust control digital value into a thrust control analog value; the control unit is specifically configured to control a hydraulic servo system and a hydraulic proportional valve, which provide thrust for the operating panel, according to the thrust control analog value, so that the single-blade thrust of the operating panel reaches the target thrust value.
Further, in an embodiment of the present invention, the sensing unit is specifically configured to:
acquiring N relative errors of tunneling direction planning data and actual measurement data of a tunneling direction of a tunneling machine tunneling guide system in a current period for N times according to a preset acquisition frequency, and calculating a first error average value of the N relative errors, wherein N is a positive integer greater than 1;
acquiring N groups of set tunneling parameter information and real-time tunneling parameter information of the tunneling machine in the current period according to a preset acquisition frequency, and calculating relative errors of all parameters in all groups of tunneling parameter information in the N groups of set tunneling parameter information and the real-time tunneling parameter information to obtain an error set;
calculating a second error average value of each parameter according to the N relative errors corresponding to each parameter in the error set;
performing digital average operation on the first error average value and the second error average value to obtain an average valueAnd acquiring the real-time tunneling effect information index according to the following formula, wherein A (x, y, z) is the real-time tunneling effect information index:
further, in an embodiment of the present invention, the intelligent decision unit is further configured to determine a tunneling mode according to the fine exploration information, and send the tunneling mode to the control unit, so that the control unit controls the tunneling machine to perform the tunneling operation according to the tunneling mode; and the intelligent decision unit is also used for generating disaster early warning information according to the tunnel geological forecast information so as to facilitate the development machine to take development disaster avoidance measures according to the disaster early warning information.
In order to achieve the above object, in another embodiment of the present invention, a remote mobile terminal command system for tunneling is provided, the system includes a tunneling robot and a command system, wherein the tunneling robot includes the tunneling robot for tunneling, and the command system is in communication connection with the tunneling robot, wherein the command system includes a mobile terminal and a cloud service unit, wherein the cloud service unit is configured to perform real-time monitoring on a tunneling construction site, perform real-time storage, analysis, display, playback, and management on tunneling operation data transmitted by a wireless manner on the tunneling construction site, and support real-time human-machine interaction between the command system and the tunneling machine to implement a tunneling mode for remotely controlling the tunneling machine to perform construction operation, and update, play, and manage a decision model of an intelligent decision unit, The command system issues a tunneling operation task to provide real-time support for the intelligent decision unit;
the mobile terminal is used for loading an autonomously developed command system application program to access a cloud service unit at the cloud end in real time for man-machine interaction, allowing a user to control the tunneling machine in a remote control mode, and allowing the user to remotely monitor the operation condition of a tunneling construction site in real time.
The remote mobile terminal command system for tunneling can perform on-line monitoring, autonomous decision feedback control and unmanned tunneling operation on a tunneling process, realize unmanned operation or remote control of the tunneling machine, is particularly suitable for tunneling construction scenes of rock tunnels and underground passages in all weather, dangerous areas, unmanned areas or extreme conditions, and can be autonomously adapted to the use of tunneling machines with different surrounding rocks, different diameters and different performances and the use of the same tunneling machine in different stages of the whole life cycle so as to improve the tunneling construction quality and the construction efficiency and realize the fine management of engineering construction.
In addition, the remote mobile terminal command system for tunneling according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the mobile terminal is further configured to receive a tunneling operation instruction, and send the tunneling operation instruction to the tunneling robot, so as to control the tunneling robot to perform tunneling operation according to the tunneling operation instruction.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a structural view of a tunneling robot for tunneling according to an embodiment of the present invention;
FIG. 2 is a block diagram of an intelligent decision making system according to one embodiment of the present invention;
FIG. 3 is a flow diagram of an intelligent decision model update according to one embodiment of the present invention;
fig. 4 is a block diagram of a control system of the intelligent tunneling robot according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a remote mobile terminal command system for tunneling according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A tunneling robot and a remote mobile terminal command system for tunnel boring according to embodiments of the present invention will be described below with reference to the accompanying drawings, and first, a tunneling robot for tunnel boring according to embodiments of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a structural view of a tunneling robot for tunneling according to an embodiment of the present invention.
As shown in fig. 1, the tunneling robot for tunneling includes: the system comprises a data line 1, a main body excavator 5 of the tunneling robot, a sensing unit 2 arranged on the excavator, an intelligent decision unit 3 and a control unit 4.
The sensing unit 2 is used for sensing tunneling operation data in real time during tunneling operation of the tunneling machine according to set tunneling parameter information in the current period, wherein the tunneling operation data comprises tunnel surrounding rock state information, tunnel geological forecast information, fine exploration information, accurate lining information, real-time tunneling effect information indexes and real-time tunneling parameter information;
the intelligent decision unit 3 is used for receiving the tunneling operation data sent by the sensing unit and generating the set tunneling parameter information of the next period according to a preset prediction algorithm, the tunneling operation data and the expected tunneling effect prediction;
and the control unit 4 is used for receiving the set tunneling parameter information and controlling the tunneling machine to carry out tunneling operation of the next period according to the set tunneling parameter information.
Further, still include: command system and the cloud service unit in the command system.
Specifically, the invention provides an intelligent Tunnel Boring Robot (TBR), which utilizes a sensing unit 2 to acquire surrounding rock state information, Boring parameter information, geological forecast information, Boring effect information, fine exploration information and accurate lining information in the Boring process of a Boring machine in real time and feed back the information to a cloud service unit in real time; the intelligent decision unit 3 is used for analyzing the sensing data in real time, evaluating the tunneling effect of the tunneling machine, autonomously making a tunneling strategy decision in real time and feeding back decision information to the cloud service unit in real time; then, the control unit 4 adjusts the running and operating states of the development machine in real time and feeds back the state information to the cloud service unit in real time according to the machine decision information; and finally, the tunneling machine 5 carries out tunneling action implementation and continuously feeds back tunneling operation data to the sensing unit 2 in real time. The remote mobile terminal command system 6 monitors site construction in real time, remotely controls human-computer interaction 5, and sends tunneling operation tasks to the intelligent decision unit 3 through the cloud service unit.
