Terahertz communication platform OMC system signaling tracking and analyzing method

Abstract

The invention provides a terahertz communication platform OMC system signaling tracking and analyzing method. The method comprises the following steps: acquiring an original signaling code stream reported by a managed device through an OMC system; performing fingerprint feature extraction on the original signaling code stream to generate a signaling fingerprint; matching the signaling fingerprint with a preset analysis routing table, and selecting a corresponding signaling analysis strategy according to a matching result; calling a signaling tracking dynamic library to analyze the original signaling code stream through a signaling analysis strategy to obtain an analysis result; and displaying and storing the analysis result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device management, in particular to a method for signaling tracking and analysis of an OMC system of a terahertz communication platform. BACKGROUND

[0002] In the field of OMC system operation and maintenance of a terahertz communication platform, the signaling tracking and analysis function plays a crucial role in ensuring the reliability and stability of the communication link. However, existing technical solutions face many challenges in actual application. Traditional signaling tracking systems usually adopt a static analysis mechanism, relying on pre-set and fixed protocol analysis libraries, which is difficult to adapt to the characteristics of diverse device types and frequent protocol version iterations in the terahertz communication scenario. When new devices are connected or protocols are updated, the entire analysis dynamic library often needs to be redeveloped and deployed, resulting in slow system response and low operation and maintenance efficiency. At the same time, the signaling formats of devices from different manufacturers differ significantly, and the code stream structure is complex and variable, making it difficult for a general parser to accurately identify and analyze all signaling types, often resulting in analysis errors or information missing. In addition, real-time processing of massive signaling data poses a severe challenge to system performance, and full analysis of invalid signaling consumes a large amount of computing resources, while the identification efficiency of critical fault signaling is not high. Existing systems lack effective knowledge accumulation and sharing mechanisms, and valuable analysis experience accumulated by operation and maintenance personnel in practice cannot be effectively converted into the optimization capabilities of the system itself, resulting in repeated occurrence of similar problems and difficulty in continuously improving analysis accuracy and efficiency. SUMMARY

[0003] In view of this, the present application proposes a method for signaling tracking and analysis of an OMC system of a terahertz communication platform to solve the problems existing in the prior art.

[0004] To achieve the above-mentioned purpose, the present application proposes a method for signaling tracking and analysis of an OMC system of a terahertz communication platform, comprising: obtaining original signaling code streams reported by managed devices through an OMC system; extracting fingerprint features from the original signaling code streams to generate signaling fingerprints; matching the signaling fingerprints with a pre-set analysis routing table, and selecting a corresponding signaling analysis strategy according to the matching result; analyzing the original signaling code streams through a signaling analysis strategy to obtain analysis results by calling a signaling tracking dynamic library; and displaying and storing the analysis results.

[0005] Optionally, the process of extracting fingerprint features from the original signaling code streams comprises: extracting one or more key bytes from the front end of the original signaling code streams, and generating a feature signature for uniquely representing the signaling type and version according to the key bytes.

[0006] Optionally, the construction process of the parsing routing table comprises: acquiring historical learning records, wherein the historical learning records comprise a dynamic library adopted each time successful signaling parsing is performed, a parsing efficiency index, and a corresponding signaling fingerprint, and a corresponding most efficient parsing path is obtained by analyzing the signaling fingerprint; acquiring a manual strategy, wherein the manual strategy comprises a mapping relationship between a signaling fingerprint and a corresponding parsing dynamic library and processing strategy; constructing a parsing routing table according to the historical learning records and the manual strategy.

[0007] Optionally, the signaling parsing strategy comprises a straight-through strategy, a preferred strategy, and a fast discard strategy, wherein the straight-through strategy is used to directly call a default signaling tracking dynamic library when a signaling fingerprint matches a default parsing dynamic library; the preferred strategy is used to preferentially call a corresponding parsing dynamic library when the signaling fingerprint matches a historical record indicating that there is a more efficient parsing dynamic library; the parsing dynamic library is a signaling tracking dynamic library; and the fast discard strategy is used to skip dynamic library calling and directly generate a structured result marked as invalid signaling when the signaling fingerprint identifies that the original signaling code stream is a signaling type that does not need to be tracked in the current task.

