A port communication optical cable routing detection method and system based on digital twinning

Abstract

The application provides a port communication optical cable route detection method and system based on digital twinning. A high-precision digital twin model of the port environment and the distribution of the communication optical cable is constructed to ensure consistency and generate a high-precision digital twin environment. Based on the high-precision digital twin environment, a long short-term memory network is used to predict the performance decline or failure of the communication optical cable and generate a potential failure prediction report. Based on the potential failure prediction report, an ant colony algorithm is applied to search for an optimal inspection path in the high-precision digital twin environment to generate an optimized inspection path and an inspection task list. Based on the optimized inspection path and the inspection task list, actual inspection is guided, and the prediction accuracy is evaluated to improve the operation and maintenance efficiency and system reliability. The technical solution provided by the application can provide a technical means for real-time monitoring, accurate prediction and effective management of the state of the communication optical cable to improve the reliability and operation and maintenance efficiency of the port communication system.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart port technology, and in particular to a port communication optical cable routing detection method and system based on digital twins. Background Art

[0002] With the rapid development of global trade, ports, as crucial nodes in international trade, have a high level of information technology development that is directly related to logistics efficiency and service quality. As critical infrastructure for information transmission, the stability and reliability of port communication optical cables are crucial. However, due to the complex and changing port environment, including factors such as climatic conditions and marine erosion, communication cables are prone to aging and damage, leading to communication interruptions and seriously affecting port operational efficiency.

[0003] The operation and maintenance of existing port communication optical cables primarily relies on regular manual inspections and simple online monitoring systems. While manual inspections can detect some obvious problems, they suffer from long inspection cycles, limited coverage, and low efficiency. While existing online monitoring systems can monitor some key parameters in real time, they lack comprehensive consideration of environmental factors and are unable to accurately predict future failures. This results in insufficient preventive maintenance and difficulty responding to emergencies.

[0004] Manual inspections rely on the experience and judgment of inspectors, which is not only time-consuming and labor-intensive, but also prone to missing hidden fault points and failing to achieve comprehensive coverage. Existing online monitoring systems primarily focus on basic parameters of communication optical cables, such as signal strength and temperature, but lack comprehensive analysis of environmental factors (such as humidity and salt spray corrosion), resulting in inaccurate fault predictions. Existing solutions lack intelligent fault prediction and path optimization tools, making it impossible to dynamically adjust inspection plans based on actual conditions and difficult to achieve refined management. This results in unstable operation of the port communication system, frequent failures, and impacts on the normal operation of the port. Summary of the Invention

[0005] The embodiments of the present application provide a port communication optical cable routing detection method and system based on digital twins, which are used to solve the problem of poor inspection effect of port communication optical cables in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a port communication optical cable routing detection method based on digital twins, comprising:

[0007] Build a high-precision digital twin model of the port environment and communication cable distribution to synchronize the status information of the port communication cables with environmental variables in real time, ensure the consistency between the model and the actual environment, and generate a high-precision digital twin environment;

[0008] Based on the high-precision digital twin environment, historical environmental data and communication cable performance data are analyzed, and a long short-term memory network is used to predict performance degradation or failure of the communication cable under specific environmental conditions, thereby generating a potential failure prediction report.

[0009] Based on the potential fault prediction report, an ant colony algorithm is applied in the high-precision digital twin environment to automatically search for the optimal inspection path, monitor and record key performance indicators of the communication optical cable, and generate an optimized inspection path and inspection task list;

[0010] Based on the optimized inspection path and inspection task list, the actual inspection work is guided, the actual inspection results are compared with the predicted data in the high-precision digital twin model, and the prediction accuracy is evaluated and optimized to improve the operation and maintenance efficiency and system reliability of the port communication optical cable.

[0011] In a second aspect, an embodiment of the present application provides a port communication optical cable routing detection system based on digital twins, including:

[0012] Build a module to construct a high-precision digital twin model of the port environment and communication cable distribution, so as to synchronize the status information of the port communication cables with environmental variables in real time, ensure the consistency between the model and the actual environment, and generate a high-precision digital twin environment;

[0013] An analysis module, based on the high-precision digital twin environment, analyzes historical environmental data and communication cable performance data, uses a long short-term memory network to predict performance degradation or failure of the communication cable under specific environmental conditions, and generates a potential failure prediction report;

[0014] A monitoring module, based on the potential fault prediction report, applies an ant colony algorithm to automatically search for an optimal inspection path in the high-precision digital twin environment, monitors and records key performance indicators of the communication optical cable, and generates an optimized inspection path and inspection task list;

[0015] The optimization module guides the actual inspection work based on the optimized inspection path and inspection task list, compares the actual inspection results with the predicted data in the high-precision digital twin model, and evaluates and optimizes the prediction accuracy to improve the operation and maintenance efficiency and system reliability of the port communication optical cable.

[0016] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a port communication optical cable routing detection method based on digital twins as described in any one of the first aspects.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a port communication optical cable routing detection method based on digital twins as described in the first aspect.

[0018] In an embodiment of the present application, a high-precision digital twin model of the port environment and the distribution of communication optical cables is constructed to synchronize the status information of the port communication optical cables with the environmental variables in real time, ensure the consistency of the model with the actual environment, and generate a high-precision digital twin environment; based on the high-precision digital twin environment, historical environmental data and communication optical cable performance data are analyzed, and a long short-term memory network is used to predict the performance degradation or failure of the communication optical cables under specific environmental conditions, and generate a potential fault prediction report; based on the potential fault prediction report, an ant colony algorithm is applied to automatically search for the optimal inspection path in the high-precision digital twin environment, monitor and record the key performance indicators of the communication optical cables, and generate an optimized inspection path and inspection task list; based on the optimized inspection path and inspection task list, actual inspection work is guided, the actual inspection results are compared with the predicted data in the high-precision digital twin model, and the prediction accuracy is evaluated and optimized to improve the operation and maintenance efficiency and system reliability of the port communication optical cables. By building a high-precision digital twin model, real-time monitoring and dynamic management of port communication optical cables and their environment can be achieved, reducing the workload of manual inspections and improving operation and maintenance efficiency. By using long-short-term memory networks to analyze historical data, it is possible to predict possible future failures, allowing maintenance personnel to take preventive measures before failures occur, thereby reducing failure rates and repair costs. The application of ant colony algorithms in the digital twin environment automatically searches for the optimal inspection path, ensuring the efficiency and comprehensiveness of the inspection work, while reducing the uncertainty and risk in the inspection process. By comparing and analyzing the actual inspection results with the predicted data in the digital twin model, it is possible to continuously optimize The accuracy of the prediction model is improved, thereby improving the reliability and stability of the entire port communication system; based on the high-precision digital twin environment and optimized inspection paths, it provides port managers with detailed data support, which helps to make more scientific and reasonable operation and maintenance decisions and improve management level; through accurate prediction and optimized inspection strategies, unnecessary on-site operations are reduced, energy consumption and environmental pollution are reduced, in line with the principle of sustainable development; this method integrates multiple advanced technologies such as digital twins, big data analysis, AI prediction and optimization algorithms, which not only solves practical problems in the operation and maintenance of port communication optical cables, but also provides new ideas and directions for technological progress and development in related fields.