In one embodiment of the invention, the intelligent decision-making system comprises a sensing unit, an intelligent decision-making unit, a control unit, a heading machine, a remote mobile terminal command system and a cloud service unit.
Wherein, the heading machine is an intelligent tunneling Robot (TBR) body.
The sensing unit is used for acquiring surrounding rock state information, tunneling parameter information, geological forecast information, tunneling effect information, fine exploration information and accurate lining information in the tunneling process of the tunneling machine in real time and feeding the information back to the cloud service unit in real time.
The intelligent decision unit receives surrounding rock state information, geological forecast information, fine exploration information, accurate lining information, tunneling effect information and tunneling parameter information in the tunneling process of the tunneling machine, which are acquired by the sensing unit, forms training data by using the data acquired by the sensing unit, generates a prediction algorithm through data training, and finally obtains the optimal tunneling parameter of the next tunneling period through the tunneling parameter of the previous tunneling period through the prediction algorithm and feeds back the decision information to the cloud service unit in real time.
The control unit adjusts the operation and working states of the heading machine in real time according to machine decision information, namely optimal heading parameters, drives the heading machine to make corresponding actions and feeds back state information to the cloud service unit in real time, the sensing unit performs data acquisition on heading effects generated according to current control parameters, then forms new training data together with the control parameters, and performs updating of the intelligent decision model, so that a periodically-circulating closed-loop system is formed.
Further, in one embodiment of the invention, the tunnel surrounding rock state information comprises rock uniaxial saturation compressive strength Rc(x, y, z) and the number of joints J per volume of rock massv(x, y, z), surrounding rock grade W (x, y, z), tunneling parameter informationIncluding a single-blade thrust Ft(x, y, z), single blade torque Tn(x, y, z), cutterhead speed N (x, y, z), propulsion speed V (x, y, z), and heading guide D (x, y, z).
Further, in an embodiment of the present invention, the sensing unit includes a leading geology detection subunit, and the leading geology detection subunit is configured to obtain the tunnel geology forecast information by detecting with a horizontal ultra-long drilling technology.
Specifically, the sensing information acquired by the sensing unit in real time comprises surrounding rock state information, geological forecast information, tunneling parameter information, tunneling effect information, fine exploration information and accurate lining information, the acquired sensing information is fed back to the cloud service unit in a wireless transmission mode in real time, the geological forecast information is detected by an advanced geological detection system which has a long detection distance and can effectively detect the characteristics of bad geological bodies such as faults, broken zones and the like occurring in front of a tunnel face and takes a horizontal super-long drilling technology as a core to provide corresponding detection data Forcasting (x, y, z), early warning and forecast are provided for disasters such as rock burst, tunneling machine blocking, water inrush and the like, the tunneling effect information comprises tunneling direction planning data DirectionDesign (x, y, z) in a design drawing and actual measurement data Directionasurement (x) of the tunneling direction of the tunneling machine tunneling guide system, y, z) and actual measurement data of main parameters of the heading machinet(x, y, z), single blade torque Tn(x, y, z), a cutter head rotating speed N (x, y, z), a propelling speed V (x, y, z) and a tunneling effect index value A (x, y, z) calculated based on the data, wherein fine exploration information comprises information fineexpansion (x, y, z) such as ground stress, rock mass strength, geological structure, osmotic pressure distribution and the like, fine geological condition exploration is carried out by a tunnel geological exploration technology of horizontal ultra-long drilling, a three-dimensional fine geological model is established based on the information, support is provided for obtaining surrounding rock state information and tunneling effect information, and accurate lining information predistringlinking (x, y, z) is provided by an accurate lining intelligent operation system and comprises intelligent evaluation information of local collapse risk, intelligent collapse branchProtection decision information and tunnel bottom accumulated slag cleaning information.
The surrounding rock state information comprises rock uniaxial saturated compressive strength, joint number of unit rock volume and surrounding rock grade, the surrounding rock in the area of the tunneling construction area is subjected to coring and sample reserving in advance, a surrounding rock joint graph is drawn, and indoor tests are performed to obtain the uniaxial saturated compressive strength R of the surrounding rock according to the retrieved rock corec(x, y, z), counting the joint number J of the unit rock mass volume according to the classification standard of the engineering rock mass according to the surrounding rock joint map near the coring pile numberv(x, y, z) value, J number of joints per volume of rock massvAnd in the process of converting the (x, y, z) value into a completeness index value Kv (x, y, z), calculating to obtain a continuous surrounding rock grade W (x, y, z) by adopting a fitting interpolation method. The tunneling parameter information includes a single-blade thrust Ft(x, y, z), single blade torque Tn(x, y, z), a cutter head rotating speed N (x, y, z), a propelling speed V (x, y, z) and a tunneling guide D (x, y, z), wherein the single-cutter thrust, the single-cutter torque, the cutter head rotating speed, the propelling speed and the tunneling guide are acquired in real time by sensors arranged on the tunneling machine. The tunneling effect information comprises tunneling direction planning data DirectionDesign (x, y, z) in a design drawing, actual measurement data DirectionDesign (x, y, z) of the tunneling direction of a tunneling machine tunneling guide system and single-blade thrust F of actual measurement data of main parameters of the tunneling machinet(x, y, z), single blade torque Tn(x, y, z), a cutter head rotation speed N (x, y, z), a propulsion speed V (x, y, z), and a tunneling effect index value a (x, y, z) calculated based on these data.
Further, in an embodiment of the present invention, the preset prediction algorithm includes a tunneling parameter prediction algorithm with a neural network structure as a core; and the intelligent decision unit is also used for training and generating a tunneling parameter prediction algorithm according to historical tunneling operation data.
