Performance optimization-based PLC (Programmable Logic Controller) optical splitter control system, method and equipment

Abstract

The invention provides a PLC optical splitter control system, method and equipment based on performance optimization, and relates to the technical field of optoelectronic equipment, and the system comprises an environment sensing module which collects an environment data set, and builds an environment fitting scene; the task reading module is used for acquiring a network load demand and establishing a task target; the fitting construction module is used for uploading an environment fitting scene and a task target to the central control unit, reading equipment state data and executing control optimization under multi-target optimization; and the intelligent control module performs performance optimization control on the PLC optical splitter according to the control optimization result. According to the invention, the technical problem that the performance of the PLC optical splitter is unstable because the working state of the PLC optical splitter is difficult to dynamically adjust according to the actual network flow and load in the prior art can be solved, the task target is set according to the network load demand, the performance optimization control is carried out, and the performance stability of the PLC optical splitter is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optoelectronic devices, and particularly relates to a PLC optical splitter control system, method and device based on performance optimization. BACKGROUND

[0002] The PLC optical splitter is used for distributing optical signals in an optical communication network to achieve effective distribution and transmission of optical signals. However, the conventional PLC optical splitter lacks flexibility in design and is difficult to dynamically adjust its working state according to actual network traffic and load requirements, resulting in that the performance of the optical splitter cannot reach the optimal state when the network traffic fluctuates greatly, thereby affecting the overall efficiency and stability. The PLC optical splitter usually adopts a fixed splitting ratio and channel configuration and cannot respond to changes in network traffic in real time. During traffic peaks, congestion of optical signals occurs in the splitting process, thereby reducing transmission efficiency; and during traffic valleys, resources are wasted because the optical splitter cannot reduce resource consumption according to actual requirements. Due to the lack of flexibility, the PLC optical splitter cannot dynamically change its working state according to actual requirements when the network load fluctuates, thereby leading to unstable performance.

[0003] In summary, the prior art has the technical problem of unstable performance of the PLC optical splitter due to the difficulty of dynamically adjusting the working state of the PLC optical splitter according to actual network traffic and load. SUMMARY

[0004] The purpose of the present application is to provide a PLC optical splitter control system, method and device based on performance optimization to solve the technical problem of unstable performance of the PLC optical splitter due to the difficulty of dynamically adjusting the working state of the PLC optical splitter according to actual network traffic and load in the prior art.

[0005] In view of the above problems, the present application provides a PLC optical splitter control system, method and device based on performance optimization.

[0006] In a first aspect, the present application provides a PLC optical splitter control system based on performance optimization, wherein the PLC optical splitter control system based on performance optimization comprises: an environment perception module configured to collect an environment data set of a PLC optical splitter, perform environment perception according to the environment data set, and establish an environment fitting scenario; a task reading module configured to obtain network load requirements of the PLC optical splitter, and establish a task target of the PLC optical splitter according to the network load requirements; a fitting construction module configured to read device state data of the PLC optical splitter after uploading the environment fitting scenario and the task target to a central control unit, perform control optimization under multi-objective optimization, and establish a control optimization result; and an intelligent control module configured to perform performance optimization control of the PLC optical splitter according to the control optimization result.

[0007] Optionally, the task parsing unit is used to parse the task objective and establish a quality importance value for signal quality optimization; the port importance value establishment unit is used to establish a port balance importance value for the PLC optical splitter based on the device status data; the importance evaluation unit is used to evaluate the device stability importance of the PLC optical splitter based on the environmental fitting scenario and the device status data, and establish a stability importance value; the control optimization unit is used to configure a multi-objective optimization function using the quality importance value, the port balance importance value, and the stability importance value, and to perform control optimization using the multi-objective optimization function.

[0008] Optionally, the interval construction subunit is used to construct the limit control interval for each port in the PLC optical splitter; the environmental adaptation and fitting subunit is used to perform environmental adaptation and fitting within the limit control interval in the environmental fitting scenario, using the device state data as the initial state data of the port, and to establish the environmental adaptation and fitting result; the constraint optimization subunit is used to perform control optimization of a multi-objective optimization function using the environmental adaptation and fitting result as a constraint condition.

[0009] Optionally, the following channels are provided: an initial solution creation channel for creating an initial solution within the search space based on the constraints; a fitness calculation channel for calculating the fitness of the initial solution using the multi-objective optimization function to generate fitness calculation results; a solution state identification channel for identifying the solution state based on the fitness calculation results and establishing a following space and a conflict space; an iterative update channel for performing iterative updates of the initial solution after adding random perturbations to the following space and the conflict space; and a result optimization channel for completing control optimization based on the iterative update results.

[0010] Optionally, the distance calculation subchannel is used to calculate the similarity distance between any solution in the initial solution and the following space and the opposing space; the random update subchannel is used to perform following or opposing iteration based on the random perturbation after selecting the following space or opposing space according to the similarity distance, so as to complete the iterative update of the initial solution.

[0011] Optionally, the perception feedback module is used to establish a feedback window, perform status monitoring of the PLC optical splitter within the feedback window, establish status monitoring feedback, perform control verification using the status monitoring feedback and the control optimization results, generate perception feedback, and perform system self-optimization management based on the perception feedback.

[0012] Optionally, the task load prediction module is used to predict the load change trend based on historical control data and the network load demand, and establish a task load prediction result; the adaptive update module is used to perform adaptive updates based on the task load prediction result and the control optimization result.

[0013] Optionally, a redundant port configuration module is used to configure redundant ports. After the performance optimization control of the PLC optical splitter is performed based on the control optimization results, port anomaly monitoring is performed. When the port anomaly monitoring results meet a preset threshold, the redundant port is activated to perform abnormal port replacement and report the anomaly.

[0014] Secondly, this application also provides a performance-optimized PLC optical splitter control method, wherein the performance-optimized PLC optical splitter control method includes: collecting environmental datasets of the PLC optical splitter; performing environmental perception based on the environmental datasets to establish an environmental fitting scenario; obtaining the network load requirements of the PLC optical splitter; establishing the task objectives of the PLC optical splitter based on the network load requirements; uploading the environmental fitting scenario and the task objectives to the central control unit; reading the device status data of the PLC optical splitter; performing control optimization under multi-objective optimization to establish control optimization results; and performing performance optimization control of the PLC optical splitter based on the control optimization results.

