CN-121728490-B - Underground communication link optimization method and system based on self-organizing network

CN121728490BCN 121728490 BCN121728490 BCN 121728490BCN-121728490-B

Abstract

The invention provides a method and a system for optimizing an underground communication link based on a self-organizing network, which relate to the technical field of communication links and comprise the steps of obtaining communication node information, constructing a network topology structure, determining network characteristic parameters, constructing a link quality prediction model to evaluate link quality, carrying out dynamic partitioning on the network, establishing a node priority ranking table to select an optimal transmission path, monitoring the link state in real time, and triggering node self-organizing reconstruction when necessary. The invention can improve the connectivity, stability and transmission efficiency of the underground communication network, reduce the communication delay and enhance the safety of underground operation.

Inventors

WU YUE
FAN YAWEI
WU BING

Assignees

北京阳光金力科技发展有限公司

Dates

Publication Date: 20260505
Application Date: 20260227

Claims (8)

1. The method for optimizing the underground communication link based on the self-organizing network is characterized by comprising the following steps: acquiring position information, channel state information and service load information of a plurality of communication nodes distributed underground; Based on the position information and the channel state information, constructing a network topology structure reflecting the communication relation among nodes through a topology discovery mechanism, and analyzing the connectivity, node distribution density and topology stability of the network topology structure to determine network characteristic parameters; Constructing a link quality prediction model which fuses network state perception according to the network characteristic parameters, and judging the link quality level among all communication nodes by utilizing the link quality prediction model to form a predicted link quality evaluation result; Combining the predicted link quality evaluation result with the service load information, carrying out dynamic partition division on a communication network, determining transmission nodes in each partition, establishing a node priority ranking table, and selecting an optimal transmission path according to the node priority ranking table; Re-calculating a target path based on the updated network characteristic parameters, and establishing a communication link to execute data transmission; Combining the predicted link quality evaluation result with the service load information, performing dynamic partition division on a communication network, determining transmission nodes in each partition, and establishing a node priority ranking table, wherein selecting an optimal transmission path according to the node priority ranking table comprises: Based on the link quality predicted value in the predicted link quality evaluation result, identifying communication node connection with the link quality predicted value lower than a preset quality threshold in a network topology structure, carrying out deep analysis on the communication node connection by combining a link perception mechanism, taking the analysis result as a partition boundary identifier, and carrying out dynamic partition division on the communication network; Continuously monitoring the state in each network partition based on the link perception mechanism, extracting service load information and position information of each communication node, and identifying the communication node with load lower than a load threshold value as a candidate transmission node of the network partition through a resource scheduling algorithm; calculating the path quality comprehensive score and the residual transmission capacity of the nodes according to the resource scheduling algorithm and combining the predicted link quality evaluation result and the service load information aiming at the candidate transmission nodes in each network partition, and establishing a node priority ranking table according to the score result; Selecting a transmission node which is highest in order and meets the monitoring requirement of the link perception mechanism as a relay node based on the node priority order table, and constructing an optimal transmission path from a source node to a data sink node; the step of calculating the path quality comprehensive score and the residual transmission capacity of the node based on the resource scheduling algorithm and combining the predicted link quality evaluation result and the service load information, and the step of establishing a node priority ranking table according to the score result comprises the following steps: Extracting a link quality predicted value from each communication node to a data sink node in a predicted link quality evaluation result, carrying out weighted fusion on the link quality predicted value and the path transmission hop count based on a resource scheduling algorithm, and calculating to obtain a path quality comprehensive score from each communication node to the data sink node; acquiring service load information of each communication node, wherein the service load information comprises the current buffer queue length of the node and the maximum buffer capacity of the node; The resource scheduling algorithm determines the residual transmission capacity of each communication node based on the ratio relation between the current buffer queue length of the node and the maximum buffer capacity of the node, and performs product operation on the residual transmission capacity serving as a load balancing factor and the path quality comprehensive score to obtain a node comprehensive score comprehensively considering the path quality and the node load; and descending order arrangement is carried out on each communication node according to the numerical value of the node comprehensive score, so as to form a node priority ranking table showing the node transmission priority level.
2. The method of claim 1, wherein constructing a network topology reflecting connectivity between nodes by a topology discovery mechanism based on the location information and the channel state information, and analyzing connectivity, node distribution density, and topology stability of the network topology to determine network characteristic parameters comprises: Calculating the space distance between communication nodes based on the position information, combining the signal intensity and the channel fading characteristic in the channel state information, determining the reachability relation between the communication nodes through a topology discovery mechanism, and constructing a network topology structure comprising the connection relation between the node identifiers and the nodes; In the executing process of the topology discovery mechanism, collecting neighbor discovery response information of each communication node, counting the number of neighbor nodes, and calculating to obtain connectivity reflecting the overall connectivity of the network by combining the number of connected subgraphs existing in the network; dividing the underground space into a plurality of area units according to the position information, counting the distribution condition of communication nodes in each area unit, and determining node distribution density reflecting the uniformity of the space distribution based on variance analysis of the area node distribution; By the periodic execution of the topology discovery mechanism, the change condition of the connection relation between nodes in a preset time window is recorded, and the topology stability reflecting the dynamic change degree of the network topology structure is obtained by calculation according to the change frequency of the connection relation; and carrying out layered weighted mapping on the connectivity, the node distribution density and the topological stability, setting dynamic adjustment coefficients according to the importance degree of each parameter, and fusing to form network characteristic parameters.
