A method and system for topology self-identification and route optimization of concentrators and collectors

Abstract

This invention relates to the field of adaptive routing optimization technology, specifically disclosing a method and system for topology self-identification and routing optimization of concentrators and collectors. The method includes: performing multi-layer heterogeneous network topology modeling; performing high-speed link status monitoring based on a preliminary topology map; completing global loop detection and link optimization based on the link status matrix; configuring primary and backup paths for each node using a weighted robust multi-dimensional path selection method; and monitoring path status in real time and dynamically switching paths. Compared to existing technologies that mainly use 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, which cannot achieve rapid path switching and high-reliability routing redundancy guarantees, this application avoids routing interruptions and performance degradation by using a multi-dimensional path selection method and cross-level and cross-protocol adaptive switching strategies, thereby improving the overall network reliability.

Description

Technical Field

[0001] This invention relates to the field of adaptive routing optimization technology, and in particular to a method and system for topology self-identification and routing optimization of concentrators and collectors. Background Technology

[0002] Currently, smart city sensing networks are widely used in various scenarios such as traffic signal control, environmental monitoring, public safety, intelligent lighting, and energy management. With the rapid increase in the number of nodes and the continuous expansion of network scale, the communication network between concentrators and collectors in the sensing network exhibits complex characteristics of multi-level, cross-protocol, and dynamic variability. However, most existing technologies adopt static topology configuration and fixed routing table maintenance methods, lacking real-time perception and adaptive adjustment capabilities of network status, resulting in problems such as poor adaptability, slow switching, and susceptibility to environmental interference.

[0003] Furthermore, most current concentrators rely on heartbeat packets, MAC address tables, and static routing rules for link detection. This approach often suffers from topology recognition delays in complex, multi-layered, and dynamic scenarios, and is prone to routing table oscillations. 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 fewest hops. At the same time, traditional primary and backup paths share many links or relay nodes, which means that even if a switch to a backup path is made under sudden events or extreme conditions, communication reliability cannot be effectively guaranteed.

[0005] Therefore, there is an urgent need for a topology self-identification and routing optimization method for concentrators and collectors, which 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 overall network reliability, stability and resilience. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a topology self-identification and route optimization method for concentrators and collectors. This method aims to solve the technical problems in existing technologies that mainly use 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, where it is impossible to achieve rapid path switching and high-reliability route redundancy assurance.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for topology self-identification and route optimization of concentrators and collectors.

[0008] The topology self-identification and route optimization method for the concentrator and collector includes:

[0009] Step S10: Perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city sensing network through a concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and a hybrid active-passive discovery mechanism; and output a preliminary topology map. and node identifier mapping table;

[0010] Step S20: Based on the preliminary topology map And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. Each edge in the process initiates a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring, and state matrix update; and outputs a link state matrix.

[0011] Step S30: Based on the link state matrix, perform preliminary topology mapping using a concentrator. Perform global loop detection and link closure or retention operations, and output a loop-free optimized topology graph. Link Reservation and Closure Instruction Table ;

[0012] Step S40: Optimize the topology graph using acyclic methods via a concentrator. Link Reservation and Closure Instruction Table A weighted robust multidimensional path selection method is used to optimize the topology graph for acyclic graphs. Each node is configured with a primary path and a backup path, and cross-level and cross-protocol configuration and path parameter distribution are performed, outputting a path configuration table M;

[0013] Step S50: Optimize the topology graph for acyclic graphs The main path and backup path of each node are monitored in real time and the execution path is dynamically switched.

[0014] Preferably, in step S10, the multi-layer heterogeneous network includes an industrial Ethernet network, an RS485 bus network, a low-power local area network, a wireless Mesh subnet, and a power line carrier communication subnet.

[0015] The steps for unified allocation of node identifiers specifically include: the concentrator assigns globally unique identifiers (IDs) to all collector nodes of different protocols and subnets in the multi-layer heterogeneous network, and establishes a node identifier mapping table between globally unique identifiers (IDs) and physical addresses;

[0016] The specific steps for abstracting the link relationship include: abstracting the physical or logical connections between all collector nodes into edges in the topology graph;

[0017] The steps of the hybrid active-passive discovery mechanism specifically include: the concentrator sends topology exploration messages to known nodes, collects the neighbor list, and simultaneously listens for existing data frames or heartbeat responses within the network to infer undeclared adjacency relationships, thereby refining the topology graph and finally outputting a preliminary topology graph. And node identifier mapping table.