[0019] Historical port environmental data and communication cable performance data were collected and preprocessed to generate a structured dataset. This data was then organized chronologically and fed into a time series prediction model for training. This model was then trained to learn the relationship between environmental factors and communication cable performance. Based on this model, a long-short-term memory network was used to predict performance degradation or failure under specific environmental conditions. Preliminary fault prediction data was generated, the scope of the failure was analyzed, and a fault risk assessment report was generated based on the actual layout. Ultimately, targeted preventive measures and recommendations were developed, and a potential fault prediction report was generated. By collecting and preprocessing historical data and leveraging a long-short-term memory network to learn the relationship between environmental factors and communication cable performance, the system can more accurately predict faults under specific environmental conditions. The generated fault risk assessment and potential fault prediction reports provide scientific decision-making support for port managers, helping them develop targeted preventive measures and improving the efficiency of operations and maintenance. By predicting and preventing faults in advance, the system ensures timely maintenance of communication cables, improves the reliability and stability of the port's communication system, and safeguards the port's normal operations. Intelligent fault prediction and optimized inspection route planning reduce the workload and frequency of manual inspections, lowering operation and maintenance costs. The system provides port managers with detailed data support and a scientific basis for decision-making, helping them make more rational and efficient operations and maintenance decisions, improving management and operational efficiency. This data-driven decision-making approach enhances the scientific nature and foresight of management. By predicting potential faults in advance, preventive measures can be taken promptly, enhancing emergency response capabilities. Pre-fault intervention reduces the impact of sudden failures on port operations and ensures business continuity and stability.

[0020] Based on a structured data set, the data is sorted in time series to ensure the temporal continuity and logic of the data and generate an ordered data sequence; the ordered data sequence is normalized to reduce the impact of data magnitude differences and generate standardized data input; the standardized data input is used to input the time series prediction model for training, learn the relationship between environmental factors and communication optical cable performance, and generate a preliminary training model; the model is evaluated and parameters are optimized through cross-validation to improve the model's generalization ability and prediction accuracy, and generate an optimized training model; the optimized training model is used to test new environmental condition data, verify the model's prediction effect, and generate a final training model that learns the relationship between environmental factors and communication optical cable performance. By sorting the data in time series, the temporal continuity and logic of the data were ensured, and an ordered data series was generated, providing a high-quality data foundation for subsequent model training. Normalization of the ordered data series reduced the impact of data magnitude differences and generated standardized data input, making model training more stable and accurate. The standardized data input was used to train the time series prediction model, and the model was evaluated and parameters optimized through cross-validation, which improved the model's generalization ability and prediction accuracy, ensuring its applicability under different environmental conditions. The optimized training model was tested with new environmental condition data to verify the model's prediction results, and a final training model was generated to learn the relationship between environmental factors and communication cable performance, ensuring the model's reliability and stability and improving the accuracy of fault prediction. The generated training model provides scientific decision-making support for port managers, helping them better understand and predict the performance changes of communication cables under different environmental conditions, formulate effective preventive measures, and improve the efficiency and effectiveness of operation and maintenance management. By predicting and preventing faults in advance, the timely maintenance of communication cables is ensured, improving the reliability and stability of the port's communication system, ensuring the normal operation of the port, and reducing business losses caused by communication interruptions.

[0021] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 A flowchart of a port communication optical cable routing detection method based on digital twins provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the structure of a port communication optical cable routing detection system based on digital twins provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0027] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0028] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0029] Figure 1 A flowchart of a port communication optical cable routing detection method based on digital twin is provided for the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0030] 101. Build a high-precision digital twin model of the port environment and communication cable distribution to synchronize the status information of the port communication cables with environmental variables in real time, ensure the consistency between the model and the actual environment, and generate a high-precision digital twin environment;

[0031] Create a virtual model that closely matches the actual port environment and communication cable distribution, capable of reflecting real-world status information and environmental variables in real time. Collect environmental data such as the port's geographic information, building layout, and climate conditions. Obtain technical parameters such as the physical location, connection method, and age of the communication cables. Use 3D modeling software and GIS technology to construct a digital twin model. Implement data interfaces so the model can receive real-time data updates from sensors and other monitoring equipment.

[0032] 102. Based on the high-precision digital twin environment, analyze historical environmental data and communication cable performance data, use a long short-term memory network to predict performance degradation or failure of the communication cable under specific environmental conditions, and generate a potential failure prediction report;

[0033] Based on historical data analysis, it predicts possible performance degradation or failure in the future to provide a basis for preventive maintenance; integrates historical environmental data and communication optical cable performance data; uses time series prediction model training such as long short-term memory networks to learn the relationship between environmental factors and optical cable performance; inputs expected environmental conditions, predicts the performance of optical cables in specific environments, and generates potential fault prediction reports.

[0034] Optionally, step 102 analyzes historical environmental data and communication optical cable performance data based on the high-precision digital twin environment, uses a long short-term memory network to predict performance degradation or failure of the communication optical cable under specific environmental conditions, and generates a potential fault prediction report, specifically including the following steps:

[0035] Based on historical port environmental data and communication cable performance data, data is collected and preprocessed to generate a structured data set;

[0036] Based on the structured data set, the data is organized in chronological order and input into a time series prediction model for training to obtain a training model for learning the relationship between environmental factors and communication optical cable performance;

[0037] Based on the training model of the relationship between the learning environmental factors and the performance of the communication optical cable, the data under the expected environmental conditions are analyzed and processed, and the performance degradation or failure of the communication optical cable under the specific environmental conditions is predicted using a long short-term memory network to generate preliminary fault prediction data;

[0038] Based on the preliminary fault prediction data, analyze the fault impact range and generate a fault risk assessment report based on the actual layout of the port communication optical cables;

[0039] Based on the fault risk assessment report, targeted preventive measures and recommendations are formulated to support the operation and maintenance management of port communication optical cables and generate potential fault prediction reports.

