The mine mining system mainly includes three parts: development, mining and mining. It involves the development of roadway, ground pressure management, perforation, blasting, rock filling, filling, air supply and water supply. The efficient operation of each operating system is a prerequisite for a mine to reach the design scale. Therefore, its operation must be predicted and analyzed. In the case of existing mining process and mining equipment, how to make full use of underground mining process information, reasonably determine the mining process parameters, and make the underground mining system operate at the best state, is currently the most concerned about underground mining in China. One of the key issues to be solved. The multi-agent system modeling method firstly defines a single agent according to the research system, assigns certain behaviors and parameters to each agent, and then determines the interaction rules according to the relationship between the agents and the agent and the environment. Interaction, the various agents are linked into a whole to form a complete Multi-Agent system. The multi-agent mining system model of the underground uranium mine is analyzed, and the following three kinds of Agent structures are mainly included. System reliability R1(t) Where n is the number of devices in the loading system, n=4; μi is the repair rate of the i-th device; λi is the failure rate of the i-th device. The transport operation includes transportation and upgrading. It is an important part of the mining process. It is necessary to consider the reliability of the transport system. This paper considers the case where two transport branches share a hoist. The transport subsystem includes mines, chutes, etc.; the lift subsystem consists of hoists and cabling systems. The reliability model and reliability block diagram are shown in Figure 5 and Figure 6. The reliability of the transport subsystem consisting of parallels is Then, the reliability of the entire carrier system is The equipment in the underground uranium mining system is repairable and the life distribution is subject to an exponential distribution, ie The reliability of the delivery system is In the whole mining system, involving the process of development, mining, mining, etc., the production system and the transporting and unloading system in each process can be regarded as a series repairable system consisting of n equipments, and each equipment is reliable. The degree is Ri (i = 1, 2, ..., n), and i represents a drilling machine , a rock loader, an excavator, a hoist, a mine car, and the like. According to the relationship between system reliability and cost, the relationship between the reliability of the i-th equipment and the maintenance cost Xi: In the formula, αi, βi are regression coefficients. Converted to solve R1, R2,..., Rn under certain conditions of maintenance cost, the constraint is Then the above problem can be changed to make Substituting constraints, there are From this, the equations for R1, R2, ..., Rn can be obtained, and R'1, R'2, ..., R'n can be solved. System reliability at this time In the process of underground uranium mining, the factors affecting the stability of the roadway include the lithology of the surrounding rock, the shape and size of the section, the location of the roadway, and the rock breaking method. In order to ensure the safety of the roof of the working face and provide a safe and stable working environment for rock blasting, mining, transportation, etc., it is necessary to carry out mine roadway support. Commonly used support methods include bolt support and shotcrete support. Although both can be used independently, they are often used in combination to make the support effect better. By applying the multi-agent theory to the underground uranium mining system, the multi-agent structure of the process flow of the underground uranium mining system is constructed, and the agent type and the reliability impact of each process flow in the system are analyzed. To promote the multi-agent distributed autonomy and function expansion of the mining system, which can effectively solve the structural problems of the underground uranium mining system with multi-agent structure, thus providing reference for mining design and rationalizing and realizing mine exploitation. To improve the economic benefits of mines has important practical significance and application prospects.
