Capacity expansion optimization method based on PON port data analysis

Abstract

The invention provides a capacity expansion optimization method based on PON port data analysis, and belongs to the technical field of PON port capacity expansion, and the method comprises the steps: 1, obtaining PON port configuration flow, peak flow utilization rate and average flow utilization rate; 2, obtaining PON port user data and user development data through a CRM system; 3, calculating a PON port score by using a weighted summation method; and step 4, comparing with a PON port threshold, and screening out PON ports exceeding the threshold. Broadband service development is prevented from being affected by insufficient PON port capability, and customer perception is synchronously improved.

Description

Technical Field

[0001] This invention relates to the field of PON port expansion technology, and in particular to an expansion optimization method based on PON port data analysis. Background Technology

[0002] Currently, PON port expansion is handled solely by maintenance personnel based on the number of connected users. They determine whether expansion is needed by counting the number of users connected to each PON port. Expansion is only initiated when the number of connected users exceeds the nominal capacity, to prevent resource exhaustion and hinder customer growth. However, this manual approach is not only inefficient but also lagging, severely impacting the development, effectiveness, and efficiency of broadband services.

[0003] The traditional method of determining whether a PON port needs expansion based on the number of users connected to it has the following problems or defects.

[0004] 1. This is a handling method used during the incident, which has already caused losses to users and business development.

[0005] 2. The consideration of factors is too simplistic, ignoring the impact of the performance of the PON port itself.

[0006] 3. Ignoring the impact of business development trends on PON port utilization can easily lead to a situation of "sudden increase in full load". Summary of the Invention

[0007] To address the aforementioned technical challenges, this invention provides a capacity expansion optimization method based on PON port data analysis. Through technological innovation, a predictive model based on multi-dimensional quantitative analysis and adjustable weights is constructed to accurately identify PON port expansion needs in advance and guide scientific decision-making. This aims to fundamentally change the traditional passive response model, proactively optimize network resource allocation, significantly reduce operator investment and maintenance costs, and provide users with a seamless, high-quality network experience. This, in turn, powerfully drives operator business growth and enhances market competitiveness, achieving the dual goals of economic and social benefits.

[0008] This invention fully considers the factors influencing PON port expansion, analyzing whether expansion is necessary based on the PON port's inherent performance and user development data. It overcomes the limitations of traditional single-factor approaches to PON port expansion, enhancing proactive management capabilities, improving user experience, and boosting market business expansion capabilities.

[0009] The technical solution of this invention is: A capacity expansion optimization method based on PON port data analysis, including Step 1: Obtain the PON port configuration traffic, peak traffic utilization, and average traffic utilization; Step 2: Obtain PON port user data and user development data through the CRM system; Step 3: Calculate the PON port score using a weighted summation method; Step 4: Compare with the PON port threshold and filter out PON ports that exceed the threshold.

[0010] Furthermore, Step 1 specifically includes Log in to the access network management system and extract the PON port configuration file and PON port performance file; the configuration file is for extracting the theoretical bandwidth of the PON port.

[0011] Peak traffic utilization and average traffic utilization are calculated by obtaining peak traffic values, average traffic, and the theoretical bandwidth of the configuration file.

[0012] The development data in Step 2 includes the rate of change in high-bandwidth users, the rate of change in high-definition ITV users, and the rate of change in broadband users.

[0013] By associating the logical ID of the user connected to the PON port with the CRM system, the development status of the OLT user located at that PON port can be obtained.

[0014] The analysis revealed the change rates of high-bandwidth users, high-definition ITV users, and broadband users.

[0015] Step 3 specifically includes Different weights are assigned to PON port data based on their importance. The overall weight of PON port data is 40%, and when allocated to sub-items, peak traffic utilization is weighted at 10% for both uplink and downlink, and average traffic utilization is weighted at 10% for both uplink and downlink.

[0016] Assign 60% weight to user change data, and allocate the following weights to sub-items: 20% weight for high-bandwidth user change rate, 20% weight for HD ITV user change rate, and 20% weight for broadband user change rate.

[0017] PON port score S = Σ (Pn * (sub-item)).

[0018] The weighting is normalized to ensure that the sum of the coefficients equals 1.

