Optical transmission equipment hardware fault linkage method and equipment for communication operation and maintenance fault simulation

Abstract

The invention discloses an optical transmission equipment hardware fault linkage method and equipment for communication operation and maintenance fault simulation, and belongs to the technical field of power system communication. The method comprises the five steps of fault feature standardized modeling, difficulty level intelligent division, training mode linkage configuration, linkage rule dynamic generation and multi-module data collaborative updating. According to the method, data normalization is realized by constructing a standardized fault feature library, fault difficulty grading is realized by a dynamic weight algorithm, stepped training is realized by training mode linkage configuration, adaptive rule generation is realized by a linkage rule engine, scene consistency is realized by multi-module data collaboration, and finally, the fault handling capability of operation and maintenance personnel is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system communication, and more particularly to a communication operation and maintenance fault simulation optical transmission equipment hardware fault linkage method and device. BACKGROUND

[0002] As the core bearing unit of the power communication network, the optical transmission equipment needs to ensure the real-time transmission of key services such as dispatching instructions, relay protection signals and monitoring data, and its operation and maintenance capability directly determines the reliability of power grid dispatching automation and operation monitoring. Fault analysis and disposal of power communication equipment is an important work content of communication operation and maintenance personnel, and is also an important skill requirement for communication professionals in communication professional training specification and skill level evaluation standard. The demand for practical training is large every year. Fault simulation training has become a core support means, but the existing technology has the following problems: 1. The optical transmission equipment hardware fault rule adaptability is insufficient, and the fault correlation rule needs to be manually written. It is difficult to dynamically adapt to the hardware differences of different models and different firmware versions of equipment (such as the cross-board physical slot definition and interface type of Optix OSN 3500 and Optix OSN 9800), the rule writing is time-consuming and has limited coverage, and it is difficult to match the operation and maintenance scene of multiple models of equipment coexisting in the field.

[0003] 2. The fixed hardware fault combination configuration is adopted, and the fault difficulty is not dynamically adjusted in combination with the operation error rate and fault disposal proficiency of different students in the field training. It is difficult to realize hierarchical training (such as basic fault practice for new people and complex chain fault examination for experienced operation and maintenance personnel), and it is difficult to match the actual ability demand of fast positioning and standard disposal of optical transmission equipment hardware faults in the field.

[0004] Therefore, in view of the above status, the present application focuses on optical transmission equipment hardware fault simulation, and focuses on solving four problems: 1. Solve the low efficiency problem of manually configuring fault rules according to the equipment model in the prior art, design fault rules automatically generated based on the hardware model and firmware version of the optical transmission equipment, and realize the adaptation of hardware faults such as optical modules and cross-boards.

[0005] 2. Solve the deviation problem of ignoring protocol characteristics, redundancy configuration and service priority in the prior art, realize the conduction simulation of “fault-protocol response-service impact”, and match the conduction path with the actual scene.

[0006] 3. Solve the problem of only visualizing no data and no operation association in the prior art, realize the linkage of three-dimensional scene device model, real-time data (temperature, optical power), operation behavior (hot plug steps) and safety specifications.

[0007] 4. The application solves the problem of fixed scene and mode linkage in the prior art, realizes dynamic adjustment of fault difficulty based on the error rate of trainees, such as reducing to intermediate scene when the error rate is greater than or equal to 50%, and links the parameter configuration of the "practice-examination" mode, such as providing step prompts in the learning mode and hiding prompts in the examination mode, to shorten the training period. SUMMARY

[0008] Therefore, the application provides a communication operation and maintenance fault simulation optical transmission equipment hardware fault linkage method and device to solve the above problems.