[0040] Assume that a port needs to predict the performance degradation or failure of its communication optical cables within the next month;

[0041] Obtain environmental data such as temperature, humidity, and wind speed from the weather station for the past year, and performance data such as transmission rate, signal attenuation, and bit error rate from the optical cable monitoring system for the same time period; clean the data, remove outliers and missing values, format the data into a unified structure, and generate a structured data set; arrange the structured data set in chronological order to ensure the temporal continuity and logic of the data; input the organized data into the long-short-term memory network model, train the model to learn the relationship between environmental factors and communication optical cable performance, and generate a training model; analyze and process data under expected environmental conditions: input expected environmental condition data for the next month (such as weather forecast data) into the The trained long-short-term memory network model predicts the performance of communication optical cables under specific environmental conditions and generates preliminary fault prediction data. The model output results show that on certain dates and times, optical cables in specific areas may experience signal attenuation and increased bit error rates. Based on the preliminary fault prediction data, the scope of possible impact of these faults is analyzed, and a fault risk assessment report is generated in combination with the actual layout of the optical cables. Based on the fault risk assessment report, specific preventive maintenance measures are formulated, such as increasing the inspection frequency during predicted high-incidence periods of faults, replacing optical cable sections that may have problems in advance, and generating potential fault prediction reports to support the operation and maintenance management of port communication optical cables.

[0042] Through this series of steps, port management departments can take measures in advance to avoid or reduce the failure of communication optical cables and improve the operational efficiency and reliability of the port.

[0043] This application considers that the formula is primarily used to construct and train a prediction model based on a long short-term memory network to predict performance degradation or failure of communication optical cables under specific environmental conditions. These formulas and models are designed to solve practical problems in the operation and maintenance of communication optical cables. By leveraging historical data and current environmental conditions, they can accurately predict optical cable performance, thereby improving operation and maintenance efficiency and system reliability.

[0044] Optionally, based on the training model for learning the relationship between environmental factors and communication optical cable performance, data under expected environmental conditions is analyzed and processed, and a long short-term memory network is used to predict performance degradation or failure of the communication optical cable under specific environmental conditions, thereby generating preliminary fault prediction data, including:

[0045] Initialize the weight matrix to ensure that the initial values ​​of the weights are reasonably distributed and accelerate model convergence;

[0046] Define an exponentially decaying learning rate strategy with periodic restarts to dynamically adjust the learning rate and improve model training results;

[0047] Initialize the weight matrix using the following calculation formula :

[0048]

[0049] in, It is from the interval A uniformly distributed random number in ; and are the number of neurons in the input layer and the output layer respectively; Is a regularization coefficient used to adjust the initial distribution range of weights, usually taking a small positive value; Is a bias term used to further adjust the initial value of the weight, usually taking a small positive value or zero;

[0050] The exponentially decaying learning rate strategy with periodic restarts is calculated using the following formula:

[0051]

[0052] in, is in the time step The learning rate; is the initial learning rate; is the learning rate decay coefficient, usually a small positive number; is the current training iteration number; is the period of learning rate restart; is the index of the current cycle, satisfying ;

[0053] Based on the initialization weights and the exponential decay learning rate strategy with periodic restarts, the nonlinear expression ability and prediction performance of the model are enhanced. The long short-term memory network is calculated by the following formula:

[0054]

[0055] in, is in the time step The input data includes environmental variables and communication cable performance data under specific environmental conditions; is in the time step The hidden state of At time steps The activation values ​​of the input gate, forget gate, and output gate; is the LeakyReLU activation function, ; is the weight matrix used to calculate the linear combination of input gate, forget gate, output gate and cell state; is a bias term that adjusts the result of the linear combination; tanh is a hyperbolic tangent activation function that converts the input into a value between -1 and 1 for updating the cell state; : Indicates the time step New cell state candidate value; is in the time step The cell state; is in the time step The hidden state of

[0056] Based on the hidden state ,The performance degradation is predicted through the output layer, the fault conditions of the communication optical cable under specific environmental conditions are evaluated, and preliminary fault prediction data are generated.

[0057] Assume that a port needs to predict the performance degradation or failure of its communication optical cables within the next month;

[0058] Number of neurons in the input layer (Assume that the input data includes 10 features such as temperature, humidity, wind speed, etc.); Number of neurons in the output layer ; Regularization coefficient ; Bias term

[0059] The weight matrix is ​​calculated as

[0060]

[0061] The exponential decay learning rate strategy with periodic restart is defined as, the initial learning rate , learning rate decay coefficient , learning rate restart cycle , the current number of training iterations , current cycle index ; Calculate the learning rate

[0062]

[0063] enter ; ; is the weight matrix after initialization; is the bias term

[0064] Calculate the activation value of each gate as

[0065]

[0066]

[0067] is in the time step The input data includes environmental variables and communication cable performance data under specific environmental conditions; is in the time step The hidden state of At time steps The activation values ​​of the input gate, forget gate, and output gate; is the LeakyReLU activation function, ; is the weight matrix used to calculate the linear combination of input gate, forget gate, output gate and cell state; is a bias term that adjusts the result of the linear combination; tanh is a hyperbolic tangent activation function that converts the input into a value between -1 and 1 for updating the cell state; : Indicates the time step New cell state candidate value; is in the time step The cell state; is in the time step The hidden state of Mapping to specific performance indicators (such as signal attenuation and bit error rate); based on the prediction results of the output layer, evaluating the fault conditions of communication optical cables under specific environmental conditions and generating preliminary fault prediction data.

[0068] Through this series of steps, port management departments can accurately predict the performance degradation or failure of communication optical cables under specific environmental conditions, take measures in advance to avoid or reduce the failure of communication optical cables, and improve the operational efficiency and reliability of the port.

[0069] Optionally, based on the structured data set, the data is organized in chronological order and input into a time series prediction model for training to obtain a training model for learning the relationship between environmental factors and communication optical cable performance, specifically comprising the following steps:

[0070] Based on the structured data set, the data is sorted in time series to ensure the temporal continuity and logic of the data and obtain an ordered data sequence;

[0071] Based on the ordered data sequence, normalizing the data to reduce the impact of data magnitude differences and generate standardized data input;

[0072] Using the standardized data input, inputting into a time series prediction model for training, so as to learn the relationship between environmental factors and communication optical cable performance, and generate a preliminary training model;

[0073] Based on the preliminary training model, the model is evaluated and parameters are optimized through cross-validation to improve the generalization ability and prediction accuracy of the model, and an optimized training model is generated;

[0074] The optimized training model is used to test new environmental condition data, verify the model prediction effect, and generate a training model that learns the relationship between environmental factors and communication optical cable performance.

