Optical fiber signal and energy co-transmission power regulation system and method based on quality of service perception

Abstract

This invention discloses a fiber optic power control system and method for information-energy co-transmission based on service quality awareness, belonging to the interdisciplinary field of fiber optic communication and radio frequency signal transmission. Addressing the shortcomings of existing fiber optic information-energy co-transmission technologies, such as fixed power allocation and lack of service awareness, this invention collects remote service quality, equipment energy status, and fiber optic link parameters. After spatiotemporal alignment, data cleaning, and feature fusion preprocessing, a multi-objective optimization decision model is constructed to solve for the optimal control parameters. The laser bias current and modulation depth are dynamically adjusted to achieve dynamic allocation of energy and information power. A closed-loop control is then formed through effect evaluation. The system includes a central office transmitter, a single-mode fiber optic channel, and a remote receiver. This invention achieves service-driven power control, improving optical power resource utilization and system robustness, and ensuring stable operation of remote equipment.

Description

Technical Field

[0001] This invention relates to the intersection of optical fiber communication and radio frequency signal transmission, specifically to an optical fiber signal-power co-transmission power regulation system and method based on service quality perception. Background Technology

[0002] With the rapid development of 5G mobile communication, the Internet of Things (IoT), and the Industrial Internet, a massive number of low-power devices are being deployed in every corner of cities. The continuous and reliable operation of these devices faces two major challenges: the need for high-speed, low-latency data backhaul and the issue of a continuous and stable energy supply, especially in scenarios where power access is difficult or wiring is inconvenient.

[0003] Optical fiber signal-energy co-transmission technology has emerged, utilizing the same optical fiber to simultaneously carry information and energy. The basic principle is that at the central office, high-frequency information signals and DC or low-frequency energy signals are multiplexed onto the same optical carrier and transmitted through the optical fiber to remote nodes. At the remote nodes, photodetectors convert the optical signals into electrical signals, and power dividers or filters separate the high-frequency information components and DC energy components for data recovery and equipment power supply, respectively.

[0004] However, existing fiber optic signal-power co-transmission technologies have significant problems and drawbacks. First, power allocation is fixed and rigid, unable to adapt to dynamically changing service demands and environments, resulting in low resource utilization efficiency. Second, they lack the ability to perceive service quality and cannot adopt different signal-power allocation strategies based on service types. Third, energy supply and demand are mismatched, easily leading to energy surplus or shortage. Fourth, the system has poor robustness; communication or power supply performance deteriorates sharply when link loss fluctuates.

[0005] Therefore, there is an urgent need in this field for new systems and methods that can sense the quality requirements of remote services and the energy status of equipment, and adjust the power allocation of information and energy sharing in real time and accurately, so as to achieve efficient and adaptive utilization of limited optical power resources. Summary of the Invention

[0006] The purpose of this invention is to provide a fiber optic power control system and method based on service quality awareness, so as to solve the problem that the power allocation of existing technologies is fixed and rigid, and cannot adapt to the dynamic changes in service needs and environment.

[0007] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0008] The fiber optic power regulation method based on service quality awareness includes the following steps:

[0009] S1. Collect service quality indicators of multi-source heterogeneous remote service units, energy status indicators of remote nodes, and transmission parameters of fiber optic links.

[0010] S2. Perform preprocessing operations such as spatiotemporal alignment, data cleaning, and feature fusion on the multi-source heterogeneous information collected in step S1 to obtain standardized feature vectors.

[0011] S3. Construct a multi-objective optimization decision model. Input the standardized feature vector obtained in step S2 into the multi-objective optimization decision model, and solve for the optimal value of the control parameter vector. ,in This is the laser bias current. The modulation depth of the information signal. Power allocation factor;

[0012] The objective function of the multi-objective optimization decision model is to maximize the combined value of service quality assurance effectiveness and energy transmission efficiency, and its specific expression is as follows: , The overall objective function of the multi-objective optimization decision model is... These are the sub-objective functions for service quality assurance effectiveness and energy transmission efficiency, respectively.