[0015] Thirdly, this application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the performance-optimized PLC optical splitter control system described in any of the first aspects above.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: The system employs an environmental perception module to collect environmental datasets from the PLC optical splitter, perform environmental perception based on these datasets, and establish an environmental fitting scenario. A task reading module acquires the network load requirements of the PLC optical splitter and establishes its task objectives based on these requirements. A fitting construction module uploads the environmental fitting scenario and task objectives to the central control unit, reads the device status data of the PLC optical splitter, performs control optimization under multi-objective optimization, and establishes the control optimization results. An intelligent control module optimizes the performance control of the PLC optical splitter based on the control optimization results. In other words, by collecting data from the environment in which the PLC optical splitter operates, determining the PLC optical splitter's objectives based on actual needs, and dynamically adjusting its control parameters, intelligent control of the PLC optical splitter is achieved, improving its performance stability.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the performance-optimized PLC optical splitter control system of this application.

[0020] Figure 2 This is a flowchart illustrating the performance-optimized PLC optical splitter control method of this application.

[0021] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0022] Figure reference numerals: 11 Environmental perception module, 12 Task reading module, 13 Fitting and construction module, 14 Intelligent control module, 300 Bus, 301 Receiver, 302 Processor, 303 Transmitter, 304 Memory, 305 Bus interface. Detailed Implementation

[0023] This application provides a performance-optimized PLC optical splitter control system, method, and device, solving the technical problem in existing technologies where the PLC optical splitter's performance is unstable due to its inability to dynamically adjust its operating state according to actual network traffic and load. By collecting data from the environment where the PLC optical splitter is located, determining the PLC optical splitter's targets based on actual needs, and dynamically adjusting the PLC optical splitter's control parameters, intelligent control of the PLC optical splitter is achieved, improving its performance stability.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a performance-optimized PLC optical splitter control system, wherein the performance-optimized PLC optical splitter control system is used to implement the performance-optimized PLC optical splitter control method, and the performance-optimized PLC optical splitter control system includes: The environment perception module 11 is used to collect the environment dataset of the PLC optical splitter, perform environment perception based on the environment dataset, and establish an environment fitting scene.

[0026] Specifically, environmental data related to the operation of the PLC optical splitter is collected through sensors and monitoring equipment installed around the PLC optical splitter. This includes factors such as temperature, humidity, vibration, fiber optic attenuation, and signal strength in the environment where the PLC optical splitter is located. Environmental datasets typically include environmental factors affecting optical signal transmission, such as temperature changes, fiber optic attenuation, and equipment operating status. By collecting these environmental datasets, a comprehensive understanding of the environment in which the PLC optical splitter operates can be obtained.

[0027] Environmental perception is performed based on environmental datasets. Environmental data is collected and analyzed to determine the impact of the environment on the PLC optical splitter. Through the perception and analysis of environmental data, an environmental fitting scenario is established, simulating the equipment's operating state based on actual data under different environmental conditions. The environmental fitting scenario not only includes the impact of the current environment but also predicts the possible performance of the PLC optical splitter under different environmental conditions. For example, it simulates how the performance of the optical splitter changes under different environments, predicts possible signal attenuation, and formulates optimization schemes.

[0028] Environmental perception refers to the real-time sensing and identification of the impact of environmental factors on the operation of a PLC optical splitter through the analysis and processing of environmental datasets, thereby achieving a sense of the PLC optical splitter's working environment. For example, collecting environmental datasets for the PLC optical splitter shows that the temperature fluctuates between 20℃ and 25℃, and the humidity remains between 40% and 50%. Based on this data, an environmental fitting scenario is constructed to achieve perception of the PLC optical splitter's working environment. After establishing the environmental fitting scenario, the device's operating state is automatically adjusted based on the real-time sensed environmental data and the predictions of the fitted scenario. By collecting environmental datasets for environmental perception and establishing environmental fitting scenarios, the stability and reliability of the PLC optical splitter can be accurately understood.

[0029] The task reading module 12 is used to obtain the network load requirements of the PLC optical splitter and establish the task target of the PLC optical splitter based on the network load requirements.

[0030] Specifically, this involves obtaining the network load requirements of the PLC optical splitter, i.e., the amount of optical communication signals and bandwidth required for the PLC optical splitter to process. Network load requirements typically include factors such as the data traffic volume, the optical signal transmission rate, and the number of channels the optical splitter needs to handle, reflecting the data volume, real-time performance, and reliability requirements of the network. Real-time data monitoring is performed using network traffic analysis tools (such as traffic analyzers and bandwidth managers) to obtain the real-time data traffic status of each channel in the network.

[0031] After acquiring network load requirements, the PLC optical splitter needs to formulate task objectives based on the current network conditions. Based on network load requirements, the bandwidth allocation of the optical splitter is optimized to ensure full utilization of network bandwidth and avoid resource waste. For example, if the network load is low during a certain period, the bandwidth allocation of the optical splitter can be reduced to lower energy consumption; conversely, during peak network load periods, the optical splitter needs to increase bandwidth resources to meet demand. Based on network load requirements, the signal distribution across different channels should be ensured to be as balanced as possible to reduce attenuation and latency caused by signal congestion or imbalance. For example, if a channel has a high load, the signal transmission strategy can be adjusted to ensure effective signal distribution across different channels and avoid overload. During low-load periods, unnecessary optical resource consumption is reduced. By dynamically adjusting the operating status of the optical splitter (e.g., reducing the activation of certain channels), wasted optical resources are avoided when network load is low. Different quality of service (QoS) indicators are set for different application requirements (such as video streaming, voice communication, etc.), and the corresponding resource allocation is adjusted through the PLC optical splitter to ensure that key performance indicators such as latency, bandwidth, and throughput meet the requirements.

[0032] Based on network load requirements and the existing network load conditions, specific task objectives are generated, including bandwidth allocation objectives, latency control objectives, and energy efficiency optimization objectives. The bandwidth allocation objective refers to dynamically allocating bandwidth to each channel according to load demand, ensuring that high-load channels receive sufficient bandwidth while low-load channels receive appropriately reduced resources. The latency control objective refers to setting minimum latency requirements for applications requiring low latency (such as real-time video streaming or voice communication) and ensuring latency is kept within acceptable limits by adjusting the splitter's operating mode. The energy efficiency optimization objective refers to achieving energy efficiency optimization and reducing unnecessary power consumption by dynamically adjusting the optical splitter's operating mode (e.g., shutting down some inactive channels) when the network load is low.