As shown in fig. 2, the intelligent decision unit 3 implements an intelligent decision by starting a tunneling operation in a tunneling period to form an intelligent decision cycle, automatically adjusting tunneling parameters of the tunneling machine, and performing unmanned tunneling operation in a multi-stage joint control and multi-system cooperation manner:
the tunneling parameter prediction algorithm with the neural network algorithm as the core determines the tunneling mode of the tunneling machine by using fine exploration information (x, y, z) obtained by a sensing unit, then according to the geological forecast information Forcasting (x, y, z) obtained by the perception unit, the construction period disasters such as rock burst, water inrush and the like are pre-warned and forecasted, and corresponding control measures are taken according to the disaster conditions, then, the sensing unit is utilized to obtain surrounding rock state information, accurate lining information and tunneling operation data fed back by the tunneling machine, namely real-time tunneling effect information and tunneling parameter information, the tunneling parameters used in the next tunneling period of the tunneling machine for on-site tunneling operation are predicted, realizing efficient tunneling operation on the premise of determining a tunneling mode of the tunneling machine and eliminating or eliminating disasters in the construction period, and forming next construction period circulation with an accurate lining intelligent operation system;
the tunneling parameter prediction algorithm carries out training and updating by periodically loading real-time historical empirical data, then carries out real-time prediction on the tunneling parameters of the next tunneling period by using current perception information, wherein the current perception information is the characteristics of training data required by the period updating algorithm and mainly comprises rock uniaxial saturated compressive strength Rc(x, y, z) and the number of joints J per volume of rock massv(x, y, z), wall rock class W (x, y, z), single blade thrust Ft(x, y, z), single blade torque Tn(x, y, z), a cutter head rotating speed N (x, y, z), a propelling speed V (x, y, z), a tunneling guide D (x, y, z) and a tunneling effect index value A (x, y, z), wherein the tunneling parameters of the tunneling machine in the next tunneling period are target parameters to be estimated by a prediction algorithm of the decision system, and the tunneling parameters of the tunneling machine in the next tunneling period and the action of the accurate lining intelligent operation system are directly determined.
One tunneling period consists of one tunneling stroke tunneling time and step-changing reset time, one tunneling period is divided into 10 equal parts, and each equal part utilizes the sensing unit to perform sensing measurement once.
As shown in fig. 3, the tunneling parameter prediction algorithm mainly performs intelligent real-time prediction on tunneling parameters, and related tunneling construction process data, such as surrounding rock state information, geological forecast information, fine exploration information, accurate lining information, tunneling effect information, tunneling parameter information and the like, obtained by the sensing unit are subjected to a prediction algorithm (predictionmodel (x)) to obtain tunneling parameter data of the next period. The prediction formula is as follows:
the cloud service unit provides support for periodic updating of a tunneling parameter prediction algorithm, taking a neural network algorithm as an example, the prediction algorithm selects a convolutional neural network structure, the input characteristic data type of the structure has spatial three-dimensional coordinates (x, y and z), and the uniaxial saturated compressive strength of rock before tunnelingNumber of joints per unit volume of rock massGrade of wall rock Wi-1(x, y, z), single blade thrustTorque of single knifeCutter head rotating speed Ni-1(x, y, z), propulsion speed Vi-1(x, y, z) and a tunneling effectiveness index value Ai-1(x, y, z), uniaxial saturated compressive strength of the rock after excavationNumber of joints per unit volume of rock massGrade of wall rock Wi(x，y，z) single blade thrustTorque of single knifeCutter head rotating speed Ni(x, y, z), propulsion speed Vi(x, y, z) heading guide Di(x, y, z) and a tunneling effectiveness index value Ai(x, y, z), the output target of the training model being a heading parameter, i.e. the single-blade thrust F of the heading machinet(x, y, z), single blade torque Tn(x, y, z), cutterhead speed N (x, y, z), propulsion speed V (x, y, z) and heading guide D (x, y, z). Meanwhile, in order to consider that the tunneling effect data of multi-pass circular tunneling at the same position of a tunnel or an underground passage has correlation, as the tunneling operation effect is slowly accumulated and then gradually approaches to the expected tunneling effect, the feature data of the latest 5 tunneling periods are selected to form a feature matrix, when the 210 training sample matrices are accumulated, model training is carried out on the updating of the prediction algorithm, the prediction algorithm is updated, and then the updated prediction algorithm is synchronized into the intelligent decision unit of the tunneling machine.
The data characteristic of the ith tunneling period of the spatial three-dimensional coordinates (x, y, z) is represented as:
the input data for training the prediction algorithm is:
Xi(x，y，z)＝[xixi-1xi-2xi-3xi-4]
thus XiAnd (x, y, z) is a two-dimensional matrix of 21 x 5, and the latest tunneling history characteristic data in the same area is formed. The tunneling parameter data of the ith tunneling period of the spatial three-dimensional coordinates (x, y, z) are as follows:
the neural network structure of the intelligent decision model is shown in fig. 3, and the model is constructed based on the convolutional neural network structure and mainly comprises a plurality of convolutional layers, a pooling layer and a final full-connection layer for outputting to realize intelligent prediction of the tunneling parameters.
Next, referring to fig. 3, a training process of the tunneling parameter prediction algorithm is further described, and after tunneling construction is performed on a region with an nth tunneling period space three-dimensional coordinate (x, y, z), characteristic data x of the process is obtainedi(x, y, z) and then combining historical data of the first 5 tunneling cycles of the tunneling position, wherein the sample training data of the position is as follows:
after the sample data is iterated through the neural network structure shown in fig. 3, the sample data is trained to obtain the weight parameters and the bias parameters of each layer of network, that is, the model data of the network structure:
K＝[k1，k2，k3，k4，k5]，B＝[b1，b2，b3，b4，b5]
knweight data for each network layer, bnAnd the two parameters jointly form a prediction algorithm of the tunneling parameters for the paranoia parameters of each network layer.
Further, in one embodiment of the present invention, the control unit comprises an electrical control subunit, wherein the electrical control subunit is established by a plurality of PLCs communicatively connected to each other by a network communication interface; and the electrical control subunit is used for controlling the corresponding tunneling devices in the tunneling machine through the plurality of PLCs to execute tunneling actions according to the set tunneling parameter information.
