Link self-healing method and device, equipment and storage medium
By generating a feature parameter mapping library and online status monitoring, the problem of rigid link configuration is solved, and adaptive optimization and proactive recovery under individual module differences and environmental changes are realized, thereby improving link reliability and operation and maintenance automation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-04-03
AI Technical Summary
In existing technologies, individual differences in modules and changes in the operating environment lead to rigid link configurations, a lack of dynamic adaptability, and an inability to automatically adjust parameters, resulting in low system availability and low operational efficiency.
By generating a feature parameter mapping library, the system can query and optimize parameter configurations based on module and device identifiers, continuously monitor the link status during operation, and trigger parameter fine-tuning to achieve adaptive optimization and proactive recovery.
It achieves adaptive optimization and proactive recovery of the link, improves the reliability of high-speed links and the level of operation and maintenance automation, and ensures that the equipment operates at near-optimal performance under different hardware combinations.
Smart Images
Figure CN121786110A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and in particular to a link self-healing method, apparatus, device and storage medium. Background Technology
[0002] Currently, the industry mainly adopts two configuration schemes: one is a configuration method based on static lookup tables, where the device reads the module identification information through buses such as I2C and calls the corresponding fixed parameter combinations according to a preset mapping table; the other is to use general default parameter configuration, which applies one or a limited number of predefined parameters to similar modules, or directly uses the chip's factory default values. While these schemes achieve plug-and-play functionality to some extent, their configuration process relies entirely on pre-set static data and lacks dynamic perception and adaptive capabilities to the actual operating environment.
[0003] The existing technology has the following main shortcomings: First, the configuration strategy is rigid and cannot adapt to individual differences in modules, hardware tolerances of equipment, and changes in the operating environment. As a result, the configuration results are only generally usable rather than optimal, and the signal integrity margin is not fully utilized or can not meet actual needs. Second, there is a lack of online adaptive and active recovery mechanisms. When the link experiences performance degradation or even interruption due to temperature drift, device aging, or external interference, it cannot automatically adjust parameters to restore communication. Manual intervention is required, resulting in low system availability and low operation and maintenance efficiency. Summary of the Invention
[0004] This application provides a link self-healing method, apparatus, device, and storage medium, which can solve the pain points of rigid parameter configuration, lack of dynamic adaptability, and reliance on manual operation and maintenance in high-speed links, and ultimately achieve a comprehensive improvement in network ports in three dimensions: high performance, high reliability, and automated maintenance.
[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a link self-healing method, the method comprising: A module insertion event was detected; the module identification information was read. Based on the current device identifier and the module identifier information, the first optimized parameter configuration is queried from the pre-generated first feature parameter mapping library. The first feature parameter mapping library contains at least one set of optimized parameter configurations corresponding to the combination of device model and module model. The first optimized parameter configuration is sent to the physical layer chip of the port to complete the initial port configuration; Continuously monitor the operating status parameters of the physical layer chip; When the operating status parameters meet the preset self-healing trigger conditions, the parameters are fine-tuned based on the parameter configuration of the current physical layer chip application until the link returns to normal or the maximum number of fine-tuning times is reached.
[0006] In some possible implementations, the first feature parameter mapping library is generated through the following steps: S201. Set up the test environment by inserting the module under test into the port of the test equipment, and connecting the port of the test equipment to the test instrument. S202. Control the physical layer chip of the test equipment to traverse multiple sets of parameter configurations within a predefined parameter range; S203. For each set of parameter configurations, a high-speed training code pattern is sent through the physical layer chip; wherein, the high-speed training code pattern is used to evaluate the signal quality of the link; S204. Receive the signal quality evaluation results corresponding to each set of parameter configurations returned by the test instrument; S205. Compare all signal quality assessment results and select the parameter configuration corresponding to the best signal quality assessment result as the optimized parameter configuration of the combination of the test equipment model and the module under test model. S206. Repeat steps S201 to S205 for combinations of multiple device models and multiple module models to form a first feature parameter mapping library.
[0007] In some possible implementations, the signal quality assessment result is obtained based on digital eye diagram analysis of high-speed training codes, including at least one of eye height and eye width values.
[0008] In some possible implementations, the operating state parameters include at least one of the physical layer chip's signal lock-in state, bit error count, and link protocol state.
