Radio and television optical cable route planning method and system, electronic equipment and storage medium

Abstract

The invention discloses a broadcasting and television optical cable route planning method and system, electronic equipment and a storage medium. The method comprises the following steps: constructing a weighted cost map by obtaining user opening rate data, geographic information data and existing network resource data of a planning area; searching on the weighted cost map by adopting a business-driven path search algorithm to generate one or more candidate paths; performing comprehensive evaluation on each candidate path by using a multi-objective scoring function; for the evaluated candidate paths, calculating the number of required optical cable fiber cores through a capacity estimation model according to the path characteristics of the candidate paths and the service requirements of the served area; and outputting an optimal optical cable route planning scheme, a corresponding material list and an investment budget based on the evaluation result and the capacity estimation result. According to the invention, routing planning can be carried out based on the service opening rate, and automatic compliance check and accurate capacity estimation are realized, so that the problems of insufficient service awareness, low design efficiency, poor investment benefit and the like in a traditional method are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of cable television network planning and design technology, and in particular to a cable television optical cable routing planning method, system, electronic device and storage medium based on service activation rate and multi-objective optimization. Background Technology

[0002] Traditional fiber optic cable routing planning for cable television networks relies heavily on the personal experience of designers and simple geographical distance calculations, resulting in several technical shortcomings: 1. Lack of business orientation: Planning decisions fail to fully consider the user service activation rate and potential business value in different areas, leading to low network investment efficiency. This often results in over-investment in low-value areas and under-investment in high-value areas. 2. Poor design compliance: Manual design makes it difficult to fully and accurately understand and apply complex cable television industry design standards (such as GY / T5069-2012, YD / T5058, etc.), easily leading to violations such as excessive pole spacing, insufficient bending radius, and inadequate safety distance from power lines, creating hidden dangers for subsequent network operation. 3. Inaccurate resource estimation: Material lists and project budgets rely on manual statistics, which are labor-intensive, inefficient, and prone to errors. The budget deviates significantly from actual costs, typically by more than ±20%, resulting in resource waste or budget underestimation. 4. Low design efficiency: Completing routing planning for a single area usually takes several hours or even days, failing to meet the needs of rapid network construction and optimization.

[0003] In the existing technology, although some patents involve data collection or fault diagnosis of broadcast networks (such as CN113507644A and CN115119242A), none of them have solved the problem of intelligent decision-making in the routing planning stage and cannot overcome the inherent defects of the planning method based on human experience.

[0004] Therefore, there is an urgent need for a method and system that can automate and intelligently plan optical cable routes, integrate business value into the core of planning decisions, and ensure the compliance and economy of the design. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, electronic device, and storage medium for cable television routing planning. This invention enables routing planning based on service activation rates, achieving automatic compliance checks and accurate capacity estimation, thereby effectively solving problems such as insufficient service awareness, low design efficiency, and suboptimal investment returns in traditional methods.

[0006] The technical solution of this invention: a method for planning the routing of optical cables for broadcasting, comprising the following steps: Step 1: Obtain user activation rate data, geographic information data, and existing network resource data for the planned area; Step 2: Based on the user activation rate data, geographic information data, and existing network resource data, construct a weighted cost map; wherein, for each grid cell in the map, its passage cost is jointly determined by the land use type basic cost, the broadcasting policy regional cost factor, the existing pipeline resource utilization factor, the terrain difficulty coefficient, and the service activation rate driving factor. Step 3: Using a business-driven path search algorithm, search the weighted cost map to generate one or more candidate paths; Step four: Use a multi-objective scoring function to comprehensively evaluate each candidate path. The multi-objective scoring function includes at least economic indicators, construction feasibility indicators, technical compliance indicators, and business value indicators. Step 5: For the evaluated candidate paths, calculate the required number of optical fiber cores based on their path characteristics and the service needs of the area they serve using a capacity estimation model. Step 6: Based on the evaluation results and capacity estimation results, output the optimal optical cable routing plan, the corresponding bill of materials, and the investment budget.