[0008] Optionally, after the parsing result is obtained, the method further comprises: storing the parsing result, the original signaling code stream, and parsing result correction data in association to form a parsing trace snapshot; wherein the parsing result correction data is a corrected parsing result; based on record data that is different in the parsing trace snapshot, performing rule mining to generate a parsing rule patch package; in a subsequent signaling parsing process, loading and executing the parsing rule patch package to correct the parsing result and output a corrected parsing result.

[0009] Optionally, the generation process of the parsing rule patch package comprises: periodically scanning a parsing case library to identify records in which the parsing result and the parsing result correction data are different; wherein the case library stores the parsing trace snapshot; performing byte-level pattern analysis on the original signaling code stream corresponding to the records in which the parsing result and the parsing result correction data are different to identify a stable pattern, wherein the stable pattern is a constant byte sequence with a fixed offset or a stable pattern that meets a specific bit mask; based on the stable pattern, generating one or more conditional judgment rules, wherein a condition part of the conditional judgment rule is based on specific byte content of the original signaling code stream, and an action part is an override or correction of a specific field in the preliminary parsing result; after the newly generated rules are integrated, de-duplicated, and conflict-detected, the rules are packaged to obtain a versioned parsing rule patch package.

[0010] Optionally, after generating the parsing rule patch package, the process also includes: performing federated knowledge synchronization based on the parsing rule patch package; Specifically, the locally generated parsing rule patch package is encrypted and uploaded to the central rule knowledge base; and parsing rule patch packages uploaded by other OMC systems are queried, pulled and verified from the central rule knowledge base, and the parsing rules uploaded by other OMC systems are merged into the local patch set for application.

[0011] Optionally, the signaling tracing dynamic library can be invoked using JNA technology.

[0012] On the other hand, the present invention provides a signaling tracing and parsing system for a terahertz communication platform OMC system, for performing the above-described method.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention effectively solves the aforementioned technical challenges by introducing an intelligent signaling tracing and parsing method. The solution innovatively establishes a streaming fingerprint extraction and intelligent routing mechanism. By performing a lightweight feature scan on the signaling code stream before complete parsing, unique signaling fingerprint features are generated. Based on historical learning and a parsing routing table constructed using manual strategies, intelligent optimization of the parsing path is achieved. The system can automatically select the most efficient parsing dynamic library and even directly filter invalid signaling, significantly reducing computational resource consumption and improving signaling processing efficiency. More importantly, this invention constructs a complete parsing capability evolution system. Through a parsing source tracing snapshot module, it captures the entire chain of data—original signaling, preliminary parsing results, and manual adjudication results—forming a traceable parsing case library. Based on a rule mining engine, it performs in-depth analysis of discrepancies in cases, automatically generating deterministic parsing rule patches. With the help of a federated knowledge synchronization mechanism, it achieves rule sharing and collaborative evolution among multiple systems, enabling the entire signaling parsing system to continuously learn and self-optimize. Attached Figure Description

[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings: Figure 1 This is a flowchart illustrating the collaborative process of the signaling tracing function in an embodiment of the present invention. Figure 2 This is the signaling tracing component in the embodiments of the present invention; Figure 3 This is a schematic diagram of the method flow in an embodiment of the present invention. Detailed Implementation

[0015] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0016] In the Operation & Management Center (OMC) system for airship platforms, the objects managed and communicated by the OMC system fall into several categories: remote platform systems and payload equipment, near-end platform systems and equipment, and remote-to-near-end communication and data links. Remote platform systems and payloads include various communication, navigation, and telemetry payload devices, terahertz communication equipment (such as optical transmitters), remote platform networking equipment, and other supporting equipment. Furthermore, various components of remote platforms (such as airship systems) can also be included in the OMC system, but these platform-type devices often have dedicated management and control systems for unified management (such as airship flight control platforms). Near-end platform systems and equipment include local receiving devices for communication, navigation, and telemetry payload data, local networking equipment, local terahertz communication equipment (such as optical receivers), supporting communication alignment turntable equipment, and other equipment. In addition, the equipment in the remote and near-end communication links and service links is also managed, but its management status and indicator data are reflected through the communication equipment at both ends. In the above context, the managed objects and communication objects can communicate with the OMC system through existing unified communication protocols and methods.

