CN109630154B - Tunneling robot for tunneling and remote mobile terminal command system - Google Patents
Tunneling robot for tunneling and remote mobile terminal command system Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK 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 OR ROCK 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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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a tunneling robot for tunneling and a remote mobile terminal command system, wherein the robot comprises: the intelligent tunneling robot comprises a body tunneling machine of the tunneling robot, a sensing unit, an intelligent decision unit and a control unit, wherein the sensing unit is arranged on the tunneling machine and is in communication connection with the intelligent decision unit; the sensing unit senses tunneling operation data in real time in the current period when the tunneling machine performs tunneling operation according to the set tunneling parameter information; the intelligent decision unit receives the tunneling operation data sent by the sensing unit, and predicts and generates set tunneling parameter information of the next period according to a preset prediction algorithm, the tunneling operation data and the expected tunneling effect; the control unit receives and controls the heading machine to carry out the tunneling operation of the next period according to the set tunneling parameter information. The robot can perform on-line monitoring, autonomous decision feedback control and unmanned tunneling operation on the tunneling process, and unmanned operation or remote control of the tunneling machine is realized.
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 major economy in the world, and the water resource cross-domain configuration, hydropower development and traffic construction are required to be large. A batch of major infrastructures such as water conservancy and hydropower engineering, railway, highway traffic engineering and the like are sequentially started to be built, and a tunnel is a building control engineering for water conservancy and hydropower engineering and traffic construction.
The requirements of the water conservancy and hydropower construction in China on the development machine are increased in an explosive manner, for example, the total length of a deep-buried long tunnel of a first-period engineering of a south-to-north transfer line in planning is 264km, the longest tunnel section is 73km, the maximum buried depth is 1150m, and the required quantity of the development machine is nearly 20; the total length of the diversion project in the planned Yunnan is over 650km, the project is composed of more than 60 tunnels, and 2 heading machines are adopted for tunneling; the total length of the started diversion tunnel is 98.30km, the maximum burial depth exceeds 2000m, the diversion tunnel passes through 39.08km of the main ridge section of the Qinling, and 2 heading machines are adopted; the power station of the Yu pine reservoir has 6 diversion tunnels with the single tunnel length of about 30km, and is estimated to need 6 heading machines.
The construction weight point of the traffic engineering is transferred to the east strait region blocked by the west mountain region and the water region with complex topography and geology, and a large number of hard mountain region tunnels and river-crossing and sea-crossing tunnels are constructed in the future. According to statistics, by 2030, china will build 4.5 kilometers of high-speed rail, and 10 years later, china will build a ten thousand kilometer highway railway tunnel, and the extra-length tunnel exceeds 2000 kilometers. The deep and long traffic tunnel engineering to be built is increasingly constructed by adopting a development machine, taking the Bohai sea strait cross-sea tunnel in the demonstration as an example, planning the whole length to be about 120 km, and the demonstration by Chinese engineering institute proves that the optimal scheme is 'taking the development machine as the main and taking the drilling and blasting method as the auxiliary', and the total requirement is about 15 development machines.
Compared with the conventional drilling and blasting method, the construction of the heading machine has poor adaptability to poor geological hole sections, is easily influenced by fault fracture zones, weak stratum, gushing water, high ground stress and other poor geological hole sections in the construction of large-burial-depth long tunnels, has the construction risks of abnormal damage to the main bearing cutterhead of the heading machine, low rock breaking efficiency, low tunneling speed, even machine clamping, trapped and the like, loses tens of thousands to hundreds of millions of yuan, and even causes the complete machine to be scrapped and the catastrophic results of casualties. In addition, in the traditional construction mode, the master control room operator evaluates the surrounding rock state parameters through a slow test tunneling process, and repeatedly adjusts the tunneling parameters until the tunneling parameters remain stable, and finally, the master control room operator gives an instruction to perform action implementation by the execution mechanism of the tunneling machine. On the one hand, the operation mode can cause a great deal of construction time cost, on the other hand, when surrounding rock conditions are changed greatly, actual parameter conditions of the current surrounding rock cannot be effectively perceived in real time, so that tunneling parameters cannot adapt to the current tunneling environment, abnormal abrasion of a cutter and reduction of rock breaking performance of a cutterhead system are caused, damage and shutdown of a key component of a hard rock tunneling machine are caused under serious conditions, the normal service life of the hard rock tunneling machine is influenced, errors and even errors caused by the fact that an executing mechanism of the tunneling machine is controlled manually are quite unfavorable for fine control of the tunneling machine, tunneling construction quality cannot be expected due to light weight, and serious construction accidents of machine destruction and death are caused.
