Topology self-identification and routing optimization method and system for concentrator and collector

Abstract

The invention relates to the technical field of adaptive routing optimization, and particularly discloses a topology self-recognition and routing optimization method and system for a concentrator and a collector, and the method comprises the steps: carrying out the topology modeling of a multi-layer heterogeneous network; executing high-speed link state monitoring based on the initial topological graph; global loop detection and link optimization are completed based on the link state matrix; a weighted robust multi-dimensional path selection method is adopted to configure a main path and a standby path for each node; and monitoring the path state in real time and dynamically switching. In the prior art, a static route, a single protocol path or a fixed main and standby path is mainly adopted, and especially in a cross-protocol, cross-level and dynamic multi-node environment in a smart city, rapid path switching and high-reliability route redundancy guarantee cannot be realized. Due to the fact that the multi-dimensional path selection method and the cross-level and cross-protocol self-adaptive switching strategy are adopted, the problems of routing interruption and performance reduction are avoided, and the reliability of the whole network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of adaptive routing optimization, and in particular to a method and system for topology self-identification and routing optimization of a concentrator and a collector. Background Art

[0002] Currently, smart city perception networks are widely used in a variety of scenarios, including traffic signal control, environmental monitoring, public safety, intelligent lighting, and energy management. With the rapid growth in the number of nodes and the continued expansion of network scale, the communication network between concentrators and collectors in perception networks has become complex, multi-layered, cross-protocol, and dynamically adaptable. However, existing technologies mostly rely on static topology configuration and fixed routing table maintenance, lacking real-time awareness and adaptive adjustment capabilities for network status. This leads to problems such as poor adaptability, slow handover, and susceptibility to environmental interference.

[0003] Furthermore, most current concentrators rely on link detection based on heartbeat packets, MAC address tables, and static routing rules. This approach often leads to topology recognition delays and routing table oscillations in complex, multi-layer dynamic scenarios. Traditional loop detection mechanisms (such as Spanning Tree Protocol (STP) or RSTP) converge slowly in industrial hybrid networks and cannot support millisecond-level dynamic switching.

[0004] Furthermore, most current routing mechanisms only consider the shortest path or the least number of hops. At the same time, traditional primary and backup paths share many links or relay nodes, resulting in the inability to effectively guarantee communication reliability even if switching to the backup path in emergencies or extreme conditions.

[0005] Therefore, there is an urgent need for a topology self-identification and routing optimization method for concentrators and collectors that can achieve millisecond-level link status monitoring, rapid switching of primary and backup paths, and cross-layer and cross-protocol adaptive optimization to improve the reliability, stability, and resilience of the overall network. Summary of the Invention

[0006] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose a topology self-identification and routing optimization method for concentrators and collectors, aiming to solve the technical problem that the existing technology mainly adopts static routing, single protocol paths or fixed primary and backup paths, especially in smart cities involving cross-protocol, cross-level and dynamic multi-node environments, and cannot achieve fast path switching and high-reliability routing redundancy.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a topology self-identification and routing optimization method for a concentrator and a collector, The topology self-identification and routing optimization method of the concentrator and collector includes: Step S10: Perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city perception network through the concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and active-passive hybrid discovery mechanism; and output a preliminary topology map and node identification mapping table; Step S20: Based on the preliminary topology map and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the link starts a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring and state matrix update; and outputs the link state matrix; Step S30: Based on the link state matrix, the concentrator performs the preliminary topology map Perform global loop detection and link shutdown or retention operations, and output a loop-free optimized topology map and Link Retention Close Instruction Table ; Step S40: Optimizing the topology using a loop-free concentrator and Link Retention Close Instruction Table A weighted robust multi-dimensional path selection method is used to optimize the topology graph for acyclic Each node in the process configures the primary path and the backup path, performs cross-level and cross-protocol configuration and path parameter distribution, and outputs the path configuration table M; Step S50: Optimizing the topology graph for acyclicity The primary and backup paths of each node in the system are monitored in real time and dynamic path switching is performed.