[0075] Imagine a large seaport adopting advanced digital twin technology and machine learning algorithms to predict and prevent potential failures of communication cables in order to improve the stability and reliability of its communication network.

[0076] Environmental data and communication optical cable performance data (including signal attenuation, transmission rate, bit error rate, etc.) from the past ten years were collected. After data cleaning, denoising and format conversion, a structured data set was generated. During the data cleaning process, records with too many missing values ​​were deleted and outliers were smoothed. The data format conversion ensured that all data could be input into the subsequent analysis model in a unified format; the structured data set was sorted in chronological order to ensure the temporal continuity and logic of the data, and then data of different magnitudes were converted into a unified standard range to reduce the impact of data magnitude differences on model training; using standardized data input, a long short-term memory network was selected to handle long-term dependency problems in sequence data for predicting performance changes of communication optical cables; during the training process, the model parameters, such as the learning rate and the number of hidden layers, were continuously adjusted to learn the relationship between environmental factors and the performance of communication optical cables; after the initial training was completed, the model was evaluated using the K-fold cross-validation method, and the model parameters were optimized through multiple iterations to improve the model's generalization ability and prediction accuracy.

[0077] Through the above steps, the port can enhance the stability and reliability of its communication network, accurately predict and effectively prevent potential failures that may occur in communication optical cables.

[0078] Optionally, based on the training model for learning the relationship between environmental factors and communication optical cable performance, analyzing and processing data under expected environmental conditions, using a long short-term memory network to predict performance degradation or failure of the communication optical cable under specific environmental conditions, and generating preliminary fault prediction data, specifically includes the following steps:

[0079] Based on the training model of the relationship between the learning environmental factors and the performance of the communication optical cable, analyzing and processing the data under the expected environmental conditions, ensuring that the time series characteristics of the input data are consistent with the training data, and generating formatted input data;

[0080] Based on the formatted input data, using the training model for learning the relationship between environmental factors and communication optical cable performance, predicting a performance change trend of the communication optical cable under specific environmental conditions to generate a preliminary prediction result;

[0081] Based on the preliminary prediction results, evaluating the performance degradation or failure of the communication optical cable under specific environmental conditions to generate preliminary fault assessment data;

[0082] Based on the preliminary fault assessment data, combined with the actual layout of the port communication optical cables and historical fault data, analyze the fault impact range and generate an impact range analysis report;

[0083] Based on the impact range analysis report, the preliminary fault assessment data is corrected and optimized to ensure the accuracy and practicality of the prediction, and preliminary fault prediction data is generated.

[0084] Suppose a port wants to use a long short-term memory network to predict the performance degradation of its communication cables under specific weather conditions;

[0085] Environmental data and communication cable performance data from the past five years were collected from the port's historical database. The data was cleaned, denoised, and normalized to generate a structured dataset. This structured dataset was organized chronologically and fed into a long-short-term memory network for training. During the training process, the model learns the relationship between environmental factors and the performance of communication optical cables; assuming that the current prediction is for the performance of communication optical cables within the next week, the expected environmental condition data within this week is formatted to ensure that it is consistent with the time series characteristics of the training data; the trained model is used to predict the formatted input data to obtain the performance change trend of the communication optical cable within the next week; based on the prediction results, the performance degradation of the communication optical cable under specific environmental conditions is evaluated, and preliminary fault assessment data is generated; combined with the actual layout of the port communication optical cable and historical fault data, the scope of possible impact of the fault is analyzed, and an impact range analysis report is generated; based on the impact range analysis report, the preliminary fault assessment data is corrected and optimized to ensure the accuracy and practicality of the prediction; based on the preliminary fault prediction data, a fault risk assessment report is generated to assess the risk of communication optical cable failure in the next week; based on the fault risk assessment report, targeted preventive measures and suggestions are formulated, such as strengthening optical cable maintenance, adjusting optical cable layout, etc., to support the operation and maintenance management of port communication optical cables.

[0086] Ports can not only accurately predict the performance degradation or failure of communication optical cables under specific future environmental conditions, but also conduct effective risk management and formulate response measures based on this.

[0087] 103. Based on the potential fault prediction report, apply an ant colony algorithm to automatically search for an optimal inspection path in the high-precision digital twin environment, monitor and record key performance indicators of the communication optical cable, and generate an optimized inspection path and inspection task list;

[0088] Use algorithms to automatically plan the most effective inspection routes to ensure the efficiency and comprehensiveness of inspection work; determine the areas and optical cable sections that require key inspection based on potential fault prediction reports; apply optimization algorithms such as ant colony algorithms to find the inspection path with the lowest cost, shortest time and covering all key points in the digital twin environment; record the key performance indicators of the optical cable during the inspection process, such as signal strength and temperature changes, to form an inspection report.

[0089] Optionally, in step 103, based on the potential fault prediction report, an ant colony algorithm is applied in the high-precision digital twin environment to automatically search for an optimal inspection path, monitor and record key performance indicators of the communication optical cable, and generate an optimized inspection path and inspection task list, which specifically includes the following steps:

[0090] Based on the potential fault prediction report, potential fault points and priorities of the communication optical cable are analyzed and processed to generate a fault point list;

[0091] Based on the fault point list, an ant colony algorithm is applied in the high-precision digital twin environment to search for inspection paths, combining inspection cost, time, and path length, to generate a preliminary inspection path plan;

[0092] Based on the preliminary inspection path plan, the inspection process is simulated in the high-precision digital twin environment, key performance indicators of the communication optical cable are monitored and recorded, and inspection data records are generated;

[0093] Based on the inspection data records, combined with actual inspection needs and resource constraints, the preliminary inspection route plan is optimized, the inspection sequence and time schedule are adjusted to ensure inspection efficiency and quality, and an optimized inspection route is generated;

[0094] Based on the optimized inspection path, a detailed inspection task list is compiled to guide the actual inspection work, and an optimized inspection path and inspection task list are generated.

