Self-organizing topology management method for distributed wireless communication link of urban power distribution network

Abstract

The invention discloses a self-organizing topology management method for a distributed wireless communication link of an urban power distribution network, and belongs to the technical field of power grid communication control. The method comprises the following steps: dividing frequency domain channels to obtain a channel division set; identifying interference among nodes in the high-density area to obtain an interference graph and dividing same-frequency neighborhoods; performing path mapping and node association modeling to obtain a node association weight matrix; performing path selection configuration to obtain a distributed path tree; quantifying a node communication state to obtain a switching threshold control table; and replacing the link maintenance logic to obtain a dynamic topology evolution result. According to the method, based on an interference adjacency graph construction mechanism of a channel division structure and a neighborhood decomposition strategy under multi-dimensional interference constraint, an interference action boundary between nodes is accurately identified under a high-density deployment condition, and a same-frequency communication neighborhood region with a physical isolation effect is extracted; a stable and reliable topological basic structure is provided for path selection configuration and state quantification, and the self-organizing evolution capability of a distributed wireless communication link in a complex interference environment is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power grid communication control, and particularly relates to a self-organizing topology management method for distributed wireless communication links of urban power distribution networks. BACKGROUND

[0002] With the rapid evolution of urban power distribution networks towards intelligence and distribution, power distribution automation systems based on wireless communication have become a key support means for ensuring efficient and reliable operation of power distribution systems. In the face of the rapid increase in the number of power communication terminal devices, high node deployment density, and complex environmental interference factors, traditional fixed topology structures cannot adapt to the needs of network dynamic evolution and frequent changes in communication links. Therefore, self-organizing topology management technology has gradually become an important development direction for the evolution of urban power distribution communication networks.

[0003] In the prior art, the construction of interference relationships between nodes generally adopts a fixed communication radius model or an adjacent determination method based on received signal strength, which cannot accurately depict the link interference overlap phenomenon caused by spectrum reuse in high-density deployment scenarios, especially under adjacent frequency or same frequency communication conditions, which easily leads to ambiguous node association relationships and cannot form a stable and effective interference identification structure.

[0004] Therefore, how to solve the problems of link interference overlap phenomenon and ambiguous node association relationships in high-density deployment scenarios is a technical problem to be solved by the present application. SUMMARY

[0005] The present application aims to provide a self-organizing topology management method for distributed wireless communication links of urban power distribution networks to solve the problems raised in the background.

[0006] The present application is achieved in that a self-organizing topology management method for distributed wireless communication links of urban power distribution networks comprises the following steps:

[0007] Step S1: performing frequency domain channel division on node physical perception results to obtain a channel division set;

[0008] Step S2: performing high-density area node interference identification using the channel division set to obtain an interference graph and a same frequency neighborhood division;

[0009] Step S3: performing path mapping and node association modeling on the interference graph and the neighborhood division to obtain a node association weight matrix;

[0010] Step S4: performing path selection configuration on the node association weight matrix to obtain a distributed path tree;

[0011] Step S5: performing node communication state quantization on the distributed path tree to obtain a switching threshold control table;

[0012] Step S6: using the control table to perform link maintenance logic replacement, obtaining the dynamic topology evolution result.

[0013] Preferably, the step S1 divides the node physical perception result in the frequency domain channel, obtaining a channel division set, specifically:

[0014] Step S1-1: using a link quality perception model to perceive the physical link of the node; identifying the physical communication possibility between each Mesh node and its adjacent node; forming a physical reachability matrix of all point pairs meeting the channel quality condition, which represents the topological basis of the possible communication link between nodes in the network;

[0015] The link quality perception model quantitatively identifies the physical link state between each Mesh node in the urban power distribution network based on the ETX mechanism. By periodically broadcasting probe data packets between nodes, the bidirectional reception rate and loss rate are counted, and then the ETX value between each pair of nodes is calculated, representing the expected number of hops required for reliable data transmission.

[0016] According to the set ETX threshold, all node pairs with ETX values below the threshold are included in the physical reachable link set, and an initial physical link reachability matrix is constructed.

[0017] Step S1-2: dividing the available channels by calling the standard spectrum template division method, dividing the frequency band into several non-overlapping channel groups;

[0018] The standard spectrum template division method adopts the ITU-R recommended template to standardize the division of the 5.8GHz frequency band according to the 20MHz channel bandwidth. The division result is a plurality of independent channel groups with a center frequency separated by 20MHz. Each channel group meets the minimum spacing requirement and there is no spectrum overlap, thereby forming a plurality of physical channel division units that do not interfere with each other and can be independently multiplexed.

[0019] Step S1-3: using the channel conflict table construction mechanism to analyze whether the channel set of each node pair of the physical link overlaps;

[0020] Step S1-4: introducing channel division rules to divide all links in the region into several frequency domain isolation regions, outputting a node-to-channel mapping table as the frequency domain channel division result;

[0021] The node physical perception result is divided into the following frequency domain channels, which include:

[0022] Node reachability matrix M link : a binary matrix indicating whether there is physical communication capability between all Mesh nodes, with a dimension of N×N, where N is the total number of nodes;

[0023] Node-channel mapping table T map : the table structure of channel set corresponding to each node, in the form of set array:

[0024] T map = {(n1,{f1,f3,f7}),(n2,{f1,f2}),...,(n N ,{f5,f8})};

[0025] Node reachability matrix M link and node-channel mapping table T map as the channel partition set.