[0009] In some possible implementations, the parameter fine-tuning based on the parameter configuration of the current physical layer chip application includes: Centered on the equalizer parameters of the current physical layer chip application, the parameter values are adjusted within a preset range according to a preset step size. After adjustment, the changes in the operating status parameters are monitored, and parameter configurations that improve the operating status parameters are selected.
[0010] In some possible implementations, the method further includes: Once the link is restored to normal through parameter fine-tuning, record the parameter configuration applied when the link was successfully restored. When the same module is inserted into the same device again, the recorded parameter configuration is applied first for the initial port configuration.
[0011] Secondly, this application provides a link self-healing device, the device comprising: The detection module is used to detect module insertion events and read module identification information; The query module is used to query the first optimized parameter configuration from a pre-generated first feature parameter mapping library based on the current device identifier and the module identifier information. The first feature parameter mapping library contains at least one set of optimized parameter configurations corresponding to the combination of device model and module model. The configuration module is used to send the first optimized parameter configuration to the physical layer chip of the port to complete the initial port configuration; The monitoring module is used to continuously monitor the operating status parameters of the physical layer chip; The self-healing module is used to fine-tune the parameters based on the current physical layer chip application parameter configuration when the running status parameters meet the preset self-healing trigger conditions, until the link returns to normal or the maximum number of fine-tuning times is reached.
[0012] Thirdly, this application provides a computing device, including a memory and a processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.
[0013] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.
[0014] Fifthly, this application provides a computer program product comprising one or more computer instructions, wherein when the computer instructions are executed by a computer, the computer performs the method as described in any one of the first aspects.
[0015] As can be seen from the above technical solution, this application has at least the following beneficial effects: In this application, a module insertion event is detected, and the module identification information is read. Based on the current device identification and module identification information, a first optimized parameter configuration is queried from a pre-generated first feature parameter mapping library, and the first optimized parameter configuration is sent to the physical layer chip of the port to complete the initial port configuration. The operating status parameters of the physical layer chip are continuously monitored. When the operating status parameters meet the preset self-healing trigger conditions, the parameters are fine-tuned based on the parameter configuration applied by the current physical layer chip until the link returns to normal or the maximum number of fine-tuning times is reached. In the prior art, parameter configuration relies on static mapping or general default values, which cannot adapt to individual differences of modules and dynamic environmental changes, and lacks online self-healing capabilities, resulting in suboptimal link performance and high operation and maintenance costs. It can be seen that this application achieves adaptive optimization of port parameters and active self-healing of the link by establishing a refined device-module parameter mapping library offline and combining online status monitoring and trigger-based fine-tuning mechanism, which greatly improves the reliability, performance and operation and maintenance automation level of high-speed links.
[0016] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description
[0017] Figure 1 A flowchart of a link self-healing method provided in an embodiment of this application; Figure 2 A flowchart illustrating a method for generating a first feature parameter mapping library provided in this application embodiment; Figure 3 This is a schematic diagram of a link self-healing device provided in an embodiment of this application; Figure 4 This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation
[0018] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.
[0019] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0020] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first: Currently, various pluggable optical modules and copper cable modules are widely used in data centers and network equipment. To ensure signal integrity in high-speed links, port physical layer parameters need to be precisely configured according to the module type. Existing technologies mainly use static parameter mapping based on module identifiers or general default parameters for configuration. However, as transmission rates increase to 400G, 800G, and even higher, factors such as individual module differences, hardware tolerances, and changes in the operating environment make it difficult for static configuration to achieve optimal performance. Furthermore, it cannot automatically adjust parameters to restore the connection when link performance deteriorates or is interrupted, requiring manual intervention, resulting in low system availability and operational efficiency.
[0021] In view of this, embodiments of this application provide a link self-healing method. In this method, after the device detects the insertion of a module, it obtains matching physical layer parameters from a pre-generated optimization parameter mapping library based on the device and module identifiers and automatically configures them. During operation, the device continuously monitors the link status and automatically triggers a parameter fine-tuning process when an anomaly is detected, thereby realizing adaptive optimization and active recovery of the link.
[0022] This method can be applied to various types of processing devices, such as network switches, routers, server network interface cards, data center interconnection devices, or telecommunications transmission equipment, to achieve automated configuration of physical layer parameters for high-speed ports and link self-healing.