[0033] The task objective refers to the performance goals that the PLC optical splitter needs to achieve based on the network load requirements. These goals include optimal signal allocation, maximized bandwidth utilization, optimized resource allocation, reduced signal attenuation, and improved transmission efficiency—in other words, the desired state of the PLC optical splitter. For example, suppose the network load during a certain period is as follows: the data traffic transmitted in the network is 10Gbps; the bandwidth requirement for two real-time video streams is 2Gbps; and the remaining data traffic is 8Gbps, including some low-latency data streams and some background data streams. Based on the network load requirements, the task objective of the PLC optical splitter is set as follows: the bandwidth allocation objective is to allocate 2Gbps of bandwidth to the video streams to ensure their real-time performance, and allocate the remaining 8Gbps to ensure that other data streams also receive sufficient bandwidth support; the latency control objective is to require the latency of the video streams to not exceed 50ms; for the background data streams, the latency can be slightly delayed without affecting data integrity; and the energy efficiency objective is to shut down some inactive optical channels during low-load periods to reduce power consumption. By acquiring the network load requirements of the PLC optical splitter and establishing task objectives based on these requirements, we can ensure that the PLC optical splitter performs optimally in network communication.

[0034] The fitting construction module 13 is used to upload the environmental fitting scenario and the task objective to the central control unit, read the device status data of the PLC optical splitter, perform control optimization under multi-objective optimization, and establish the control optimization result.

[0035] Furthermore, the fitting construction module 13 in the performance-optimized PLC optical splitter control system is also used for: The task parsing unit is used to parse the task objective and establish a quality importance value for signal quality optimization; the port importance value establishment unit is used to establish a port balance importance value for the PLC optical splitter based on the device status data; the importance evaluation unit is used to evaluate the device stability importance of the PLC optical splitter based on the environmental fitting scenario and the device status data, and establish a stability importance value; the control optimization unit is used to configure a multi-objective optimization function using the quality importance value, port balance importance value, and stability importance value, and to perform control optimization using the multi-objective optimization function.

[0036] Specifically, both the environmental simulation scenario and the task objective are uploaded to the central control unit. The central control unit acts as a control center, receiving data from various devices and performing unified scheduling and optimization based on the task objective and environmental data. The central control unit reads the device status data of the PLC optical splitter, including but not limited to port input / output power, temperature, and voltage, to assess the device's current operating status. This device status data is the real-time operating status data of the PLC optical splitter, including the device's temperature, load, signal strength, transmission rate, and power consumption, reflecting the device's operating performance at the current moment.

[0037] Analyzing the task objectives is crucial during equipment operation, determining the direction and focus of optimization. In the multi-objective optimization problem of PLC optical splitters, task objectives include not only signal quality but also load balancing, equipment stability, and more. Analyzing the task objectives helps identify the specific directions for optimization and determine the relative importance of each objective. When analyzing task objectives, relevant optimization objectives are extracted based on information such as the current network traffic and bandwidth requirements of the PLC optical splitter. For example, when network traffic is high, signal quality optimization may be given higher priority to ensure the quality of high-bandwidth signal transmission; while when network traffic is low, resource saving and power consumption optimization may become the primary objectives.

[0038] The importance value for signal quality optimization quantifies the impact of signal quality on the overall performance of a device. Calculating the importance value requires considering various signal parameters, including but not limited to signal attenuation, signal distortion, and noise interference. Real-time monitoring and collection of signal attenuation, distortion, and noise data from the device are used to adjust signal optimization targets based on task requirements. For example, video streams and voice communications have higher signal quality requirements, while ordinary data streams have relatively less stringent requirements. The importance value for signal quality optimization is calculated using a weighted summation method. For instance, if the signal quality of a video stream is critical, its importance value can be set to 0.7, while the importance value for an ordinary data stream can be set to 0.3.

[0039] Based on device status data, the port balance is evaluated. The port balance importance value reflects the balance of load distribution among the ports. For example, some ports may be overloaded due to excessive traffic, while others may be lightly loaded. Based on these factors, signal allocation and resource allocation strategies are adjusted to ensure load balance across all ports and improve overall device performance. Port balance is a crucial optimization objective in the optimization of PLC optical splitters. The port balance importance value reflects how resources are allocated evenly according to the load and performance of each port to ensure that each port operates at its optimal state.

[0040] Equipment stability refers to the ability of equipment to maintain long-term, efficient, and stable operation under different environmental conditions and workloads. The importance value of stability is determined by analyzing equipment status data (such as equipment temperature, failure rate, signal stability, etc.) and environmental scenarios (such as temperature and humidity) to assess the current stability of the equipment and determine its importance value. Based on the equipment's status and environmental data, the stability of the equipment under different operating conditions is evaluated, including its continuous operating capability and failure rate.

[0041] Based on the importance values ​​of signal quality, port balancing, and stability, a multi-objective optimization function is configured to balance multiple optimization directions (such as signal quality, load balancing, and device stability) and find the optimal solution. The multi-objective optimization function combines multiple objectives (such as signal quality, port balancing, and device stability). Each objective needs a weight value (i.e., its importance value), and the objective function is configured according to these weights. The signal quality optimization objective is assigned a quality importance value, and its weight can be set according to the requirements of the actual task objective (e.g., higher priority for video streams); the port balancing optimization objective uses the port balancing importance value to balance the traffic load of each port based on the device's port load; the weight of the stability optimization objective is determined by the stability importance value, prioritizing device stability and preventing device failure due to excessive load or environmental changes. For example, the multi-objective optimization function is F(x) = w1·f1(x) + w2·f2(x) + w3·f3(x), where F(x) is the overall objective of the objective function, f1(x), f2(x), and f3(x) represent signal quality, port equalization, and device stability, respectively, and w1, w2, and w3 are the weights of the corresponding objectives, determined based on their respective importance values.