Further, in an embodiment of the present invention, the electrical control subunit includes an a/D conversion function module, a PLC control program module, and a D/a conversion function module, both the a/D conversion function module and the D/a conversion function module are in communication connection with the PLC control program module, and when the tunneling parameter information is set to include a single-blade thrust, the a/D conversion function module is configured to output a target thrust value corresponding to a voltage value according to the voltage value corresponding to the single-blade thrust; the PLC control program module is used for calculating a numerical value and a current thrust value of a thrust gear of the operating panel, calculating a thrust difference value of a target thrust value and the current thrust value, and obtaining a thrust control numerical value according to a preset algorithm and the thrust difference value; the D/A conversion function module is used for converting the thrust control digital value into a thrust control analog value; and the control unit is specifically used for controlling a hydraulic servo system and a hydraulic proportional valve which provide thrust for the operating panel according to the thrust control analog value so as to enable the single-blade thrust of the operating panel to reach a target thrust value.
Specifically, as shown in fig. 4, the control unit completes the electrical control task of the whole heading machine with a plurality of PLCs as the core, a plurality of PLCs establish communication through a network communication interface, and then construct a complete electrical control system of the heading machine, the PLC system automatically resets and adjusts the gear of the cutter head thrust, the cutter head torque, the heading guidance, the cutter head rotation speed and the heading speed of the heading machine according to the optimal heading parameter given by the intelligent decision unit, and then drives the heading machine to make a corresponding action, and the heading machine continuously feeds back the heading operation data to the sensing unit in real time while executing the action, taking the automatic control of the cutter head thrust as an example for explanation:
the thrust gear setting potentiometer on the operation disc is automatically reset and adjusted by machine related parameters given by the intelligent decision unit, the parameters are a voltage value, the voltage value is sent to an A/D conversion function module in a PLC system, the A/D conversion function module converts the voltage value into a digital quantity, the digital quantity represents a cutter disc thrust given value, a PLC control program obtains a difference value by comparing a cutter disc thrust set value with an actual thrust value of a cutter disc detected by a pressure sensor, then a control quantity is obtained by calculating through an automatic control algorithm according to the difference value, the PLC control system converts the control quantity into an analog quantity by using the D/A conversion function module and controls and adjusts the pressure of a thrust liquid pressure cylinder of the cutter disc through a hydraulic servo system and a hydraulic proportional valve, so that the actual thrust of the cutter disc approaches to the set value of the cutter disc thrust gear, the purpose of automatically controlling the thrust of the cutter head is achieved.
Further, in an embodiment of the invention, the intelligent decision unit is further configured to determine a tunneling mode according to the fine exploration information, and send the tunneling mode to the control unit, so that the control unit controls the tunneling machine to perform tunneling operation according to the tunneling mode; and the intelligent decision unit is also used for generating disaster early warning information according to the tunnel geological forecast information so as to facilitate the development machine to take development disaster evasion measures according to the disaster early warning information.
The heading machine is a full-face hard rock Tunnel heading machine for rock stratum, consists of a heading machine main machine system and a heading machine rear matching system, and is an intelligent tunneling Robot (TBR) body.
The tunneling mode of the tunneling machine comprises two modes, wherein the first mode is a double-shield tunneling mode under the condition that a tunnel rock stratum is stable, namely a supporting shoe oil cylinder is propped out, a supporting shoe is tightly pressed against a rock wall, and the supporting shoe and the rock wall provide support for tunneling through static friction force, and the mode can realize synchronization of multiple procedures such as tunneling, step changing, duct piece supporting, backfilling and grouting and the like; the second mode adopts single shield mode to tunnel when tunnel rock stratum is unstable, the supporting shoe can not obtain enough counter-force, assists promptly to push away the hydro-cylinder and acts on the section of jurisdiction terminal surface and provides the support for the tunnelling, the process of stepping can not go on simultaneously under this mode, and the section of jurisdiction is assembled the process in-process, and the auxiliary cylinder still plays the effect that compresses tightly the section of jurisdiction.
Furthermore, the command system comprises a cloud service unit, a mobile terminal and a user, wherein the mobile terminal comprises a smart phone, a tablet personal computer and a portable notebook computer, and is used for loading an autonomously developed command system application program (app) to access the cloud service unit at the cloud end in real time for man-machine interaction, enabling the user to control the heading machine in a remote control mode, enabling the user to monitor the field heading construction operation condition in real time and remotely, and issuing a heading operation task to the intelligent decision unit remotely through the cloud service unit; the user is a tunneling machine operator and a tunneling construction commander which are trained on site in a remote control mode.
The cloud service unit in the command system can monitor the on-site tunneling construction in real time, store, analyze, display, playback and manage the tunneling operation data transmitted in a wireless mode on site in real time, support the command system and the tunneling machine to perform real-time man-machine interaction so as to realize a mode of remotely controlling the tunneling machine to perform construction operation, and simultaneously update a decision model of the intelligent decision unit and issue a tunneling operation task to the intelligent decision unit by the command system to provide real-time support.
Further, in an embodiment of the present invention, the sensing unit is specifically configured to: acquiring N relative errors of the planning data of the tunneling direction and the actual measurement data of the tunneling direction of the tunneling machine tunneling guide system in the current period for N times according to a preset acquisition frequency, and calculating a first error average value of the N relative errors, wherein N is a positive integer greater than 1;
acquiring N groups of set tunneling parameter information and real-time tunneling parameter information of the tunneling machine in the current period according to a preset acquisition frequency, and calculating relative errors of all parameters in all groups of tunneling parameter information in the N groups of set tunneling parameter information and the real-time tunneling parameter information to obtain an error set;
calculating a second error average value of each parameter according to the N relative errors corresponding to each parameter in the error set;
carrying out digital average operation on the first error average value and the second error average value to obtain an average valueAnd obtains the real-time tunneling effect information index according to the following formula,wherein, A (x, y, z) is a real-time tunneling effect information index:
specifically, the tunneling effect index value A (x, y, z) is determined by tunneling direction planning data (x, y, z) in a design drawing, actual measurement data (x, y, z) of the tunneling direction of a tunneling machine tunneling guide system and actual measurement data of main parameters of the tunneling machine, namely, single-blade thrust Ft(x, y, z), single blade torque Tn(x, y, z), the rotating speed N (x, y, z) of the cutter head and the propelling speed V (x, y, z) are determined together, and the specific method is as follows:
step 1: calculating relative errors of heading direction planning data (x, y, z) in a design drawing in a heading period and actual measurement data Directionasummary (x, y, z) of the heading direction of a heading guide system of the heading machine, namely respectively calculating 10 relative error values, wherein N is 10, and then calculating the average value of the 10 relative error values
Step 2: calculating relative errors between a set value of main parameters of the tunneling machine and actual measured values detected by a sensor in a tunneling period, namely calculating the relative errors of the main parameters for 10 times respectively, and then calculating the average relative errors of the main parameters for 10 times respectively, namely calculating the average relative errors of the main parameters for 10 times respectivelyTorque of single knifeRotational speed of cutter headPropulsion speed
And step 3: mathematically averaging the average relative error structure obtained in steps 1 to 2:
and 4, step 4: calculating the index value of the tunneling effect
According to the tunneling robot for tunneling provided by the embodiment of the invention, an intelligent flow of a tunneling process can be realized, the online real-time monitoring and intelligent feedback control can be carried out on the tunneling process, the unmanned operation or remote control of the tunneling machine is realized, the self-adaptive adjustment of working parameters of the tunneling machine to a permission state is guided in the tunneling process, the real-time, intelligent, efficient and high-quality tunneling process is ensured, the tunneling construction quality and efficiency of tunnels and underground passages are improved, and the intelligent control of the construction of rock tunnels and underground passage tunneling equipment and the fine management of the tunneling process in the fields of hydraulic and hydroelectric engineering, railways, road traffic engineering and the like are realized.
