A method, system, equipment, and medium for laying high-fiber optical cables and upgrading communication networks.

Abstract

This invention belongs to the field of data processing technology, specifically disclosing a method, system, equipment, and medium for laying high-fiber-core optical cables and upgrading communication networks. The method includes extracting the duct well coordinates and geotag data of reserved fiber cores from a resource pool database, performing preliminary correction processing on coordinate deviations to obtain a corrected set of coordinates with meter-level accuracy; based on the corrected set of coordinates with meter-level accuracy, using the Dijkstra algorithm to calculate potential routing paths from service triggering points to available fiber cores, determining the number and length of curved duct segments in the potential routing paths, and obtaining a list of optimized routing options. The purpose of this invention is to solve the problems of low fiber optic resource scheduling efficiency, unstable signal quality, and service response delays during the laying of high-fiber-core optical cables and upgrading of communication networks, caused by low accuracy of reserved fiber core geographical location information, asynchronous resource allocation and physical status, and lack of dynamic routing optimization mechanisms.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, system, equipment, and medium for laying high-fiber optical cables and upgrading communication networks. Background Technology

[0002] In fiber optic communication networks, high-fiber-core cable laying systems are core infrastructure supporting high-speed data transmission and service expansion, crucial for ensuring network stability and scalability. With the surge in information technology demands, particularly in scenarios like 5G, cloud computing, and the Internet of Things (IoT), networks need to respond quickly to service growth and ensure flexible allocation of fiber optic resources. Centralized fiber core reserved resource pool management technology manages reserved fiber optic resources through a unified platform, aiming to improve resource utilization efficiency and response speed. However, existing technologies exhibit significant shortcomings when dealing with highly dynamic service scenarios, urgently requiring solutions to adapt to the actual needs of complex network environments. Existing methods often struggle to balance the dynamic allocation of resource pools with the adaptation to physical layer constraints when managing reserved fiber optic resources. Current solutions largely rely on static resource registration, lacking precise mapping of fiber optic physical characteristics and geographical location, resulting in the inability to quickly match suitable fiber optic resources during service expansion. For example, the system may select unsuitable fiber cores due to inaccurate geographical information, leading to a decline in signal transmission quality. Furthermore, existing technologies lag in state updates, with inconsistencies between logical layer resource states and actual physical layer states, increasing the cost of manual intervention. These issues collectively limit the resource pool's scheduling capabilities in highly dynamic scenarios. The core technical challenge lies in achieving accurate management of fiber optic resource geographic location information. Fiber optic geographic location information is not only the foundation of resource scheduling but also directly impacts routing and signal transmission quality. Because fiber optic laying involves complex duct environments, if geographic markings are not highly accurate (e.g., meter-level), the system may select nearby but unusable fiber cores when scanning the resource pool. For example, during an urgent service expansion in a certain area, the system might call reserved fiber cores based on junction box numbers and duct well coordinates, but coordinate deviations could cause the selected fiber cores to travel through curved ducts, increasing losses and affecting signal quality. Even if priority rules differentiate between urgent and routine services, coordinate deviations can still cause conflicts between the system's automatically updated status and actual splicing preparation, requiring manual verification and causing allocation delays. Therefore, achieving accurate recording and real-time updating of fiber optic geographic location information in centralized resource pool management is a key issue for rapidly responding to service expansion needs. Inaccurate geographic location information not only affects the rapid call-up of fiber cores but may also lead to allocation failures due to physical layer limitations (such as insufficient duct capacity or fiber core length). This requires the system to not only establish a precise numbering system and database mapping in resource pool management, but also to ensure real-time synchronization between the logical layer state and the actual physical layer status to avoid scheduling conflicts caused by information discrepancies. In highly dynamic business scenarios, the accuracy of geographic location information directly determines whether the resource pool can efficiently support network expansion. For example, when a sudden surge in traffic demand occurs in a certain area, the system needs to quickly retrieve fiber cores from the resource pool that meet the required rate level and type. However, if geographic coordinate deviations are not corrected in time, the selected fiber cores may not meet the actual routing requirements, thus affecting the efficiency of business deployment.Therefore, how to improve the accuracy of geographic location information and achieve status synchronization through a centralized management platform has become the key problem to be solved in this study. Summary of the Invention

[0003] This invention provides a method, system, equipment, and medium for laying high-fiber-core optical cables and upgrading communication networks. The purpose is to solve the problems of low fiber optic resource scheduling efficiency, unstable signal quality, and delayed service response caused by low accuracy of reserved fiber core geographical location information, asynchronous resource allocation and physical status, and lack of dynamic routing optimization mechanism during the laying of high-fiber-core optical cables and upgrading of communication networks.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for laying high-fiber-core optical cables and upgrading communication networks includes: extracting the duct well coordinates and geomarking data of reserved fiber cores from a resource pool database, performing preliminary correction processing on the coordinate deviations to obtain a corrected set of coordinates with meter-level accuracy; calculating potential routing paths from service trigger points to available fiber cores using the Dijkstra algorithm based on the corrected set of coordinates with meter-level accuracy, determining the number and length of curved duct segments in the potential routing paths, and obtaining a list of optimized routing options; acquiring path data from the list of optimized routing options, applying a neural network model to each path data to predict potential loss values, and determining that if the potential loss value is lower than a preset threshold, the path data is identified as a candidate route, resulting in a set of filtered candidate routes; matching service requirements from fiber core type attributes and status identifiers to the filtered set of candidate routes, determining whether the type attributes are consistent with single-mode or multimode fiber requirements, and obtaining a type-matched fiber core allocation scheme; updating the resource pool database using the type-matched fiber core allocation scheme. The logical status identifiers in the source pool database are compared with the physical layer fusion preparation data. If the logical status identifiers show availability, the allocation operation is determined to be executable, and a final allocation instruction is obtained. Using the final allocation instruction, the signal quality indicators during the route remapping process are monitored to determine whether the signal quality indicators are maintained within a specified range, and real-time monitoring feedback data is obtained. Based on the real-time monitoring feedback data, the state synchronization is automatically adjusted. If the real-time monitoring feedback data indicates a deviation, all relevant identifiers are updated through the resource pool database mapping to obtain a complete resource pool state synchronization result. For the complete resource pool state synchronization result, backup path data is extracted from the filtered candidate route set, and the neural network model is used to perform secondary prediction on the backup path data to determine whether the potential loss value of the secondary prediction is lower than the preset threshold, thereby obtaining a backup allocation scheme. Based on the backup allocation scheme, the backup status identifiers in the type-matching fiber core allocation scheme are updated to obtain the extended final allocation instruction.