[0042] After configuring multiple objective optimization functions, an optimization algorithm is used for control optimization, that is, adjusting the operating parameters of the equipment to achieve the optimization objectives. The configured multi-objective optimization functions are input into the optimization algorithm. The optimization algorithm will iterate through the objective functions multiple times, adjusting the parameters of the PLC optical splitter to gradually approach the optimal solution. For example, initial values ​​for the equipment parameters are set, and multiple candidate solutions (i.e., initial states) are randomly generated. In each iteration, the optimization algorithm will continuously adjust the equipment configuration (such as signal transmission bandwidth, port resource allocation, equipment power, etc.) based on the weights of the objective functions and the calculation results. The algorithm stops when the predetermined optimization accuracy or the maximum number of iterations is reached.

[0043] By employing a multi-objective optimization function, considering factors such as signal quality, port load balancing, and device stability, the system ensures efficient and stable operation of the equipment in complex network environments. Through real-time acquisition and dynamic adjustment of device status data, the performance of the optical splitter is optimized, enabling it to maintain optimal operating conditions under varying loads and environments.

[0044] Furthermore, the fitting construction module 13 in the performance-optimized PLC optical splitter control system is also used for: The interval construction subunit is used to construct the limit control interval of each port in the PLC optical splitter; the environmental adaptation adjustment fitting subunit is used to perform environmental adaptation adjustment fitting within the limit control interval in the environmental fitting scenario, using the device state data as the initial state data of the port, and establish the environmental adaptation adjustment fitting result; the constraint optimization subunit is used to perform control optimization of a multi-objective optimization function using the environmental adaptation adjustment fitting result as a constraint condition.

[0045] The initial solution creation channel is used to create an initial solution within the search space based on the constraints. The fitness calculation channel is used to calculate the fitness of the initial solution using the multi-objective optimization function and generate fitness calculation results. The solution state identification channel is used to identify the solution state based on the fitness calculation results and establish a following space and a conflict space. The iterative update channel is used to perform iterative updates of the initial solution after adding random perturbations to the following space and the conflict space. The result optimization channel is used to complete control optimization based on the iterative update results.

[0046] The distance calculation subchannel is used to calculate the similarity distance between any solution in the initial solution and the following space and the opposing space; the random update subchannel is used to perform following or opposing iterations based on the random perturbation after selecting the following space or opposing space according to the similarity distance, so as to complete the iterative update of the initial solution.

[0047] Specifically, in a PLC optical splitter, each port has a permissible operating range. Limits refer to the performance limitations of each port, such as bandwidth, power, and load. These limits are typically determined by the device's hardware specifications and environmental factors, defining the maximum and minimum values ​​that the port can operate at to ensure stable and efficient operation within those ranges. For example, suppose the port's maximum bandwidth is 100Mbps, its minimum bandwidth is 10Mbps, its maximum power is 10W, and its minimum power is 1W. These limit control ranges ensure that the PLC optical splitter will not overload or fail when operating within specific ranges, ensuring device stability and reliability.

[0048] In the environmental fitting scenario, device state data is used as the initial port state data, meaning it serves as the starting point for environmental adaptation adjustments. Initial port state data refers to the current state data of each port of the device before the optimization process, including basic parameters such as bandwidth, load, and power. Based on the device state data and the environmental fitting scenario, environmental adaptation adjustments are fitted within the limit control range to simulate the impact of different operating conditions and environmental changes on the device. For example, when the temperature rises, the device may increase power consumption or bandwidth requirements. Based on the environmental fitting results, the parameters of each port are adjusted within the limit control range. For example, if port 1 has a high load (70%) and a high temperature (30℃), the port's bandwidth is increased from 50Mbps to 60Mbps, while the power is adjusted according to temperature changes to ensure efficiency and stability.

[0049] The environmental adaptation fitting results are the device status data after adaptive adjustment, reflecting the device's operating state under new environmental conditions. The fitting results represent the optimized port configuration, ensuring the device can adapt to different working environments and provide optimal performance in practical applications. For example, suppose the port status data of a PLC optical splitter is as follows: Port 1: Bandwidth 50Mbps, Load 70%, Power 5W, Temperature 30℃; Port 2: Bandwidth 60Mbps, Load 60%, Power 6W, Temperature 30℃; Port 3: Bandwidth 40Mbps, Load 50%, Power 4W, Temperature 30℃; Port 4: Bandwidth 45Mbps, Load 65%, Power 5W, Temperature 30℃. By analyzing these status data and environmental data (such as temperature), environmental adaptation fitting is performed. Temperature increase: Assuming the temperature rises to 35℃, it is predicted that this may affect the port's power consumption and bandwidth requirements; High load: Ports 1 and 4 have high loads, requiring increased bandwidth for these two ports to avoid overload. Bandwidth and power adjustments: Port 1: Bandwidth increased from 50Mbps to 55Mbps, power increased to 4.8W; Port 4: Bandwidth increased from 45Mbps to 50Mbps, power remained at 5W. Fitting results: The adjusted device status data are as follows: Port 1: Bandwidth 55Mbps, load 72%, power 4.8W, temperature 35℃; Port 2: Bandwidth 60Mbps, load 60%, power 6W, temperature 35℃; Port 3: Bandwidth 40Mbps, load 50%, power 4W, temperature 35℃; Port 4: Bandwidth 50Mbps, load 65%, power 5W, temperature 35℃.

[0050] The environmental adaptation fitting results are used as constraints to limit the range of solutions during the optimization process. For example, the device's bandwidth cannot exceed 100Mbps, and its power cannot exceed 10W, ensuring that the optimized solution is reasonable and feasible. In multi-objective optimization problems, the search space refers to the set of all possible solutions. Each solution corresponds to a set of device parameters, such as bandwidth, power, and load. The goal of optimization is to find the optimal solution within this search space. An initial solution is created within the search space based on the constraints; that is, a set of initial device parameters is selected as the starting point, and the optimization algorithm starts from this point and gradually improves. The initial solution is based on the environmental adaptation fitting results, ensuring that each solution meets the device's operational constraints.

[0051] The fitness of the initial solutions is calculated using a multi-objective optimization function, yielding the fitness results. The fitness value is generated by evaluating the merits of each initial solution through the multi-objective optimization function calculation. For example, if a solution has a signal quality of 90%, port equalization of 80%, and device stability of 95%, then the fitness value of this solution is 0.4 * signal quality + 0.3 * port equalization + 0.3 * stability = 0.885.

