Fttrb network real-time dynamic parameter adjustment method, system, device and medium

Abstract

The application discloses a kind of FTTRB network real-time dynamic parameter adjustment method, system, equipment and medium, is related to data processing technical field, method includes: the parameter data of cascaded light unit is collected, parameter data in preset time period is cached and forms parameter data sequence;The i th fluctuation and the i th variation are obtained, if the i th variation is less than first preset threshold, then the contrast cascaded light unit is obtained, the contrast fluctuation and the contrast variation are obtained;The first check proportion of the i th cascaded light unit is obtained, the second check proportion of the i th cascaded light unit is obtained;If the first check amount or the second check amount of the i th cascaded light unit exceeds second preset threshold, then according to the i th fluctuation, contrast fluctuation, the i th variation and contrast variation parameter adjustment amount is obtained, and the parameter data of the i th cascaded light unit is compensated according to parameter adjustment amount.The application has the advantages of cascaded precision, dynamic self-adaptability and local isolation.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, system, device, and medium for real-time dynamic parameter adjustment of an FTTRB network. Background Technology

[0002] In Fiber to the Room (FTTRB) networks, optical units are typically cascaded and deployed in diverse physical environments on the user side (such as homes or offices with frequent temperature fluctuations). Key parameters within these units, such as optical transmit power, receive sensitivity, and bias current, are highly susceptible to real-time, nonlinear drift caused by environmental interference (such as local temperature differences, equipment vibration, and unstable power supply). This drift not only affects the performance of individual nodes but also, due to the cascaded structure, creates a progressive and cumulative effect, leading to a deterioration in the optical signal-to-noise ratio, an increase in the bit error rate, and significant fluctuations in the user experience across the entire optical link. Current technologies generally employ fixed threshold warnings or periodic global calibration strategies, which have key drawbacks.

[0003] Specifically, preset static thresholds are difficult to adapt to the differences in the magnitude of changes of different nodes in a dynamic environment, which can easily lead to misjudgment of normal environmental fluctuations (such as small-range shifts caused by short-term temperature differences) or omission of truly abnormal nodes (parameters continuously jumping over a large range). Secondly, in cascaded scenarios, the abnormal drift of one node may be absorbed or masked by subsequent nodes. Existing methods evaluate the absolute parameter range (such as the amount of change) or overall fluctuation (such as the amount of fluctuation) of each node separately, lacking an effective assessment of the dynamic correlation of parameters between nodes. It is impossible to accurately identify whether the source of drift is the degradation of the node's own devices or the conduction interference from the previous stage, making it difficult to accurately locate compensation measures (such as insufficient compensation cannot cure the problem, while overcompensation may disrupt the link balance). Summary of the Invention

[0004] To address the technical problem in existing technologies that cannot identify local abnormal drift (distinguished from normal environmental fluctuations or conduction effects) in a cascaded architecture in real time and intelligently and compensate for dynamic parameter adjustments accordingly, thus failing to ensure consistently superior end-to-end optical performance under complex environments, this invention provides a method, system, device, and medium for real-time dynamic parameter adjustment in FTTRB networks.

[0005] A method for real-time dynamic parameter adjustment in an FTTRB network includes: deploying sensors within multiple cascaded optical units (OIRs) in the FTTRB network, collecting parameter data from each OIR based on the sensors, obtaining a preset time period, caching the parameter data for each OIR for the preset time period, and forming a parameter data sequence; obtaining the i-th fluctuation and i-th change based on the parameter data sequence of the i-th OIR; if the i-th change is less than a first preset threshold, then selecting an OIR adjacent to the i-th OIR as a comparison OIR, and adjusting the comparison... The parameter data sequence of the cascaded optical units is used to obtain the comparison fluctuation and comparison change. The first verification ratio of the i-th cascaded optical unit is obtained based on the i-th fluctuation and the comparison fluctuation, and the second verification ratio of the i-th cascaded optical unit is obtained based on the i-th change and the comparison change. If the first verification value or the second verification value of the i-th cascaded optical unit exceeds the second preset threshold, the parameter adjustment value of the i-th cascaded optical unit is obtained based on the i-th fluctuation, the comparison fluctuation, the i-th change, and the comparison change, and the parameter data of the i-th cascaded optical unit is compensated based on the parameter adjustment value.

[0006] Optionally, obtaining the i-th fluctuation quantity based on the parameter data sequence of the i-th cascaded optical unit includes: traversing all adjacent pairs of data points in the parameter data sequence of the i-th cascaded optical unit, and calculating the absolute value of the parameter value difference of each pair of adjacent data points; adding the absolute values ​​of all parameter value differences in the parameter data sequence of the i-th cascaded optical unit to obtain a cumulative value, and defining the cumulative value as the i-th fluctuation quantity.

[0007] Optionally, obtaining the i-th change based on the parameter data sequence of the i-th cascaded optical unit includes: identifying the highest and lowest parameter values ​​in the parameter data sequence of the i-th cascaded optical unit; subtracting the lowest parameter value from the highest parameter value to obtain the range of change, and defining the range of change as the i-th change.

[0008] Optionally, obtaining the first verification ratio of the i-th cascaded optical unit based on the i-th fluctuation amount and the comparison fluctuation amount includes: subtracting the comparison fluctuation amount from the i-th fluctuation amount to obtain the fluctuation amount difference; dividing the absolute value of the fluctuation amount difference by the i-th fluctuation amount to obtain the first ratio value, and defining the first ratio value as the first verification ratio of the i-th cascaded optical unit.

[0009] Optionally, obtaining the second verification ratio of the i-th cascaded optical unit based on the i-th change and the comparison change includes: subtracting the comparison change from the i-th change to obtain the change difference; dividing the absolute value of the change difference by the i-th change to obtain the second ratio value, and defining the second ratio value as the second verification ratio of the i-th cascaded optical unit.

[0010] Optionally, obtaining the parameter adjustment amount of the i-th cascaded optical unit based on the i-th fluctuation amount, the comparative fluctuation amount, the i-th change amount, and the comparative change amount includes: subtracting the comparative fluctuation amount from the i-th fluctuation amount to obtain the fluctuation amount difference, subtracting the comparative change amount from the i-th change amount to obtain the change amount difference, using the fluctuation amount difference as the main adjustment component, and using the change amount difference as the auxiliary adjustment component; weighting and fusing the main adjustment component and the auxiliary adjustment component to generate the original adjustment amount, and determining the parameter adjustment amount of the i-th cascaded optical unit based on the positive or negative sign of the original adjustment amount and the physical quantity type corresponding to the parameter data of the i-th cascaded optical unit.

