Broken rock mass tunnel construction method based on multi-machine cooperative autonomous task planning

Abstract

The invention relates to a fractured rock mass tunnel construction method based on multi-machine collaborative autonomous task planning, belongs to the field of tunnel construction, and is particularly suitable for a fractured rock mass tunnel environment. According to the method, a construction task is decomposed into a plurality of subtasks by establishing a construction task model and combining geologic features of fractured rock mass and tunnel design requirements. A plurality of construction devices including but not limited to a heading machine, a supporting machine, a muck conveyor and the like are adopted to cooperatively execute different tasks. And through an autonomous task planning system, according to field real-time feedback information, a construction strategy and task distribution of all equipment are dynamically adjusted, and efficient cooperative operation of working procedures such as tunneling, supporting and muck cleaning in fractured rock mass tunnel construction is achieved. Based on the multi-machine cooperation and autonomous planning technology, the problems that traditional manual intervention is frequent, the equipment utilization rate is low and the construction efficiency is low in the fractured rock mass tunnel construction are solved, and the construction efficiency and safety are remarkably improved. According to the method, real-time monitoring and evaluation of the construction environment are realized through an integrated sensor network and an intelligent control algorithm, so that each device can autonomously adjust the working strategy according to the environment change, and unnecessary shutdown and equipment damage are avoided. Meanwhile, the method can reduce the manual participation degree, reduces personal errors and potential safety hazards, and is suitable for the complex construction environment of the fractured rock mass tunnel.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel construction, in particular to a broken rock tunnel construction method based on multi-machine collaborative autonomous task planning, which is suitable for broken rock tunnel construction under complex geological conditions. Background Art

[0002] Tunnel construction often faces many challenges under geological conditions of broken rock, such as unstable tunnel rock formations, harsh construction environment, and difficult equipment operation, which results in traditional construction methods being unable to meet the needs of efficient and safe construction. In existing technologies, tunnel construction generally relies on manual operation, using a single or small number of construction equipment to complete tasks such as excavation, support, and slag removal. This traditional construction method lacks coordination between equipment, has low operating efficiency, and cannot respond to complex geological changes in broken rock tunnels in real time. In addition, due to frequent manual intervention, the safety risks of construction workers are also high.

[0003] With the development of construction equipment and automation technology, the concept of multi-equipment collaborative operation has gradually emerged, but the existing multi-machine collaborative construction still mainly relies on human control, lacks systematic task planning and intelligent autonomous control means, resulting in the inability to handle dynamic changes in the construction process in a timely manner, and the construction efficiency and safety cannot be fully guaranteed. Therefore, how to achieve multi-machine collaborative operation in broken rock tunnel construction and improve construction efficiency and safety through autonomous task planning and control has become a problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing a broken rock tunnel based on multi-machine collaborative autonomous task planning, aiming to solve the problems of low construction efficiency, frequent manual intervention and great safety hazards in the prior art through the collaborative work of an autonomous task planning system and multiple construction equipment. To achieve the above purpose, the present invention proposes a method for constructing a broken rock tunnel based on multi-machine collaborative autonomous task planning, comprising the following steps:

[0005] -a) By establishing a construction task model, construction tasks such as tunnel excavation, support and soil transportation are decomposed into multiple subtasks;

[0006] -b) using multiple construction equipment, including excavation equipment, support equipment and muck transport equipment, to collaboratively complete the subtasks;

[0007] -c) Dynamically assign tasks to each device and adjust construction strategies based on real-time feedback information through an autonomous task planning system;

[0008] -d) Through the integrated sensor network, the construction environment is monitored in real time, and combined with the intelligent control algorithm, the corresponding task planning decisions are generated; You are writing an invention patent, and the claims are as above. Please write the specification in detail in combination with the claims.

[0009] -e) During the construction process, the operating parameters and task sequence of the equipment are automatically optimized according to the changes in the geological conditions of the broken rock mass to ensure the stability and safety of the construction process.

[0010] Preferably, the construction task model includes geological characteristics of the broken rock tunnel, tunnel design parameters and performance parameters of the construction equipment.

[0011] The construction method according to claim 1, characterized in that the autonomous task planning system comprises:

[0012] -a) Real-time monitoring module, used to collect status information of various equipment and tunnel environment data during the construction process;

[0013] -b) a task allocation module, used to dynamically adjust the tasks of each construction equipment according to the data collected by the real-time monitoring module;

[0014] -c) Autonomous planning module, used to generate new construction strategies based on current geological conditions and construction progress.