Next, a remote mobile terminal command system for tunneling proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 5 is a schematic structural diagram of a remote mobile terminal command system for tunneling according to an embodiment of the present invention.
As shown in fig. 5, the remote mobile terminal commanding system includes: a tunneling robot and a command system.
The tunneling robot comprises the tunneling robot for tunneling, and the command system is in communication connection with the tunneling robot, wherein the command system comprises a mobile terminal and a cloud service unit.
The cloud service unit is used for monitoring the on-site tunneling construction in real time, storing, analyzing, displaying, replaying and managing tunneling operation data transmitted in a wireless mode on site in real time, supporting a command system and a tunneling machine to perform real-time man-machine interaction so as to realize a mode of remotely controlling the tunneling machine to perform construction operation, and simultaneously updating a decision model of the intelligent decision unit and issuing a tunneling operation task to the intelligent decision unit by the command system to provide real-time support for the intelligent decision unit;
and the mobile terminal is used for loading an autonomously developed command system application program (app) to access the cloud service unit at the cloud end in real time for man-machine interaction, controlling the tunneling machine in a remote control mode for a user, and remotely monitoring the field tunneling construction operation condition in real time for the user.
Further, in an embodiment of the present invention, the mobile terminal is further configured to receive a tunneling operation instruction, and send the tunneling operation instruction to the tunneling robot, so as to control the tunneling robot to perform tunneling operation according to the tunneling operation instruction.
It should be noted that the foregoing explanation of the embodiment of the tunneling robot for tunneling is also applicable to a remote mobile terminal command system for tunneling of this embodiment, and details are not repeated here.
According to the remote mobile terminal command system for tunneling provided by the embodiment of the invention, the method can carry out real-time and effective on-line monitoring on hard rock tunnel excavation construction under different geological conditions, different construction modes and different construction stages, realizes unmanned operation or remote control of the excavator by autonomous intelligent decision feedback control and unmanned excavation construction operation of the excavator, the method accurately and effectively controls the tunneling parameters, avoids the influence of human factors, effectively ensures the tunneling construction quality, has the characteristics of detection, analysis, decision and control of cyclic closed loop realization, is particularly suitable for the scenes of all-weather, dangerous areas, unmanned areas or extreme condition hard rock tunnel tunneling construction operation, the construction quality of rock tunnels and underground passage engineering can be effectively improved, the engineering construction efficiency is improved, and the rock tunnel and underground passage engineering construction in the fields of hydraulic and hydroelectric engineering, railways, highway traffic engineering and the like is led to the fine management era.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A tunneling robot for tunneling, characterized by comprising: the self-body excavator of the tunneling robot, a sensing unit arranged on the excavator, an intelligent decision unit and a control unit, wherein the sensing unit is in communication connection with the intelligent decision unit, the intelligent decision unit is in communication connection with the control unit, wherein,
the sensing unit is used for sensing tunneling operation data in a current period in real time when the tunneling machine performs tunneling operation according to set tunneling parameter information, wherein the tunneling operation data comprises tunnel surrounding rock state information, tunnel geological forecast information, fine exploration information, accurate lining information, real-time tunneling effect information indexes and real-time tunneling parameter information;
the intelligent decision unit is used for receiving the tunneling operation data sent by the sensing unit and generating the set tunneling parameter information of the next period according to a preset prediction algorithm, the tunneling operation data and the expected tunneling effect prediction;
and the control unit is used for receiving the set tunneling parameter information and controlling the tunneling machine to carry out tunneling operation of the next period according to the set tunneling parameter information.
2. The tunneling robot according to claim 1,
the tunnel surrounding rock state information comprises rock uniaxial saturation compressive strength Rc(x, y, z) and the number of joints J per volume of rock massv(x, y, z), surrounding rock grade W (x, y, z), tunneling parameter information including single-blade thrust Ft(x, y, z), single blade torque Tn(x, y, z), cutterhead speed N (x, y, z), propulsion speed V (x, y, z), and heading guide D (x, y, z).
3. The tunneling robot of claim 1, wherein the sensing unit comprises a leading geology detection subunit,
and the advanced geological detection subunit is used for detecting by using a horizontal ultra-long drilling technology to obtain the tunnel geological forecast information.
4. The tunneling robot of claim 1, wherein the preset prediction algorithm comprises a neural network structure-based tunneling parameter prediction algorithm;
and the intelligent decision unit is also used for training and generating the tunneling parameter prediction algorithm according to historical tunneling operation data.
5. The tunneling robot of claim 1, wherein the control unit comprises an electrical control subunit, wherein the electrical control subunit is established by a plurality of PLCs communicatively connected to one another via a network communication interface;
and the electrical control subunit is used for controlling corresponding tunneling devices in the tunneling machine to execute tunneling actions according to the set tunneling parameter information through the plurality of PLCs.