[0018] Preferably, step S30, the steps of global loop detection and performing link closure or retention operations, specifically include: first, using a depth-first search to determine whether a closed loop exists and marking the loop set; then, calculating the link cost for each link within the loop set. When link overhead If the link cost exceeds the preset threshold, the link is shut down. Then, a false alarm suppression mechanism is executed. If no actual loopback occurs in multiple tests, the link is considered a false alarm and is retained.

[0019] Preferably, in step S30, the link cost The calculation formula is: ,in, The average one-way latency of the link required to send data from node i to node j; This represents the maximum single-hop latency threshold that the sensing data or control commands can tolerate in the current application network. The average link bandwidth available for sending data from node i to node j; Link health is an indicator used to reflect the overall communication quality of a link. , and These represent the importance factors of latency, bandwidth, and health in link overhead, respectively.

[0020] Preferably, in step S20, based on the preliminary topology map... And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. The process of initiating a state monitoring mechanism for each edge, including heartbeat mechanism design, event-driven monitoring, and state matrix update, and outputting the link state matrix, specifically includes:

[0021] Step S201: High-speed heartbeat mechanism initialization and scheduling table generation: The concentrator generates a corresponding heartbeat scheduling table for each link based on the preliminary topology map and node identifier mapping table, and sets the heartbeat period and heartbeat timeout threshold for each node;

[0022] Step S202: Establishment of microsecond-level event-driven monitoring mechanism: The concentrator is configured with 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 disconnection signals. After an event is triggered, the current heartbeat waiting is immediately interrupted, and physical or link disconnection events at the link layer are processed first.

[0023] Step S203: When a physical or link disconnection event is triggered or the heartbeat timeout threshold is exceeded, the concentrator actively sends an instant probe message to the corresponding node and simultaneously sends a parallel verification request to neighboring nodes to confirm whether the heartbeat loss is caused by short-term interference.

[0024] 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 heartbeat statistics, real-time detection and parallel verification results, and outputs the link state matrix.

[0025] Preferably, in step S40, the topology graph is optimized using an acyclic method via a concentrator. Link Reservation and Closure Instruction Table A weighted robust multidimensional path selection method is used to optimize the topology graph for acyclic graphs. The steps involved in configuring primary and backup paths for each node, performing cross-level and cross-protocol configuration and path parameter distribution, and outputting the path configuration table M include:

[0026] Step S401: Optimize the topology graph based on acyclic graphs Link Reservation and Closure Instruction Table The concentrator generates a set of candidate legal reachable paths for each node, and calculates multi-dimensional performance metrics for each candidate path in the set, including total latency, minimum path bandwidth, number of cross-levels, number of protocol switching, and path redundancy.

[0027] Step S402: Apply the weighted robust multidimensional path selection method to each candidate path according to the multidimensional performance index to calculate the comprehensive score, select the path with the highest score as the main path, and select the path with the second highest score and the least overlap with the main path in links or nodes as the backup path.

[0028] Step S403: Perform cross-level and cross-protocol compatibility verification on the selected and backup paths, including: confirming whether LoRa, Ethernet and 4G multi-protocol mixing is supported, and confirming whether the gateway interface configuration and protocol conversion module interface configuration are supported; if any unsupported cases are detected, return to S402 to select a new path;

[0029] Step S404: Package the detailed links, node order, protocol switching instructions, heartbeat timeout threshold, backup activation conditions, and cross-layer configuration parameters in the primary path and backup path, and output the path configuration table M.

[0030] Preferably, in step S50, the topology graph is optimized for acyclic graphs. The process involves real-time status monitoring and dynamic path switching for each node's primary and backup paths. Specifically, this includes: if the health of any link in the primary path falls below a preset threshold, automatically switching to the backup path and synchronously updating the global routing table; simultaneously, adaptively adjusting path priorities based on the health difference before and after the switch, and recording path switching logs and dynamic link health evolution information; if a node remains offline for a preset monitoring period, marking it as failed and optimizing the topology from a loop-free perspective. Delete it.