[0009] In order to achieve the above purpose, the application adopts the following technical solutions: A communication operation and maintenance fault simulation optical transmission equipment hardware fault linkage method, comprising the following steps: S100: Constructing an optical transmission equipment hardware fault feature library; S200: Based on the fault feature library, quantifying the fault combination complexity by a dynamic weight algorithm, and automatically dividing the fault difficulty level according to a preset difficulty level threshold; S300: Linking the fault difficulty, prompt mechanism and scoring parameters according to the training mode selected by the trainee, wherein the training mode includes a practice mode and an examination mode; S400: Based on the fault feature library, difficulty level and training mode, dynamically generating a training rule package by a rule engine, wherein the rule package adopts a JSON format and includes fault configuration, training mode, scoring parameters and three-dimensional scene parameters; S500: Establishing a data synchronization mechanism between the fault feature library, rule engine, trainee end and three-dimensional scene, updating the fault correlation state, trainee operation data and scene parameters in real time, and ensuring the consistency of the training scene.

[0010] Further, the fault feature library includes four core fields of device identification, fault phenomenon, alarm level and disposal measures, and is mapped at the field level with the optical transmission equipment configuration parameter table.

[0011] Further, the S100 further includes data preprocessing: The Z-score algorithm is used for abnormal value detection of numerical value type data, and the Z-score confidence interval is set to ±2.58, and when |Z|>2.58, the abnormal value is marked; Linear interpolation method is used to supplement missing data; The data range is checked, and the numerical value exceeding the normal range of the equipment is automatically marked.

[0012] Further, the formula of the dynamic weight algorithm is: Fault combination complexity = ∑(single fault weight x regional distribution coefficient x fault correlation index). wherein the single-fault weight is assigned based on the alarm level; The regional distribution coefficient is a cross-regional fault coefficient or a same-region fault coefficient; The fault correlation index is a correlated fault index or an independent fault index.

[0013] Further, the S200 further comprises a random configuration engine: The Mersenne Twister pseudo-random number generator is used to extract a fault combination from the fault feature library; SHA-256 hash values are calculated for the extracted fault combination, and compared with historical combinations to ensure no duplication; The proportion of optical transmission equipment faults, power supply equipment faults, and auxiliary equipment faults in the fault combination is not less than 1:1:1.

[0014] Further, in the S500: The WebSocket protocol is used to push student operation data to the rule engine in real time; The student operation data includes fault identification time consumption, error handling times, and violation operation times; The rule engine calculates the student score and synchronously updates to the teaching plan list and the teacher end.

[0015] Further, the rule engine uses the production rule representation method IF-THEN to build the inference mechanism.

[0016] Further, it further comprises: The fault features are synchronized to the three-dimensional scene to realize the linkage of the device model state and real-time data, including indicator light color change, temperature value dynamic display, and violation operation warning.

[0017] Further, the three-dimensional scene restores the machine room layout in a 1:1 ratio, and supports positioning the device through screen position coding or two-dimensional coordinates.

[0018] On the other hand, the present application also provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize a communication operation and maintenance fault simulation optical transmission equipment hardware fault linkage method.

[0019] Compared with the prior art, the application provides a communication operation and maintenance fault simulation optical transmission equipment hardware fault linkage method and device, a "optical transmission equipment hardware-fault-operation" knowledge graph, a hardware-driven dynamic rule engine and a three-dimensional hardware virtual-real linkage simulation technology are fused to construct a "optical transmission equipment hardware fault rule generation-hardware fault conduction simulation-hardware operation verification-hardware training adaptation" whole process system, solve the problems of traditional training, such as static hardware fault rules, distorted hardware fault scenes and missing hardware operation verification, and support the digital improvement of the hardware fault disposal ability of the operation and maintenance personnel. The specific beneficial effects are as follows: 1. The prior art needs manual writing of fault rules, and only covers basic fault types. The application can automatically update fault rules according to the specific model and firmware version of the optical transmission equipment through a "device firmware version-fault rule mapping mechanism". The application design improves the adaptation efficiency of fault rules, and covers fault types from basic faults to complex faults, adapting to the deployment requirements of existing transmission network equipment.

