Optical network unit management method

Abstract

The invention belongs to the technical field of data processing, and discloses an optical network unit management method. Comprising the following steps: 1, deploying a plurality of sensors on an optical network unit (ONU), connecting an ONU core operation component, and collecting ONU operation state data, service data and energy consumption data in real time; and step 2, establishing a service demand model and an operation health model for ONUs in different scenes. ONU operation data are collected in real time through multiple sensors, and the sub-health state can be identified in advance in combination with the operation health model, so that the fault response time is shortened, and the equipment fault rate is reduced; the bandwidth is dynamically allocated based on the business demand model, waste caused by static configuration is avoided, the bandwidth utilization rate of the residential area at night is optimized, and bandwidth waste in the idle period of a commercial office area is reduced.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, and more specifically, to a method for managing optical network units. Background Technology

[0002] Currently, Optical Network Units (ONUs), as terminal devices in fiber optic access networks, are widely used in residential communities, commercial office areas, and other scenarios. However, existing ONU management suffers from several pain points: On the one hand, there is a lack of accurate monitoring of the real-time operating status of ONUs. Issues such as excessively high ONU temperatures and unbalanced bandwidth utilization are difficult to detect in a timely manner, which can easily lead to equipment failures or service interruptions. On the other hand, management strategies are mostly static configurations (such as fixed bandwidth allocation and 24-hour full-power operation), which cannot adapt to fluctuations in service volume (such as peak video service in residential communities at night and idle operation during the day). This not only wastes bandwidth resources but also increases unnecessary energy consumption. In addition, traditional operation and maintenance rely on manual on-site troubleshooting, resulting in long fault response times (averaging more than 30 minutes) and low operation and maintenance efficiency.

[0003] Therefore, there is an urgent need for an intelligent optical network unit management method and system to solve the problems of insufficient real-time monitoring, static policies, and inefficient operation and maintenance. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution, comprising the following steps: Step 1: Deploy various sensors in the Optical Network Unit (ONU) and connect them to the core operating components of the ONU to collect ONU operating status data, service data, and energy consumption data in real time. The operating status data includes ONU temperature, bandwidth utilization, packet loss rate, and power supply voltage. The service data includes user service type and real-time bandwidth requirements. The energy consumption data includes ONU real-time power and cumulative power consumption. Step 2: Establish service demand models and operational health models for ONUs in different scenarios. The service demand model satisfies the formula Btype_min ≤ Buser ≤ Btype_max, where Btype_min and Btype_max are the minimum and maximum bandwidth requirements for the corresponding service type (e.g., video service, data service), and Buser is the real-time user bandwidth requirement of the ONU. Establish an ONU energy consumption model, such as ONU energy consumption Eonu = Ponu * tonu * f(Butil), where Ponu is the rated power of the ONU, tonu is the ONU operating time, and f(Butil) is a coefficient function related to the ONU bandwidth utilization rate Buutil (the higher the bandwidth utilization rate, the closer the coefficient function value is to 1, and vice versa). Step 3: Collect the data gathered in Step 1 in real time, compare the operational status data with the operational health model, and compare the business data with the business demand model. Combine the ONU energy consumption model to evaluate the resource cost and operation and maintenance efficiency under different management strategies. For example, the bandwidth dynamic adjustment cost △Bcost=Cband*tadj*h(Butil-Btarget), where Cband is the unit bandwidth adjustment cost, tadj is the bandwidth adjustment duration, and h(Butil-Btarget) is a coefficient function related to the difference between the actual bandwidth utilization Buli and the target bandwidth utilization Btarget. Develop the optimal management strategy based on the analysis results. Step 4: The optimal management strategy is converted into control commands through the optical network control center (linked with the OLT) and sent to the corresponding ONU for execution. At the same time, the execution status of the ONU commands and changes in operating parameters are monitored in real time. Step 5: Regularly summarize historical ONU operation data, business data, and management strategy execution effect data. Utilize machine learning algorithms to optimize business demand models, operational health models, and management strategies to improve strategy adaptability and management efficiency.

[0005] Furthermore, the various sensors include a temperature sensor (monitoring the temperature of the ONU chip), a flow sensor (monitoring bandwidth usage), a voltage and current sensor (monitoring power supply status), and a service identification sensor (identifying the user's service type). Furthermore, the business demand model and operational health model are dynamically adjusted based on the business volume fluctuation patterns and historical fault data of the region where the ONU is located (such as residential areas or commercial office areas).

