Optimized cabling management method and system for optical cable branching connection based on big data analysis

Abstract

This invention discloses a method and system for optimizing optical cable branching and splicing management based on big data analysis, relating to the field of optical cable network operation and maintenance technology. It achieves accurate collection and association of fiber core data across the entire link through standardized physical identifiers, breaking down data silos across regions and levels. Secondly, it performs full-node verification of planned fiber cores through a reverse step-by-step verification decision function, eliminating pseudo-idle resources and identifying purely implicit idle fiber cores. Then, it constructs a digital resource map through multi-source data fusion, achieving full-domain visualization of fiber core topology and status. Simultaneously, it generates fiber core profiles based on multi-dimensional features, completing precise matching of needs and resources. Finally, it combines a nonlinear superposition cumulative impact model to assess scheduling disturbance risks, balancing resource utilization and the stability of in-use services, thus achieving intelligent optimization management of optical cable branching and splicing cabling.

Description

Technical Field

[0001] This invention relates to the field of optical fiber network operation and maintenance technology, specifically to a method and system for optimizing optical fiber branching and splicing cabling management based on big data analysis. Background Technology

[0002] In today's era of rapid development in communication technology, the construction and optimization of communication networks, as key infrastructure for information transmission, are of paramount importance. In the early stages of communication network construction, network operators need to plan the optical fiber network topology based on predictions of future business demands and rationally allocate the number of fiber cores. Optical fiber networks typically employ multi-level branching cabling, starting from the trunk optical fiber and extending gradually through various branching points to the user access point. Fiber core resources are allocated step-by-step during this process. This planning model aims to meet the ever-growing business demands of the future and ensure that the network has sufficient capacity and stability.

[0003] However, traditional fiber optic network planning models based on long-term forecasts have revealed numerous problems in actual operation. Due to the high uncertainty of actual business development, planning often deviates from market dynamics, leading to rapid saturation of fiber core resources in some areas, unable to meet new business demands; while fiber cores in other areas remain idle for extended periods, resulting in resource waste. This is particularly true in multi-level branching networks, where a large number of fiber cores are implicitly idle. These cores have been allocated at the planning level but are not used in actual operations. Existing technologies struggle to accurately monitor the planning and allocation status and real-time occupancy of fiber cores in multi-level branching networks. There are significant shortcomings in the use of status, physical connection paths, and cross-level scheduling potential for refined, dynamic, and cascading tracking and comprehensive analysis. Maintenance personnel find it difficult to proactively and promptly identify these potential allocable resources across regions and sub-levels, especially fiber cores that have been nominally allocated to upstream regions but have not actually been consumed. When resource shortages occur in local areas of the network, due to the lack of effective means to revitalize these hidden idle resources, the only option is often to invest heavily in new construction or renovation. This not only increases the sunk costs of optical cable facilities assets but also leads to low resource utilization and severely limited investment returns. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for optimizing optical cable branching and splicing based on big data analysis. By acquiring real-time data and fiber core planning and allocation information, it uses big data analysis and reverse step-by-step verification decision functions to accurately identify hidden idle fiber cores, constructs a digital resource map to achieve resource visualization, generates a portrait of hidden idle fiber cores to provide a refined evaluation basis, and generates a scientific and reasonable scheduling guidance scheme based on multi-dimensional evaluation and nonlinear superposition model.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, a method for optimizing optical cable branching and splicing management based on big data analysis, the method comprising: S1: Obtain the fiber core connection relationship and service occupancy status information recorded by the mobile terminal. The connection relationship and service occupancy status information are associated with the physical identifier, which is pre-set at the fiber core connection point of the various levels of branch facilities of the optical cable network. S2: Obtain fiber core planning and allocation information, including the original planned service area, allocation level, and planning path of the fiber core; S3: Based on fiber core planning and allocation information, as well as connection relationships and service occupancy status information, big data analysis technology is used to correlate and mine cross-regional and cross-level data to obtain hidden idle fiber cores in the optical cable network. S4: Based on fiber core planning and allocation information, service occupancy status information, implicit idle status of implicit idle fiber cores, and cascaded paths from the source connection point through various levels of branching points to the end branching point, construct a digital resource map for resource visualization. S5: Generate a profile of the hidden idle fiber core based on the physical routing information, branching level information, idle duration information, and original planned service area information of the hidden idle fiber core. S6: When a fiber core resource request for the target area is received, based on the digital resource map and the profile of hidden idle fiber cores, candidate hidden idle fiber cores that can be scheduled to the target area are screened, and a scheduling guidance scheme is generated.