[0011] Optionally, compensating the parameter data of the i-th cascaded optical unit according to the parameter adjustment amount includes: obtaining the compensation direction and compensation amplitude according to the parameter adjustment amount of the i-th cascaded optical unit; matching the control parameters of the adjustment element of the i-th cascaded optical unit according to the compensation direction and compensation amplitude, generating a control signal, and sending the control signal to the driving circuit of the i-th cascaded optical unit so that its output terminal continuously outputs according to the compensation direction and compensation amplitude.

[0012] A real-time dynamic parameter adjustment system for an FTTRB network is also provided. The system includes: an acquisition module, used to deploy sensors within multiple cascaded optical units in the FTTRB network, collect parameter data of each cascaded optical unit based on the sensors, acquire a preset time period, cache the parameter data of each cascaded optical unit for the preset time period, and form a parameter data sequence; and a first data processing module, used to acquire the i-th fluctuation and i-th change based on the parameter data sequence of the i-th cascaded optical unit. If the i-th change is less than a first preset threshold, the module acquires the cascaded optical unit adjacent to the i-th cascaded optical unit as a comparison cascaded optical unit, and adjusts the parameters according to the comparison threshold. The system comprises: a first data processing module for obtaining comparison fluctuation and comparison change values ​​from the parameter data sequence of the cascaded optical units; a second data processing module for obtaining a first verification ratio of the i-th cascaded optical unit based on the i-th fluctuation and comparison fluctuation, and a second verification ratio of the i-th cascaded optical unit based on the i-th change and comparison change value; and a dynamic parameter adjustment module for obtaining a parameter adjustment value of the i-th cascaded optical unit based on the i-th fluctuation, comparison fluctuation, i-th change, and comparison change value if the first or second verification value of the i-th cascaded optical unit exceeds a second preset threshold, and compensating the parameter data of the i-th cascaded optical unit based on the parameter adjustment value.

[0013] An electronic device is also provided, comprising: a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement a method for real-time dynamic parameter adjustment of an FTTRB network.

[0014] A non-transitory computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements a method for real-time dynamic parameter adjustment of an FTTRB network.

[0015] The beneficial effects of this invention are reflected in:

[0016] In the entire FTTRB network real-time dynamic parameter adjustment method, a high-fidelity dynamic benchmark is constructed by using a distributed sensor network and time-aligned data sequences. The interference types are decoupled using two indicators: fluctuation (cumulative intensity of high-frequency instantaneous disturbance) and change (global range of low-frequency slow drift). Furthermore, when the change does not exceed the first dynamic threshold, the fluctuation and change of neighboring nodes are compared. The dynamic coupling anomaly degree between nodes (the interference superposition illusion caused by penetration and conduction effects) is quantified by the first and second verification ratio. The second threshold, which is combined with physical quantity characteristics and topological weights, is adaptively used to achieve accurate differentiation between local real anomalies, random environmental fluctuations, and front-stage conducted interference. Furthermore, the parameter adjustment amount with clear physical meaning is generated by weighted fusion of the fluctuation difference (main component) and the change difference (secondary component). Two-level targeted intervention is achieved through closed-loop control of compensation direction and amplitude: deep compensation is implemented for local faults such as device aging (e.g., laser bias correction), and dynamic isolation is performed for conducted interference (e.g., receiver gain suppression). Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a partial flowchart of the FTTRB network real-time dynamic parameter adjustment method of the present invention;

[0019] Figure 2 This is a schematic diagram of another part of the process of the real-time dynamic parameter adjustment method for FTTRB network of the present invention;

[0020] Figure 3 This is a schematic diagram illustrating the steps of the FTTRB network real-time dynamic parameter adjustment method of the present invention;

[0021] Figure 4 This is a schematic diagram of part of step S2 in the real-time dynamic parameter adjustment method for FTTRB network of the present invention;

[0022] Figure 5 This is a schematic diagram of another part of the steps in S2 of the FTTRB network real-time dynamic parameter adjustment method of the present invention;

[0023] Figure 6 This is a schematic diagram of part of step S3 in the real-time dynamic parameter adjustment method for FTTRB network of the present invention;

[0024] Figure 7 This is a schematic diagram of another part of step S3 in the real-time dynamic parameter adjustment method for FTTRB networks of the present invention;

[0025] Figure 8 This is a schematic diagram of part of step S4 in the real-time dynamic parameter adjustment method for FTTRB networks of the present invention;

[0026] Figure 9 This is a schematic diagram of another part of step S4 in the FTTRB network real-time dynamic parameter adjustment method of the present invention;

[0027] Figure 10 This is a block diagram illustrating an electronic device according to an embodiment of the present invention.

[0028] Figure label:

[0029] 700 - Electronic device; 701 - Processor; 702 - Memory; 703 - Multimedia component; 704 - I / O interface; 705 - Communication component. 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 only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] like Figures 1 to 3 As shown, a method for real-time dynamic parameter adjustment of an FTTRB network is provided. In one embodiment, the method includes:

[0034] S1. Deploy sensors in multiple cascaded optical units in the FTTRB network, collect parameter data of each cascaded optical unit based on the sensors, obtain a preset time period, cache the parameter data of each cascaded optical unit for the preset time period, and form a parameter data sequence.

[0035] S2. Obtain the i-th fluctuation amount and the i-th change amount according to the parameter data sequence of the i-th cascaded optical unit. If the i-th change amount is less than the first preset threshold, obtain the cascaded optical unit adjacent to the i-th cascaded optical unit as the comparison cascaded optical unit, and obtain the comparison fluctuation amount and comparison change amount according to the parameter data sequence of the comparison cascaded optical unit.

[0036] S3. Obtain the first verification ratio of the i-th cascaded optical unit based on the i-th fluctuation amount and the comparison fluctuation amount, and obtain the second verification ratio of the i-th cascaded optical unit based on the i-th change amount and the comparison change amount.