[0007] In the above-mentioned cable TV optical cable routing planning method, the passage cost in step two is calculated using the following formula: ; In the formula: Basic costs for land use types, Regional cost factors for broadcasting policies; The utilization factor of existing pipeline resources; The terrain difficulty coefficient; This is a driver of business activation rate.

[0008] In the aforementioned cable television optical cable routing planning method, the service-driven path search algorithm mentioned in step three is the A* search algorithm, where the heuristic function of the A* search algorithm is: ; In the formula: Let n be the Euclidean distance from node n to target t; For the cost item of service activation rate, This is a policy cost item.

[0009] The previous method for planning cable routing in the broadcasting industry, wherein the multi-objective scoring function is expressed as: ; In the formula: As an economic indicator, For construction feasibility indicators, As a technical compliance indicator, As a business value indicator, , , and These are the weights of the corresponding indicators.

[0010] The aforementioned method for planning cable routing for broadcasting includes calculating technical compliance indicators such as compliance with the bending radius of the path, compliance with pole spacing restrictions, and compliance with safe distances from power lines.

[0011] The capacity estimation model in the aforementioned cable TV optical cable routing planning method is expressed as follows: ; In the formula: Based on the fiber core count required for basic business operations, This is the dynamic redundancy factor; where, ; In the formula: For business type The single-point fiber core requirement, For the number of business points, Total number of business types; ; In the formula, As the length redundancy factor, As a complexity redundancy factor, As a policy redundancy factor, This is a reliability redundancy factor.

[0012] The aforementioned cable TV optical cable routing planning method also includes hierarchical service protection: based on the user activation rate level of the areas through which the final determined optical cable route passes, differentiated minimum fiber core counts, redundancy coefficients, and sets of guaranteed service types are configured for different levels of areas.

[0013] A system for implementing the method described above includes: The data acquisition module is used to collect user activation rate data, geographic information data, and existing network resource data; The cost map calculation module receives data from the data acquisition module and constructs a weighted cost map. The path optimization module is connected to the cost map calculation module and is used to obtain the weighted cost map and execute a business-driven path search algorithm to generate candidate paths. The capacity estimation module is connected to the path optimization module and is used to obtain candidate path information and calculate the core requirement based on the dynamic redundancy model. The service assurance module is connected to the capacity estimation module and the data acquisition module. It is used to implement a tiered service assurance strategy based on user activation rate data and to provide service configuration constraints for different regions to the capacity estimation module. The results output module, connected to the path optimization module and the capacity estimation module, is used to receive the optimal path and fiber core demand information, and generate the final routing scheme, bill of materials and investment budget.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described above.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses the service activation rate as the core decision factor to prioritize the coverage of high-value user areas by optical cable routes, which significantly improves capital utilization efficiency and return on investment.

[0017] 2. The automated process of this invention reduces the traditional manual planning work that takes several hours to minutes, greatly improving efficiency and accelerating network construction.

[0018] 3. This invention incorporates complex broadcasting industry design specifications into the algorithm model, automatically conducting strict technical compliance checks during the planning stage, fundamentally eliminating non-compliant designs caused by human negligence, and improving the robustness and security of network infrastructure.

[0019] 4. The present invention uses a dynamic capacity estimation model based on business needs and path characteristics, which changes the previous model that relied on rough estimation and enables accurate prediction of resources such as the number of optical fiber cores, effectively reducing material waste and budget deviation. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0022] Example: Cable TV optical cable routing planning method, such as Figure 1 As shown, it includes the following steps: Step 1: Obtain user activation rate data, geographic information data, and existing network resource data for the planning area. In this step, the user activation rate data refers to the ratio of "activated users / total potential users" for broadcast services (home broadband, 4K live streaming, etc.) within the area, broken down by street and grid (high ≥60%, medium 30%-60%, low 10%-30%, extremely low <10%). The geographic information data includes terrain elevation gradient, land use type (highway / farmland / building area, etc.), special terrain (rivers / mountains), and policy boundaries (core signal protection zones, military restricted areas, policy support zones, and other controlled areas). The existing network resource data refers to existing optical cable routes, pipeline locations and remaining capacity, coordinates of equipment such as optical distribution boxes, optical fiber core utilization rate, pipeline integrity, and fault frequency.