[0008] Preferably, in step S10, the multi-layer heterogeneous network includes industrial Ethernet, RS485 bus network, low-power local area network, wireless mesh subnet and power carrier communication subnet; The steps of uniformly allocating node identifiers specifically include: the concentrator allocates a globally unique identifier ID to all collector nodes of different protocols and subnets in the multi-layer heterogeneous network, and establishes a node identifier mapping table between the globally unique identifier ID and the physical address; The steps of abstractly representing link relationships specifically include: abstracting the physical or logical connections between all collector nodes into edges in a topology graph; The steps of the active-passive hybrid discovery mechanism include: the concentrator sends topology exploration messages to known nodes, collects neighbor lists, monitors existing data frames or heartbeat responses in the network, infers adjacency relationships that are not explicitly declared, and then improves the topology map, and finally outputs a preliminary topology map. and node identifier mapping table.

[0009] Preferably, in step S30, the steps of global loop detection and link closing or retaining operation specifically include: firstly using depth-first search to determine whether there is a closed loop, marking the loop set, and calculating the link cost for each link in the loop set. , when the link cost When the link cost is greater than the preset link cost threshold, the link is closed; then the false positive suppression mechanism is executed and verified through detection packets. If there is no actual loopback in multiple tests, it is determined to be a false positive and the link is retained.

[0010] Preferably, in step S30, the link overhead The calculation formula is: ,in, is the average one-way link delay required to send data from node i to node j; The maximum single-hop delay threshold that can be tolerated by sensing data or control instructions in the current application network; is the average link bandwidth available for sending data from node i to node j; Link health is an indicator that reflects the overall communication quality of the link; 、 and They represent the importance factors of delay, bandwidth and health in link overhead respectively.

[0011] Preferably, in step S20, based on the preliminary topology map and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the state monitoring mechanism is started, including the heartbeat mechanism design, event-driven monitoring and state matrix update; and the steps of outputting the link state matrix include: Step S201: High-speed heartbeat mechanism initialization and schedule generation: The concentrator generates a corresponding heartbeat schedule for each link based on the preliminary topology map and node identification mapping table, and sets the heartbeat period and heartbeat timeout threshold for each node; Step S202: Establishing a microsecond-level event-driven monitoring mechanism: The concentrator configures a link event listener to monitor in real time the physical or link disconnection events of the link layer, including Ethernet link down events and wireless mesh node loss signals. When an event is triggered, the current heartbeat wait is immediately interrupted, and the physical or link disconnection events of the link layer are processed first. Step S203: When a physical or link disconnection event is triggered or the heartbeat timeout threshold is exceeded, the concentrator proactively sends an immediate detection message to the corresponding node and simultaneously sends a parallel verification request to the neighboring node to confirm whether the heartbeat loss is caused by short-term interference; Step S204: Link health index calculation and dynamic state matrix update: The concentrator determines the link heartbeat success rate, link average packet loss rate and link signal quality ratio based on the heartbeat statistics, real-time detection and parallel verification results, and outputs the link state matrix.

[0012] Preferably, in step S40, the concentrator uses the loop-free optimization topology and Link Retention Close Instruction Table A weighted robust multi-dimensional path selection method is used to optimize the topology graph for acyclic Each node in the process configures a primary path and a backup path, performs cross-level and cross-protocol configuration and path parameter distribution, and outputs a path configuration table M, specifically including: Step S401: Optimizing the topology graph based on acyclicity and Link Retention Close Instruction Table ,The concentrator generates a set of legal and reachable path candidate sets for each node, and ,calculates multi-dimensional performance indicators for each candidate path in the legal and reachable ,path candidate set, including total delay, path minimum bandwidth, number of cross-layer ,number of protocol switching and path redundancy; Step S402: Apply a weighted robust multi-dimensional path selection method to each candidate path based on the multi-dimensional performance indicators to calculate a comprehensive score. The path with the highest score is selected as the primary path, and the path with the second highest score and the least overlap with the primary path in links or nodes is selected as the backup path. Step S403: Perform cross-layer and cross-protocol compatibility verification on the selected and backup paths, including: confirming whether LoRa, Ethernet and 4G multi-protocol mixing is supported, confirming whether the interface configuration of the gateway and the interface configuration of the protocol conversion module are supported; if it is detected that there is any support, return to S402 to reselect the path; Step S404: Pack the detailed links, node sequences, protocol switching instructions, heartbeat timeout thresholds, backup enabling conditions, and cross-layer configuration parameters in the primary path and backup path, and output a path configuration table M.

[0013] Preferably, in step S50, the topology graph is optimized for acyclic The steps of real-time status monitoring of the primary and backup paths of each node in the network and dynamic path switching are as follows: if the health of any link in the primary path is detected to be lower than the preset threshold, the network automatically switches to the backup path and updates the global routing table simultaneously; at the same time, the path priority is adaptively adjusted according to the health difference of the path before and after the switch, and the path switching log and the dynamic evolution information of the link health are recorded; if a node is detected to be continuously offline within the preset monitoring period, it is marked as failed and removed from the loop-free optimization topology map. Delete in.