In order to solve the construction problems of poor geological tunnel sections such as large faults, broken zones, high ground stress, rock burst, gushing water and the like and other problems faced by tunnel construction, an intelligent tunnel tunneling robot (Tunnel Boring Robot, TBR) is developed, an intelligent decision theory and an optimal control method of tunneling parameters of a tunneling machine are explored, a set of basic theory, practical method, supporting software and key technology for real-time advanced detection and intelligent decision of tunneling of poor geological conditions of tunnel construction are finally formed, serious disaster accidents of tunneling of the tunneling machine are avoided and prevented, safety, high efficiency and high quality tunneling of the tunneling machine are guaranteed, and the intelligent tunnel tunneling robot has extremely important significance in improving the core competitiveness of intelligent tunnel construction and tunnel machine manufacturing in China.
Aiming at the construction problems of large faults, broken zones, high ground stress, rock burst, gushing water and other bad geological tunnel sections under the complex geological conditions faced by the current tunnel construction, the surrounding rock state parameters can be guessed only by the artificial experience of operators in a main control room in the related operation mode, and the problems of time and labor consumption, abnormal abrasion and even damage of cutters and cutterheads of the tunneling machine, damage to other key parts of the tunneling machine and the like caused by continuously trying different tunneling parameters are solved, so that the tunneling construction quality brought by manually controlling the executing mechanism of the tunneling machine to implement the tunneling action cannot reach the expected and even serious construction accidents are caused.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, 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 on a tunneling process, and realize unmanned operation or remote control of a tunneling machine.
Another object of the present invention is to propose a remote mobile terminal command system for tunnelling.
To achieve the above object, in one aspect, an embodiment of the present invention provides a tunneling robot for tunneling, including: the intelligent tunneling robot comprises a body tunneling machine 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 the current period in real time when the tunneling machine 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, 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; the control unit is used for receiving the set tunneling parameter information and controlling the tunneling machine to perform 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 the tunneling process, realize unmanned operation or remote control of the tunneling machine, is particularly suitable for tunneling construction scenes of rock tunnels and underground channels in all-weather, dangerous areas, unmanned areas or limit conditions, can be autonomously adapted to use of tunneling machines with different surrounding rocks, different diameters and different performances and different stages of the whole life cycle of the same tunneling machine, and is used for improving tunneling construction quality, construction efficiency and realizing fine management of engineering construction.
In addition, a 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 present invention, the tunnel surrounding rock state information includes rock uniaxial saturation compressive strength R c (x, y, z), number of joints per unit rock volume J v (x, y, z), surrounding rock grade W (x, y, z), tunneling parameter information including single-blade thrust F t (x, y, z), single blade torque T n (x, y, z), cutterhead rotational speed N (x, y, z), advance speed V (x, y, z), heading direction D (x, y, z).
Further, in an embodiment of the present invention, the sensing unit includes a leading geological detection subunit, and the leading geological detection subunit is configured to detect and obtain the tunnel geological forecast information by using a horizontal ultra-long drilling technique.
Further, in an embodiment of the present invention, the preset prediction algorithm includes a tunneling parameter prediction algorithm using a neural network structure as a core; the intelligent decision unit is further used for generating the tunneling parameter prediction algorithm according to historical tunneling operation data training.
Further, in one embodiment of the invention, the control unit comprises an electrical control subunit, wherein the electrical control subunit is established by a plurality of PLCs, which are communicatively connected to each other through a network communication interface; and the electrical control subunit is used for controlling the 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 one 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 communicatively connected to 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; the PLC control program module is used for calculating the number value and the current thrust value of the thrust gear of the operating panel, calculating the thrust difference 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; 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 used for controlling a hydraulic servo system and a hydraulic proportional valve for providing thrust for the operation panel according to the thrust control simulation value, so that the single-blade thrust of the operation 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 the tunneling direction of a tunneling machine tunneling guide system in N current periods 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 the set tunneling parameter information and the real-time tunneling parameter information of the tunneling machine in N groups of current periods according to a preset acquisition frequency, and calculating the relative errors of all the parameters in each group of tunneling parameter information in the N groups of set tunneling parameter information and the real-time tunneling parameter information to acquire an error set;
calculating a second error average value of each parameter according to 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 one 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; the intelligent decision unit is further used for generating disaster early warning information according to the tunnel geological forecast information so that the tunneling machine can take tunneling disaster avoidance measures according to the disaster early warning information.
