An open optical transmission system full-dimension data acquisition method and system

Abstract

The application discloses an open optical transmission system full-dimension data collection method and system, relates to the optical transmission technical field, and aims to solve the problem that in the open optical transmission system, the data integrity and consistency face severe challenges due to communication link interference and differences between multi-vendor equipment, so that the system performance analysis has an information blind area, and it is difficult to accurately diagnose and locate problems. The method comprises the following steps: constructing a dynamic semantic topology graph, receiving full-dimension data collected from each network device in the open optical transmission system, checking the logical constraints between the associated data based on the dynamic semantic topology graph, marking the full-dimension data that does not satisfy the logical constraints as potential conflict data, performing causal tracing and intelligent elimination on the potential conflict data, determining the potential root cause of the conflict and correcting the conflict data to obtain corrected full-dimension data, and generating a standardized network data view based on the corrected full-dimension data.

Description

Technical Field

[0001] This application relates to the field of optical transmission technology, and in particular to a method and system for full-dimensional data acquisition in an open optical transmission system. Background Technology

[0002] In modern communication networks, open optical transmission systems play a crucial role in carrying massive data streams. These systems are typically deployed over vast geographical areas, connecting data centers, metropolitan area networks (MANs), and access networks, and their stable operation is vital for various digital services. To ensure network stability and high-quality service transmission, operations teams deploy advanced, multi-dimensional data acquisition systems designed to collect various operational information from these heterogeneous devices. However, in actual network operations, especially in certain special environments, traditional data acquisition methods face severe challenges in data integrity and consistency. This results in information blind spots in system performance analysis, making it difficult to accurately diagnose and locate problems. Summary of the Invention

[0003] This application provides a method and system for full-dimensional data acquisition in open optical transmission systems, aiming to solve the problems that in open optical transmission systems, data integrity and consistency face severe challenges due to communication link interference and differences in equipment from multiple vendors. This leads to information blind spots in system performance analysis, making it difficult to accurately diagnose and locate problems, and causing maintenance personnel to be guided in the wrong direction for troubleshooting, resulting in a significant decrease in fault diagnosis efficiency.

[0004] Firstly, to address the aforementioned technical problems, this invention provides a method for acquiring full-dimensional data in an open optical transmission system. This method includes: constructing a dynamic semantic topology graph, which characterizes the physical connections and causal relationships between all network entities in the open optical transmission system. These network entities include optical transponders, optical amplifiers, and optical fiber links; receiving full-dimensional data collected from various network devices in the open optical transmission system, including optical signal transmission quality data, internal hardware operating status data, and network operation event information; the optical signal transmission quality data characterizes the quality status of optical signals during transmission in the optical fiber link; the internal hardware operating status data characterizes the operating status of the core hardware modules of the network devices; and the network operation event information characterizes network operation status such as network topology changes, configuration updates, alarm triggering, and clearing; verifying the logical constraints between related data based on the dynamic semantic topology graph, and marking full-dimensional data that does not meet the logical constraints as potential conflict data; performing causal tracing and intelligent resolution on the potential conflict data to determine the potential root causes of the conflicts and correct the conflict data to obtain corrected full-dimensional data; and generating a standardized network data view based on the corrected full-dimensional data, which is used for upper-layer network monitoring and fault diagnosis applications.

[0005] Secondly, this application provides a full-dimensional data acquisition system for an open optical transmission system. The system includes: a construction unit for constructing a dynamic semantic topology graph, which characterizes the physical connections and causal relationships between all network entities in the open optical transmission system. These network entities include optical transponders, optical amplifiers, and optical fiber links. A receiving unit is used to receive full-dimensional data collected from various network devices in the open optical transmission system. This full-dimensional data includes optical signal transmission quality data, device internal hardware operating status data, and network operation event information. The optical signal transmission quality data characterizes the quality status of the optical signal during transmission in the optical fiber link. The device internal hardware operating status data... The system comprises four parts: a dynamic data unit and a generation unit. The dynamic data unit is used to characterize the operational status of the core hardware modules of network devices; network operation event information is used to characterize network operation status such as network topology changes, configuration updates, alarm triggering and clearing; a marking unit is used to verify the logical constraints between related data based on the dynamic semantic topology graph, marking all-dimensional data that does not meet the logical constraints as potential conflict data; a determination unit is used to perform causal tracing and intelligent resolution of potential conflict data, determining the potential root causes of conflicts and correcting the conflict data to obtain corrected all-dimensional data; and a generation unit is used to generate a standardized network data view based on the corrected all-dimensional data, which is used for upper-layer network monitoring and fault diagnosis applications.

[0006] This application has at least the following beneficial effects: The open optical transmission system full-dimensional data acquisition method disclosed in this application, by constructing a dynamic semantic topology graph, can comprehensively characterize the physical connections and causal relationships between network entities, providing an accurate topological basis for data verification. By receiving multi-dimensional full-dimensional data, including optical signal transmission quality, device hardware operating status, and network operation event information, the comprehensiveness of data acquisition is ensured. Based on this, the method can verify the logical constraints between related data based on the dynamic semantic topology graph, effectively identify and mark potentially conflicting data, solving the problems of data inconsistency and information blind spots in traditional methods. Furthermore, by performing causal tracing and intelligent resolution on potentially conflicting data, the potential root causes of conflicts can be determined and the data corrected, thereby overcoming the problems of misjudgment and low diagnostic efficiency caused by data corruption or processing differences in existing technologies. Finally, based on the corrected full-dimensional data, a standardized network data view is generated, providing high-quality data with unified format and consistent logic for upper-layer network monitoring and fault diagnosis applications, significantly improving the accuracy and efficiency of fault diagnosis, avoiding maintenance personnel being misled by false information, and thus effectively solving the severe challenges of data integrity and consistency raised in the background technology. Attached Figure Description

[0007] Figure 1This is a flowchart illustrating a full-dimensional data acquisition method for an open optical transmission system provided in this application. Detailed Implementation

[0008] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0009] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0010] In modern communication networks, open optical transmission systems serve as critical infrastructure carrying massive data streams, and their stable operation is essential for various digital services. However, in actual operation and maintenance, especially in complex environments composed of equipment from multiple vendors, traditional data acquisition methods face severe challenges in terms of data integrity and consistency. When the communication link connecting to the management interface of remote network devices is subject to intermittent interference, management data packets may suffer minor damage during transmission, causing the central data processing platform to receive data that appears valid but is actually corrupted. Due to differences in data formats and parsing logic among different vendors' equipment, this corrupted data is processed in different ways, ultimately leading to contradictions and inconsistencies between multi-dimensional data collected from different devices or from the same device at different times, severely affecting the accuracy and efficiency of fault diagnosis.

[0011] In view of the above problems, this application provides a method for full-dimensional data acquisition of open optical transmission systems. By introducing a dynamic semantic topology graph and combining full-dimensional data acquisition, verification, causal tracing and intelligent resolution, this application can effectively identify and correct potential conflicting data, and finally generate a standardized and highly reliable network data view, thereby significantly improving the accuracy and efficiency of fault diagnosis in open optical transmission systems and solving the problem of insufficient data integrity and consistency in traditional data acquisition methods.

[0012] The following specific embodiments will provide a detailed introduction and explanation of the full-dimensional data acquisition method for the open optical transmission system provided in this application.

[0013] Reference Figure 1 This application provides a method for full-dimensional data acquisition in an open optical transmission system, which may include the following steps: S1. Construct a dynamic semantic topology graph.

[0014] The dynamic semantic topology graph is used to characterize the physical connection and causal relationship between all network entities in the open optical transmission system. The network entities include optical transceivers, optical amplifiers, and optical fiber links.