[0006] Furthermore, the management strategies include dynamic bandwidth allocation, ONU sleep / wake-up control (reducing power during idle periods), fault pre-repair instructions (such as triggering port reset when packet loss exceeds a threshold), and service priority scheduling. The technical effects and advantages of the optical network unit management method of the present invention are as follows: This invention collects ONU operating data in real time through multiple sensors and combines it with an operating health model to identify sub-health states (such as temperatures approaching thresholds) in advance, shortening fault response time and reducing equipment failure rates. It also dynamically allocates bandwidth based on a business demand model, avoiding waste caused by static configuration, optimizing bandwidth utilization in residential areas during evenings, and reducing bandwidth waste during idle periods in commercial office areas. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of an optical network unit management method according to the present invention. Detailed Implementation

[0008] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] A method for managing optical network units The method includes the following steps: Step 1: Deploy multiple sensors in the Optical Network Unit (ONU) and connect them to the ONU core operating components to collect ONU operating status data, service data, and energy consumption data in real time; wherein, the operating status data includes ONU temperature (typically -10℃~70℃), bandwidth utilization (0%~100%), packet loss rate (0%~10%), and power supply voltage (typically 12V / 24V); the service data includes user service type (video, data, voice), real-time bandwidth requirement (1Mbps~1000Mbps); and the energy consumption data includes ONU real-time power (5W~20W) and cumulative power consumption; Step 2: Establish service demand models and operating health models for ONUs in different scenarios, wherein the service demand model satisfies the formula Btype_min≤Buser≤Btype_max (e.g., video service Btype_min=20Mbps, Btype_max=100Mbps, data service Btype_min=5Mbps, Btype_max=50Mbps), Btype_min and Btype_max are... x represents the minimum and maximum bandwidth requirements for the corresponding service type, and Buser represents the real-time user bandwidth requirement of the ONU; establish an ONU energy consumption model, such as ONU energy consumption Eonu=Ponu*tonu*f(Butil), where Ponu is the rated power of the ONU (e.g., 10W), tonu is the ONU operating time (unit: h), and f(Butil) is a coefficient function related to the ONU bandwidth utilization rate Butil (e.g., f=1 when Butil≥80%, f=0.5 when Butil≤30%); Step 3: Real-time collection of data from Step 1 The collected data is compared with the operational status data and the operational health model (e.g., ONU temperature > 60℃ is judged as sub-healthy, packet loss rate > 1% is judged as abnormal), and with the business data and the business demand model (e.g., video service Buser < 20Mbps is judged as insufficient bandwidth). Combined with the ONU energy consumption model, the resource cost and operation and maintenance efficiency under different management strategies are evaluated, such as the bandwidth dynamic adjustment cost △Bcost = Cband * tadj * h (Butil - Btarget) (Cband = 0.01 yuan / Mbps・h, tadj = 0.1h, Btarget=70%, h is the difference coefficient); Based on the analysis results, formulate the optimal management strategy (e.g., increase bandwidth when bandwidth is insufficient, trigger cooling fans when temperature is too high, and initiate hibernation when idle); Step 4, through the optical network control center (linked with the OLT), convert the optimal management strategy into control commands and send them to the corresponding ONU for execution (e.g., "increase video service bandwidth to 30Mbps" "initiate hibernation mode, reduce power to 5W"), while monitoring the ONU command execution status and changes in operating parameters in real time (e.g., whether the bandwidth adjustment meets the target, whether the power decreases after hibernation); Step 5, regularly summarize the historical operating data, service data, and management strategy execution effect data of the ONU (e.g., weekly summary), and use machine learning algorithms (gradient descent algorithm to optimize model thresholds, random forest algorithm to optimize strategy priority) to optimize the service demand model, operational health model, and management strategy, improving strategy adaptability and management efficiency (e.g., increase bandwidth in advance according to historical peak periods to reduce fault response time).

[0010] Preferably, the multiple sensors include a temperature sensor (monitoring ONU chip temperature, accuracy ±0.5℃), a flow sensor (monitoring bandwidth occupancy, sampling rate 1 time / second), a voltage and current sensor (monitoring power supply status, accuracy ±0.1V), and a service identification sensor (identifying user service type, accuracy ≥95%).

[0011] Preferably, the business demand model and the operational health model are dynamically adjusted according to the business volume fluctuation pattern and historical fault data of the area where the ONU is located (e.g., the upper limit of data service bandwidth demand is increased in commercial office areas from 9:00 to 18:00 on weekdays, and the ONU temperature warning threshold is lowered from 60℃ to 58℃ in residential areas based on historical fault data).