[0014] The present invention also provides a topology self-identification and routing optimization system for a concentrator and a collector, comprising: The topology modeling and analysis module is used to perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city perception network through the concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and active and passive hybrid discovery mechanisms; and output a preliminary topology map and node identification mapping table; High-speed status monitoring module for monitoring the status of and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the link starts a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring and state matrix update; and outputs the link state matrix; The global loop detection and control module is used to monitor the preliminary topology map through the concentrator based on the link state matrix. Perform global loop detection and link shutdown or retention operations, and output a loop-free optimized topology map and Link Retention Close Instruction Table ; Multi-dimensional path selection and configuration module for optimizing topology graphs with loop-free routing through concentrators and Link Retention Close Instruction Table A weighted robust multi-dimensional path selection method is used to optimize the topology graph for acyclic Each node in the process configures the primary path and the backup path, performs cross-level and cross-protocol configuration and path parameter distribution, and outputs the path configuration table M; Dynamic switching monitoring and control module for optimizing topology for loop-free operation The primary and backup paths of each node in the system are monitored in real time and dynamic path switching is performed.

[0015] The present invention also provides a topology self-identification and routing optimization device for a concentrator and a collector, comprising: a memory, a processor, and a topology self-identification and routing optimization program for the concentrator and the collector stored in the memory and runnable on the processor. When the topology self-identification and routing optimization program for the concentrator and the collector is executed by the processor, a topology self-identification and routing optimization method for the concentrator and the collector is implemented.

[0016] The present invention also provides a computer program product, including a topology self-identification and routing optimization program for a concentrator and a collector, which implements the topology self-identification and routing optimization method for the concentrator and the collector when executed by a processor.

[0017] The beneficial effect of the present invention is that compared with the traditional technology of using static routing or single heartbeat detection scheme, the present invention introduces multi-dimensional performance indicators and weighted robust multi-dimensional path selection method, realizing real-time and accurate link health assessment and automatic switching of primary and backup paths, and can complete switching within milliseconds to ensure data continuity and network stability.

[0018] The present invention adopts cross-level and cross-protocol path adaptation and global loop control mechanisms to avoid routing failures caused by switching between different protocols and different subnets. It can maintain high-reliability communications in complex heterogeneous environments and is suitable for multiple scenarios such as smart transportation, environmental monitoring, and emergency management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a flow chart of a first embodiment of a method for topology self-identification and routing optimization of a concentrator and a collector according to the present invention.

[0021] Figure 2 This is a schematic diagram of a device for a topology self-identification and routing optimization method of a concentrator and a collector according to the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Example 1: Figure 1 FIG. 1 is a flow chart of a first embodiment of a method for topology self-identification and routing optimization of a concentrator and a collector according to the present invention, and provides a first embodiment of a method for topology self-identification and routing optimization of a concentrator and a collector according to the present invention.

[0024] In a first embodiment, the topology self-identification and route optimization method of the concentrator and collector includes: Step S10: Perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city perception network through the concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and active-passive hybrid discovery mechanism; and output a preliminary topology map and node identification mapping table; It's important to note that within smart city sensor networks, various subsystems (such as traffic signals, environmental monitoring, emergency alarms, and smart lighting) typically utilize different communication protocols (e.g., Ethernet, RS485, LoRa, NB-IoT, and Wi-Fi) and reside at different layers (e.g., backbone, access, and terminal). Traditional approaches often rely on independent network segments or static configurations, requiring manual allocation of routes and node identifiers, which is inefficient and prone to errors.

[0025] It is understandable that the present invention proposes automatically assigning globally unique identifiers (IDs) to all connected collector nodes through a concentrator. For example, Ethernet nodes can use MAC addresses, RS485 nodes can use serial port logical numbers, LoRa nodes use unique serial numbers (DevEUI), and NB-IoT nodes use SIM identifiers (IMSI or ICCID). Unified identification allows all protocol nodes to be centrally identified and managed, avoiding the problems of node identification conflicts and repeated configurations in traditional methods. In this step, the preliminary topology map automatically generated by the concentrator includes not only the physical connection information between nodes, but also logical adjacency relationships, link attributes (such as bandwidth, latency, and signal-to-noise ratio), and cross-layer and cross-protocol information. This mechanism allows subsequent optimization during dynamic monitoring and routing selection to be directly based on the complete graph model, eliminating the need for repeated scanning and updates, reducing network-wide broadcast and convergence overhead, and improving overall management efficiency and network stability.