[0005] In one aspect of this disclosure, the method of extracting the coordinates and geotag data of the reserved fiber core pipeline wells from the resource pool database, performing preliminary correction processing on the coordinate deviation, and obtaining a corrected set of coordinates with meter-level accuracy includes: Obtain the coordinates and geotag data of the reserved fiber core pipeline well from the resource pool database to determine the initial coordinate set; The coordinate deviation values ​​are calculated based on the initial coordinate set, and the deviation distribution is obtained. If there are outliers in the deviation distribution, the outliers are smoothed using a Kalman filter to obtain a smoothed coordinate set; The matching consistency is determined by matching a smoothed coordinate set with the geotagged data. By adjusting the coordinate offset based on the consistency matching results, the offset correction coordinates are obtained; The least squares method is applied to fit the offset correction coordinates to determine the set of fitted coordinates. Obtain the accuracy index of the fitted coordinate set to obtain a coordinate set with meter-level accuracy.

[0006] In one aspect of this disclosure, the step of calculating potential routing paths from the service trigger point to available fiber cores using the Dijkstra algorithm based on the corrected meter-level precision coordinate set, determining the number and length of curved pipe segments in the potential routing paths, and obtaining a list of optimized routing options includes: Potential path data is obtained by calibrating a meter-level coordinate set; Dijkstra's algorithm is used to calculate the potential paths from the trigger point to the available fiber core, resulting in a set of paths. The number of curved segments is assessed based on the path set, and quantitative indicators are determined. If the quantity index exceeds the preset threshold, the filtering path is obtained by filtering the set of paths through the information processing stage; The length of the curved section is evaluated based on the filtering path to determine the length index; The fused index is obtained by fusing length and quantity indicators. The K-means algorithm is used to cluster and fuse metrics to determine the optimized route list.

[0007] In one aspect of this disclosure, the step of obtaining path data from the optimized route option list, applying a neural network model to each path data to predict a potential loss value, and determining the path data as a candidate route if the potential loss value is lower than a preset threshold, thereby obtaining a filtered candidate route set, includes: Obtain path data, and calculate the potential loss value for each path data using a neural network model to obtain a loss value sequence; Based on the loss value sequence, if the loss value is lower than a preset threshold, the path data is determined as a preliminary candidate, and a preliminary candidate set is obtained; Cluster analysis was used to group the initial candidate set, resulting in grouped path clusters; For grouped path clusters, the loss values ​​are arranged using a sorting algorithm to obtain a sorted path list; If the loss value of the first path in the sorted path list is lower than the average loss value, then the path is determined to be the preferred route, and a subset of preferred routes is obtained. Based on the preferred route subset, adjacent path data are merged to obtain the merged route path; The final set of candidate routes is determined by verifying the connectivity of the merged route paths.

[0008] In one aspect of this disclosure, the step of matching service requirements from fiber core type attributes and status identifiers for the filtered candidate route set, determining whether the type attribute is consistent with single-mode or multimode fiber requirements, and obtaining a type-matched fiber core allocation scheme includes: Obtain the fiber core type attribute and status identifier through the candidate route set to determine the initial list that matches the business requirements; Based on the initial list, determine the type attribute. If the type attribute meets the single-mode requirement, mark it as a single-mode matching group and obtain the single-mode matching group. Obtain the status identifier from the single-mode matching group, determine whether the status identifier is available, and obtain the set of available single-mode fiber cores; For the available single-mode fiber core set to match the multimode requirements, if the multimode requirements are not met, the corresponding fiber core is excluded, and a type-compatible fiber core set is determined. Load balancing is calculated using type-compatible fiber core groups, and the fiber core distribution is clustered using the K-means algorithm to obtain a balanced distribution subset; Extract priority identifiers from the balanced allocation subset, and if the priority identifier is higher than a preset threshold, it is allocated to the main path to obtain the main path allocation scheme. Verify the backup paths according to the primary path allocation scheme. If the status identifiers of the backup paths are consistent, they are integrated to determine the final fiber core allocation scheme.

[0009] In one aspect of this disclosure, updating the logical status identifier and physical layer fusion preparation data in the resource pool database through the type-matched fiber core allocation scheme, and determining that the allocation operation is executable if the logical status identifier indicates availability, and obtaining the final allocation instruction, includes: Type matching is performed by obtaining the fiber core type to determine the matching result; If the type match is successful, the logical status identifier and physical layer data are obtained from the resource pool database to determine whether the status identifier is in an available state. Update the status identifier in the resource pool database based on the judgment result to obtain the updated status data; The updated status data is used to generate welding preparation data, and the welding preparation is confirmed to be complete. If the fusion splicing preparation is complete, an allocation instruction is generated based on the fiber core type and status data, and the instruction content is obtained; Data synchronization is performed using allocation instructions, the resource pool database is updated, and synchronization is confirmed to be complete. The final allocation result is generated by verifying the execution status of the data verification instructions through synchronous completion.

[0010] In one aspect of this disclosure, the step of using the final allocation instruction to monitor signal quality indicators during the route remapping process, determining whether the signal quality indicators remain within a specified range, and obtaining real-time monitoring feedback data includes: Obtain signal quality metrics during route remapping by extracting signal strength and bit error rate data from network devices using a preset acquisition protocol to obtain an initial signal dataset. The initial signal dataset is classified using the support vector machine algorithm to determine whether the signal strength and bit error rate are within the preset range, and the classification result is obtained. If the classification results show that the signal quality exceeds the preset range, an adaptive filter is used to smooth out the outliers in the time series and generate an optimized signal dataset. Based on the optimized signal dataset, the signal stability index is calculated, and the mean-variance analysis method is used to determine the degree of signal fluctuation, thus obtaining the stability assessment results. Based on the stability assessment results, route state data is obtained, and time series analysis is used to detect abnormal changes during the route remapping process to generate a route state sequence. The routing state sequence is grouped by cluster analysis to determine whether the routing state of each group meets the expected pattern, and real-time monitoring feedback data of route remapping is obtained. By using real-time monitoring feedback data, the dynamic thresholds of signal quality indicators are updated, and the signal change trend is continuously tracked through sliding window technology to generate a real-time monitoring data stream.

[0011] In another aspect, this disclosure also relates to a high-fiber-core optical cable laying and communication network upgrade system, comprising: The coordinate correction module is used to perform preliminary correction of coordinate deviation by extracting the coordinates and geomarking data of the reserved fiber core pipeline well from the resource pool database, and obtain a set of corrected coordinates with meter-level accuracy. The routing optimization module is used to calculate the potential routing path from the service trigger point to the available fiber core using the Dijkstra algorithm based on the corrected meter-level precision coordinate set, determine the number and length of the curved pipe segments in the potential routing path, and obtain a list of optimized routing options. The loss prediction module is used to obtain path data from the optimized route option list, apply a neural network model to each path data to predict the potential loss value, and if the potential loss value is lower than a preset threshold, then the path data is determined as a candidate route, and a set of filtered candidate routes is obtained. The type matching module is used to match service requirements from fiber core type attributes and status identifiers for the filtered candidate route set, determine whether the type attribute is consistent with the requirements of single-mode or multimode fiber, and obtain a type-matched fiber core allocation scheme. The status update module is used to update the logical status identifier and physical layer fusion preparation data in the resource pool database according to the fiber core allocation scheme that matches the type. If the logical status identifier shows that it is available, the allocation operation is determined to be executable and the final allocation instruction is obtained. The quality monitoring module is used to monitor the signal quality indicators during the route remapping process using the final allocation instruction, determine whether the signal quality indicators are maintained within a specified range, and obtain real-time monitoring feedback data. The synchronization adjustment module is used to automatically adjust the state synchronization based on the real-time monitoring feedback data. If the real-time monitoring feedback data indicates a deviation, all relevant identifiers are updated through the resource pool database mapping to obtain a complete resource pool state synchronization result. The backup prediction module is used to extract backup path data from the filtered candidate route set based on the complete resource pool status synchronization result, use the neural network model to perform secondary prediction on the backup path data, determine whether the potential loss value of the secondary prediction is lower than the preset threshold, and obtain a backup allocation scheme. The allocation extension module is used to update the standby status flag in the type-matching fiber core allocation scheme according to the standby allocation scheme, so as to obtain the extended final allocation instruction.