[0019] Step 4 specifically includes Set the PON port threshold to 80 points, filter out all PON ports with scores exceeding 80 points, and develop capacity expansion plans for these PON ports. This invention fully considers both PON port performance and user development trends, providing guidance for PON port expansion. Furthermore, it quantifies the impact of various indicators on PON port expansion through weighted summation, making the process more standardized. The weights are freely adjustable to ensure that highly influential sub-items contribute significantly to the overall expansion recommendations. This solution can accurately analyze the PON ports requiring expansion in advance, providing a better customer experience and improving market expansion rates compared to the in-process analysis of older solutions.

[0020] The beneficial effects of this invention are Social benefits: Improving the quality of the public's network experience: By accurately predicting expansion needs in advance, resources can be replenished before network congestion occurs, effectively avoiding poor experiences such as slow network speeds, video stuttering, and game lag caused by insufficient PON port bandwidth, and significantly improving the internet satisfaction and digital life quality of broadband users.

[0021] Ensuring the stable operation of critical services: For critical applications such as home office, online education, telemedicine, and smart homes that rely on stable networks, this solution can provide more reliable bandwidth guarantees, reduce the risk of service interruptions caused by insufficient network capacity, and support the stability and continuity of digital services in society.

[0022] Promoting Information Equity and Accessibility: More precise and efficient network planning helps operators optimize investment and allocate resources more effectively to areas with urgent needs, especially in areas with rapid user growth or emerging markets. This enables faster network deployment or upgrades, narrowing the digital divide and promoting the inclusiveness of information services.

[0023] Promoting green and energy-saving development: The "expansion on demand" model avoids the blind over-allocation of resources, reduces unnecessary equipment procurement, deployment and operation energy consumption, conforms to the national "dual carbon" strategic goal, and helps the communications industry develop in a green and low-carbon manner.

[0024] Economic benefits: Reduce operator CAPEX (capital expenditure): Precise investment: Investment is only made in PON ports that truly need expansion, avoiding the waste of resources in a "sprinkling pepper" manner or blind premature investment, and greatly improving the efficiency of capital utilization.

[0025] Reduce redundant equipment: Accurate advance forecasting avoids configuring too much redundant equipment to cope with uncertain risks, thus reducing procurement costs.

[0026] Reduce operator OPEX (operating expenses): Reduced fault handling costs: Preventative capacity expansion significantly reduces user complaints, fault tickets, and emergency repair costs (manpower, materials, vehicles, etc.) caused by network congestion.

[0027] Optimize operations and maintenance manpower: Shift operations and maintenance personnel from passive "firefighting" (handling congestion during events) to more efficient proactive planning and preventative maintenance, thereby improving operations and maintenance efficiency.

[0028] Reduce energy costs: Reduce unnecessary equipment operation and directly save on electricity expenses. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a schematic diagram of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0031] This invention relates to a PON port capacity expansion and optimization method based on PON port user data, user development data, peak traffic utilization rate, and average traffic utilization rate (the above traffic is displayed separately for uplink and downlink), which avoids the impact of insufficient PON port capacity on broadband service development and simultaneously improves customer experience.

[0032] Step 1: Obtain the PON port configuration traffic, peak traffic utilization (uplink and downlink), and average traffic utilization (uplink and downlink).

[0033] Log in to the access network management system and extract the PON port configuration file and PON port performance file. The configuration file is for extracting the theoretical bandwidth of the PON port. Peak traffic utilization (uplink and downlink) and average traffic utilization (uplink and downlink) are calculated by obtaining peak traffic values ​​(uplink and downlink), average traffic (uplink and downlink), and the theoretical bandwidth of the configuration file.

[0034] Step 2: Obtain PON port user data and user development data through the CRM system. The development data includes the change rate of high-bandwidth users, the change rate of HD ITV users, and the change rate of broadband users.

[0035] By associating the logical ID of the user connected to the PON port with the CRM system, the development status of the OLT user located at that PON port can be obtained.

[0036] The analysis revealed the change rates of high-bandwidth users, high-definition ITV users, and broadband users.

[0037] Step 3: Calculate the PON port score using a weighted summation method.

[0038] Different weights are assigned to PON port data based on their importance. The overall weight of PON port data is 40%, and when allocated to sub-items, peak traffic utilization is weighted at 10% for both uplink and downlink, and average traffic utilization is weighted at 10% for both uplink and downlink.