[0012] Preferably, the management strategy includes dynamic bandwidth allocation (adjusting as needed to avoid waste), ONU sleep-wake control (reducing power during idle periods to save energy), fault pre-repair instructions (such as triggering port reset when the packet loss rate exceeds the threshold to reduce fault propagation), and service priority scheduling (voice service priority > video service > data service).

[0013] An optical network unit management system This system is applied to the aforementioned optical network unit management method, including: Data acquisition module: Used to collect data from various sensors deployed in the ONU and data from the core operating components of the ONU, and transmit them to the data processing center; This module communicates with the sensors and the core components of the ONU via RS485 or Ethernet to ensure that the data acquisition frequency is ≥1 time / minute and the data transmission delay is ≤100ms, so as to avoid data lag affecting the judgment; Modeling and Analysis Module: Constructs ONU service demand model and operational health model, performs model matching and efficiency and cost assessment on real-time collected data; this module updates model threshold parameters hourly based on real-time data (e.g., increases the upper limit of video service bandwidth demand by 10% during peak hours) to ensure that the model adapts to service fluctuations. Strategy Decision Module: Based on the evaluation results of the modeling and analysis module, and combined with preset operation and maintenance goals (such as bandwidth utilization ≥80%, fault response time ≤5s, and energy consumption reduction 15%), the optimal management strategy is formulated. When formulating the strategy, the service experience (video service stuttering rate ≤0.5%), operation and maintenance costs (bandwidth adjustment energy consumption increment ≤5%) and equipment lifespan (ONU temperature does not exceed 60℃ for a long time) are comprehensively considered to avoid a single goal orientation. Equipment control module: Receives control commands from the policy decision module, communicates with the ONU via optical network protocol (GPON / EPON) to realize command issuance and execution status feedback; supports remote batch command issuance, when the number of ONUs is ≥100, the command issuance completion time is ≤30s, and supports network outage caching (temporarily stores commands when the network is disconnected, and automatically resends them after the network is connected) to ensure that commands are not lost; The optimization learning module conducts long-term analysis of ONU historical data and strategy execution effect data, and uses gradient descent and random forest algorithms to optimize model parameters and management strategies. Through continuous learning, the strategy's response speed to business fluctuations is improved by 20%, the accuracy of fault pre-repair is improved by 15%, and the adaptability and effectiveness of the management system are continuously improved. Example 1

[0014] ONU Management in Residential Communities Data Acquisition: Temperature sensors, flow sensors, voltage and current sensors, and service identification sensors are deployed on the ONU devices in each building of the community to collect data in real time, including ONU temperature (e.g., up to 55℃ at midday in summer), bandwidth utilization (up to 90% at 8 pm), power supply voltage (12V±0.2V), user service type (mainly video services at night, accounting for 70%), and real-time bandwidth requirements (average video service requirement of 30Mbps). Model Establishment: Based on the characteristics of community services, a service demand model (video service Btype_min=20Mbps, Btype_max=50Mbps, data service Btype_min=5Mbps, Btype_max=20Mbps), an operational health model (ONU temperature ≤60℃, packet loss rate ≤1%, bandwidth utilization ≤95%), and an energy consumption model (Eonu=10W*tonu*f(Butil), f=1 when Butil≥90%, f=0.4 when Butil≤30%) were established. Strategy Assessment: At 8 PM, the traffic sensor detected a bandwidth utilization rate of 90%, and the service identification sensor showed that video services accounted for 70% of the bandwidth. Some users had a bandwidth utilization rate of 18Mbps (lower than the video service bandwidth utilization rate of 20Mbps). Based on the energy consumption model, the cost of "increasing bandwidth to 50Mbps" versus "maintaining the existing bandwidth" was assessed: After the increase, ΔBcost = 0.01 yuan / Mbps·h * 0.1h * h(90%-70%) = 0.02 yuan, and the lag issue could be resolved. Therefore, "increasing bandwidth to 50Mbps" was chosen. Command execution: The optical network control center sends the command "increase ONU video service bandwidth to 50Mbps" to the corresponding ONU via the GPON protocol. Real-time monitoring shows that the bandwidth adjustment is completed within 10 seconds, and the user's video service stuttering rate drops from 5% to 0.3%. Model optimization: Weekly summarization of ONU data from the community revealed that the bandwidth peak on Friday evenings occurred one hour earlier than usual (reaching 90% at 7 PM). The optimization learning module adjusted the peak-hour parameters of the business demand model using the random forest algorithm, advancing the bandwidth pre-adjustment time from 8 PM to 7 PM to avoid lag at the beginning of the peak. At the same time, based on historical temperature data, the summer ONU temperature warning threshold was lowered from 60℃ to 58℃ to reduce the risk of high-temperature failures.