[0020] 2. The prior art only deduces fault conduction based on topological relationship, ignoring protocol and redundancy characteristics. The application designs a fusion engine of "knowledge graph + topological weight analysis", wherein the knowledge graph constructs the association relationship of "device entity-fault entity-protocol entity-business entity", restores the fault chain conduction process, and effectively trains the fault analysis ability of the communication operation and maintenance personnel.

[0021] 3. The simulation scene of the prior art only realizes device visualization, without data and operation association. The application designs a "three-dimensional scene-real-time data-violation warning" trinity linkage mechanism. The three-dimensional scene restores the layout of the machine room according to 1:1, the real-time data synchronizes the set device operation parameters, and the violation warning is triggered through "operation interception + voice prompt". The application upgrades the operation verification from "pure visual cognition" to "data-driven + behavior correction" practical training.

[0022] 4. The prior art adopts fixed scene examination, without difficulty adjustment and mode linkage. The application designs a "student end operation data-teacher end rule optimization" double-end cooperation mechanism. The student end collects operation data in real time, such as fault identification time consumption, disposal error rate and violation times. The teacher end dynamically adjusts the fault complexity through an "error rate-difficulty mapping algorithm", and simultaneously links the parameter configuration of "practice-examination" mode, so that the training period is shortened, the training pertinence is improved, and the training is better than the fixed scene examination mode without difference. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description only aim to explain part of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0024] Figure 1 The method flowchart provided by the present application is shown in the figure. Figure 2 The overall architecture of the fault linkage rule generation system provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0026] Referring to Figure 1 The embodiment of the present application discloses a kind of optical transmission equipment hardware fault linkage methods for communication operation and maintenance fault simulation, comprising the following steps: S100: build optical transmission equipment hardware fault feature library; S200: based on the fault feature library, the complexity of fault combination is quantified by dynamic weight algorithm, and the difficulty level is automatically divided according to the preset difficulty level threshold; S300: according to the training mode selected by student, linkage configuration fault difficulty, prompt mechanism and scoring parameter, wherein training mode includes practice mode and examination mode; S400: based on the fault feature library, difficulty level and training mode, real training rule package is dynamically generated by rule engine, the rule package adopts JSON format and includes fault configuration, training mode, scoring parameter and three-dimensional scene parameter; S500: establish the data synchronization mechanism between fault feature library, rule engine, student end and three-dimensional scene, real-time update fault correlation state, student operation data and scene parameter, ensure that real training scene consistency.

[0027] Specifically, the application realizes data normalization by constructing a standardized fault feature library, fault difficulty grading by a dynamic weight algorithm, ladder training by a training mode linkage configuration, self-adaptive rule generation by a linkage rule engine, and scene consistency by multi-module data collaboration, thereby ultimately improving the fault handling capability of operation and maintenance personnel. The method includes five steps of fault feature standardization modeling, difficulty level intelligent division, training mode linkage configuration, linkage rule dynamic generation, and multi-module data collaborative updating (S100-S500).

[0028] In one specific embodiment, the implementation steps of the application are as shown below: 1. Fault feature standardization modeling: This step aims to establish a unified fault data specification to provide a high-quality data source for subsequent rule generation, and is based on device configuration and fault handling standards: (1) Fault feature library design: A four-dimensional core field structure of "device identifier-fault phenomenon-alarm level-disposal measures" is constructed, each field is mapped at the field level with the "optical transmission device hardware fault configuration table", and the specific definitions are as follows: Device identifier: associated with the screen position code and device model of the "device configuration module". The screen position code is standardized according to the "region code-cabinet number-slot number" standard, such as R-03-05-01 representing the 3rd region, the 5th cabinet, and the 1st slot.

[0029] Fault phenomenon: using the format of "serial number + structured description + alarm code", such as "serial number 01 - single board working temperature out of limit (uploading TEMP_OVER alarm: code 0x001, FAN_FAIL alarm: code 0x002)", the description needs to conform to the set fault phenomenon expression specification.