6. The tunneling robot of claim 5, wherein the electrical control subunit comprises an A/D conversion function module, a PLC control program module and a D/A conversion function module, both the A/D conversion function module and the D/A conversion function module are in communication connection with the PLC control program module, and when the set tunneling parameter information includes a single-blade thrust,
the A/D conversion function module is used for outputting a target thrust value corresponding to the voltage value according to the voltage value corresponding to the single-blade thrust;
the PLC control program module is used for calculating the quantity value and the current thrust value of the thrust gear of the operating panel, calculating the thrust difference value between the target thrust value and the current thrust value, and obtaining a thrust control digital value according to a preset algorithm and the thrust difference value;
the D/A conversion function module is used for converting the thrust control digital value into a thrust control analog value;
the control unit is specifically configured to control a hydraulic servo system and a hydraulic proportional valve, which provide thrust for the operating panel, according to the thrust control analog value, so that the single-blade thrust of the operating panel reaches the target thrust value.
7. The tunneling robot of claim 2, wherein the sensing unit is specifically configured to:
acquiring N relative errors of tunneling direction planning data and actual measurement data of a tunneling direction of a tunneling machine tunneling guide system in a current period for N times according to a preset acquisition frequency, and calculating a first error average value of the N relative errors, wherein N is a positive integer greater than 1;
acquiring N groups of set tunneling parameter information and real-time tunneling parameter information of the tunneling machine in the current period according to a preset acquisition frequency, and calculating relative errors of all parameters in all groups of tunneling parameter information in the N groups of set tunneling parameter information and the real-time tunneling parameter information to obtain an error set;
calculating a second error average value of each parameter according to the N relative errors corresponding to each parameter in the error set;
performing digital average operation on the first error average value and the second error average value to obtain an average valueAnd acquiring the real-time tunneling effect information index according to the following formula, wherein A (x, y, z) is the real-time tunneling effect information index:
8. the tunneling robot according to claim 1,
the intelligent decision unit is further used for determining a tunneling mode according to the fine exploration information and sending the tunneling mode to the control unit so that the control unit controls the tunneling machine to perform tunneling operation according to the tunneling mode;
and the intelligent decision unit is also used for generating disaster early warning information according to the tunnel geological forecast information so as to facilitate the development machine to take development disaster avoidance measures according to the disaster early warning information.
9. A remote mobile terminal command system for tunneling, characterized in that the system comprises a tunneling robot and a command system, wherein the tunneling robot comprises the tunneling robot for tunneling according to any one of claims 1-7, the command system and the tunneling robot are in communication connection, wherein the command system comprises a mobile terminal and a cloud service unit, wherein,
the cloud service unit is used for monitoring a tunneling construction site in real time, storing, analyzing, displaying, replaying and managing tunneling operation data transmitted in a wireless mode from the tunneling construction site in real time, supporting a command system to perform real-time man-machine interaction with a tunneling machine so as to realize a tunneling mode for remotely controlling the tunneling machine to perform construction operation, updating a decision model of an intelligent decision unit, and issuing a tunneling operation task to the intelligent decision unit by the command system to provide real-time support for the intelligent decision unit;
the mobile terminal is used for loading an autonomously developed command system application program to access a cloud service unit at the cloud end in real time for man-machine interaction, allowing a user to control the tunneling machine in a remote control mode, and allowing the user to remotely monitor the operation condition of a tunneling construction site in real time.
10. The system of claim 9,
the mobile terminal is further used for receiving a tunneling operation instruction and sending the tunneling operation instruction to the tunneling robot so as to control the tunneling robot to conduct tunneling operation according to the tunneling operation instruction.
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| CN114006770A (en) * | 2021-12-28 | 2022-02-01 | 国网瑞嘉(天津)智能机器人有限公司 | Data processing method, device and equipment of robot operation platform and storage medium |
Citations (39)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3424606A1 (en) * | 1984-07-04 | 1986-01-16 | Stein, Dietrich, Dr.-Ing., 4630 Bochum | CONTROL DEVICE FOR UNDERGROUND DRIVING ON ANY ROAD |
| JPH04120395A (en) * | 1990-09-12 | 1992-04-21 | Shimizu Corp | Surveying system for natural ground conduction |
| EP0534338A2 (en) * | 1991-09-24 | 1993-03-31 | Bodenseewerk Gerätetechnik GmbH | Control system for a tunnel boring machine |