[0012] In another aspect, this disclosure also relates to an electronic device, comprising: A memory on which computer programs are stored; A processor is used to execute the computer program in the memory to implement the above-described method for laying high-fiber optical cables and upgrading communication networks.

[0013] In another aspect, this disclosure also relates to a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the above-described method for laying high-fiber optical cables and upgrading communication networks.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention extracts pipeline well coordinates and geotag data from a resource pool database, corrects the coordinates to meter-level accuracy, uses Dijkstra's algorithm to calculate potential routing paths and evaluate curved pipeline segments, combines a neural network model to predict path loss, filters low-loss candidate routes, matches single-mode or multi-mode fiber requirements, and generates a type-matching allocation scheme. Simultaneously, by updating logical state and physical layer data, it ensures the allocation is executable, monitors signal quality in real time, automatically adjusts state synchronization, maps and updates resource pool identifiers, generates backup allocation schemes, and optimizes the final command. This invention significantly improves the intelligence level and resource utilization efficiency of fiber optic routing allocation, ensuring signal quality and network stability. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is one of the flowcharts for a high-fiber-core optical cable laying and communication network upgrade method according to the present invention.

[0017] Figure 2 This is the second flowchart of a method for laying high-fiber optical cables and upgrading communication networks according to the present invention.

[0018] Figure 3 This is the third flowchart of a method for laying high-fiber optical cables and upgrading communication networks according to the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to embodiments. These embodiments are merely some, not all, of the embodiments described. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the protection scope of the present invention.

[0020] Please see Figures 1-3 As shown in the figure, this embodiment discloses a method for laying high-fiber optical cables and upgrading communication networks, which may specifically include: Step 1: Extract the coordinates and geotag data of the reserved fiber core pipeline wells from the resource pool database, perform preliminary correction of coordinate deviations, and generate a set of corrected coordinates with meter-level accuracy.

[0021] Specifically, the resource pool database stores the geographic information of all reserved fiber cores in the fiber optic network, including the latitude and longitude coordinates of the duct shafts and geographic marker data, such as street names or building numbers. The extraction process is achieved through database queries to generate an initial set of coordinates.

[0022] For example, in a city's fiber optic network, the initial coordinate set might contain the latitude and longitude data for 100 manholes. Coordinate deviation correction calculates the deviation value and generates a deviation distribution by comparing the actual coordinates with the database records. The deviation distribution reflects the error range of the coordinate data. For example, the coordinate deviation of a certain pipeline well might be 5 meters. In one embodiment, deviation correction employs statistical analysis methods to calculate the mean and variance of the deviation and identify outliers. Outliers are typically caused by data entry errors or equipment positioning deviations. For example, if a coordinate point deviates from the normal range by more than 10 meters, an outlier is detected. A Kalman filter is then used to smooth the coordinates, generating a smoothed coordinate set. The Kalman filter dynamically adjusts the coordinate estimation by combining historical coordinate data with current measurements, thus reducing errors.

[0023] For example, in a complex terrain area, filters can effectively correct coordinate deviations caused by terrain interference. It should be noted that the accuracy of smoothed coordinate sets typically reaches the meter level, meeting the requirements of fiber optic network allocation.

[0024] Step 1.1: Based on the matching of the smoothed coordinate set and the geotagged data, determine the consistency of the match, adjust the coordinate offset, and generate offset correction coordinates. The geotagged data includes the physical location information of the pipeline well. For example, it may be located near an intersection or building. Match consistency is determined by comparing the correspondence between smoothed coordinates and geographic tags to identify any discrepancies.

[0025] For example, in a city network, if the smoothed coordinates of a manhole show it as being on a certain street, but the geotag indicates it's located on another street, this is considered a mismatch. In one possible implementation, the matching process determines a consistency score by calculating the Euclidean distance between the coordinates and the geotag. Coordinates with scores below a preset threshold require offset adjustments.

[0026] For example, offset adjustment might move the coordinate point 2 meters towards the center of the geotag, generating offset-corrected coordinates. It's important to note that offset adjustment must consider the network topology to ensure that the adjusted coordinates do not affect path connectivity. Using offset-corrected coordinates, the system can provide high-precision data support for subsequent path calculations.

[0027] Step 1.2: For the offset correction coordinates, apply the least squares method to fit and generate a set of fitted coordinates. The least squares method generates a more accurate set of coordinates by optimizing the error between the coordinate points and the geographic markers.

[0028] For example, in a regional fiber optic network, the least squares method might fit a smooth path curve based on the coordinate data of multiple manholes, reducing positioning errors. In one embodiment, the fitting process takes into account the physical distribution of the manholes. For example, manholes are typically arranged linearly along streets. The accuracy index of the fitted coordinate set is obtained by calculating the root mean square of the residuals. For example, if the residual value is less than 1 meter, the accuracy is considered to have reached the meter level. It should be noted that the fitting process must avoid overfitting to ensure that the coordinate set reflects the actual network layout. Through verification of the accuracy indicators, a corrected meter-level accurate coordinate set is generated, providing a reliable data foundation for subsequent route calculations.

[0029] Step 2: Based on the corrected meter-level precision coordinate set, the shortest path algorithm is used to calculate the potential routing path from the service trigger point to the available fiber core, determine the number and length of the curved pipe segments in the path, and generate a list of optimized routing options.

[0030] Specifically, the corrected meter-level precision coordinate set contains the accurate location information of the pipeline well, providing high-precision input for path calculation. The shortest path algorithm calculates the optimal path from the service trigger point to the available fiber core by constructing a network topology graph.

[0031] For example, in a city fiber optic network, the service trigger point might be a data center. The algorithm calculates all possible paths from the data center to the target manhole, generating a path set. Each path in the path set contains manhole sequence and path length information. In one embodiment, the algorithm uses a weighted graph model, employing path length and the number of bends as weights to optimize computational efficiency. It should be noted that the number and length of bends directly affect signal transmission quality and require special evaluation. The evaluation process determines the quantity index by statistically analyzing the bends in each path.

[0032] For example, if a path contains 3 curved sections with a total length of 50 meters, it is recorded as quantity index 3 and length index 50 meters. If the quantity index exceeds a preset threshold, For example, if there are more than 5 bends, the path set is filtered through the information processing stage to generate a filtered path. The filtering process removes paths with too many bends or excessive lengths to ensure path quality.