[0023] Step 2: Based on the user activation rate data, geographic information data, and existing network resource data, construct a weighted cost map; wherein, for each grid cell in the map, its passage cost is jointly determined by the land use type basic cost, the broadcasting policy regional cost factor, the existing pipeline resource utilization factor, the terrain difficulty coefficient, and the service activation rate driving factor. In this step, user activation rate, geographic information data, and existing network resources are transformed into "grid unit access cost" to form a quantitative spatial cost model. Then, the planning area in the spatial cost model is divided into grid units at a fixed scale (e.g., 100m × 100m), and the access cost of each unit is calculated using a five-factor product formula: ; In the formula: Basic costs for land use types, Regional cost factors for broadcasting policies; The utilization factor of existing pipeline resources; The terrain difficulty coefficient; This is a driver of business activation rate.

[0024] Among them, the basic cost of land use type is set according to land use type: highway is set at 1.0, green belt is set at 3.0, ordinary open space is set at 5.0, farmland is set at 8.0, building area is set at 100.0, and water area is set at 1000.0; The regional cost factor for broadcasting policies is set according to the broadcasting policy region: 1000.0 for core signal protection zones, 10000.0 for military restricted zones, 0.5 for policy support zones, and 1.0 for ordinary zones; The utilization factor for existing pipeline resources is determined based on the existing pipeline resources: 0.2 if existing pipelines exist, and 1.0 if no existing pipelines exist. The terrain difficulty coefficient is determined using the following formula: ; In the formula: This represents the elevation gradient value. The service activation rate driver factor is determined by the service activation rate P, where: P≥60%, the value is 0.3; 30%≤P<60%, the value is 0.8; 10%≤P<30%, the value is 1.5; and P<10%, the value is 3.0.

[0025] Step three involves employing a business-driven path search algorithm to search the weighted cost map and generate one or more candidate paths. This algorithm is an optimized path generation scheme based on an improved A* algorithm. Its core principle is to integrate factors such as business value, policy constraints, and terrain costs into the path decision, ultimately outputting the optimal fiber optic cable route that is "high in business value, low in cost, and compliant and feasible." The core of the algorithm is an improved heuristic function, which is essentially a comprehensive quantitative model of "estimated cost + value orientation," with the following formula logic: ; In the formula: Let n be the Euclidean distance from node n to target t, used to ensure basic path convenience and avoid excessive detours; The service activation rate cost item is assigned a value based on the user activation rate of the grid unit. The value is -50 for the high activation rate area (≥60%), 0 for the medium activation rate area (30%≤P<60%), 300 for the low activation rate area (10%≤P<30%), and 100 for the extremely low activation rate area (<10%). The lower the activation rate, the higher the path cost, which directly reflects the business value orientation. As a policy cost item, it is assigned a value based on the policy area to which the node belongs. The core signal protection area is assigned 500, the military restricted area is assigned 2000 (significantly increasing costs and prohibiting crossing), and the ordinary area is assigned 0 to ensure path compliance.

[0026] The algorithm's execution flow is as follows: First, load the constructed weighted cost map, the coordinates of the starting point (e.g., the core data center), and the ending point (e.g., the target coverage area). Then, using the starting point as the initial node, establish an open list (nodes to be explored) and a closed list (explored nodes), and calculate the initial heuristic value of the starting point. Next, select the node with the lowest heuristic value from the open list, expand its adjacent grid cells (up, down, left, right / diagonal), and calculate the comprehensive cost of each adjacent node (basic distance + business cost + policy cost + grid access cost in the weighted cost map). Nodes exceeding the planning scope, policy-restricted areas (e.g., military restricted areas), or with excessively high costs are eliminated. Nodes that are compliant and have the optimal cost are added to the open list. When the target node is added to the closed list, the search stops, and the node path from the starting point to the ending point is traced in reverse; this is the candidate optimal path.