[0015] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0016] An "open optical transmission system" refers to an interoperable optical network system composed of optical transmission equipment (such as optical transponders and optical amplifiers) and optical fiber links from different manufacturers.

[0017] "Network entities" refer to the basic components that make up an open optical transmission system, including optical transponders, optical amplifiers, and optical fiber links. Optical transponders are responsible for the electro-optical and photoelectric conversion of optical signals; optical amplifiers are used to amplify optical signals to compensate for transmission losses; and optical fiber links are the physical medium for optical signal transmission.

[0018] A "dynamic semantic topology graph" is a graph structure used to characterize the physical connections and causal relationships between network entities. "Physical connections" refer to the actual physical connections between network entities, such as an optical fiber link connecting an optical transceiver and an optical amplifier; "causal relationships" refer to the impact of a change in the state of one network entity on another, such as a failure of an optical amplifier potentially causing a degrade in the quality of the optical signal received by its downstream optical transceiver.

[0019] "Full-dimensional data" refers to data collected from various network devices in an open optical transmission system, covering multiple aspects, including optical signal transmission quality data, internal hardware operating status data, and network operation event information.

[0020] "Optical signal transmission quality data" is used to characterize the quality status of optical signals during transmission in optical fiber links, such as optical power, optical signal-to-noise ratio, and bit error rate.

[0021] "Internal hardware operating status data" is used to characterize the operating status of the core hardware modules of network devices, such as CPU utilization, memory usage, temperature, fan speed, etc.

[0022] "Network operation event information" is used to characterize network operation status such as network topology changes, configuration updates, alarm triggering and clearing, such as link interruption, device restart, parameter modification, optical power alarm, etc.

[0023] "Potential conflict data" refers to all-dimensional data that, after logical constraint verification, is found to be inconsistent with the preset logical constraints. This data may contain errors, inconsistencies, or anomalies.

[0024] "Causal tracing" refers to identifying the root cause of a conflict by analyzing potential conflict data and its associated network entities and historical data.

[0025] "Intelligent resolution" refers to correcting potentially conflicting data based on the results of causal tracing, combined with preset rules and algorithms, so that it is restored to a state that conforms to logical constraints.

[0026] "Standardized network data view" refers to processed and corrected full-dimensional data presented in a unified format and logical structure, which is convenient for upper-layer network monitoring and fault diagnosis applications to access.

[0027] The core of the full-dimensional data acquisition method for open optical transmission systems proposed in this application lies in ensuring the integrity, consistency, and accuracy of the acquired data through a series of steps.

[0028] First, constructing a dynamic semantic topology graph is the foundation of the entire data acquisition and processing workflow. The dynamic semantic topology graph is used to characterize the physical connections and causal relationships between all network entities in an open optical transmission system. Network entities include optical transponders, optical amplifiers, and fiber optic links. For example, an initial topology can be manually drawn or imported through a network management system, with optical transponders, optical amplifiers, and fiber optic links as nodes, and their physical connections as edges. Simultaneously, based on the physical principles of optical transmission and the characteristics of each network device, the inherent relationships between changes in the state of different network entities can be predefined as causal relationships. For example, when the output optical power of an optical amplifier decreases, the optical power received by its downstream optical transponder may also decrease; this is a causal relationship. As the configuration of the open optical transmission system is updated, such as adding equipment, adjusting links, or modifying parameters, the dynamic semantic topology graph can be adjusted in real time to ensure it remains consistent with the actual operating state of the network.

[0029] S2. Receive full-dimensional data collected from various network devices in the open optical transmission system.

[0030] The comprehensive data includes optical signal transmission quality data, internal hardware operating status data, and network operation event information. Optical signal transmission quality data is used to characterize the quality status of optical signals during transmission in optical fiber links. Internal hardware operating status data is used to characterize the operating status of core hardware modules of network devices. Network operation event information is used to characterize network operation status such as network topology changes, configuration updates, alarm triggering and clearing.

[0031] Receiving multi-dimensional data collected from various network devices in the open optical transmission system serves as the input for data processing. This multi-dimensional data includes optical signal transmission quality data, internal hardware operating status data, and network operation event information. Optical signal transmission quality data characterizes the quality status of optical signals during transmission through the fiber optic link. For example, metrics such as optical power, optical signal-to-noise ratio, and bit error rate can be obtained by periodically polling the optical module interfaces of each network device. Internal hardware operating status data characterizes the operational status of the core hardware modules of the network devices. For example, information such as CPU utilization, memory usage, temperature, and fan speed can be collected in real time using methods such as SNMP (Simple Network Management Protocol) or Netconf (Network Configuration Protocol). Network operation event information characterizes the network operating status, including network topology changes, configuration updates, and alarm triggering and clearing. For example, events such as link interruptions, device restarts, parameter modifications, and optical power alarms can be obtained in real time by subscribing to event notifications or alarm logs of network devices. This multi-dimensional data is aggregated into a central data processing platform, providing raw input for subsequent verification and resolution.

[0032] S3. Verify the logical constraints between related data based on the dynamic semantic topology graph, and mark all-dimensional data that do not meet the logical constraints as potential conflict data.

[0033] Based on a dynamic semantic topology graph, the system verifies the logical constraints between related data and marks full-dimensional data that does not meet these constraints as potentially conflicting data. For example, a series of consistency rules can be preset to characterize the logical constraints that should be satisfied between full-dimensional data corresponding to related network entities. One rule could be, "If the optical transponders and optical amplifiers at both ends of an optical fiber link are working normally, then the bit error rate of the optical fiber link should be lower than a certain threshold." When new full-dimensional data is received, the system locates all historical full-dimensional data that have a physical connection or causal relationship with the current data based on the dynamic semantic topology graph. Then, the currently received full-dimensional data and historical full-dimensional data are substituted into the preset set of consistency rules for verification. If any data is found to not meet these logical constraints, it is marked as potentially conflicting data. For example, if an optical amplifier reports normal output optical power, but its downstream optical transponder reports abnormally low received optical power, then there is a logical conflict between these two data points, and they will be marked as potentially conflicting data.

[0034] S4. Perform causal tracing and intelligent resolution on potential conflict data, identify the potential root causes of the conflict, and correct the conflict data to obtain corrected full-dimensional data.

[0035] Causal tracing and intelligent resolution of potentially conflicting data identify the potential root causes of the conflicts and correct the conflicting data to obtain corrected full-dimensional data. Causal tracing aims to find the root cause of data conflicts. For example, it can obtain the collection time information corresponding to the potentially conflicting data and, based on the dynamic semantic topology graph, trace back all upstream network entities that affected the generation and transmission of the potentially conflicting data, starting from the network entity corresponding to the potentially conflicting data. Combining the chronological order of the collection time information, the potential root cause event of the potentially conflicting data can be determined. For example, if a brief power fluctuation is found in an optical amplifier before the collection time of the potentially conflicting data, this may be the root cause of the abnormal data in downstream devices. Intelligent resolution corrects the conflicting data based on the identified root cause. For example, it can obtain multiple sets of associated full-dimensional data corresponding to the potentially conflicting data, which come from network entities that have physical connections or causal relationships with the potentially conflicting data. Then, it calculates the confidence score for each set of associated full-dimensional data, which characterizes the reliability of the corresponding full-dimensional data in reflecting the actual operating state of the network. For example, the confidence score can be calculated based on management link quality data, data transmission verification results, and historical data deviation information. Management link quality data characterizes the operational status of the management link transmitting associated full-dimensional data; data transmission verification results characterize the integrity verification status of the associated full-dimensional data during transmission; historical data deviation information characterizes the degree of deviation between the associated full-dimensional data and historical data of the same type from the same device. Based on the confidence score, the acceptance priority of each group of associated full-dimensional data is determined. Finally, according to the acceptance priority and preset optical transmission physical laws, potentially conflicting data is corrected to obtain corrected full-dimensional data. For example, if data from a device with better management link quality has a higher confidence score, that data is prioritized for correction.