[0033] For example, in a high-density urban network, filtering might retain paths with fewer than three bends. By evaluating the bend length of the filtered paths, a length metric is generated, which is then fused with a quantity metric to produce a fused metric. This fused metric comprehensively reflects the transmission performance of the path. For example, a fusion metric for a certain path might be 0.6, indicating high overall performance. The K-means clustering algorithm is used to group the fusion metrics, generating a list of optimized routing options.

[0034] For example, the list may contain three sets of paths, representing low, medium, and high performance paths, for subsequent allocation.

[0035] In one possible implementation, the generation of the optimized routing option list takes into account a variety of business scenarios.

[0036] For example, in a real-time communication scenario, the system prioritizes the path with the fewest bends to reduce signal latency. In a long-distance transmission scenario, the system might prioritize a path with a shorter total length but slightly more bends to balance loss and distance. Path selection for various scenarios is achieved through cluster analysis to ensure the list covers different business needs. It should be noted that the path calculation and evaluation process needs to interact with the resource pool database in real time to ensure the accuracy and availability of path data.

[0037] For example, a route may be temporarily unavailable due to pipeline maintenance and needs to be removed from the list. Through multi-scenario optimization, the routing option list can provide flexible support for subsequent loss prediction and fiber core allocation.

[0038] Step 3: Obtain path data from the optimized route option list, apply a neural network model to predict potential loss values, and if the potential loss value is lower than a preset threshold, then determine the path data as a candidate route and obtain the filtered candidate route set.

[0039] The neural network model is a multilayer perceptron model, whose input layer receives feature vectors. ,in This is the total path length. This represents the total number of curved sections. Encoding for single-mode / multimode fiber type, Historical average degradation rate; predicted potential output loss of the output layer. The hidden layer uses the ReLU activation function, expressed as follows: This is to handle the nonlinear mapping relationship between features.

[0040] Specifically, the optimized routing option list includes potential paths filtered in step 2. Each path includes data such as well shaft coordinates, path length, and bend information. A neural network model is used to predict the potential loss value for each path, which reflects the degree of signal attenuation during fiber optic transmission. The neural network model is typically trained based on historical transmission data and environmental factors, with input features including path length, number of bends, and fiber core type.

[0041] For example, in a city fiber optic network, a model might predict signal attenuation based on the number of manholes a path passes through and environmental interference factors. In one embodiment, after acquiring path data, the features of each path are input into a neural network model to generate a sequence of loss values. This sequence contains the predicted loss value for each path, typically expressed in decibels.

[0042] For example, the predicted loss value for one path might be 0.5 dB, while for another it might be 1.2 dB. It's important to note that the neural network model uses a multi-layered neuron structure combined with activation functions to process input features and generate continuous loss value outputs. To improve prediction accuracy, the model might employ a convolutional neural network or a recurrent neural network structure, depending on the complexity of the path data. Based on the loss value sequence, it is determined whether the loss value of each path is below a preset threshold. For example, 0.8 dB. If the loss value of a certain path is lower than the threshold, it is marked as a preliminary candidate path, and a preliminary candidate set is generated.

[0043] For example, in a data center scenario, the initial candidate set might contain 10 paths with low loss values, suitable for high-bandwidth services. It's important to note that the selection of the preset threshold is based on service requirements and network performance demands, and is typically set by the network administrator according to the actual scenario. In one possible implementation, cluster analysis is performed on the initial candidate set, dividing the paths into multiple path clusters. The cluster analysis uses the K-means algorithm, grouping paths with similar performance based on loss value and path length.

[0044] For example, in a regional fiber optic network, clustering might group short-distance, low-loss paths into clusters, prioritizing them for real-time communication services. Cluster analysis can further optimize the selection of candidate paths, ensuring that the grouping results meet service requirements. For each cluster of paths, a sorting algorithm is used to rank the paths by loss value, generating a ranked path list. The sorting algorithm typically employs quicksort to ensure computational efficiency.

[0045] For example, paths in a cluster might be arranged from lowest to highest loss value, with the first path having the lowest loss value. If the loss value of the first path is lower than the average loss value, For example, if the value is 0.6 dB below the average value of paths within the cluster, it is marked as a priority route, and a priority route subset is generated. Based on the priority route subset, adjacent path data is merged to generate a merged route path. Adjacent paths refer to paths that are geographically consecutive or share the same pipe shaft; merging them reduces the complexity of path switching.

[0046] For example, in a city fiber optic network, two paths sharing the same manhole might be merged into a single, longer path to simplify the allocation process. It's important to note that the merging process must ensure path connectivity and signal transmission stability. By verifying the connectivity of the merged routes, a final set of candidate routes is determined. Connectivity verification involves checking the reachability of each manhole in the path and identifying any points of interruption.

[0047] For example, if a merged route passes through a manhole that is obstructed by construction, that route is eliminated. The final candidate route set contains several paths that meet the loss and connectivity requirements for subsequent fiber core allocation.

[0048] Step 4: For the filtered candidate route set, match the service requirements from the fiber core type attribute and status identifier, and determine whether the type attribute is consistent with the single-mode or multimode fiber requirement to obtain a type-matched fiber core allocation scheme. In one embodiment, each path in the candidate route set is associated with a corresponding fiber core resource. The fiber core type attribute includes single-mode fiber or multimode fiber, and the status identifier reflects the availability or occupancy status of the fiber core. By obtaining the fiber core type attribute and status identifier, an initial list matching the service requirements is generated.

[0049] For example, in a corporate leased line scenario, business requirements might necessitate the use of single-mode fiber to support long-distance, low-loss transmission. The initial list would contain core information for all single-mode fibers. Based on this initial list, it would determine whether the fiber core type attributes meet the single-mode fiber requirements; if so, it would be marked as a single-mode matching group.

[0050] For example, if a fiber core's type attribute indicates it's single-mode fiber and its bandwidth capacity meets service requirements, it will be included in the single-mode matching group. It should be noted that single-mode fiber is typically used for long-distance, high-bandwidth transmission, while multimode fiber is suitable for short-distance, high-throughput scenarios; the matching process must be performed according to the specific requirements of the service. The status identifier is obtained from the single-mode matching group to determine if the fiber core is in an available state, generating a set of available single-mode fiber cores. The status identifier typically includes states such as "available," "occupied," or "faulty."

[0051] For example, in a regional network, a single-mode fiber core might be marked as "faulty" due to maintenance and thus excluded from the available set. It's important to note that the status identification is based on real-time updates from the resource pool database to ensure accurate allocation. In one possible implementation, multimode fiber requirements are matched against the available single-mode fiber core set. If the service requirements also involve multimode fiber, the cores in the single-mode matching group undergo a secondary screening, removing cores that do not meet the multimode requirements and generating a type-compatible core group.

[0052] For example, in a hybrid network scenario, services may require some paths to use multimode fiber to support high throughput. After screening, fiber cores that simultaneously meet both single-mode and multimode requirements are retained. Load balancing is calculated using type-compatible fiber core groups, and the fiber core distribution is grouped using the K-means clustering algorithm to generate a balanced allocation subset. Load balancing considers factors such as the current load, historical usage frequency, and bandwidth capacity of the fiber cores.