[0095] Suppose a port needs to automatically search for the optimal inspection route within a high-precision digital twin environment based on potential fault prediction reports. Based on these reports, the port identifies potential fault points and their priorities for the communication optical cable within the next month. For example, fault points A, B, and C are located at Pier 1, Pier 3, and Pier 5, with high, medium, and low priorities, respectively. These fault points and their priorities are compiled into a list, which serves as the basis for subsequent inspection route optimization. Based on this list of fault points, the ant colony algorithm is applied within the high-precision digital twin environment to search for an inspection route, taking into account inspection cost, time, and path length. By simulating the foraging behavior of ants, the ant colony algorithm finds the optimal path from the starting point to all fault points and back to the starting point. The algorithm outputs a preliminary inspection route plan, for example: starting point -> Pier 1 -> Pier 3 -> Pier 5 -> starting point. Based on this preliminary inspection route plan, the inspection process is simulated within the high-precision digital twin environment. During the simulation, key performance indicators of the communication optical cable, such as signal strength and temperature fluctuations, are recorded. This data is compiled into inspection data records for subsequent route optimization. Based on these inspection data records, the initial inspection route plan is optimized in light of actual inspection needs and resource constraints. For example, the inspection sequence is adjusted to ensure that high-priority fault points are inspected first while minimizing inspection time and costs. The optimized inspection route might be: starting point -> Pier 1 -> Pier 5 -> Pier 3 -> starting point. Based on the optimized inspection route, a detailed inspection task list is compiled. The task list should include the specific location of each fault point, inspection time, required tools, and staffing to guide the actual inspection work. The optimized inspection route and task list are compiled into a report and submitted to port management to ensure the efficiency and comprehensiveness of the inspection work.

[0096] Through the above steps, port management departments can use potential fault prediction reports and advanced optimization algorithms to automatically generate optimal inspection routes and task lists, ensuring the efficiency and comprehensiveness of inspection work and improving the maintenance level of port communication optical cables.

[0097] This application considers that this formula is used to construct a multi-objective optimization model for inspection paths, integrating multiple optimization objectives through a comprehensive objective function, including minimizing path length, minimizing inspection costs, maximizing inspection efficiency, maximizing safety, and optimizing dynamic factors. Each objective function has its own specific expression, which is combined into a comprehensive objective function through a weighted sum method;

[0098] Optionally, an ant colony algorithm is applied in the high-precision digital twin environment to search for inspection paths in combination with inspection cost, time, and path length to generate a preliminary inspection path plan, including:

[0099] Update pheromones to provide the latest reference values ​​for path selection and accelerate the optimal path discovery;

[0100] Set the transition probability formula to dynamically guide movement decisions to improve the efficiency and accuracy of path search;

[0101] Update pheromones using the following calculation formula:

[0102]

[0103] in, Indicates that at time step Time Path Pheromone concentration on; is the pheromone evaporation rate, which is usually a positive number less than 1; Indicates that at time step Time Path The newly added pheromone concentration, , is the number of ants, Indicates the Ants in time step Time Path The contribution of ; is a constant representing the total amount of pheromone; It is The total length of the path traveled by the ants;

[0104] The transition probability is calculated using the following formula:

[0105]

[0106] in, Indicates that at time step When, Ant slave node Move to Node probability; It is the pheromone importance factor; is the heuristic information importance factor; Represents heuristic information, for the path The reciprocal distance of , is a node and nodes the distance between them; Indicates the Only ants on the node The next node set that can be selected when belong Nodes in Represents heuristic information, for the path The reciprocal distance of , is a node and nodes the distance between them;

[0107] Based on the updated pheromone and transition probability, the multi-factor comprehensive nonlinear relationship is introduced, and the comprehensive path evaluation is calculated using the following calculation formula:

[0108]

[0109] in, is in the time step When, Ant slave node Move to Node Comprehensive evaluation score; They are the weight factors of reliability, efficiency, safety and cost, and dynamic factors; Is the path At time step reliability when , Is the path At time step The failure rate when is a tuning parameter; Is the path At time step The efficiency of , is a node and nodes The distance between is a small positive number to prevent division by zero errors; Is the path At time step Safety when , Is the path At time step The risk value when is a standard deviation parameter used to control the risk weight decay; Indicates the path At time step The cost of , Is the path At time step The actual cost of is a small positive number to prevent division by zero errors; Indicates the path At time step Dynamic factors when , Is the path At time step Dynamic factors affecting time, is an adjustment parameter used to control the weight of dynamic factors;

[0110] Dynamically adjust the pheromone concentration on the path, repeatedly calculate the transition probability, and select the optimal path in the high-precision digital twin environment;

[0111] Through multiple rounds of iteration, a preliminary inspection path plan is generated.

[0112] Assume that a port has five terminals (nodes), numbered 1 to 5. The ant colony algorithm is used to generate a preliminary inspection path plan in a high-precision digital twin environment.

[0113] The initialization parameters are the number of ants ; Pheromone evaporation rate Total amount of pheromones ; Pheromone importance factor ; Heuristic information importance factor ; Initial pheromone concentration (all paths); the distance between nodes is, the distance from node 1 to node 2 is 2 kilometers, the distance from node 1 to node 3 is 3 kilometers, the distance from node 1 to node 4 is 4 kilometers, the distance from node 1 to node 5 is 5 kilometers, the distance from node 2 to node 3 is 2 kilometers, the distance from node 2 to node 4 is 3 kilometers, the distance from node 2 to node 5 is 4 kilometers, the distance from node 3 to node 4 is 2 kilometers, the distance from node 3 to node 5 is 3 kilometers, and the distance from node 4 to node 5 is 2 kilometers; suppose 5 ants choose the following paths respectively, ant 1 is 1->2->3->4->5->1, the total length of the path , Ant 2 is 1->3->2->4->5->1, the total path length ,Ant ->4->2->3->5->1, total path length , ant 4 is 1->5->2->3->4->1, the total path length , Ant 5 is 1->2->4->3->5->1, the total path length ; Calculate the new pheromone concentration on each path as ,in ; For each path, the new pheromone concentrations are ; Update pheromone concentration to ,in For each path, the pheromone concentration is , , ; Assume that in the second iteration, the next node set that the first ant can choose when it is at node 1 is ; Heuristic information ,therefore , ; , ; Assume that the first ant chooses path 1-2, calculate the comprehensive evaluation score of path 1-2: , The comprehensive evaluation score is . .

[0114] Through the above steps, the ant colony algorithm generates a preliminary inspection path plan in a high-precision digital twin environment. Through multiple iterations, the pheromone concentration will be gradually updated, the transfer probability will be dynamically adjusted, and the optimal inspection path will eventually be selected.

[0115] Optionally, based on the fault point list, an ant colony algorithm is applied in the high-precision digital twin environment to search for inspection paths in combination with inspection cost, time, and path length to generate a preliminary inspection path plan, specifically including the following steps:

[0116] Based on the fault point list, the location information and priority of each fault point are analyzed and processed to generate detailed information of the fault point;

[0117] Based on the detailed information of the fault point, combined with the inspection cost, time and path length, the parameters of the ant colony algorithm are set and processed to generate algorithm parameters suitable for port optical cable inspection;

[0118] Using the algorithm parameters adapted to port optical cable inspection, an ant colony algorithm is applied in the high-precision digital twin environment to search for possible paths from the starting point to each fault point, simulating the pheromone release and perception behavior of ants to generate multiple candidate inspection paths;

[0119] Based on the multiple candidate inspection paths, updating the pheromone concentration on the path to converge to a better solution and generate an optimized candidate inspection path;

[0120] Based on the optimized candidate inspection paths, the best path is selected and a preliminary inspection path plan is generated by combining inspection efficiency, cost and path safety.