[0030] Alarm level: divided into emergency, important, and secondary, and the level determination is based on the built-in alarm grading standard.

[0031] Disposal measures: associated with the standard operation process of "fault phenomenon", such as "single board working temperature out of limit" corresponding to "check dust screen → troubleshoot fan → adjust computer room air conditioner → replace single board", which conforms to the defined fault disposal specification.

[0032] The optical transmission device configuration parameter table is shown in Table 1.

[0033] Table 1 Optical transmission device configuration parameter table

[0034] Specifically, the optical transmission device hardware fault configuration table is shown in Table 2.

[0035] Table 2 Optical transmission device hardware fault configuration table

[0036] (2) Data preprocessing mechanism: An anomaly detection module based on the Z-score algorithm is embedded. The numerical data in the "fault feature library" are configured, and the specific process is as follows: Data collection: Extract numerical fields from the fault feature library. The field range should meet the set device operating parameter threshold.

[0037] Set the Z-score confidence interval to ±2.58. The formula is Z=(X-μ) / σ (X is the original data, μ is the mean, and σ is the standard deviation). When |Z|>2.58, it is marked as an outlier, such as temperature 50℃, Z=2.8>2.58, and it is determined to be abnormal.

[0038] Missing value supplement: Linear interpolation method is used to supplement missing data. For example, if 3 data are missing in the "single board temperature" field, the interpolation is calculated based on the previous and next 3 valid data to ensure data integrity ≥99%.

[0039] Data verification: Verify whether the numerical data meets the set parameter range. If not, it is automatically marked and prompted "data exceeds the normal range of the device, please check the fault configuration".

[0040] (3) Association mapping rule: Establish a two-way mapping relationship between "fault feature library-device physical configuration-three-dimensional scene" to ensure the correspondence between fault data and physical and virtual devices: Forward mapping: Through screen position coding and two-dimensional coordinates (X axis: column 001-999, Y axis: row 001-999, coordinate rule: standardized definition), associate the fault feature to the physical device location, such as the device associated with the "single board temperature limit" fault at "screen position R-03-05-01 (coordinates 003, 005)".

[0041] Reverse mapping: When the physical device configuration changes, the system automatically reads the configuration file of the new device and updates the "device identification" and "fault phenomenon" fields in the fault feature library.

[0042] Three-dimensional scene mapping: Synchronize the fault feature to the three-dimensional scene, such as the "single board temperature limit" fault triggering the "temperature value dynamic red (from 40℃ to 60℃) + indicator light from green (0, 255, 0) to orange (255, 165, 0)" of the corresponding device in the three-dimensional scene, which meets the set three-dimensional scene state linkage standard.

[0043] 2. Intelligent difficulty level division: This step realizes the automatic configuration of the training difficulty based on the fault feature data, and the parameters and logic match the set task difficulty requirements.

[0044] (1) Hierarchical parameter system: Set two levels of basic thresholds, intermediate (3 faults) and advanced (5 faults), and introduce a dynamic weight algorithm to quantify the complexity of fault combinations. The algorithm formula and parameter definition are as follows: Fault combination complexity = Σ (single fault weight x regional distribution coefficient x fault correlation index) Single fault weight: based on alarm level assignment, urgent = 3, important = 2, minor = 1, assignment based on built-in alarm level corresponding to fault handling difficulty (urgent fault needs to be responded within 15 minutes, handling steps ≥ 5 steps; minor fault needs to be responded within 30 minutes, handling steps ≤ 2 steps).

[0045] Regional distribution coefficient: cross-regional fault coefficient = 1.2 (increasing fault location difficulty), same-region fault coefficient = 1.0, region division based on set room region coordinate rules.

[0046] Fault correlation index: correlation fault index = 1.15 (increasing the difficulty of analyzing chain faults), independent fault index = 1.0, correlation determined based on "fault phenomenon-cause" correlation in "fault feature library".