[0006] Furthermore, S3 specifically includes: For fiber cores in the optical cable network that are in the planning and allocation state, big data analysis technology is used to correlate and mine data across regions and levels. Starting from the terminal branch point, based on the connection relationship and service occupancy status information, a reverse step-by-step verification decision function is used to verify the actual service status information of each level of branch point through which the fiber core flows in reverse step-by-step upward. When the actual service status information of at least one of the branch points at each level is detected to indicate that the branch point is marked as a test-occupied state, a reserved state, or has a historical service record within a preset time period, the fiber core in the planning allocation state is determined to be a non-hidden idle fiber core. If a fiber core in the planning and allocation state has no service occupation in its actual service status information from the end of its planned path branch point to all upstream branch points or up to the source connection point, and is not determined to be a non-latent idle fiber core, then the fiber core is identified as a latent idle fiber core.

[0007] Furthermore, in S3, a reverse step-by-step verification decision function is used to verify the actual service status information of each level of branching points through which the fiber core flows in a reverse step-by-step upward manner. Its function expression is: ,in, f This indicates the target fiber core to be verified. This indicates the i-th branch node through which the fiber core flows. State represents the set of node states, including test occupancy, reserved status, and historical service records existing within a preset time period. For indicator functions, when node The value is 1 when the state is j, and 0 otherwise. Let j be the decision weight factor corresponding to state j. .

[0008] Furthermore, S4 specifically includes: Obtain the first mapping relationship between the fiber core identifier and the predetermined service area in the fiber core planning and allocation information; Obtain the actual occupancy marker and occupancy status type of the fiber core from the service occupancy status information; Obtain the core identifier and hidden vacancy status marker of the identified hidden vacant cores; Extract cascaded path information from the source connection point through various branching points to the terminal branching point from the network topology database, including the node identifier, port connection relationship and hierarchy depth of each branching node; The first mapping relationship, the actual occupancy mark and occupancy status type of the fiber core, the implicit idle status mark, and the cascaded path information are associated and integrated with the fiber core identifier as the primary key to construct a digital resource map containing node-link topology relationships and status attributes of each node.

[0009] Furthermore, S5 specifically includes: Based on the digital resource map, the physical routing information of the hidden idle fiber cores is extracted. The physical routing information includes the location identifier sequence of the source connection point, branch points at all levels and the terminal branch point through which the fiber core passes. Based on the cascaded path, determine the branching level information of the hidden idle fiber core in the optical cable network, that is, the number of branching levels of the fiber core from the source connection point; Based on the historical records of service occupancy status information, calculate the idle time information of the implicit idle fiber core from the end of the most recent service occupancy to the current time; Obtain the original planned service area information of the implicitly idle fiber core recorded in the fiber core planning and allocation information; The physical routing information, branch level information, idle time information, and original planned service area information are structurally integrated to generate the implicit idle fiber core profile.

[0010] Furthermore, S6 specifically includes: Based on digital resource maps, identify in-use fiber cores that share physical support with candidate hidden idle fiber cores in the same optical cable and the same branching equipment, and obtain information on neighboring in-use fiber cores. Based on the scheduling operation type and location of candidate latent idle fiber cores, and combined with information on nearby in-use fiber cores, assess the risk level of physical disturbances introduced by scheduling operations to nearby in-use fiber cores. Based on the type of candidate hidden idle fiber cores, the estimated adjustment length, and the estimated number of fusion splices and connectors, the adjusted link optical power attenuation value is calculated, and the link optical power attenuation value is compared with the optical power threshold required by the service type corresponding to the fiber core resource requirement to obtain the optical power attenuation comparison result. Based on the port availability of the branch facility and the risk level of physical disturbance, the technical feasibility of adjusting candidate hidden idle fiber cores to meet fiber core resource requirements is determined, and the technical feasibility assessment results are obtained. Based on the optical power attenuation comparison results and the technical feasibility assessment results, a scheduling guidance scheme is generated.