[0026] Preferably, the step S2 uses the channel partition set to identify the interference between nodes in the high-density area, to obtain an interference graph and a same-frequency neighborhood partition, specifically:

[0027] Step S2-1: constructing an interference adjacency graph to determine which physically adjacent nodes cause same-frequency interference in the communication process, specifically:

[0028] Constructing an interference adjacency graph G intf =(V,E intf ), wherein V is a set of Mesh network nodes participating in communication, that is, each node in the graph represents a physical device with wireless communication capability; E intf is a set of interference edges;

[0029] The generation condition of E intf is:

[0030]

[0031] Wherein, M link (i,j)=1 indicates that there is physical communication reachability between nodes i and j; T map (i) indicates the available channel set of node i; the non-empty intersection of the channel sets of the two indicates the potential risk of same-frequency conflict;

[0032] Step S2-2: constructing an interference strength weighting model;

[0033] Step S2-3: performing same-frequency neighborhood isolation sub-domain partitioning, and using a weighted clustering method to perform domain partitioning:

[0034]

[0035] The constraint condition is:

[0036] All C m are disjoint sets of V, UC m =V, and for m≠n;

[0037] The number of subdomains k can be set by a density threshold of high interference areas or a maximum node number limit strategy.

[0038] Preferably, the interference strength weighting model in step S2-2 is constructed, specifically:

[0039] Based on each interference edge (i, j), combining the channel occupancy rate and the adjacent distance factor, the interference strength weight function w ij The interference strength weighting model is constructed, and the interference strength weight function w ij :

[0040]

[0041] Where O ij is the conflict risk value reflected by the minimum channel idle degree of the intersection of the channel sets of nodes i and j, d ij represents the physical distance between nodes i and j; α, β are normalization weight coefficients, and ε is a small positive number to prevent division by zero.

[0042] Preferably, in step S3, the path mapping and node association modeling of the interference graph and the neighborhood division are performed, specifically:

[0043] Step S3-1: Construct the cross-subdomain coupling degree matrix Σ:

[0044] According to the interference graph G intf and the subdomain structure, the average connection density between all pairs of subdomains is calculated to describe the interference coupling strength between different subdomains, and the cross-subdomain coupling degree matrix Σ is:

[0045]

[0046] Where ∑ ij represents the average path coupling degree between subdomain i and subdomain j; P ij represents the set of all paths from a node in subdomain i to a node in subdomain j; w p represents the comprehensive path association weight of path p; |P ij | represents the number of paths in the path set P ij ;

[0047] Step S3-2: On the interference graph, set the hop number upper limit H, and perform directed path enumeration on all node pairs (i, j);

[0048] Step S3-3: Associated impact aggregation:

[0049] For each path, the following operations are performed:

[0050] The interference strengths of all edges in the path are multiplied and superimposed.

[0051] A multiplicative suppression is given to the cross-domain path by using the coupling degree function σ;

[0052] According to the output interference graph G intf and the sub-domain structure, the average connection density between all pairs of sub-domains is calculated, which is the coupling degree, calculated by the following formula:

[0053]

[0054] wherein ∈ is a small constant to avoid division by zero; γ is a cross-domain coupling penalty coefficient, used to strengthen the suppression of cross-domain paths;

[0055] Step S3-4: Finally, the influence strength of the node pair is summarized after the path length is normalized, and the correlation matrix value of the node pair (i, j) is generated.

[0056] Preferably, the correlation matrix of the node pair (i, j) in step S3-4 is generated, specifically:

[0057] A rel (i, j) represents the path correlation strength between node i and node j, which is the node correlation weight matrix, and the calculation formula is as follows:

[0058]

[0059] wherein, represents the set of all feasible paths from node i to node j on the interference graph G intf , |p| represents the number of hops contained in the path p, (u, v) ∈ p represents the lth edge in the path p, and w uv is the interference matrix element w ij converted into the weight w uv of the edge (i, j) in the graph structure, where u = i, v = j.

[0060] Preferably, the node correlation weight matrix in step S4 is configured for path selection, and a distributed path tree is obtained, specifically:

[0061] Step S4-1: The node correlation weight matrix is inversely mapped to construct a path feasibility metric;

[0062] The weighted shortest path calculation technology is used to construct an interference-aware cost graph for each node, and the correlation strength A rel (i, j) is regarded as the path cost, and through inverse mapping or normalization processing, the high-interference path is set as high cost, so as to suppress it from being selected as the main path;

[0063] Step S4-2: Based on the interference-aware cost map, each node independently uses a distributed path tree construction algorithm to expand its reachable path set from the local node to the whole network, and continuously adopts a path cost minimization strategy during path expansion;

[0064] Step S4-3: The path tree is locally optimized by a path redundancy pruning algorithm, and the path branches with weak path strength across interference sub-domains are first retained;

[0065] Step S4-4: The final output is the local path tree table entry maintained by each node, including the next hop forwarding node to the upstream node and the path cost index used.