[0047] (2) Random configuration engine: Develop a pseudo-random number generator based on the Mersenne Twister algorithm, and select faults matching the current room type from the "fault feature library" (selection conditions based on set room type and fault association rules). According to the number of faults required by the difficulty level, generate pseudo-random numbers, and extract faults from the candidate set. Calculate the SHA-256 hash value of the extracted fault combination, and compare it with the hash value of the historical fault combination. If there is no repetition, it is retained, otherwise it is re-extracted. Ensure that the proportion of optical transmission equipment faults, power supply equipment faults, and auxiliary equipment faults in the extracted fault combination is ≥1:1:1, avoiding excessive concentration of single type faults.

[0048] (3) Verification mechanism: Set up multi-dimensional compliance check, automatically filter invalid combinations through Boolean logic operation, and check rules completely match the set difficulty requirements and fault configuration logic.

[0049] Quantity verification: intermediate faults need to meet fault number ≤ 3, advanced faults need to meet fault number ≤ 5, if not, prompt "current selected X faults, need to select X-X".

[0050] Alarm level verification: intermediate faults need to contain at least 1 important alarm (weight ≥ 2) and 1 urgent alarm; advanced faults need to contain at least 1 urgent alarm (weight = 3) and 1 minor alarm (weight = 1), increasing the difficulty of fault identification.

[0051] Region and relevance check: no more than 2 on-screen device failures, and the number of associated failures ≤ 50% of the total number of failures.

[0052] Passing the check: if the check fails, the system automatically retains the failures that meet the rules and replaces only the rule violations, without the need to re-draw all the failures, improving configuration efficiency.

[0053] 3. Dynamic generation of linkage rules:

[0054] This step is based on the designed "practice, assessment" two types of training mode, linking fault difficulty and parameter configuration, to achieve step-by-step training.

[0055] (1) Difficulty linkage configuration:

[0056] According to the target differences of the training mode, adjust the complexity and prompt mechanism of the fault combination, the specific parameters are as follows: Practice mode: the goal is to strengthen the proficiency of fault handling. The difficulty of the fault can be selected as intermediate or advanced; only the alarm level is prompted, and the fault reason and handling steps are not prompted; after the student completes the handling, the system provides real-time feedback on the "correctness of the operation".

[0057] Assessment mode: the goal is to verify the comprehensive handling ability. The difficulty of the fault can be selected as intermediate or advanced; all prompts are hidden, and only the fault phenomenon is displayed; based on the set assessment standard, set the project quality dimension to 85 points and the time efficiency dimension to 15 points, with a weight of 8.5:1.5, and the score data is synchronized to the teacher end.

[0058] (2) Task association configuration:

[0059] Associate the training mode with the set fault tasks to ensure that the tasks and mode targets match. The fault task association "assessment" mode, the task content is "handling complex faults for specific equipment", matching the comprehensive ability verification of the assessment mode. The association logic is in the fault association configuration interface, after selecting the training mode, the system automatically filters the incompatible task types, such as selecting the "assessment" mode, only displaying fault tasks to avoid mode and task mismatch.

[0060] 4. Generation and management of training rule package: This step builds a core rule engine to realize the intelligent association of "fault characteristics-difficulty level-training mode", generates adaptive training rules, and the core logic matches the set lesson plan generation specification.

[0061] (1) Rule engine architecture

[0062] Use the production rule representation method IF-THEN to build the reasoning mechanism, some key rules are as follows: Rule 1: IF (Difficulty Level = Intermediate AND Training Mode = Learning), THEN (Number of Faults = 3 AND Associated Faults = 0 AND Trigger Full Process Prompt AND Task Type = Inspection Task).

[0063] Rule 2: IF (Difficulty Level = Advanced AND Training Mode = Practice), THEN (Number of Faults = 4-5 AND Associated Faults = 1 AND Alarm Level Only AND Task Type = Inspection Task).