[0017] The airship platform serves as the carrier, carrying remote equipment, remote payloads, remote networking equipment, remote terahertz transceivers, and a system management OMC platform. The relevant near-end equipment is placed on the ground, and its system management OMC platform is also located on the ground. The remote system management OMC platform and the near-end system management OMC platform also interact with each other. The aforementioned remote equipment consists of the airship platform's control and environmental sensing devices, the remote payload consists of the relevant data acquisition devices, and the remote networking equipment ensures data transmission between the remote equipment, remote payload, and system management OMC platform.

[0018] The main business processes of the managed devices in the OMC system of its airship platform are as follows: remote devices and remote payloads aggregate service data through optical switches; optical transmitters transmit service data through remote transmission links; the local system receives service data through optical receivers; the local optical switch distributes service data to the application platform; and the system management OMC platform manages the devices.

[0019] The OMC system of the terahertz communication platform includes basic functions such as topology management, configuration management, performance management, alarm management, security management, log management, and signaling tracing. A subsequent solution is provided for the signaling tracing function.

[0020] This embodiment proposes a signaling tracing and parsing method for the OMC system of a terahertz communication platform, such as... Figure 3 As shown, the description is as follows: The terahertz communication platform OMC system signaling tracing and parsing method provided by this invention constructs an enhanced closed-loop system integrating intelligent preprocessing and continuous learning and evolution. The entire process begins with the task creation and distribution phase. Operation and maintenance personnel create tracing or failure observation tasks through the signaling tracing view of the OMC system, set specific parameters, and then the signaling tracing manager persistently stores the task. The task is then converted into a device-recognizable protocol format through the signaling tracing adapter and distributed to the target device via the device agent.

[0021] Once the target device reports the signaling stream based on the tracking conditions, the system enters the data reporting and intelligent preprocessing stage. The core innovation of this stage lies in the introduction of an intelligent routing mechanism. Before calling the dynamic library for full parsing, the signaling tracing adapter first activates the streaming fingerprint extractor to quickly scan the key bytes at the front end of the stream, generating a lightweight "signaling fingerprint." This fingerprint is then matched against the parsing routing table, which is maintained by historical successful parsing records and manual strategies. This ultimately generates parsing instructions, which may employ a pass-through strategy to call the default dynamic library, a preferred strategy to call a more efficient specific version of the dynamic library, or a fast-discard strategy to directly filter invalid signaling, thus significantly saving system resources.

[0022] During the bitstream parsing and result correction phases, the system demonstrates continuous learning capabilities. The adapter, based on the parsing instructions, uses JNA technology to call the corresponding signaling tracing dynamic library to obtain preliminary parsing results. Simultaneously, the system automatically captures and correlates the original signaling payload, the preliminary parsing results, and the final decision result after manual correction, forming a parsing traceability snapshot with version information and storing it in the case library. The rule mining engine periodically scans the case library, identifies fixed byte patterns by comparing and analyzing records with discrepancies, automatically generates parsing rules in the form of "condition-action," and packages them into versioned parsing rule patch packages.

[0023] To achieve broader capability enhancement, the system also incorporates a knowledge-sharing and collaborative evolution mechanism. Through the federated knowledge synchronization module, newly generated parsing rule patch packages are encrypted and uploaded to the central rule knowledge base. Other OMC instances in the network can subscribe to and pull these patch packages, which, after verification, are applied to their local signaling parsing processes. This allows the parsing experience of a single site to be quickly shared across the entire network, enabling the continuous evolution of collective capabilities.