[0011] Furthermore, the assessment of the risk level of physical disturbance introduced by the scheduling operation to neighboring in-use fiber cores based on the scheduling operation type and location of candidate latently idle fiber cores, combined with information on neighboring in-use fiber cores, specifically includes: Obtain historical scheduling operation records for the target area associated with neighboring in-use fiber cores. The historical scheduling operation records include the number of historical operations, the type of historical operations, and the time information of the historical operations. Based on historical scheduling operation records, a nonlinear superposition cumulative impact calculation model is used to calculate the degree of cumulative disturbance impact already experienced by adjacent in-use fiber cores; The scheduling operation type of the candidate hidden idle fiber core, the operation location of the candidate hidden idle fiber core, the information of the adjacent in-use fiber cores, and the degree of cumulative disturbance impact are input into the preset risk assessment model or rule set. The physical disturbance risk level is output based on a preset risk assessment model or rule set.

[0012] Furthermore, the expression for the nonlinear superposition cumulative effect calculation model is as follows: Where C represents the cumulative disturbance impact experienced by the adjacent in-use fiber core; m represents the total number of historical operations; This represents the basic disturbance contribution value of the k-th historical operation, and its value is determined by the operation type through a preset mapping relationship. This is a nonlinear superposition influence factor function, whose value is determined by the time interval between the k-th operation and the previous operation. And the types of two adjacent operations together determine it; when hour, .

[0013] Furthermore, the aforementioned For a nonlinear superposition of influence factors, if And the first If the type of the next operation is the same as that of the previous operation, then ;like ,but Approaching 1.

[0014] On the other hand, a fiber optic branching and splicing optimization cabling management system based on big data analysis includes: First acquisition module: used to acquire the connection relationship and service occupancy status information of the fiber cores entered by the mobile terminal; The second acquisition module is used to acquire fiber core planning and allocation information. Hidden Idle Fiber Core Identification Module: Based on fiber core planning and allocation information, connection relationships, and service occupancy status information, it uses big data analysis technology and a reverse step-by-step verification decision function to identify hidden idle fiber cores; Map building module: used to build digital resource maps; Image generation module: used to generate images of hidden idle fiber cores; Scheduling decision module: Based on digital resource maps and implicit idle fiber core profiles, it uses a nonlinear superposition cumulative impact calculation model to assess the risk of physical disturbances and generate scheduling guidance schemes.

[0015] Compared with existing technologies, this method and system for optimizing fiber optic branching and splicing cabling management based on big data analysis has the following advantages: This invention effectively solves the technical problem of accurately identifying hidden idle fiber cores across regions and levels in existing optical cable branching and splicing cabling management by combining big data analysis technology with reverse step-by-step verification decision functions and nonlinear superposition cumulative impact calculation models. It realizes refined and dynamic tracking and comprehensive analysis of fiber core planning and allocation status, real-time occupancy status, and cascaded paths. By constructing a digital resource map, it provides a global visual management view of optical cable network resources, facilitating rapid understanding of resource distribution and status. By generating hidden idle fiber core profiles, it provides a refined evaluation basis for fiber core scheduling decisions. During the scheduling process, combined with physical disturbance risk assessment and optical power verification, it significantly improves the scientific nature and safety of scheduling operations and effectively avoids interference with in-use fiber cores during scheduling.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart for optimizing cabling management methods for optical cable branching splices based on big data analysis; Figure 2 A flowchart for S6, a fiber optic branch splicing optimization cabling management method based on big data analysis; Figure 3 The structural block diagram of the fiber optic branch splicing optimization cabling management system based on big data analysis. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] like Figure 1 As shown, embodiments of the present invention propose a method for optimizing and managing optical cable branching splices based on big data analysis. This method employs a data processing mechanism that utilizes cross-regional and cross-level data association mining, reverse step-by-step verification decision-making, digital resource map visualization construction, multi-dimensional fiber core profile matching, and nonlinear disturbance risk assessment. This addresses industry pain points such as difficulty in identifying hidden idle fiber cores in optical cable networks, high degree of blindness in resource scheduling, and significant risks associated with splicing operations disturbing in-use fiber cores. It achieves refined management and control of optical cable fiber core resources, efficient mining of hidden resources, and low-risk optimized scheduling.