[0015] Example 2: ONU Management in Commercial Office Area Data Acquisition: Sensors are deployed on ONUs in the office area to collect data in real time on ONU temperature (50℃ at noon on weekdays), bandwidth utilization (85% on weekdays from 9 am to 6 pm, dropping to 20% on weekends), service type (data services are the main service on weekdays, accounting for 60%), and real-time bandwidth requirements (average data service requirement of 40Mbps). Model establishment: Establish a business requirement model (data service Btype_min=30Mbps, Btype_max=80Mbps), an operational health model (ONU temperature ≤58℃, packet loss rate ≤0.8%), and an energy consumption model (Eonu=15W*tonu*f(Butil), f=1 when Butil≥80%, f=0.3 when Butil≤20%). Strategy Assessment: Over the weekend, bandwidth utilization dropped to 20%, and ONU temperature reached 40℃ (below the healthy threshold). Based on the energy consumption model, the energy consumption of "starting hibernation mode" and "maintaining operation" was evaluated: After hibernation, the power consumption dropped to 5W, saving 10W per hour. Therefore, "starting hibernation mode" was selected. Command execution: The control center issues the command "ONU starts sleep mode, power reduced to 5W". The device control module monitors and displays that the ONU enters sleep mode within 5 seconds, and the power is reduced from 15W to 4.8W. Model optimization: Monthly data aggregation revealed that data service demand drops to 25Mbps during weekdays from 12:00 to 13:00 (below Btype_min=30Mbps). The optimization learning module uses gradient descent algorithm to lower data service Btype_min to 25Mbps, avoiding unnecessary bandwidth allocation and reducing energy consumption by approximately 12kWh per month.

[0016] Since the electronic device described in this embodiment is the electronic device used to implement the post-urological surgery vital sign monitoring data processing method of this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the post-urological surgery vital sign monitoring data processing method described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any electronic device used by those skilled in the art to implement the post-urological surgery vital sign monitoring data processing method of this application embodiment falls within the scope of protection of this application.

[0017] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0018] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for users of ordinary technical skills, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for managing optical network units, characterized in that, Includes the following steps: Step 1: Deploy various sensors in the optical network unit and connect them to the core operating components to collect ONU operating status data, service data, and energy consumption data in real time. The operating status data includes ONU temperature, bandwidth utilization, packet loss rate, and power supply voltage. The service data includes user service type and real-time bandwidth requirements. The energy consumption data includes ONU real-time power and cumulative power consumption. Step 2: Establish service demand models and operational health models for ONUs in different scenarios. The service demand model satisfies the formula Btype_min≤Buser≤Btype_max, where Btype_min and Btype_max are the minimum and maximum bandwidth requirements for the corresponding service type, respectively, and Buser is the real-time user bandwidth requirement of the ONU. Establish an ONU energy consumption model, such as ONU energy consumption Eonu=Ponu*tonu*f(Butil), where Ponu is the rated power of the ONU, tonu is the ONU operating time, and f(Butil) is a coefficient function related to the ONU bandwidth utilization rate Butil. Step 3: Collect the data collected in Step 1 in real time, compare the operational status data with the operational health model, compare the business data with the business demand model, and combine the ONU energy consumption model to evaluate the resource cost and operation and maintenance efficiency under different management strategies. For example, the bandwidth dynamic adjustment cost △Bcost=Cband*tadj*h, where Cband is the unit bandwidth adjustment cost, tadj is the bandwidth adjustment duration, and h is a coefficient function related to the difference between the actual bandwidth utilization rate Buli and the target bandwidth utilization rate Btarget; formulate the optimal management strategy based on the analysis results. Step 4: The optical network control center converts the optimal management strategy into control commands and sends them to the corresponding ONUs for execution, while simultaneously monitoring the ONU command execution status and changes in operating parameters in real time. Step 5: Regularly summarize historical ONU operation data, business data, and management strategy execution effect data. Utilize machine learning algorithms to optimize business demand models, operational health models, and management strategies to improve strategy adaptability and management efficiency.

2. The optical network unit management method according to claim 1, characterized in that, The various sensors include temperature sensors, flow sensors, voltage and current sensors, and service identification sensors.

3. The optical network unit management method according to claim 1, characterized in that, The business demand model and operational health model are dynamically adjusted based on the business volume fluctuation patterns and historical fault data of the region where the ONU is located.

4. The optical network unit management method according to claim 1, characterized in that, The management strategy includes dynamic bandwidth allocation, ONU sleep / wake-up control, fault pre-repair instructions, and service priority scheduling.

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