[0023] To make the technical solution of this application clearer and easier to understand, a link self-healing method provided by an embodiment of this application will be described below with reference to the accompanying drawings. Figure 1 As shown, this figure is a flowchart of a link self-healing method provided in an embodiment of this application.
[0024] The method includes: S101. The processing device detects a module insertion event and reads the module identification information.
[0025] When a pluggable module (such as a QSFP-DD optical module (Quad Small Form-factor Pluggable - Double Density) or a DAC copper cable module (Direct Attach Copper) is inserted into the physical port of the processing device, the port management unit in the processing device detects the module insertion event. Subsequently, the processing device accesses the module's internal identification information area (such as EEPROM (Electrically Erasable Programmable Read-Only Memory)) via the I²C bus (Inter-Integrated Circuit, a serial communication bus commonly used for low-speed device management) to read the module's identification information. This information typically includes key characteristics such as the module's manufacturer name, model, serial number, and supported speeds. This step enables automatic module identification, providing crucial input for subsequent adaptive parameter configuration.
[0026] S102. The processing device queries the first optimized parameter configuration from the pre-generated first feature parameter mapping library based on the current device identifier and module identifier information.
[0027] The processing device, combining its own device identifier (such as device model and hardware version number) with the identifier information read from the module, initiates a query request to the first feature parameter mapping library pre-generated in the system. This first feature parameter mapping library is a structured parameter database that stores optimized physical layer parameter configurations corresponding to different combinations of device models and module models. The purpose of this step is to quickly complete port initialization using known optimal parameter configurations, avoiding inefficient trial-and-error processes, thereby improving port activation speed and link reliability.
[0028] Specifically, the first feature parameter mapping library contains at least one set of optimized parameter configurations corresponding to combinations of device model and module model; this first feature parameter mapping library is generated by the following method. To make the technical solution of this application clearer and easier to understand, the following describes a method for generating a first feature parameter mapping library provided by an embodiment of this application, in conjunction with the accompanying drawings. For example... Figure 2 As shown, this figure is a flowchart of a method for generating a first feature parameter mapping library according to an embodiment of this application. The method includes: S201. Set up the test environment by inserting the module under test into the port of the test equipment and connecting the port of the test equipment to the test instrument.
[0029] This step begins by setting up a standardized test platform in a laboratory environment. The specific steps include: inserting the module under test (e.g., a 100G-SR4 optical module from a specific manufacturer) into the target port of a specific type of test equipment (e.g., a switch or router); connecting the other end of this port to a test instrument that supports high-speed signal quality analysis (e.g., a switch supporting multi-port and digital eye diagram displays) via fiber optic cable or cable. The purpose of this step is to construct a controllable and reproducible evaluation environment, providing a physical basis for subsequent parameter optimization.
[0030] S202, Control the physical layer chip of the test equipment to traverse multiple sets of parameter configurations within the predefined parameter range.
[0031] Through automated test scripts or control software, commands are sent to the physical layer chip (i.e., SerDes PHY (Serializer / Deserializer Physical Layer), responsible for serializing and deserializing signals and physical layer signal processing) in the test equipment. These commands instruct the chip to iterate through multiple different parameter configuration combinations within a pre-defined parameter range (e.g., pre-emphasis level, equalizer gain, FEC mode (Forward Error Correction)) in a specific order or randomly. The purpose of this step is to systematically explore the impact of different parameters on link performance, providing a data foundation for selecting the optimal configuration.
[0032] S203. For each set of parameter configurations, high-speed training codes are sent through the physical layer chip.
[0033] After each set of parameter configurations takes effect, the physical layer chip controlling the test equipment sends a high-speed training code (such as a PRBS31 pseudo-random binary sequence) to the peer test instrument. This is a special test signal sequence with a known structure, designed to fully stimulate the high-frequency response of the channel in order to comprehensively evaluate the signal transmission quality. The purpose of this step is to provide quantifiable and comparable performance evaluation input, giving subsequent analysis an objective basis.
[0034] S204. Receive the signal quality evaluation results returned by the test instrument for each set of parameter configurations.