[0037] S4. If the first verification value or the second verification value of the i-th cascaded optical unit exceeds the second preset threshold, the parameter adjustment value of the i-th cascaded optical unit is obtained according to the i-th fluctuation value, the comparison fluctuation value, the i-th change value and the comparison change value, and the parameter data of the i-th cascaded optical unit is compensated according to the parameter adjustment value.

[0038] In this embodiment, it should be noted that in S1, the physical deployment of an embedded sensor network for each independent node in the cascaded optical units (such as optical line terminals (OLTs), optical network units (ONUs), and repeater amplifiers) of the FTTRB network is the basis for dynamic parameter adjustment. These sensors are specifically deployed according to the physical characteristics of the target parameters (such as photodetectors corresponding to optical emission power, thermistors corresponding to temperature, and current probes corresponding to bias current). Their core function is to capture the original state of key operating parameters within each optical unit in real time. Because the cascaded structure faces non-uniform interference in the user-side environment (such as a sudden temperature rise in a node due to local heat sources, or mechanical displacement caused by vibration), the sensors of each node need to independently collect specific physical quantities (such as the laser drive current value of this unit, the sensitivity voltage of the optical receiver module, etc.), rather than relying on the global average value of the network. This design stems from the inherent defect of cascaded links—interference has local conduction (such as an abnormal bias current in the preceding unit being transmitted to subsequent nodes through optical signals), and anomalies in a single node may be amplified or masked by the cascade. By deploying distributed sensors, the independent parameter trajectories of each node under real-world operating conditions can be acquired, providing raw data support for subsequent identification of "node anomalies" and "pre-existing conducted interference." The data acquisition frequency needs to be set according to the dynamic characteristics of the parameters (e.g., temperature sampled at the second level, current sampled at the millisecond level) to ensure complete coverage of typical environmental disturbance cycles (e.g., air conditioning start-up and shutdown cycles, equipment vibration decay cycles) within a preset time period, avoiding the omission of key fluctuation patterns due to insufficient sampling.

[0039] Furthermore, an independent buffer is allocated for each cascaded optical unit to store the raw parameters collected by the sensors in timestamp order, forming a continuous parameter data sequence for a preset time period (usually 10 minutes). This mechanism allows each node to form its own "parameter behavior profile" by accumulating continuous observations over a period of time; for example, the temperature parameter sequence of a certain node may show a slow and gradual rise (natural ambient temperature rise) or a sudden spike (affected by local equipment heat dissipation), and its fluctuation pattern implies the uniqueness of the interference experienced by that node; the buffer time period needs to cover typical interference cycles (such as 5 minutes to adapt to most thermal inertia scenarios), if it is too short, it will not be able to capture complete fluctuation characteristics, and if it is too long, it will increase the burden of invalid data.

[0040] Furthermore, the temporal alignment of the sequence data is a core prerequisite for subsequent comparison of the dynamics of adjacent nodes; only by ensuring strict consistency of the time window can we distinguish between genuine anomalies (such as continuous oscillations occurring only at node i) and transmission effects (such as fluctuations at node i originating from parameter drift in preceding nodes). Each data point in the sequence carries a timestamp to ensure that the collected values ​​at the same time can be accurately matched when accessing data from adjacent nodes, eliminating misjudgments caused by time deviations.

[0041] In S2, the i-th cascaded optical unit is represented as any one of the cascaded optical units, where "i" is a positive integer and does not exceed the number of cascaded optical units. Two key dynamic indicators are extracted from the parameter data sequence: fluctuation and change. The calculation of fluctuation requires traversing the parameter sequence of the i-th cascaded optical unit within a preset time period (e.g., the optical unit's receiver sensitivity voltage sequence), performing parameter value difference operations on each pair of adjacent data points in the sequence (e.g., timestamps t1 and t2, t2 and t3), and then summing the absolute values ​​of all differences. This indicator is essentially the cumulative disturbance intensity of the parameters in the short time domain. For example, if a node experiences frequent micro-jumps in its bias current (lasting for several milliseconds) due to power supply ripple, the absolute value of the difference between its adjacent sampling points will remain consistently high, accumulating to form a significant fluctuation. In contrast, change focuses on the global offset within a preset time period. By identifying the highest and lowest parameter values ​​in the sequence (e.g., the maximum power of an optical transmitter unit is 5.1mW and the minimum is 4.9mW within 10 minutes), the range of change is calculated based on the difference between the two values. This indicator reflects the long-term drift of parameters due to slowly changing factors (such as a slow rise in ambient temperature). Combining these two factors can distinguish between different types of disturbances: high-frequency instantaneous disturbances (dominated by fluctuation) and low-frequency slowly changing drifts (dominated by change), providing a classification basis for subsequent anomaly localization. It is important to note that the calculation at this stage relies entirely on the node's own historical data to avoid introducing external references that could lead to premature misjudgments. This lays the logical foundation for subsequent comparisons with neighboring nodes—only when the node itself has not experienced extreme drift (the change has not exceeded the threshold) is it necessary to verify the source of the anomaly through neighboring nodes.

[0042] Furthermore, data comparison of adjacent nodes (such as the (i-1)th or (i+1)th nodes) is only initiated when the change in the i-th node is less than the first preset threshold. The first preset threshold needs to be dynamically set according to the physical quantity characteristics: for example, the temperature change threshold is ±3℃ (exceeding this is considered a device malfunction), while the bias current threshold is set to ±2mA (due to higher current sensitivity). Specifically, the dynamic response characteristics of different parameters determine the baseline value of the first preset threshold; for example, optical emission power: affected by the laser aging curve and ambient temperature, the threshold is based on the allowable instantaneous fluctuation range specified in the device manual (such as ±0.5dBm), and adjusted in conjunction with factory calibration data; for example, bias current: due to extreme sensitivity to power supply ripple, the threshold needs to be less than the critical value for signal-to-noise ratio degradation (for example, current jumps exceeding ±2% will cause a sharp increase in the bit error rate); for example, temperature parameters: based on thermal inertia characteristics (such as the heat dissipation coefficient of the optical module), the threshold is set to 1.5 times the normal range of ambient temperature change (for example, the hourly temperature change in an office area is usually ≤±3℃, so the threshold is set to ±4.5℃). Additionally, it can be obtained through a limited number of experiments. For example, during deployment, the natural fluctuation baseline value of each node in a non-interference state (such as during low-load periods at night) can be collected (for 24 hours). Three times its standard deviation can be taken as the first preset threshold. For example, if the standard deviation of the natural fluctuation of the current of a certain node is 0.05mA, then the initial threshold is set to 0.15mA to avoid misjudging the inherent noise of the equipment as abnormal.