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Since the 1980s, China has made a wealth of practical research results on the reliability of mining systems. By using computer simulation methods to analyze the position and capacity of the mine, as well as the train operation of the hoist and rail transportation system, etc. The safety production and efficiency improvements have contributed to the reliability of the mine production system. However, with the advancement of technology and the development of intelligent management, some high-yield and high-efficiency mines with higher requirements for system reliability have emerged, which has led to further analysis and research on the reliability of mining systems. Yuan Yanbin et al [1-4] used fuzzy mathematics to evaluate the reliability of transportation system, established a system of judging factors, analyzed the current reliability status of transportation system and its existing problems and deficiencies, making the evaluation results more objective and objective. science. Shen Ying et al [5] used the Agent simulation system to simulate the reliability of the running mission process, and analyzed the reliability of the equipment system based on the Agent-oriented mission environment, and studied the characteristics of the system reliability under different environmental conditions. The practical system reliability theory and method laid the foundation and provided the basis for the reliability engineering design, development and demonstration of equipment system. Yang Fan [6] used the linear fuzzy number method to determine the AND gate fuzzy operator and the OR gate fuzzy operator, and calculated the importance of the wind path by Fussell-Vesely value method. Based on this, the ventilation network fuzzy fault was established. The tree model calculates the ventilation fuzzy fault tree model through specific examples, and obtains the failure data and importance analysis results of the fault tree basic events to verify the fuzzy fault tree model. Generation Jing Xia et al [7] and the use of computer simulation reliability theory, established under coal mine belt conveyor reliability simulation model to calculate the reliability index of the system, according to a reliability model to mine the tape Pingmei The conveyor is numerically simulated, and the results are calculated and analyzed by computer, which proves that this method is feasible. Liu Fuming et al [8] established a flexible series system and analyzed the reliability mathematical model by analyzing the three operational relationships between the warehouse system, the warehouse system and the buffer warehouse and their mutual transformation laws, and obtained the computational flexibility. The calculation formula for the reliability of the series system. According to the requirement of coal mining intensity in open pit mine, the distribution law of the repair time of the warehouse system and the warehouse system is used to obtain the optimal stock capacity of the buffer tank under the normal operating conditions of the system, and the optimal remaining space. .
In foreign countries, in the 1960s, the United States, Australia and other developed countries began to use computer simulation to solve the local production technology problems of underground mines, and did not conduct a comprehensive research and analysis of the mining system. With the development of computer simulation technology, the mining system has been widely used, and professional simulation software based on the entire mine production process has begun to appear, which has promoted the economic benefits and modern management level of the enterprise. In the early 1970s, the West German Essen Mining Research Institute developed a general rail transit system simulation model that was later used as a common standard software for mine transportation. The model was later integrated into the mine production system simulation model as an essential component. . In the 1980s, ISI and a mining company used the professional computer simulation language GPSS, combined with the experience of the mine delivery system software at that time, and jointly developed a general simulation software for underground delivery systems [9-11].
With the rapid development of computer technology and the emergence of intelligent computing technology, intelligent optimization technology has become the main technology to solve mining design. Multi-Agent System (MAS) adopts distributed architecture, which is agile, flexible and real-time. The advantage of each agent has a certain independent function, and a single
The structural relationship between Agent and Agent is dynamically adjustable. The mining system architecture composed of different functions of Agent coupling has adaptive, self-organizing and good coordination performance, and can complete complicated operation through different negotiation methods. Therefore, the application of multi-agent technology has important practical significance for solving the problem of distributed autonomy and integration optimization of mining systems.
1 mining system design
Based on the mining process and characteristics of underground uranium mines, combined with hybrid multi-agent structure, an underground uranium mining system based on multi-agent technology is established to decompose the various process operations designed in the mining process into corresponding Agent models. It is mainly composed of human-computer interaction interface, main control agent, development agent, acquisition agent, mining agent, ventilation agent, power supply agent, water supply agent, wear-blasting agent, loading agent, transportation agent, support agent and other agents. The specific structure is shown in Figure 1. 
1.1 Master Agent
The main control agent belongs to the management layer and is responsible for receiving the initial information input by the human-machine interface and the feedback information of other agents. At the same time, it also needs to issue mining information to other agents, so it needs the ability of information processing and reasoning decision. Its internal structure is shown in Figure 2.