[0027] Step four: Use a multi-objective scoring function to comprehensively evaluate each candidate path. The multi-objective scoring function includes at least economic indicators, construction feasibility indicators, technical compliance indicators, and business value indicators. In this step, the core process involves using a multi-objective scoring function to quantify, score, and rank the candidate paths generated by the business-driven path search, ultimately selecting the optimal routing solution. The multi-objective scoring function abandons traditional single-dimensional evaluation methods (such as considering only distance or cost), transforming the four key dimensions affecting the practicality of fiber optic routing (economic, construction, compliance, and business) into quantifiable indicators. A weighted sum is then used to obtain a comprehensive score (out of 100). A higher score indicates a more significant overall advantage of the path and is the core basis for ranking candidate paths. The multi-objective scoring function is expressed as follows: ; In the formula: As an economic indicator, For construction feasibility indicators, As a technical compliance indicator, As a business value indicator, , , and These are the weights for the corresponding indicators, with values ​​of 0.25, 0.30, 0.25, and 0.20 respectively.

[0028] Among them, the economic indicator measures the construction cost; the shorter the path length, the lower the construction cost and the higher the score.

[0029] Calculation formula: ; In the formula, L is the total path length in meters.

[0030] In this formula, when the path length does not exceed 50 kilometers, the shorter the length, the higher the score; when it exceeds 50 kilometers, it is calculated as 50 kilometers to avoid extremely long paths receiving too low a score.

[0031] The feasibility index measures the difficulty of construction; the lower the difficulty, the easier it is to implement, and the higher the score.

[0032] Calculation formula: ; In the formula: The construction difficulty index has a value range of [0,1].

[0033] In this formula, =0, score 100 (no construction difficulty). When the score is 1, the score is 0 (construction is not feasible), which directly relates to the difficulty of implementing the path.

[0034] Technical compliance indicators measure compliance with regulations. Strict adherence to broadcasting industry standards (such as GY / T 5069-2012) is required. The fewer violations, the higher the score.

[0035] Calculation formula: ; In the formula: This indicates a point deduction for bending compliance; 10 points will be deducted for each point with a bending angle <15°. This indicates a violation of the rules regarding the length of the route segment, with 5 points deducted for each segment where the pole spacing is less than 50m. This indicates a deduction for violations of power line safety distance regulations. 20 points will be deducted for each intersection point where the distance to the power line is less than 2.0m.

[0036] Business value metrics measure business potential. The more high-value (high activation rate) areas the path covers, the greater the business revenue potential and the higher the score.

[0037] Calculation formula: ; In the formula: This represents the four activation rate levels mentioned above. This represents the area ratio of the corresponding level region within the path. The values ​​are assigned as weights: 1.0 for high activation rate, 0.6 for medium activation rate, 0.2 for low activation rate, and -0.3 for extremely low activation rate, guiding the path towards high-value areas.

[0038] The multi-objective scoring function achieves multi-dimensional equilibrium and optimality, which avoids the one-sidedness of focusing only on cost and ignoring compliance, or focusing only on business and ignoring construction, and ensures that the evaluation results are objective and traceable through quantitative scoring, providing a scientific basis for routing decisions.