[0031] This invention also provides a topology self-identification and route optimization system for concentrators and collectors, comprising:

[0032] The topology modeling and analysis module is used to perform unified topology modeling and analysis on multi-layer heterogeneous networks in the smart city sensing network through a concentrator. This includes unified node identifier allocation, abstract representation of link relationships, and a hybrid active-passive discovery mechanism; and outputs a preliminary topology map. and node identifier mapping table;

[0033] High-speed condition monitoring module, used for initial topology map And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. Each edge in the process initiates a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring, and state matrix update; and outputs a link state matrix.

[0034] The global loop detection and control module is used to analyze the preliminary topology map through a concentrator based on the link state matrix. Perform global loop detection and link closure or retention operations, and output a loop-free optimized topology graph. Link Reservation and Closure Instruction Table ;

[0035] The multi-dimensional path selection and configuration module is used to optimize the topology graph using acyclic methods via a concentrator. Link Reservation and Closure Instruction Table A weighted robust multidimensional path selection method is used to optimize the topology graph for acyclic graphs. Each node is configured with a primary path and a backup path, and cross-level and cross-protocol configuration and path parameter distribution are performed, outputting a path configuration table M;

[0036] Dynamic switching monitoring and control module is used to optimize the topology graph for acyclic graphs. The main path and backup path of each node are monitored in real time and the execution path is dynamically switched.

[0037] The present invention also provides a topology self-identification and route optimization device for concentrators and collectors, comprising: a memory, a processor, and a topology self-identification and route optimization program for concentrators and collectors stored in the memory and executable on the processor. When the topology self-identification and route optimization program for concentrators and collectors is executed by the processor, the topology self-identification and route optimization method for concentrators and collectors is implemented.

[0038] The present invention also provides a computer program product, including a topology self-identification and route optimization program for a concentrator and a data collector, wherein the topology self-identification and route optimization program for the concentrator and the data collector, when executed by a processor, implements the topology self-identification and route optimization method for the concentrator and the data collector.

[0039] The beneficial effects of this invention are as follows: Compared with traditional technologies that use static routing or single heartbeat detection schemes, this invention introduces multi-dimensional performance indicators and a weighted robust multi-dimensional path selection method, which realizes real-time and accurate link health assessment and automatic switching of primary and backup paths. The switching can be completed within milliseconds, ensuring data continuity and network stability.

[0040] This invention employs a cross-level, cross-protocol path adaptation and global loop control mechanism to avoid routing failures caused by switching between different protocols and subnets. It can maintain high-reliability communication in complex heterogeneous environments and is suitable for various applications such as intelligent transportation, environmental monitoring, and emergency management. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the first embodiment of a topology self-identification and route optimization method for concentrators and collectors according to the present invention.

[0043] Figure 2 This is a schematic diagram of a device for a topology self-identification and routing optimization method for a concentrator and a collector according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the topology self-identification and routing optimization method for concentrators and collectors of the present invention, which presents the first embodiment of the topology self-identification and routing optimization method for concentrators and collectors of the present invention.

[0046] In the first embodiment, the topology self-identification and route optimization method of the concentrator and collector includes:

[0047] Step S10: Perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city sensing network through a concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and a hybrid active-passive discovery mechanism; and output a preliminary topology map. and node identifier mapping table;

[0048] It should be noted that in a smart city sensing network, various subsystems (such as traffic signals, environmental monitoring, emergency alarms, and smart lighting) typically use different communication protocols (such as Ethernet, RS485, LoRa, NB-IoT, and Wi-Fi) and are located at different layers (such as backbone network, access layer, and terminal layer). Traditional methods often use independent network segments or static configurations, requiring manual allocation of routes and node identifiers, which is inefficient and prone to errors.

[0049] Understandably, this invention proposes to automatically assign 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 can use unique serial numbers (DevEUI), and NB-IoT nodes can use SIM identifiers (IMSI or ICCID). This unified identifier allows for centralized identification and unified management of all protocol nodes, avoiding the problems of node identifier conflicts and redundant configurations in traditional methods. In this step, the preliminary topology map automatically generated by the concentrator includes not only 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 dynamic monitoring and routing selection to be directly optimized 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.