3. The method of claim 2, wherein calculating the spatial distance between the communication nodes based on the location information, and determining the reachability relationship between the communication nodes through the topology discovery mechanism in combination with the signal strength and the channel fading characteristics in the channel state information, and constructing the network topology including the connection relationship between the node identifiers and the nodes comprises: calculating the space distance between communication nodes according to the position information, and identifying a communication node pair with a linear propagation path and a communication node pair with a diffraction propagation path based on the geometric constraint relation between the space distance and the underground roadway to obtain a propagation path type identifier; respectively calculating channel fading characteristics of the linear propagation path and the diffraction propagation path according to signal intensity in channel state information and the propagation path type identifier through a topology discovery mechanism, wherein the linear propagation path is calculated based on the spatial distance fading characteristic, and the diffraction propagation path is calculated based on the multipath superposition effect; Comprehensively evaluating the signal strength and the channel fading characteristics, judging that the corresponding communication node pairs have a reachability relationship when the comprehensive evaluation result meets the communication quality constraint condition, and distributing connection weights for the communication nodes with the reachability relationship; and constructing a network topology structure comprising node identifiers, connection relations among nodes and connection weights based on the topology discovery mechanism according to the reachability relation and the connection weights.
4. The method of claim 1 wherein constructing a link quality prediction model that blends network state awareness based on the network characteristic parameters, and wherein determining a link quality level between the communication nodes using the link quality prediction model, and forming a predicted link quality assessment result comprises: Extracting connectivity, node distribution density and topological stability in network characteristic parameters, acquiring channel state information and service load information among communication nodes in a historical time period, and constructing a link quality prediction model fused with network state perception based on time sequence relativity among the connectivity, the node distribution density, the topological stability, the channel state information and the service load information; Inputting network characteristic parameters, channel state information and service load information at the current moment into the link quality prediction model to obtain a link quality prediction value among communication nodes in a future time period; Based on the link quality predicted value, combining with a preset link quality grading standard, dividing the link quality level among all communication nodes into a plurality of quality grades, and distributing a quality weight coefficient for each quality grade, wherein the quality weight coefficient reflects the influence degree of different quality grades on the path selection; And combining the link quality predicted value with the quality grade to form a predicted link quality evaluation result.
5. The method of claim 1, wherein recalculating the target path based on the updated network characteristic parameters and establishing a communication link for data transmission comprises: and dynamically adjusting weight coefficients of each communication node in target path selection according to the network state change trend, constructing a path optimization strategy reflecting the dynamic adaptability of the network, determining a target path based on the path optimization strategy, and establishing a communication link to execute data transmission.
6. A downhole communication link optimization system based on an ad hoc network for implementing the method of any of the preceding claims 1-5, comprising: A first unit, configured to obtain location information, channel state information, and traffic load information of a plurality of communication nodes distributed downhole; The second unit is used for constructing a network topology structure reflecting the communication relation among the nodes through a topology discovery mechanism based on the position information and the channel state information, analyzing the connectivity, the node distribution density and the topology stability of the network topology structure and determining network characteristic parameters; a third unit, configured to construct a link quality prediction model fused with network state awareness according to the network feature parameters, and determine a link quality level between each communication node by using the link quality prediction model, so as to form a predicted link quality evaluation result; A fourth unit, configured to dynamically partition a communication network by combining the predicted link quality evaluation result and the traffic load information, determine transmission nodes in each partition, establish a node priority ranking table, and select an optimal transmission path according to the node priority ranking table; A fifth unit, configured to recalculate a target path based on the updated network characteristic parameter, and establish a communication link to perform data transmission; Combining the predicted link quality evaluation result with the service load information, performing dynamic partition division on a communication network, determining transmission nodes in each partition, and establishing a node priority ranking table, wherein selecting an optimal transmission path according to the node priority ranking table comprises: Based on the link quality predicted value in the predicted link quality evaluation result, identifying communication node connection with the link quality predicted value lower than a preset quality threshold in a network topology structure, carrying out deep analysis on the communication node connection by combining a link perception mechanism, taking the analysis result as a partition boundary identifier, and carrying out dynamic partition division on the communication network; Continuously monitoring the state in each network partition based on the link perception mechanism, extracting service load information and position information of each communication node, and identifying the communication node with load lower than a load threshold value as a candidate transmission node of the network partition through a resource scheduling algorithm; calculating the path quality comprehensive score and the residual transmission capacity of the nodes according to the resource scheduling algorithm and combining the predicted link quality evaluation result and the service load information aiming at the candidate transmission nodes in each network partition, and establishing a node priority ranking table according to the score result; Selecting a transmission node which is highest in order and meets the monitoring requirement of the link perception mechanism as a relay node based on the node priority order table, and constructing an optimal transmission path from a source node to a data sink node; the step of calculating the path quality comprehensive score and the residual transmission capacity of the node based on the resource scheduling algorithm and combining the predicted link quality evaluation result and the service load information, and the step of establishing a node priority ranking table according to the score result comprises the following steps: Extracting a link quality predicted value from each communication node to a data sink node in a predicted link quality evaluation result, carrying out weighted fusion on the link quality predicted value and the path transmission hop count based on a resource scheduling algorithm, and calculating to obtain a path quality comprehensive score from each communication node to the data sink node; acquiring service load information of each communication node, wherein the service load information comprises the current buffer queue length of the node and the maximum buffer capacity of the node; The resource scheduling algorithm determines the residual transmission capacity of each communication node based on the ratio relation between the current buffer queue length of the node and the maximum buffer capacity of the node, and performs product operation on the residual transmission capacity serving as a load balancing factor and the path quality comprehensive score to obtain a node comprehensive score comprehensively considering the path quality and the node load; and descending order arrangement is carried out on each communication node according to the numerical value of the node comprehensive score, so as to form a node priority ranking table showing the node transmission priority level.
7. An electronic device, comprising: A processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 5.
8. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 5.