[0024] Finally, in the results display and persistence phase, the signaling tracing manager receives the final parsing results after rule patching, persists them, and displays them to operations personnel through front-end JS components in the form of tables and interactive signaling sequence diagrams, clearly presenting the flow, timing, and detailed content of the signaling. The entire process, through three core components—intelligent routing to reduce invalid calculations, rule learning to correct parsing errors, and federated sharing to accelerate network-wide evolution—constitutes a self-optimizing and continuously accurate intelligent signaling tracing and parsing system.

[0025] The above content is described in detail: The signaling tracing module is used to monitor and analyze signaling data in the network to help troubleshoot communication problems. Signaling data refers to the control information flow exchanged between devices in the network for establishing, maintaining, controlling, terminating communication connections, and managing the devices themselves. In simple terms, it can be understood as the management dialogue or control commands of communication devices.

[0026] The main functions to be implemented are as follows: Signaling tracing settings: Configure the signaling tracing task, specifying the signaling types and devices to be traced.

[0027] Signaling data parsing: Collects, decodes, and presents signaling trace data, supporting tabular and flowchart formats.

[0028] Failure Watch Settings: Configure failure watch tasks and specify the failure scenarios that need to be monitored.

[0029] Failure Observation Analysis: Collects, decodes, and presents failure observation data, supporting tabular and flowchart formats.

[0030] The signaling tracing collaboration process is as follows: The collaborative flowchart for signal tracing function is as follows: Figure 1 As shown, failure observation is similar and will not be repeated. The signaling tracing function collaboration process is as follows: user initiates signaling tracing task creation → signaling tracing module creates and persists the task → queries task information and issues it → adaptation layer converts instructions and issues them to device agent → device agent reports signaling data → adaptation layer parses the data and constructs a signaling sequence diagram → persists and displays the signaling tracing data.

[0031] The relevant information regarding signaling tracing is as follows: Signal trace view: The signaling tracing view is displayed in a GUI format and is mainly used to display signaling tracing tasks, signaling tracing result parsing, failure observation tasks, and failure observation result parsing.

[0032] Component architecture: The intra-module architecture of the signaling tracing component is as follows: Figure 2As shown, it specifically includes the signaling tracing view GUI sub-component, the signaling tracing manager (including failure observation management), the signaling tracing adapter, the signaling tracing parsing dynamic library, the device type manager, and the signaling tracing log manager.

[0033] Key business logic: 1. Signaling tracing process: The signaling tracing process is triggered by operations and maintenance personnel from the OMC interface. After setting the tracing task conditions, the tracing task is converted into a tracing execution message agreed upon by the device code and sent to the device agent. Upon receiving the tracing instruction message, the device agent distributes it to the corresponding device, triggering the tracing process.

[0034] Based on the tracking conditions, the device reports the signaling tracing stream to the OMC. After receiving the signaling message, the OMC's signaling tracing adapter calls the signaling tracing dynamic library to parse the stream and distributes the parsed stream data to the signaling tracing manager for formatting and display.

[0035] The process described above, specifically the invocation of the signaling tracing library, requires the use of JNA technology. This is because signaling tracing dynamic libraries are typically provided by the device's service modules, implemented in C / C++, and distributed as dynamic link libraries. To invoke these dynamic libraries in Java, they need to be encapsulated using JNA technology.

[0036] In order to better achieve the above, the following process optimization methods are provided: When the system executes the standard signaling tracing process, it simultaneously triggers the parsing and tracing snapshot module. This module immediately captures and correlates three key data points after the signaling tracing adapter completes parsing by calling the signaling tracing dynamic library: the original signaling payload received from the device agent, the preliminary parsing result output by the signaling tracing dynamic library, and the final ruling result generated by maintenance personnel after manual correction on the OMC human-machine interface. These three data points are persistently stored as an indivisible parsing and tracing snapshot in a dedicated parsing case library. Each snapshot includes a timestamp, device type identifier, and dynamic library version number.