[0039] Step 5: For the evaluated candidate paths, calculate the required number of optical fiber cores based on their path characteristics and the service needs of the area they serve using a capacity estimation model. This step, based on the characteristics of the optimal candidate path and the service area's business requirements, accurately calculates the number of optical fiber cores using a capacity estimation model that combines basic requirements and dynamic redundancy, balancing service carrying capacity and resource utilization. The capacity estimation model is expressed as follows: ; In the formula: Based on the fiber core count required for basic business operations, This is the dynamic redundancy factor; where, ; In the formula: For business type The single-point fiber core requirement, such as 4K live streaming and home broadband, requires 2 fiber cores per point; This refers to the number of service points, such as 15 residential communities requiring home broadband access. =15, Total number of business types; ; In the formula, The length redundancy factor is calculated as follows: L is the path length; the longer the path, the higher the redundancy. The complexity redundancy factor is calculated as follows: , The number of bend points increases with more bends; As a policy redundancy factor, a value of 1.3 is used for paths passing through core signal protection zones, and a value of 1.0 is used for other areas; As a reliability redundancy factor, it is set to 1.2 for backbone transmission and 1.0 for branch transmission.

[0040] This formula means that the basic number of fiber cores is multiplied by the dynamic redundancy factor and then rounded up to obtain the final number of fiber cores required for the optical cable (e.g., if the calculation result is 23.1, take 24 cores).

[0041] Step 6: Based on the evaluation results and capacity estimation results, output the optimal optical cable routing plan, the corresponding bill of materials, and the investment budget.

[0042] This step is the final implementation output of the entire planning process. Its core is integrating the optimal path from multi-objective evaluation with the fiber core requirements from capacity estimation, generating a deliverable package that is directly implementable, data-accurate, and with clearly defined responsibilities, providing a complete basis for project implementation. Core outputs include: 1. Optimal optical cable routing plan: including a route overview map (marking the start point, end point, and key nodes such as the location of optical distribution boxes), detailed path coordinates (accurate to the meter level), and a description of the laying method (duct / aerial / direct burial, determined in combination with terrain and existing resources); at the same time, it clearly defines the area served by the path, the types of services covered (such as 4K live broadcast, emergency broadcast) and the penetration rate distribution.

[0043] Additional information: Route compliance description (listing compliant points with no violations or those that have been rectified), and tips on key and difficult points of construction (such as construction precautions for long-distance sections and sections with many bends).

[0044] 2. Precise Material List: Based on route length, fiber core quantity, and laying method, materials are categorized into "main materials + auxiliary materials + consumables," with quantities precisely matching project requirements (including dynamic redundancy reserves). Main contents: Main material: Optical cable (model such as GYTA-48B1, length, number of fiber cores, determined based on capacity estimation results); Auxiliary materials: junction boxes, optical distribution boxes, poles (for overhead installation), pipes (for new pipe installation), etc., calculated according to the number of path nodes and the laying length; Consumables: Optical cable splices, fasteners, protective materials, etc., should be matched according to a reasonable proportion of the main materials used.

[0045] 3. Detailed investment budget: Based on the material list, combined with market prices, construction labor costs, management fees, etc., the budget is broken down into "itemized costs + total budget" to ensure that the error is controlled within ±5%.

[0046] Main content: Itemized costs: Material procurement costs (calculated based on material unit price × quantity), construction costs (priced based on laying length and construction difficulty), transportation costs, management fees, and unforeseen expenses (reserved at 5%-8% of the total cost); Total Budget: Summarize the costs of each item, clarify the total investment amount and the cost per unit length, and provide an investment recovery estimate (based on the business value calculation of the service area).

[0047] Ultimately, the construction department can organize on-site construction based on the routing plan and material list, clarifying the construction scope and material requirements; the procurement department can make precise purchases according to the material list to avoid material shortages or stockpiling; the finance department can allocate funds based on the investment budget to control project costs; and the decision-making department can evaluate the project's feasibility and investment benefits through the deliverables package, serving as the final basis for project initiation and implementation.