[0121] Suppose a port requires regular inspections of its communication cables to ensure their proper operation. The following are the steps for implementing this solution: Based on historical data and a predictive model, a prediction report is generated containing potential fault points and their priorities for the communication cables. The location and priority of each fault point are extracted from the prediction report to generate a detailed list of the fault points. The parameters of the ant colony algorithm are set based on factors such as the detailed information of the fault point, inspection cost, time, and path length. For example, the pheromone volatility coefficient is set to 0.5, the pheromone intensity is set to 10, and the number of ants is set to 50. In a high-precision digital twin environment, the ant colony algorithm is applied to search for possible paths from the starting point to each fault point. The pheromone release and perception behavior of ants is simulated to generate multiple candidate inspection paths. The pheromone concentration along the candidate inspection paths is updated based on the quality of the candidate inspection paths (such as path length and inspection time). The optimal path has a higher pheromone concentration, guiding the ants to converge toward the optimal solution. Based on the optimized candidate inspection paths, the optimal path is selected as the preliminary inspection route plan, taking into account factors such as inspection efficiency, cost, and path safety. The inspection process is simulated in the digital twin environment, and inspections are carried out according to the preliminary plan. Key performance indicators of the communication optical cable (such as transmission rate and bit error rate) are monitored and recorded. The preliminary inspection route plan is optimized based on actual inspection needs and resource constraints (such as the number of inspectors and inspection time windows). The inspection sequence and schedule are adjusted to ensure inspection efficiency and quality. Based on the optimized inspection route plan, a detailed inspection task list is compiled. The list includes information such as the division of labor of inspectors, inspection time and location, and key performance indicators to be monitored.

[0122] Through the above steps, the port can realize automatic planning and optimization of the inspection path of communication optical cables, improve inspection efficiency and quality, and reduce inspection costs.

[0123] 104. Based on the optimized inspection path and inspection task list, guide the actual inspection work, compare the actual inspection results with the predicted data in the high-precision digital twin model, evaluate and optimize the prediction accuracy, so as to improve the operation and maintenance efficiency and system reliability of the port communication optical cable.

[0124] By comparing actual inspection results with the prediction model, the accuracy of the prediction model is continuously optimized and operation and maintenance efficiency is improved; the data collected in the actual inspection is compared with the predicted data in the digital twin model; the differences between the two are analyzed to identify deviations in the model prediction; the model parameters are adjusted according to the analysis results and the prediction algorithm is optimized; the digital twin model is updated regularly to ensure that it can better reflect the actual situation, improve the accuracy of fault prediction and the reliability of the system.

[0125] Optionally, step 104 guides actual inspection work based on the optimized inspection path and inspection task list, compares actual inspection results with predicted data in the high-precision digital twin model, evaluates and optimizes prediction accuracy, and thereby improves the operation and maintenance efficiency and system reliability of the port communication optical cable. Specifically, the following steps are included:

[0126] Based on the optimized inspection route and inspection task list, plan and guide the actual inspection work, execute the inspection tasks, and generate actual inspection records;

[0127] Based on the actual inspection records, the performance changes and fault conditions of the communication optical cables during the inspection process are collected, and the inspection results are sorted, analyzed, and processed to generate actual inspection results;

[0128] Based on the actual inspection results, the predicted data in the high-precision digital twin model is compared to evaluate the accuracy of the predicted data, identify prediction deviations, and generate a prediction accuracy evaluation report;

[0129] Based on the forecast accuracy assessment report, analyze the major forecast deviations, identify the causes of the deviations, propose improvement measures, and generate a forecast model optimization plan;

[0130] Based on the prediction model optimization scheme, the high-precision digital twin model is updated and optimized to improve the prediction accuracy of the model, and an optimized digital twin model is generated to improve the operation and maintenance efficiency and system reliability of the port communication optical cable.

[0131] Suppose a port management department needs to guide actual inspections based on an optimized inspection route and task list. The department then compares actual inspection results with the predicted data from a high-precision digital twin model to evaluate and optimize prediction accuracy. Based on the optimized inspection route and task list, the department then plans and executes actual inspection tasks. For example, inspectors follow the predetermined route and task list to sequentially inspect the communication cables at Piers 1 and 3, recording the inspection time, location, and test indicators to generate an actual inspection record. During the inspection, the department collects performance changes and fault conditions for the communication cables, organizes and analyzes the inspection results, and generates an actual inspection report. For example, the department records performance indicators such as signal strength and temperature changes for the Pier 1 optical cable, as well as the presence of faults. The department then compares the actual inspection results with the predicted data from the high-precision digital twin model to evaluate the accuracy of the predicted data and generate a prediction accuracy evaluation report. For example, the actual signal strength of the Pier 1 optical cable is compared with the predicted signal strength to evaluate the accuracy of the prediction. Based on the prediction accuracy evaluation report, the department analyzes any significant deviations, identifies the causes of the deviations, proposes improvement measures, and generates a prediction model optimization plan. For example, if the signal strength prediction error for the Pier 1 optical cable is large, analysis suggests that this may be due to the model not fully accounting for the impact of environmental factors. Improvement measures such as increasing the weight of environmental factors are proposed. Based on the prediction model optimization plan, the high-precision digital twin model is updated and optimized to improve the model's prediction accuracy, generating an optimized digital twin model. For example, model parameters can be adjusted to increase the weight of environmental factors, and the model can be retrained to improve prediction accuracy.

[0132] Through the above steps, port management departments can continuously optimize the accuracy of the prediction model, improve the efficiency of inspection work and the operation and maintenance level of communication optical cables, and ensure the reliability and stability of the port communication system.

[0133] Figure 2 A structural diagram of a port communication optical cable routing detection system based on digital twin is provided for the embodiment of the present application. Figure 2 As shown, the device includes:

[0134] Build Module 21 to construct a high-precision digital twin model of the port environment and communication cable distribution, synchronizing the status information of the port communication cables with environmental variables in real time, ensuring consistency between the model and the actual environment, and generating a high-precision digital twin environment.