[0037] The rule mining engine module periodically scans the parsing case library, searching for records where the preliminary parsing results differ from the final ruling. For each discrepancy record, the engine performs in-depth byte-level pattern analysis on the corresponding original signaling payload. By comparing multiple original signaling payloads pointing to the same final ruling, the engine can identify constant byte sequences or stable patterns conforming to specific positioning masks that appear at fixed offsets. Based on these patterns, the engine generates deterministic parsing rules, which exist in the form of conditional statements. The condition part is based on specific byte content of the original signaling payload, and the action part is to overwrite or modify specific fields in the preliminary parsing results. After integration, deduplication, and conflict detection, the newly generated rules are packaged into a versioned parsing rule patch package, which is an executable file independent of the signaling tracing dynamic library.

[0038] In the subsequent signaling tracing process, after completing the standard call to the signaling tracing dynamic library, the signaling tracing adapter immediately loads and executes the currently effective parsing rule patch package. The rule engine takes the original signaling payload and preliminary parsing results as input and matches the rules in the patch package sequentially. When the condition of a rule is met, the engine executes the corresponding correction action, generating a corrected parsing result; if no rule matches, the corrected parsing result remains consistent with the preliminary parsing result. This enhanced parsing process is completely transparent to the user, and the system ultimately outputs the corrected parsing result to the signaling tracing manager for display.

[0039] To achieve cross-system knowledge sharing, the federated knowledge synchronization module is responsible for distributing and synchronizing parsing rule patches. When an OMC instance generates a new parsing rule patch package, its federated knowledge synchronization module encrypts the patch package and related metadata and uploads it to the central rule knowledge base. Other OMC instances periodically query and pull new patch packages from the rule knowledge base according to a predefined subscription policy. After verifying integrity and security, consumer instances incorporate the new rules into their local patch sets, thereby directly applying this externally sourced knowledge in subsequent signaling tracing, achieving a collective evolution of parsing capabilities.

[0040] Through the above implementation methods, this invention establishes a complete closed loop from problem discovery and knowledge extraction to collaborative evolution while maintaining the existing system architecture. This system can transform the individual experience of operations and maintenance personnel into reusable automated rules, and through a federated sharing mechanism, continuously improve the accuracy and efficiency of signaling parsing across the entire network operations and maintenance system, while ensuring the reliability and traceability of the process.

[0041] Before formally calling the dynamic library for full parsing, a lightweight preprocessing stage is used to identify the "fingerprint" of the signaling code stream. Based on this fingerprint, the most suitable parsing strategy or dynamic library version is intelligently selected, thereby avoiding invalid parsing attempts and directly entering the most efficient parsing path.

[0042] Detailed description is as follows: The entire process begins with the signaling tracing adapter receiving the raw binary stream reported by the device. Before performing full parsing by calling the signaling tracing dynamic library via the JNA interface, as is the conventional procedure, the system inserts an uncommon preprocessing step.

[0043] This step is implemented by a streaming fingerprint extractor. Instead of performing deep decoding of the entire bitstream, this extractor quickly scans the first few key bytes at fixed or variable offsets of the bitstream with very low overhead. Its goal is to extract a "traffic fingerprint" that uniquely identifies the signaling type and version. This fingerprint is not a complex hash value, but a lightweight signature consisting of key bytes, such as {offset_2: 0xA1, offset_4: 0x0F}.

[0044] The fingerprint is then sent to a routing table for matching. This routing table is a high-performance data structure continuously updated in memory, and its entries consist of three parts: fingerprint, recommendation dynamic library, and parsing configuration. Its data comes from two sources: Historical learning: The system records logs for each successful parsing, including the final dynamic library used, CPU time consumed, and the fingerprint of the corresponding bitstream. By analyzing this data, the system can learn that "for a certain type of fingerprint signaling, using a specific version of a dynamic library is the most efficient way to parse it."