[0048] Furthermore, the method of the present invention also includes hierarchical service assurance, which configures differentiated minimum fiber core count, redundancy coefficient, and guaranteed service type set for different levels of areas based on the user activation rate level of the areas traversed by the finally determined optical cable route, as shown in Table 1:

[0049] Table 1 The invention will be further described below with reference to specific examples: I. Implementation Background A certain county covers a total area of ​​1,200 square kilometers, encompassing urban areas, towns, and remote rural areas. The existing cable television network suffers from insufficient bandwidth in high-value areas (urban areas), resource waste in low-value areas (remote rural areas), and poor planning compliance. The goal of this plan is to optimize fiber optic cable routing, cover 200,000 potential users, ensure services for four types of businesses—4K live streaming, home broadband, enterprise leased lines, and emergency broadcasting—and improve investment efficiency and service quality.

[0050] II. Data Acquisition and Preprocessing 1. User activation rate data Data was extracted from county-level radio and television CRM and BOSS systems, and combined with township sampling surveys, to calculate the service activation rate in each region: urban areas (high penetration rate, ≥60%), townships (medium penetration rate, 30%-60%), and remote rural areas (low / very low penetration rate, <30%).

[0051] High-value services (4K live streaming, enterprise dedicated lines) activation rates: 28% in urban areas, 5% in townships, and 1% in rural areas.

[0052] 2. Geographic information data Obtain 1:10000 basic geographic data from the local natural resources bureau, including land use types (roads, building areas, farmland, water areas, etc.), elevation gradient values, and policy area boundaries (1 core signal protection zone and 1 military restricted zone).

[0053] Topographic data shows that the urban area has a gentle terrain. ≤0.005), some sections of the road in the township have gentle slopes (0.005 < ≤0.02), remote rural areas including mountainous areas ( >0.02).

[0054] 3. Existing network resource data Exporting existing optical fiber and pipeline resources from the OSS system: Urban areas have a complete pipeline network (with sufficient remaining capacity), some sections of townships have overhead optical fiber, and remote rural areas only have simple optical fiber in the core villages.

[0055] Verify the current resource status: the integrity rate of urban pipelines is 95%, and the utilization rate of overhead optical cable cores in rural areas is 70%.

[0056] III. Construction of Weighted Cost Map Divide the grid into 100m × 100m units, calculate the passage cost using a formula, and provide an example of key factor values: Urban areas (highways + general areas + existing pipelines + high completion rate): =1.0、 =1.0、 =0.2、 =1.01、 =0.3, =1.0×1.0×0.2×1.01×0.3=0.0606.

[0057] Remote rural mountainous areas (farmland + ordinary areas + no existing pipelines + extremely low coverage rate): =8.0、 =1.0、 =1.0、 =1.05、 =3.0, =8.0×1.0×1.0×1.05×3.0=25.2.

[0058] IV. Path Search and Comprehensive Evaluation 1. Candidate Path Generation Starting from the county-level core data center, candidate paths are generated for three target areas (eastern urban area, township cluster, and core remote rural village). An improved A* algorithm is adopted, and heuristic functions are incorporated into business and policy cost items to avoid military restricted areas and high-cost mountainous areas.

[0059] 2. Multi-objective scoring according to =0.25× +0.30× +0.25× +0.20× Calculate the scores, select the top 3 candidate paths, and the final optimal path score is as follows: Economic indicators: Total route length 32 kilometers, =100×(1-32000 / 50000)=36 points; Construction feasibility indicators: Construction difficulty index =0.15, =100×(1-0.15)=85 points; Technical compliance indicators: No violations of bending, pole spacing, or power line safety distance requirements. =100 points; Business value metrics: The area coverage percentages for high / medium / low / extremely low activation rates are 40% / 35% / 15% / 10%, respectively. =1.0×0.4+0.6×0.35+0.2×0.15+(-0.3)×0.1=0.55 points; Overall Score: =0.25×36+0.30×85+0.25×100+0.20×0.55≈71.61 points.

[0060] V. Capacity Estimation 1. Baseline fiber core count (Nbase) Business needs: Eastern part of the city (5 4K live streaming service points) =2; 12 home broadband service points. =1; 3 dedicated enterprise line service points. =4), Township Cluster (8 home broadband service points), =1; 6 emergency broadcast service points. =1), remote rural areas (4 emergency broadcast service points), =1).