[0035] After receiving the high-speed training code, the testing instrument analyzes it and generates a set of quantitative signal quality assessment results, such as the eye height (representing the noise tolerance in the vertical direction of the signal) and eye width (representing the tolerance range of signal timing jitter) of the eye diagram. The purpose of this step is to convert the characteristics of the analog signal into recordable and comparable digital indicators, providing criteria for parameter optimization.
[0036] The signal quality assessment results are obtained based on digital eye diagram analysis of high-speed training codes, including at least one of eye height and eye width values.
[0037] S205. Compare all signal quality assessment results and select the parameter configuration corresponding to the best signal quality assessment result as the optimized parameter configuration for the combination of test equipment model and module under test model.
[0038] The control system compares and analyzes the signal quality evaluation results corresponding to all the parameter configurations it has traversed, and selects the set of parameter configurations that optimizes the eye height and / or eye width (e.g., maximum eye height and maximum eye width). This set of configurations is recognized as the optimized parameter configuration under the combination of the test equipment model and the module under test model, also known as the "golden parameters". The purpose of this step is to determine the most suitable physical layer parameters for the current hardware combination through an automated selection mechanism, thereby optimizing link performance.
[0039] S206. Repeat steps S201 to S205 for combinations of multiple device models and multiple module models to form a first feature parameter mapping library.
[0040] For different models of processing equipment and modules, the above testing and optimization process is repeated. All device-module combinations and their corresponding optimized parameter configurations are structured and stored, ultimately forming a complete first characteristic parameter mapping library. This mapping library can exist in the form of firmware tables, database files, etc., and can be pre-installed before the equipment leaves the factory or imported through later upgrades. The purpose of this step is to build a performance knowledge base covering a wide range of hardware combinations, providing core data support for the adaptive configuration of online equipment. Its benefits include significantly reducing on-site debugging costs, improving the plug-and-play capability of equipment, and ensuring that the link can operate at near-optimal performance under different hardware combinations.
[0041] S103. The processing device sends the first optimized parameter configuration to the physical layer chip of the port to complete the initial port configuration.
[0042] The management unit (such as a CPU or dedicated management chip) in the processing device writes the queried first optimized parameter configuration into the corresponding configuration register of the physical layer chip (SerDes PHY) of the target port through an internal bus (such as MDIO, Management Data Input / Output) or configuration interface. These parameters typically include pre-emphasis parameters (used to compensate for high-frequency signal loss at the transmitting end), equalizer parameters (such as CTLE continuous-time linear equalizer parameters, used to compensate for channel distortion at the receiving end), and FEC mode, etc. After the parameter configuration is completed, the physical layer chip will reinitialize based on the new parameters and attempt to establish a link. The purpose of this step is to apply the offline optimized golden configuration to the actual working environment, ensuring that the port is in an optimal or near-optimal performance state at the initial establishment, thereby achieving plug-and-play and high-performance startup.
[0043] S104. The processing device continuously monitors the operating status parameters of the physical layer chip.
[0044] The operating status parameters include at least one of the following: the signal lock-in status of the physical layer chip, the bit error count, and the link protocol status.
[0045] After the port completes its initial configuration and enters normal operating status, the management unit of the processing device will initiate a continuous monitoring task. This task periodically polls or obtains real-time operating status parameters provided by the physical layer chip via interrupts, mainly including: Signal lock-in state refers to whether the receiver's CDR (Clock and Data Recovery) circuit has successfully extracted a stable clock signal from the received serial bitstream and achieved correct alignment of the data bitstream. This is the foundation of physical layer connections.
[0046] Bit error count, typically provided by the FEC decoder or bit error monitoring circuitry within the physical layer chip, counts the number of bit errors that occur and are corrected or detected within a specific time window. This is the most direct quantitative indicator for measuring the quality of the link signal.
[0047] Link protocol status refers to whether the link layer protocol (such as Ethernet's Auto-Negotiation and Link Training) running above the physical layer has completed negotiation and successfully entered the Link Up state, indicating that the logical channel is ready to transmit data frames.
[0048] The role of continuous monitoring is to provide a real-time, multi-dimensional diagnostic view of the link's health status, serving as the data foundation for subsequent self-healing decisions. Its beneficial effect is the realization of proactive awareness of link performance, rather than passively waiting for failures to occur.
[0049] S105. When the running status parameters meet the preset self-healing trigger conditions, the parameters are fine-tuned based on the parameter configuration of the current physical layer chip application until the link returns to normal or the maximum number of fine-tuning times is reached.