[0053] For example, in a data center network, the algorithm might group low-load fiber cores together and prioritize their allocation for new services. Cluster analysis ensures a balance between performance and resource utilization in fiber core allocation. Priority identifiers are extracted from the balanced allocation subsets to determine if the priority exceeds a preset threshold. For example, a priority value greater than 0.7. If the condition is met, the corresponding fiber core will be assigned to the main path, generating a main path allocation scheme.

[0054] For example, in a high-priority service scenario, the highest-priority fiber core will be allocated to the primary path to ensure transmission quality. It should be noted that the priority threshold is set based on the urgency of the service and network resource conditions. Based on the primary path allocation scheme, the availability of backup paths is verified. If the status indicator of the backup path matches that of the primary path, the two are integrated to generate the final fiber core allocation scheme. Backup paths are used to provide redundancy support and ensure network reliability.

[0055] For example, in a city fiber optic network, a backup path might be selected from a different conduit shaft than the primary path to avoid a single point of failure. After consolidation, the allocation scheme includes details of the fiber core allocation for both the primary and backup paths.

[0056] Step 5: Update the logical status identifier and physical layer fusion preparation data in the resource pool database using the fiber core allocation scheme that matches the type. If the logical status identifier shows that it is available, the allocation operation is determined to be executable and the final allocation instruction is obtained.

[0057] Specifically, the type-matching fiber core allocation scheme includes fiber core allocation information for both the primary and backup paths. By obtaining the fiber core type and performing type matching, it is confirmed whether the allocation scheme is consistent with business requirements.

[0058] For example, in a corporate leased line scenario, the allocation scheme might specify the use of particular fiber core numbers for single-mode fiber. The matching process ensures that these fiber cores meet the service bandwidth and distance requirements. If the type match is successful, logical status identifiers and physical layer data are retrieved from the resource pool database to determine if the status identifier indicates an available state. The logical status identifier reflects the allocation status of the fiber core, while the physical layer data includes the location and connection status of the splice.

[0059] For example, if the logical status of a fiber core is "available" and the physical layer data shows that its splice point is ready, then allocation is considered feasible. Based on the judgment result, the status flag in the resource pool database is updated, and updated status data is generated.

[0060] For example, the status of the allocated fiber core is updated from "available" to "occupied," and the allocation time and service number are recorded. It should be noted that the database update uses a transaction processing mechanism to ensure data consistency and reliability. Based on the updated status data, splicing preparation data is generated to confirm that splicing preparation is complete. The splicing preparation data includes the specific location of the splice point, equipment configuration, and operational requirements.

[0061] For example, in a fiber optic network, fusion splice preparation may involve the physical connection of optical fibers within a duct shaft. After fusion splicing is complete, an instruction is generated containing fiber core numbers and splice point information. In one embodiment, an allocation instruction is generated based on fiber core type and status data, including a specific allocation scheme and execution steps.

[0062] For example, an instruction might specify the allocation of a particular single-mode fiber core to a specific service, including the connection order of fusion splices. Data synchronization is performed through the allocation instruction, updating the resource pool database and ensuring the real-time nature of all relevant data. The synchronized data is then used to verify the instruction execution status and generate the final allocation result. The verification process checks whether the allocation instruction has been executed correctly. For example, it confirms whether the fiber core has been successfully assigned to the specified path. If the verification is successful, the final assignment result is generated for subsequent network monitoring.

[0063] Step 6 involves using the final allocation command to monitor signal quality indicators during the route remapping process, determining whether these indicators remain within a specified range, and obtaining real-time monitoring feedback data. In one possible implementation, route remapping refers to dynamically adjusting the fiber optic path based on service requirements or network status during network operation. Signal quality indicators include signal strength and bit error rate, and initial signal datasets are extracted from network devices using a preset acquisition protocol.

[0064] For example, in a city's fiber optic network, a data acquisition protocol might periodically obtain signal strength data via an optical power meter. A support vector machine (SVM) algorithm is then used to classify the initial signal dataset, determining whether the signal strength and bit error rate are within preset ranges. The SVM constructs a classification hyperplane to categorize the signal data into "normal" and "abnormal" classes.

[0065] For example, if the bit error rate of a certain path exceeds 10^-6, it is marked as abnormal. The classification result reflects the overall state of signal quality. If the classification result shows that the signal quality exceeds the preset range, the signal strength time series data within the preset time window is extracted, and the Kalman filter algorithm is used to estimate the state of the time series, filtering out false degradation indicators caused by instantaneous equipment jitter, and generating a smoothed true attenuation trend value; if the true attenuation trend value is consistently lower than the communication standard threshold, the backup route remapping mechanism is directly triggered, rather than denoising the indicator itself, to generate an optimized signal dataset. The adaptive filter dynamically adjusts the filtering parameters according to the temporal characteristics of the signal to reduce the impact of environmental interference.

[0066] For example, in a noisy industrial park network, filters can effectively reduce the interference of external electromagnetic interference on signals. Based on the optimized signal dataset, signal stability indices are calculated, and the degree of signal fluctuation is determined using mean-variance analysis.

[0067] For example, signal stability metrics might be derived by calculating the standard deviation of signal strength; a smaller standard deviation indicates a more stable signal. The stability assessment results provide a basis for subsequent route adjustments. In one embodiment, based on the stability assessment results, route state data is acquired, and time-series analysis methods are used to detect abnormal changes during the route remapping process.

[0068] For example, time series analysis can detect whether the signal strength of a path drops sharply within a short period of time, generating a routing state sequence. This sequence contains information about the dynamic changes in the path. Cluster analysis is then used to group the routing state sequences and determine whether each group conforms to an expected pattern.

[0069] For example, the expected pattern might require signal strength fluctuations to be controlled within 5%. If a group of routes deviates from the expected state, it is marked as an abnormal path. Real-time monitoring feedback data for route remapping is ultimately generated for dynamically adjusting network configuration. Using this real-time monitoring feedback data, dynamic thresholds for signal quality indicators are updated, and a sliding window technique is used to continuously track signal change trends, generating a real-time monitoring data stream.

[0070] For example, sliding window technology can dynamically adjust the bit error rate threshold by analyzing signal data from the most recent 10 minutes, ensuring the real-time performance and accuracy of monitoring.

[0071] Step 7: Based on the real-time monitoring feedback data, automatically adjust the status synchronization. If the real-time monitoring feedback data indicates a deviation, update all relevant identifiers through the resource pool database mapping to obtain the complete resource pool status synchronization result.

[0072] Specifically, real-time monitoring feedback data includes dynamic information on signal quality indicators and routing status. Data analysis is used to determine whether any state synchronization deviations exist.

[0073] For example, if the signal strength of a certain path remains lower than expected, it may indicate a discrepancy between the fiber core allocation status and the actual status, suggesting a synchronization deviation. If a deviation exists, relevant identifiers are retrieved from the resource pool database mapping to generate a set of identifiers to be updated. This set of identifiers contains the fiber core numbers and status information that need to be adjusted.