[0050] For example, in a typical smart transportation scenario, a concentrator manages 300 nodes. These include signal controller nodes at intersections connected via Ethernet, surveillance camera nodes connected via wireless Mesh, air quality sensor nodes connected via LoRa, and smart streetlight nodes connected via NB-IoT. During initialization, the concentrator automatically generates a unique ID for each node, establishes a global node identifier mapping table, and automatically constructs a preliminary topology graph based on adjacency information, achieving unified modeling and management of all nodes. In this example, the concentrator completes node identifier registration and preliminary topology generation within 5 seconds, significantly improving efficiency compared to traditional segmented manual configuration schemes.

[0051] Step S20: Based on the preliminary topology map And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. Each edge in the process initiates a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring, and state matrix update; and outputs a link state matrix.

[0052] It should be noted that in step S20, based on the preliminary topology map... And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. The process involves initiating a state monitoring mechanism for each edge, including heartbeat mechanism design, event-driven monitoring, and state matrix updates, and outputting the link state matrix. Specifically, this includes: high-speed heartbeat mechanism initialization and scheduling table generation: the concentrator generates a corresponding heartbeat scheduling table for each link based on the initial topology map and node identifier mapping table, and sets the heartbeat period and heartbeat timeout threshold for each node; establishment of a microsecond-level event-driven monitoring mechanism: 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 disconnection signals. Upon event triggering, the current heartbeat waiting is immediately interrupted, prioritizing the handling of physical or link disconnection events at the link layer; when a physical or link disconnection event is triggered or exceeds the heartbeat timeout threshold, the concentrator actively sends an instant probe message to the corresponding node and simultaneously sends a parallel verification request to neighboring nodes to confirm whether the heartbeat loss is due to short-term interference; calculation of link health indicators and dynamic state matrix updates: the concentrator determines the link heartbeat success rate, average link packet loss rate, and link signal quality ratio based on heartbeat statistics, instant probes, and parallel verification results, and outputs the link state matrix.

[0053] It should be understood that in existing technologies, node and link status monitoring largely relies on low-frequency heartbeat packet monitoring at fixed intervals (e.g., 1-5 seconds). This can only detect whether a node is completely disconnected, and cannot accurately identify real-time quality changes in the link. Due to the low heartbeat frequency, anomaly detection is delayed and recovery response is slow, which is particularly insufficient in emergency commands or high real-time scenarios (such as intelligent traffic accident alarms and urban emergency broadcasts). This invention, by designing a high-speed heartbeat mechanism, microsecond-level event-driven monitoring, and dynamic updating of the state matrix, can detect link anomalies at the millisecond level, significantly reducing detection latency. Furthermore, the status monitoring mechanism proposed in this invention does not simply detect the physical connectivity of a single link, but 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 achieving dynamic quantitative tracking of link health. This trend-prediction-based link status monitoring method enables the concentrator to identify potential link degradation risks in advance. Even if the link is not currently completely interrupted, it can execute path switching or early warning strategies in advance based on the performance degradation trend, maximizing the protection of 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 according to network load pressure, real-time application priority, and historical link performance. For example, for high-priority emergency alarm nodes, their link monitoring threshold 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 enables the concentrator to have global adaptive optimization capabilities, ensuring the priority of critical links and reducing network-wide oscillations in multi-task scenarios, significantly enhancing the resilience and reliability of the smart city sensing network.

[0054] Step S30: Based on the link state matrix, perform preliminary topology mapping using a concentrator. Perform global loop detection and link closure or retention operations, and output a loop-free optimized topology graph. Link Reservation and Closure Instruction Table ;

[0055] It should be noted that the steps for global loop detection and performing link closure or retention operations specifically include: first, using a depth-first search to determine whether a closed loop exists and marking the loop set; then, calculating the link cost for each link within the loop set. When link overhead If the link cost exceeds a preset threshold, the link is shut down. Then, a false positive suppression mechanism is executed. Verification is performed using probe packets; if no actual loopback occurs in multiple tests, it is considered a false alarm, and the link is retained. Link cost The calculation formula is: ,in, The average one-way latency of the link required to send data from node i to node j; This represents the maximum single-hop latency threshold that the sensing data or control commands can tolerate in the current application network. The average link bandwidth available for sending data from node i to node j; Link health is an indicator used to reflect the overall communication quality of a link. , and These represent the importance factors of latency, bandwidth, and health in link overhead, respectively.