In order to achieve the above purpose, another embodiment of the present invention provides a remote mobile terminal command system for tunneling, where the system includes a tunneling robot and a command system, where the tunneling robot includes the tunneling robot for tunneling, and the command system is in communication connection with the tunneling robot, where the command system includes a mobile terminal and a cloud service unit, where the cloud service unit is configured to monitor a tunneling construction site in real time, store, analyze, display, play back and manage tunneling operation data transmitted by the tunneling construction site in a wireless manner in real time, and support real-time man-machine interaction between the command system and the tunneling machine to remotely control a tunneling mode in which the tunneling machine performs construction operation, and update a decision model of an intelligent decision unit and provide real-time support for the intelligent decision unit by the tunneling operation task under the command system;
the mobile terminal is used for loading an autonomously developed command system application program to access a cloud service unit of the cloud end in real time for human-computer interaction, and is used for a user to control the heading machine by using a remote control mode, and the user can 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 the tunneling process, realize unmanned operation or remote control of the tunneling machine, is particularly suitable for tunneling construction scenes of rock tunnels and underground channels in all-weather, dangerous areas, unmanned areas or limit conditions, can be autonomously adapted to use of tunneling machines with different surrounding rocks, different diameters and different performances and different stages of the whole life cycle of the same tunneling machine, and is used for improving tunneling construction quality, construction efficiency and realizing fine management of engineering construction.
In addition, a 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 invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a block diagram of a tunneling robot for tunneling according to an embodiment of the present invention;
FIG. 2 is a block diagram of an intelligent decision system according to one embodiment of the invention;
FIG. 3 is a flow chart of an intelligent decision model update according to one embodiment of the invention;
FIG. 4 is a block diagram of a control system of an 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 one embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
A tunneling robot and a remote mobile terminal command system for tunneling according to an embodiment of the present invention will be described below with reference to the accompanying drawings, and first, a tunneling robot for tunneling according to an embodiment 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 body development machine 5 of the development robot, a sensing unit 2, an intelligent decision unit 3 and a control unit 4, wherein the sensing unit 2, the intelligent decision unit 3 and the control unit 4 are arranged on the development machine.
The sensing unit 2 is used for sensing tunneling operation data in the current period in real time when the tunneling machine performs tunneling operation according to the set tunneling parameter information, wherein the tunneling operation data comprise 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 predicting and generating set tunneling parameter information of the next period according to a preset prediction algorithm, the tunneling operation data and the expected tunneling effect;
and the control unit 4 is used for receiving the set tunneling parameter information and controlling the tunneling machine to perform tunneling operation of the next period according to the set tunneling parameter information.
Further, the method further comprises the following steps: a command system and a cloud service unit in the command system.
Specifically, the invention provides an intelligent tunneling robot (Tunnel Boring Robot, TBR), which utilizes a sensing unit 2 to acquire 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 a tunneling machine in real time and feeds back the information to a cloud service unit in real time; the intelligent decision unit 3 is utilized to analyze the perceived data in real time, the tunneling effect of the tunneling machine is estimated, the tunneling strategy decision is made by the machine autonomously in real time, the tunneling strategy decision is made autonomously, and the decision information is fed back to the cloud service unit in real time; then, the control unit 4 adjusts the running and working states of the heading machine in real time according to the machine decision information and feeds back the state information to the cloud service unit in real time; finally, the tunneling machine 5 performs tunneling action, and continuously feeds tunneling operation data back to the sensing unit 2 in real time. The remote mobile terminal command system 6 monitors site construction in real time, remotely controls man-machine interaction 5, and discovers work tasks to the intelligent decision unit 3 through the cloud service unit.
In one embodiment of the invention, the intelligent control system comprises a sensing unit, an intelligent decision unit, a control unit, a heading machine, a remote mobile terminal command system and a cloud service unit.
The development machine is an intelligent tunneling robot (Tunnel Boring 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 back the information 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, the surrounding rock state information, geological forecast information, fine exploration information, accurate lining information, tunneling effect information and tunneling parameter information are acquired by the sensing unit, training data are composed by the aid of the data acquired by the sensing unit, a prediction algorithm is generated through data training, and finally optimal tunneling parameters of the next tunneling period are obtained through the aid of the tunneling parameters of the last tunneling period through the prediction algorithm, and decision information is fed back to the cloud service unit in real time.