[0026] For example, in a typical smart transportation scenario, a concentrator manages 300 nodes. These include intersection signal controller nodes connected via Ethernet, surveillance camera nodes via wireless mesh, air quality sensor nodes via LoRa, and smart streetlight nodes via NB-IoT. During initialization, the concentrator automatically generates a unique ID for each node, establishes a global node ID mapping table, and automatically constructs a preliminary topology based on adjacency information, enabling unified modeling and management of all nodes. In this example, the concentrator completes node ID registration and preliminary topology generation within 5 seconds, significantly improving efficiency compared to traditional, segmented, manual configuration solutions.

[0027] Step S20: Based on the preliminary topology map and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the link starts a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring and state matrix update; and outputs the link state matrix; It should be noted that in step S20, based on the preliminary topology map and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the network initiates a status monitoring mechanism, including heartbeat mechanism design, event-driven monitoring, and status matrix update; and outputs the link status matrix. The steps specifically include: high-speed heartbeat mechanism initialization and schedule table generation: The concentrator generates a corresponding heartbeat schedule for each link based on the preliminary topology map and node identification mapping table, and sets the heartbeat period and heartbeat timeout threshold for each node; microsecond-level event-driven monitoring mechanism establishment: The concentrator configures a link event listener to monitor physical or link disconnection events at the link layer in real time, including Ethernet link down events and wireless mesh node loss signals. When an event is triggered, the current heartbeat wait is immediately interrupted and physical or link disconnection events at the link layer are prioritized; when a physical or link disconnection event is triggered or exceeds the heartbeat timeout threshold, the concentrator actively sends an immediate detection message to the corresponding node and simultaneously sends a parallel verification request to the neighboring node to confirm whether the heartbeat loss is caused by short-term interference; link health indicator calculation and dynamic status matrix update: The concentrator determines the link heartbeat success rate, link average packet loss rate, and link signal quality ratio based on heartbeat statistics, immediate detection, and parallel verification results, and outputs the link status matrix.

[0028] It should be understood that existing technologies often rely on low-frequency heartbeat monitoring with a fixed period (e.g., 1-5 seconds). This can only detect complete node disconnection and cannot accurately identify real-time changes in link quality. The low heartbeat frequency leads to delayed anomaly detection and slow recovery response, which is particularly problematic in emergency commands or high-real-time scenarios (such as smart traffic accident alerts and city emergency broadcasts). The present invention, through the design of a high-speed heartbeat mechanism, microsecond-level event-driven monitoring, and dynamic state matrix updates, can detect link anomalies within milliseconds of occurrence, significantly reducing detection latency. The proposed state monitoring mechanism goes beyond simply monitoring the physical connectivity of a single link. Instead, it comprehensively evaluates multi-dimensional performance indicators such as the link's heartbeat success rate, packet loss rate, and signal quality ratio, thereby constructing a link stability trend model and dynamically tracking link health. This trend-based link status monitoring approach enables the concentrator to proactively identify potential link degradation risks. Even if the link is not yet completely disconnected, it can proactively implement path switching or early warning strategies based on performance degradation trends, maximizing node service continuity and overall system stability. By introducing an adaptive threshold adjustment mechanism based on multi-dimensional performance indicators into the state matrix, the concentrator can dynamically adjust the heartbeat frequency and switching threshold based on network load pressure, real-time application priority, and historical link performance. For example, for high-priority emergency alarm nodes, their link monitoring thresholds can be automatically tightened to achieve more agile performance degradation detection; while for non-critical environmental sampling nodes, the monitoring frequency can be appropriately relaxed to reduce network load. This mechanism provides the concentrator with global adaptive optimization capabilities, allowing it to simultaneously safeguard critical link priorities and reduce network-wide oscillation in multi-tasking scenarios, significantly enhancing the resilience and reliability of the smart city perception network.