[0066] Preferably, the step S5 quantifies the node communication state of the distributed path tree to obtain a switching threshold control table, specifically:

[0067] Step S5-1: The distributed path cost is standardized to a communication stability index by a Hop cost aggregation technique or a weighted hop number conversion method, forming a path cost evaluation;

[0068] Step S5-2: A consistency check method is used to identify whether multiple paths are concentrated in the same interference sub-domain, forming an interference redundancy identification;

[0069] Step S5-3: The path cost evaluation and interference redundancy are fused, and a multi-factor scoring model is used to generate a comprehensive score for each path, and the communication state level is divided according to the score result;

[0070] Step S5-4: A network state threshold division mechanism is used to set a dynamic triggering condition for the communication state level, and finally a switching threshold control table is formed.

[0071] Preferably, the step S6 uses the control table to replace the link maintenance logic to obtain a dynamic topology evolution result, specifically:

[0072] Path state persistence monitoring: each node periodically confirms its current path state level according to the control table;

[0073] When the state level of a path continuously falls below a set threshold, it enters a link maintenance pending state; a sliding window composed of historical state values is monitored, and the following path degradation accumulation criterion model is used:

[0074]

[0075] Where Γ i (t) is the degradation accumulation of path i at time t; S i (t-k) is the communication state level of path i at time k; θ is the switching level threshold set in the control table; is an indicator function, returns 1 if the condition is satisfied, otherwise returns 0; T is the window size, representing the observation length;

[0076] Link replacement logic matching:

[0077] For the path node satisfying the link degradation condition, the replacement evaluation is carried out according to the recommended alternative path in the control table;

[0078] The replacement path needs to satisfy the following logical matching conditions:

[0079]

[0080] wherein, path cost of the alternative path j; path cost of the current path i; interference isolation degree of the alternative path; interference isolation degree of the current path; β: path cost replacement factor;

[0081] Path switching and topology reconstruction: when the alternative path satisfies the condition, the node takes it as a new main path and updates the local path;

[0082] The topology connection relationship between nodes forms the following update matrix model:

[0083]

[0084] wherein, A (t) is the topology adjacency matrix at time t; ΔA (t) is the adjacency relationship change matrix caused by path switching; represents that the matrix is superimposed according to the rule, that is, the structure update in the topology evolution process;

[0085] Dynamic topology evolution output formation:

[0086] The results of all node path replacement and topology reconstruction are converged to a new network connection state, which is the dynamic topology evolution result formed under the action of path maintenance and link evolution logic, and the topology evolution result is used for path configuration and monitoring in the next period.

[0087] Compared with the prior art, the present application has the following improvements and advantages:

[0088] By setting the interference adjacency graph construction mechanism based on the channel division structure and the neighborhood decomposition strategy under the multi-dimensional interference constraint, the interference boundary between nodes can be accurately identified under high-density deployment conditions, and the same frequency communication neighborhood area with physical isolation effect is extracted, which provides a stable and reliable topology structure for the subsequent path selection configuration and state quantization, thereby significantly improving the self-organizing evolution ability of the distributed wireless communication link in a complex interference environment. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 The flowchart of the method of the application is shown.

[0090] Figure 2 The flowchart of the interference identification between nodes in the high-density area in the application is shown.

[0091] Figure 3 The flowchart of the link maintenance logic replacement in the application is shown.

[0092] Figure 4 The communication network topology diagram is shown.

[0093] Figure 5 The performance comparison diagram of the power distribution communication network routing algorithm is shown. DETAILED DESCRIPTION

[0094] The application is further described below in conjunction with the drawings.

[0095] As shown in Figure 1 , a self-organizing topology management method for a distributed wireless communication link of a city power distribution network, the method comprising the following steps:

[0096] Step S1: performing frequency domain channel division on the node physical perception results to obtain a channel division set, specifically:

[0097] First, perform node physical link perception, use a link quality perception model, and identify the physical communication possibility between each Mesh node and its adjacent nodes;

[0098] All point pairs meeting the channel quality condition are formed into a physical reachability matrix, which represents the topology basis of the possible communication links between nodes in the network.

[0099] Then, perform available channel division, divide the 5.8GHz frequency band into several non-overlapping channel groups by calling a standard spectrum template division method;

[0100] Use a spectrum detection module to scan the occupation intensity and background interference intensity on each channel, and use a CCA (Clear Channel Assessment) mechanism to judge the availability of each channel;

[0101] The local channel available set of each Mesh node is recorded and compared with the reachable neighbor nodes to form the channel co-occurrence structure table between nodes.

[0102] Finally, node channel mapping establishment and division structure construction are performed. The channel conflict table construction mechanism is used to analyze whether the channel set of each physical link (node pair) overlaps;

[0103] A channel adjustable graph structure is constructed, and each edge of the graph represents a link that has a shareable channel.