[0043] Furthermore, this judgment mechanism stems from the conduction characteristics of cascaded networks—if a node itself has undergone drastic changes (e.g., the change exceeds a threshold), it indicates that its components may have failed (e.g., laser aging), and in this case, it itself is the source of the problem, eliminating the need for comparison with neighboring nodes (because the drastic change has masked the conduction effect). Conversely, if the node's change is slight (e.g., temperature fluctuations within ±0.5℃), but an anomaly is subsequently detected (S3-S4 stages), it may be due to interference from the preceding stage being conducted through the optical link. For example, a sudden drop in the optical power of the preceding node (i-1) causes a passive increase in the receiving sensitivity of this node (i). Although its own change is small, its fluctuation may be abnormally high (due to frequent compensation for preceding stage jitter). In this case, by obtaining the comparison of fluctuation / change values ​​of neighboring nodes (i-1), the conduction path can be identified (the verification ratio calculation in S3). This design avoids two major misjudgments of traditional methods from the source: 1) missing detection of nodes with drastic anomalies (directly intercepted by the change threshold); 2) misjudging conducted interference (tracing the true source through neighboring node data).

[0044] In S3, interference propagation paths in the cascaded network are made explicit through quantitative comparison of the dynamic characteristics of neighboring nodes. First, the first verification ratio is calculated, taking the relative difference between the fluctuation of the i-th node (characterizing short-term disturbance intensity) and the fluctuation of adjacent comparison nodes. Its physical essence is to measure the coupling consistency of parameter fluctuations between nodes—if the network is in a stable state (e.g., all nodes are affected by temperature changes in the same environment), the fluctuations of adjacent nodes should be proportional. The second verification ratio focuses on comparing neighboring nodes for parameter changes (long-term drift amplitude), used to capture the propagation effect of slowly changing drifts (e.g., the gradual reduction in optical power due to laser aging in the preceding node). This mechanism design corresponds to cascaded networks where single-node parameter anomalies could be due to either a fault in the node itself (requiring local compensation) or interference propagation from the preceding stage (requiring blocking the propagation path). For example, if the receiving sensitivity of an ONU node (node ​​i) experiences high-frequency jitter (high fluctuation) within a preset time period, and the fluctuation of the adjacent OLT node (comparison node) is significantly lower, the calculated first verification ratio will be significantly larger (e.g., close to 1), indicating that the jitter of node i is not a problem with its own receiving module, but rather caused by unstable optical power transmitted by the OLT. Conversely, if the fluctuations of the two are close (ratio approaching 0), it points to a local fault in node i. Similarly, when the temperature of node i exhibits abnormal unidirectional drift (high change), while the change in adjacent nodes is small, a sudden increase in the second verification ratio will expose a heat dissipation failure in that node (rather than environmental temperature conduction).

[0045] Furthermore, the calculation results of the first and second verification ratios need to be compared with the second preset threshold to trigger compensation. To eliminate differences in units of different physical quantities, the second preset threshold is established on a relative scale. For fluctuation verification, the initial threshold is set to 0.5 (50%), based on the percentage difference in fluctuation between adjacent nodes. The physical basis is that in a steady-state FTTRB network, adjacent nodes typically experience fluctuation differences ≤30% due to shared environmental interference sources (such as power supply from the same cabinet). When the ratio >50%, it significantly exceeds the upper limit of random noise (3σ criterion). For change verification, the initial threshold is set to 0.4 (40%) for slowly varying drift. The physical basis is that long-term drift in cascaded optical links has spatial continuity (e.g., a ±5℃ temperature drift in the preceding node leads to a ±4℃ drift in the following node), and a difference >40% indicates a local decoupling anomaly. Furthermore, the collected historical verification ratio data (network stability period >72 hours) is fitted with a Weibull distribution, and the upper limit of the 95% confidence interval is used as the dynamic benchmark. For example, if the fluctuation verification ratio of a certain office network is ≤0.42 in 95% of cases, then the initial threshold is set to 0.42.

[0046] Furthermore, under stable operating conditions, the fluctuation / change of adjacent nodes may have slight differences due to differences in environmental factors (such as uneven local heat dissipation of equipment), and the corresponding verification ratio is usually below 0.3 (30% difference). Therefore, the second preset threshold needs to be higher than this baseline value (such as setting it to 0.5) to avoid misjudging random disturbances as abnormalities. For example, if a node experiences a brief temperature fluctuation of ±1.5℃ due to the periodic air supply of the air conditioner, although the temperature difference with the adjacent node reaches 1℃, the verification ratio is still lower than the threshold because the fluctuation duration is short and does not exceed the time window percentage (<10%). When the ratio exceeds the threshold, the source needs to be located by combining the direction of the difference (positive and negative signs of the verification ratio); if the first verification ratio is too high, and the fluctuation of the i-th node is significantly greater than that of the neighboring nodes, it indicates that the disturbance originates from the i-th node itself (such as the power supply ripple amplification caused by capacitor aging), otherwise it originates from the conduction of the previous stage (such as the optical signal jitter caused by abnormal power supply of the neighboring node); if the second verification ratio is too high, and the change of the i-th node is significantly higher than that of the neighboring nodes, it points to a local slow-change fault (such as the heat sink continuously heating up due to dust accumulation), otherwise it indicates that the drift is caused by the accumulation of the previous stage (such as the attenuation of incident light caused by the increase of optical cable joint loss over time).

[0047] Furthermore, for example, in a certain FTTRB office deployment, one ONU (node ​​i) experienced periodic sudden changes in optical power. S3 calculations revealed that its second check ratio reached 0.7 (far exceeding the 0.5 threshold), and its own fluctuation was higher than that of neighboring nodes. Tracing the source, it was determined that the ONU's internal laser temperature compensation circuit had failed (a local fault). In another case, the ONU's first check ratio abnormally reached 0.6, but its own fluctuation was lower than that of neighboring nodes. The cause was traced to noise from the power module of the front-end switch being conducted through the fiber optic link (conductive interference).

