Communication network design and configuration method suitable for mine ventilation scene

Abstract

A deep well ventilation system is a core link for guaranteeing safe production of a mine and is also a key technical challenge for realizing efficient and green mining of an ultra-large deep well mine. Ventilation energy-saving optimization is an important subject which must be solved by deep well mining. In a deep well ventilation system, reasonable configuration of a communication network structure is a basic guarantee for achieving real-time monitoring of underground environment data and reliable transmission of control instructions. The main contradiction faced by the current deep well communication network design lies in that the network redundancy design introduced for improving the reliability of data transmission can enhance the fault-tolerant capability of the system, but also causes the problems of complex network topology structure and increased construction cost at the same time. In order to solve the technical problem, the invention provides a communication network design and configuration method suitable for a mine ventilation scene. According to the method, a quantitative relation model of the network construction cost and the safety performance is innovatively established, and the optimal network node configuration scheme is determined through target optimization. Particularly, according to the method, the high reliability of the ring network and the high efficiency of the tree network are fused, so that the optimal design of the communication network topology structure is realized, and the network economy is remarkably improved while the system reliability is ensured.

Description

Technical Field

[0001] The present invention relates to a communication network design and configuration method applicable to mine ventilation scenarios, belonging to the field of industrial automation and control engineering. Background Art

[0002] In recent years, the rapid development of industrial network automation technology has promoted the wide application of intelligent manufacturing, industrial Internet of Things (IIoT), and digital transformation. Modern industrial networks have evolved from traditional fieldbuses (such as PROFIBUS, Modbus) to industrial Ethernet (such as PROFINET, EtherCAT) and wireless communication (such as 5G, WiFi 6), with higher real-time performance, reliability, and bandwidth. However, in complex industrial environments (such as mines, petrochemicals), the network still faces challenges of anti-interference, low latency, and high reliability, and there is an urgent need to optimize the communication architecture for specific scenarios.

[0003] In deep-shaft mine mining, the ventilation system is the core to ensure safe production, and a reliable underground communication network is the key infrastructure for realizing intelligent ventilation monitoring. Due to the complex mine environment (such as high humidity, much dust, electromagnetic interference), traditional communication technologies are difficult to meet the requirements of real-time data collection and remote control. An efficient underground communication network can monitor key parameters such as gas concentration, wind speed, temperature, and humidity in real time, and transmit control instructions through low-latency transmission to ensure the dynamic adjustment of the ventilation system, thereby reducing energy consumption and improving safety. In addition, a stable communication network can also support personnel positioning, emergency broadcasting, and equipment status monitoring, comprehensively improving the intelligent management level of the mine. Therefore, optimizing the underground communication network structure and balancing reliability, real-time performance, and cost are important technical supports for realizing green and efficient deep-shaft mining. Summary of the Invention

[0004] Aiming at the problem that although the introduction of network redundancy design can enhance the system fault tolerance ability, it also leads to the complication of the network topology structure and the increase of construction costs, the present invention proposes a communication network design and configuration method applicable to mine ventilation scenarios. This method innovatively establishes a quantitative relationship model between network construction costs and safety performance, and determines the optimal network node configuration scheme through target optimization. By integrating the high reliability of the ring network and the high efficiency of the tree network, this method realizes the optimal design of the communication network topology structure, significantly improving the network economy while ensuring the system reliability.

[0005] A communication network design and configuration method applicable to mine ventilation scenarios specifically includes the following five parts:

[0006] The first part: stratify the mine ventilation system;

[0007] Specifically, it includes the following steps:

[0008] Step (1): Stratify the ventilation and communication system in the order from the surface system to the underground system. The switch ring network layer is denoted as A, the integrated base station layer is denoted as B, the fan station layer is denoted as C, and the variable-frequency fan layer is denoted as D;

[0009] Step (2): Count the number of underground ventilation tunnels and denote it as Q, that is, the number of variable-frequency fans required for the variable-frequency fan layer E is Q;

[0010] Step (3): The network architecture design starts from layer D, determines the optimal number of sites for the upper layer C according to the function, and finally determines the number of sites for layer A;