[0013] Wherein: the business quality assurance utility function is: In the formula, N represents the total number of business types. Let be the weighting coefficient for the i-th type of business. Let represent the quality level of the i-th type of service under control parameter u. Let i be the quality utility function corresponding to the i-th type of business. Let i be the quality utility function for the i-th type of business. The normalized quality level for the i-th type of business;

[0014] Its expression is:

[0015] In the formula, Let be the steepness parameter of the utility function for the i-th type of business quality. Let be the quality satisfaction threshold for the i-th type of service; the energy transmission efficiency function is: In the formula, For the overall efficiency of the energy harvesting chain, The actual energy harvesting power is defined by control parameter u, and λ is the battery aging cost weighting coefficient. Costs associated with battery aging due to improper charging and discharging;

[0016] The constraints of the multi-objective optimization decision model include:

[0017] Device safety constraints: In the formula, This is the minimum bias current for the laser. Maximum bias current of the laser; signal integrity constraints: In the formula, The minimum modulation depth of the information signal;

[0018] Power budget constraints: In the formula, This represents the total transmit power of the optical carrier. This represents the system's minimum total transmit power. This represents the system's maximum total transmit power.

[0019] Fiber nonlinear constraints: In the formula, The nonlinear efficiency coefficient of the optical fiber. For optical carrier signal power, The maximum permissible nonlinear interference power;

[0020] Receiver sensitivity constraints: In the formula, For optical carrier communication information power, For photodetector responsivity, This represents the minimum received power at the receiving end.

[0021] S4. Based on the optimal value of the control parameter vector obtained in step S3, adjust the bias current of the transmitter laser and the modulation depth of the information signal to achieve dynamic allocation of optical carrier energy power and information power.

[0022] S5. Collect and adjust the business quality indicators and energy status indicators after regulation, evaluate the regulation effect, update the decision strategy library based on the evaluation results, and return to step S1 to form closed-loop regulation.

[0023] In a further embodiment, in step S1, the service quality indicators include data throughput. End-to-end transmission delay latency jitter Packet error rate Business priority identifier The energy state index includes the remaining power. Real-time charging power, real-time discharging power, terminal voltage, operating current, and device temperature; the fiber optic link transmission parameters include attenuation coefficient. Effective refractive index Link loss value.

[0024] In a further embodiment, in step S2, the spatiotemporal alignment operation employs a hybrid algorithm of dynamic time warping and Kalman filtering to interpolate and resample periodic index sequences and perform timestamp correction on non-periodic event data; the data cleaning operation adopts a confidence-based strategy to perform anomaly detection, logic verification, and trend verification; the feature fusion operation employs normalization processing and correlation analysis. The normalization processing method includes one or more of min-max standardization, z-score standardization, and decimal scaling standardization, and the correlation analysis adopts a hybrid method based on mutual information and Granger causality test.

[0025] In a further embodiment, step S3 employs a decomposition-based multi-objective evolutionary algorithm to solve the multi-objective optimization decision model. This algorithm decomposes the original multi-objective problem into M single-objective sub-problems, each sub-problem corresponding to a reference vector, which are uniformly distributed in the objective space. The algorithm's crossover probability... With the probability of mutation The expression is adaptively adjusted based on the individual diversity contribution and the degree of convergence, as follows:

[0026] ;

[0027] ;

[0028] In the formula, Based on the basic crossover probability, This is the crossover probability adjustment amount. The contribution to diversity of the i-th individual. Based on the probability of mutation, This is the adjustment amount for the mutation probability. Let be the convergence index for the i-th individual.

[0029] In a further embodiment, in step S4, the operation of adjusting the bias current of the transmitting laser and the modulation depth of the information signal is achieved through a digitally controlled current source and a modulator driving circuit at the transmitting end; the digitally controlled current source converts the digital set value of the bias current into an analog voltage, and then converts it into a driving current through a transconductance amplifier; the modulator driving circuit uses digital predistortion technology to compensate for the nonlinear characteristics of the modulator.

[0030] In a further embodiment, step S5, the specific operation for evaluating the control effect is as follows: calculating the system utility value U after control, and comparing the system utility value U with a preset system utility threshold. Remaining battery power With the preset energy safety threshold Compare; when and When the current control parameter vector is not met, the control parameter vector is stored in the strategy library; if the results of three consecutive evaluations do not meet the above conditions, the safety mode is activated and the laser bias current is fixed. Modulation depth security value .