The control unit adjusts the running and working states of the heading machine in real time according to the machine decision information, namely the optimal heading parameters, drives the heading machine to make corresponding actions and feeds back the state information to the cloud service unit in real time, and the sensing unit collects data according to the heading effect generated by the current control parameters, forms new training data together with the control parameters and updates an intelligent decision model, so that a periodic and circulated closed-loop system is formed.
Further, in one embodiment of the invention, the tunnel surrounding rock state information includes rock uniaxial saturation compressive strength R c (x, y, z), number of joints per unit rock volume J v (x, y, z), surrounding rock grade W (x, y, z), tunneling parameter information including single-blade thrust F t (x, y, z), single blade torque T n (x, y, z), cutterhead rotational speed N (x, y, z), advance speed V (x, y, z), heading direction D (x, y, z).
Further, in one embodiment of the present invention, the sensing unit includes a leading geological detection subunit, and the leading geological detection subunit is configured to detect and obtain the tunnel geological forecast information by using the horizontal ultra-long drilling technology.
Specifically, the sensing information obtained in real time by the sensing unit comprises surrounding rock state information, geological forecast information, tunneling parameter information, tunneling effect information, fine exploration information and precise lining information, the obtained sensing information is fed back to the cloud service unit in real time in a wireless transmission mode, the geological forecast information is obtained by detecting and providing corresponding detection data forming (x, y, z) by an advanced geological detection system taking a horizontal ultra-long drilling technology as a core and having the characteristics of detecting faults, broken zones and other bad geological bodies which are far away from and can effectively detect occurrence in front of a face, early detection data forming (x, y, z) are provided for providing early warning and providing early warning for disasters such as rock burst, a heading machine card machine, water burst and the like, and the tunneling effect information comprises tunneling direction planning data DirectDesign (x, y, z) in a design drawing, actual measurement data Directosum (x, y, z) of the heading direction of a heading machine guiding system and actual measurement data single-tool thrust F of a main parameter of the heading machine t (x, y, z), single blade torque T n (x, y, z), cutter head rotating speed N (x, y, z), advancing speed V (x, y, z), and tunneling effect index value A (x, y, z) calculated based on the data, wherein fine exploration information comprises information FineExposure (x, y, z) such as ground stress, rock strength, geological structure, osmotic pressure distribution and the like, fine geological condition exploration acquisition 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 acquiring surrounding rock state information and tunneling effect information, and precise lining information precision is provided by a precise lining intelligent operation system and comprises intelligent assessment information of local collapse risk, intelligent collapse support decision information and tunnel bottom slag cleaning information.
The surrounding rock state information comprises rock uniaxial saturated compressive strength, joint number of unit rock mass volume and surrounding rock grade, the surrounding rock of the area where the tunneling construction area is located is core-taken in advance for sample reservation, a surrounding rock joint diagram is drawn, and the surrounding rock joint diagram is drawn according to the retrieved rockCore, and the uniaxial saturated compressive strength R of the core is obtained by performing indoor test c (x, y, z) counting the joint number J of the unit rock volume according to the engineering rock grading standard according to the surrounding rock joint diagram near the coring pile number v (x, y, z) value, number J of joints per unit rock mass volume v In the process of converting the (x, y, z) value into the integrity index value Kv (x, y, z), a fitting interpolation method is adopted to calculate and obtain the continuous surrounding rock grade W (x, y, z). The tunneling parameter information comprises single-blade thrust F t (x, y, z), single blade torque T n (x, y, z), cutterhead speed N (x, y, z), propulsion speed V (x, y, z), tunneling guide D (x, y, z), wherein single-blade thrust, single-blade torque, cutterhead speed, propulsion speed and tunneling guide are acquired in real time by sensors mounted on the heading machine. The tunneling effect information comprises tunneling direction planning data DirectionDesign (x, y, z) in a design drawing, actual measurement data DirectionMessaging (x, y, z) of the tunneling direction of a tunneling guiding system of the tunneling machine, and single-cutter thrust F of actual measurement data of main parameters of the tunneling machine t (x, y, z), single blade torque T n (x, y, z), a cutterhead rotational speed N (x, y, z), a thrust 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; the intelligent decision unit is also used for generating a tunneling parameter prediction algorithm according to the historical tunneling operation data training.