[0011] Part Two: Construct the objective function for the optimal network nodes of mine ventilation;

[0012] Specifically, it includes the following steps:

[0013] Step (1): Construct the negative impact factor of network nodes. Define the first negative factor of network nodes as economy, denoted as M, where M is the number of nodes in the upper-layer network. Define the second negative factor of network nodes as management complexity H, and H = α·M β , where α represents the basic coefficient, β represents the complexity index. Define the third negative factor of network nodes as the expansion of the attack surface, denoted as S, and S = p·M·C v , where p represents the topology amplification factor, C v represents the average vulnerability coefficient;

[0014] Step (2): Construct the positive impact factor of network nodes. Define the first positive factor of network nodes as fault isolation, denoted as F, and F = 1 - G eff / G total , where G eff is the number of nodes affected by a single node failure in the upper level, that is, the number of nodes in the current network layer connected by each upper-layer node. G total is the total number of nodes in the current layer network. G eff satisfies G eff = G total / M; Define the second positive factor of node security as path redundancy R, and R = N path / N min , where N path is the actual number of paths, and N min is the minimum required number of paths. When the network adopts a tree-type network structure, N path = N min , and R = 1;

[0015] Step (3): Construct the objective function for mine ventilation network nodes as:

[0016]

[0017] Where J represents the optimal network configuration coefficient, and ω1, ω2, ω3, ω4, and ω5 respectively represent the first positive factor coefficient of the network node, the second positive factor coefficient of the network node, the first negative factor coefficient of the network node, the second negative factor coefficient of the network node, and the third negative factor coefficient of the network node;

[0018] Part Three: Calculate the weights of the coefficients in the objective function of the mine ventilation network nodes;

[0019] Step (1): Set the priority relationships of the four factor coefficients. Denote the priority degree of the path redundancy R as K1 times that of the fault isolation F, denote the priority degree of the economy as K2 times that of the fault isolation F, denote the priority degree of the management complexity as K3 times that of the fault isolation F, and denote the priority degree of the attack surface expansion as K4 times that of the fault isolation F;

[0020] Step (2): Normalize the coefficients of the two positive factors of the network nodes and the two negative factors of the network nodes. ω1 = 1 / (1 + K1 + K2 + K3 + K4), ω2 = K1 / (1 + K1 + K2 + K3 + K4), ω3 = K2 / (1 + K1 + K2 + K3 + K4), ω4 = K3 / (1 + K1 + K2 + K3 + K4), ω5 = K4 / (1 + K1 + K2 + K3 + K4);

[0021] Part Four: Obtain the optimal number of nodes in each layer of the node network according to the objective function;

[0022] Specifically, it includes the following steps:

[0023] Step (1): Since the higher-level network is more important, more complex, and less vulnerable, repeat the second part before calculating the new layer to obtain new parameters. Denote the objective function parameters of the fan station layer as α C , β C , p C , C v C ; Denote the objective function parameters of the comprehensive base station layer as α B , β B , p B , C v B ; Denote the objective function parameters of the switch ring network layer as α A , β A , p A , C v A ;

[0024] Step (2): Calculate the optimal number of stations to be built on the fan station layer C. Using the curve fitting function of Matlab, with the number of nodes M on the upper layer as the independent variable and the optimal network configuration coefficient J as the dependent variable, when J is maximized, the corresponding number of nodes is denoted as M C , and at this time M C is denoted as the optimal number of nodes on the fan station layer;

[0025] Step (3): Repeat step (2) of the fourth part. Solve the optimal number of nodes M of the comprehensive base station in the order of gradually solving from the underground network system to the above-ground network system B ;

[0026] Step (4): Repeat step (2) of the fourth part. Calculate the relevant influencing factors according to the ring network, and the optimal number of nodes M of the switch ring network A ;

[0027] Part Five: Configure a data monitoring host and a remote maintenance sub-station for the network to complete the design of the network architecture.

[0028] The beneficial effects of the present invention are as follows:

[0029] 1. The present invention effectively quantifies the relationship between the cost of network construction nodes and network security, and can quickly and efficiently complete the preliminary design of the communication network for deep shaft mines.