[0029] Step S30: Based on the link state matrix, the concentrator performs the preliminary topology map Perform global loop detection and link shutdown or retention operations, and output a loop-free optimized topology map and Link Retention Close Instruction Table ; It should be noted that the steps of global loop detection and link closing or retaining operations specifically include: first using depth-first search to determine whether a closed loop exists, marking the loop set, and calculating the link cost for each link in the loop set. , when the link cost When the link cost is greater than the preset threshold, the link is closed; then the false positive suppression mechanism is executed and verified by the detection packet. If there is no actual loopback in multiple tests, it is determined to be a false positive and the link is retained. The calculation formula is: ,in, is the average one-way link delay required to send data from node i to node j; The maximum single-hop delay threshold that can be tolerated by sensing data or control instructions in the current application network; is the average link bandwidth available for sending data from node i to node j; Link health is an indicator that reflects the overall communication quality of the link; 、 and They represent the importance factors of delay, bandwidth and health in link overhead respectively.

[0030] Understandably, traditional loop detection often relies on passive convergence using spanning tree protocols (such as STP or RSTP), typically considering only link connectivity or link cost (such as fixed port priority) while ignoring real-time link performance metrics. This invention introduces a weighted calculation of link cost using three dimensions: latency, bandwidth, and health. This allows the concentrator to more accurately consider actual link quality and service requirements when determining whether to close looped links, avoiding the closure of high-quality links that should be retained simply due to cost.

[0031] It should be understood that multiple detection and verification based on the false positive suppression mechanism can effectively reduce false loop determinations caused by transient topology jitter, packet loss, or temporary link instability, preventing the accidental shutdown of non-loop links and ensuring network connectivity and overall performance. Furthermore, the dynamically adjusted factors in the cost calculation enable the concentrator to flexibly allocate link resources based on real-time load, node importance, or task priority, improving the service quality assurance capabilities of the entire smart city perception network.

[0032] For example, in a simulation experiment on a multi-intersection signal control network for smart transportation, the concentrator performed global loop detection on a preliminary topology containing 300 links. Fifteen temporary loops were introduced in the test, of which 10 links had high latency (about 100ms), low bandwidth (about 1Mbps), and degraded links with a health of only 60%. The traditional spanning tree protocol-based solution takes about 2 to 3 seconds to complete convergence, and does not consider performance when shutting down links, resulting in some high-quality links being mistakenly disconnected and a 30% decrease in overall link utilization. However, using the method of the present invention, the concentrator completes loop detection within 200ms, prioritizes shutting down links with degraded quality based on link overhead, and only shuts down 9 links, while the remaining high-quality links are accurately retained. The overall network availability is improved by 27%, and the number of link switching jitters is reduced by about 80%, significantly improving the coordinated response efficiency and robustness of traffic signals.

[0033] Step S40: Optimizing the topology using a loop-free concentrator and Link Retention Close Instruction Table A weighted robust multi-dimensional path selection method is used to optimize the topology graph for acyclic Each node in the process configures the primary path and the backup path, performs cross-level and cross-protocol configuration and path parameter distribution, and outputs the path configuration table M; It should be noted that the concentrator, based on a loop-free optimized topology graph and a link retention and shutdown instruction table, first generates a set of available path candidates for each node. These candidates are then comprehensively scored using the Weighted Robust Multidimensional Path Selection (WRMPS) method. This path scoring not only considers traditional path length or hop count, but also multi-dimensional metrics such as link latency, bandwidth, health, path redundancy, cross-layer penalty factors, and protocol switching penalty factors. The primary path prioritizes the path with the highest overall score and optimal performance, while the backup path selects the suboptimal path with the least physical overlap with the primary path. This ensures maximum isolation between the primary and backup paths in terms of physical links and logical structure, guaranteeing independence and reliability during switchover. A path optimization algorithm based on global topology analysis at the concentrator selects primary and backup paths by comprehensively scoring and weighting candidate paths based on multi-dimensional performance metrics, taking into account physical link independence and protocol compatibility. This method not only considers traditional path hop count or static cost, but also incorporates multi-dimensional performance metrics.

[0034] Understandably, traditional path selection methods rely solely on shortest path algorithms (such as Dijkstra) or fixed routing configurations, often ignoring dynamic link performance metrics and network-level protocol compatibility. This leads to rapid path performance degradation and high handover failure rates in multi-service scenarios. This invention introduces cross-layer, cross-protocol adaptive configuration, allowing paths to simultaneously incorporate multiple communication media, such as wired (e.g., Ethernet), wireless (e.g., LoRa, Mesh), and cellular networks (e.g., 4G / 5G). The concentrator automatically performs path protocol adaptation, layer-level jump logic control, and interface mapping, achieving end-to-end optimization across systems and network standards.