[0031] A fiber optic signal-energy co-transmission power regulation system based on service quality awareness includes a central office transmitting terminal system, a single-mode fiber optic channel, and a remote node receiving and power supply terminal system. The central office transmitting terminal system includes a service quality awareness module, a regulation unit, an adjustable laser driver circuit, and a signal modulation and synthesis circuit. The service quality awareness module is used to collect remote service quality indicators. The regulation unit is used to execute steps S2 to S5 of the above method. The adjustable laser driver circuit is used to adjust the laser bias current according to control parameters. The signal modulation and synthesis circuit is used to adjust the modulation depth of the information signal and synthesize the energy signal and information signal. The single-mode fiber optic channel is used to transmit the synthesized optical carrier signal. The remote node receiving and power supply terminal system includes a photoelectric conversion and separation module, an energy management unit, and a feedback information transmission module. The photoelectric conversion and separation module is used to convert the optical signal into an electrical signal and separate the energy component and information component. The energy management unit is used to perform maximum power point tracking, battery state estimation, and dynamic power allocation. The feedback information transmission module is used to transmit remote energy state indicators and link parameters back to the central office transmitting terminal system.

[0032] In a further embodiment, the feedback information transmission module uses wavelength division multiplexing technology to transmit information back, the feedback laser operates in the 1310nm band, the modulation method is on / off keyed modulation, and the transmission rate is adjusted within the range of 1kbps to 100kbps.

[0033] In a further embodiment, the maximum power point tracking algorithm in the energy management unit adopts a perturbation observation method based on model prediction, and the battery state estimation adopts an extended Kalman filter algorithm, while simultaneously estimating the battery's remaining power SOC, internal resistance, and health status.

[0034] The present invention has the following beneficial effects:

[0035] This enables a paradigm shift from "link-driven" to "business-driven," elevating power regulation from physical layer parameters to application layer service quality, thus closely linking power allocation strategies with actual user experience.

[0036] Achieving dynamic adaptation and global resource optimization, the system can continuously optimize power allocation based on service load, service type, and equipment energy status, thus realizing Pareto optimization of communication performance and energy supply.

[0037] Significantly enhances system reliability and equipment endurance, implements on-demand power replenishment by monitoring equipment energy status in real time, avoids unexpected downtime of remote equipment, and ensures service quality indicators for high-priority services.

[0038] Significantly improves system environmental robustness. When link performance fluctuates, the system can automatically detect and compensate for changes, prioritize ensuring basic communication connections and uninterrupted power supply to devices, and has fault mitigation capabilities.

[0039] It features good scalability and deployment flexibility. The control unit can be implemented using the software-defined concept, the strategy algorithm can be flexibly customized and upgraded online, and the feedback channel can be implemented using existing optical fibers through wavelength division multiplexing, reducing deployment costs.

[0040] The above-mentioned method and system, through a closed-loop control mechanism, break through the limitations of fixed power allocation, achieve a dynamic balance between service quality and energy supply, improve the utilization efficiency of limited optical power resources, and enhance the system's ability to cope with link loss fluctuations and changes in service demands, fundamentally ensuring the stable operation of remote low-power equipment. Attached Figure Description

[0041] Figure 1 The diagram illustrates the specific steps of the method of the present invention.

[0042] Figure 2 This is a block diagram of the system of the present invention. Detailed Implementation

[0043] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0044] The fiber optic signal-energy co-transmission power regulation method based on service quality perception revolves around the core process of multi-source information acquisition, preprocessing, decision-making, power regulation, and closed-loop iteration. The central office transmitting terminal system is responsible for decision-making and regulation execution, the remote node receiving and power supply terminal system is responsible for signal reception, energy management, and status feedback, and the single-mode fiber channel undertakes the transmission of optical carrier signals, forming a closed-loop architecture of "perception-decision-execution-evaluation".

[0045] Multi-source sensing information acquisition: Acquiring service quality indicators of remote service units, energy status indicators of remote nodes, and transmission parameters of fiber optic links.

[0046] Service quality indicators include data throughput, end-to-end transmission latency, latency jitter, packet error rate, and service priority identifiers. Corresponding data collection methods are employed for different types of network devices. For devices supporting traditional network management standards, simple network management protocols (SMART) are used for active polling or trap reception to acquire data. For devices supporting modern network management frameworks, network configuration protocols based on the YANG data model or expressive state transfer application programming interfaces (APIs) are used to obtain structured performance data. For devices that cannot directly provide standardized quality reports, deep packet inspection engines are used to extract packet features, identify service types, and statistically analyze quality indicators.