[0074] For example, in a local area network, the identifiers to be updated might include fiber cores that need to be reassigned due to signal attenuation. For the set of identifiers to be updated, an identifier update operation is performed through database mapping to generate the updated identifier status.

[0075] For example, the status of a fiber core is updated from "occupied" to "available" for reallocation. Update operations are performed in batches to improve efficiency. Based on the updated identifier status, intermediate results for resource pool status synchronization are generated. These intermediate results contain all updated fiber core statuses and allocation information. A consistency check algorithm is used to verify the consistency between the intermediate results and real-time monitoring feedback data.

[0076] For example, the verification algorithm checks whether the updated state matches the signal quality data. If the consistency check passes, a complete resource pool state synchronization result is generated, including the latest state and allocation scheme of all fiber cores. The synchronization result is stored in the database mapping through the resource management module, updating the resource pool state and ensuring the real-time nature and accuracy of network resource information.

[0077] Step 8: Based on the complete resource pool status synchronization results, extract backup path data from the filtered candidate route set. Use a neural network model to perform secondary prediction on the backup path data, and determine whether the potential loss value of the secondary prediction is lower than a preset threshold to obtain a backup allocation scheme. In one embodiment, the complete resource pool status synchronization results provide the latest fiber core and path information. Backup path data is extracted from the candidate route set to generate candidate route data. Backup path data typically includes potential paths not assigned as primary paths, retained as redundancy options. Backup path data is extracted from the candidate route data, and principal component analysis (PCA) is used to generate a path feature set. PCA extracts key features such as path length and number of bends through dimensionality reduction, generating path feature data.

[0078] For example, in a city network, principal component analysis might simplify path features to two main dimensions: distance and loss. Multilayer perceptrons (MLPs) are then used to predict potential loss values ​​from the path feature data. The MLP uses a multilayer neural network structure to predict signal attenuation values ​​based on path features.

[0079] For example, the predicted loss value for a backup path might be 0.4 dB. If the potential loss value is lower than a preset threshold, For example, if the value is 0.8 dB, Dijkstra's algorithm is used to generate alternative allocation schemes. Allocation priorities are obtained from the allocation scheme data, and the priorities are sorted using the quicksort algorithm to generate an optimized allocation scheme.

[0080] In this embodiment, the nodes in Dijkstra's algorithm To the node Path weights between The calculation formula is defined as follows:

[0081] in, For nodes With nodes The actual length of the pipe between them; This represents the number of bends within that section of the pipe. This is the length penalty coefficient; The base for bending penalty; This is the bending exponential factor. This formula maps the nonlinear effect of the number of bends on signal loss into a weight value that the algorithm can handle.

[0082] For example, the highest priority backup path will be given priority for redundancy allocation. Based on the optimized allocation scheme, a resource mapping function is used to map the scheme to the resource pool state, generating the final allocation result. The final allocation result is then used to verify the resource pool state using a consistency check function, ensuring that the allocation scheme is consistent with the database information.

[0083] For example, a check function verifies the availability of fiber cores for backup paths. If the verification passes, a final, confirmed backup allocation scheme is generated for network redundancy configuration. In one possible implementation, the generation of the backup allocation scheme considers multiple scenarios.

[0084] For example, in a data center network, a backup path might be preferentially selected from a completely independent conduit shaft to improve fault tolerance. In a long-distance transmission scenario, a backup path might be selected from low-loss single-mode fiber to ensure signal quality. Backup schemes for various scenarios are generated through cluster analysis to ensure the flexibility and reliability of allocation. It should be noted that the verification process for backup allocation schemes is similar to that for primary path allocation, requiring checks on path connectivity and state consistency.

[0085] For example, in a regional network, verification might include checking whether the backup path traverses a known faulty area. Through multi-scenario verification, backup allocation schemes can effectively support dynamic network adjustment and fault recovery.

[0086] Step 9: Extract the initial data of each path in the fiber optic network to be allocated, query the backup scheme based on the pre-established database, update the backup status flag in the fiber core allocation scheme with matching type, and obtain the extended final allocation instruction.

[0087] Specifically, the fiber optic network to be allocated comprises multiple paths, each consisting of data such as duct well coordinates, fiber core type, and status identifier. A pre-established database stores historical allocation schemes and backup scheme information, with backup schemes typically including potential paths not used by the primary path and their status identifiers.

[0088] For example, in a city's fiber optic network, a database might record the fiber core allocation history of all manholes within a certain area, including the availability of single-mode and multimode fibers. When querying backup plans, the system retrieves initial data related to the current allocation plan by matching the fiber core type to the service requirements, generating a set of backup status identifiers. This set of backup status identifiers contains availability information for each path. For example, "available", "occupied", or "faulty".

[0089] In one embodiment, database queries are based on Structured Query Language (SCL) to quickly extract relevant data through keyword matching. It should be noted that the database design needs to support high-concurrency access to meet the demands of large-scale network allocation. By analyzing the set of standby status identifiers, a decision tree algorithm is used to determine the degree of type matching and generate an update priority sequence. The decision tree algorithm constructs multi-layer decision nodes based on features such as fiber core type, path length, and historical usage frequency, outputting a priority score for each path.

[0090] For example, in a data center scenario, a decision tree might prioritize fiber cores with high bandwidth capacity that are not frequently used. Through this priority sequence, the system can quickly identify the path suitable for the current business needs.

[0091] Step 9.1: If there is a conflict identifier in the update priority sequence, the backup plan data is integrated through the information processing stage to determine the conflict resolution path and obtain the adjusted priority sequence.

[0092] Specifically, conflict indicators typically appear when multiple paths are competing for the same fiber core resource. For example, two paths may simultaneously request the allocation of the same single-mode fiber core. The information processing stage compares the path priority scores and business requirements, integrates backup plan data, and determines the conflict resolution path.

[0093] For example, in a corporate leased line network, if two paths have similar priority scores, the system may evaluate the path's signal loss and distance, selecting the superior path as the primary path, while the other path is assigned to a backup plan. It should be noted that the information processing may involve a weighted scoring mechanism, comprehensively considering factors such as path length, fiber core status, and business urgency. In one possible implementation, conflict resolution paths are determined through iterative comparison, with each iteration updating the priority sequence until no conflict markers remain. The adjusted priority sequence includes the optimized path ranking, ensuring the rationality of resource allocation.

[0094] For example, in a regional fiber optic network, the adjusted sequence may prioritize the allocation of shorter and less lossy paths to improve transmission efficiency.

[0095] Step 9.2: For the adjusted priority sequence, obtain the extended instruction template and generate a preliminary extended structure. The extended instruction template is a predefined set of rules used to guide the extension of the fiber core allocation scheme.

[0096] For example, the template might specify that after the primary path allocation is completed, at least one backup path must be configured for each primary path. The initial extension structure generates an allocation framework containing primary and backup paths based on the path information in the priority sequence. In one embodiment, the extension instruction template might include constraints such as allocation priority, fiber core type, and path connectivity.