[0056] Understandably, traditional loop detection relies heavily on passive convergence via spanning tree protocols (such as STP or RSTP), typically considering only link connectivity or link cost (such as fixed port priorities), neglecting real-time link performance metrics. This invention introduces a three-dimensional weighted calculation of link overhead based on latency, bandwidth, and health, enabling the concentrator to more accurately consider actual link quality and service requirements when deciding whether to close a loop link, avoiding the closure of valuable links that should be retained due to purely cost-driven decisions.

[0057] It should be understood that multiple probe verifications based on the false positive suppression mechanism can effectively reduce erroneous loop determinations caused by instantaneous topology jitter, packet loss, or brief link instability, preventing the accidental closure of non-loop links and ensuring network connectivity and overall performance. Furthermore, the dynamically adjusted factors in the overhead calculation enable the concentrator to flexibly allocate link resources based on real-time load, node importance, or task priority, thereby improving the service quality assurance capabilities of the entire smart city sensing network.

[0058] For example, in a simulation experiment of a smart traffic multi-intersection signal control network, the concentrator performed global loop detection on an initial topology containing 300 links. Fifteen temporary loops were introduced in the test, 10 of which were degraded links with high latency (approximately 100ms), low bandwidth (approximately 1Mbps), and a health rate of only 60%. Traditional spanning tree protocol-based solutions require approximately 2-3 seconds to converge, and performance is not considered when closing links, leading to the incorrect disconnection of some high-quality links and a 30% decrease in overall link utilization. However, using the method of this invention, the concentrator completes loop detection within 200ms, prioritizing the closure of degraded links based on link overhead, closing only 9 links while accurately preserving the remaining high-quality links. This improves overall network availability by 27%, reduces link switching jitter by approximately 80%, and significantly improves the coordinated response efficiency and robustness of traffic signals.

[0059] Step S40: Optimize the topology graph using acyclic methods via a concentrator. Link Reservation and Closure Instruction Table A weighted robust multidimensional path selection method is used to optimize the topology graph for acyclic graphs. Each node is configured with a primary path and a backup path, and cross-level and cross-protocol configuration and path parameter distribution are performed, outputting a path configuration table M;

[0060] It should be noted that the concentrator, based on a loop-free optimized topology graph and combined with a link reservation and closure instruction table, first generates a set of available path candidates for each node, and then comprehensively scores the candidate paths using the Weighted Robust Multidimensional Path Selection Method (WRMPS). Path scoring considers not only traditional path length or hop count, but also a comprehensive set of multidimensional indicators such as link latency, bandwidth, health, path redundancy, cross-level penalty factors, and protocol switching penalty factors. The primary path is prioritized based on the path with the highest comprehensive score and optimal performance, while the backup path is selected based on the second-best path with the least physical overlap with the primary path, ensuring maximum isolation between the primary and backup paths in terms of physical links and logical structure, guaranteeing independence and reliability during switching. A path optimization algorithm based on concentrator global topology analysis is proposed, which comprehensively scores and weights candidate paths using multidimensional performance indicators, combining physical link independence and protocol compatibility to select the primary and backup paths. This method not only considers traditional path hop count or static cost, but also introduces multidimensional performance indicators.

[0061] Understandably, traditional path selection methods, based solely on shortest path algorithms (such as Dijkstra's) or fixed route configurations, often neglect dynamic performance metrics of links and network layer protocol compatibility. This leads to rapid path performance degradation and high handover failure rates when facing multi-service scenarios. This invention introduces cross-layer, cross-protocol adaptive configuration, allowing paths to simultaneously include multiple communication media such as wired (e.g., Ethernet), wireless (e.g., LoRa, Mesh), and cellular networks (e.g., 4G / 5G). The concentrator automatically completes path protocol adaptation, layer hop logic control, and interface mapping, achieving end-to-end optimization across systems and network standards.