[0050] The monitoring logic continuously evaluates the acquired operational status parameters and compares them with preset self-healing trigger conditions. Once any condition is met, the self-healing process is immediately triggered. Condition A (Signal Available, No Connection): When the signal lock state is locked, but the link protocol state fails to enter Link Up for an extended period, this indicates that the physical layer is synchronized, but protocol negotiation fails due to poor signal quality. This is a common soft fault.
[0051] Condition B (Bit Error Rate Exceeded): When the number of bit errors per unit time exceeds a preset safety threshold. This indicates that although the link is still operational, signal integrity has significantly deteriorated, the bit error rate has increased, and this may affect upper-layer applications in the long term.
[0052] Condition C (Renegotiation after Link Disruption): When the established link protocol status is detected to change from Link Up to Down, renegotiation of the protocol begins. This is usually caused by transient interruptions or severe interference.
[0053] The setting of these trigger conditions enables the system to better identify various abnormal scenarios ranging from performance degradation to complete shutdown, and to initiate repairs in a timely manner to prevent the fault from escalating.
[0054] The parameter fine-tuning based on the current physical layer chip application parameter configuration includes: Centered on the equalizer parameters of the current physical layer chip application, the parameter values are adjusted within a preset range according to a preset step size. After adjustment, the changes in the operating status parameters are monitored, and parameter configurations that improve the operating status parameters are selected.
[0055] Parameter fine-tuning is the core action of the self-healing process. The system uses the parameters of the current application (especially receiver equalizer parameters sensitive to signal quality, such as CTLE gain) as the center point, and systematically tries different parameter values within a preset, small adjustment range (e.g., center value ± N steps) according to a preset step size (i.e., the minimum increment for each adjustment). After each attempt, the system briefly waits and re-evaluates the operating status parameters (e.g., observing whether the bit error count decreases or whether protocol negotiation is successful). Through this closed-loop feedback iterative search, the system can adaptively find a new parameter point that can restore link stability under the current environment. This mechanism enables parameter adaptation under changes in the operating environment (such as temperature drift or device aging), compensating for potential deviations in the offline "golden configuration" or the impact of environmental changes. Its beneficial effect is a significant improvement in the long-term operational reliability and environmental adaptability of the link.
[0056] In some embodiments, during the self-healing process, if the link status parameters still fail to return to normal after reaching a preset maximum number of fine-tuning iterations (e.g., 10 iterations) (e.g., the bit error count still exceeds the threshold, or the protocol link cannot be established), the system determines that the self-healing attempt has failed. At this time, the management unit of the processing device generates a clear self-healing failure alarm event and sends the alarm to the Network Management System (NMS), the operations and maintenance platform, or the administrator terminal via a system management interface (e.g., SNMP Trap, Syslog, REST API, etc.). The alarm information includes at least: the port identifier that triggered self-healing, the module identifier, the reason for triggering self-healing, a detailed record of the fine-tuning attempt, and a snapshot of the link status at the time of final failure. The purpose of this alarm mechanism is to promptly notify operations and maintenance personnel to perform manual intervention when the system's automatic recovery capability reaches its limit, preventing business damage due to prolonged abnormal states. Its beneficial effect lies in achieving effective synergy between automation and manual supervision, ensuring the integrity of closed-loop fault management.
[0057] In some embodiments, after the link is restored to normal through parameter fine-tuning, the parameter configuration applied when the link is successfully restored is recorded; when the same module is inserted into the same device again, the recorded parameter configuration is applied first for the initial port configuration.
[0058] This is an enhanced personalized learning feature. Once the self-healing process successfully restores the connection, the system binds the successfully applied parameter configuration (which may differ from the initial first optimized parameter configuration) to the specific module and device identifiers, saving it as a personalized parameter record. Subsequently, when the system detects the same module inserted into the same device again, it will prioritize checking and applying this personalized record before or after querying the general first feature parameter mapping library. This step aims to accumulate and reuse experience. Its beneficial effects are significant: for modules whose optimal parameters deviate due to individual differences or specific usage environments, the system can directly apply verified and effective personalized configurations after a successful self-healing learning process, thus avoiding repeated triggering of the self-healing process, further accelerating port readiness, and improving the user experience.