[0097] For example, in a city fiber optic network, the initial expansion structure might designate a single-mode fiber core as the primary path, while simultaneously allocating a backup path for that path. It's important to note that the design of the expansion instruction template needs to consider the network topology and service requirements to ensure the scalability of the allocation scheme. Through the initial expansion structure, the system can provide a basic framework for subsequent packetization and optimization.

[0098] Step 9.3: Based on the initial extended structure, the K-means clustering algorithm is used to group the fiber core allocation schemes and determine the extended state after grouping. The K-means clustering algorithm divides the allocation schemes into multiple groups based on path length, loss value, and fiber core type.

[0099] For example, in a data center network, the algorithm might group short-distance, low-loss paths into one group, suitable for high real-time services; and long-distance paths into another group, suitable for low-priority transmissions. The expanded state after grouping reflects the characteristic distribution of each group of paths. For example, the average loss or average length of a group of paths. In one possible implementation, the K-means clustering algorithm determines the optimal grouping result by iteratively optimizing the cluster centers.

[0100] For example, the system might set the number of clusters to 3, representing high, medium, and low priority paths respectively. It's important to note that the grouping process must ensure the connectivity and availability of each group of paths, avoiding the inclusion of unavailable paths in the allocation scheme. The expanded state after grouping provides data support for subsequent command integration.

[0101] Step 9.4: If the expanded state after grouping meets the preset threshold, the final instruction is integrated through the data merging process to generate a complete allocation instruction. The preset threshold typically includes indicators such as path loss, connectivity, and load balancing.

[0102] For example, the average loss of a group must be below 0.8 dB, and all paths must remain connected. The data merging process integrates the path data from the group to generate a complete allocation instruction that includes the primary path and backup paths.

[0103] For example, in a regional fiber optic network, the merging process might combine a high-priority group's primary path with a low-loss backup path to form a final instruction. It's important to note that the data merging process needs to check the compatibility between paths to ensure that the primary and backup paths do not share the same point of failure. In one embodiment, the complete allocation instruction includes information such as fiber core number, path coordinates, and allocation priority, which network devices then use to perform the allocation operation. A verification process checks the completeness and executability of the instruction, generating a final confirmed allocation instruction.

[0104] For example, verification might include checking whether all allocated fiber cores are available, and if any are unavailable, readjusting the allocation scheme.

[0105] It should be noted that the methods for determining the preset thresholds (including the preset threshold for potential loss value, the preset threshold for the number of bends, and the threshold for abnormal range, etc.) involved in this embodiment all follow the following data processing mechanism: Obtain the log data of successfully laid and scheduled fiber optic paths in the target communication network over a limited number of historical periods (e.g., 2000 times in the past 12 months); extract the physical and logical parameter values ​​(such as the corresponding loss value, number of bends, etc.) of all high-quality samples whose signal transmission quality remains within a predetermined communication standard (e.g., bit error rate below $10^{-6}$ and no packet loss); after removing the highest and lowest 5% outliers from the extracted parameter value dataset using a box plot method, calculate the arithmetic mean of the remaining valid samples, and add a 10% engineering redundancy tolerance to this arithmetic mean. Finally, set the calculation result as the system's preset threshold. For example, if the average loss value of high-quality paths in the historical samples is 0.6 dB, after adding 10% redundancy, the preset threshold for potential loss value entered by the system is 0.66 dB.

[0106] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for laying high-fiber optical cables and upgrading communication networks, characterized in that, include: By extracting the coordinates and geomarking data of the reserved fiber cores from the resource pool database, the coordinate deviation is initially corrected to obtain a set of corrected coordinates with meter-level accuracy. Based on the corrected meter-level precision coordinate set, the Dijkstra algorithm is used to calculate the potential routing path from the service trigger point to the available fiber core, determine the number and length of the curved pipe segments in the potential routing path, and obtain a list of optimized routing options. Obtain path data from the optimized route option list, apply a neural network model to each path data to predict the potential loss value, and if the potential loss value is lower than a preset threshold, determine the path data as a candidate route to obtain a filtered set of candidate routes. For the filtered candidate route set, the service requirements are matched from the fiber core type attribute and status identifier, and it is determined whether the type attribute is consistent with the single-mode or multimode fiber requirements to obtain a fiber core allocation scheme with matching type. By using the fiber core allocation scheme that matches the type, the logical status identifier and physical layer fusion preparation data in the resource pool database are updated. If the logical status identifier shows that it is available, the allocation operation is determined to be executable, and the final allocation instruction is obtained. Using the final allocation instruction, monitor the signal quality indicators during the route remapping process, determine whether the signal quality indicators are maintained within the specified range, and obtain real-time monitoring feedback data; Based on the real-time monitoring feedback data, the state synchronization is automatically adjusted. If the real-time monitoring feedback data indicates a deviation, all relevant identifiers are updated through the resource pool database mapping to obtain a complete resource pool state synchronization result. Based on the complete resource pool status synchronization results, backup path data is extracted from the filtered candidate route set, and the neural network model is used to perform secondary prediction on the backup path data. It is then determined whether the potential loss value of the secondary prediction is lower than the preset threshold to obtain a backup allocation scheme. Based on the backup allocation scheme, update the backup status flag in the fiber core allocation scheme that matches the type to obtain the extended final allocation instruction.

2. The method for laying high-fiber optical cables and upgrading communication networks according to claim 1, characterized in that: The process involves extracting the coordinates and geotag data of the reserved fiber core pipeline wells from the resource pool database, performing preliminary correction on the coordinate deviations, and obtaining a corrected set of coordinates with meter-level accuracy, including: Obtain the coordinates and geotag data of the reserved fiber core pipeline well from the resource pool database to determine the initial coordinate set; The coordinate deviation values ​​are calculated based on the initial coordinate set, and the deviation distribution is obtained. If there are outliers in the deviation distribution, the outliers are smoothed using a Kalman filter to obtain a smoothed coordinate set; The matching consistency is determined by matching a smoothed coordinate set with the geotagged data. By adjusting the coordinate offset based on the consistency matching results, the offset correction coordinates are obtained; The least squares method is applied to fit the offset correction coordinates to determine the set of fitted coordinates. Obtain the accuracy index of the fitted coordinate set to obtain a coordinate set with meter-level accuracy.

3. The method for laying high-fiber optical cables and upgrading communication networks according to claim 1, characterized in that: Based on the corrected meter-level precision coordinate set, the Dijkstra algorithm is used to calculate the potential route path from the service trigger point to the available fiber core. The number and length of curved pipe segments in the potential route path are determined to obtain a list of optimized route options, including: Potential path data is obtained by calibrating a meter-level coordinate set; Dijkstra's algorithm is used to calculate the potential paths from the trigger point to the available fiber core, resulting in a set of paths. The number of curved segments is assessed based on the path set, and quantitative indicators are determined. If the quantity index exceeds the preset threshold, the filtering path is obtained by filtering the set of paths through the information processing stage; The length of the curved section is evaluated based on the filtering path to determine the length index; The fused index is obtained by fusing length and quantity indicators. The K-means algorithm is used to cluster and fuse metrics to determine the optimized route list.