[0036] S5. Generate a standardized network data view based on the corrected full-dimensional data.

[0037] The standardized network data view is used for upper-layer network monitoring and fault diagnosis applications.

[0038] A standardized network data view is generated based on the corrected full-dimensional data. The corrected full-dimensional data is updated to the central data storage unit of the central data processing platform in the open optical transmission system, while retaining the original data and complete processing records. To distinguish the data source and processing status, a first-type tag is added to the full-dimensional data corrected by the intelligent resolution step according to the acceptance priority and preset optical transmission physical laws; a second-type tag is added to the missing full-dimensional data inferred from related full-dimensional data with physical connections or causal relationships by the intelligent resolution step; and a third-type tag is added to conflicting data marked by the intelligent resolution step that cannot be corrected by preset rules. Based on all data in the updated central data storage unit, a standardized network data view with a unified format and consistent logic is generated. This standardized network data view can be called by upper-layer network monitoring and fault diagnosis applications, providing operation and maintenance personnel with accurate and reliable network operation status information.

[0039] The proposed method for full-dimensional data acquisition in open optical transmission systems, by introducing a dynamic semantic topology graph, achieves comprehensive modeling of the physical connections and causal relationships between network entities, enabling the system to gain a deeper understanding of network behavior. Unlike traditional methods that focus only on a single device or a single data dimension, this application can receive and process full-dimensional data, including optical signal transmission quality data, internal hardware operating status data, and network operation event information, thus providing a more comprehensive view of network operation. Regarding data verification, based on the dynamic semantic topology graph and a pre-set set of consistency rules, this application can effectively identify and mark potentially conflicting data, avoiding misjudgments caused by data inconsistency in traditional methods. More importantly, this application introduces a causal tracing and intelligent resolution mechanism, which can determine the potential root causes of conflicts and correct conflicting data, significantly improving data accuracy and reliability. By calculating the confidence score of the associated full-dimensional data and correcting it according to the acceptance priority, this application can more intelligently handle complex data conflict scenarios. Ultimately, this application generates a standardized network data view, which not only unifies the data format and logic but also clearly displays the data processing status through different types of markers, providing high-quality data support for upper-layer network monitoring and fault diagnosis applications. Compared to the insufficient data integrity and consistency issues in existing technologies, this application effectively solves the data corruption and inconsistency caused by communication link interference, significantly improves the accuracy and efficiency of fault diagnosis, reduces operation and maintenance costs, and avoids resource waste caused by false information.

[0040] In some embodiments described above in this application, a method for full-dimensional data acquisition in open optical transmission systems is proposed, which involves constructing a dynamic semantic topology graph to characterize the physical connections and causal relationships between network entities. However, if the construction process of this dynamic semantic topology graph fails to fully consider the dynamic changes and complex internal relationships of the network, the topology graph may not accurately reflect the actual operating state of the network in real time, thereby affecting the accuracy of subsequent data verification and conflict resolution.

[0041] In this regard, this application further proposes steps for constructing a dynamic semantic topology graph, including: An initial dynamic semantic topology graph is stored using a preset graph storage structure. The nodes in the preset graph storage structure correspond to the network entities, and the edges in the preset graph storage structure correspond to the physical connection relationships and causal influence relationships between the network entities. Based on the physical principles of optical transmission and the characteristics of each network device, association rules corresponding to the causal relationship are predefined. These association rules are used to characterize the intrinsic connections between the state changes of different network entities. The system receives configuration update information from the open optical transmission system, adjusts the nodes and edges of the initial dynamic semantic topology graph according to the configuration update information, so that the dynamic semantic topology graph keeps the actual operating state of the open optical transmission system consistent in real time, and obtains the dynamic semantic topology graph.

[0042] Specifically, a pre-defined graph storage structure can be understood as a structure specifically designed for efficient storage and retrieval of graph data, such as an adjacency matrix, adjacency list, or a more advanced graph database structure. Here, nodes refer to various network entities in an open optical transmission system, including optical transceivers, optical amplifiers, and fiber optic links. Edges represent the physical connections between these network entities, such as the fiber optic connection between an optical transceiver and an optical amplifier, and the potential causal relationships between them, such as the impact of changes in optical amplifier gain on the output optical power of the optical transceiver.

[0043] Causal relationships refer to the inherent logical connections between changes in the states of network entities. To accurately capture these relationships, a series of association rules need to be predefined based on the physical principles of optical transmission, such as optical signal attenuation, dispersion, and nonlinear effects, as well as the device characteristics of each network device, such as the modulation method of optical transponders and the gain characteristics of optical amplifiers. These association rules specifically characterize how a change in the state of one network entity might affect the states of other related network entities. For example, an increase in the attenuation of a fiber optic link might lead to a decrease in the received optical power of its downstream optical transponders.

[0044] In practical applications, open optical transmission systems are dynamically changing systems, and their network topology and configuration parameters are updated according to service requirements or maintenance operations. Therefore, this application adjusts the nodes and edges of the initial dynamic semantic topology graph in real time by receiving configuration update information from the open optical transmission system, such as the addition of new devices, link adjustments, and parameter modifications. This adjustment ensures that the dynamic semantic topology graph remains consistent with the actual operating state of the open optical transmission system in real time, thereby providing an accurate topological foundation for subsequent full-dimensional data collection, verification, and resolution.

[0045] Through the above technical solution, this application can construct a highly accurate and real-time updated dynamic semantic topology graph. This topology graph can not only comprehensively represent the physical connections between network entities, but also deeply depict the causal relationships between them, providing a solid and dynamic semantic foundation for the verification and conflict resolution of full-dimensional data. Compared with constructing only a static topology graph or one that lacks real-time updating capabilities, the solution of this application significantly improves the ability to understand the complex dynamic behavior of open optical transmission systems, thereby effectively improving the accuracy and reliability of data acquisition, verification, and correction, and providing a more accurate and real-time network data view for upper-layer network monitoring and fault diagnosis applications.

[0046] In some preferred embodiments, a specific example is given below. Assume an open optical transmission system comprising an optical transponder A, an optical amplifier B, and an optical fiber link C. When initially constructing the dynamic semantic topology graph, a graph database (e.g., Neo4j) can be used as the default graph storage structure. The optical transponder A, optical amplifier B, and optical fiber link C are modeled as nodes in the graph. The optical transponder A and optical amplifier B are connected via optical fiber link C, which is represented in the graph by an edge between node A and node B indicating a physical connection, with an attribute indicating that it is connected via optical fiber link C.

[0047] Furthermore, based on the physical principles of optical transmission, association rules can be predefined. For example, a rule can be defined as: "If the attenuation value of optical fiber link C increases beyond the threshold X, the output optical power of optical amplifier B may decrease, thereby affecting the received optical power of optical transceiver A." This rule is stored as a causal relationship and associated with the corresponding nodes and edges.

[0048] When the system receives configuration update information, such as when a system administrator adds an optical attenuator to fiber optic link C or adjusts the gain parameter of optical amplifier B, this information is captured by the system. Based on this information, the system adjusts the dynamic semantic topology graph in real time. For example, if an optical attenuator is added, its attenuation attribute on the corresponding edge of fiber optic link C will be updated, or a node representing the optical attenuator will be added to the graph and its related connections will be adjusted. If the gain parameter of optical amplifier B is adjusted, the attributes of node B will be updated, potentially triggering a re-evaluation of the causal rules related to that gain parameter. In this way, the dynamic semantic topology graph can always remain consistent with the actual operating state of the open optical transmission system in real time, ensuring its accuracy in data acquisition and analysis.