[0035] It should be understood that the path configuration table M output in this step not only contains each path's link sequence, bandwidth utilization plan, protocol switching strategy, and cross-layer logic information, but also includes multi-dimensional dynamic configuration data such as switching thresholds, backup activation conditions, and historical health parameters. The concentrator distributes the complete path configuration to all nodes and relay nodes at once, pre-configuring primary and backup paths. Subsequently, when the primary path degrades or becomes disconnected, nodes can seamlessly switch based on this pre-configured information without having to request the concentrator or rebroadcast the route across the entire network. This significantly reduces switching delays and mitigates the risk of network-wide oscillation.

[0036] Step S50: Optimizing the topology graph for acyclicity The primary and backup paths of each node in the system are monitored in real time and dynamic path switching is performed.

[0037] It should be noted that the steps of real-time status monitoring and dynamic path switching of the primary and backup paths of each node in the loop-free optimization topology diagram specifically include: if it is detected that the health of any link in the primary path is lower than the preset threshold, the path is automatically triggered to switch to the backup path, and the global routing table is synchronously updated to ensure that all subsequent data flows immediately adopt the new forwarding path; at the same time, the concentrator adaptively adjusts the priority of the primary and backup paths based on the comprehensive health difference of the paths before and after the switch, automatically re-evaluates the backup path, generates a new backup path when necessary, and updates the path configuration table M; in addition, the system will record the path switching log (including trigger time, reason, health after switching, switching time) and the dynamic evolution information of the link health in real time for subsequent network operation and maintenance and optimization; if it is detected that the node is continuously offline within a preset continuous monitoring period (for example, 3 heartbeat detection periods), the concentrator will mark the node as failed and completely delete it from the loop-free optimization topology diagram to avoid redundant route occupation and the generation of ghost nodes.

[0038] As can be understood, this step not only implements passive failover after a failure but also supports pre-degradation detection and active failover, preemptively preventing the risk of communication interruption caused by a complete link outage. By coordinating updates with the concentrator's global routing table, all neighboring nodes can instantly perceive the current node's path status and rapidly reconfigure routes, significantly reducing packet loss and latency jitter during failover. Furthermore, the health difference-driven path priority adaptive mechanism ensures that nodes remain on optimal or near-optimal paths for extended periods, significantly improving overall network stability and resource utilization efficiency. By recording path switch logs and dynamic link health evolution information, the concentrator can not only be used for subsequent root cause analysis and network backtracking verification, but also support self-learning optimization (such as using machine learning to further predict link degradation trends), thereby continuously improving the accuracy of future path selection and failover strategies. Compared to traditional passive failover methods that rely solely on heartbeat timeouts, this method, based on real-time performance evaluation and multi-dimensional dynamic health management, enables faster, more stable, and more granular self-healing and enhanced system resilience.

[0039] Embodiment 2: In addition, the present invention provides a topology self-identification and routing optimization system for a concentrator and a collector, which adopts a topology self-identification and routing optimization method for a concentrator and a collector in the above embodiment, and can solve the technical problem of topology self-identification and routing optimization for a concentrator and a collector. Compared with the prior art, the beneficial effects of the topology self-identification and routing optimization system for a concentrator and a collector provided by the present invention are the same as the beneficial effects of the topology self-identification and routing optimization method for a concentrator and a collector provided by the above embodiment, and other technical features of the topology self-identification and routing optimization system for a concentrator and a collector are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0040] Example 3: The present invention provides a topology self-identification and routing optimization device for a concentrator and a collector, please refer to Figure 2 A topology self-identification and route optimization device for a concentrator and collector includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for topology self-identification and route optimization for a concentrator and collector described in the first embodiment. The topology self-identification and route optimization device for a concentrator and collector in this embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The topology self-identification and route optimization device for a concentrator and collector is merely an example and should not limit the functionality or scope of use of this embodiment of the present invention. A topology self-identification and route optimization device for a concentrator and collector may include a processing device 1001, which can perform various appropriate actions and processes based on a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the topology self-identification and route optimization device for the concentrator and collector. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An I / O interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003; and a communication device 1009. The communication device 1009 can allow a topology self-identification and route optimization device of a concentrator and collector to communicate with other devices wirelessly or by wire to exchange data. Figure 2 1 shows a topology self-identification and route optimization device of a concentrator and a collector with various systems, but it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented or possessed instead.