[0045] Manual strategy: Operations personnel can manually specify rules, such as "All signaling with fingerprint feature X should be parsed directly using the beta dynamic library optimized for Y model devices".

[0046] Next comes the intelligent scheduling phase. After parsing the routing table and matching it based on fingerprints, a clear "parsing instruction" is output. This instruction no longer simply calls the default dynamic library, but instead includes one of the following three strategies: Straight-through strategy: If the fingerprint explicitly points to the default dynamic library, it will be called directly; this is the normal path.

[0047] Preferred strategy: Fingerprint matching to historical records indicates that another version of the dynamic library (such as a more lightweight dedicated parser) is 30% more efficient at parsing this type of signaling than the default library. The system will prioritize calling this preferred dynamic library.

[0048] Fast discard strategy: If the fingerprint identifies the stream as a useless heartbeat or broadcast signaling, and the user task is not tracking such signaling, the system will no longer call any dynamic libraries for full parsing. Instead, it will directly generate a structured result of "invalid signaling," thereby saving significant computational resources.

[0049] Finally, the signaling tracing adapter, based on the instructions for parsing the routing table, calls the recommended dynamic library via JNA (or chooses not to), and passes the resulting structured data to the signaling tracing manager. The manager organizes the data according to the usual process and displays it through a front-end JS component.

[0050] In addition, the organization and display of signaling code stream interactive data requires a dedicated JS component (the specific component needs to be explored and technically tested during development). It must be able to support the direction and timing of signaling, and also allow clicking on each signaling stream to view the specific code stream data.

[0051] 2. Failure Observation Process: Similar to signaling tracing, it only tracks, analyzes, and displays failed signaling flows; specific details will not be elaborated further.

[0052] Signaling tracing interface The signaling tracing interface includes the CRUD interface for signaling tracing tasks, the interface for issuing tracing tasks, and the interface for reporting tracing data; as well as the CRUD interface for failure observation tasks, the interface for issuing failure observation tasks, and the interface for reporting failure signaling data.

[0053] Signal tracing library design: Signaling tracing task list: Table name: t_omc_sig_tracetask_base Fields include: Task ID, Task Name, Tracking Object Parameters, Task Type, Task Status, Device Number, Creator, Creation Time, Start Time, End Time, Device Type, Remarks, etc.

[0054] The signaling tracing task table, as shown in Table 1, is used to store and manage basic information about signaling tracing tasks, including task parameters, status, execution time range, etc.

[0055] Table 1

[0056] The TRACETARGET field stores tracking conditions, such as signaling type and filter parameters, in JSON format, which facilitates flexible configuration. TASKTYPE distinguishes between signaling tracing and failure observation, facilitating task classification and management; TASKSTATE controls the activation and suspension status of tasks, determining whether to send tracking commands to the device; BEGINTIME / ENDTIME are used to set the time window for task execution, supporting scheduled and periodic tasks; DEVDN + DEVTYPE uniquely identifies the device object being tracked, and supports tracking by device type or specific device.

[0057] Signaling tracing stream record table: Table name: t_omc_sig_tracetask_data Fields include: record ID, task ID, reporting timestamp, split sequence number, signaling data (hexadecimal numeric string format), etc.

[0058] The signaling tracing stream record table, as shown in Table 2, is used to store the raw signaling stream data collected by the signaling tracing task and supports the split storage of large amounts of signaling data.