[0039] Assign 60% weight to user change data, and allocate the following weights to sub-items: 20% weight for high-bandwidth user change rate, 20% weight for HD ITV user change rate, and 20% weight for broadband user change rate.

[0040] PON port score S = Σ (Pn * (sub-item)).

[0041] Note: Weighting is performed using normalization to ensure that the sum of the coefficients equals 1.

[0042] Step 4: Compare with the PON port threshold and filter out PON ports that exceed the threshold.

[0043] Set the PON port threshold to 80 points, filter out all PON ports with scores exceeding 80 points, and develop capacity expansion plans for these PON ports.

[0044] 2. Data Structure Design PON port performance

[0045] User development data Expansion guidance model based on PON port performance and user trends: Establish a method that simultaneously considers two key factors: the current performance indicators of the PON port and the development trend of user scale, as the core basis for generating PON port expansion guidance.

[0046] 2. Weighted Quantitative Evaluation System: The weighted summation mathematical method is used to quantify and standardize various sub-indicators (such as bandwidth utilization, user growth rate, service type requirements, etc.) that affect PON port expansion decisions.

[0047] 3. Configurable weighting factors: The weights in the model can be freely adjusted, allowing key sub-indicators that have a greater impact on scaling decisions to be assigned higher weights, thus giving them a larger share (greater contribution) in the final decision.

[0048] 4. Precise Pre-Expansion Prediction: The core advantage of this solution lies in its ability to accurately analyze and identify the specific PON ports that will need expansion before the actual expansion demand occurs (the pre-expansion stage). This changes the lagging model of traditional solutions that rely on "in-process analysis" (i.e., analysis when the problem has already occurred or is in the process of occurring).

[0049] 5. Enhance user experience and market expansion: By enabling accurate pre-emptive capacity expansion prediction, this solution aims to proactively optimize network resources, avoid network congestion or service quality degradation caused by insufficient capacity, thereby improving the network experience for end users and ultimately helping to enhance the market expansion capabilities of service providers.

[0050] The above description is merely a preferred embodiment of the present invention and is used only to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A capacity expansion optimization method based on PON port data analysis, characterized in that, include Step 1: Obtain the PON port configuration traffic, peak traffic utilization, and average traffic utilization; Step 2: Obtain PON port user data and user development data through the CRM system; Step 3: Calculate the PON port score using a weighted summation method; Step 4: Compare with the PON port threshold and filter out PON ports that exceed the threshold.

2. The method according to claim 1, characterized in that, Step 1 specifically includes Log in to the access network management system and extract the PON port configuration file and PON port performance file; the configuration file is for extracting the theoretical bandwidth of the PON port.

3. The method according to claim 2, characterized in that, Peak traffic utilization and average traffic utilization are calculated by obtaining peak traffic values, average traffic, and the theoretical bandwidth of the configuration file.

4. The method according to claim 1, characterized in that, The development data in Step 2 includes the rate of change in high-bandwidth users, the rate of change in high-definition ITV users, and the rate of change in broadband users.

5. The method according to claim 4, characterized in that, By associating the logical ID of the user connected to the PON port with the CRM system, the development status of the OLT user to which the PON port is located can be obtained; The analysis revealed the change rates of high-bandwidth users, high-definition ITV users, and broadband users.

6. The method according to claim 5, characterized in that, Step 3 specifically includes PON port data is assigned different weights based on its importance. The overall weight of PON port data is 40%, and when allocated to sub-items, peak traffic utilization is assigned 10% for both uplink and downlink, and average traffic utilization is assigned 10% for both uplink and downlink. Assigning 60% weight to user change data, and allocating it to sub-items as follows: 20% weight for high-bandwidth user change rate, 20% weight for HD ITV user change rate, and 20% weight for broadband user change rate; PON port score S = Σ (Pn * (sub-item)).

7. The method according to claim 6, characterized in that, The weighting is normalized to ensure that the sum of the coefficients equals 1.

8. The method according to claim 7, characterized in that, Step 4 specifically includes Set the PON port threshold to 80 points, filter out all PON ports with scores exceeding 80 points, and develop capacity expansion plans for these PON ports.

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