[0104] Channel division rules (such as the maximum independent channel coverage) are introduced to divide all links in the region into several frequency domain isolated regions.

[0105] A node-to-channel mapping table is output as the frequency domain channel division result, which includes:

[0106] Node reachability matrix M link : a binary matrix indicating whether there is a physical communication capability between all Mesh nodes, with a dimension of N x N, where N is the total number of nodes.

[0107] Node-channel mapping table T map : a set array structure of the available channel set corresponding to each node.

[0108] T map = {(n1, {f1, f3, f7}), (n2, {f1, f2}),..., (n N , {f5, f8})};

[0109] Node reachability matrix M link and node-channel mapping table T map are passed as input parameters to step S2.

[0110] Step S2: interference identification between nodes in high-density areas using channel division sets to obtain an interference graph and same-frequency neighborhood division, specifically:

[0111] Step S2 is based on the node reachability matrix M link and the node-channel mapping table T map output by step S1, combined with the physical distance between nodes and channel sharing, to identify same-frequency conflict neighborhoods in interference-intensive areas and construct an interference graph structure.

[0112] First, an interference adjacency graph is constructed according to the data in M link and T map , first determine which physically adjacent nodes may cause same-frequency interference during communication, and construct an interference adjacency graph G intf = (V, E intf), interference edge set E intf The generation condition is:

[0113]

[0114] Wherein, M link (i,j) = 1 represents that there is physical communication reachability between nodes i and j; T map (i) represents the available channel set of node i; the intersection of the two channel sets is not empty, indicating that there is a potential same frequency conflict risk;

[0115] Then the interference strength weighting model is constructed, based on each interference edge (i,j), combining channel occupancy rate and adjacent distance factors, the interference strength weight function w ij is constructed:

[0116]

[0117] Wherein, O ij is the conflict risk value reflected by the minimum channel idle degree of nodes i and j in the intersection of their channel sets, d ij represents the physical distance between nodes i and j (obtained by RSSI), and α, β are normalization weight coefficients, and ε is a small positive number to prevent division by zero.

[0118] Finally, the same frequency neighborhood isolation sub-domain division is carried out, and the weighted clustering method is used for domain division:

[0119]

[0120] The constraint condition is: all C m is the disjoint set of V (∪C m = V, and For m ≠ n);

[0121] The number of sub-domains k can be set by the density threshold of high interference area or the maximum node number limit strategy.

[0122] The final output structure is the interference isolation domain division structure table, indicating that the same frequency interference dense area is divided into multiple isolated sub-domains, and the subsequent scheduling strategy can be based on the sub-domain to independently control the spectrum resource.

[0123] Step S2 differs significantly from existing Mesh spectrum planning technologies in four aspects: First, the interference identification method is upgraded from the traditional static conflict table judgment to a dynamic mechanism based on the joint judgment of channel co-occurrence probability, channel cleanliness index and node proximity; second, the weight model construction introduces the dual consideration of physical distance and real-time channel cleanliness, replacing the fixed conflict matrix method; third, the subdomain partitioning objective is changed from the traditional maximum independent set partitioning to emphasizing high coupling within the domain and cross-domain isolation, thus providing a structural foundation for subsequent scheduling structure optimization; fourth, the regional dynamic adaptability is significantly improved, supporting adaptive frequency domain isolation in high-density hotspot areas, breaking through the limitations of static configuration and manual management.

[0124] Step S3: Perform path mapping and node association modeling on the interference graph and neighborhood partitioning to obtain the node association weight matrix, specifically:

[0125] Step S3, based on step S2, further utilizes the weighted interference map structure G output. intf =(V,E) intf The structure of the interference isolation subdomain partitioning {C1,C2,…,C} is w) k}, perform path mapping and node association modeling;

[0126] Step S3 outputs the node association weight matrix. Where A rel (i,j) represents the path association strength between node i and node j, and its calculation formula is as follows:

[0127]

[0128] in, Indicating in interference graph G intf The set of all feasible paths from node i to node j; |p|: represents the number of hops (edges) in path p; (u,v)∈p: represents the l-th edge in path p; w uv : The interference matrix element w in step S2 ij Convert to the weight w of edge (i,j) in a graph structure uv , note u=i, v=j;

[0129] σ C(u),C(v) The coupling degree between subdomains C(u) and C(v) is represented by the following formula:

[0130]

[0131] Where ∈ is a small constant to avoid division by zero; γ is the cross-domain coupling penalty coefficient, used to strengthen the suppression of cross-domain paths.

[0132] like Figure 2As shown, step S3 includes the following processes in the process of path mapping and node association modeling:

[0133] Construct the cross-subdomain coupling matrix Σ:

[0134] According to the interference graph G output by step S2 intf and the subdomain structure, calculate the average connection density (i.e. coupling) between all pairs of subdomains, which is used to characterize the interference coupling strength between different subdomains;

[0135] Path set Enumeration:

[0136] On the interference graph, set the upper limit of the number of hops H (usually 2-4 hops), and perform directed path enumeration on all node pairs (i,j), to ensure that the model only calculates local reachability influence paths;

[0137] Associated impact aggregation:

[0138] For each path, perform the following operations:

[0139] Multiply and superimpose the interference strengths of all edges in the path;

[0140] Use the coupling function σ to give multiplicative suppression to cross-domain paths;

[0141] Finally, the influence strength of the node pair is summarized after being normalized by the path length, to generate the node pair (i,j) association matrix value.