[0135] An analysis module 22 analyzes historical environmental data and communication cable performance data based on the high-precision digital twin environment, uses a long short-term memory network to predict performance degradation or failure of the communication cable under specific environmental conditions, and generates a potential failure prediction report;

[0136] The monitoring module 23 applies an ant colony algorithm to automatically search for an optimal inspection path in the high-precision digital twin environment based on the potential fault prediction report, monitors and records key performance indicators of the communication optical cable, and generates an optimized inspection path and inspection task list;

[0137] The optimization module 24 guides the actual inspection work based on the optimized inspection path and inspection task list, compares the actual inspection results with the predicted data in the high-precision digital twin model, evaluates and optimizes the prediction accuracy, and thus improves the operation and maintenance efficiency and system reliability of the port communication optical cable.

[0138] Figure 2 The port communication optical cable routing detection system based on digital twin can perform Figure 1 The implementation principles and technical effects of the digital twin-based port communication optical cable routing detection method described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the digital twin-based port communication optical cable routing detection system described in the above embodiment has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0139] In one possible design, Figure 2 A port communication optical cable routing detection system based on digital twin of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0140] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0141] The processing component 32 is used to: construct a high-precision digital twin model of the port environment and the distribution of communication optical cables to synchronize the status information of the port communication optical cables with environmental variables in real time, ensure the consistency of the model with the actual environment, and generate a high-precision digital twin environment; based on the high-precision digital twin environment, analyze historical environmental data and communication optical cable performance data, use long and short-term memory networks to predict the performance degradation or failure of communication optical cables under specific environmental conditions, and generate a potential fault prediction report; based on the potential fault prediction report, apply the ant colony algorithm to automatically search for the optimal inspection path in the high-precision digital twin environment, monitor and record the key performance indicators of the communication optical cables, and generate an optimized inspection path and inspection task list; based on the optimized inspection path and inspection task list, guide the actual inspection work, compare the actual inspection results with the predicted data in the high-precision digital twin model, evaluate and optimize the prediction accuracy, so as to improve the operation and maintenance efficiency and system reliability of the port communication optical cables.

[0142] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0143] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0144] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0145] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0146] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0147] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0148] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a port communication optical cable routing detection method based on digital twin.

[0149] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0150] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0151] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A port communication optical cable routing detection method based on digital twin, characterized in that: include: Build a high-precision digital twin model of the port environment and communication cable distribution to synchronize the status information of the port communication cables with environmental variables in real time, ensure the consistency between the model and the actual environment, and generate a high-precision digital twin environment; Based on the high-precision digital twin environment, historical environmental data and communication cable performance data are analyzed, and a long short-term memory network is used to predict performance degradation or failure of the communication cable under specific environmental conditions, thereby generating a potential failure prediction report. Based on the potential fault prediction report, an ant colony algorithm is applied in the high-precision digital twin environment to automatically search for the optimal inspection path, monitor and record key performance indicators of the communication optical cable, and generate an optimized inspection path and inspection task list; Based on the optimized inspection path and inspection task list, the actual inspection work is guided, the actual inspection results are compared with the predicted data in the high-precision digital twin model, and the prediction accuracy is evaluated and optimized to improve the operation and maintenance efficiency and system reliability of the port communication optical cable.

2. The port communication optical cable routing detection method based on digital twin according to claim 1 is characterized in that: Based on the high-precision digital twin environment, historical environmental data and communication cable performance data are analyzed, and a long short-term memory network is used to predict the performance degradation or failure of the communication cable under specific environmental conditions, and a potential fault prediction report is generated, which specifically includes the following steps: Based on historical port environmental data and communication cable performance data, data is collected and preprocessed to generate a structured data set; Based on the structured data set, the data is organized in chronological order and input into a time series prediction model for training to obtain a training model for learning the relationship between environmental factors and communication optical cable performance; Based on the training model of the relationship between the learning environmental factors and the performance of the communication optical cable, the data under the expected environmental conditions are analyzed and processed, and the performance degradation or failure of the communication optical cable under the specific environmental conditions is predicted using a long short-term memory network to generate preliminary fault prediction data; Based on the preliminary fault prediction data, analyze the fault impact range and generate a fault risk assessment report based on the actual layout of the port communication optical cables; Based on the fault risk assessment report, targeted preventive measures and recommendations are formulated to support the operation and maintenance management of port communication optical cables and generate potential fault prediction reports.

3. The port communication optical cable routing detection method based on digital twin according to claim 2 is characterized in that: Based on the structured data set, the data is organized in chronological order and input into a time series prediction model for training to obtain a training model for learning the relationship between environmental factors and communication optical cable performance, specifically including the following steps: Based on the structured data set, the data is sorted in time series to ensure the temporal continuity and logic of the data and obtain an ordered data sequence; Based on the ordered data sequence, normalizing the data to reduce the impact of data magnitude differences and generate standardized data input; Using the standardized data input, inputting into a time series prediction model for training, so as to learn the relationship between environmental factors and communication optical cable performance, and generate a preliminary training model; Based on the preliminary training model, the model is evaluated and parameters are optimized through cross-validation to improve the generalization ability and prediction accuracy of the model, and an optimized training model is generated; The optimized training model is used to test new environmental condition data, verify the model prediction effect, and generate a training model that learns the relationship between environmental factors and communication optical cable performance.

4. The port communication optical cable routing detection method based on digital twin according to claim 2 is characterized in that: Based on the training model of the relationship between the learning environmental factors and the performance of the communication optical cable, data under expected environmental conditions are analyzed and processed, and a long short-term memory network is used to predict the performance degradation or failure of the communication optical cable under specific environmental conditions, thereby generating preliminary fault prediction data, which specifically includes the following steps: Based on the training model of the relationship between the learning environmental factors and the performance of the communication optical cable, analyzing and processing the data under the expected environmental conditions, ensuring that the time series characteristics of the input data are consistent with the training data, and generating formatted input data; Based on the formatted input data, using a long short-term memory network to predict the performance change trend of the communication optical cable under specific environmental conditions, and generate preliminary prediction results; Based on the preliminary prediction results, evaluating the performance degradation or failure of the communication optical cable under specific environmental conditions to generate preliminary fault assessment data; Based on the preliminary fault assessment data, combined with the actual layout of the port communication optical cables and historical fault data, analyze the fault impact range and generate an impact range analysis report; Based on the impact range analysis report, the preliminary fault assessment data is corrected and optimized to ensure the accuracy and practicality of the prediction, and preliminary fault prediction data is generated.