[0059] Based on the above, the link self-healing method provided in this application, through a complete technical closed loop of offline optimization database construction, online configuration query, continuous status monitoring, intelligent trigger fine-tuning, and personalized experience learning, not only solves the problem of rigid static parameter configuration but also endows high-speed links with the ability to self-perceive, self-diagnose, and self-repair during operation. This method transforms traditional passive and discrete operation and maintenance actions into proactive, continuous, and intelligent system-native functions, fundamentally improving the reliability, availability, and automation level of high-speed interconnect systems, and providing key technical support for the efficient and stable operation of data centers, high-performance computing, and next-generation communication networks.
[0060] The above text combined Figures 1 to 2 The link self-healing method provided in the embodiments of this application has been described in detail. The apparatus and device provided in the embodiments of this application will be described below with reference to the accompanying drawings.
[0061] This application also provides a link self-healing device, such as... Figure 3 As shown in the figure, this is a schematic diagram of a link self-healing device provided in an embodiment of this application. The device includes: Detection module 301 is used to detect module insertion events and read module identification information; The query module 302 is used to query the first optimized parameter configuration from the pre-generated first feature parameter mapping library based on the current device identifier and the module identifier information. The first feature parameter mapping library contains at least one set of optimized parameter configurations corresponding to the combination of device model and module model. Configuration module 303 is used to send the first optimized parameter configuration to the physical layer chip of the port to complete the initial port configuration; The monitoring module 304 is used to continuously monitor the operating status parameters of the physical layer chip; The self-healing module 305 is used to fine-tune the parameters based on the current physical layer chip application parameter configuration when the running status parameters meet the preset self-healing trigger conditions, until the link returns to normal or the maximum number of fine-tuning times is reached.
[0062] In some possible implementations, the device further includes: Build a module to perform the following steps: S201. Set up the test environment by inserting the module under test into the port of the test equipment, and connecting the port of the test equipment to the test instrument. S202. Control the physical layer chip of the test equipment to traverse multiple sets of parameter configurations within a predefined parameter range; S203. For each set of parameter configurations, a high-speed training code pattern is sent through the physical layer chip; wherein, the high-speed training code pattern is used to evaluate the signal quality of the link; S204. Receive the signal quality evaluation results corresponding to each set of parameter configurations returned by the test instrument; S205. Compare all signal quality assessment results and select the parameter configuration corresponding to the best signal quality assessment result as the optimized parameter configuration of the combination of the test equipment model and the module under test model. S206. Repeat steps S201 to S205 for combinations of multiple device models and multiple module models to form a first feature parameter mapping library.
[0063] In some possible implementations, the signal quality assessment result is obtained based on digital eye diagram analysis of high-speed training codes, including at least one of eye height and eye width values.
[0064] In some possible implementations, the operating state parameters include at least one of the physical layer chip's signal lock-in state, bit error count, and link protocol state.
[0065] In some possible implementations, the self-healing module 305 is specifically used to adjust the parameter values within a preset range according to a preset step size, centered on the receiver equalizer parameters of the current physical layer chip application, monitor the changes in the operating status parameters after adjustment, and select parameter configurations that improve the operating status parameters.
[0066] In some possible implementations, the device further includes: The recording module is used to record the parameter configuration applied when the link is successfully restored after fine-tuning the parameters. When the same module is inserted into the same device again, the recorded parameter configuration is applied first for the initial port configuration.
[0067] The link self-healing device according to the embodiments of this application can correspond to the execution of the method described in the embodiments of this application, and the other operations and / or functions of each module / unit of the link self-healing device are respectively for implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.
[0068] This application also provides a computing device. For example... Figure 4 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 400 includes a bus 401, a processor 402, a communication interface 403, and a memory 404. The processor 402, the memory 404, and the communication interface 403 communicate with each other via the bus 401.
[0069] Bus 401 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0070] Processor 402 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).
[0071] The communication interface 403 is used for communication with external devices. For example, if the computing device is a first switch, the communication interface 403 can be used for communication between the first switch and a first user terminal, or for communication between the first switch and a second switch.
[0072] Memory 404 may include volatile memory, such as random access memory (RAM). Memory 404 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).
[0073] The memory 404 stores executable code, and the processor 402 executes the executable code to perform the aforementioned link self-healing method.