[0030] 2. The present invention is aimed at the deep shaft mine scenario, integrates two typical network structures, combines the high reliability of the ring network and the low cost of the tree type, and can ensure the economy and reliability of the network structure design.

[0031] 3. The objective function constructed by the present invention will change according to the selected network structure type, enabling the method to flexibly adapt to the special working conditions of various different scenarios. Description of the Drawings

[0032] Figure 1 is a schematic diagram of the network structure of the mine ventilation system;

[0033] Figure 2 is the fitting effect of the optimal network configuration coefficient of the fan layer;

[0034] Figure 3 is the fitting effect of the optimal network configuration coefficient of the comprehensive base station layer;

[0035] Figure 4 is the fitting effect of the optimal network configuration coefficient of the switch ring network layer. Detailed Embodiments

[0036] The present invention will be further described in detail below with reference to the drawings.

[0037] As Figure 1 , a communication network design and configuration method applicable to the mine ventilation scenario, includes the following five parts:

[0038] The first part: Stratify the mine ventilation system;

[0039] Specifically, it includes the following steps:

[0040] Step (1): Stratify the ventilation communication system in the order from the surface system to the underground system. The switch ring network layer is denoted as A, the integrated base station layer is denoted as B, the fan station layer is denoted as C, and the variable-frequency fan layer is denoted as D;

[0041] Step (2): Count the number of underground ventilation tunnels and denote it as Q. In this example, Q = 12, that is, the number of variable-frequency fans required for the variable-frequency fan layer E is 12;

[0042] Step (3): The network architecture design starts from level D, determines the optimal number of stations for the upper level C according to the function, and finally determines the number of stations for level A;

[0043] The second part: Construct the optimal network node objective function for mine ventilation;

[0044] Specifically, it includes the following steps:

[0045] Step (1): Construct the negative impact factor of the network node. Define the first negative factor of the network node as economy, denoted as M, where M is the number of nodes in the upper-level network. Define the second negative factor of the network node as management complexity H, H = α·M β , where α represents the basic coefficient, β represents the complexity index. Define the third negative factor of the network node as the expansion of the attack surface, denoted as S, S = p·M·C v , where p represents the topology amplification factor, C v represents the average vulnerability coefficient;

[0046] Step (2): Construct the positive impact factor of the network node. Define the first positive factor of the network node as fault isolation, denoted as F, F = 1 - G eff / G total , where G eff is the number of nodes affected by a single node failure in the upper level, that is, the number of nodes in the current network level connected by each upper-level node. G total is the total number of nodes in the current level network. G eff satisfies G eff = G total / M; Define the second positive factor of node security as path redundancy R, R = N path / N min , where N path is the actual number of paths, N minFor the minimum required number of paths, when the network adopts a tree - type network structure, N path = N min , R = 1;

[0047] Step (3): Construct the objective function of the mine ventilation network nodes as follows:

[0048]

[0049] Among them, J represents the best network configuration coefficient, and ω1, ω2, ω3, ω4, ω5 represent the first positive factor coefficient of the network node, the second positive factor coefficient of the network node, the first negative factor coefficient of the network node, the second negative factor coefficient of the network node, and the third negative factor coefficient of the network node respectively;

[0050] Part Three: Calculate the weights of the coefficients in the objective function of the mine ventilation network nodes;

[0051] Step (1): Set the priority relationships of the four factor coefficients. Denote the priority degree of the path redundancy R as K1 times that of the fault isolation F, denote the priority degree of the economy as K2 times that of the fault isolation F, denote the priority degree of the management complexity as K3 times that of the fault isolation F, and denote the priority degree of the attack surface expansion as K4 times that of the fault isolation F; According to the underground working conditions requirements, after expert scoring, set K1 as 0.2, K2 as 0.5, K3 as 0.3, and K4 as 1.5;