[0021] The method in this embodiment specifically includes: The connection relationship and service occupancy status information of the fiber cores are obtained from the mobile terminal. The connection relationship and service occupancy status information are associated with the physical identifier, which is pre-set at the fiber core connection point of the branch facilities at all levels of the optical cable network. Obtain fiber core planning and allocation information, including the original planned service area, allocation level, and planning path of the fiber core; Based on fiber core planning and allocation information, as well as connection relationships and service occupancy status information, big data analysis technology is used to correlate and mine cross-regional and cross-level data to obtain hidden idle fiber cores in the optical cable network. Based on fiber core planning and allocation information, service occupancy status information, implicit idle status of implicit idle fiber cores, and cascaded paths from the source connection point through various levels of branching points to the end branching point, a digital resource map for resource visualization is constructed. Based on the physical routing information, branching level information, idle duration information, and original planned service area information of the hidden idle fiber cores, a hidden idle fiber core profile is generated. When a fiber core resource request for a target area is received, a candidate hidden idle fiber core that can be scheduled to the target area is selected based on the digital resource map and the profile of hidden idle fiber cores, and a scheduling guidance scheme is generated.

[0022] Specifically, this invention first achieves accurate collection and association of fiber core data across the entire link through standardized physical identifiers, breaking down data silos across regions and levels; secondly, it performs full-node verification of planned fiber cores through a reverse step-by-step verification decision function, eliminating pseudo-idle resources and identifying purely implicit idle fiber cores; thirdly, it constructs a digital resource map through multi-source data fusion, achieving full-domain visualization of fiber core topology and status; simultaneously, it generates fiber core profiles based on multi-dimensional features, completing accurate matching of demand and resources; finally, it combines a nonlinear superposition cumulative impact model to assess scheduling disturbance risks, balancing resource utilization and the stability of in-use services, and achieving intelligent optimization management of optical cable branching and splicing cabling.

[0023] Optionally, the step of obtaining implicitly available fiber cores in the optical cable network by using big data analysis technology to correlate and mine cross-regional and cross-level data based on fiber core planning and allocation information, connection relationships, and service occupancy status information includes: For fiber cores in the optical cable network that are in the planning and allocation state, big data analysis technology is used to correlate and mine data across regions and levels. Starting from the terminal branch point, based on the connection relationship and service occupancy status information, a reverse step-by-step verification decision function is used to verify the actual service status information of each level of branch point through which the fiber core flows in reverse step-by-step upward. When the actual service status information of at least one of the branch points at each level is detected to indicate that the branch point is marked as a test-occupied state, a reserved state, or has a historical service record within a preset time period, the fiber core in the planning allocation state is determined to be a non-hidden idle fiber core. If a fiber core in the planning and allocation state has no service occupation in its actual service status information from the end of its planned path branch point to all upstream branch points or up to the source connection point, and is not determined to be a non-latent idle fiber core, then the fiber core is identified as a latent idle fiber core.

[0024] Specifically, starting from the end branch point, the verification is traced upstream along the fiber core cascade path, covering all branch nodes in the entire link, achieving a state verification without blind spots, and avoiding resource misjudgment caused by local node occupancy.

[0025] Among them, test occupancy, reserved status, and historical service records within the period are all used as the criteria for determining invalid idleness. Only planned fiber cores with no service associations across the entire link can be defined as implicitly idle fiber cores to ensure high accuracy of the mining results. The function expression of the reverse step-by-step verification decision function is as follows: In the formula, This indicates the target fiber core to be verified; This indicates the i-th branch node through which the fiber core flows; State represents the set of node states, including test occupancy, reserved status, and historical business records existing within a preset time period. For indicator functions, when node The value is 1 when the state is j, and 0 otherwise. Let j be the decision weight factor corresponding to state j. ∈(0,1], when the function calculation result When S(f) = 0, it is determined to be a hidden idle fiber core; when S(f) = 0, it is determined to be a non-hidden idle fiber core.