Description

Underground communication link optimization method and system based on self-organizing network Technical Field The present invention relates to communication link technologies, and in particular, to a method and a system for optimizing an underground communication link based on an ad hoc network. Background The underground communication network is used as an important infrastructure of underground engineering such as mining, tunnel construction and the like, and has a key effect on guaranteeing the safety of operators and improving the production efficiency. With the development of intelligent mining technology, the number of devices and sensors in a downhole environment has increased dramatically, placing higher demands on the stability and reliability of communication networks. Conventional downhole communication systems mainly employ wired communication modes, such as optical fibers, leaky cables, and the like, but in complex and variable downhole environments, these fixedly-deployed communication facilities are susceptible to damage. The wireless communication technology is gradually introduced into the field of underground communication due to flexibility and deployment convenience, and a self-organizing network is constructed by deploying a plurality of wireless communication nodes so as to realize reliable transmission of data. The underground environment is complex and changeable, the roadway structure is complex, the number of barriers such as rock walls and supporting equipment is large, the signal propagation is affected by serious attenuation and multipath effects, and the quality of a communication link is unstable. In the underground operation process, the mining face is continuously pushed, the position of a communication node needs to be frequently adjusted, the network topology structure is dynamically changed, and the traditional static routing algorithm is difficult to adapt to the high-dynamic environment. The service demands of different areas in the pit have large difference, such as different requirements of applications such as video monitoring, equipment control, environment monitoring and the like on bandwidth and time delay, but the existing communication system lacks an intelligent allocation mechanism for network resources, and cannot flexibly optimize a transmission path according to the service load characteristics, so that the utilization efficiency of the network resources is low. In order to solve the problems, a method for optimizing the underground communication link, which can sense the network state, predict the link quality and dynamically adjust the communication path, is needed to improve the stability, the reliability and the resource utilization efficiency of the underground communication network and meet the requirements of intelligent mine construction on high-quality communication guarantee. Disclosure of Invention The embodiment of the invention provides a method and a system for optimizing an underground communication link based on a self-organizing network, which can solve the problems in the prior art. In a first aspect of an embodiment of the present invention, a method for optimizing a downhole communication link based on an ad hoc network is provided, including: acquiring position information, channel state information and service load information of a plurality of communication nodes distributed underground; Based on the position information and the channel state information, constructing a network topology structure reflecting the communication relation among nodes through a topology discovery mechanism, and analyzing the connectivity, node distribution density and topology stability of the network topology structure to determine network characteristic parameters; constructing a link quality prediction model which fuses network state perception according to the network characteristic parameters, and judging the link quality level among all communication nodes by utilizing the link quality model to form a predicted link quality evaluation result; Combining the predicted link quality evaluation result with the service load information, carrying out dynamic partition division on a communication network, determining transmission nodes in each partition, establishing a node priority ranking table, and selecting an optimal transmission path according to the node priority ranking table; and recalculating a target path based on the updated network characteristic parameters, and establishing a communication link to execute data transmission. Based on the position information and the channel state information, constructing a network topology structure reflecting the connectivity relation among nodes through a topology discovery mechanism, and analyzing connectivity, node distribution density and topology stability of the network topology structure to determine network characteristic parameters comprises the following steps: Calculating the space distance between