1.2 Exploring Agent, Mining Agent, Mining Agent
Exploring Agents, Mining Agents, and Mining Agents not only have the functions of the main control agent, but also have certain planning functions. They receive the tasks issued by the main control agent, and at the same time give the wearer, the agent, the agent, and the support. The Agent issues the corresponding tasks, and according to the feedback information of the wearer agent, the loading agent, the agent, and the support agent, further planning decisions are made, and the optimal solutions are respectively fed back to the relevant agents. Its internal structure is shown in Figure 3.
1.3 Other Agents
Ventilation Agent, Power Supply Agent, Water Supply Agent, Explosive Agent, Loading Agent, Transportation Agent and Support Agent only need to receive corresponding task information, and make reasonable reasoning and planning decision according to their own knowledge base, and get the optimal operation of the agent. The program is fed back to the corresponding task release agent. Its internal structure is shown in Figure 4. 2 Reliability analysis of mining system
2.1 wearer agent reliability
The blasting operation is the first operation in the mining process of underground uranium mines. The effect of blasting directly affects whether the subsequent operations can be smoothly carried out and the cost is mined. There are many factors related to wearing explosive agents: drilling equipment, aperture, hole spacing, row spacing, ultra-deep, falling ore and explosive unit consumption. The relationship between these factors is complex, and it is still not possible to express it with specific function expressions. Therefore, adaptive fuzzy inference method is chosen for modeling.
In this paper, the main blasting parameters of the underground uranium mine, the aperture, hole spacing, row spacing, ultra-deep, falling ore and explosive unit consumption are used to establish the model of the blasting agent, with the amount of ore falling as the output and the rest as input.
Proceed as follows:
Step1: Generate training data and test data.
Step2: Determine the type and number of membership functions of the input variable.
Step3: The initial ANFIS structure is generated by a non-clustering function (genfis1) or a clustering function (genfis2). Ii=genfis2(trnData,bounds)
Step4: Set the parameters in ANFIS.
Step5: Train ANFIS with the anfis function.
[Oi,trnErr,stepSize,outfismat1,chkErr]...=anfis([trndatin,trndatout],Ii,[50,0,0.1],[],[chkdatin,chkdatout])
Step6: Verify the performance of the FIS.
Step7: Call the e valfis function to get the final output. f=e valfis(X,O)
2.2 Acquisition Agent Reliability
The loading operation refers to the operation of excavating the ore from the explosive pile and loading it into the transportation equipment through the mining equipment. It is a central link in the whole production process of the mine, and its efficiency directly affects the mining intensity, production capacity and economic benefits of the mine. The equipment used mainly includes excavators, ore hoppers, rock loaders, mine trucks, etc., which can be regarded as a repairable series system composed of equipment for analysis. The reliability index of the tandem production line based on the equipment failure rate can be expressed as the system validity A1.
2.3 Transportation Agent Reliability
The reliability of the two rows of subsystems in the transport subsystem is
2.4 Support Agent Reliability
The parameters of bolt support mainly include bolt length, bolt density, rod diameter, inter-rod spacing, and bolt tray area. The process parameters of shotcrete support mainly include the working wind pressure, water pressure, water-cement ratio of the jet, the distance and inclination of the nozzle from the sprayed surface, and the thickness of one shot.
The support agent can be designed as shown in Figure 7.
According to the current parameters of the working face as the condition for judging the reliability of the support system, combined with the Matlab neural network toolbox for data training, the reasonable support parameters are obtained, which provides theoretical support for the roadway support.
3 Conclusion
references
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[3] Bai Xiaoping, Zhang Xiaowei. Dynamic reliability modeling and evaluation of belt storage system with storage bin [J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2008, 37(S1): 284-288.
[4] Yuan Yanbin, Liang Wei. Fuzzy comprehensive evaluation of mine transportation system reliability by entropy weight method [J]. Metal mines, 2011 (2): 29-31.
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Author: Daijian Yong, Li Haitao; School of Nuclear Engineering, University of South China Resources;
Article source: "Modern Mining"; 2016.7;
Copyright:
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