[0026] For example, a target fiber core in a certain planning and allocation state is selected. It flows through a 3-level branching node. , , Preset decision weight factors The value is uniformly set to 0.8, with a preset verification period of 90 days. This has been confirmed through data collection. , There are no abnormal states, and the indicator function takes the value 0; With no test occupancy, reserved status, and no historical business records within 90 days, the indicator function takes the value 0. Substituting this into the function yields... =1, indicating that the fiber core is a hidden vacant fiber core; if among them If a reserved state exists, and the indicator function takes the value 1, then... =0 indicates a non-hidden idle fiber core, thus completing accurate identification.

[0027] Optionally, constructing the digital resource map for resource visualization includes: Obtain the first mapping relationship between the fiber core identifier and the predetermined service area in the fiber core planning and allocation information; Obtain the actual occupancy marker and occupancy status type of the fiber core from the service occupancy status information; obtain the fiber core identifier and implicit idle status marker of the identified implicit idle fiber cores; Extract cascaded path information from the source connection point through various branching points to the terminal branching point from the network topology database, including the node identifier, port connection relationship and hierarchy depth of each branching node; The first mapping relationship, the actual occupancy mark and occupancy status type of the fiber core, the implicit idle status mark, and the cascaded path information are associated and integrated with the fiber core identifier as the primary key to construct a digital resource map containing node-link topology relationships and status attributes of each node.

[0028] Specifically, using the unique identifier of the fiber core as the core association primary key, the system integrates four types of data: planning attributes, business status, idle attributes, and topology path, eliminating the problem of data dimension fragmentation.

[0029] The constructed digital resource map can intuitively display the physical topology, hierarchical distribution, and real-time status of the entire network of fiber cores. It supports full-domain visual retrieval and status monitoring for management personnel, providing visual data support for subsequent resource scheduling. It eliminates the need for manual on-site verification and significantly improves resource management efficiency.

[0030] Optionally, such as Figure 2 As shown, the generation of the latent idle fiber core image includes: Based on the digital resource map, the physical routing information of the hidden idle fiber cores is extracted. The physical routing information includes the location identifier sequence of the source connection point, branch points at all levels and the terminal branch point through which the fiber core passes. Based on the cascaded path, determine the branching level information of the hidden idle fiber core in the optical cable network, that is, the number of branching levels of the fiber core from the source connection point; Based on the historical records of service occupancy status information, calculate the idle time information of the implicit idle fiber core from the end of the most recent service occupancy to the current time; Obtain the original planned service area information of the implicitly idle fiber core recorded in the fiber core planning and allocation information; The physical routing information, branch level information, idle time information, and original planned service area information are structurally integrated to generate the implicit idle fiber core profile.

[0031] Specifically, the resource attributes of implicit idle fiber cores are quantified through four-dimensional features, physical routing information clarifies the scheduling and construction path, divergence level information defines the difficulty of resource scheduling, idle duration information assesses the priority of idle resources, original planned service area information matches cross-regional scheduling adaptability, and structured profiling realizes standardized and tagged management of fiber core resources, providing a quantitative basis for demand matching and improving the accuracy and efficiency of scheduling and screening.

[0032] Optionally, the process of screening candidate latent idle fiber cores that can be scheduled to the target region and generating a scheduling guidance scheme includes: Based on digital resource maps, identify in-use fiber cores that share physical support with candidate hidden idle fiber cores in the same optical cable and the same branching equipment, and obtain information on neighboring in-use fiber cores. Based on the scheduling operation type and location of candidate latent idle fiber cores, and combined with information on nearby in-use fiber cores, assess the risk level of physical disturbances introduced by scheduling operations to nearby in-use fiber cores. Based on the type of candidate hidden idle fiber cores, the estimated adjustment length, and the estimated number of fusion splices and connectors, the adjusted link optical power attenuation value is calculated, and the link optical power attenuation value is compared with the optical power threshold required by the service type corresponding to the fiber core resource requirement to obtain the optical power attenuation comparison result. Based on the port availability of the branch facility and the risk level of physical disturbance, the technical feasibility of adjusting candidate hidden idle fiber cores to meet fiber core resource requirements is determined, and the technical feasibility assessment results are obtained. Based on the optical power attenuation comparison results and the technical feasibility assessment results, a scheduling guidance scheme is generated.