[0064] Rule 3: IF (Difficulty Level = Advanced AND Training Mode = Assessment), THEN (Number of Faults = 5 AND Associated Faults = 1-2 AND Hide All Hints AND Task Type = Fault Task AND Scoring Dimensions = Identification Time + Correctness of Handling + Standardized Operation).

[0065] Rule 4: IF (Student error rate ≥ 50% AND current mode = assessment), THEN (automatically reduce difficulty to intermediate AND switch mode to practice AND retain correctly handled faults).

[0066] The rule engine's reasoning is triggered when a student selects a training mode, after a student completes a fault handling task, and when the lesson plan is saved, and when the rule compliance is verified.

[0067] (2) Scene generation logic

[0068] The "fault combination, mode parameters, score configuration, and 3D scene parameters" are packaged into a JSON-formatted training rule package. The fields of the rule package completely match the set teaching plan data specifications. A typical JSON structure is as follows: { "ruleVersion":"V+YYYYMMDD+3-digit serial number", / / e.g., V20240520001, matches the set version rule. "faultConfig":[ {"faultId":"G-001","deviceId":"R-03-05-01","deviceModel":"OOptixOptix OSN 3500","faultPhenomenon":"Board operating temperature exceeded limit","alarmLevel":"Important"}, {"faultId":"G-002","deviceId":"R-03-05-02","deviceModel":"OOptixOptix OSN 3500","faultPhenomenon":"Insufficient optical module receive power","alarmLevel":"Emergency"} ], "trainingMode":"Assessment", "scoreConfig":{"timeWeight":0.3,"correctWeight":0.5,"standardWeight":0.2,"passScore":80}, / / Scoring parameters, matching the assessment mode requirements "3DSceneConfig":{"sceneId":"500kVSubstation_01","deviceCoord":{"X":"003","Y":"005"},"lightStatus":{"normal": "(0,255,0)","urgent":"(255,0,0)"}}, / / 3D scene parameters, conforming to the set scene linkage standards "taskType":"Faulty Task", "taskName":"500kV Substation Equipment Room OOptix OSN 3500 Fault Task" / / Matches the set task naming rules } After the rule package is generated, it is encrypted with MD5 to ensure transmission integrity. The encrypted data is then stored in the training database. When students download the rule package, they need to verify that the MD5 value matches the server value. If they do not match, they need to download it again.

[0069] (3) Version control mechanism

[0070] Each rule generation operation is assigned a unique version number, in the format of "V+YYYYMMDD+3-digit serial number", such as V20240520001.

[0071] Version storage: Saves historical configuration snapshots, including rule package JSON files, generation time, operators, associated lesson plan IDs, matching the set lesson plan ID encoding rules, and supports querying and backtracking by "time, mode, difficulty".

[0072] Version iteration trigger: When the set list of device models is updated or the fault handling specifications are revised, the rule version iteration is automatically triggered and the iteration reason is marked, such as "Device model list update: Added OOptix OSN 9800 M24".

[0073] 5. Multi-module data collaborative update: This step establishes a full-link data synchronization mechanism from "fault feature library - rule engine - student terminal - 3D scene" to ensure the consistency of the training scene and match the core logic with the set fault list, task association, and lesson plan saving requirements.

[0074] (1) State synchronization mechanism: Once a fault is associated with a training task, the "Association Status" field of the corresponding record in the fault list is updated in real time. The specific logic is as follows: Status value definition: The format is "Associated with [Training Mode]_[Task Type] (Difficulty: Level X)", such as "Associated with Assessment_Fault Task (Difficulty: Advanced)". The status value is synchronized with the set fault association operation logic.

[0075] Status Lock: A fault that has already been associated cannot be associated again before the "training task is not completed". The lock duration is the same as the duration of the training task. For example, a 2-hour training task corresponds to a 2-hour lock. The lock will be automatically unlocked after the task is completed.

[0076] Status rollback: If the training task is canceled, the system will automatically roll back the "Associated Status" to "Unassociated" and update the operation buttons in the fault list. The "Edit / Delete" button will change from grayed out to available.