[0052] Based on the fitness calculation results, any solutions in the initial solution are classified according to their fitness values. Solutions with higher fitness values ​​enter the follower space; these solutions exhibit better performance and satisfy the system's main optimization objective. Solutions with lower fitness values ​​are assigned to the conflict space; solutions in the conflict space have performance far removed from the target state and cannot effectively improve the overall system performance, thus these solutions receive less attention. The follower space refers to the region in multi-objective optimization where solutions have high fitness and satisfy the optimization objective. The conflict space, in contrast to the follower space, refers to the region where solutions have low fitness and cannot effectively satisfy the optimization objective. For example, suppose the initial solution includes three solutions: Solution 1: Signal quality 90%, Port equalization 85%, Device stability 95%; Solution 2: Signal quality 70%, Port equalization 60%, Device stability 80%; Solution 3: Signal quality 50%, Port equalization 40%, Device stability 60%. After fitness calculation, the fitness values ​​are as follows: Solution 1 has a fitness value of 0.9, Solution 2 has a fitness value of 0.6, and Solution 3 has a fitness value of 0.4. Based on the fitness calculation results, solution 1 is assigned to the following space, while solutions 2 and 3 are assigned to the conflicting space.

[0053] The similarity distance between the initial solution and both the following and conflicting spaces is calculated using Euclidean distance. Generally, the smaller the similarity distance, the more similar the solution is to the corresponding space. Based on the calculated similarity distance, it is determined whether the solution belongs to the following or conflicting space. If the similarity distance between the initial solution and the following space is small, it indicates that the solution is close to the optimal state, and it is classified as belonging to the following space. Conversely, if the similarity distance between the solution and the following space is low, but the similarity distance with the conflicting space is high, it is classified as belonging to the conflicting space.

[0054] Random perturbations are used to further optimize the solution. By randomly varying certain parameters of the solution, random perturbations allow the optimization algorithm to avoid stagnation in local optima and thus explore the global optimum. When the initial solution is in the following space, its parameters are fine-tuned through random perturbations while maintaining its general orientation, further improving its fitness. For example, adjusting the port configuration of the optical splitter or optimizing signal quality might bring its performance closer to the optimum. When the initial solution is in the opposing space, larger random perturbations are used to adjust certain parameters of the solution, causing it to move towards the following space. In this case, the perturbation might be more drastic, such as changing the configuration of the PLC optical splitter or reallocating resources, thereby gradually improving the fitness of the solution.

[0055] After random perturbation, the fitness of the new solution is calculated, and its position in the target space is evaluated. After several rounds of perturbation, the performance of the solution is gradually improved until it reaches an acceptable level of optimization or meets the stopping criterion. Follower iteration refers to further optimizing the solutions in the follower space, usually refining certain parameters to improve fitness; while resistance iteration perturbs or adjusts the solutions in the resistance space, gradually improving their performance, which may lead them into the follower space.

[0056] The solutions in the follower and conflict spaces are adjusted through random perturbations. Solutions in the follower space are fine-tuned to move closer to a better solution, such as minor adjustments to port configuration, signal quality, or device stability. Solutions in the conflict space typically require larger adjustments; larger random perturbations improve the fitness of the solution, gradually guiding it towards the follower space. After random perturbation, the initial solution is iteratively updated. Through multiple optimizations and adjustments, the fitness of the solution gradually improves. After each iteration, the system calculates the fitness of the solution based on various objectives (such as signal quality, port equalization, and device stability). A solution with high fitness indicates a superior current configuration. Based on the fitness calculation results, various parameters of the solution are adjusted. For example, if the signal quality is already good but the device stability is poor, the system increases the optimization effort for stability, making appropriate adjustments. The iterative process continues until a stopping condition is met (such as the maximum number of iterations, fitness reaching a certain threshold, or the solution reaching a stable state). The result of each update gradually approaches the globally optimal solution.

[0057] After multiple iterations and updates, an optimized solution was obtained. Based on this final solution, the operating state of the PLC optical splitter was optimized, dynamically adjusting its configuration according to actual network traffic and load demands to ensure optimal performance under both high and low traffic conditions. The control optimization results generate a final control strategy, which is used to adjust the configuration of each port of the PLC optical splitter to ensure efficient optical signal distribution and transmission stability. For example, when the network load is high, the splitting ratio and signal enhancement mode may be adjusted; while under low load, power consumption and signal redundancy may be reduced. By combining environmental fitting scenarios and port limit control ranges, the system adapts to different network loads and environmental changes, ensuring that the performance of the optical splitter is always optimal. Resource allocation is adjusted according to actual needs to avoid unnecessary resource waste or equipment overload, ensuring efficient system operation. Through multi-objective optimization, the system achieves a balance in optimizing signal quality, port balance, and equipment stability, improving the stability and reliability of the PLC optical splitter.

[0058] The intelligent control module 14 is used to perform performance optimization control of the PLC optical splitter based on the control optimization results.

[0059] Specifically, based on the control optimization results, the performance of the PLC optical splitter is optimized. After calculation using a multi-objective optimization algorithm, the control optimization results generate an optimal parameter configuration scheme, including parameters such as signal strength, bandwidth, and power consumption for each port, and considering trade-offs between multiple objectives. For example, the signal strength of port 1 is adjusted to 80mW, the bandwidth to 8Gbps, and the power consumption to 3W; the signal strength of port 2 is adjusted to 70mW, the bandwidth to 7Gbps, and the power consumption to 2.5W. The control optimization results represent the optimal operating state of the PLC optical splitter under different network loads and environmental conditions.

[0060] The PLC optical splitter undergoes performance optimization control based on the optimal parameter configuration scheme obtained from the control optimization results. This involves adjusting the device's operating status in real time based on calculated parameters such as signal strength, bandwidth, and power to achieve the optimal operating point. For example, if the signal strength of port 1 is determined to be 80mW, the bandwidth 8Gbps, and the power consumption 3W, the central control unit of the PLC optical splitter will adjust the operating parameters of port 1 accordingly, ensuring operation within these values. After adjustment, the performance indicators of the PLC optical splitter, such as signal quality, port load, and power consumption, continue to be monitored in real time. If the actual operating status of the device changes (e.g., network load fluctuations, ambient temperature changes), adjustments will be made promptly according to the preset control strategy. By adjusting signal strength and bandwidth in real time, high-quality signal transmission is ensured, avoiding signal attenuation, loss, or interference, thus improving the stability and reliability of network transmission. Reasonable configuration of signal strength and bandwidth for each port optimizes the overall performance of the PLC optical splitter, increases network transmission rate, reduces latency, and improves the efficiency of the entire optical communication network.