[0049] In some embodiments described above in this application, a method for receiving full-dimensional data collected from various network devices in an open optical transmission system is proposed. Specifically, receiving full-dimensional data collected from various network devices in an open optical transmission system may include the following steps: The optical signal transmission quality indicators of each network device are collected according to a preset collection period. The optical signal transmission quality indicators are used to characterize the quality status of the optical signal during transmission in the optical fiber link. The internal operating status data of each network device is collected in real time according to a preset collection frequency. The internal operating status data is used to characterize the operating status of the core hardware modules of the network device. Real-time acquisition of network operation event information of open optical transmission system, the network operation event information is used to characterize network topology changes, device configuration parameter updates, and the triggering and clearing of various alarm events; The optical signal transmission quality indicators, the internal operating status data of the device, and the network operation event information are used as the full-dimensional data.

[0050] Specifically, optical signal transmission quality indicators refer to various parameters used to measure the transmission performance of optical signals in fiber optic links, such as optical power, optical signal-to-noise ratio (OSNR), and bit error rate (BER). The collection of these indicators typically requires a certain periodicity to reflect the long-term trend and periodic fluctuations in optical signal quality. The preset collection period can be configured according to the needs of the network operator and the characteristics of the optical transmission system, for example, collecting data every few minutes or hours to balance data freshness and system overhead.

[0051] Internal operating status data refers to the real-time operating parameters of core hardware modules (such as CPU, memory, power supply, and optical modules) within network devices (e.g., optical repeaters, optical amplifiers). This data is crucial for assessing the health of the equipment and predicting potential failures. Because hardware status can change rapidly, a preset sampling frequency is used for real-time data collection, such as once per second or every few seconds, to ensure timely detection of abnormal states.

[0052] In practical applications, network operation event information refers to various critical events occurring in open optical transmission systems, including but not limited to changes in network topology (such as link interruption, equipment going online or offline), updates to equipment configuration parameters (such as wavelength adjustment, power setting), and the triggering and clearing of various alarm events (such as low optical power alarm, abnormal temperature alarm). These events are usually sudden and therefore need to be collected in real time to ensure that the system can respond immediately and record all critical network status changes.

[0053] Therefore, by integrating optical signal transmission quality indicators, internal equipment operating status data, and network operation event information, the aforementioned multi-dimensional data can be formed. This multi-dimensional data set provides a comprehensive understanding of the operating status of the open optical transmission system, laying the foundation for subsequent data verification, causal tracing, and intelligent resolution.

[0054] The above technical solutions ensure higher integrity and timeliness of data collected from open optical transmission systems. Specifically, by differentiating between optical signal transmission quality indicators, internal device operating status data, and network operation event information, and employing different collection cycles and frequencies based on their characteristics, data loss or lag caused by a single collection strategy can be avoided. This allows subsequent data verification to be based on more comprehensive and accurate data, thereby improving the accuracy of identifying potential conflicting data. Simultaneously, it provides richer and more refined time-series data for causal tracing, helping to more accurately pinpoint the root cause of conflicts. Ultimately, the obtained corrected, full-dimensional data will be more reliable, providing a solid data foundation for upper-layer network monitoring and fault diagnosis applications.

[0055] Specifically, the steps described above, which involve verifying the logical constraints between related data based on a dynamic semantic topology graph and marking all-dimensional data that does not meet the logical constraints as potentially conflicting data, can be further refined.

[0056] The step of verifying the logical constraints between related data based on the dynamic semantic topology graph, and marking all-dimensional data that does not meet the logical constraints as potentially conflicting data, includes: Based on the dynamic semantic topology graph, locate all historical full-dimensional data that have a physical connection or causal relationship with the currently received full-dimensional data; Obtain a preset set of consistency rules, which is used to characterize the logical constraints that should be satisfied between the full-dimensional data corresponding to the associated network entities; The currently received full-dimensional data and the historical full-dimensional data from the same period are substituted into the consistency rule set for verification one by one, and all full-dimensional data that do not meet the logical constraints are marked as potentially conflicting data.

[0057] The locating of all historical concurrent full-dimensional data that has a physical connection or causal relationship with the currently received full-dimensional data refers to identifying other network entities and their corresponding historical data related to the currently received full-dimensional data by utilizing the physical connection and causal relationship between network entities represented by the dynamic semantic topology graph. For example, if the currently received data is the optical signal transmission quality data of a certain optical transponder, the topology graph will locate the optical fiber link, optical amplifier, and other related optical transponders that are physically connected to that transponder within the same time period. This historical concurrent full-dimensional data is the basis for consistency verification.

[0058] Furthermore, acquiring a pre-defined set of consistency rules refers to the system pre-storing a series of rules used to determine data consistency. These consistency rule sets can be understood as a knowledge base built upon the physical principles of optical transmission, equipment operating characteristics, and network configuration logic. Their purpose is to define the logical relationships that should be satisfied between different network entities and between different types of data. For example, the attenuation value of an optical fiber link should have a specific mathematical relationship with the received and transmitted optical power of the optical transceivers at both ends; or, when an optical amplifier fails, the downstream optical signal transmission quality should exhibit corresponding degradation.

[0059] Specifically, the process of substituting the currently received full-dimensional data with the aforementioned historical full-dimensional data into the consistency rule set for verification involves the system combining the currently collected data with relevant data located from historical data and comparing each rule in the consistency rule set with the data. If any set of data fails to meet the preset logical constraints, the relevant full-dimensional data in that set will be marked as potentially conflicting data. For example, if the transmitted optical power data of an optical transceiver does not conform to the consistency rules of the optical power budget with the attenuation data of the downstream optical fiber link and the received optical power data of the receiving optical transceiver, these data will be marked as potentially conflicting data. The purpose of this step is to identify potentially erroneous, abnormal, or inconsistent data before it enters the subsequent processing flow, providing targets for subsequent causal tracing and intelligent resolution.

[0060] Through the above technical solutions, this application enables more refined and accurate conflict identification across all dimensions of open optical transmission system data. By introducing a dynamic semantic topology graph for associated data location, the comprehensiveness of the verification is ensured, avoiding misjudgments due to missing relevant data. Simultaneously, the pre-defined set of consistency rules provides clear physical and logical basis for the data verification process, improving the accuracy and reliability of conflict identification. Therefore, potential problems in the data can be discovered earlier and more accurately, laying a solid foundation for subsequent causal tracing and intelligent resolution, thereby improving the reliability and data quality of the entire data acquisition method.

[0061] In some of the embodiments described above in this application, a step of causal tracing of potentially conflicting data is proposed. To implement this step more specifically, this application further proposes a detailed causal tracing method.

[0062] Specifically, the steps for causal tracing of potential conflict data include: Acquire the collection time information corresponding to the potential conflict data, wherein the collection time information is used to characterize the time point at which the potential conflict data is reported from the network device; Based on the dynamic semantic topology graph, starting from the network entities corresponding to the potential conflict data, we trace back to all upstream network entities that affect the generation and transmission of the potential conflict data. Obtain the operational data of upstream network entities corresponding to the time information collected, and determine the potential root cause events of potential conflicting data by combining the chronological order of the collected time information.

[0063] The collection time information refers to the timestamp when potential conflict data is reported from its source network device to the central data processing platform. This timestamp is crucial for subsequent causal tracing because it provides a time reference for the event, enabling precise location of the point in time when the conflict data was generated.