As shown in fig. 2, the intelligent decision unit 3 implements intelligent decision by starting a tunneling cycle tunneling operation to form an intelligent decision cycle, automatically adjusts tunneling parameters of the tunneling machine, and performs unmanned tunneling operation in cooperation with multiple cascade control and multiple systems:
the method comprises the steps of determining a tunneling mode of a tunneling machine by using fine exploration information FineExpletion (x, y, z) obtained by a sensing unit according to a tunneling parameter prediction algorithm taking a neural network algorithm as a core, then carrying out early warning and prediction on construction period disasters such as rock burst, water burst and the like according to geological prediction information Forcasting (x, y, z) obtained by the sensing unit, taking corresponding control measures according to disaster conditions, obtaining surrounding rock state information, accurate lining information and tunneling operation data fed back by the tunneling machine by using the sensing unit, namely real-time tunneling effect information and tunneling parameter information, predicting tunneling parameters used in a next tunneling period of the tunneling machine of on-site tunneling operation, realizing efficient tunneling operation on the premise of determining the tunneling mode of the tunneling machine and removing or eliminating the construction period disasters, and forming a next construction period cycle with the accurate lining intelligent operation system;
The tunneling parameter prediction algorithm performs training update by periodically loading real-time historical experience data, and then predicts tunneling parameters of the next tunneling period in real time by using current perception information, wherein the current perception information is the characteristics of training data required by the period update algorithm and mainly comprises rock uniaxial saturated compressive strength R c (x, y, z), number of joints per unit rock volume J v (x, y, z), surrounding rock grade W (x, y, z), single-blade thrust F t (x, y, z), single blade torque T n (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 next tunneling period are target parameters to be estimated by a prediction algorithm of the decision system, and the tunneling parameters of the next tunneling period and the actions of the accurate lining intelligent operation system are directly determined.
The tunneling period consists of tunneling time of a tunneling stroke and step change reset time, the tunneling period is divided into 10 equal parts, and each equal part is subjected to sensing measurement by a sensing unit.
As shown in fig. 3, the tunneling parameter prediction algorithm mainly predicts tunneling parameters intelligently in real time, and related tunneling construction process data obtained by the sensing unit, such as surrounding rock state information, geological forecast information, fine exploration information, accurate lining information, tunneling effect information, tunneling parameter information and the like, is predicted by the prediction algorithm (PredictionModel (X)) to obtain next period tunneling parameter data. The prediction formula is as follows:
The cloud service unit provides support for periodically updating a tunneling parameter prediction algorithm, and takes a neural network algorithm as an example, the prediction algorithm selects a convolutional neural network structure, the input characteristic data type of the structure of the convolutional neural network structure has spatial three-dimensional coordinates (x, y, z), and the uniaxial saturated compressive strength of rock before tunnelingNumber of joints per volume of rock mass +.>Surrounding rock grade W i-1 (x, y, z), single-blade thrust +.>Single blade torque->Cutter head rotating speed N i-1 (x, y, z), propulsion speed V i-1 (x, y, z) and a tunneling effect index value A i-1 (x, y, z) uniaxial saturation compressive strength of the tunneled rock +.>Number of joints per volume of rock mass +.>Surrounding rock grade W i (x, y, z), single-blade thrust +.>Single blade torqueCutter head rotating speed N i (x, y, z), propulsion speed V i (x, y, z), tunneling guide D i (x, y, z) tunneling effect index valueA i (x, y, z), the output target of the training model is the tunneling parameter, namely the single-blade thrust F of the tunneling machine t (x, y, z), single blade torque T n (x, y, z), cutterhead speed N (x, y, z), advance speed V (x, y, z) and heading direction D (x, y, z). Meanwhile, in order to consider that tunneling effect data of multi-cycle tunneling in the same position of a tunnel or an underground passage has correlation, the tunneling operation effect is slowly accumulated and then gradually approaches to the expected tunneling effect, the characteristic data of the latest 5 tunneling periods are selected to form a characteristic matrix, model training is carried out when 210 training sample matrixes are accumulated in the updating of a prediction algorithm, the prediction algorithm is updated, and then the updated prediction algorithm is synchronized into an intelligent decision unit of the tunneling machine.
The data characteristic of the ith tunneling cycle of the spatial three-dimensional coordinates (x, y, z) is expressed as:
the input data for training the predictive algorithm is:
X i (x，y，z)＝[x i x i-1 x i-2 x i-3 x i-4 ]
thus X is i (x, y, z) is a two-dimensional matrix of 21 x 5, which constitutes the latest tunneling history feature data in the same region. The tunneling parameter data of the ith tunneling period of the space three-dimensional coordinates (x, y, z) are as follows:
the neural network structure of the intelligent decision model is shown in fig. 3, the model is constructed based on the convolutional neural network structure and mainly comprises a plurality of convolutional layers, a pooling layer and a last full-connection layer output to realize intelligent prediction of tunneling parameters.