[0061] Furthermore, the performance-optimized PLC optical splitter control system also includes: a perception feedback module, used to establish a feedback window, perform status monitoring of the PLC optical splitter within the feedback window, establish status monitoring feedback, perform control verification using the status monitoring feedback and the control optimization results, generate perception feedback, and perform system self-optimization management based on the perception feedback.

[0062] Specifically, a specific time period or condition range is defined as a feedback window in the central control unit of the PLC optical splitter. Its function is to periodically or under specific conditions monitor the equipment status. The feedback window is a monitoring area for the PLC optical splitter's operating status within a given time or condition range. It is used to periodically or in real-time monitor the equipment status, performing status detection and feedback generation within the window to ensure the system always receives the latest operating status information.

[0063] Within the feedback window, sensors monitor various status data of the PLC optical splitter in real time, including signal quality, port load, and power consumption, to understand the PLC optical splitter's operating status. Status monitoring feedback refers to the real-time acquisition and recording of the PLC optical splitter's operating status, including signal quality, port load, and power consumption, through the sensing feedback module. This information is used to verify the effectiveness of the system control strategy and make adjustments based on the feedback results.

[0064] Control verification is performed using status monitoring feedback and control optimization results, generating perception feedback. The control optimization results include the optimal device parameter configuration derived from the optimization algorithm, while the status monitoring feedback reflects the device's current actual operating status. If the control optimization results and actual monitoring results are consistent, the optimization scheme is effective; if a discrepancy exists, perception feedback is generated, and the control strategy is adjusted based on this feedback to optimize device operation. Perception feedback is an assessment of the current system performance, containing information on whether the device is operating normally according to the optimization strategy. If any mismatch or performance degradation exists, the perception feedback will indicate areas for improvement. For example, the perception feedback might indicate insufficient bandwidth on port 2, leading to traffic congestion and a discrepancy with the expected bandwidth configuration in the control optimization results. Based on this feedback, the bandwidth or other parameters of port 2 can be adjusted to achieve efficient operation.

[0065] Based on the feedback, the system automatically adjusts and optimizes the operating state of the PLC optical splitter, including dynamically adjusting bandwidth, signal strength, and power consumption. For example, suppose that during a status monitoring process, the following data is detected: the signal strength of port 1 is 75mW, lower than the optimized result of 80mW; the bandwidth of port 2 is 6Gbps, lower than the optimized result of 7Gbps; and the power consumption of port 3 is 2.8W, slightly higher than the optimized result of 2.5W. Feedback is provided to the system indicating that the current state has not met the expected control optimization. Based on this, self-optimization management is performed, automatically adjusting the following parameters: increasing the signal strength of port 1 to 80mW; increasing the bandwidth of port 2 to 7Gbps; and reducing the power consumption of port 3 to 2.5W. After these adjustments, the optimal operating state is finally achieved, thereby ensuring the performance and stability of the optical splitter in practical applications.

[0066] Furthermore, the performance-optimized PLC optical splitter control system also includes: a task load prediction module, used to predict load change trends based on historical control data and the network load demand, and establish task load prediction results; and an adaptive update module, used to perform adaptive updates based on the task load prediction results and the control optimization results.

[0067] Specifically, this involves acquiring historical control data, which includes the PLC optical splitter's past operating data and control strategies over different time periods, including the device's operating status, performance data, and optimized control parameters. Network load demand refers to the amount of network traffic or load that the PLC optical splitter needs to handle within a specific time period. This typically includes information such as traffic volume, bandwidth requirements, and transmission rate, and usually fluctuates with time, application, and network conditions.

[0068] Based on historical control data and network load demand, the load fluctuations of the PLC optical splitter are analyzed, and time series forecasting algorithms (such as the ARIMA model) are used to predict the load change trend over a future period. Unit root tests (such as the ADF test) are used to check whether the time series data is stationary. If the data exhibits a trend or seasonality, the ARIMA model needs to be differencing to make the data stationary. If the time series data is not stationary, the trend or seasonality is eliminated through differencing. For example, the difference in the data is taken (e.g., the data at time t minus the data at time t-1). If the data exhibits seasonality (e.g., daily or monthly periodic fluctuations), seasonal differencing or the SARIMA model (Seasonal ARIMA) is used for processing. The parameters for the ARIMA model are selected, including p, d, and q. p is the number of autoregressive terms, representing the order of the AR component, typically chosen using the ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function). d is the difference order, indicating how many times the data is differencing to make it stationary. The purpose of differencing is to eliminate trends (non-stationarity) in the data, making it more suitable for modeling. First differencing calculates the difference between the current value and the previous value, second differencing differs the data after the first differencing, and so on. q is the number of moving average terms, representing the order of the MA (Moving Average) component, which predicts the current error using the average of past errors, typically chosen using the ACF and PACF plots. After selecting the ARIMA model parameters, historical control data is used to fit the ARIMA model, generating estimated parameters for the model.

[0069] After fitting the model, the ARIMA model is used to predict future load, typically as a point estimate. The model's predictive performance can be viewed by plotting a prediction curve. Once the prediction results are obtained, the model's predictive performance needs to be evaluated, such as by measuring the mean squared error. After training and evaluation, a load change trend prediction model is obtained, which predicts the trend of load demand changes over future time periods based on historical load data.

[0070] By analyzing historical control data and network load demands, future load trends are predicted, resulting in a task load forecast—the predicted value of future load demand, typically representing the required network traffic, bandwidth, or other related resources over a future period. Based on the task load forecast and control optimization results, the PLC optical splitter's control parameters are dynamically adjusted to better adapt to future load demands, ensuring performance stability and efficient resource utilization. For example, if the predicted load increases, more bandwidth or port resources are pre-allocated to avoid bottlenecks during peak load periods; when the load is high, the PLC optical splitter's splitting ratio, signal strength, or port configuration is adjusted to handle higher demands; and load balancing is performed based on the predicted load distribution to ensure even load distribution across ports and prevent overload of certain ports.

[0071] After the adaptation and update, the updated control strategy is executed, and the operating status of the PLC optical splitter is adjusted to adapt to upcoming load changes. Parameters such as port bandwidth and signal strength are automatically adjusted, enabling the optical splitter to efficiently handle upcoming loads. By predicting load change trends, load change trends are identified in advance, and corresponding adjustments are made, thereby avoiding performance degradation or resource overload caused by sudden load increases.