[0077] (2) Progress feedback chain: The student-side training operation data is pushed to the rule engine in real time via the WebSocket protocol, as detailed below: Transmission protocol parameters: WebSocket RFC 6455 standard is adopted, transmission frequency is 1 time / second, data frame size is 1024 bytes by default, and the reconnection mechanism is automatic reconnection after disconnection, 3 retries, with an interval of 5 seconds.

[0078] Push data content includes: trainee ID, task ID, fault identification time, number of errors handled, and number of violations. The data fields match the set training mode scoring dimensions.

[0079] Results Calculation and Synchronization: The rule engine calculates the assessment results based on the preset algorithm: "Final Score = (Recognition Time Compliance Rate × 30%) + (Processing Accuracy Rate × 50%) + (Operation Standardization Rate × 20%)". The results are then synchronized to the "Training Status" field in the set lesson plan list, such as "Completed, Score 85", and pushed to the student performance panel on the instructor's end.

[0080] On the other hand, embodiments of the present invention also disclose an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a hardware fault linkage method for optical transmission equipment to simulate communication operation and maintenance faults.

[0081] On the other hand, see Figure 2 This invention also discloses a fault linkage rule generation system, which is divided into three levels: data, algorithm, and application. This presents the system implementation architecture of the technical solution of this invention, with each level's modules deeply integrated with the technical solution. Data layer: Equipment database, fault database, and real-time log database provide basic data storage such as equipment configuration, fault characteristics, and operation logs for "standardized modeling of fault characteristics". It is the core data support for realizing automatic generation of fault rules and scenario consistency.

[0082] Algorithm layer: Anomaly detection corresponds to Z-score outlier detection in the "data preprocessing mechanism", weight calculation supports dynamic weight algorithm for "intelligent division of difficulty level", rule engine realizes generative rule reasoning for "dynamic generation of linkage rules", and data synchronization ensures full-link state synchronization for "multi-module data collaborative update". It is the core algorithm of intelligent hierarchical classification, rule generation and data collaboration in the technical solution.

[0083] Application Layer: The fault configuration module provides an operation entry point for "standardized modeling of fault features and intelligent classification of difficulty levels". The training mode management module carries the mode configuration function of "dynamic generation of linkage rules". The scoring module realizes the assessment score calculation of "multi-module data collaborative update". The anomaly handling module covers the entire process of anomaly identification and handling. It is the functional implementation of the technical solution on the user side and supports the interaction and management of the entire training process.

[0084] Specifically, the present invention achieves the following beneficial effects through the above technical solution: 1. An innovative three-dimensional mapping model of "transmission hardware model - firmware version - fault characteristics" is constructed. By standardizing hardware identifiers (including slot, model, and redundancy configuration) and associating them with fault phenomena at the field level, and combining the hardware parameter preprocessing logic of Z-score outlier detection, the automatic generation of fault rules for different OTN / SDH transmission hardware (such as Optix OSN 9800 M24 and ZXONE S385) is realized, solving the problems of low efficiency and incomplete coverage of traditional manual rule compilation.

[0085] 2. A fusion deduction scheme of "hardware knowledge graph + topology weight" is proposed. The knowledge graph clarifies the relationship between "optical transmission equipment hardware entity - fault entity - protocol entity". The topology weight introduces hardware distance (same slot / across slots) and hardware redundancy coefficient to calculate the transmission impact, which can reconstruct the hardware chain process and break through the limitations of traditional topology deduction.

[0086] 3. Design a three-in-one linkage mechanism of "3D hardware model - real-time parameters - violation warning", restore the hardware structure of optical transmission equipment at a 1:1 scale, synchronize hardware operating parameters, embed hardware operation specification verification logic, trigger the hardware model red box warning + voice prompt when operation is violated, realize the hardware operation closed-loop verification of "fault recognition - standardized operation - effect feedback", and fill the gap of traditional simulation only visualization without operation verification.