5. The port communication optical cable routing detection method based on digital twin according to claim 1 is characterized in that: Based on the potential fault prediction report, an ant colony algorithm is applied in the high-precision digital twin environment to automatically search for the optimal inspection path, monitor and record the key performance indicators of the communication optical cable, and generate an optimized inspection path and inspection task list, specifically including the following steps: Based on the potential fault prediction report, potential fault points and priorities of the communication optical cable are analyzed and processed to generate a fault point list; Based on the fault point list, an ant colony algorithm is applied in the high-precision digital twin environment to search for inspection paths, combining inspection cost, time, and path length, to generate a preliminary inspection path plan; Based on the preliminary inspection path plan, the inspection process is simulated in the high-precision digital twin environment, key performance indicators of the communication optical cable are monitored and recorded, and inspection data records are generated; Based on the inspection data records, combined with actual inspection needs and resource constraints, the preliminary inspection route plan is optimized, the inspection sequence and time schedule are adjusted to ensure inspection efficiency and quality, and an optimized inspection route is generated; Based on the optimized inspection path, a detailed inspection task list is compiled to guide the actual inspection work, and an optimized inspection path and inspection task list are generated.

6. The method according to claim 5, characterized in that Based on the fault point list, the ant colony algorithm is applied in the high-precision digital twin environment to search for inspection paths, combining inspection cost, time, and path length, and generate a preliminary inspection path plan, including: Based on the fault point list, the location information and priority of each fault point are analyzed and processed to generate detailed information of the fault point; Based on the detailed information of the fault point, combined with the inspection cost, time and path length, the parameters of the ant colony algorithm are set and processed to generate algorithm parameters suitable for port optical cable inspection; Using the algorithm parameters adapted to port optical cable inspection, an ant colony algorithm is applied in the high-precision digital twin environment to search for possible paths from the starting point to each fault point, simulating the pheromone release and perception behavior of ants to generate multiple candidate inspection paths; Based on the multiple candidate inspection paths, updating the pheromone concentration on the path to converge to a better solution and generate an optimized candidate inspection path; Based on the optimized candidate inspection paths, the best path is selected and a preliminary inspection path plan is generated by combining inspection efficiency, cost, and path safety. The process of updating the pheromone concentration on the path includes: Update pheromones to provide the latest reference values ​​for path selection and accelerate the optimal path discovery; Set the transition probability formula to dynamically guide movement decisions to improve the efficiency and accuracy of path search; Update pheromones using the following calculation formula: ; in, Indicates that at time step Time Path Pheromone concentration on; is the pheromone evaporation rate, which is usually a positive number less than 1; Indicates that at time step Time Path The newly added pheromone concentration, , is the number of ants, Indicates the Ants in time step Time Path The contribution of ; is a constant representing the total amount of pheromone; It is The total length of the path traveled by the ants; The method of updating the pheromone concentration on the paths based on the plurality of candidate inspection paths to converge to a more optimal solution and generate an optimized candidate inspection path includes: The transition probability is calculated using the following formula: ; in, Indicates that at time step When, Ant slave node Move to Node probability; It is the pheromone importance factor; is the heuristic information importance factor; Represents heuristic information, for the path The reciprocal distance of , is a node and nodes the distance between them; Indicates the Only ants on the node The next node set that can be selected when belong Nodes in Represents heuristic information, for the path The reciprocal distance of , is a node and nodes the distance between them; Based on the updated pheromone and transition probability, the multi-factor comprehensive nonlinear relationship is introduced, and the comprehensive path evaluation is calculated using the following calculation formula: ; in, is in the time step When, Ant slave node Move to Node Comprehensive evaluation score; They are the weight factors of reliability, efficiency, safety and cost, and dynamic factors; Is the path At time step reliability when , Is the path At time step The failure rate when is a tuning parameter; Is the path At time step The efficiency of , is a node and nodes The distance between is a small positive number to prevent division by zero errors; Is the path At time step Safety when , Is the path At time step The risk value when is a standard deviation parameter used to control the risk weight decay; Indicates the path At time step The cost of , Is the path At time step The actual cost of is a small positive number to prevent division by zero errors; Indicates the path At time step Dynamic factors when , Is the path At time step Dynamic factors affecting time, is an adjustment parameter used to control the weight of dynamic factors; The pheromone concentration on the path is dynamically adjusted, the transfer probability is repeatedly calculated, and the optimal path is selected as the optimized candidate inspection path in the high-precision digital twin environment.

7. The port communication optical cable routing detection method based on digital twin according to claim 1 is characterized in that: Based on the optimized inspection path and inspection task list, actual inspection work is guided, actual inspection results are compared with the predicted data in the high-precision digital twin model, and the prediction accuracy is evaluated and optimized to improve the operation and maintenance efficiency and system reliability of the port communication optical cable. Specifically, the following steps are included: Based on the optimized inspection route and inspection task list, plan and guide the actual inspection work, execute the inspection tasks, and generate actual inspection records; Based on the actual inspection records, the performance changes and fault conditions of the communication optical cables during the inspection process are collected, and the inspection results are sorted, analyzed, and processed to generate actual inspection results; Based on the actual inspection results, the predicted data in the high-precision digital twin model is compared to evaluate the accuracy of the predicted data, identify prediction deviations, and generate a prediction accuracy evaluation report; Based on the forecast accuracy assessment report, analyze forecast deviations, identify the causes of the deviations, propose improvement measures, and generate a forecast model optimization plan; Based on the prediction model optimization scheme, the high-precision digital twin model is updated and optimized to improve the prediction accuracy of the model, and an optimized digital twin model is generated to improve the operation and maintenance efficiency and system reliability of the port communication optical cable.

8. A port communication optical cable routing detection system based on digital twin, characterized in that: include: Build a module to construct a high-precision digital twin model of the port environment and communication cable distribution, so as to synchronize the status information of the port communication cables with environmental variables in real time, ensure the consistency between the model and the actual environment, and generate a high-precision digital twin environment; An analysis module, based on the high-precision digital twin environment, analyzes historical environmental data and communication cable performance data, uses a long short-term memory network to predict performance degradation or failure of the communication cable under specific environmental conditions, and generates a potential failure prediction report; A monitoring module, based on the potential fault prediction report, applies an ant colony algorithm to automatically search for an optimal inspection path in the high-precision digital twin environment, monitors and records key performance indicators of the communication optical cable, and generates an optimized inspection path and inspection task list; The optimization module guides the actual inspection work based on the optimized inspection path and inspection task list, compares the actual inspection results with the predicted data in the high-precision digital twin model, and evaluates and optimizes the prediction accuracy to improve the operation and maintenance efficiency and system reliability of the port communication optical cable.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a port communication optical cable routing detection method based on digital twins as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a port communication optical cable routing detection method based on digital twins as described in any one of claims 1 to 7 is implemented.

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