[0052] Step (2): Normalize the coefficients of the two network node positive factors and the two network node negative factors. ω1 = 1 / (1 + K1 + K2 + K3 + K4)=0.287, ω2 = K1 / (1 + K1 + K2 + K3 + K4)=0.057, ω3 = K2 / (1 + K1 + K2 + K3 + K4)=0.14, ω4 = K3 / (1 + K1 + K2 + K3 + K4)=0.086, ω5 = K4 / (1 + K1 + K2 + K3 + K4)=0.43;

[0053] Part Four: Obtain the optimal number of nodes in each layer of the node network according to the objective function;

[0054] Specifically, it includes the following steps:

[0055] Step (1): Since the upper - level network is more important, more complex, and has fewer vulnerabilities, repeat Part Two before calculating the new layer to obtain new parameters. Denote the objective function parameters of the fan station layer as α C , β C , p C , C v C ; Denote the objective function parameters of the comprehensive base station layer as α B , β B , pB and C v B ; Denote the objective function parameters of the switch ring network layer as α A and β A and p A and C v A ; The basic coefficient α can take values from 1.2 to 1.5, the typical value of the complexity exponent β is from 0.8 to 1.5, p for the tree network topology usually takes values from 1.3 to 1.8, p for the ring network topology usually takes values from 1.5 to 2.0, and the average vulnerability coefficient C v usually takes values from 0.5 to 0.7. Then take α C = 1.2, β C = 0.8, p C = 1.8, C v C = 0.7; α B = 1.2, β B = 1.1, p B = 1.8, C v B = 0.6; α A = 1.2, β A = 1.3, p A = 2.0, C v A = 0.5;

[0056] Step (2): Calculate the optimal number of stations to be built for C in the fan station layer. Using the curve fitting function of matlab, with the number of nodes M in the upper layer as the independent variable and the optimal network configuration coefficient S as the dependent variable, when S is maximized, the corresponding number of nodes is denoted as M C , and at this time M C is denoted as the optimal number of nodes in the fan station layer;

[0057] The Matlab fitting effect is as Figure 2 shown. The maximum point of the fitted function is 3, so the optimal number of nodes in the fan station layer is 3, and M C is 3;

[0058] Step (3): Repeat step (2) of the fourth part. Solve the optimal number of nodes M of the integrated base station in the order of gradually solving from the underground network system to the above-ground network system B ; At this time, the current network level is the fan station layer, the number of stations is 3, and the upper level is the integrated base station;

[0059] The Matlab fitting effect is as Figure 3 shown. The maximum point of the fitted function is 2, so the optimal number of nodes in the fan station layer is 2, and M Bis 2; that is, one integrated base station connects to two fan stations, and one integrated base station connects to one fan station;

[0060] Step (3): Repeat step (2) of the fourth part, calculate the relevant influence factors according to the ring network, and the optimal number of nodes M of the switch ring network A ; At this time, the current network level is the integrated basic level, the number of stations is 2, and the upper level is the ring network station level;

[0061] The Matlab fitting effect is as Figure 4 shown. The maximum point of the fitted function is 2, so the optimal number of nodes in the fan station layer is 2, and M A is 2;

[0062] Part Five: Configure a data monitoring host and a remote maintenance sub-station for the network to complete the design of the network architecture.

[0063] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principles and purposes of the present invention, several improvements, substitutions, variations and embellishments can still be made, and these improvements, substitutions, variations and embellishments should also be regarded as the protection scope of the present invention.

[0064] The content not described in detail in this specification belongs to the prior art well-known to those of ordinary skill in the art.

Claims

1. A communication network design and configuration method suitable for mine ventilation scenarios, characterized in that: It includes the following five parts: Part I: Stratifying the mine ventilation system; The specific steps include: Step (1): The ventilation communication system is layered in the order from the above-ground system to the underground system, with the switch ring network layer being recorded as A, the integrated base station layer being recorded as B, the fan station layer being recorded as C, and the variable frequency fan layer being recorded as D; Step (2): Count the number of underground ventilation tunnels and record it as Q, that is, the number of variable frequency fans required for variable frequency fan layer E is Q; Step (3): The network architecture design starts from level D, determines the optimal number of sites for the previous level C based on the function, and finally determines the number of sites for level A; Part II: Constructing the optimal network node objective function for mine ventilation; Part III: Calculate the weights of the coefficients in the objective function of the mine ventilation network nodes; Part 4: Find the optimal number of nodes at each level in the node network based on the objective function; Part 5: Configure data monitoring host and remote maintenance substation for the network to complete the design of the network architecture.