[0142] The innovation of step S3 "path mapping and node association modeling using interference graph and neighborhood division, to obtain the node association weight matrix" lies in the following aspects: this step is different from the traditional way of measuring node connection degree only according to the physical topology structure in power communication network, and for the first time introduces a heterogeneous graph structure constructed based on interference adjacency relationship, uses the interference strength weight w ij converted from step S2 as the graph edge attribute, and combines the multi-level neighborhood division mechanism to perform path mapping, thereby realizing multi-scale modeling of node association. At the same time, by constructing an asymmetric directional association weight function, the non-equivalent interference influence relationship between nodes is explicitly represented, which overcomes the problem of symmetric edge weight and lack of interference direction expression in traditional graph models. Compared with existing mature technologies, step S3 not only enhances the adaptability of the graph model to the 5.8G Mesh link interference environment, but also realizes more refined expression of the logical relationship between nodes, providing a structural input basis for subsequent path selection and communication control.

[0143] Step S4: Node association weight matrix for path selection configuration, to obtain a distributed path tree, specifically:

[0144] Step S4 first carries out input data and initial preparation, based on the node association weight matrix A output by step S3 rel , the weight matrix is inversely mapped to construct a "path feasibility metric";

[0145] Then the reverse conversion of the interference weight is carried out, and using the mature "weighted shortest path calculation technology", a "disturbance-aware cost map" is constructed for each node. In this map, the original association strength A rel (i,j) is regarded as the path "cost", and through reverse mapping or normalization processing, the high-interference path is set as high cost, so as to suppress it from being selected as the main path;

[0146] Subsequently, based on the above converted path cost map, each node independently uses a distributed path tree construction algorithm to expand its reachable path set from the local to the whole network, and continuously uses the path cost minimization strategy in the path expansion process;

[0147] Then interference redundant path elimination and local optimization are carried out, and further through the mature "path redundancy pruning algorithm", the path tree is locally optimized, and the path branch with weak path strength across the interference sub-domain is preferentially retained;

[0148] Finally, the output structure is formatted, and the final output is the local path tree table entry maintained by each node, including its next-hop forwarding node to the upstream node and the path cost index used. The structure can be directly used for spectrum scheduling and network reconstruction logic.

[0149] Step S5: Quantize the node communication state of the distributed path tree to obtain a switching threshold control table, specifically:

[0150] In step S5, the local path tree table entry output by step S4 is first subjected to path state quantization processing:

[0151] Through the mature Hop cost aggregation technology or weighted hop number conversion method, the distributed path cost is standardized to a communication stability index to form a path cost evaluation;

[0152] Using the interference sub-domain distribution consistency checking method, it is identified whether multiple paths are concentrated in the same interference sub-domain, thereby increasing the conflict risk, and forming interference redundancy identification.

[0153] Subsequently, the communication state index fusion and grading are carried out, the path cost evaluation and the interference redundancy are fused, a multi-factor scoring model is used to generate a comprehensive score for each path, and according to the score result, the following communication state levels are divided:

[0154] First (optimal): low cost, high isolation path;

[0155] Second (available): medium cost, good isolation;

[0156] Level 3 (to be switched) : low path redundancy, obvious cost increase;

[0157] Level 4 (switch recommended) : path in high conflict sub-domain;

[0158] Level 5 (disabled) : path unavailable or cost exceeds threshold.

[0159] Finally, the switching threshold control table is generated, and a mature network state threshold division mechanism is used to set dynamic trigger conditions for the above communication state levels, and finally a control table is formed, including node ID, current path state level, corresponding score value, switching trigger condition, and recommended switching target path.

[0160] Step S6: link maintenance logic replacement using the control table to obtain the dynamic topology evolution result, specifically:

[0161] Step S6 is based on the switching threshold control table generated in step S5 to make replacement selection for inefficient paths in the current network, thereby realizing dynamic updating of the topology structure, and enabling the system to adapt to factors such as interference, failure, and link quality changes. The evolution ability, step S6 includes the following sub-processes in the execution process:

[0162] Path state persistence monitoring:

[0163] Each node periodically confirms its current path state level according to the control table;

[0164] When the state level of a path is continuously below a certain threshold (such as "level 4" or below), it enters the link maintenance pending state;

[0165] The sliding window based on the historical state value is monitored, and the following path degradation accumulation criterion model is used:

[0166]

[0167] Where Γ i (t) is the degradation accumulation of path i at time t; S i (t-k) is the communication state level of path i at time k; θ is the switching level threshold set in the control table; is an indicator function, returns 1 if the condition is met, otherwise returns 0; T is the window size, representing the observation length;