[0064] Furthermore, the dynamic semantic topology graph not only represents the physical connections between network entities but also includes their causal relationships. During causal tracing, this dynamic semantic topology graph is used to trace back along a predefined causal chain, starting from the network entity that detected the conflicting data, to find all upstream network entities that could have caused the conflict. These upstream network entities may be devices that directly provide the data source, or they may be intermediate devices that affect data transmission or processing, such as optical transceivers, optical amplifiers, or fiber optic links.

[0065] Specifically, the operational data corresponding to the collection time information of upstream network entities refers to the various operational statuses, performance indicators, or event information recorded by these upstream network entities at or before the time when potential conflict data is reported. By collecting this data, comprehensive background information can be provided for analyzing the causes of potential conflicts, such as optical signal transmission quality data, internal hardware operational status data of equipment, and network operational event information.

[0066] Therefore, combining the chronological order of data collection time refers to comparing the timestamps of potential conflict data with the operational data of upstream network entities to determine which upstream events might precede or coincide with the occurrence of conflict data, thereby establishing potential causal relationships. For example, if an upstream device experiences a fault alarm or configuration update before the occurrence of conflict data, this event may be the potential root cause of the conflict data.

[0067] The above technical solution enables precise causal tracing of potentially conflicting data in open optical transmission systems. Compared to traditional methods that only detect data conflicts without pinpointing their root causes, this application provides detailed information on the generation and transmission paths of conflicting data and identifies potential root cause events leading to data inconsistencies. This significantly improves the accuracy and efficiency of data conflict diagnosis, providing a solid foundation for subsequent intelligent resolution and network fault diagnosis, thereby enhancing the reliability and practicality of the entire data acquisition system.

[0068] In some of the embodiments described above in this application, when tracing the causal relationship of potential conflict data to determine its potential root causes, the focus is mainly on analyzing the temporal correlation between changes in the operational data of upstream network entities and the potential conflict data. However, in practical applications, the generation of potential conflict data may not only stem from operational anomalies of the network entities themselves, but may also be due to data distortion or errors caused by anomalies in the management link during data transmission from network devices to the central data processing platform. Failure to fully consider the operational status of the management link may result in an incomplete or inaccurate judgment of potential root causes.

[0069] In this regard, this application further proposes the following steps for determining the potential root cause events of the potential conflict data by combining the aforementioned chronological order of the data collection time information: Obtain the operational data of the upstream network entity corresponding to the time information collected; Based on the chronological order of the collected time information, determine the temporal correlation between the changes in the operational data of each upstream network entity and the potential conflict data; The management link corresponding to the potential conflict data acquires the quality data corresponding to the collection time information in the collection time information. The management link is used to realize full-dimensional data transmission between network devices and the central data processing platform. Based on the quality data of the management link, determine whether there is any abnormality in the operating status of the management link corresponding to the collection time information; If the management link is in an abnormal operating state, and the abnormal operating state of the management link is temporally correlated with the potential conflict data, then the abnormal operating state of the management link is identified as a potential root cause event of the potential conflict data.

[0070] Specifically, the management link refers to the communication path that enables full-dimensional data transmission between network devices and the central data processing platform. The operational status of this management link directly affects the completeness and accuracy of the collected full-dimensional data. The quality data of the management link can be understood as various indicators characterizing the link's performance and health status, such as packet loss rate, latency, bandwidth utilization, and bit error rate. By monitoring and analyzing this quality data in real time, it's possible to determine whether there are any anomalies in the management link's operational status at a specific data collection time. For example, a sudden increase in the management link's packet loss rate or a significant increase in latency indicates an anomaly in its operational status. Furthermore, determining whether there is a temporal correlation between the abnormal operational status of the management link and potential conflict data involves analyzing whether the time point of the management link's anomaly coincides with or has a close time window with the time point when the potential conflict data is collected or reported. If the two are highly correlated in time, it indicates that the anomaly of the management link is highly likely to be the direct cause of the potential conflict data.

[0071] Through the above technical solution, this application can more comprehensively and accurately identify the potential root causes of conflicting data. Especially when there are anomalies in the data transmission link itself, it can promptly identify and locate the abnormal operating state of the management link as the potential root cause, avoiding misjudgments caused by data transmission problems. This not only improves the efficiency and accuracy of fault diagnosis but also helps distinguish between network entity faults and data transmission faults, providing a more reliable basis for subsequent fault resolution and system maintenance, thereby enhancing the overall robustness and reliability of the full-dimensional data acquisition method for open optical transmission systems.

[0072] In some preferred embodiments, it is assumed that the optical signal transmission quality data (e.g., OSNR value) reported by an optical transponder in an open optical transmission system significantly conflicts with historical data or data from adjacent devices, and is marked as potentially conflicting data. First, the system traces upstream network entities affecting the OSNR value of the optical transponder based on a dynamic semantic topology map, such as upstream optical amplifiers and fiber optic links, and obtains their operational data corresponding to the time point of potential conflict data acquisition. Simultaneously, this application obtains the quality data of the management link between the optical transponder and the central data processing platform within the same time period, including the link's packet loss rate and latency. If analysis reveals that the operational data of the upstream optical amplifier and fiber optic link are normal, but the management link exhibits a persistently high packet loss rate during the potential conflict data reporting period, and this high packet loss rate has a strong temporal correlation with the OSNR data anomaly, then the system will no longer merely suspect a problem with the optical transponder or upstream optical path, but will instead identify the abnormal operating state of the management link as the potential root cause of the potential conflict data. Therefore, subsequent intelligent resolution and fault diagnosis will prioritize investigating and repairing the management link, rather than incorrectly diagnosing optical transponder or optical path faults.

[0073] Traditional intelligent conflict resolution methods often struggle to accurately determine the true operational status of a network when dealing with complex and variable multi-dimensional data in open optical transmission systems, especially when faced with multiple conflicting data sources or questionable data reliability. Simply identifying and correcting conflicts may result in inaccurate corrections, particularly in complex open optical transmission systems with diverse and unreliable data sources. Without addressing these issues, the corrected multi-dimensional data may fail to accurately reflect the network's operational status, thus affecting the accuracy of upper-layer network monitoring and fault diagnosis applications. To address this, this application proposes a more refined intelligent conflict resolution method that accurately corrects potentially conflicting data by assessing data reliability and incorporating the physical laws of optical transmission.

[0074] In this regard, this application further proposes intelligent digestion steps including: Obtain multiple sets of associated full-dimensional data corresponding to the potential conflict data, wherein the associated full-dimensional data comes from network entities that have physical connections or causal relationships with the potential conflict data; Calculate the confidence score for each group of associated full-dimensional data. The confidence score is used to characterize the degree to which the corresponding full-dimensional data reflects the true operating state of the network. Based on the confidence scores, the acceptance priority of each group of related full-dimensional data is determined; Based on the acceptance priority and the preset physical laws of optical transmission, the potential conflict data is corrected to obtain corrected full-dimensional data.

[0075] Specifically, acquiring multiple sets of associated full-dimensional data corresponding to potential conflict data means that when a certain full-dimensional data is marked as potential conflict data, the system, based on a dynamic semantic topology graph, identifies and collects full-dimensional data reported by all network entities that are physically connected to or have a causal relationship with the potential conflict data. This associated full-dimensional data may include historical data from the same device at different times, or data from adjacent devices or other devices affected by the same event. Its purpose is to provide more comprehensive reference information for correcting conflict data.

[0076] Calculating the confidence score for each set of related full-dimensional data can be understood as quantifying the reliability of each set of collected related full-dimensional data in reflecting the actual operating status of the network by evaluating factors such as its source, transmission process, and deviation from historical data. The confidence score is a numerical value, such as a floating-point number between 0 and 1; a higher value indicates more reliable data. Its purpose is to provide a quantitative basis for subsequent data acceptance.