[0087] 4. Construct a dual-end collaborative mechanism of "student-side hardware operation data - instructor-side rule optimization" to collect students' hardware operation indicators in real time. Through the "error rate - fault difficulty" mapping algorithm, dynamically adjust the hardware fault combination. When the error rate is ≥50%, reduce the number of faults and shield cascading faults. When the error rate is ≤20%, add associated hardware faults. At the same time, link the hardware operation prompt strategy of the "practice-assessment" mode to realize personalized adaptation of optical transmission equipment hardware fault training and shorten the training cycle.

[0088] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0089] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulating hardware fault linkage in optical transmission equipment during communication operation and maintenance, characterized in that, Includes the following steps: S100: Construct a hardware fault feature library for optical transmission equipment; S200: Based on the fault feature library, the complexity of fault combination is quantified by a dynamic weighting algorithm, and the fault difficulty level is automatically divided according to a preset difficulty level threshold. S300: Based on the training mode selected by the trainee, the fault difficulty, prompting mechanism and scoring parameters are configured in conjunction. The training mode includes practice mode and assessment mode. S400: Based on the fault feature library, difficulty level and training mode, a training rule package is dynamically generated by the rule engine. The rule package adopts JSON format and includes fault configuration, training mode, scoring parameters and three-dimensional scene parameters. S500: Establishes a data synchronization mechanism between the fault feature library, rule engine, student terminal, and 3D scene to update fault association status, student operation data, and scene parameters in real time, ensuring the consistency of training scenarios.

2. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 1, characterized in that, The fault feature database includes four core fields: device identifier, fault phenomenon, alarm level, and handling measures, and is mapped at the field level to the optical transmission equipment configuration parameter table.

3. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 1, characterized in that, S100 further includes data preprocessing: The Z-score algorithm is used to detect outliers in numerical data. The confidence interval of the Z-score is set to ±2.

58. When |Z|>2.58, it is marked as an outlier. Missing data were supplemented using linear interpolation. The system verifies the data range and automatically marks values ​​that exceed the normal range of the device.

4. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 1, characterized in that, The formula for the dynamic weighting algorithm is: Fault combination complexity = ∑(single fault weight × regional distribution coefficient × fault correlation index); Among them, the single fault weight is assigned based on the alarm level; The regional distribution coefficient can be either a cross-regional fault coefficient or a same-regional fault coefficient. The fault correlation index is either a correlated fault index or an independent fault index.

5. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 1, characterized in that, The S200 also includes a random configuration engine: Fault combinations were extracted from the fault feature library using the Mersenne Twister pseudo-random number generator; Calculate the SHA-256 hash value for the extracted fault combinations and compare it with historical combinations to ensure no duplicates; In the fault combination, the proportion of optical transmission equipment faults, power supply equipment faults, and auxiliary equipment faults shall not be less than 1:1:

1.

6. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 1, characterized in that, In S500: The WebSocket protocol is used to push student operation data to the rule engine in real time. Trainee operation data includes time spent on fault identification, number of errors handled, and number of violations. The rules engine calculates student scores and updates them synchronously to the lesson plan list and the instructor's end.

7. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 1, characterized in that, The rule engine uses the production rule representation IF-THEN to construct the reasoning mechanism.

8. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 1, characterized in that, Also includes: The fault characteristics are synchronized to the 3D scene to realize the linkage between the equipment model status and real-time data, including indicator light color changes, dynamic display of temperature values, and warnings of illegal operations.

9. The method for hardware fault linkage of optical transmission equipment in communication operation and maintenance fault simulation according to claim 8, characterized in that, The three-dimensional scene replicates the computer room layout at a 1:1 scale and supports device positioning via screen location coding or two-dimensional coordinates.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a hardware fault linkage method for optical transmission equipment for communication operation and maintenance fault simulation as described in any one of claims 1 to 9.

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