[0059] Table 2

[0060] Data splitting mechanism: When the length of a single signaling data exceeds 1000 characters, the system will split it into multiple records for storage, and the order will be identified by the SUBNO field; Storage format: The TRACEDATA field stores the original signaling code stream in hexadecimal string format, which facilitates subsequent parsing and processing; Task association: By associating TASKID with the signaling tracing task table, the tracing task to which each signaling data belongs can be traced; Time stamp: RPTTIME records the time when signaling data is reported, and is used for signaling timing analysis and troubleshooting; Index optimization: Create an index on TASKID to improve the efficiency of querying signaling data by task.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A signaling tracing and parsing method for a terahertz communication platform OMC system, characterized in that, include: Obtain the raw signaling code stream reported by the managed device through the OMC system; Fingerprint features are extracted from the original signaling code stream to generate a signaling fingerprint; The signaling fingerprint is matched with a preset parsing routing table, and the corresponding signaling parsing strategy is selected based on the matching result. The signaling parsing strategy calls the signaling tracing dynamic library to parse the original signaling code stream and obtain the parsing results; the parsing results are then displayed and stored.

2. The method according to claim 1, characterized in that, The process of extracting fingerprint features from the original signaling code stream includes: For the front end of the original signaling code stream, one or more keyword sections are used to generate a feature signature that uniquely represents the signaling type and version.

3. The method according to claim 1, characterized in that, The process of constructing the routing table includes: Obtain historical learning records, which include the dynamic library used for each successful signaling parsing, parsing efficiency indicators, and corresponding signaling fingerprints. Analyze the signaling fingerprints to obtain the most efficient parsing path. Obtain the manual strategy, wherein the manual strategy includes the mapping relationship between the signaling fingerprint and the corresponding parsing dynamic library and processing strategy; A parsing routing table is constructed based on historical learning records and manual strategies.

4. The method according to claim 1, characterized in that, The signaling parsing strategy includes: a pass-through strategy, a preferred strategy, and a fast discard strategy. The pass-through strategy directly calls the default signaling tracing dynamic library when the signaling fingerprint matches a default parsing dynamic library. The preferred strategy prioritizes calling the corresponding parsing dynamic library when the signaling fingerprint matches a historical record indicating the existence of a more efficient parsing dynamic library. The parsing dynamic library is the signaling tracing dynamic library. The fast discard strategy skips the dynamic library call and directly generates a structured result marked as invalid signaling when the signaling fingerprint identifies the original signaling code stream as a signaling type that the current task does not need to trace.

5. The method according to claim 1, characterized in that, After obtaining the parsing results, the following is also included: The parsing result, the original signaling code stream, and the parsing result correction data are stored together to form a parsing traceability snapshot; wherein the parsing result correction data is the corrected parsing result. Based on the record data with discrepancies in the parsed source snapshot, rule mining is performed to generate a parsing rule patch package; In the subsequent signaling parsing process, the parsing rule patch package is loaded and executed to correct the parsing results and output the corrected parsing results.

6. The method according to claim 5, characterized in that, The process of generating the parsing rule patch package includes: The parsing case library is periodically scanned to identify records where the parsing results differ from the corrected parsing data; wherein the case library stores parsing source snapshots; Byte-level pattern analysis is performed on the original signaling code streams corresponding to records with differences to identify stable patterns, wherein the stable pattern is a constant byte sequence at a fixed offset or a stable pattern that conforms to a specific positioning mask. Based on the stable mode, one or more condition judgment rules are generated. The condition part of the condition judgment rule is based on the specific byte content of the original signaling code stream, and the action part is to overwrite or modify specific fields in the preliminary parsing result. After integrating, deduplicating, and detecting conflicts in the newly generated rules, a versioned parsing rule patch package is obtained.

7. The method according to claim 5, characterized in that, After generating the parsing rule patch package, the process also includes: performing federated knowledge synchronization based on the parsing rule patch package; Specifically, the locally generated parsing rule patch package is encrypted and uploaded to the central rule knowledge base; and parsing rule patch packages uploaded by other OMC systems are queried, pulled and verified from the central rule knowledge base, and the parsing rules uploaded by other OMC systems are merged into the local patch set for application.

8. The method according to claim 1, characterized in that, The signaling tracing dynamic library is invoked using JNA technology.

9. A terahertz communication platform OMC system signaling tracing and parsing system, characterized in that, Used to perform the method described in any one of claims 1-8.

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