[0168] Link replacement logic matching:

[0169] For path nodes that meet the link degradation condition, replacement evaluation is performed according to the recommended alternative path in the control table;

[0170] The replacement path needs to meet the following logic matching conditions:

[0171]

[0172] wherein, is the path cost of alternative path j, is the path cost of alternative path j, is the path cost of current path i, is the interference isolation of alternative path; is the interference isolation of current path; β is the path cost substitution factor;

[0173] Path switching and topology reconfiguration:

[0174] When the alternative path meets the conditions, the node will take it as the new main path and update the local path

[0175] The update of the path tree will be based on the following update propagation principles:

[0176] The update is only broadcast to the neighbor nodes after the path changes;

[0177] All neighbor nodes update their local topology view accordingly;

[0178] The topology connection relationship between nodes forms the following update matrix model:

[0179]

[0180] wherein, A (t) is the topology adjacency matrix at time t; ΔA (t) is the adjacency relationship change matrix caused by path switching; ⊕ represents the matrix according to the rules of superposition, that is, the structure update in the topology evolution process;

[0181] The dynamic topology evolution output forms:

[0182] Finally, all the node path substitution and the results of topology reconfiguration converge to the new network connection state;

[0183] This state is the dynamic topology evolution result formed by the system under the action of path maintenance and link evolution logic;

[0184] The topology evolution result can be used for path configuration and monitoring in the next period.

[0185] Compared with existing mature technologies (such as an open shortest path first (OSPF) routing protocol based on static entry updating or a link quality indicator (LQI) mechanism based on single-point updating strategy), the step S6 adopts a link replacement mechanism driven by path state level, does not rely on global network synchronization state, introduces an interference isolation degree parameter D as a path screening factor, and is different from a mechanism based on only link quality LQI. Topology updating is modeled in an incremental adjacency matrix superposition manner, has formal structure evolution expression capability, and is not a traditional entry updating logic.

[0186] Embodiment two: the embodiment also provides a computer device suitable for a case of the self-organizing topology management method of the distributed wireless communication link of the urban power distribution network, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the self-organizing topology management method of the distributed wireless communication link of the urban power distribution network proposed in the above embodiment.

[0187] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the self-organizing topology management method of the distributed wireless communication link of the urban power distribution network proposed in the above embodiment.

[0188] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. In addition, the input device can be an external keyboard, touchpad or mouse, etc.

[0189] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts of the prior art that contribute to the present application or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0190] More specific examples (non-exhaustive list) of the computer readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optical scanning of the paper or other medium, followed by editing, interpreting or processing, if necessary, in other suitable ways, to obtain the program electronically, and then storing it in the computer memory.

[0191] In order to verify the effect of the method of the present application, the following experiments are carried out for verification:

[0192] Communication node distribution: simulation of 100 nodes / km 2 High-density urban power distribution network environment, including substation, smart meter, fault indicator and other typical devices. Interference model: inject WiFi / 4G / industrial equipment and other dynamic interference sources, signal-to-noise ratio range SNR=10-20dB. Spectrum resources: 8 available wireless channels, working at 5.8GHz frequency band, dynamic occupancy rate 30%-70%. Simulation duration: 1000 time steps, single node data packet sending amount 100.

[0193] As shown in Figure 4 100 nodes / km 2The high-density urban power distribution communication network topology schematic diagram, the core elements include: communication node model: blue dot represents the power distribution network communication node, according to the mixed distribution of dense urban area (40%), suburban area (30%), industrial area (30%). Interference model: red dot is a dynamic interference source, the pink translucent area represents its influence range (radius 100-300 meters). Environment model: gray polygon area simulates building shielding, attenuation coefficient 0.2-0.8. Link model: red solid line is the current communication main path (generated based on the "minimum interference-optimal path" principle), the green dotted line is the standby reachable path, which can be automatically switched to the optimal green path when the main path communication quality is seriously interfered.

[0194] Figure 4 The densely connected area corresponds to the high-frequency communication path in the "interference subdomain", which reflects the algorithm's ability to improve interference resistance through multi-path redundancy; the sparse area represents the low-coupling path across the subdomain, which embodies the effectiveness of the interference avoidance strategy. Through the density of the connection and the redundancy of the path, the robustness of the topology generated by the algorithm is intuitively reflected (i.e., the connection is dense (multiple paths are available), and the anti-interference and fault tolerance are strong).

[0195] As shown in Figure 5 , the performance of the scheme, the OSPF protocol and the LQI-AODV protocol in the same frequency conflict rate, end-to-end delay and packet loss rate is shown. From the data distribution, the scheme has obvious advantages in the same frequency conflict rate, delay index and packet loss rate compared with the comparison algorithm, and after double Y-axis visualization, the comparison effect of different indexes is clearer.

[0196] The method controls the same frequency conflict rate in the range of 0.12-0.18 through dynamic channel allocation and interference subdomain division technology, and the surface spectrum resource management algorithm can effectively avoid the same frequency interference, especially in the high-density node area.