[0077] In practical applications, determining the acceptance priority of each group of related full-dimensional data based on confidence scores means that after obtaining the confidence scores of all related full-dimensional data, the system will sort these scores according to their ranking to determine which data sources should be prioritized when correcting potentially conflicting data. For example, the data with the highest confidence score will be given the highest acceptance priority, meaning it is considered to be the data closest to the current true state. The purpose is to provide a basis for decision-making in conflict correction and avoid blind correction.

[0078] Furthermore, based on the acceptance priority and preset physical laws of optical transmission, potentially conflicting data is corrected to obtain corrected full-dimensional data. This means that after determining the acceptance priority of each related full-dimensional data, the system combines known physical laws in the field of optical transmission (such as optical power conservation, signal attenuation models, dispersion accumulation, etc.) to perform logical reasoning and numerical adjustments on the potentially conflicting data. For example, if high-priority data shows a certain optical power value, and potentially conflicting data contradicts it, but the physical laws support the high-priority data, then the potentially conflicting data will be corrected to a value consistent with the high-priority data and the physical laws. The purpose is to ensure that the corrected data is not only logically consistent but also conforms to the operating laws of the actual physical world.

[0079] Through the above technical solutions, this application can significantly improve the accuracy and reliability of intelligent resolution in the full-dimensional data acquisition method of open optical transmission systems. By introducing confidence scoring and acceptance priority, the system can intelligently identify and prioritize the acceptance of more reliable data sources, effectively avoiding erroneous corrections caused by inconsistent data quality. Furthermore, by combining the correction with preset optical transmission physical laws, it ensures that the corrected data is not only logically consistent but also conforms to the operating laws of the actual physical world, thereby greatly improving the authenticity and usability of the correction results. This refined intelligent resolution mechanism enables upper-layer network monitoring and fault diagnosis applications to make decisions based on more accurate and reliable data, thereby improving the overall operating efficiency and fault handling capabilities of the open optical transmission system.

[0080] In some preferred embodiments, a specific example is given below. Suppose that in an open optical transmission system, there is a significant discrepancy between the optical power data reported by an optical transceiver (potential conflict data) and the received optical power data reported by a neighboring optical amplifier, and causal tracing fails to identify a single root cause. In this case, the system acquires multiple sets of correlated, multi-dimensional data related to the potential conflict data. For example, this may include historical optical power data of the optical transceiver for a period of time before the conflict occurred, temperature sensor data inside the optical transceiver, and attenuation coefficient data of the optical fiber link connecting the optical transceiver.

[0081] The system will calculate the confidence score for each of these correlated full-dimensional data. For example, if the historical optical power data comes from a long-term stable and well-calibrated sensor, its confidence score may be high; if the fiber optic link attenuation coefficient data is obtained recently through precise measurement, its confidence score may also be high; while if the internal temperature sensor data has periodic fluctuations and is not significantly correlated with changes in optical power, its confidence score may be low.

[0082] Based on these confidence scores, the system determines the acceptance priority of each group of related full-dimensional data. It is assumed that historical optical power data and fiber optic link attenuation coefficient data are given a higher acceptance priority.

[0083] Finally, the system corrects the optical power data of potential conflicts based on this high-priority data and preset physical laws of optical transmission (e.g., optical power attenuates during transmission in an optical fiber link, and the attenuation amount is related to the link length and attenuation coefficient). For example, if the high-priority data and physical laws infer that the optical transponder should output a specific optical power value, and the potential conflict data deviates significantly, the system will correct the potential conflict data to the inferred value. In this way, even in the presence of multi-source conflict information, the system can obtain a correction result that is closer to the actual situation based on the reliability of the data and physical constraints.

[0084] In some embodiments of this application, when intelligently resolving potential conflict data, it is necessary to calculate the confidence score for each group of associated full-dimensional data to characterize the degree to which the corresponding full-dimensional data reflects the actual operating state of the network. Specifically, the calculation of the confidence score for each group of associated full-dimensional data includes: Obtain the management link quality data, data transmission verification results, and historical data deviation information corresponding to the associated full-dimensional data; The management link quality data is used to characterize the operational status of the management link that transmits the associated full-dimensional data; The data transmission verification result is used to characterize the integrity verification status of the associated full-dimensional data during the transmission process; The historical data deviation information is used to characterize the degree of deviation between the associated full-dimensional data and historical data of the same type from the same device; Based on the management link quality data, data transmission verification results, and historical data deviation information, the confidence score of the associated full-dimensional data is calculated.

[0085] Specifically, management link quality data refers to the operational status information of the management link used to transmit associated full-dimensional data, such as packet loss rate, latency, and bandwidth utilization. This data reflects whether the data has been interfered with or damaged during transmission, and its purpose is to assess the reliability of the data source. Data transmission verification results refer to the results of integrity verification (such as CRC check, hash check, etc.) of the associated full-dimensional data during transmission, and its purpose is to confirm whether the data has been tampered with or lost during transmission. Historical data deviation information refers to the degree of difference between the currently received associated full-dimensional data and historical data of the same type from the same device. For example, it can be obtained by calculating the deviation of the current data from the historical average or trend line, and its purpose is to identify whether the data has abnormal fluctuations or deviates from the normal range.

[0086] The aforementioned technical solution enables a more refined assessment of the credibility of data from different sources and across various dimensions. This multi-dimensional and comprehensive confidence scoring mechanism avoids misjudgments that may arise from a single indicator, significantly improving the accuracy and reliability of data correction during intelligent resolution. Consequently, it ensures that the final standardized network data view more realistically and accurately reflects the actual operating status of the open optical transmission system, providing high-quality data support for upper-layer network monitoring and fault diagnosis applications.

[0087] In some of the embodiments described above in this application, a method for generating standardized network data views based on corrected full-dimensional data is proposed. However, in practical applications, if the corrected data is simply used to generate the view, it may lead to difficulties in data traceability, or upper-layer applications may be unable to effectively distinguish the original state, corrected state, and whether there are unresolved conflicts in the data, thereby affecting the accuracy and efficiency of network monitoring and fault diagnosis.

[0088] In this regard, this application further proposes the following steps for generating a standardized network data view based on the revised full-dimensional data: The corrected full-dimensional data is updated to the central data storage unit of the central data processing platform in the open optical transmission system, while retaining the original data and complete processing records of the full-dimensional data. The first type of label is added to the full-dimensional data after the intelligent resolution step is corrected according to the acceptance priority and the preset physical law of optical transmission. The second type of label is added to the missing full-dimensional data generated by the intelligent resolution step based on the associated full-dimensional data with physical connection or causal relationship. The third type of label is added to the conflict data marked by the intelligent resolution step that cannot be corrected by the preset rules. Based on all the data in the updated central data storage unit, a standardized network data view with a unified format and consistent logic is generated.

[0089] Specifically, updating the corrected full-dimensional data to the central data storage unit of the central data processing platform in the open optical transmission system refers to storing the data, after causal tracing and intelligent resolution processing, in a centralized and accessible storage area. Simultaneously, the original full-dimensional data and complete processing records are retained to ensure data traceability, facilitating subsequent auditing, review, and deeper fault analysis. The original data provides a snapshot of the true network state before processing, while the complete processing records detail the entire process from data reception, verification, tracing to resolution and correction, including the applied rules, the basis for correction, and a comparison of data before and after correction.