[0061] calculate: =(5×2+12×1+3×4)+(8×1+6×1)+(4×1)=(10+12+12)+(8+6)+4=52 cores.

[0062] 2. Dynamic redundancy factor ( ) Length redundancy ( ): 1.0 + 0.1 × (32000 / 50000) = 1.064; Redundancy in complexity ( There are 8 bending points, 1.0 + 0.02 × 8 = 1.16; Policy redundancy ( ( ): Not passing through the core signal protection zone, take 1.0; Reliability redundancy ( ): Backbone transmission, take 1.2; =max(1.064,1.16,1.0,1.2)=1.2.

[0063] 3. Total number of fiber cores =⌈52×1.2⌉=⌈62.4⌉=64 cores.

[0064] VI. Output Results 1. Optimal route planning scheme Route: Core computer room → Eastern urban pipeline reuse section → Township overhead section → Rural direct burial section, passing through 15 key nodes (including 8 optical distribution box locations). Laying method: pipeline laying is used in urban areas, overhead laying is used in towns, and direct burial is used in rural areas; Service coverage: Covering 8 subdistricts, 12 townships, and 20 core villages, serving 120,000 potential users.

[0065] 2. List of Materials Main material: 32 km of GYTA-64B1 optical cable; Auxiliary materials: 22 junction boxes, 8 optical distribution boxes, 12 kilometers of overhead pole lines, and 8 kilometers of direct-buried protective pipes; Consumables: 64 sets of optical cable splices and 3,000 fasteners.

[0066] 3. Investment Budget Itemized costs: material procurement cost 896,000 yuan, construction cost 688,000 yuan, transportation cost 52,000 yuan, management cost 124,000 yuan, and unforeseen expenses 80,000 yuan; Total budget: 1.84 million yuan; cost per unit length: 57,500 yuan / km. Return on investment: The estimated annual new business revenue is RMB 1.36 million, and the investment payback period is approximately 1.35 years.

[0067] VII. Implementation Results Planning efficiency: Reduced from 4 hours of traditional manual work to 15 minutes, an efficiency increase of 16 times; Investment accuracy: Budget error is controlled within ±5%, and coverage of high-value areas has increased from 65% to 92%; Compliance: The design compliance rate reached 98%, with no rework due to violations; Resource utilization: Material waste was reduced by 35%, fiber core configuration was tailored to business needs, and user complaint rate decreased by 38%.

[0068] In summary, this invention can perform routing planning based on service activation rates, achieve automatic compliance checks and accurate capacity estimation, thereby effectively solving problems such as insufficient service awareness, low design efficiency, and poor investment returns in traditional methods.

[0069] Example 2: A system for implementing the method described above, comprising: The data acquisition module is used to collect user activation rate data, geographic information data, and existing network resource data; The cost map calculation module receives data from the data acquisition module and constructs a weighted cost map. The path optimization module is connected to the cost map calculation module and is used to obtain the weighted cost map and execute a business-driven path search algorithm to generate candidate paths. The capacity estimation module is connected to the path optimization module and is used to obtain candidate path information and calculate the core requirement based on the dynamic redundancy model. The service assurance module is connected to the capacity estimation module and the data acquisition module. It is used to implement a tiered service assurance strategy based on user activation rate data and to provide service configuration constraints for different regions to the capacity estimation module. The results output module, connected to the path optimization module and the capacity estimation module, is used to receive the optimal path and fiber core demand information, and generate the final routing scheme, bill of materials and investment budget.

[0070] Example 3: This example provides an electronic device that implements Example 1, which is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit, at least one storage unit, a bus connecting different system components (including the storage unit and the processing unit), and a display unit.

[0071] The storage unit stores program code, which can be executed by the processing unit to perform the steps described in the method section of Embodiment 1 above, according to various exemplary embodiments of the present invention.

[0072] The storage unit may include readable media in the form of volatile storage units, such as random access memory (RAM) and / or cache storage units, and may further include read-only memory (ROM).