[0047] Energy status indicators include remaining power, real-time charging power, real-time discharging power, terminal voltage, operating current, and equipment temperature. These indicators are collected by a high-precision energy monitoring circuit integrated at a remote node. The monitoring circuit is electrically connected to the energy storage device and the load equipment to achieve real-time data acquisition.

[0048] The transmission parameters of an optical fiber link include attenuation coefficient, effective refractive index, and link loss. These parameters are collected by a link detection module to provide a physical layer basis for subsequent power regulation.

[0049] After the data acquisition is completed, the remote node transmits the energy status index and link parameters back to the central office through the feedback information transmission module. The feedback information transmission module adopts wavelength division multiplexing technology, the feedback laser operates in the 1310nm band, the modulation method is on-off keyed modulation, and the transmission rate is adaptively adjusted in the range of 1kbps to 100kbps according to the amount of feedback information and channel conditions.

[0050] Multi-source heterogeneous information preprocessing: The collected multi-source heterogeneous information is preprocessed by spatiotemporal alignment, data cleaning and feature fusion to obtain standardized feature vectors.

[0051] The spatiotemporal alignment operation employs a hybrid algorithm combining dynamic time warping and Kalman filtering to address the issues of varying sampling rates and timestamp discrepancies across different data sources. For highly periodic index sequences, interpolation resampling is used to align them to a unified sampling grid. For event-driven aperiodic data, a correction model for event timestamps is established, and time synchronization is achieved using a reference clock source. Simultaneously, distributed matching techniques are employed to eliminate systemic biases introduced by different measurement locations and methods across different data sources, thus realizing spatial alignment.

[0052] The data cleaning operation employs a hybrid cleaning strategy based on confidence assessment. Before each data point enters the processing flow, it is assigned an initial confidence score based on the reliability of the data source, the standardization of the collection method, and the quality record of historical data. The cleaning process consists of three levels: first, anomaly detection is performed through statistical distribution; second, logical verification is performed based on business rules; and finally, trend verification is performed through time-series patterns. For detected suspicious data, correction, interpolation reconstruction, weighted smoothing, or selective removal measures are taken according to the confidence score and the degree of anomaly.

[0053] Feature fusion normalizes the cleaned multi-source data, selecting one or more of min-max standardization, z-score standardization, or decimal scaling standardization based on the data characteristics of the features to eliminate the influence of dimensions. The normalized features are organized into multi-dimensional feature vectors and input into the association analysis engine. A hybrid method based on mutual information and Granger causality tests is used to calculate the statistical correlation between features, analyze the causal dependencies and temporal influence patterns among features, and store the analysis results in the form of an association graph for feature selection and weight allocation in subsequent decision-making processes, ultimately yielding standardized feature vectors.

[0054] Construction and solution of multi-objective optimization decision model: Construct a multi-objective optimization decision model, input the standardized feature vector into the multi-objective optimization decision model, and solve to obtain the optimal value of the control parameter vector.

[0055] Model parameter definition: The control parameter vector is a three-dimensional vector. ,in Let be the laser bias current, m be the modulation depth of the information signal, and ρ be the power allocation coefficient. The objective function of the model is to maximize the combined value of service quality assurance effectiveness and energy transmission efficiency, expressed as follows:

[0056] ;

[0057] The service quality assurance utility function is as follows: ;

[0058] N represents the total number of business types. The weighting coefficient for the i-th type of business is determined by the business priority and the service level agreement. The quality level of the i-th type of service under control parameter u; Let the quality utility function for the i-th type of business be expressed as:

[0059] ;

[0060] is the steepness parameter of the quality utility function for the i-th type of business, reflecting the business's sensitivity to quality changes; Let be the quality satisfaction threshold for the i-th type of service. The energy transmission efficiency function is:

[0061] ;

[0062] For the overall efficiency of the energy harvesting chain, The actual energy harvesting power is defined by control parameter u, and λ is the battery aging cost weighting coefficient. Costs associated with battery aging due to improper charging and discharging.

[0063] The constraint conditions for the model cover five categories: device safety, signal integrity, power budget, fiber nonlinearity, and receiver sensitivity. The specific expressions are as follows:

[0064] Device safety constraints: , This is the minimum bias current for the laser. This is the maximum bias current for the laser, ensuring that the laser operates within a safe operating range.

[0065] Signal integrity constraints: , This is the minimum modulation depth of the information signal, ensuring the demodulation capability of the modulated signal.