[0041] Embodiment 4: The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for topology self-identification and route optimization of a concentrator and a collector. The computer program product provided by the present invention can solve the technical problem of topology self-identification and route optimization of a concentrator and a collector. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the method for topology self-identification and route optimization of a concentrator and a collector provided in the above-mentioned embodiment, and are not further described here.

[0042] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are performed.

[0043] It should be understood that the various parts disclosed in the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0044] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for topology self-identification and routing optimization of a concentrator and a collector, characterized in that: Methods include: Step S10: Perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city perception network through the concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and active-passive hybrid discovery mechanism; and output a preliminary topology map and node identification mapping table; Step S20: Based on the preliminary topology map and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the link starts a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring and state matrix update; and outputs the link state matrix; Step S30: Based on the link state matrix, the concentrator performs the preliminary topology map Perform global loop detection and link shutdown or retention operations, and output a loop-free optimized topology map and Link Retention Close Instruction Table ; Step S40: Optimizing the topology using a loop-free concentrator and Link Retention Close Instruction Table A weighted robust multi-dimensional path selection method is used to optimize the topology graph for acyclic Each node in the process configures the primary path and the backup path, performs cross-level and cross-protocol configuration and path parameter distribution, and outputs the path configuration table M; Step S50: Optimizing the topology graph for acyclicity The primary and backup paths of each node in the system are monitored in real time and dynamic path switching is performed.

2. A method for topology self-identification and routing optimization of a concentrator and a collector according to claim 1, characterized in that: In step S10, the multi-layer heterogeneous network includes industrial Ethernet, RS485 bus network, low-power local area network, wireless mesh subnet and power carrier communication subnet; The steps of uniformly allocating node identifiers specifically include: the concentrator allocates a globally unique identifier ID to all collector nodes of different protocols and subnets in the multi-layer heterogeneous network, and establishes a node identifier mapping table between the globally unique identifier ID and the physical address; The steps of abstractly representing link relationships specifically include: abstracting the physical or logical connections between all collector nodes into edges in a topology graph; The steps of the active-passive hybrid discovery mechanism include: the concentrator sends topology exploration messages to known nodes, collects neighbor lists, monitors existing data frames or heartbeat responses in the network, infers adjacency relationships that are not explicitly declared, and then improves the topology map, and finally outputs a preliminary topology map. and node identifier mapping table.

3. The method for topology self-identification and routing optimization of a concentrator and a collector according to claim 1, characterized in that: In step S30, the steps of global loop detection and link closing or retaining operation specifically include: firstly using depth-first search to determine whether there is a closed loop, marking the loop set, and calculating the link cost for each link in the loop set. , when the link cost When the link cost is greater than the preset link cost threshold, the link is closed; then the false positive suppression mechanism is executed and verified through detection packets. If there is no actual loopback in multiple tests, it is determined to be a false positive and the link is retained.

4. A method for topology self-identification and routing optimization of a concentrator and a collector as claimed in claim 3, characterized in that: In step S30, the link cost The calculation formula is: ,in, is the average one-way link delay required to send data from node i to node j; The maximum single-hop delay threshold that can be tolerated by sensing data or control instructions in the current application network; is the average link bandwidth available for sending data from node i to node j; Link health is an indicator that reflects the overall communication quality of the link; 、 and They represent the importance factors of delay, bandwidth and health in link overhead respectively.

5. The method for topology self-identification and routing optimization of a concentrator and a collector according to claim 1, characterized in that: In step S20, based on the preliminary topology map and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the state monitoring mechanism is started, including heartbeat mechanism design, event-driven monitoring and state matrix update; The steps of outputting a link state matrix include: Step S201: High-speed heartbeat mechanism initialization and schedule generation: The concentrator generates a corresponding heartbeat schedule for each link based on the preliminary topology map and node identification mapping table, and sets the heartbeat period and heartbeat timeout threshold for each node; Step S202: Establishing a microsecond-level event-driven monitoring mechanism: The concentrator configures a link event listener to monitor in real time the physical or link disconnection events of the link layer, including Ethernet link down events and wireless mesh node loss signals. When an event is triggered, the current heartbeat wait is immediately interrupted, and the physical or link disconnection events of the link layer are processed first. Step S203: When a physical or link disconnection event is triggered or the heartbeat timeout threshold is exceeded, the concentrator proactively sends an immediate detection message to the corresponding node and simultaneously sends a parallel verification request to the neighboring node to confirm whether the heartbeat loss is caused by short-term interference; Step S204: Link health index calculation and dynamic state matrix update: The concentrator determines the link heartbeat success rate, link average packet loss rate and link signal quality ratio based on the heartbeat statistics, real-time detection and parallel verification results, and outputs the link state matrix.