4. The method for laying high-fiber optical cables and upgrading communication networks according to claim 1, characterized in that: The process involves obtaining path data from the optimized route option list, applying a neural network model to each path data point to predict its potential loss value, and determining the path data as a candidate route if the potential loss value is lower than a preset threshold, thus obtaining a filtered set of candidate routes, including: Obtain path data, and calculate the potential loss value for each path data using a neural network model to obtain a loss value sequence; Based on the loss value sequence, if the loss value is lower than a preset threshold, the path data is determined as a preliminary candidate, and a preliminary candidate set is obtained; Cluster analysis was used to group the initial candidate set, resulting in grouped path clusters; For grouped path clusters, the loss values ​​are arranged using a sorting algorithm to obtain a sorted path list; If the loss value of the first path in the sorted path list is lower than the average loss value, then the path is determined to be the preferred route, and a subset of preferred routes is obtained. Based on the preferred route subset, adjacent path data are merged to obtain the merged route path; The final set of candidate routes is determined by verifying the connectivity of the merged route paths.

5. The method for laying high-fiber optical cables and upgrading communication networks according to claim 1, characterized in that: The process of matching service requirements from fiber core type attributes and status identifiers for the filtered candidate route set, determining whether the type attribute is consistent with the requirements of single-mode or multimode fiber, and obtaining a type-matched fiber core allocation scheme includes: Obtain the fiber core type attribute and status identifier through the candidate route set to determine the initial list that matches the business requirements; Based on the initial list, determine the type attribute. If the type attribute meets the single-mode requirement, mark it as a single-mode matching group and obtain the single-mode matching group. Obtain the status identifier from the single-mode matching group, determine whether the status identifier is available, and obtain the set of available single-mode fiber cores; For the available single-mode fiber core set to match the multimode requirements, if the multimode requirements are not met, the corresponding fiber core is excluded, and a type-compatible fiber core set is determined. Load balancing is calculated using type-compatible fiber core groups, and the fiber core distribution is clustered using the K-means algorithm to obtain a balanced distribution subset; Extract priority identifiers from the balanced allocation subset, and if the priority identifier is higher than a preset threshold, it is allocated to the main path to obtain the main path allocation scheme. Verify the backup paths according to the primary path allocation scheme. If the status identifiers of the backup paths are consistent, they are integrated to determine the final fiber core allocation scheme.

6. The method for laying high-fiber optical cables and upgrading communication networks according to claim 1, characterized in that: The process involves updating the logical status identifier and physical layer fusion preparation data in the resource pool database using the fiber core allocation scheme that matches the type. If the logical status identifier indicates availability, the allocation operation is determined to be executable, and a final allocation instruction is obtained, including: Type matching is performed by obtaining the fiber core type to determine the matching result; If the type match is successful, the logical status identifier and physical layer data are obtained from the resource pool database to determine whether the status identifier is in an available state. Update the status identifier in the resource pool database based on the judgment result to obtain the updated status data; The updated status data is used to generate welding preparation data, and the welding preparation is confirmed to be complete. If the fusion splicing preparation is complete, an allocation instruction is generated based on the fiber core type and status data, and the instruction content is obtained; Data synchronization is performed using allocation instructions, the resource pool database is updated, and synchronization is confirmed to be complete. The final allocation result is generated by verifying the execution status of the data verification instructions through synchronous completion.

7. The method for laying high-fiber optical cables and upgrading communication networks according to claim 1, characterized in that: The step of using the final allocation instruction to monitor signal quality indicators during the route remapping process, determining whether the signal quality indicators remain within a specified range, and obtaining real-time monitoring feedback data includes: Obtain signal quality metrics during route remapping by extracting signal strength and bit error rate data from network devices using a preset acquisition protocol to obtain an initial signal dataset. The initial signal dataset is classified using the support vector machine algorithm to determine whether the signal strength and bit error rate are within the preset range, and the classification result is obtained. If the classification results show that the signal quality exceeds the preset range, an adaptive filter is used to smooth out the outliers in the time series and generate an optimized signal dataset. Based on the optimized signal dataset, the signal stability index is calculated, and the mean-variance analysis method is used to determine the degree of signal fluctuation, thus obtaining the stability assessment results. Based on the stability assessment results, route state data is obtained, and time series analysis is used to detect abnormal changes during the route remapping process to generate a route state sequence. The routing state sequence is grouped by cluster analysis to determine whether the routing state of each group meets the expected pattern, and real-time monitoring feedback data of route remapping is obtained. By using real-time monitoring feedback data, the dynamic thresholds of signal quality indicators are updated, and the signal change trend is continuously tracked through sliding window technology to generate a real-time monitoring data stream.

8. A high-fiber-core optical cable laying and communication network upgrade system, characterized in that, include: The coordinate correction module is used to perform preliminary correction of coordinate deviation by extracting the coordinates and geomarking data of the reserved fiber core pipeline well from the resource pool database, and obtain a set of corrected coordinates with meter-level accuracy. The routing optimization module is used to calculate the potential routing path from the service trigger point to the available fiber core using the Dijkstra algorithm based on the corrected meter-level precision coordinate set, determine the number and length of the curved pipe segments in the potential routing path, and obtain a list of optimized routing options. The loss prediction module is used to obtain path data from the optimized route option list, apply a neural network model to each path data to predict the potential loss value, and if the potential loss value is lower than a preset threshold, then the path data is determined as a candidate route, and a set of filtered candidate routes is obtained. The type matching module is used to match service requirements from fiber core type attributes and status identifiers for the filtered candidate route set, determine whether the type attribute is consistent with the requirements of single-mode or multimode fiber, and obtain a type-matched fiber core allocation scheme. The status update module is used to update the logical status identifier and physical layer fusion preparation data in the resource pool database according to the fiber core allocation scheme that matches the type. If the logical status identifier shows that it is available, the allocation operation is determined to be executable and the final allocation instruction is obtained. The quality monitoring module is used to monitor the signal quality indicators during the route remapping process using the final allocation instruction, determine whether the signal quality indicators are maintained within a specified range, and obtain real-time monitoring feedback data. The synchronization adjustment module is used to automatically adjust the state synchronization based on the real-time monitoring feedback data. If the real-time monitoring feedback data indicates a deviation, all relevant identifiers are updated through the resource pool database mapping to obtain a complete resource pool state synchronization result. The backup prediction module is used to extract backup path data from the filtered candidate route set based on the complete resource pool status synchronization result, use the neural network model to perform secondary prediction on the backup path data, determine whether the potential loss value of the secondary prediction is lower than the preset threshold, and obtain a backup allocation scheme. The allocation extension module is used to update the standby status flag in the type-matching fiber core allocation scheme according to the standby allocation scheme, so as to obtain the extended final allocation instruction.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor is configured to execute the computer program in the memory to implement the method for laying high-fiber optical cables and upgrading communication networks as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for laying high-fiber optical cables and upgrading communication networks as described in any one of claims 1 to 7.

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