[0072] Furthermore, the performance-optimized PLC optical splitter control system also includes a redundant port configuration module, which is used to configure redundant ports. After the performance optimization control of the PLC optical splitter is performed through the control optimization results, port anomaly monitoring is performed. When the port anomaly monitoring result meets the preset threshold, the redundant port is activated to perform abnormal port replacement and report the anomaly.

[0073] Specifically, redundant ports are additional backup ports configured in a PLC optical splitter. They are in standby mode during normal operation. When an anomaly is detected in the primary port, the redundant port is activated and takes over the primary port's operation, ensuring system stability and reliability. Redundant ports are typically set to standby mode during initial device configuration and do not participate in normal workload processing; they are only activated when the primary port fails. For example, suppose a PLC optical splitter has four ports, two of which are primary ports, and the other two are redundant ports. Under normal circumstances, the primary ports handle data transmission, while the redundant ports are in standby mode. The bandwidth of the standby ports is usually set to a lower standby level.

[0074] After the PLC optical splitter performs performance optimization control based on the control optimization results, it performs port anomaly monitoring, which involves real-time monitoring of the operating status of each port, including port bandwidth, throughput, signal quality, and port loss. By monitoring the port status of the PLC optical splitter, it determines in real time whether the port is operating normally. When a port's indicator exceeds a preset threshold range, i.e., meets the preset threshold, the port is considered to be abnormal. The preset threshold is a set of standard values ​​defined by the system to determine whether an anomaly exists. If the monitoring results exceed the threshold range, the port is considered to be abnormal.

[0075] When port anomaly monitoring detects an abnormal port status, and the monitoring results exceed preset thresholds (such as port bandwidth falling below a set value, throughput reduction exceeding 50%, etc.), the redundant port activation process is initiated. The redundant port will be activated within a preset limit control range in the system, taking over the work of the faulty port. For example, assuming port A fails, port B (the redundant port) will be automatically activated, and data traffic will be switched from port A to port B to ensure normal data transmission. After the redundant port is activated, the port replacement operation is automatically completed, allowing the optical splitter to continue operating normally, preventing the performance of the entire system from being affected by the failure of one port. At this time, the background will perform some data verification operations to ensure that the bandwidth, throughput, and other resources of the redundant port are fully prepared and meet the current load requirements. Simultaneously, port failure information will be transmitted to the management platform or user through an error reporting mechanism. For example, the management platform will receive a message: Port A has failed, and redundant port B has taken over the work.

[0076] By configuring redundant ports and real-time port anomaly monitoring, the system can quickly switch to redundant ports when a port fails, ensuring continuous operation of the optical splitter and greatly improving system reliability and stability. The rapid activation of redundant ports effectively avoids service interruptions caused by a single port failure. The PLC optical splitter can resume normal operation in a very short time after a failure, with almost no impact on network services.

[0077] In summary, the performance-optimized PLC optical splitter control system provided in this application has the following technical advantages: The system employs an environmental perception module to collect environmental datasets from the PLC optical splitter, perform environmental perception based on these datasets, and establish an environmental fitting scenario. A task reading module acquires the network load requirements of the PLC optical splitter and establishes its task objectives based on these requirements. A fitting construction module uploads the environmental fitting scenario and task objectives to the central control unit, reads the device status data of the PLC optical splitter, performs control optimization under multi-objective optimization, and establishes the control optimization results. An intelligent control module optimizes the performance control of the PLC optical splitter based on the control optimization results. In other words, by collecting data from the environment in which the PLC optical splitter operates, determining the PLC optical splitter's objectives based on actual needs, and dynamically adjusting its control parameters, intelligent control of the PLC optical splitter is achieved, improving its performance stability.

[0078] Example 2: Based on the same inventive concept as the performance-optimized PLC optical splitter control system in Example 1, this application also provides a performance-optimized PLC optical splitter control method. Please refer to the appendix. Figure 2 The performance-optimized PLC optical splitter control method includes: The system collects environmental datasets of the PLC optical splitter, performs environmental perception based on the datasets, and establishes an environmental fitting scenario. It also acquires the network load requirements of the PLC optical splitter and establishes task objectives based on these requirements. After uploading the environmental fitting scenario and task objectives to the central control unit, it reads the device status data of the PLC optical splitter, performs control optimization under multi-objective optimization, and establishes the control optimization results. Finally, it performs performance optimization control of the PLC optical splitter based on these results.

[0079] Furthermore, the step of uploading the environmental fitting scenario and the task objective to the central control unit, and then reading the device status data of the PLC optical splitter, includes: The task objective is analyzed, and a quality importance value for signal quality optimization is established. A port balance importance value for the PLC optical splitter is established based on the device status data. The device stability importance of the PLC optical splitter is evaluated based on the environmental fitting scenario and the device status data, and a stability importance value is established. A multi-objective optimization function is configured using the quality importance value, port balance importance value, and stability importance value, and control optimization is performed using the multi-objective optimization function.

[0080] Furthermore, the step of performing control optimization using the multi-objective optimization function includes: Construct the limit control range for each port in the PLC optical splitter; in the environmental fitting scenario, use the device state data as the initial state data of the port, perform environmental adaptation adjustment fitting within the limit control range, and establish the environmental adaptation adjustment fitting result; use the environmental adaptation adjustment fitting result as a constraint condition, and perform control optimization using a multi-objective optimization function.

[0081] Furthermore, the step of using the environmental adaptation and fitting results as constraints to perform control optimization using a multi-objective optimization function includes: Within the search space, an initial solution is created based on the constraints; the fitness of the initial solution is calculated using the multi-objective optimization function to generate fitness calculation results; solution state identification is performed based on the fitness calculation results to establish a following space and a conflict space; after adding random perturbations to the following space and the conflict space, the initial solution is iteratively updated; control optimization is completed based on the iterative update results.

[0082] Furthermore, the iterative update of the initial solution after adding random perturbations to the following space and the conflicting space includes: For any solution in the initial solution, calculate the similarity distance between the arbitrary solution and the following space and the opposing space; based on the similarity distance, after selecting the following space or the opposing space, perform following or opposing iterations based on the random perturbation to complete the iterative update of the initial solution.