[0033] Specifically, this step first identifies nearby in-use fiber cores to avoid the risk of service interruption, then assesses the disturbance level through a quantitative model, combines optical power attenuation verification to ensure service transmission quality, and finally determines the feasibility based on port resources and risk level, outputting a feasible scheduling solution to achieve a dual balance between security and efficiency.

[0034] Optionally, the assessment of the risk level of physical disturbance introduced by the scheduling operation to neighboring used fiber cores based on the scheduling operation type and location of candidate latently idle fiber cores, combined with information on neighboring used fiber cores, specifically includes: Obtain historical scheduling operation records for the target area associated with neighboring in-use fiber cores. The historical scheduling operation records include the number of historical operations, the type of historical operations, and the time information of the historical operations. Based on historical scheduling operation records, a nonlinear superposition cumulative impact calculation model is used to calculate the degree of cumulative disturbance impact already experienced by adjacent in-use fiber cores; The scheduling operation type of the candidate hidden idle fiber core, the operation location of the candidate hidden idle fiber core, the information of the adjacent in-use fiber cores, and the degree of cumulative disturbance impact are input into the preset risk assessment model or rule set. The physical disturbance risk level is output based on a preset risk assessment model or rule set.

[0035] Specifically, to fully consider the cumulative disturbance effect of historical operations and avoid performance degradation of in-use fiber cores caused by the superposition of a single low-risk operation, a nonlinear model is used to quantify the cumulative impact. Combined with real-time operating parameters, dynamic risk level classification is achieved, with three risk levels: low, medium, and high. This provides a quantitative basis for optimizing and adjusting the scheduling scheme. The expression for the nonlinear superposition cumulative impact calculation model is as follows: Where C represents the cumulative disturbance impact experienced by the adjacent in-use fiber core; m represents the total number of historical operations; This represents the basic disturbance contribution value of the k-th historical operation, and its value is determined by the operation type through a preset mapping relationship. This is a nonlinear superposition influence factor function, whose value is determined by the time interval between the k-th operation and the previous operation. The type of the two adjacent operations determines the result; when k=1, =1.

[0036] Furthermore, the aforementioned For a nonlinear superposition of influence factors, if And if the type of the k-th operation is the same as the previous operation, then >1; if ,but Approaching 1, where, The preset time interval threshold, ranging from 7 to 30 days, is used to define the boundary for judging short-period superimposed disturbances.

[0037] For example, select a fiber core that is adjacent to the fiber core in use, whose total number of historical operations is m=2, and the first fusion splicing operation. =0.3, second jumper adjustment operation =0.2, the time interval between the two operations =5 days, less than the preset threshold of 10 days, and all operations are physical connection operations. =1.2, substituting into the model, we get C=0.54; inputting the cumulative disturbance value and the parameters of this fusion splicing operation into the risk rule set, we determine it to be a low-risk level, which meets the scheduling safety requirements. At the same time, we calculate that the optical power attenuation value after the fiber core is scheduled is 0.8dB, which is lower than the target service threshold of 1.5dB, so the branch port is available. Finally, we generate a scheduling guidance scheme that includes construction path, operation specifications, and risk warnings.

[0038] Optionally, such as Figure 3 As shown, the present invention also provides an optical cable branching and splicing optimization cabling management system based on big data analysis. This system is applicable to the methods described in any of the above embodiments, and the system includes: The first acquisition module is used to acquire the connection relationship and service occupancy status information of the fiber cores entered by the mobile terminal; The second acquisition module is used to acquire fiber core planning and allocation information; The hidden idle fiber core identification module identifies hidden idle fiber cores based on fiber core planning and allocation information, connection relationships, and service occupancy status information, using big data analysis technology and a reverse step-by-step verification decision function. The map construction module is used to construct digital resource maps; the profile generation module is used to generate profiles of hidden idle fiber cores. The scheduling decision module, based on digital resource maps and implicit idle fiber core profiles, uses a nonlinear superposition cumulative impact calculation model to assess the risk of physical disturbances and generate scheduling guidance schemes.