[0048] In S4, the confidence level of the anomaly identification of the calibration ratio is transformed into an executable physical compensation action. The parameter adjustment amount is generated through the coupling calculation of fluctuation difference and change difference. The main adjustment component (fluctuation difference) captures the absolute difference of short-term disturbances between nodes, reflecting the instantaneous stress intensity of conducted interference; the auxiliary adjustment component (change difference) quantifies the relative deviation of long-term drift, indicating the slow cumulative effect of device aging. The two are weighted and fused (e.g., 90% for the main component and 10% for the auxiliary component) to form the original adjustment amount. This weighted fusion corresponds to the rapid propagation of fluctuation interference (requiring immediate suppression), while drift problems need to be suppressed to prevent continuous deterioration (preventing cascading accumulation). The key physical quantity type adaptation is reflected in: converting the adjustment direction according to parameter characteristics. For example, the optical transmit power needs to maintain a stable upper limit (downward compensation when out of tolerance), while the receive sensitivity needs to maintain a lower limit (upward compensation when insufficient). This conversion ensures that the compensation action conforms to the inherent operating range of the optical device. For example, the fluctuation difference of a certain relay node shows that its transient disturbance intensity is significantly higher than that of its neighboring nodes (negative value of the principal component), while the change difference indicates that its long-term optical power is slowly decreasing (negative value of the auxiliary component). After weighted fusion, a negative adjustment amount is generated. Combined with the optical emission characteristics (over-tolerance downward compensation), the final decision is to increase the driving current to suppress the down-drift of the emission power.

[0049] Furthermore, the compensation execution stage achieves dynamic adaptation through the precise output of control signals from the adjustment elements: the compensation direction is determined by the sign of the parameter adjustment (positive values ​​correspond to enhancement parameters, negative values ​​correspond to suppression parameters), and the amplitude is taken as the absolute value of the adjustment, transforming the abstract adjustment into specific drive control. For example, for optical receiver sensitivity compensation, the voltage control curve of the adjustable attenuator is matched to generate a signal to increase the receiver gain; for temperature drift, it is mapped to the thermoelectric cooler drive current adjustment.

[0050] Furthermore, to illustrate, if a user node experiences high-frequency jitter in its receiving sensitivity due to power supply interference, it is identified as conducted interference (abnormal first check ratio) in stage S3. The parameter adjustment amount generated in stage S4 controls the output current of the adjustable gain amplifier of the node to continuously decrease, suppressing its own gain abnormality while blocking the interference from propagating backward, thus protecting the subsequent three-stage nodes from being affected.

[0051] In summary, the real-time dynamic parameter adjustment method for the entire FTTRB network constructs a high-fidelity dynamic benchmark using a distributed sensor network and time-aligned data sequences. It decouples interference types using two indicators: fluctuation (cumulative intensity of high-frequency instantaneous disturbances) and change (global range of low-frequency gradual drift). Furthermore, when the change does not exceed the first dynamic threshold, it triggers a comparison between the fluctuation and change of neighboring nodes. The dynamic coupling anomaly between nodes (the illusion of interference superposition caused by penetration and conduction effects) is quantified using the first and second verification ratios. A second threshold, adaptively determined by physical quantity characteristics and topological weights, accurately distinguishes between local real anomalies, random environmental fluctuations, and upstream conducted interference. Further, a physically meaningful parameter adjustment amount is generated by weighted fusion of the fluctuation difference (principal component) and the change difference (auxiliary component). Two-level targeted intervention is achieved through closed-loop control of the compensation direction and amplitude: deep compensation for local faults such as device aging (e.g., laser bias correction) and dynamic isolation of conducted interference (e.g., receiver gain suppression).

[0052] like Figure 4 As shown, in one embodiment, obtaining the i-th fluctuation quantity in S2 based on the parameter data sequence of the i-th cascaded optical unit includes:

[0053] S21. Traverse all adjacent data points in the parameter data sequence of the i-th cascaded optical unit and calculate the absolute value of the parameter value difference of each pair of adjacent data points.

[0054] S22. Add up the absolute values ​​of all parameter differences in the parameter data sequence of the i-th cascaded optical unit and obtain the cumulative value, and define the cumulative value as the i-th fluctuation quantity.

[0055] In this embodiment, it should be noted that in S21, the microscopic dynamic characteristics of the parameter sequence are captured. This is achieved by traversing all adjacent data points of the parameter data sequence at the i-th node (e.g., consecutive sampling points at times t1 and t2, t2 and t3, etc.), calculating the absolute difference between each pair of adjacent parameter values. Essentially, this operation extracts the instantaneous intensity of parameter changes within a continuous time slice, its physical meaning being similar to performing a differential operation on the parameter change trajectory. For example, in the bias current parameter sequence, if the current value between adjacent millisecond-level sampling points repeatedly jumps (small but frequent) due to power supply ripple, the absolute value of the difference between each pair of adjacent points will be significantly non-zero; conversely, if the current is in an ideal steady state, the difference between adjacent points will approach zero. This mechanism is extremely sensitive to high-frequency disturbances and can effectively identify transient anomalies (such as nanosecond-level jitter caused by capacitor charging and discharging) that may be masked by existing averaging calculations. During implementation, it is necessary to ensure the completeness of the traversal (covering the entire preset time period) to avoid omitting the first and last data points of the sequence.

[0056] In step S22, the absolute differences between all adjacent points calculated in S21 are summed to obtain the fluctuation index of the i-th node. This summation operation has a core physical significance: by aggregating all instantaneous changes in the short time domain, a scalar value reflecting the overall instability of the parameter is generated. For example, if a certain optical emitting unit experiences intermittent fan malfunctions within a preset time period, causing repeated small temperature jumps in the laser (the absolute value of the temperature difference between adjacent second-level samples remains consistently high), the summation will form a significant fluctuation peak. The advantage of this design is that it eliminates the directional influence of disturbances (both upward and downward jumps contribute positive values) and amplifies the cumulative effect of continuous weak disturbances (the summation of multiple 0.1% small jumps can achieve the same warning level as a single 1% mutation). In cascaded scenarios, high fluctuations are usually strongly correlated with conducted interference (such as anomalies in the preceding node causing continuous compensation jitter in this node), so this index provides a key input for subsequent neighbor node comparisons. It should be noted that the summed value is not averaged (e.g., divided by the number of time points) to retain absolute disturbance intensity information to adapt to different time windows.