[0083] Furthermore, the performance-optimized PLC optical splitter control method further includes: A feedback window is established, and the status monitoring of the PLC optical splitter is performed within the feedback window. Status monitoring feedback is established, and control verification is performed using the status monitoring feedback and the control optimization results. Perception feedback is generated, and system self-optimization management is performed based on the perception feedback.

[0084] Furthermore, the performance-optimized PLC optical splitter control method includes: Based on historical control data and the network load demand, load change trends are predicted, and task load prediction results are established; the task load prediction results and control optimization results are then used for adaptation and updates.

[0085] Furthermore, the performance-optimized PLC optical splitter control method includes: Configure redundant ports. After optimizing the performance control of the PLC optical splitter based on the control optimization results, perform port anomaly monitoring. When the port anomaly monitoring results meet the preset threshold, activate the redundant ports to perform abnormal port replacement and report the anomaly.

[0086] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The performance-optimized PLC optical splitter control system and specific examples in Embodiment 1 are also applicable to the performance-optimized PLC optical splitter control method in this embodiment. Through the foregoing detailed description of the performance-optimized PLC optical splitter control system, those skilled in the art can clearly understand the performance-optimized PLC optical splitter control method in this embodiment; therefore, for the sake of brevity, it will not be described in detail here. Regarding the method disclosed in the embodiments, since it corresponds to the system disclosed in the embodiments, the description is relatively simple; relevant details can be found in the system section description.

[0087] Example 3: Based on the same inventive concept as the performance-optimized PLC optical splitter control system in Example 1, this application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the performance-optimized PLC optical splitter control system described in any one of Examples 1 above.

[0088] Appendix Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In this document, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0089] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0090] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A performance-optimized PLC optical splitter control system, characterized in that, include: The environmental perception module is used to collect environmental datasets from the PLC optical splitter, perform environmental perception based on the environmental datasets, and establish an environmental fitting scenario. The task reading module is used to obtain the network load requirements of the PLC optical splitter and establish the task target of the PLC optical splitter based on the network load requirements. The fitting construction module is used to upload the environmental fitting scenario and the task objective to the central control unit, read the device status data of the PLC optical splitter, perform control optimization under multi-objective optimization, and establish the control optimization result. The intelligent control module is used to optimize the performance of the PLC optical splitter based on the control optimization results.

2. The performance-optimized PLC optical splitter control system as described in claim 1, characterized in that, The fitting construction module includes: The task parsing unit is used to parse the task objective and establish a quality importance value for signal quality optimization. A port importance value establishment unit is used to establish the port balanced importance value of the PLC optical splitter based on the device status data. The importance evaluation unit is used to evaluate the importance of the PLC optical splitter's stability based on the environmental fitting scenario and the equipment status data, and to establish a stability importance value. The control optimization unit is used to configure a multi-objective optimization function using the quality importance value, port balance importance value, and stability importance value, and to perform control optimization using the multi-objective optimization function.

3. The performance-optimized PLC optical splitter control system as described in claim 2, characterized in that, The control optimization unit includes: The interval construction sub-unit is used to construct the limit control interval of each port in the PLC optical splitter; An environmental adaptation and fitting subunit is used to perform environmental adaptation and fitting within the limit control range in the environmental fitting scenario, using the device state data as the port initial state data, and to establish the environmental adaptation and fitting result. The constraint optimization subunit is used to perform controlled optimization of a multi-objective optimization function by taking the environmental adaptation and adjustment fitting results as constraints.

4. The performance-optimized PLC optical splitter control system as described in claim 3, characterized in that, The constrained optimization subunit includes: An initial solution creation channel is used to create an initial solution within the search space based on the constraints. The fitness calculation channel is used to calculate the fitness of the initial solution using the multi-objective optimization function and generate the fitness calculation result. The solution state identification channel is used to identify the solution state based on the fitness calculation results and establish the following space and the conflict space. An iterative update channel is used to perform iterative updates of the initial solution after adding random perturbations to the following space and the conflicting space; The result optimization channel is used to complete control optimization based on the iterative update results.

5. The performance-optimized PLC optical splitter control system as described in claim 4, characterized in that, The iterative update channel includes: The distance calculation sub-channel is used to calculate the similarity distance between any solution in the initial solution and the following space and the opposing space. The random update sub-channel is used to perform follow or conflict iterations based on the random perturbation after selecting the follow space or conflict space according to the similarity distance, so as to complete the iterative update of the initial solution.

6. The performance-optimized PLC optical splitter control system as described in claim 1, characterized in that, The performance-optimized PLC optical splitter control system also includes: The perception feedback module is used to establish a feedback window, perform status monitoring of the PLC optical splitter within the feedback window, establish status monitoring feedback, perform control verification using the status monitoring feedback and the control optimization results, generate perception feedback, and perform system self-optimization management based on the perception feedback.

7. The performance-optimized PLC optical splitter control system as described in claim 1, characterized in that, The performance-optimized PLC optical splitter control system also includes: The task load prediction module is used to predict load change trends based on historical control data and the network load demand, and to establish task load prediction results. An adaptive update module is used to perform adaptive updates based on the task load prediction results and the control optimization results.

8. The performance-optimized PLC optical splitter control system as described in claim 1, characterized in that, The performance-optimized PLC optical splitter control system also includes: The redundant port configuration module is used to configure redundant ports. After the performance optimization control of the PLC optical splitter is performed based on the control optimization results, port anomaly monitoring is performed. When the port anomaly monitoring results meet the preset threshold, the redundant port is activated to perform abnormal port replacement and report the anomaly.

9. A PLC optical splitter control method based on performance optimization, characterized in that, Executed by the performance-optimized PLC optical splitter control system according to any one of claims 1 to 8, the performance-optimized PLC optical splitter control method includes: Collect environmental datasets from the PLC optical splitter, perform environmental perception based on the environmental datasets, and establish an environmental fitting scenario. Obtain the network load requirements of the PLC optical splitter, and establish the task objectives of the PLC optical splitter based on the network load requirements; After uploading the environmental fitting scenario and the task objective to the central control unit, the device status data of the PLC optical splitter is read, control optimization under multi-objective optimization is performed, and control optimization results are established. The performance optimization control of the PLC optical splitter is performed based on the control optimization results.

10. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to execute the performance-optimized PLC optical splitter control system according to any one of claims 1 to 8.

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