[0074] Specifically, in achieving Figure 3 In the case of the illustrated embodiment, and Figure 3 When the modules or units of the link self-healing device described in the embodiments are implemented in software, the following steps are performed: Figure 3 The software or program code required for the functions of each module / unit can be partially or wholly stored in memory 404. Processor 402 executes the program code corresponding to each unit stored in memory 404 to execute the aforementioned link self-healing method.
[0075] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned link self-healing method.
[0076] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.
[0077] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic) or wireless (e.g., infrared, wireless, microwave, etc.) means.
[0078] When the computer program product is executed by a computer, the computer performs any of the aforementioned link self-healing methods. The computer program product can be a software installation package; when any of the aforementioned link self-healing methods is required, the computer program product can be downloaded and executed on the computer.
[0079] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.
[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.
Claims
1. A link self-healing method, characterized in that, The method includes: A module insertion event was detected; the module identification information was read. Based on the current device identifier and the module identifier information, the first optimized parameter configuration is queried from the pre-generated first feature parameter mapping library. The first feature parameter mapping library contains at least one set of optimized parameter configurations corresponding to the combination of device model and module model. The first optimized parameter configuration is sent to the physical layer chip of the port to complete the initial port configuration; Continuously monitor the operating status parameters of the physical layer chip; When the operating status parameters meet the preset self-healing trigger conditions, the parameters are fine-tuned based on the parameter configuration of the current physical layer chip application until the link returns to normal or the maximum number of fine-tuning times is reached.
2. The method according to claim 1, characterized in that, The first feature parameter mapping library is generated through the following steps: S201. Set up the test environment by inserting the module under test into the port of the test equipment, and connecting the port of the test equipment to the test instrument. S202. Control the physical layer chip of the test equipment to traverse multiple sets of parameter configurations within a predefined parameter range; S203. For each set of parameter configurations, a high-speed training code pattern is sent through the physical layer chip; wherein, the high-speed training code pattern is used to evaluate the signal quality of the link; S204. Receive the signal quality evaluation results corresponding to each set of parameter configurations returned by the test instrument; S205. Compare all signal quality assessment results and select the parameter configuration corresponding to the best signal quality assessment result as the optimized parameter configuration of the combination of the test equipment model and the module under test model. S206. Repeat steps S201 to S205 for combinations of multiple device models and multiple module models to form a first feature parameter mapping library.
3. The method according to claim 2, characterized in that, The signal quality assessment results are obtained based on digital eye diagram analysis of high-speed training codes, including at least one of eye height and eye width values.
4. The method according to claim 1, characterized in that, The operating status parameters include at least one of the following: the signal lock-in status of the physical layer chip, the bit error count, and the link protocol status.
5. The method according to claim 1, characterized in that, The self-healing triggering conditions include at least one of the following: physical layer signal is detected but no link protocol connection is established, the bit error count per unit time exceeds a preset threshold, and protocol negotiation is performed again after the link protocol connection is interrupted.
6. The method according to claim 1, characterized in that, The parameter fine-tuning based on the current physical layer chip application parameter configuration includes: Centered on the equalizer parameters of the current physical layer chip application, the parameter values are adjusted within a preset range according to a preset step size. After adjustment, the changes in the operating status parameters are monitored, and parameter configurations that improve the operating status parameters are selected.
7. The method according to claim 1, characterized in that, The method further includes: Once the link is restored to normal through parameter fine-tuning, record the parameter configuration applied when the link was successfully restored. When the same module is inserted into the same device again, the recorded parameter configuration is applied first for the initial port configuration.
8. A link self-healing device, characterized in that, The device includes: The detection module is used to detect module insertion events and read module identification information; The query module is used to query the first optimized parameter configuration from a pre-generated first feature parameter mapping library based on the current device identifier and the module identifier information. The first feature parameter mapping library contains at least one set of optimized parameter configurations corresponding to the combination of device model and module model. The configuration module is used to send the first optimized parameter configuration to the physical layer chip of the port to complete the initial port configuration; The monitoring module is used to continuously monitor the operating status parameters of the physical layer chip; The self-healing module is used to fine-tune the parameters based on the current physical layer chip application parameter configuration when the running status parameters meet the preset self-healing trigger conditions, until the link returns to normal or the maximum number of fine-tuning times is reached.
9. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 7.