[0062] It should be understood that the path configuration table M output in this step not only includes the link sequence, bandwidth utilization plan, protocol switching strategy, and cross-layer logic information for each path, but also 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, realizing pre-configuration of primary and backup paths. Subsequently, when the performance of the primary path degrades or the link breaks, the nodes can directly switch seamlessly based on the pre-configured information without needing to request the concentrator again or rebroadcast the route across the entire network, significantly reducing switching latency and mitigating the risk of network-wide instability.

[0063] Step S50: Optimize the topology graph for acyclic graphs The main path and backup path of each node are monitored in real time and the execution path is dynamically switched.

[0064] It should be noted that the steps for real-time status monitoring and dynamic path switching of the primary and backup paths for each node in the loop-free optimization topology graph include: if the health of any link in the primary path is detected to be lower than a preset threshold, the path is automatically switched to the backup path, and the global routing table is updated synchronously 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 difference in the overall health of the paths before and after the switch, automatically re-evaluates the backup path, and generates a new backup path and updates the path configuration table M when necessary; in addition, the system records the path switching log (including trigger time, reason, health after the switch, and switch time) and the dynamic evolution information of the link health in real time for subsequent network operation and maintenance and optimization; if a node is detected to be continuously offline within a preset continuous monitoring period (e.g., 3 heartbeat detection periods), the concentrator marks the node as failed and completely deletes it from the loop-free optimization topology graph to avoid redundant route occupation and ghost node generation.

[0065] Understandably, this step not only achieves passive switching after a failure but also supports link performance pre-degradation detection and proactive switching, preventing the risk of communication interruption caused by a complete link failure. Through coordinated updates with the concentrator's global routing table, all neighboring nodes can instantly perceive the current node's path status and quickly reconstruct routes, significantly reducing packet loss rate and latency jitter during switching. Furthermore, the path priority adaptive mechanism driven by health difference ensures that nodes remain on optimal or near-optimal paths for extended periods, greatly improving overall network stability and resource utilization efficiency. By recording path switching logs and dynamic evolution information of link health, the concentrator can not only be used for subsequent anomaly 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 switching strategies. Compared to traditional methods that rely solely on passive switching based on heartbeat timeouts, this invention, based on real-time performance evaluation and multi-dimensional dynamic health management, enables faster, more stable, and finer-grained self-healing recovery and enhanced system resilience.

[0066] Example 2: Furthermore, the concentrator and collector topology self-identification and route optimization system provided by the present invention employs the concentrator and collector topology self-identification and route optimization method of the above embodiments, which can solve the technical problem of concentrator and collector topology self-identification and route optimization. Compared with the prior art, the beneficial effects of the concentrator and collector topology self-identification and route optimization system provided by the present invention are the same as the beneficial effects of the concentrator and collector topology self-identification and route optimization method of the above embodiments, and other technical features of the concentrator and collector topology self-identification and route optimization system are the same as the features disclosed in the above embodiments, and will not be repeated here.

[0067] Example 3: This invention provides a topology self-identification and route optimization device for concentrators and collectors. Please refer to... Figure 2 A topology self-identification and routing optimization device for a concentrator and data 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, which, when executed by the at least one processor, enable the at least one processor to perform the topology self-identification and routing optimization method for a concentrator and data collector as described in Embodiment 1 above. The topology self-identification and routing optimization device for a concentrator and data collector in this embodiment of the invention may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This topology self-identification and routing optimization device for a concentrator and data collector is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the invention. A topology self-identification and routing optimization device for a concentrator and data collector may include a processing unit 1001, which can perform various appropriate actions and processes according to 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 routing optimization device for the concentrator and data collector. The processing unit 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 can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003; and communication device 1009. The communication device 1009 allows a concentrator and collector topology self-identification and routing optimization device to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 2 The diagram illustrates a topology self-identification and routing optimization device with various systems for concentrators and collectors; however, it should be understood that implementation or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0068] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for topology self-identification and route optimization of a concentrator and data collector. The computer program product provided by this invention can solve the technical problem of topology self-identification and route optimization of a concentrator and data collector. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the topology self-identification and route optimization method for a concentrator and data collector provided in the above embodiments, and will not be repeated here.

[0069] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0070] It should be understood that the various parts disclosed in this 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 may be combined in any suitable manner in one or more embodiments or examples.