| DE3943591C2 (en) * | 1988-03-19 | 1993-11-11 | Paurat Gmbh | Control system for tunnelling appts. |
| JPH11125093A (en) * | 1997-10-22 | 1999-05-11 | Kajima Corp | Method and device for excavation control of tbm |
| JPH11141275A (en) * | 1997-11-13 | 1999-05-25 | Okumura Corp | Drilling direction automatic control device of tunnel excavator |
| JP2001311389A (en) * | 2000-05-01 | 2001-11-09 | Mitsubishi Heavy Ind Ltd | Control device for tunnel boring machine |
| EP1395001A1 (en) * | 2002-08-20 | 2004-03-03 | Hitachi, Ltd. | Hierarchical mobile packet communication network and method of communication thereof |
| CN1877080A (en) * | 2006-06-28 | 2006-12-13 | 上海隧道工程股份有限公司 | Method for autocontrolling excavation direction of underground excavator using virtual track |
| CN101614124A (en) * | 2008-06-27 | 2009-12-30 | 中铁隧道集团有限公司 | Open type hard rock mole driving and secondary lining method for synchronously constructing |
| CN101995861A (en) * | 2010-09-07 | 2011-03-30 | 中国矿业大学 | Remote monitoring method and system for development machine |
| CN102102522A (en) * | 2010-12-30 | 2011-06-22 | 浙江大学 | Track and posture composite control method in shield tunneling process |
| CN102322294A (en) * | 2011-05-31 | 2012-01-18 | 中铁二十局集团第一工程有限公司 | Comprehensive geological prediction method for karst tunnel construction |
| CN102736124A (en) * | 2012-06-14 | 2012-10-17 | 北京市市政工程研究院 | Tunnel excavation surrounding rock dynamic refined classification method based on integrated parameters |
| CN102797504A (en) * | 2012-08-31 | 2012-11-28 | 中铁二十一局集团有限公司 | Remote three-dimensional digital alarm method and facility of deformation stability of primary tunnel supporting body |
| CN103713335A (en) * | 2014-01-07 | 2014-04-09 | 山东大学 | Comprehensive advance geological detection system carried by tunnel boring machine |
| CN103867205A (en) * | 2014-03-11 | 2014-06-18 | 西安科技大学 | Remote control system and remote control method of heading machine |
| CN203658603U (en) * | 2014-01-07 | 2014-06-18 | 山东大学 | Comprehensive advanced geological detection system carried by tunnel boring machine |
| CN104343457A (en) * | 2013-07-29 | 2015-02-11 | 昌永生 | Control system of full face rock tunnel boring machine |
| CN104653193A (en) * | 2014-12-22 | 2015-05-27 | 天津大学 | Energy theory-based prediction method for stress of TMB (tunnel boring machine) disk hob |
| CN105652866A (en) * | 2014-11-24 | 2016-06-08 | 丁金堂 | Automatic measurement and error correcting system for heading machine |
| CN105697023A (en) * | 2016-02-05 | 2016-06-22 | 清华大学 | Tunnel geological exploring method and system and mini-type heading machine |
| WO2016141630A1 (en) * | 2015-03-11 | 2016-09-15 | 山东大学 | Tunnel boring machine rock breaking seismic source and active source three-dimensional seismic combined advanced detection system |
| CN106321108A (en) * | 2016-09-13 | 2017-01-11 | 浙江大学 | On-line surrounding rock strength identification method of hard rock heading equipment |
| CN106593534A (en) * | 2016-12-18 | 2017-04-26 | 河北科技大学 | Intelligent tunnel construction security monitoring system |
| CN106777568A (en) * | 2016-11-29 | 2017-05-31 | 天津大学 | Full face rock tunnel boring machine cutterhead tunnels the determination method of load |
| CN107346032A (en) * | 2017-06-21 | 2017-11-14 | 湖北省交通规划设计院股份有限公司 | A kind of wireless controlled passes the Tunnel prediction system and method for acceleration transducer |
| CN107407147A (en) * | 2015-03-24 | 2017-11-28 | 日立造船株式会社 | Rock tunnel(ling) machine |
| CN107461204A (en) * | 2017-09-22 | 2017-12-12 | 山西焦煤集团有限责任公司 | Development machine tunnels automatic orientation system and its control method |
| CN107503757A (en) * | 2017-09-20 | 2017-12-22 | 中建交通建设集团有限公司 | A kind of shield tunnel intelligence construction accessory system and application method based on big data technology |
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| CN107860374A (en) * | 2017-11-02 | 2018-03-30 | 中铁四局集团第工程有限公司 | A kind of tunnel tunnel face excavation line intelligent positioning measuring method |
| CN207177908U (en) * | 2017-09-22 | 2018-04-03 | 山西焦煤集团有限责任公司 | Development machine tunnels automatic orientation system |
| CN108491590A (en) * | 2018-03-05 | 2018-09-04 | 北京交通大学 | Shield driving parameter prediction method based on grey system model |
| CN108643930A (en) * | 2018-05-08 | 2018-10-12 | 中铁工程装备集团有限公司 | A kind of TBM constructing tunnels real time early warning method |
| CN108678760A (en) * | 2018-05-07 | 2018-10-19 | 华能西藏雅鲁藏布江水电开发投资有限公司 | A kind of construction method of rock tunnel(ling) machine and rock tunnel(ling) machine |
| CN108681288A (en) * | 2018-06-28 | 2018-10-19 | 上海电器科学研究所（集团）有限公司 | A kind of novel maintenance system based on cloud platform |
| CN108798690A (en) * | 2018-06-01 | 2018-11-13 | 中国科学院武汉岩土力学研究所 | Realize the combination type T BM and geology detecting driving method of geology detecting |
| CN108844528A (en) * | 2018-05-28 | 2018-11-20 | 中铁五局集团有限公司 | A kind of tunnel survey system |
-
2019
- 2019-01-24 CN CN201910068376.9A patent/CN109630154B/en active Active
Patent Citations (39)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3424606A1 (en) * | 1984-07-04 | 1986-01-16 | Stein, Dietrich, Dr.-Ing., 4630 Bochum | CONTROL DEVICE FOR UNDERGROUND DRIVING ON ANY ROAD |
| DE3943591C2 (en) * | 1988-03-19 | 1993-11-11 | Paurat Gmbh | Control system for tunnelling appts. |
| JPH04120395A (en) * | 1990-09-12 | 1992-04-21 | Shimizu Corp | Surveying system for natural ground conduction |
| EP0534338A2 (en) * | 1991-09-24 | 1993-03-31 | Bodenseewerk Gerätetechnik GmbH | Control system for a tunnel boring machine |
| JPH11125093A (en) * | 1997-10-22 | 1999-05-11 | Kajima Corp | Method and device for excavation control of tbm |
| JPH11141275A (en) * | 1997-11-13 | 1999-05-25 | Okumura Corp | Drilling direction automatic control device of tunnel excavator |