[0039] Specifically, each module adopts a distributed data interaction architecture. The first acquisition module is wirelessly compatible with mobile terminals, supporting real-time data entry and physical identifier binding. The hidden idle fiber core identification module has a built-in big data mining engine and decision function calculation unit to achieve millisecond-level fiber core status verification. The scheduling decision module integrates a risk assessment model and optical power calculation unit to automatically output standardized scheduling schemes. The entire system operates automatically without manual intervention, adapting to the large-scale management and control needs of large-scale optical cable networks.

[0040] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for optimizing fiber optic cable branching and splicing management based on big data analysis, characterized in that: The method includes: S1: Obtain the fiber core connection relationship and service occupancy status information recorded by the mobile terminal. The connection relationship and service occupancy status information are associated with the physical identifier, which is pre-set at the fiber core connection point of the various levels of branch facilities of the optical cable network. S2: Obtain fiber core planning and allocation information, including the original planned service area, allocation level, and planning path of the fiber core; S3: Based on fiber core planning and allocation information, as well as connection relationships and service occupancy status information, big data analysis technology is used to correlate and mine cross-regional and cross-level data to obtain hidden idle fiber cores in the optical cable network. S4: Based on fiber core planning and allocation information, service occupancy status information, implicit idle status of implicit idle fiber cores, and cascaded paths from the source connection point through various levels of branching points to the end branching point, construct a digital resource map for resource visualization. S5: Generate a profile of the hidden idle fiber core based on the physical routing information, branching level information, idle duration information, and original planned service area information of the hidden idle fiber core. S6: When a fiber core resource request for the target area is received, based on the digital resource map and the profile of hidden idle fiber cores, candidate hidden idle fiber cores that can be scheduled to the target area are screened, and a scheduling guidance scheme is generated.

2. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 1, characterized in that, S3 specifically includes: For fiber cores in the optical cable network that are in the planning and allocation state, big data analysis technology is used to correlate and mine data across regions and levels. Starting from the terminal branch point, based on the connection relationship and service occupancy status information, a reverse step-by-step verification decision function is used to verify the actual service status information of each level of branch point through which the fiber core flows in reverse step-by-step upward. When the actual service status information of at least one of the branch points at each level is detected to indicate that the branch point is marked as a test-occupied state, a reserved state, or has a historical service record within a preset time period, the fiber core in the planning allocation state is determined to be a non-hidden idle fiber core. If a fiber core in the planning and allocation state has no service occupation in its actual service status information from the end of its planned path branch point to all upstream branch points or up to the source connection point, and is not determined to be a non-latent idle fiber core, then the fiber core is identified as a latent idle fiber core.

3. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 2, characterized in that, In S3, a reverse step-by-step verification decision function is used to verify the actual service status information of each level of branching points through which the fiber core flows in a reverse step-by-step upward manner. Its function expression is: ,in, f This indicates the target fiber core to be verified. This indicates the i-th branch node through which the fiber core flows. State represents the set of node states, including test occupancy, reserved status, and historical service records existing within a preset time period. For indicator functions, when node The value is 1 when the state is j, and 0 otherwise. Let j be the decision weight factor corresponding to state j. .

4. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 1, characterized in that, S4 specifically includes: Obtain the first mapping relationship between the fiber core identifier and the predetermined service area in the fiber core planning and allocation information; Obtain the actual occupancy marker and occupancy status type of the fiber core from the service occupancy status information; Obtain the core identifier and hidden vacancy status marker of the identified hidden vacant cores; Extract cascaded path information from the source connection point through various branching points to the terminal branching point from the network topology database, including the node identifier, port connection relationship and hierarchy depth of each branching node; The first mapping relationship, the actual occupancy mark and occupancy status type of the fiber core, the implicit idle status mark, and the cascaded path information are associated and integrated with the fiber core identifier as the primary key to construct a digital resource map containing node-link topology relationships and status attributes of each node.

5. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 1, characterized in that, S5 specifically includes: Based on the digital resource map, the physical routing information of the hidden idle fiber cores is extracted. The physical routing information includes the location identifier sequence of the source connection point, branch points at all levels and the terminal branch point through which the fiber core passes. Based on the cascaded path, determine the branching level information of the hidden idle fiber core in the optical cable network, that is, the number of branching levels of the fiber core from the source connection point; Based on the historical records of service occupancy status information, calculate the idle time information of the implicit idle fiber core from the end of the most recent service occupancy to the current time; Obtain the original planned service area information of the implicitly idle fiber core recorded in the fiber core planning and allocation information; The physical routing information, branch level information, idle time information, and original planned service area information are structurally integrated to generate the implicit idle fiber core profile.

6. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 1, characterized in that, S6 specifically includes: Based on digital resource maps, identify in-use fiber cores that share physical support with candidate hidden idle fiber cores in the same optical cable and the same branching equipment, and obtain information on neighboring in-use fiber cores. Based on the scheduling operation type and location of candidate latent idle fiber cores, and combined with information on nearby in-use fiber cores, assess the risk level of physical disturbances introduced by scheduling operations to nearby in-use fiber cores. Based on the type of candidate hidden idle fiber cores, the estimated adjustment length, and the estimated number of fusion splices and connectors, the adjusted link optical power attenuation value is calculated, and the link optical power attenuation value is compared with the optical power threshold required by the service type corresponding to the fiber core resource requirement to obtain the optical power attenuation comparison result. Based on the port availability of the branch facility and the risk level of physical disturbance, the technical feasibility of adjusting candidate hidden idle fiber cores to meet fiber core resource requirements is determined, and the technical feasibility assessment results are obtained. Based on the optical power attenuation comparison results and the technical feasibility assessment results, a scheduling guidance scheme is generated.

7. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 6, characterized in that, The assessment of the risk level of physical disturbance introduced by the scheduling operation to neighboring in-use fiber cores based on the scheduling operation type and location, combined with information on neighboring in-use fiber cores, specifically includes: Obtain historical scheduling operation records for the target area associated with neighboring in-use fiber cores. The historical scheduling operation records include the number of historical operations, the type of historical operations, and the time information of the historical operations. Based on historical scheduling operation records, a nonlinear superposition cumulative impact calculation model is used to calculate the degree of cumulative disturbance impact already experienced by adjacent in-use fiber cores; The scheduling operation type of the candidate hidden idle fiber core, the operation location of the candidate hidden idle fiber core, the information of the adjacent in-use fiber cores, and the degree of cumulative disturbance impact are input into the preset risk assessment model or rule set. The physical disturbance risk level is output based on a preset risk assessment model or rule set.

8. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 7, characterized in that, The expression for the nonlinear superposition cumulative effect calculation model is as follows: Where C represents the cumulative disturbance impact experienced by the adjacent in-use fiber core; m represents the total number of historical operations; Indicates the first The basic disturbance contribution value of the previous historical operation is determined by the operation type through a preset mapping relationship; This is a nonlinear superposition influence factor function, whose value is determined by the time interval between the k-th operation and the previous operation. And the types of two adjacent operations together determine it; when hour, .

9. The optical cable branching and splicing optimization cabling management method based on big data analysis according to claim 8, characterized in that, The For a nonlinear superposition of influence factors, if And if the type of the k-th operation is the same as that of the previous operation, then ;like ,but Approaching 1.

10. A fiber optic branching splice optimization cabling management system based on big data analysis, the system being applicable to the fiber optic branching splice optimization cabling management method based on big data analysis as described in any one of claims 1-9, characterized in that, The system includes: First acquisition module: used to acquire the connection relationship and service occupancy status information of the fiber cores entered by the mobile terminal; The second acquisition module is used to acquire fiber core planning and allocation information. Hidden Idle Fiber Core Identification Module: Based on fiber core planning and allocation information, connection relationships, and service occupancy status information, it uses big data analysis technology and a reverse step-by-step verification decision function to identify hidden idle fiber cores; Map building module: used to build digital resource maps; Image generation module: used to generate images of hidden idle fiber cores; Scheduling decision module: Based on digital resource maps and implicit idle fiber core profiles, it uses a nonlinear superposition cumulative impact calculation model to assess the risk of physical disturbances and generate scheduling guidance schemes.

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