2. According to a communication network design and configuration method suitable for mine ventilation scenarios according to claim 1, it is characterized in that: the second part constructs the optimal network node objective function for mine ventilation, specifically including the following steps: Step (1): Construct the negative impact factor of the network node, define the first negative factor of the network node as economy, denoted as M, where M is the number of nodes in the previous layer of the network, and define the second negative factor of the network node as management complexity H, H = α·M β , where α represents the basic coefficient, β represents the complexity index, and the third negative factor of the network node is defined as the expansion of the attack surface, denoted as S, S = p·M·C v , where p represents the topological amplification factor, C v represents the average vulnerability coefficient; Step (2): Construct the positive impact factor of the network node, and define the first positive factor of the network node as fault isolation, denoted as F, F = 1-G eff / G total , where G eff is the number of nodes affected by the failure of a single node at the previous level, that is, the number of nodes at the current network level connected to each node at the previous level, G total is the total number of nodes in the current level network, G eff Meet G eff =G total / M; define the second positive factor of node safety as path redundancy R, R = N path / N min , where N path is the actual number of paths, N min is the minimum number of required paths. When the network adopts a tree network structure, N path =N min , R = 1; Step (3): Construct the mine ventilation network node objective function as follows: Where J represents the optimal network configuration coefficient, ω1, ω2, ω3, ω4, and ω5 represent the first positive factor coefficient of the network node, the second positive factor coefficient of the network node, the first negative factor coefficient of the network node, the second negative factor coefficient of the network node, and the third negative factor coefficient of the network node, respectively.

3. A communication network design and configuration method suitable for mine ventilation scenarios according to claim 1, characterized in that: the third part calculates the weights of the coefficients in the objective function of the mine ventilation network node, specifically comprising the following steps: Step (1): Set the priority relationship of the four factor coefficients, record the priority of path redundancy R as K1 times of fault isolation F, record the priority of economy as K2 times of fault isolation F, record the priority of management complexity as K3 times of fault isolation F, record the priority of attack surface expansion as K4 times of fault isolation F; Step (2): Normalize the coefficients of the positive factors of the two network nodes and the negative factors of the two network nodes ω1=1 / (1+K1+K2+K3+K4), ω2=K1 / (1+K1+K2+K3+K4), ω3=K2 / (1+K1+K2+K3+K4), ω4=K3 / (1+K1+K2+K3+K4), ω5=K4 / (1+K1+K2+K3+K4).

4. A communication network design and configuration method suitable for mine ventilation scenarios according to claim 1, characterized in that: the fourth part obtains the optimal number of nodes at each level in the node network according to the objective function, specifically comprising the following steps: Step (1): Since the network at higher levels is more important, more complex, and less vulnerable, repeat the second part to obtain new parameters before calculating the next level. The objective function parameter of the wind turbine station layer is α C , β C 、p C , C v C ; Let the objective function parameter of the integrated base station layer be α B , β B 、p B , The objective function parameter of the switch ring network layer is α A , β A 、p A , Step (2): Calculate the optimal number of sites required for the wind turbine station layer C. Use the curve fitting function of Matlab, take the number of nodes M in the previous layer as the independent variable, and the optimal network configuration coefficient J as the dependent variable. When J is the largest, the corresponding number of nodes is recorded as M. C , at this time M C It is recorded as the optimal number of nodes at the wind turbine station level; Step (3): Repeat step (2) of the fourth part, and solve the optimal number of nodes M of the integrated base station in the order of gradually solving from the underground network system to the surface network system. B ; Step (4): Repeat step (2) in part 4 and calculate the relevant influencing factors according to the ring network. The optimal number of nodes M of the switch ring network is A .

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