[0090] The first type of label is added to the full-dimensional data after the intelligent resolution step, based on the acceptance priority and preset optical transmission physical laws. This refers to marking data that has been successfully corrected through the intelligent resolution algorithm, combined with the confidence score of multi-source correlation data and the physical laws of optical transmission. This first type of label can be understood as a "corrected and reliable" tag, the purpose of which is to indicate to upper-layer applications that although the data had conflicts, it has been effectively corrected through the system's internal mechanisms and has a high degree of reliability.

[0091] In practical applications, a second type of label is added to missing full-dimensional data inferred from interconnected full-dimensional data with physical connections or causal relationships through the intelligent resolution step. This means that when certain key data is missing, the system can generate this missing data through reasoning or prediction, based on the physical connections and causal relationships represented by the dynamic semantic topology graph and combined with data from other interconnected network entities. This second type of label can be understood as an "inferred generation" tag, its purpose being to distinguish that this data is not directly collected but derived from existing information through an algorithmic model, providing transparency regarding the data source for upper-layer applications.

[0092] In addition, a third type of label is added to conflict data that cannot be corrected by preset rules after being marked by the intelligent resolution process. This refers to certain complex or abnormal conflict data that, even after causal tracing and intelligent resolution attempts, cannot be effectively corrected according to preset rules. The third type of label can be understood as a "uncorrected conflict" or "requiring manual intervention" tag. Its purpose is to remind upper-level application or operation and maintenance personnel that this part of the data may have deep-seated problems and requires further manual analysis or special handling.

[0093] Finally, based on all the data in the updated central data storage unit, a standardized network data view with a uniform format and consistent logic is generated. This ensures that all processed data, regardless of its original, corrected, or inferred state, is presented to upper-layer network monitoring and fault diagnosis applications in a standardized, easy-to-understand, and easy-to-parse format, thereby improving the efficiency and accuracy of data use.

[0094] Through the aforementioned technical solution, this application significantly improves the practicality and reliability of the full-dimensional data acquisition method for open optical transmission systems. Specifically, by retaining original data and complete processing records in the central data storage unit, data traceability and auditability are greatly enhanced, making the data processing process transparent. Furthermore, adding classification tags to data in different processing states (corrected data, inferred data, and uncorrected conflicting data) provides rich data metadata for upper-layer network monitoring and fault diagnosis applications. This allows for the assessment of data reliability, source, and potential risks based on specific data tags, leading to more accurate fault location, performance analysis, and decision support. This meticulous data management and information presentation effectively avoids misjudgments caused by opaque data processing or information loss, significantly improving the efficiency and accuracy of network operation and maintenance.

[0095] In some preferred embodiments, suppose an optical transceiver in an open optical transmission system reports its optical power data at a certain moment, but this data logically conflicts with the data reported by a neighboring optical amplifier and the loss model of the optical fiber link. Through causal tracing, the system discovers that the conflict is caused by a temporary malfunction of a sensor within the optical transceiver, resulting in data anomalies. Through an intelligent resolution step, the system combines synchronous data from other related network entities (such as upstream optical amplifiers and downstream optical fiber links) and corrects the abnormal optical power data of the optical transceiver according to preset optical transmission physics laws. At this point, the corrected optical power data is updated to the central data storage unit and a first-type tag is added, indicating that it has been corrected by the system and is reliable. Simultaneously, a detailed record of the original abnormal optical power data and its correction process (including the cause of the conflict, the basis for the correction, and a comparison before and after the correction) is also retained.

[0096] Furthermore, suppose that at another moment, loss data for a certain fiber optic link fails to be successfully acquired, resulting in data loss. Based on the dynamic semantic topology map, the system identifies the physical connections between this fiber optic link and adjacent optical transponders and amplifiers. By analyzing the optical power data reported by these associated network entities, the system infers the reasonable loss value of the missing fiber optic link. This inferred loss data is then updated to the central data storage unit and labeled with a second type of tag to indicate that it was obtained through inference rather than direct acquisition.

[0097] For example, if a network device reports a series of abnormal hardware operating status data, after causal tracing and intelligent resolution, the system discovers that these data contain deep-seated logical contradictions that cannot be effectively corrected using existing preset rules. In this case, these uncorrectable conflicting data will still be updated to the central data storage unit, but a third type of tag will be added to alert upper-layer applications or operations and maintenance personnel that the data contains unresolved conflicts and may require manual intervention for further analysis.

[0098] Ultimately, the central data processing platform generates a standardized network data view with a unified format and consistent logic, based on the corrected data, inferred data, and uncorrected conflicting data with different tags (Category 1, Category 2, and Category 3) contained in the central data storage unit, as well as the original data and processing records. When upper-layer network monitoring applications access this view, they can intelligently determine the reliability and processing status of the data based on its tags. For example, they can prioritize real-time monitoring of data with Category 1 tags, use data with Category 2 tags as supplementary reference, and trigger alarms or manual review processes for data with Category 3 tags, thereby achieving more refined and intelligent network management.

[0099] In some embodiments, this application proposes a full-dimensional data acquisition system for an open optical transmission system, comprising: a construction unit for constructing a dynamic semantic topology graph, which characterizes the physical connection relationships and causal influence relationships between all network entities in the open optical transmission system, including optical transceivers, optical amplifiers, and optical fiber links; and a receiving unit for receiving full-dimensional data collected from various network devices in the open optical transmission system, including optical signal transmission quality data, device internal hardware operating status data, and network operation event information; the optical signal transmission quality data characterizes the quality status of the optical signal during transmission in the optical fiber link; and the device internal hardware operating status data characterizes the quality status of the optical signal during transmission in the optical fiber link. The system comprises four parts: a dynamic data unit and a generation unit. The dynamic data unit is used to characterize the operational status of the core hardware modules of network devices; network operation event information is used to characterize network operation status such as network topology changes, configuration updates, alarm triggering and clearing; a marking unit is used to verify the logical constraints between related data based on the dynamic semantic topology graph, marking all-dimensional data that does not meet the logical constraints as potential conflict data; a determination unit is used to perform causal tracing and intelligent resolution of potential conflict data, determining the potential root causes of conflicts and correcting the conflict data to obtain corrected all-dimensional data; and a generation unit is used to generate a standardized network data view based on the corrected all-dimensional data, which is used for upper-layer network monitoring and fault diagnosis applications.

[0100] This system aims to address the challenges of data integrity and consistency faced by traditional data acquisition methods in open optical transmission systems. By constructing a dynamic semantic topology graph through a building unit, the system can comprehensively understand the physical connections and causal influences between network entities, providing precise context for subsequent data processing. The receiving unit is responsible for aggregating multi-dimensional data from various network devices, ensuring comprehensive information. The labeling unit performs logical constraint verification on associated data based on the dynamic semantic topology graph, effectively identifying potentially conflicting data. The determination unit further performs causal tracing and intelligent resolution of potentially conflicting data, thereby identifying the root cause of the conflict and correcting the data, significantly improving the accuracy and reliability of the data. Finally, the generation unit transforms the corrected data into a standardized network data view, providing high-quality, high-reliability data support for upper-layer network monitoring and fault diagnosis applications. The collaborative work of these units forms a complete data acquisition and processing closed loop, effectively addressing data anomaly issues in complex network environments.

[0101] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An open optical transmission system full-dimension data acquisition method, characterized in that, include: A dynamic semantic topology graph is constructed to characterize the physical connection relationships and causal relationships between all network entities in the open optical transmission system. The network entities include optical transceivers, optical amplifiers, and optical fiber links. The system receives comprehensive data collected from various network devices in the open optical transmission system. This comprehensive data includes optical signal transmission quality data, internal hardware operating status data, and network operation event information. The optical signal transmission quality data characterizes the quality status of the optical signal during transmission in the optical fiber link. The internal hardware operating status data characterizes the operating status of the core hardware modules of the network devices. The network operation event information characterizes network operation status such as network topology changes, configuration updates, and alarm triggering and clearing. Based on the dynamic semantic topology graph, the logical constraints between related data are verified, and all dimensions of data that do not meet the logical constraints are marked as potentially conflicting data. The potential conflict data is subjected to causal tracing and intelligent resolution to determine the potential root causes of the conflict and correct the conflict data to obtain corrected full-dimensional data. A standardized network data view is generated based on the corrected full-dimensional data. This standardized network data view is used for upper-layer network monitoring and fault diagnosis applications.