Next, the training process of the tunneling parameter prediction algorithm will be further described with reference to fig. 3, so that the nth tunneling period space is three-dimensionally seatedAfter tunneling construction of the region marked (x, y, z), the characteristic data x of the process is obtained i (x, y, z) and then combining historical data of the first 5 tunneling cycles of the tunneling site, the sample training data of the site is:
the sample data is trained to obtain the weight parameters and the paranoid parameters of each layer of network after the neural network structure is iterated as shown in fig. 3, namely the model data of the network structure:
K＝[k 1 ，k 2 ，k 3 ，k 4 ，k 5 ]，B＝[b 1 ，b 2 ，b 3 ，b 4 ，b 5 ]
k n b for weight data of each network layer n And the two parameters form a tunneling parameter prediction algorithm for the paranoid parameters of each network layer.
Further, in one embodiment of the invention, the control unit comprises an electrical control subunit, wherein the electrical control subunit is established by a plurality of PLCs, the plurality of PLCs being communicatively connected to each other through a network communication interface; and the electrical control subunit is used for controlling the corresponding tunneling devices in the tunneling machine to execute tunneling actions according to the set tunneling parameter information through a plurality of PLCs.
Further, in one 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 of which are communicatively connected to 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 the voltage value according to the voltage value corresponding to the single-blade thrust; the PLC control program module is used for calculating the current thrust value of the thrust gear of the operation panel and the value of the quantity, calculating the thrust difference 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; 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 used for controlling the hydraulic servo system and the hydraulic proportional valve for providing thrust for the operation panel according to the thrust control simulation value so as to enable the single-blade thrust of the operation panel to reach the target thrust value.
Specifically, as shown in fig. 4, the control unit uses a plurality of PLCs as a core to complete the electrical control task of the whole machine of the heading machine, and the PLCs establish communication connection through a network communication interface, so as to construct a complete electrical control system of the heading machine, and the PLC system automatically resets and adjusts gears of the thrust force, torque, heading direction, rotational speed and propulsion speed of the cutterhead of the heading machine according to the optimal heading parameters given by the intelligent decision unit, and then drives the heading machine to make corresponding actions, and the heading machine continuously feeds back heading operation data to the sensing unit in real time while executing the actions, and is illustrated by taking automatic control of the thrust force of the cutterhead as an example:
the control method comprises the steps that a thrust gear setting potentiometer on an operation panel is automatically reset and adjusted by a machine related parameter given by an intelligent decision unit, the parameter is a voltage value, the voltage value is sent to an A/D conversion functional module in a PLC system, the A/D conversion functional module converts the voltage value into a digital quantity, the digital quantity represents a given thrust value of a cutter disc, a PLC control program obtains a difference value by comparing a set thrust value of the cutter disc with an actual thrust value of the cutter disc detected by a pressure sensor, then a control quantity is calculated by an automatic control algorithm according to the difference value, and the PLC control system converts the control quantity into an analog quantity by using a D/A conversion functional module and controls and adjusts the pressure of a pushing hydraulic 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 thrust value of the cutter disc, and the aim of automatic control of the thrust of the cutter disc is achieved.
Further, in one 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 tunneling operation according to the tunneling mode; the intelligent decision unit is further used for generating disaster early warning information according to the tunnel geological forecast information so that the heading machine can take heading disaster avoidance measures according to the disaster early warning information.
The tunneling machine is a full-face hard rock tunneling machine for rock strata, consists of a tunneling machine host system and a tunneling machine rear supporting system, and is an intelligent tunneling robot (Tunnel Boring Robot, TBR) body.
The tunneling machine tunneling mode comprises two modes, wherein the first mode is tunneling in a double-shield mode under the condition that a tunnel rock stratum is stable, namely a supporting shoe cylinder is supported out, a supporting shoe presses a rock wall, and support is provided for tunneling through static friction force between the supporting shoe and the rock wall, and the mode can realize multiple working procedures of tunneling, step changing, segment supporting, backfilling grouting and the like; the second mode is that the tunnel rock stratum is unstable, when the supporting shoe cannot obtain enough counter force, a single shield mode is adopted for tunneling, namely, the auxiliary pushing oil cylinder acts on the end face of the duct piece to provide support for tunneling, tunneling and step changing procedures cannot be carried out simultaneously in the mode, and the auxiliary oil cylinder also plays a role in compacting duct pieces in the duct piece assembling procedure.