[0073] The storage unit may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0074] A bus can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0075] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. Further, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0076] From the above description of the embodiments, those skilled in the art will readily understand that the embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0077] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section above.

Claims

1. A method for planning the routing of optical fiber cables for broadcasting, characterized in that: Includes the following steps: Step 1: Obtain user activation rate data, geographic information data, and existing network resource data for the planned area; Step 2: Based on the user activation rate data, geographic information data, and existing network resource data, construct a weighted cost map; wherein, for each grid cell in the map, its passage cost is jointly determined by the land use type basic cost, the broadcasting policy regional cost factor, the existing pipeline resource utilization factor, the terrain difficulty coefficient, and the service activation rate driving factor. Step 3: Using a business-driven path search algorithm, search the weighted cost map to generate one or more candidate paths; Step four: Use a multi-objective scoring function to comprehensively evaluate each candidate path. The multi-objective scoring function includes at least economic indicators, construction feasibility indicators, technical compliance indicators, and business value indicators. Step 5: For the evaluated candidate paths, calculate the required number of optical fiber cores based on their path characteristics and the service needs of the area they serve using a capacity estimation model. Step 6: Based on the evaluation results and capacity estimation results, output the optimal optical cable routing plan, the corresponding bill of materials, and the investment budget.

2. The cable TV optical cable routing planning method according to claim 1, characterized in that: The passage cost in step two is calculated using the following formula: ； In the formula: For land use type basic costs, Regional cost factors for broadcasting policies; The utilization factor of existing pipeline resources; The terrain difficulty coefficient; This is a driver of business activation rate.

3. The cable TV optical cable routing planning method according to claim 1, characterized in that: The business-driven path search algorithm described in step three is the A* search algorithm, where the heuristic function of the A* search algorithm is: ； In the formula: Let n be the Euclidean distance from node n to target t; For the cost item of service activation rate, This is a policy cost item.

4. The cable TV optical cable routing planning method according to claim 1, characterized in that: The multi-objective scoring function is expressed as follows: ； In the formula: As an economic indicator, For construction feasibility indicators, As a technical compliance indicator, As a business value indicator, , , and These are the weights of the corresponding indicators.

5. The cable TV optical cable routing planning method according to claim 4, characterized in that: The calculation of the technical compliance indicators includes checking compliance with the bending radius of the path, pole spacing limits, and safe distance from power lines.

6. The cable TV optical cable routing planning method according to claim 1, characterized in that: The capacity estimation model is expressed as follows: ； In the formula: Based on the fiber core count required for basic business operations, This is the dynamic redundancy factor; where, ； In the formula: For business type The single-point fiber core requirement, For the number of business points, Total number of business types; ； In the formula, As the length redundancy factor, As a complexity redundancy factor, As a policy redundancy factor, This is a reliability redundancy factor.

7. The method for planning cable routing for broadcasting optical cables according to claim 1, characterized in that: It also includes tiered service assurance: based on the user activation rate level of the areas through which the final optical cable route passes, different minimum fiber core counts, redundancy coefficients, and sets of guaranteed service types are configured for different levels of areas.

8. A system for implementing the method as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to collect user activation rate data, geographic information data, and existing network resource data; The cost map calculation module receives data from the data acquisition module and constructs a weighted cost map. The path optimization module is connected to the cost map calculation module and is used to obtain the weighted cost map and execute a business-driven path search algorithm to generate candidate paths. The capacity estimation module is connected to the path optimization module and is used to obtain candidate path information and calculate the core requirement based on the dynamic redundancy model. The service assurance module is connected to the capacity estimation module and the data acquisition module. It is used to implement a tiered service assurance strategy based on user activation rate data and to provide service configuration constraints for different regions to the capacity estimation module. The results output module, connected to the path optimization module and the capacity estimation module, is used to receive the optimal path and fiber core demand information, and generate the final routing scheme, bill of materials and investment budget.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method 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 the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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