[0057] like Figure 5 As shown, in one embodiment, obtaining the i-th change quantity based on the parameter data sequence of the i-th cascaded optical unit in S2 includes:

[0058] S23. Identify the highest and lowest parameter values ​​in the parameter data sequence of the i-th cascaded optical unit;

[0059] S24. Subtract the lowest parameter value from the highest parameter value to obtain the range of variation, and define the range of variation as the i-th change.

[0060] In this embodiment, it should be noted that in S23, the highest and lowest parameter values ​​within a preset time period are quickly located by scanning the entire parameter sequence. Extreme value identification is essentially a data compression strategy that condenses the complex fluctuation information of a long sequence into two key boundary points. Its implementation relies on efficient comparison algorithms (such as dynamically updating temporary extreme value variables) to ensure that the maximum / minimum values ​​encountered are recorded in real time during sequence traversal. For example, in an optical fiber receiver sensitivity sequence, a sudden high-power operation of the user-end equipment causes a single sharp drop in the sensitivity voltage within a 5-minute window (lowest value); simultaneously, the periodic cooling of the ambient air conditioning causes multiple small increases (highest value). At this point, the extreme point captures the boundary range of this abnormal drift. This mechanism is particularly suitable for coarse-grained evaluation of slowly varying interference, such as the slow linear decay of transmit power caused by laser aging (the minimum value continuously decreases over time). The extreme value identification result directly determines the calculation range of the change, and because it only needs to store two variables (current maximum / minimum values), it reduces computational resource consumption and adapts to the processing capability limitations of embedded optical units.

[0061] In S24, the change (range of change) is obtained by simple subtraction from the highest and lowest values ​​obtained in S23. This indicator directly quantifies the long-term drift of the parameter by the magnitude of extreme deviations, ignoring the fluctuation details within the sequence (such as high-frequency oscillations, brief recovery, etc.). For example, if the temperature parameter of an ONU rises unidirectionally from the base value to the highest point within 10 minutes due to continuous baking by a nearby heat source (the lowest value appears at the beginning and the highest value appears at the end), then the difference between the highest and lowest values ​​expresses the overall temperature rise. In cascaded network diagnostics, large changes usually indicate local hardware faults (such as a heatsink failure at a node causing unilateral temperature drift), while drift related to conduction often manifests as synchronous changes in adjacent nodes (with similar numerical values ​​of change). This indicator complements the fluctuation values ​​in S21-S22: fluctuation values ​​are good at capturing high-dynamic disturbances (such as power supply interference), while change values ​​focus on assessing slow offsets (such as device aging).

[0062] like Figure 6 As shown, in one embodiment, obtaining the first verification ratio of the i-th cascaded optical unit based on the i-th fluctuation amount and the comparison fluctuation amount in S3 includes:

[0063] S31. Subtract the comparison fluctuation from the i-th fluctuation and obtain the fluctuation difference;

[0064] S32. Divide the absolute value of the fluctuation difference by the i-th fluctuation to obtain the first ratio value, and define the first ratio value as the first verification ratio of the i-th cascaded optical unit.

[0065] In this embodiment, it should be noted that in S31, calculating the algebraic difference between the fluctuation of the i-th node and the fluctuation of adjacent nodes is essentially a quantification of the absolute deviation of high-frequency disturbances between nodes. For example, if a relay optical unit experiences a power supply filter capacitor failure, its current fluctuation will be significantly higher than that of adjacent nodes (the fluctuation difference is a large positive value), indicating that the node itself has a transient interference source; conversely, if the difference is a large negative value, it suggests that the disturbance originates from the preceding stage (such as a laser drive malfunction in the upper-level node causing compensation jitter at the receiver of this node).

[0066] In S32, the absolute deviation is converted into a standardized relative proportion by taking the ratio of the absolute value of the fluctuation difference to the fluctuation of the i-th node. The core of this design is to eliminate the differences in physical quantity units (such as the inability to directly compare current mA and temperature °C) while amplifying the relative responsibility weight of abnormal nodes. For example, when the fluctuation of node i is the benchmark value, if the fluctuation difference is close to the benchmark, the proportion approaches 1 (100%), clearly indicating that node i itself is the source of the disturbance; if the difference is close to zero, the proportion approaches 0, indicating that the cascade coupling is normal. When the proportion value is >50% (corresponding to the second preset threshold), it can be determined that the fluctuation of the node is significantly out of the group behavior of the cascade network, and targeted intervention needs to be triggered.

[0067] like Figure 7 As shown, in one embodiment, obtaining the second verification ratio of the i-th cascaded optical unit based on the i-th change and the comparison change in S3 includes:

[0068] S33. Subtract the comparison change from the i-th change and obtain the change difference;

[0069] S34. Divide the absolute value of the difference in change by the i-th change to obtain the second ratio value, and define the second ratio value as the second calibration ratio of the i-th cascaded optical unit.

[0070] In this embodiment, it should be noted that in S33, the algebraic difference between the change in the i-th node and its adjacent nodes is calculated, focusing on quantifying the degree of spatial continuity disruption caused by gradual drift. For example, if the temperature of a certain optical emitting unit continues to rise due to dust accumulation on the heat sink (positive change value), while the change in the adjacent nodes remains within the normal range, then the positive difference value indicates local thermal failure; conversely, if the change in the local node is significantly lower than that of the adjacent nodes (negative difference value), it indicates that the drift is caused by accumulation in the previous stage (such as incident light attenuation caused by aging of the optical cable connector).

[0071] In S34, the absolute value of the difference in changes is divided by the change at node i to generate a dimensionless proportion. This transformation highlights the "proportion of abnormal responsibility" in the drift amplitude of a node. For example, when the change at node i is mainly contributed by local faults (such as heat dissipation failure causing 80% of the temperature rise), even if the absolute temperature rise is not large, its second verification proportion will still increase significantly (approaching 1).