[0197] The average delay is 42-78 ms, and the 90% service request delay is <60 ms, which meets the real-time requirement of the power distribution network fault rapid response (<100 ms). The method significantly reduces the edge node communication delay through cross-domain path optimization (penalty factor α=0.7) and dynamic path switching mechanism. The low delay characteristic is crucial for remote control and real-time monitoring of the power distribution network.

[0198] The packet loss rate is 0.23, and the link maintenance mechanism based on the control table realizes the rapid replacement of the fault path to improve the packet loss rate, which is especially prominent in the communication stability of the network edge node.

[0199] The method is superior to the traditional protocol in terms of same frequency conflict suppression, delay control and packet loss rate reduction, and provides technical support for the reliable communication of urban power distribution network.

[0200] The above merely provides the implementation of the present application, but not for limiting the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of claims of the present application.

Claims

1. A self-organizing topology management method for distributed wireless communication links in urban power distribution networks, characterized in that: The method includes the following steps: Step S1: Divide the node physical sensing results into frequency domain channels to obtain a channel partition set; Step S2: Use the channel partition set to identify interference between nodes in high-density areas, and obtain the interference map and the division of the same frequency neighborhood; Step S3: Perform path mapping and node association modeling on the interference graph and neighborhood partitioning to obtain the node association weight matrix; Step S4: Configure path selection using the node association weight matrix to obtain a distributed path tree; Step S5: Quantify the node communication status of the distributed path tree to obtain the switching threshold control table; Step S6: Use the control table to replace the link maintenance logic and obtain the dynamic topology evolution result.

2. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 1, characterized in that: In step S1, the node physical sensing results are divided into frequency domain channels to obtain a channel partitioning set, specifically as follows: Step S1-1: Use the link quality awareness model to perform node physical link awareness; Identify the physical communication possibilities between each Mesh node and its neighboring nodes; construct a physical reachability matrix from all point pairs that meet the channel quality conditions, which represents the topological basis for establishing communication links between nodes in the network; The link quality awareness model quantitatively identifies the physical link status between each Mesh node in the urban power distribution network based on the ETX mechanism. It detects data packets by periodically broadcasting between nodes, counts their bidirectional reception rate and loss rate, and then calculates the ETX value between each pair of nodes, which represents the expected number of hops required for reliable data transmission. Based on the set ETX threshold, all node pairs with ETX values ​​below the threshold are included in the set of physically reachable links, and an initial physical link reachability matrix is ​​constructed. Step S1-2: By calling the standard spectrum template partitioning method, the available channels are partitioned, and the frequency band is divided into several non-overlapping channel groups; The standard spectrum template allocation method adopts the ITU-R recommended template, which standardizes the 5.8 GHz band into a channel bandwidth of 20 MHz. The allocation result is multiple independent channel groups with a center frequency 20 MHz apart. Each channel group meets the minimum spacing requirement and there is no spectrum overlap, thus forming multiple physical channel allocation units that do not interfere with each other and can be independently reused. Step S1-3: Using the channel conflict table construction mechanism, analyze whether the channel sets of each physical link node pair overlap. Step S1-4: Introduce channel partitioning rules, divide all links in the region into several frequency domain isolation regions, and output a mapping table from nodes to channels as the frequency domain channel partitioning result; The node physical sensing results are divided into the following frequency domain channels, including: Node reachability matrix : A binary matrix representing whether physical communication capability exists between all Mesh nodes, with dimensions of . ,in The total number of nodes; Node-Channel Mapping Table The channel set table structure corresponding to each node is in the form of a set array: ； Node reachability matrix and node-channel mapping table As a channel partition set.

3. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 1, characterized in that: In step S2, interference between nodes in high-density areas is identified using channel partitioning sets to obtain an interference map and a co-frequency neighborhood partition. Specifically: Step S2-1: Construct an interference adjacency graph to determine which physically adjacent nodes cause co-channel interference during communication. Specifically: Constructing an interference adjacency graph ,in, A Mesh network is a collection of nodes that participate in communication; each node in the diagram represents a physical device with wireless communication capabilities. For the set of interfering edges; The generation conditions are: ； in, Represents a node and There is physical communication reachability between them; Represents a node The set of available channels; a non-empty intersection of the two channel sets indicates a potential risk of co-channel conflict; Step S2-2: Construct an interference intensity weighting model; Step S2-3: Perform domain partitioning by isolating neighboring domains of the same frequency, using a weighted clustering method for domain partitioning: ； The constraints are: all for The non-intersection, ,and for ; Number of subdomains It can be set by density thresholds for high-interference areas or maximum node count limits.

4. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 3, characterized in that: The interference intensity weighting model constructed in step S2-2 is as follows: Based on each interference edge Combining channel occupancy and proximity factors, an interference intensity weighting function is used. Construct an interference intensity weighting model, and define the interference intensity weighting function. : ； in, For nodes and The collision risk value reflected by the minimum channel idleness in the intersection of their channel sets. , Represents a node and The physical distance between them; These are the normalized weighting coefficients. To prevent small positive numbers from being divided by zero.

5. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 1, characterized in that: In step S3, path mapping and node association modeling are performed on the interference graph and neighborhood partitioning, specifically as follows: Step S3-1: Construct the cross-subdomain coupling matrix : According to the interference map The subdomain structure is used to calculate the average connection density between all subdomain pairs, which characterizes the interference coupling strength between different subdomains, and the cross-subdomain coupling matrix is ​​also calculated. for: ； in, Subfield subdomain Average path coupling between them; Indicates from subdomain From a node to a subdomain The set of all paths to a given node in the network; Representing a path The comprehensive path association weight; Represents a set of paths The number of paths in the middle; Step S3-2: Set the upper limit for the number of hops on the interference map. For all node pairs Perform directed path enumeration; Step S3-3: Aggregation of Correlated Influences: Perform the following operations on each path: The interference intensities of all edges within the path are multiplied and summed. Using coupling function Multiplicative inhibition is applied to cross-domain paths; Based on the output interference map Given the subdomain structure, calculate the average connection density between all subdomain pairs. The average connection density is the coupling degree, calculated using the following formula: ； in, To avoid small constants that divide by zero; This is the cross-domain coupling penalty coefficient, used to strengthen the suppression of cross-domain paths; Step S3-4: Finally, after normalizing the path length, the influence strength of node pairs is summarized to generate node pairs. The correlation matrix values.

6. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 5, characterized in that: In step S3-4, node pairs are generated. The correlation matrix is ​​as follows: use Represents a node With nodes The path association strength between nodes, also known as the node association weight matrix, is calculated using the following formula: ； in, Indicating interference map upper node With nodes The set of all feasible paths Representing a path The number of hops included. Representing a path The first in Strip edge, Interference matrix elements Transform into edges in a graph structure weight ,remember .

7. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 1, characterized in that: In step S4, the node association weight matrix is ​​used to configure path selection, resulting in a distributed path tree, specifically: Step S4-1: Reverse map the node association weight matrix to construct a path feasibility measure; A weighted shortest path calculation technique is used to construct an interference-aware cost map for each node, and the correlation strength is considered. Treated as path cost, high-interference paths are set to high cost through reverse mapping or normalization, thereby suppressing their selection as the main path. Step S4-2: Based on the interference-aware cost graph, each node independently uses a distributed path tree construction algorithm to expand its reachable path set from its local location to the entire network, and continuously adopts a path cost minimization strategy during the path expansion process; Step S4-3: Optimize the path tree locally using the path redundancy pruning algorithm, first retaining the path branches with weaker cross-interference subdomain paths; Step S4-4: The final output is the local path tree table entry maintained by each node, including its next-hop forwarding node to the upstream node, and the path cost metric used.

8. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 1, characterized in that: In step S5, the node communication state of the distributed path tree is quantified to obtain the switching threshold control table, specifically as follows: Step S5-1: Standardize the distributed path cost into a communication stability index by using Hop cost aggregation technology or weighted hop count conversion method to form a path cost evaluation. Step S5-2: Use the interference subdomain distribution consistency check method to identify whether there are multiple paths concentrated in the same interference subdomain, forming interference redundancy identification; Step S5-3: Integrate path cost evaluation with interference redundancy, and use a multi-factor scoring model to generate a comprehensive score for each path, and classify the communication status level based on the scoring results; Step S5-4: Using a network status threshold division mechanism, dynamic triggering conditions are set for communication status levels, ultimately forming a switching threshold control table.

9. The self-organizing topology management method for distributed wireless communication links in urban power distribution networks according to claim 1, characterized in that: In step S6, the link maintenance logic is replaced using a control table to obtain the dynamic topology evolution result, specifically: Continuous path status monitoring: Each node periodically confirms its current path status level according to the control table; When the status level of a certain path continuously falls below a set threshold, it enters a link maintenance pending state; monitoring is based on a sliding window constructed from historical status values, and the following path degradation cumulative criterion model is adopted: ； in, For path At any moment The cumulative amount of degradation; For path in the past The communication status level at any given moment; The switching level threshold set in the control table; This is an indicator function that returns 1 if the condition is met, and 0 otherwise. The window size represents the observation length. Link substitution logic matching: For path nodes that meet the link degradation conditions, alternative evaluations are conducted based on the alternative paths recommended in the control table. The alternative path must meet the following logical matching conditions: ； in, Alternative Paths Path cost; Current path Path cost; Interference isolation of alternative paths; Interference isolation level of the current path; Path cost substitution factor; Path switching and topology reconfiguration: When a candidate path meets the conditions, the node adopts it as the new primary path and updates the local path. The topological connections between nodes form the following update matrix model: ； in, For a moment The topological adjacency matrix; This is the adjacency relationship change matrix caused by path switching; This indicates that matrices are superimposed according to rules, which is the structural update in the process of topological evolution; Dynamic topology evolution output formation: The result of replacing all node paths and reconstructing the topology is converged into a new network connection state. This state is the dynamic topology evolution result formed by the system under the action of path maintenance and link evolution logic. The topology evolution result is used for path configuration and monitoring in the next cycle.

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