Further, 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 personal computer and is used for loading an autonomously developed command system application program (app) to access the cloud service unit of the cloud for human-computer interaction in real time, controlling the development machine by the user in a remote control mode, remotely monitoring the condition of on-site development construction operation in real time, and remotely transmitting a development work task to the intelligent decision-making unit through the cloud service unit; the user is a field trained heading machine operator and a tunneling construction commander under 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, play back and manage tunneling operation data transmitted in a wireless mode on site in real time, support real-time man-machine interaction between the command system and the tunneling machine to realize a mode of remotely controlling the tunneling machine to perform construction operation, and simultaneously provide real-time support for decision model updating of the intelligent decision unit and the tunneling operation task under the command system.
Further, in one 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 the tunneling direction of a tunneling machine tunneling guide system in N current periods 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 set tunneling parameter information and real-time tunneling parameter information of the tunneling machine in N groups of current periods according to preset acquisition frequency, and calculating relative errors of all parameters in each group of tunneling parameter information in the N groups of set tunneling parameter information and the real-time tunneling parameter information to acquire an error set;
calculating a second error average value of each parameter according to N relative errors corresponding to each parameter in the error set;
the first error average value and the second error average value are subjected to digital average operation to obtain an average valueAnd acquiring a real-time tunneling effect information index according to the following formula, wherein A (x, y, z) is the real-time tunneling effect information index:
specifically, the tunneling effect index value A (x, y, z) is formed by tunneling direction planning data DirectionDesign (x, y, z) in a design drawing, actual measurement data DirectionMessaging (x, y, z) of the tunneling direction of a tunneling machine tunneling guide system and actual measurement data single-cutter thrust F of a main parameter of the tunneling machine t (x, y, z), single blade torque T n (x, y, z), cutter rotational speed N (x, y, z), propulsion speed V (x, y, z) are jointly determined, and the specific method is as follows:
step 1: calculating the actual measurement of the tunneling direction planning data DirectionDesign (x, y, z) and the tunneling direction of the tunneling machine tunneling guide system in the design drawing in a tunneling period The relative errors of the data directionmeasurment (x, y, z), i.e. the relative error values of 10 times, where N is 10, are calculated respectively, and then the relative errors of 10 times are averaged
Step 2: calculating the relative error between the main parameter set value of the heading machine and the actual measured value detected by the sensor in one heading period, namely respectively calculating the relative error of 10 times of main parameters, and then respectively calculating the average relative error of the 10 times of main parameters, namelySingle blade torque->Cutter head rotating speed->Propulsion speed
Step 3: mathematical averaging of the average relative error structure obtained in steps 1 to 2:
step 4: calculating a tunneling effect index value
According to the tunneling robot for tunneling, provided by the embodiment of the invention, the intelligent flow of the tunneling process can be realized, the online real-time monitoring and intelligent feedback control can be carried out on the tunneling process, unmanned operation or remote control of the tunneling machine is realized, the self-adaptive adjustment of the working parameters of the tunneling machine to the 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 in the fields of water conservancy and hydropower engineering, railway, highway traffic engineering and the like and the fine management of the tunneling process are realized.
A remote mobile terminal command system for tunneling 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 one embodiment of the present invention.
As shown in fig. 5, the remote mobile terminal command system includes: tunneling robots and command systems.
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 real-time man-machine interaction between the command system and the tunneling machine to realize a mode of remotely controlling the tunneling machine to perform construction operation, and simultaneously providing real-time support for the intelligent decision unit by updating a decision model of the intelligent decision unit and transmitting tunneling operation tasks under the command system;
the mobile terminal is used for loading an autonomous development command system application program (app) to access the cloud service unit of the cloud in real time for human-computer interaction, and is used for a user to control the heading machine by using a remote control mode, and the user can remotely monitor the on-site tunneling construction operation condition in real time.
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 an embodiment of a tunneling robot for tunneling is also applicable to a remote mobile terminal command system for tunneling of the embodiment, and will not be repeated herein.