2. The open optical transmission system full dimensional data acquisition method of claim 1, wherein, The construction of the dynamic semantic topology graph includes: An initial dynamic semantic topology graph is stored using a preset graph storage structure. The nodes in the preset graph storage structure correspond to the network entities, and the edges in the preset graph storage structure correspond to the physical connection relationships and causal influence relationships between the network entities. Based on the physical principles of optical transmission and the characteristics of each network device, association rules corresponding to the causal relationship are predefined. These association rules are used to characterize the intrinsic connections between the state changes of different network entities. The system receives configuration update information from the open optical transmission system, adjusts the nodes and edges of the initial dynamic semantic topology graph according to the configuration update information, so that the dynamic semantic topology graph keeps the actual operating state of the open optical transmission system consistent in real time, and obtains the dynamic semantic topology graph.

3. The method for full-dimensional data acquisition in an open optical transmission system according to claim 1, characterized in that, The receiving of full-dimensional data collected from various network devices in the open optical transmission system includes: The optical signal transmission quality indicators of each network device are collected according to a preset collection period. The optical signal transmission quality indicators are used to characterize the quality status of the optical signal during transmission in the optical fiber link. The internal operating status data of each network device is collected in real time according to a preset collection frequency. The internal operating status data is used to characterize the operating status of the core hardware modules of the network device. Real-time acquisition of network operation event information of open optical transmission system, the network operation event information is used to characterize network topology changes, device configuration parameter updates, and the triggering and clearing of various alarm events; The optical signal transmission quality indicators, the internal operating status data of the device, and the network operation event information are used as the full-dimensional data.

4. The open optical transmission system full dimensional data acquisition method of claim 1, wherein, The step of verifying the logical constraints between related data based on the dynamic semantic topology graph, and marking all-dimensional data that does not meet the logical constraints as potentially conflicting data, includes: Based on the dynamic semantic topology graph, locate all historical full-dimensional data that have a physical connection or causal relationship with the currently received full-dimensional data; Obtain a preset set of consistency rules, which is used to characterize the logical constraints that should be satisfied between the full-dimensional data corresponding to the associated network entities; The currently received full-dimensional data and the historical full-dimensional data from the same period are substituted into the consistency rule set for verification one by one, and all full-dimensional data that do not meet the logical constraints are marked as potentially conflicting data.

5. The open optical transmission system full dimensional data acquisition method of claim 1, wherein, The causal tracing of the potential conflict data includes: Obtain the collection time information corresponding to the potential conflict data, wherein the collection time information is used to characterize the time point at which the potential conflict data is reported from the network device; Based on the dynamic semantic topology graph, starting from the network entity corresponding to the potential conflict data, all upstream network entities that affect the generation and transmission of the potential conflict data are traced backward. Obtain the operational data of the upstream network entity corresponding to the collection time information, and determine the potential root cause event of the potential conflict data by combining the chronological order of the collection time information.

6. The open optical transmission system full dimensional data acquisition method of claim 5, wherein, The process of determining the potential root cause events of the potential conflicting data by combining the chronological order of the collected time information includes: Obtain the operational data of the upstream network entity corresponding to the time information collected; Based on the chronological order of the collected time information, determine the temporal correlation between the changes in the operational data of each upstream network entity and the potential conflict data; The management link corresponding to the potential conflict data acquires the quality data corresponding to the collection time information in the collection time information. The management link is used to realize full-dimensional data transmission between network devices and the central data processing platform. Based on the quality data of the management link, determine whether there is any abnormality in the operating status of the management link corresponding to the collection time information; If the management link is in an abnormal operating state, and the abnormal operating state of the management link is temporally correlated with the potential conflict data, then the abnormal operating state of the management link is identified as a potential root cause event of the potential conflict data.

7. The open optical transmission system full dimensional data acquisition method of claim 1, wherein, The intelligent digestion includes: Obtain multiple sets of associated full-dimensional data corresponding to the potential conflict data, wherein the associated full-dimensional data comes from network entities that have physical connections or causal relationships with the potential conflict data; Calculate the confidence score for each group of associated full-dimensional data. The confidence score is used to characterize the degree to which the corresponding full-dimensional data reflects the true operating state of the network. Based on the confidence scores, the acceptance priority of each group of related full-dimensional data is determined; Based on the acceptance priority and the preset physical laws of optical transmission, the potential conflict data is corrected to obtain corrected full-dimensional data.

8. The method for full-dimensional data acquisition in an open optical transmission system according to claim 7, characterized in that, The calculation of the confidence score for each group of the associated full-dimensional data includes: Obtain the management link quality data, data transmission verification results, and historical data deviation information corresponding to the associated full-dimensional data; The management link quality data is used to characterize the operational status of the management link that transmits the associated full-dimensional data; The data transmission verification result is used to characterize the integrity verification status of the associated full-dimensional data during the transmission process; The historical data deviation information is used to characterize the degree of deviation between the associated full-dimensional data and historical data of the same type from the same device; Based on the management link quality data, data transmission verification results, and historical data deviation information, the confidence score of the associated full-dimensional data is calculated.

9. The open optical transmission system full dimensional data acquisition method of claim 1, wherein, The process of generating a standardized network data view based on the corrected full-dimensional data includes: The corrected full-dimensional data is updated to the central data storage unit of the central data processing platform in the open optical transmission system, while retaining the original data and complete processing records of the full-dimensional data. The first type of label is added to the full-dimensional data after the intelligent resolution step is corrected according to the acceptance priority and the preset physical law of optical transmission. The second type of label is added to the missing full-dimensional data generated by the intelligent resolution step based on the associated full-dimensional data with physical connection or causal relationship. The third type of label is added to the conflict data marked by the intelligent resolution step that cannot be corrected by the preset rules. Based on all the data in the updated central data storage unit, a standardized network data view with a unified format and consistent logic is generated.

10. An open optical transmission system full dimensional data acquisition system, characterized by, include: A construction unit is used to construct a dynamic semantic topology graph, which is used to characterize the physical connection relationships and causal influence relationships between all network entities in the open optical transmission system. The network entities include optical transceivers, optical amplifiers, and optical fiber links. The receiving unit is used to receive multi-dimensional data collected from various network devices in the open optical transmission system. The multi-dimensional data includes optical signal transmission quality data, internal hardware operating status data of the devices, and network operation event information. The optical signal transmission quality data is used to characterize the quality status of the optical signal during transmission in the optical fiber link. The internal hardware operating status data of the devices is used to characterize the operating status of the core hardware modules of the network devices. The network operation event information is used to characterize the network operation status of network topology changes, configuration updates, alarm triggering and clearing. The marking unit is used to verify the logical constraints between related data based on the dynamic semantic topology graph, and to mark all-dimensional data that does not meet the logical constraints as potential conflict data. The determination unit is used to perform causal tracing and intelligent resolution on the potential conflict data, determine the potential root causes of the conflict and correct the conflict data to obtain corrected full-dimensional data. The generation unit is used to generate a standardized network data view based on the corrected full-dimensional data. The standardized network data view is used for upper-layer network monitoring and fault diagnosis applications.

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