[0066] Power budget constraints: , This represents the total transmit power of the optical carrier. This represents the system's minimum total transmit power. The maximum total transmit power of the system must meet the total power consumption limit of the system.

[0067] Fiber nonlinear constraints: , The nonlinear efficiency coefficient of the optical fiber. For optical carrier signal power, To maximize the allowable nonlinear interference power and avoid severe nonlinear damage.

[0068] Receiver sensitivity constraints: , For optical carrier communication information power, For photodetector responsivity, This is the minimum receiving power at the receiver to ensure reliable demodulation.

[0069] The model is solved using a decomposition-based multi-objective evolutionary algorithm to solve the multi-objective optimization decision model. The original multi-objective problem is decomposed into M single-objective sub-problems, each corresponding to a reference vector. These reference vectors are uniformly distributed in the objective space, ensuring that the final Pareto front has good distribution. The algorithm's crossover probability... With the probability of mutation The expression is adaptively adjusted based on the individual diversity contribution and the degree of convergence, as follows:

[0070] ;

[0071] ;

[0072] In the formula, Based on the basic crossover probability, This is the crossover probability adjustment amount. The contribution to diversity of the i-th individual. Based on the probability of mutation, This is the adjustment amount for the mutation probability. Let be the convergence index for the i-th individual. To meet the needs of real-time control, a multi-level decision acceleration mechanism is adopted. For decision scenarios that occur periodically, the best historical decision results are directly read from the decision cache. When time is tight, a simplified decision model is switched to reduce computational complexity. At the same time, a multi-core parallel computing architecture is used to distribute population evaluation, selection operation and mutation operation to different processing cores for simultaneous execution, thereby improving the solution efficiency.

[0073] Power Coordinated Regulation Execution: Based on the optimal value of the control parameter vector obtained by the solution, the bias current of the transmitting laser and the modulation depth of the information signal are adjusted to realize the dynamic allocation of optical carrier energy power and information power.

[0074] The adjustable bias current laser driver circuit adopts a hybrid digital-analog design, with a high-performance digitally controlled current source at its core. The digitally controlled current source receives the digital setpoint bias current from the control unit, converts it into an analog voltage via a high-precision digital-to-analog converter, and then converts it into a precise drive current via a transconductance amplifier. Current control employs a strategy combining proportional-integral-derivative (PI-DI) control and feedforward compensation. The PI-DI controller handles slow disturbances such as load changes and temperature drift, while the feedforward compensator adjusts the output in advance based on the trend of the setpoint changes, improving dynamic response speed. The driver circuit also integrates a health monitoring function, monitoring the laser's drive current, operating temperature, and output optical power in real time. Upon detecting an abnormal state, it immediately activates current-limiting protection, over-temperature protection, or an automatic shutdown mechanism.

[0075] The signal modulation and synthesis circuits handle the processing, modulation, and synthesis of the information signal. The data stream first enters the signal preprocessing module, which adaptively selects the coding scheme and modulation format based on the service type and channel conditions. Delay-sensitive services utilize low-complexity coding and high-order modulation, while power-constrained scenarios employ high-gain coding and low-order modulation. The pulse shaping filter dynamically adjusts the roll-off coefficient based on the symbol rate and available bandwidth to balance spectral efficiency and inter-symbol interference. The modulator driver circuit uses digital predistortion technology to compensate for the modulator's inherent nonlinearity. An adaptive algorithm estimates the predistortion parameters online and optimizes the predistortion function to ensure the modulator operates in the linear region. The information signal and energy signal are synthesized through a bias tee. The bias tee employs a wideband impedance matching design to ensure high synthesis efficiency across a frequency range from DC to several GHz.

[0076] Evaluation of regulation effectiveness and closed-loop iteration: Collect business quality indicators and energy status indicators after regulation, evaluate the regulation effect, update the decision strategy library based on the evaluation results, and return to the multi-source sensing information collection steps to form closed-loop regulation.

[0077] The effect evaluation calculates the system utility value after adjustment and compares it with the preset system utility threshold and the remaining power with the preset energy safety threshold. When the system utility value is greater than or equal to the system utility threshold and the remaining power is greater than or equal to the energy safety threshold, the control parameter vector for this operation is stored in the strategy library. If the above conditions are not met for three consecutive evaluations, a safety mode is activated, and the laser bias current safety value is fixed. Modulation depth security value At the same time, it reports alarm information and triggers the manual intervention interface.