6. The method for topology self-identification and routing optimization of a concentrator and a collector according to claim 1, characterized in that: In step S40, the concentrator uses the loop-free optimization topology and Link Retention Close Instruction Table A weighted robust multi-dimensional path selection method is used to optimize the topology graph for acyclic Each node in the process configures a primary path and a backup path, performs cross-level and cross-protocol configuration and path parameter distribution, and outputs a path configuration table M, specifically including: Step S401: Optimizing the topology graph based on acyclicity and Link Retention Close Instruction Table ,The concentrator generates a set of legal and reachable path candidate sets for each node, and ,calculates multi-dimensional performance indicators for each candidate path in the legal and reachable ,path candidate set, including total delay, path minimum bandwidth, number of cross-layer ,number of protocol switching and path redundancy; Step S402: Apply a weighted robust multi-dimensional path selection method to each candidate path based on the multi-dimensional performance indicators to calculate a comprehensive score. The path with the highest score is selected as the primary path, and the path with the second highest score and the least overlap with the primary path in links or nodes is selected as the backup path. Step S403: Perform cross-layer and cross-protocol compatibility verification on the selected and backup paths, including: confirming whether LoRa, Ethernet and 4G multi-protocol mixing is supported, confirming whether the interface configuration of the gateway and the interface configuration of the protocol conversion module are supported; if it is detected that there is any support, return to S402 to reselect the path; Step S404: Pack the detailed links, node sequences, protocol switching instructions, heartbeat timeout thresholds, backup enabling conditions, and cross-layer configuration parameters in the primary path and backup path, and output a path configuration table M.

7. The method for topology self-identification and routing optimization of a concentrator and a collector according to claim 1, characterized in that: In step S50, the topology graph is optimized for acyclic The steps of real-time status monitoring of the primary and backup paths of each node in the network and dynamic path switching are as follows: if the health of any link in the primary path is detected to be lower than the preset threshold, the network automatically switches to the backup path and updates the global routing table simultaneously; at the same time, the path priority is adaptively adjusted according to the health difference of the path before and after the switch, and the path switching log and the dynamic evolution information of the link health are recorded; if a node is detected to be continuously offline within the preset monitoring period, it is marked as failed and removed from the loop-free optimization topology map. Delete in.

8. A topology self-identification and routing optimization system for a concentrator and a collector, applied to a topology self-identification and routing optimization method for a concentrator and a collector according to any one of claims 1 to 7, characterized in that: The topology self-identification and routing optimization system of the concentrator and collector includes: The topology modeling and analysis module is used to perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city perception network through the concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and active and passive hybrid discovery mechanisms; and output a preliminary topology map and node identification mapping table; High-speed status monitoring module for monitoring the status of and node identification mapping table, and the preliminary topology map is mapped by the concentrator Each edge in the link starts a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring and state matrix update; and outputs the link state matrix; The global loop detection and control module is used to monitor the preliminary topology map through the concentrator based on the link state matrix. Perform global loop detection and link shutdown or retention operations, and output a loop-free optimized topology map and Link Retention Close Instruction Table ; Multi-dimensional path selection and configuration module for optimizing topology graphs with loop-free routing through concentrators and Link Retention Close Instruction Table A weighted robust multi-dimensional path selection method is used to optimize the topology graph for acyclic Each node in the process configures the primary path and the backup path, performs cross-level and cross-protocol configuration and path parameter distribution, and outputs the path configuration table M; Dynamic switching monitoring and control module for optimizing topology for loop-free operation The primary and backup paths of each node in the system are monitored in real time and dynamic path switching is performed.

9. A topology self-identification and routing optimization device for a concentrator and a collector, characterized in that: The topology self-identification and routing optimization device of the concentrator and collector includes: a memory, a processor, and a topology self-identification and routing optimization program of the concentrator and collector stored on the memory and runnable on the processor. When the topology self-identification and routing optimization program of the concentrator and collector is executed by the processor, a topology self-identification and routing optimization method of the concentrator and collector as described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a topology self-identification and routing optimization program for a concentrator and a collector. When the topology self-identification and routing optimization program for a concentrator and a collector is executed by a processor, a topology self-identification and routing optimization method for a concentrator and a collector according to any one of claims 1 to 7 is implemented.

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