[0071] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for topology self-identification and route optimization of concentrators and collectors, characterized in that, The methods include: Step S10: Perform unified topology modeling and analysis on the multi-layer heterogeneous network in the smart city sensing network through a concentrator, including unified allocation of node identifiers, abstract representation of link relationships, and a hybrid active-passive discovery mechanism; and output a preliminary topology map. and node identifier mapping table; Step S20: Based on the preliminary topology map And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. Each edge in the process initiates a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring, and state matrix update; and outputs a link state matrix. Step S30: Based on the link state matrix, perform preliminary topology mapping using a concentrator. Perform global loop detection and link closure or retention operations, and output a loop-free optimized topology graph. Link Reservation and Closure Instruction Table The steps for global loop detection and link closure or retention include: first, using a depth-first search to determine if a closed loop exists and marking the loop set; then, calculating the link cost for each link within the loop set. When link overhead If the link cost exceeds the preset threshold, the link is shut down; then the false alarm suppression mechanism is executed. If no actual loopback occurs in multiple tests, the link is considered a false alarm and is retained. Step S40: Optimize the topology graph using acyclic methods via a concentrator. Link Reservation and Closure Instruction Table A weighted robust multidimensional path selection method is used to optimize the acyclic topology. Each node is configured with a primary path and a backup path, and cross-level and cross-protocol configuration and path parameter distribution are performed, outputting a path configuration table M; Step S50: Optimize the topology graph for acyclic graphs The main path and backup path of each node are monitored in real time and the execution path is dynamically switched.

2. The method for topology self-identification and route optimization of concentrators and collectors as described in 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 line carrier communication subnet; The steps for unified allocation of node identifiers specifically include: the concentrator assigns globally unique identifiers (IDs) to all collector nodes of different protocols and subnets in the multi-layer heterogeneous network, and establishes a node identifier mapping table between globally unique identifiers (IDs) and physical addresses; The specific steps for abstracting the link relationship include: abstracting the physical or logical connections between all collector nodes into edges in the topology graph; The steps of the hybrid active-passive discovery mechanism specifically include: the concentrator sends topology exploration messages to known nodes, collects the neighbor list, and simultaneously listens for existing data frames or heartbeat responses within the network to infer undeclared adjacency relationships, thereby refining the topology graph and finally outputting a preliminary topology graph. And node identifier mapping table.

3. The method for topology self-identification and route optimization of concentrators and collectors as described in claim 1, characterized in that, In step S30, link cost The calculation formula is: ,in, The average one-way latency of the link required to send data from node i to node j; This represents the maximum single-hop latency threshold that the sensing data or control commands can tolerate in the current application network. The average link bandwidth available for sending data from node i to node j; Link health is an indicator used to reflect the overall communication quality of a link. , and These represent the importance factors of latency, bandwidth, and health in link overhead, respectively.

4. The method for topology self-identification and route optimization of concentrators and collectors as described in claim 1, characterized in that, In step S20, based on the preliminary topology map And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. Each edge in the process initiates a state monitoring mechanism, including heartbeat mechanism design, event-driven monitoring, and state matrix update; The steps for outputting the link state matrix include: Step S201: High-speed heartbeat mechanism initialization and scheduling table generation: The concentrator generates a corresponding heartbeat scheduling table for each link based on the preliminary topology map and node identifier mapping table, and sets the heartbeat period and heartbeat timeout threshold for each node; Step S202: Establishment of microsecond-level event-driven monitoring mechanism: The concentrator is configured with 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 disconnection signals. After an event is triggered, the current heartbeat waiting is immediately interrupted, and physical or link disconnection events at 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 actively sends an instant probe message to the corresponding node and simultaneously sends a parallel verification request to neighboring nodes 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 heartbeat statistics, real-time detection and parallel verification results, and outputs the link state matrix.