[0078] The model iteratively optimizes the energy management unit of the remote node to perform maximum power point tracking (MPPT), battery state estimation, and dynamic power allocation operations. MPPT is achieved using a perturbation-observation method based on model prediction, reducing power oscillations. An extended Kalman filter algorithm is used to simultaneously estimate the battery's remaining charge, internal resistance, and health status, providing a basis for charging strategies. Received energy is dynamically allocated based on load priority, battery state, and service requirements. Daily compliance records are added to the training set, and the decision model is retrained weekly to improve rule base accuracy, optimize algorithm convergence speed, and enhance the accuracy of abnormal state prediction.

[0079] This invention constructs a power regulation system driven by both service quality and energy state. Its core technical principle is as follows:

[0080] Multi-source information sensing principle: For three-layer objects—remote service units, energy equipment, and fiber optic links—multi-protocol adaptation acquisition, deep packet inspection and identification, and high-precision circuit monitoring techniques are used to acquire service quality indicators, energy status parameters, and link transmission characteristics, respectively. Then, the remote status information is transmitted back to the central office through wavelength division multiplexing reverse channel, providing comprehensive and real-time input data for control and decision-making.

[0081] Information preprocessing and fusion principles: Spatiotemporal alignment of multi-source data is achieved through a hybrid algorithm of dynamic time warping and Kalman filtering, eliminating problems such as sampling asynchrony and measurement bias; a multi-layer cleaning strategy based on confidence assessment improves data quality; feature fusion is completed by a combination of normalization and mutual information-Granger causality test to mine the correlation between service quality, energy state and link characteristics, forming a standardized feature vector.

[0082] Multi-objective optimization decision principle: Construct an optimization model with dual objectives of service quality assurance effectiveness and energy transmission efficiency, introduce physical constraints such as device safety, signal integrity, and power budget, and use a decomposition-based multi-objective evolutionary algorithm to solve for the optimal control parameter vector. Through the exploration and development capabilities of the adaptive adjustment algorithm to balance crossover probability and mutation probability, and combined with decision caching, model simplification, and parallel computing mechanisms, real-time decision-making is achieved.

[0083] Power Co-regulation Principle: Through a digital-analog hybrid drive circuit, the control parameters of the decision output are converted into precise adjustment commands for the laser bias current and modulation depth. The bias current determines the DC power component of the optical carrier, and the modulation depth determines the AC information power component. Then, the energy and information signals are efficiently synthesized through a bias tere with wideband impedance matching. After transmission through optical fiber, the receiving end achieves lossless separation of energy and information through a passive LC network. The energy management unit achieves efficient energy collection and accurate battery state estimation based on maximum power point tracking and extended Kalman filtering algorithms.

[0084] Closed-loop iterative optimization principle: By collecting and adjusting business quality and energy status indicators, comparing them with preset thresholds to complete the effect evaluation, the qualified parameters are stored in the strategy library for rapid decision-making in similar subsequent scenarios, and the unqualified parameters trigger the re-optimization process; at the same time, the qualified adjustment data are accumulated daily, and the decision model is iteratively trained weekly to continuously improve the accuracy of the rule library and the convergence speed of the algorithm, so as to realize the self-optimization and self-adaptation of the system.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for power regulation in optical fiber transmission based on quality of service awareness, characterized in that, Includes the following steps: S1: Collect service quality indicators of multi-source heterogeneous remote service units, energy status indicators of remote nodes, and transmission parameters of fiber optic links. S2: For the multi-source heterogeneous information collected in step S1, perform spatiotemporal alignment, data cleaning and feature fusion preprocessing operations in sequence to obtain standardized feature vectors; S3: Construct a multi-objective optimization decision model, input the standardized feature vector obtained in step S2 into the multi-objective optimization decision model, and solve for the optimal value of the control parameter vector; S4: Based on the optimal value of the control parameter vector obtained in step S3, adjust the bias current of the transmitter laser and the modulation depth of the information signal to achieve dynamic allocation of optical carrier energy power and information power; S5: Collect and regulate the business quality indicators and energy status indicators after regulation, evaluate the regulation effect, update the decision strategy library based on the evaluation results, and return to step S1 to form closed-loop regulation. In step S2, the spatiotemporal alignment operation employs a hybrid algorithm of dynamic time warping and Kalman filtering to interpolate and resample periodic index sequences and perform timestamp correction on non-periodic event data; the data cleaning operation uses a confidence-based strategy to perform anomaly detection, logic verification, and trend verification; the feature fusion operation employs normalization processing and correlation analysis, with normalization methods including one or more of min-max standardization, z-score standardization, and decimal scaling standardization. In step S5, the specific operation of evaluating the regulation effect is: calculating the system utility value U after regulation, comparing the system utility value U with a preset system utility threshold , the residual power , and a preset energy security threshold ; when and , storing the current control parameter vector into the strategy library; Safety mode is initiated when the result of three consecutive evaluations does not satisfy a predetermined condition, fixed bias current safety value , modulation depth safety value ; The predetermined condition is: system utility value U ≥ preset system utility threshold. And remaining power ≥Preset energy safety threshold .