[0015] Preferably, the construction equipment comprises:

[0016] -a) Roadheader, responsible for tunnel excavation;

[0017] -b) Support equipment, responsible for supporting the inner wall of the tunnel;

[0018] -c) Muck transportation equipment, responsible for cleaning and transporting the muck generated during excavation.

[0019] Preferably, the sensor network includes geological monitoring sensors, displacement sensors, pressure sensors and equipment status sensors, and the sensors are used to monitor the geological conditions of tunnel construction, equipment operating status and tunnel stability in real time.

[0020] Preferably, the intelligent control algorithms used in the construction process include machine learning algorithms and expert system algorithms, which are used to optimize construction strategies and equipment operating parameters based on historical construction data and real-time monitoring data.

[0021] Prioritize the transmission of the operating status of each device and construction environment data to the central control system through wireless communication technology for centralized monitoring and task adjustment.

[0022] Preferably, the method is applicable to the complex geological conditions of broken rock tunnels and can automatically adjust the task priority and operation sequence of the equipment according to the changes in geological conditions.

[0023] The beneficial effects of the present invention based on its technical solution are:

[0024] 1. Improve construction efficiency: The present invention enables multiple construction equipment to work together and combines with an autonomous task planning system to dynamically adjust the task allocation of each device, reducing equipment waiting time and manual intervention, and improving construction efficiency.

[0025] 2. Enhanced safety: The present invention monitors the geological conditions of the tunnel in real time through a sensor network, and automatically adjusts the construction strategy in combination with an intelligent control algorithm, thereby reducing the participation of construction personnel in dangerous environments and reducing safety risks.

[0026] 3. Adaptability to complex geological conditions: The present invention is particularly suitable for the complex construction environment of broken rock tunnels. Through real-time monitoring and dynamic adjustment, the construction plan can be flexibly adjusted according to changes in geological conditions, ensuring the stability and continuity of construction.

[0027] 4. Reduce human errors: The present invention adopts intelligent control, which reduces the dependence on manual operation, thereby reducing errors and potential mistakes in human operation.

[0028] 5. Improved economic benefits: The present invention reduces construction delays caused by improper equipment coordination and process mismatch, improves equipment utilization, thereby reducing construction costs and improving economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a broken rock tunnel construction method based on multi-machine collaborative autonomous task planning according to the present invention;

[0030] Figure 2 It is a system diagram of the construction task model in the present invention;

[0031] Figure 3 It is a schematic diagram of multiple tunnel construction equipment in the present invention;

[0032] Figure 4 It is a task planning decision flow chart in the present invention;

[0033] 1- Tunnel excavation construction task; 2- Support construction task; 3- Muck transportation construction task; 4- Muck transportation construction task; 5- Construction task model system; 6- Tunnel cooperative signal transmitting device; 7- Excavator; 8- Transport device; 9- Spray mixing device; 10- Drilling device; 11- Tunnel face; 12- Intelligent control system; 12-1- Data input terminal; 12-2- Data output terminal; 13- Data transceiver; DETAILED DESCRIPTION

[0034] In order to achieve the purpose of the present invention and to better understand the technical solution and effects, the embodiments of the present invention will be further described in conjunction with the accompanying drawings.

[0035] Please refer to Figure 1 , Figure 1 This is a flow chart of a broken rock tunnel construction method based on multi-machine collaborative autonomous task planning according to the present invention;

[0036] In order to achieve the above objectives,

[0037] A method for constructing a broken rock tunnel based on multi-machine collaborative autonomous task planning, characterized in that it comprises the following steps:

[0038] -a) By establishing a construction task model, construction tasks such as tunnel excavation, support and soil transportation are decomposed into multiple subtasks;

[0039] -b) using multiple construction equipment, including excavation equipment, support equipment and muck transport equipment, to collaboratively complete the subtasks;

[0040] -c) Dynamically assign tasks to each device and adjust construction strategies based on real-time feedback information through an autonomous task planning system;

[0041] -d) Through the integrated sensor network, the construction environment is monitored in real time, and combined with the intelligent control algorithm, the corresponding task planning decisions are generated; You are writing an invention patent, and the claims are as above. Please write the specification in detail in combination with the claims.

[0042] -e) During the construction process, according to the changes in the geological conditions of the broken rock mass, the operating parameters and task sequence of the equipment are automatically optimized to ensure the stability and safety of the construction process. Figure 2 , 1 is the tunnel excavation construction task; 2 is the support construction task; 3 is the slag transportation construction task; 4 is the slag transportation construction task; 5 is the construction task model system.