| JP2001311389A (en) * | 2000-05-01 | 2001-11-09 | Mitsubishi Heavy Ind Ltd | Control device for tunnel boring machine |
| EP1395001A1 (en) * | 2002-08-20 | 2004-03-03 | Hitachi, Ltd. | Hierarchical mobile packet communication network and method of communication thereof |
| CN1877080A (en) * | 2006-06-28 | 2006-12-13 | 上海隧道工程股份有限公司 | Method for autocontrolling excavation direction of underground excavator using virtual track |
| CN101614124A (en) * | 2008-06-27 | 2009-12-30 | 中铁隧道集团有限公司 | Open type hard rock mole driving and secondary lining method for synchronously constructing |
| CN101995861A (en) * | 2010-09-07 | 2011-03-30 | 中国矿业大学 | Remote monitoring method and system for development machine |
| CN102102522A (en) * | 2010-12-30 | 2011-06-22 | 浙江大学 | Track and posture composite control method in shield tunneling process |
| CN102322294A (en) * | 2011-05-31 | 2012-01-18 | 中铁二十局集团第一工程有限公司 | Comprehensive geological prediction method for karst tunnel construction |
| CN102736124A (en) * | 2012-06-14 | 2012-10-17 | 北京市市政工程研究院 | Tunnel excavation surrounding rock dynamic refined classification method based on integrated parameters |
| CN102797504A (en) * | 2012-08-31 | 2012-11-28 | 中铁二十一局集团有限公司 | Remote three-dimensional digital alarm method and facility of deformation stability of primary tunnel supporting body |
| CN104343457A (en) * | 2013-07-29 | 2015-02-11 | 昌永生 | Control system of full face rock tunnel boring machine |
| CN203658603U (en) * | 2014-01-07 | 2014-06-18 | 山东大学 | Comprehensive advanced geological detection system carried by tunnel boring machine |
| CN103713335A (en) * | 2014-01-07 | 2014-04-09 | 山东大学 | Comprehensive advance geological detection system carried by tunnel boring machine |
| CN103867205A (en) * | 2014-03-11 | 2014-06-18 | 西安科技大学 | Remote control system and remote control method of heading machine |
| CN105652866A (en) * | 2014-11-24 | 2016-06-08 | 丁金堂 | Automatic measurement and error correcting system for heading machine |
| CN104653193A (en) * | 2014-12-22 | 2015-05-27 | 天津大学 | Energy theory-based prediction method for stress of TMB (tunnel boring machine) disk hob |
| WO2016141630A1 (en) * | 2015-03-11 | 2016-09-15 | 山东大学 | Tunnel boring machine rock breaking seismic source and active source three-dimensional seismic combined advanced detection system |
| CN107407147A (en) * | 2015-03-24 | 2017-11-28 | 日立造船株式会社 | Rock tunnel(ling) machine |
| CN105697023A (en) * | 2016-02-05 | 2016-06-22 | 清华大学 | Tunnel geological exploring method and system and mini-type heading machine |
| CN106321108A (en) * | 2016-09-13 | 2017-01-11 | 浙江大学 | On-line surrounding rock strength identification method of hard rock heading equipment |
| CN106777568A (en) * | 2016-11-29 | 2017-05-31 | 天津大学 | Full face rock tunnel boring machine cutterhead tunnels the determination method of load |
| CN106593534A (en) * | 2016-12-18 | 2017-04-26 | 河北科技大学 | Intelligent tunnel construction security monitoring system |
| CN107346032A (en) * | 2017-06-21 | 2017-11-14 | 湖北省交通规划设计院股份有限公司 | A kind of wireless controlled passes the Tunnel prediction system and method for acceleration transducer |
| CN107503757A (en) * | 2017-09-20 | 2017-12-22 | 中建交通建设集团有限公司 | A kind of shield tunnel intelligence construction accessory system and application method based on big data technology |
| CN107461204A (en) * | 2017-09-22 | 2017-12-12 | 山西焦煤集团有限责任公司 | Development machine tunnels automatic orientation system and its control method |
| CN207177908U (en) * | 2017-09-22 | 2018-04-03 | 山西焦煤集团有限责任公司 | Development machine tunnels automatic orientation system |
| CN107632523A (en) * | 2017-09-30 | 2018-01-26 | 中铁工程装备集团有限公司 | A kind of hard rock TBM digging control parameter intelligent decision-making techniques and system |
| CN107860374A (en) * | 2017-11-02 | 2018-03-30 | 中铁四局集团第工程有限公司 | A kind of tunnel tunnel face excavation line intelligent positioning measuring method |
| CN108491590A (en) * | 2018-03-05 | 2018-09-04 | 北京交通大学 | Shield driving parameter prediction method based on grey system model |
| CN108678760A (en) * | 2018-05-07 | 2018-10-19 | 华能西藏雅鲁藏布江水电开发投资有限公司 | A kind of construction method of rock tunnel(ling) machine and rock tunnel(ling) machine |
| CN108643930A (en) * | 2018-05-08 | 2018-10-12 | 中铁工程装备集团有限公司 | A kind of TBM constructing tunnels real time early warning method |
| CN108844528A (en) * | 2018-05-28 | 2018-11-20 | 中铁五局集团有限公司 | A kind of tunnel survey system |
| CN108798690A (en) * | 2018-06-01 | 2018-11-13 | 中国科学院武汉岩土力学研究所 | Realize the combination type T BM and geology detecting driving method of geology detecting |
| CN108681288A (en) * | 2018-06-28 | 2018-10-19 | 上海电器科学研究所（集团）有限公司 | A kind of novel maintenance system based on cloud platform |
Non-Patent Citations (8)
| Title |
|---|
| DONG-FANG CHEN等: "Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling", TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, vol. 51, pages 372 - 386, XP029335819, DOI: 10.1016/j.tust.2015.09.010 * |
| ZHILI TANG等: "Stability evaluation of deep-buried TBM construction tunnel based on microseismic monitoring technology", TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, vol. 81, pages 512 - 524, XP085475870, DOI: 10.1016/j.tust.2018.08.028 * |
| 何小新;吴庆鸣;: "全断面岩石掘进机智能选型方案生成系统", 中国机械工程, no. 06, pages 55 - 60 * |
| 刘斌等: "TBM掘进前方不良地质与岩体参数的综合获取方法", 《山东大学学报(工学版)》, no. 06, pages 108 - 115 * |
| 张娜等: "TBM掘进参数智能控制系统的研究与应用", 《隧道建设(中英文)》, no. 10, pages 150 - 156 * |
| 张志奇等: "复杂地层盾构掘进速率和刀盘扭矩预测模型及其地层适应性研究", 隧道建设, vol. 36, no. 12, pages 1449 - 1455 * |
| 程德胜;汪旭;: "HSP超前地质预报技术在双护盾TBM施工隧道中的应用", 四川水力发电, no. 03, pages 184 - 188 * |
| 褚东升: "长沙地铁下穿湘江土压平衡盾构隧道掘进参数研究", 第《中国优秀硕士学位论文全文数据工程科技Ⅱ辑》, no. 2 * |
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