2. The fiber optic signal-energy co-transmission power regulation method based on service quality perception according to claim 1, characterized in that, In step S1, the service quality indicators include data throughput. End-to-end transmission delay latency jitter Packet error rate Business priority identifier The energy state index includes the remaining power. Real-time charging power, real-time discharging power, terminal voltage, operating current, and device temperature; the fiber optic link transmission parameters include attenuation coefficient α and effective refractive index. Link loss value.

3. The fiber optic signal-energy co-transmission power regulation method based on service quality awareness according to claim 1, characterized in that, In step S3, a decomposition-based multi-objective evolutionary algorithm is used to solve the multi-objective optimization decision model. This algorithm decomposes the original multi-objective problem into M single-objective sub-problems, each sub-problem corresponding to a reference vector, which are uniformly distributed in the objective space. The crossover probability of the multi-objective evolutionary algorithm... With the probability of mutation The expression is adaptively adjusted based on the individual diversity contribution and the degree of convergence, as follows: ； ； In the formula, Based on the basic crossover probability, This is the crossover probability adjustment amount. The contribution to diversity of the i-th individual. Based on the probability of mutation, This is the adjustment amount for the mutation probability. Let be the convergence index for the i-th individual. Let be the crossover probability of the i-th individual. Let be the mutation probability of the i-th individual, where i is the individual number.

4. The fiber optic signal-energy co-transmission power regulation method based on service quality awareness according to claim 1, characterized in that, In step S4, the operation of adjusting the bias current of the transmitting laser and the modulation depth of the information signal is realized through the digitally controlled current source and the modulator driving circuit of the transmitting end; the digitally controlled current source converts the digital set value of the bias current into an analog voltage, and then converts it into a driving current through a transconductance amplifier; the modulator driving circuit uses digital predistortion technology to compensate for the nonlinear characteristics of the modulator.

5. A fiber optic signal-power co-transmission power regulation system based on service quality awareness, used to execute the method described in any one of claims 1 to 4; characterized in that, This includes the central office transmitting terminal system, single-mode fiber optic channel, and remote node receiving and power supply terminal system; The central office transmitting terminal system includes a service quality sensing module, a control unit, an adjustable laser driving circuit, and a signal modulation and synthesis circuit. The service quality perception module is used to collect remote service quality indicators; the adjustable laser drive circuit is used to adjust the laser bias current according to control parameters; the signal modulation and synthesis circuit is used to adjust the modulation depth of the information signal and synthesize the energy signal and the information signal; the single-mode fiber channel is used to transmit the synthesized optical carrier signal. The remote node receiving and power supply terminal system includes a photoelectric conversion and separation module, an energy management unit, and a feedback information transmission module. The photoelectric conversion and separation module is used to convert optical signals into electrical signals and separate energy components and information components. The energy management unit is used to perform maximum power point tracking, battery state estimation, and dynamic power allocation. The feedback information transmission module is used to transmit remote energy state indicators and link parameters back to the central office transmitting terminal system.

6. The fiber optic signal-energy co-transmission power regulation system based on service quality awareness according to claim 5, characterized in that, The feedback information transmission module uses wavelength division multiplexing technology to transmit information back. The feedback laser operates in the 1310nm band, and the modulation method is on-off keying modulation. The transmission rate is adjusted within the range of 1kbps to 100kbps.

7. The fiber optic signal-energy co-transmission power regulation system based on service quality awareness according to claim 5, characterized in that, The maximum power point tracking algorithm in the energy management unit adopts a perturbation observation method based on model prediction, and the battery state estimation adopts an extended Kalman filter algorithm, which simultaneously estimates the battery's remaining power SOC, internal resistance, and health status.

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