[0072] like Figure 8 As shown, in one embodiment, obtaining the parameter adjustment amount of the i-th cascaded optical unit in S4 based on the i-th fluctuation amount, the comparative fluctuation amount, the i-th change amount, and the comparative change amount includes:

[0073] S41. Subtract the comparison fluctuation from the i-th fluctuation and obtain the fluctuation difference value, and subtract the comparison change from the i-th change and obtain the change difference value, and use the fluctuation difference value as the main adjustment component, and the change difference value as the auxiliary adjustment component;

[0074] S42. The main adjustment component and the auxiliary adjustment component are weighted and fused to generate the original adjustment amount. The parameter adjustment amount of the i-th cascaded optical unit is determined based on the positive and negative signs of the original adjustment amount and the physical quantity type corresponding to the parameter data of the i-th cascaded optical unit.

[0075] In this embodiment, it should be noted that in S41, the fluctuation difference (characterizing the absolute intensity difference of short-term disturbances between nodes) and the change difference (expressing the relative deviation of slowly changing drift) are defined as the main adjustment component and the auxiliary adjustment component, respectively. Their physical essence is to decompose the source of responsibility for the anomaly: the main component highlights the instantaneous pressure of conducted interference (such as cascade jitter caused by the noise of the preceding power supply), while the auxiliary component marks the chronic accumulation of device aging (such as laser efficiency decay). For example, if a user node experiences high-frequency fluctuations in received optical power due to a cooling fan failure in an adjacent relay unit (the fluctuation difference is significantly negative), and its own bias current drifts downwards due to capacitor aging (the change difference is slightly positive), then the main component reveals the dominant responsibility for conducted interference, and the auxiliary component indicates superimposed local degradation.

[0076] In S42, the original adjustment amount is generated by assigning a high weight (typically >90%) to the primary adjustment component. The core logic conforms to the fault propagation characteristics of fiber optic links: transient disturbances can spread rapidly through cascaded structures (e.g., affecting multiple downstream nodes in milliseconds), requiring rapid blocking; while the cascaded effects of slowly varying drifts accumulate in minutes (e.g., temperature rise propagation), allowing for secondary priority processing. For example, when the primary component shows a fluctuation difference of 2 times that of neighboring nodes (strong conducted interference), and the difference in the secondary component's change is slight (weak aging symptoms), the primary component accounts for 95% of the weight during fusion, generating a large negative adjustment amount to urgently suppress interference propagation. The critical direction adaptation is based on the physical characteristics of the parameters: positive adjustment is performed on the receiver sensitivity (compensating for enhanced signal acquisition), and negative correction is implemented on the optical transmit power (suppressing power overshoot), ensuring that all adjustment actions are strictly limited within the device's safe operating area.

[0077] like Figure 9 As shown, in one embodiment, compensating the parameter data of the i-th cascaded optical unit according to the parameter adjustment amount in S4 includes:

[0078] S43. Obtain the compensation direction and compensation magnitude based on the parameter adjustment amount of the i-th cascaded optical unit;

[0079] S44. Match the control parameters of the adjustment element of the i-th cascaded optical unit according to the compensation direction and compensation amplitude, generate a control signal, and send the control signal to the driving circuit of the i-th cascaded optical unit so that its output terminal continuously outputs according to the compensation direction and compensation amplitude.

[0080] In this embodiment, it should be noted that in S43, determining the compensation direction (positive / negative) based on the sign of the parameter adjustment amount and determining the amplitude by taking the absolute value essentially transforms the mathematical model into executable physical operation instructions. For example, when the adjustment amount of a relay node is a large negative value: if it is for the receiver sensitivity parameter, it is defined as "downward compensation" (i.e., reducing the receiver gain to suppress the conduction of interference from the preceding stage); if it is for temperature drift, it is transformed into "cooling enhancement" (increasing the TEC current to offset the temperature rise). Amplitude quantization is directly related to the hardware drive range—for example, for an adjustable attenuator, every 0.1V of voltage corresponds to a 1dB attenuation level, ensuring that the compensation accuracy is strictly matched with the physical control interface. This process can incorporate characteristic mapping tables for different optical devices (such as laser drive current-power response curves) to realize the conversion of mathematical adjustment amounts into physical control amounts.

[0081] In S44, the control signal is output to the drive circuit in a continuously gradual manner (non-step change), and the instruction is converted into a continuous analog output through the digital-to-analog converter module. The execution process combines real-time feedback monitoring to form a closed loop: for example, when a current boost signal is sent to the TEC drive circuit, temperature sensor data is read simultaneously, and if the expected cooling rate is not reached, the output amplitude is automatically increased slightly. A typical application is an ONU node whose gain jitter is caused by power supply disturbance, which generates an attenuator voltage drop signal of 0.5% per second, which gradually reduces to the target value within 60 seconds, avoiding optical power oscillation caused by instantaneous adjustment.

[0082] A real-time dynamic parameter adjustment system for FTTRB networks is also provided, the system comprising:

[0083] The acquisition module is used to deploy sensors in multiple cascaded optical units in the FTTRB network, collect parameter data of each cascaded optical unit based on the sensors, acquire a preset time period, cache the parameter data of each cascaded optical unit for the preset time period, and form a parameter data sequence.

[0084] The first data processing module is used to obtain the i-th fluctuation amount and the i-th change amount according to the parameter data sequence of the i-th cascaded optical unit. If the i-th change amount is less than the first preset threshold, the cascaded optical unit adjacent to the i-th cascaded optical unit is obtained as the comparison cascaded optical unit, and the comparison fluctuation amount and comparison change amount are obtained according to the parameter data sequence of the comparison cascaded optical unit.

[0085] The second data processing module is used to obtain the first verification ratio of the i-th cascaded optical unit based on the i-th fluctuation amount and the comparison fluctuation amount, and to obtain the second verification ratio of the i-th cascaded optical unit based on the i-th change amount and the comparison change amount.

[0086] The dynamic parameter adjustment module is used to obtain the parameter adjustment amount of the i-th cascaded optical unit based on the i-th fluctuation amount, the comparison fluctuation amount, the i-th change amount, and the comparison change amount if the first verification amount or the second verification amount of the i-th cascaded optical unit exceeds the second preset threshold, and to compensate the parameter data of the i-th cascaded optical unit based on the parameter adjustment amount.