5. The method for topology self-identification and route optimization of concentrators and collectors as described in claim 1, characterized in that, In step S40, the topology graph is optimized using a concentrator with acyclic properties. Link Reservation and Closure Instruction Table A weighted robust multidimensional path selection method is used to optimize the acyclic topology. The steps involved in configuring primary and backup paths for each node, performing cross-level and cross-protocol configuration and path parameter distribution, and outputting the path configuration table M include: Step S401: Optimize the topology graph based on acyclic graphs Link Reservation and Closure Instruction Table The concentrator generates a set of candidate legal reachable paths for each node, and calculates multi-dimensional performance metrics for each candidate path in the set, including total latency, minimum path bandwidth, number of cross-levels, number of protocol switching, and path redundancy. Step S402: Apply the weighted robust multidimensional path selection method to each candidate path according to the multidimensional performance index to calculate the comprehensive score, select the path with the highest score as the main path, and select the path with the second highest score and the least overlap with the main path in links or nodes as the backup path. Step S403: Perform cross-level and cross-protocol compatibility verification on the selected and backup paths, including: confirming whether LoRa, Ethernet and 4G multi-protocol mixing is supported, and confirming whether the gateway interface configuration and protocol conversion module interface configuration are supported; if any unsupported cases are detected, return to S402 to select a new path; Step S404: Package the detailed links, node order, protocol switching instructions, heartbeat timeout threshold, backup activation conditions, and cross-layer configuration parameters in the primary path and backup path, and output the path configuration table M.

6. The method for topology self-identification and route optimization of concentrators and collectors as described in claim 1, characterized in that, In step S50, the topology graph is optimized for acyclic graphs. The process involves real-time status monitoring and dynamic path switching for each node's primary and backup paths. Specifically, this includes: if the health of any link in the primary path falls below a preset threshold, automatically switching to the backup path and synchronously updating the global routing table; simultaneously, adaptively adjusting path priorities based on the health difference before and after the switch, and recording path switching logs and dynamic link health evolution information; if a node remains offline for a preset monitoring period, marking it as failed and optimizing the topology from a loop-free perspective. Delete it.

7. A topology self-identification and route optimization system for a concentrator and a data collector, applied to the topology self-identification and route optimization method for a concentrator and a data collector as described in any one of claims 1 to 6, characterized in that, The topology self-identification and route optimization system for the concentrator and collector includes: The topology modeling and analysis module is used to perform unified topology modeling and analysis on multi-layer heterogeneous networks in the smart city sensing network through a concentrator. This includes unified node identifier allocation, abstract representation of link relationships, and a hybrid active-passive discovery mechanism; and outputs a preliminary topology map. and node identifier mapping table; High-speed condition monitoring module, used for initial topology map And a node identifier mapping table, through a concentrator, to create a preliminary topology graph. Each edge in the process initiates 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 analyze the preliminary topology map through a concentrator based on the link state matrix. Perform global loop detection and link closure or retention operations, and output a loop-free optimized topology graph. Link Reservation and Closure Instruction Table The steps for global loop detection and link closure or retention include: first, using a depth-first search to determine if a closed loop exists and marking the loop set; then, calculating the link cost for each link within the loop set. When link overhead If the link cost exceeds the preset threshold, the link is shut down; then the false alarm suppression mechanism is executed. If no actual loopback occurs in multiple tests, the link is considered a false alarm and is retained. The multi-dimensional path selection and configuration module is used to optimize the topology graph using acyclic methods via a concentrator. Link Reservation and Closure Instruction Table A weighted robust multidimensional path selection method is used to optimize the acyclic topology. Each node is configured with a primary path and a backup path, and cross-level and cross-protocol configuration and path parameter distribution are performed, outputting a path configuration table M; Dynamic switching monitoring and control module is used to optimize the topology graph for acyclic graphs. The main path and backup path of each node are monitored in real time and the execution path is dynamically switched.

8. A topology self-identification and route optimization device for a concentrator and a data collector, characterized in that, The topology self-identification and routing optimization device for the concentrator and collector includes: a memory, a processor, and a topology self-identification and routing optimization program for the concentrator and collector stored in the memory and executable on the processor. When the topology self-identification and routing optimization program for the concentrator and collector is executed by the processor, it implements a topology self-identification and routing optimization method for the concentrator and collector according to any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes a topology self-identification and routing optimization program for concentrators and collectors. When the topology self-identification and routing optimization program for concentrators and collectors is executed by the processor, it implements a topology self-identification and routing optimization method for concentrators and collectors according to any one of claims 1 to 6.

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