[0043] The construction task model includes geological characteristics of the broken rock tunnel, tunnel design parameters and performance parameters of construction equipment.

[0044] The construction method according to claim 1, characterized in that the autonomous task planning system comprises:

[0045] -a) Real-time monitoring module, used to collect status information of various equipment and tunnel environment data during the construction process;

[0046] -b) a task allocation module, used to dynamically adjust the tasks of each construction equipment according to the data collected by the real-time monitoring module;

[0047] -c) Autonomous planning module, used to generate new construction strategies based on current geological conditions and construction progress. Figure 3 , 6 is a tunnel cooperative signal transmitting device; 7 is an excavator; 8 is a transport device; 9 is a spray mixing device; 10 is a drilling device; 11 is a tunnel face;

[0048] The construction equipment includes:

[0049] -a) Roadheader, responsible for tunnel excavation;

[0050] -b) Support equipment, responsible for supporting the inner wall of the tunnel;

[0051] -c) Muck transportation equipment, responsible for cleaning and transporting the muck generated during excavation.

[0052] The sensor network includes geological monitoring sensors, displacement sensors, pressure sensors and equipment status sensors, which are used to monitor the geological conditions of tunnel construction, equipment operation status and tunnel stability in real time. Figure 4 , 12 is an intelligent control system; 12-1 is a data input terminal; 12-2 is a data output terminal; 13 is a data transceiver.

[0053] The intelligent control algorithms used in the construction process include machine learning algorithms and expert system algorithms, which are used to optimize construction strategies and equipment operating parameters based on historical construction data and real-time monitoring data.

[0054] The construction method transmits the operating status of each device and the construction environment data to the central control system through wireless communication technology, so as to carry out centralized monitoring and task adjustment.

[0055] The method is applicable to the complex geological conditions of broken rock tunnels and can automatically adjust the task priority and operation sequence of the equipment according to the changes in the geological conditions.

Claims

1. A method for constructing a broken rock tunnel based on multi-machine collaborative autonomous task planning, characterized in that: The following steps are involved: -a) By establishing a construction task model, construction tasks such as tunnel excavation, support and soil transportation are decomposed into multiple subtasks; -b) using multiple construction equipment, including excavation equipment, support equipment and muck transport equipment, to collaboratively complete the subtasks; -c) Dynamically assign tasks to each device and adjust construction strategies based on real-time feedback information through an autonomous task planning system; -d) Real-time monitoring of the construction environment through an integrated sensor network and combined with intelligent control algorithms to generate corresponding task planning decisions; -e) During the construction process, the operating parameters and task sequence of the equipment are automatically optimized according to the changes in the geological conditions of the broken rock mass to ensure the stability and safety of the construction process.

2. The construction method according to claim 1, characterized in that: The construction task model includes geological characteristics of the broken rock tunnel, tunnel design parameters and performance parameters of construction equipment. The construction method according to claim 1, characterized in that the autonomous task planning system comprises: -a) Real-time monitoring module, used to collect status information of various equipment and tunnel environment data during the construction process; -b) a task allocation module, used to dynamically adjust the tasks of each construction equipment according to the data collected by the real-time monitoring module; -c) Autonomous planning module, used to generate new construction strategies based on current geological conditions and construction progress.

3. The construction method according to claim 1, characterized in that: The construction equipment includes: -a) Roadheader, responsible for tunnel excavation; -b) Support equipment, responsible for supporting the inner wall of the tunnel; -c) Muck transportation equipment, responsible for cleaning and transporting the muck generated during excavation.

4. The construction method according to claim 1, characterized in that: The sensor network includes geological monitoring sensors, displacement sensors, pressure sensors and equipment status sensors, and the sensors are used to monitor the geological conditions of tunnel construction, equipment operating status and tunnel stability in real time.

5. The construction method according to claim 1, characterized in that: The intelligent control algorithms used in the construction process include machine learning algorithms and expert system algorithms, which are used to optimize construction strategies and equipment operating parameters based on historical construction data and real-time monitoring data.

6. The construction method according to claim 1, characterized in that: The operating status and construction environment data of each device are transmitted to the central control system through wireless communication technology for centralized monitoring and task adjustment.

7. The construction method according to claim 1, characterized in that: The method is applicable to the complex geological conditions of broken rock tunnels and can automatically adjust the task priority and operation sequence of the equipment according to the changes in the geological conditions.

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