According to the remote mobile terminal command system for tunneling, provided by the embodiment of the invention, the real-time and effective on-line monitoring can be carried out on hard rock tunneling construction under different geological conditions, different construction modes and different construction stages, unmanned operation or remote control of the tunneling machine is realized by autonomous intelligent decision feedback control and unmanned tunneling construction operation of the tunneling machine, the influence of human factors is avoided, the tunneling construction quality is effectively ensured, the characteristics of detection, analysis, decision making and control loop closed-loop implementation are realized, the system is especially suitable for the scenes of all-weather, dangerous area, unmanned area or limit condition hard rock tunneling construction operation, the construction quality of rock tunnels and underground channel engineering can be effectively improved, the engineering construction efficiency is improved, and the rock tunnel and underground channel engineering construction in the fields of water conservancy and hydropower engineering, railway, highway traffic engineering and the like is advanced to the refined management age.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present invention. In this specification, schematic representations of the above terms are not necessarily directed 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, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (9)
1. A tunneling robot for tunneling, comprising: the body development machine of the development robot, a perception unit, an intelligent decision unit and a control unit which are arranged on the development machine, wherein the perception 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 real time in the current period 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 tunnel surrounding rock state information comprises tunneling parameter information, and the tunneling parameter information comprises single-blade thrust force Torque of single blade->Rotational speed of cutterhead>Propulsion speed->And tunneling guide->The real-time tunneling effect information index is determined by the following method: acquiring N relative errors of tunneling direction planning data and actual measurement data of the tunneling direction of a tunneling machine tunneling guiding system in N current periods according to a preset acquisition frequency, and calculating a first error average value of the N relative errors; the tunneling parameter information for calculating the second error average includes single-blade thrust +.>Torque of single blade->Rotational speed of cutterhead>And propulsion speed->Acquiring set tunneling parameter information and real-time tunneling parameter information of the tunneling machine in N groups of current periods according to a preset acquisition frequency, calculating relative errors of all parameters in each group of tunneling parameter information in the N groups of set tunneling parameter information and real-time tunneling parameter information to obtain an error set, and calculating a second error average value of each parameter according to 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 average values, and determining the real-time tunneling effect information index according to the following formula, wherein the expression of the formula is as follows:
wherein ,information index for real-time tunneling effect>N is a positive integer greater than 1, being the average value;
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;
the control unit is used for receiving the set tunneling parameter information and controlling the tunneling machine to perform tunneling operation of the next period according to the set tunneling parameter information.
2. A tunneling robot as claimed in claim 1, wherein,
the tunnel surrounding rock state information also comprises rock uniaxial saturation compressive strengthNumber of joints per volume of rock mass +.>Surrounding rock grade->。
3. A tunneling robot according to claim 1, wherein said sensing unit comprises a lead geological detection subunit,
and the advanced geological detection subunit is used for detecting and obtaining the tunnel geological forecast information by using a horizontal overlength drilling technology.
4. A tunneling robot according to claim 1, wherein said preset predictive algorithm comprises a neural network structure-based tunneling parameter predictive algorithm;
The intelligent decision unit is further used for generating the tunneling parameter prediction algorithm according to historical tunneling operation data training.
5. A tunneling robot according to claim 1, wherein said control unit comprises an electrical control subunit, wherein said electrical control subunit is established by a plurality of PLCs, said plurality of PLCs being communicatively connected to each other via a network communication interface;
and the electrical control subunit is used for controlling the 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 according to claim 5, wherein said electrical control subunit includes an a/D conversion function module, a PLC control program module, and a D/a conversion function module, both of which are communicatively connected to said PLC control program module, and when said 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 current thrust value of the thrust gear of the operation panel and the value of the quantity, calculating the thrust difference 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;
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 used for controlling a hydraulic servo system and a hydraulic proportional valve for providing thrust for the operation panel according to the thrust control simulation value, so that the single-blade thrust of the operation panel reaches the target thrust value.
7. A tunneling robot as claimed in claim 1, wherein,
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 the tunneling operation according to the tunneling mode;
the intelligent decision unit is further used for generating disaster early warning information according to the tunnel geological forecast information so that the tunneling machine can take tunneling disaster avoidance measures according to the disaster early warning information.
8. 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 a tunneling robot for tunneling according to any one of claims 1-6, the command system and the tunneling robot being communicatively connected, 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 by the tunneling construction site in a wireless mode in real time, supporting a command system and a tunneling machine to perform real-time man-machine interaction so as to realize remote control of a tunneling mode of the tunneling machine for construction operation, updating a decision model of the intelligent decision unit, and providing real-time support for the intelligent decision unit by the command system for tunneling operation tasks;
the mobile terminal is used for loading an autonomously developed command system application program to access a cloud service unit of the cloud end in real time for human-computer interaction, and is used for a user to control the heading machine by using a remote control mode and for the user to remotely monitor the operation condition of a tunneling construction site in real time.
9. The remote mobile terminal command system for tunneling according to claim 8, wherein,
the mobile terminal is also 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 perform tunneling operation according to the tunneling operation instruction.
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