[0087] In this embodiment, it should be noted that the specific method of performing the operation of the above-mentioned FTTRB network real-time dynamic parameter adjustment system has been described in detail in the embodiments of the relevant FTTRB network real-time dynamic parameter adjustment method, and will not be elaborated here.

[0088] Figure 10 This is a block diagram of an electronic device illustrating a real-time dynamic parameter adjustment method for an FTTRB network according to an exemplary embodiment. Figure 10 As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an I / O interface 704 (input / output interface), and a communication component 705.

[0089] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the aforementioned FTTRB network real-time dynamic parameter adjustment method. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0090] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described FTTRB network real-time dynamic parameter adjustment method.

[0091] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described FTTRB network real-time dynamic parameter adjustment method. For example, the computer-readable storage medium may be the memory 702 including the program instructions, which may be executed by the processor 701 of the electronic device 700 to complete the above-described FTTRB network real-time dynamic parameter adjustment method.

[0092] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described FTTRB network real-time dynamic parameter adjustment method when executed by the programmable device.

[0093] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0094] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0095] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for real-time dynamic parameter adjustment of an FTTRB network, characterized in that, include: Sensors are deployed within multiple cascaded optical units in the FTTRB network, and parameter data of each cascaded optical unit is collected based on the sensors. A preset time period is obtained, and parameter data of each cascaded optical unit for the preset time period is cached to form a parameter data sequence. Traverse all adjacent pairs of data points in the parameter data sequence of the i-th cascaded optical unit, and calculate the absolute value of the parameter value difference of each pair of adjacent data points. The absolute values ​​of all parameter differences in the parameter data sequence of the i-th cascaded optical unit are added together to obtain the cumulative value, and the cumulative value is defined as the i-th fluctuation quantity. Identify the highest and lowest parameter values ​​in the parameter data sequence of the i-th cascaded optical unit; subtract the lowest parameter value from the highest parameter value to obtain the range of variation, and define the range of variation as the i-th variation; If the i-th change is less than the first preset threshold, then the cascaded optical unit adjacent to the i-th cascaded optical unit is obtained as the comparison cascaded optical unit, and the comparison fluctuation and comparison change are obtained according to the parameter data sequence of the comparison cascaded optical unit. Subtract the comparison fluctuation from the i-th fluctuation to obtain the fluctuation difference; divide the absolute value of the fluctuation difference by the i-th fluctuation to obtain the first ratio value, and define the first ratio value as the first verification ratio of the i-th cascaded optical unit; Subtract the comparison change from the i-th change to obtain the change difference; divide the absolute value of the change difference by the i-th change to obtain the second ratio value, and define the second ratio value as the second verification ratio of the i-th cascaded optical unit; If the first or second verification value of the i-th cascaded optical unit exceeds the second preset threshold, the parameter adjustment amount of the i-th cascaded optical unit is obtained based on the i-th fluctuation value, the comparison fluctuation value, the i-th change value, and the comparison change value, and the parameter data of the i-th cascaded optical unit is compensated based on the parameter adjustment amount.

2. The method for real-time dynamic parameter adjustment of an FTTRB network according to claim 1, characterized in that, The process of obtaining the parameter adjustment amount of the i-th cascaded optical unit based on the i-th fluctuation amount, the comparative fluctuation amount, the i-th change amount, and the comparative change amount includes: Subtract the comparison fluctuation from the i-th fluctuation to obtain the fluctuation difference value, and subtract the comparison change from the i-th change to obtain the change difference value. Use the fluctuation difference value as the main adjustment component and the change difference value as the auxiliary adjustment component. The main adjustment component and the auxiliary adjustment component are weighted and fused to generate the original adjustment value. The parameter adjustment value of the i-th cascaded optical unit is determined based on the positive or negative sign of the original adjustment value and the physical quantity type corresponding to the parameter data of the i-th cascaded optical unit.

3. The method for real-time dynamic parameter adjustment of an FTTRB network according to claim 1, characterized in that, The compensation of the parameter data of the i-th cascaded optical unit based on the parameter adjustment amount includes: The compensation direction and compensation magnitude are obtained based on the parameter adjustment amount of the i-th cascaded optical unit; The control parameters of the adjustment element of the i-th cascaded optical unit are matched according to the compensation direction and compensation amplitude, and a control signal is generated and sent to the driving circuit of the i-th cascaded optical unit so that its output terminal continuously outputs according to the compensation direction and compensation amplitude.

4. A real-time dynamic parameter adjustment system for an FTTRB network, characterized in that, The system is used to implement the real-time dynamic parameter adjustment method for FTTRB networks according to any one of claims 1 to 3, the system comprising: The acquisition module is used to deploy sensors in multiple cascaded optical units in the FTTRB network, collect parameter data of each cascaded optical unit based on the sensors, acquire a preset time period, cache the parameter data of each cascaded optical unit for the preset time period, and form a parameter data sequence. The first data processing module is used to obtain the i-th fluctuation amount and the i-th change amount according to the parameter data sequence of the i-th cascaded optical unit. If the i-th change amount is less than the first preset threshold, the cascaded optical unit adjacent to the i-th cascaded optical unit is obtained as the comparison cascaded optical unit, and the comparison fluctuation amount and comparison change amount are obtained according to the parameter data sequence of the comparison cascaded optical unit. The second data processing module is used to obtain the first verification ratio of the i-th cascaded optical unit based on the i-th fluctuation amount and the comparison fluctuation amount, and to obtain the second verification ratio of the i-th cascaded optical unit based on the i-th change amount and the comparison change amount. The dynamic parameter adjustment module is used to obtain the parameter adjustment amount of the i-th cascaded optical unit based on the i-th fluctuation amount, the comparison fluctuation amount, the i-th change amount, and the comparison change amount if the first verification amount or the second verification amount of the i-th cascaded optical unit exceeds the second preset threshold, and to compensate the parameter data of the i-th cascaded optical unit based on the parameter adjustment amount.

5. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor is configured to execute the computer program in the memory to implement the FTTRB network real-time dynamic parameter adjustment method according to any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the real-time dynamic parameter adjustment method for FTTRB networks as described in any one of claims 1 to 3.

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