PLC-based photovoltaic optimizer communication method and system

Abstract

This application relates to the field of communication optimization technology, and discloses a PLC-based photovoltaic optimizer communication method and system. The method includes: initializing and frequency testing the MPPT optimizer to obtain an initial communication frequency set; monitoring and analyzing real-time communication quality indicators and environmental parameters to obtain real-time communication frequencies; classifying, packetizing, and prioritizing the data to be transmitted to obtain a priority data packet sequence; performing topology analysis and multi-path planning on the MPPT optimizer network to obtain a distributed data segment set; aggregating and performing integrity checks on the multi-path transmitted data to obtain target verification data; and compressing, encrypting, and hierarchically storing the target verification data to obtain target communication data. This application improves the efficiency of PLC-based photovoltaic optimizer communication.

Description

Technical Field

[0001] This application relates to the field of communication optimization technology, and in particular to a PLC-based communication method and system for photovoltaic optimizers. Background Technology

[0002] In photovoltaic (PV) power generation systems, the MPPT (Maximum Power Point Tracking) optimizer is a key device for improving system efficiency. Traditional MPPT optimizers primarily use RS485 or wireless communication technologies. While RS485 communication is stable and reliable, it requires additional wiring, increasing system complexity and cost. Although wireless communication is easy to install, it may face signal interference and transmission distance limitations in large-scale PV power plants. With the continuous expansion of PV power plant scale and the increasing level of intelligence, higher demands are placed on the reliability, real-time performance, and data transmission capabilities of communication systems.

[0003] While existing PLC (Power Line Carrier) communication technology can utilize existing power lines for data transmission, reducing additional wiring costs, it often faces problems such as signal attenuation, frequency interference, and communication instability in the complex electromagnetic environment of photovoltaic systems. Especially in large-scale photovoltaic power plants, due to the large number and wide distribution of equipment, traditional PLC communication struggles to meet the demands for efficient and reliable data transmission. Furthermore, existing PLC communication solutions lack real-time monitoring and dynamic adjustment capabilities for communication quality, making it difficult to adapt to the complex and variable electromagnetic environment of photovoltaic systems. This results in low communication efficiency, impacting the overall operational efficiency and management effectiveness of the photovoltaic system. Summary of the Invention

[0004] This application provides a PLC-based photovoltaic optimizer communication method and system to improve the communication efficiency of PLC-based photovoltaic optimizers.

[0005] In a first aspect, this application provides a PLC-based photovoltaic optimizer communication method, comprising: initializing and frequency testing the MPPT optimizer to obtain an initial communication frequency set; monitoring and analyzing real-time communication quality indicators and environmental parameters based on the initial communication frequency set to obtain a real-time communication frequency; classifying, packetizing, and prioritizing the data to be transmitted according to the real-time communication frequency to obtain a priority data packet sequence; performing topology analysis and multi-path planning on the MPPT optimizer network based on the priority data packet sequence to obtain a distributed data segment set; aggregating and performing integrity checks on the multi-path transmission data according to the distributed data segment set to obtain target verification data; and compressing, encrypting, and hierarchically storing the target verification data to obtain target communication data.

[0006] Secondly, this application provides a PLC-based photovoltaic optimizer communication system, the PLC-based photovoltaic optimizer communication system comprising:

[0007] The test module is used to initialize the MPPT optimizer and perform frequency testing to obtain the initial set of communication frequencies.

[0008] The analysis module is used to monitor and analyze real-time communication quality indicators and environmental parameters based on the initial set of communication frequencies, and to obtain the real-time communication frequencies.

[0009] The allocation module is used to classify, packetize, and prioritize the data to be transmitted according to the real-time communication frequency to obtain a priority data packet sequence.

[0010] The planning module is used to perform topology analysis and multi-path planning on the MPPT optimizer network based on the priority data packet sequence to obtain a set of distributed data fragments.

[0011] The inspection module is used to aggregate and perform integrity checks on the multi-path transmission data based on the distributed data fragment set to obtain target verification data;

[0012] The compression module is used to compress, encrypt, and perform layered storage processing on the target verification data to obtain target communication data.

[0013] The technical solution provided in this application accurately determines a set of high-quality initial communication frequencies for the MPPT (Maximum Power Point Tracking) optimizer through initialization and frequency testing. This step not only considers the signal strength, bit error rate, and stability of the communication frequencies but also performs comprehensive evaluation through a multi-objective optimization algorithm, thereby ensuring that the selected frequencies can maintain efficient and stable communication in complex and ever-changing communication environments. By monitoring and analyzing real-time communication quality indicators and environmental parameters, the communication frequencies can be dynamically adjusted to the optimal state, further improving communication efficiency. This adaptive capability allows the photovoltaic optimizer network to flexibly adjust communication parameters according to current environmental conditions and communication needs, ensuring smooth and efficient communication. In terms of data transmission, a classification, packetization, and priority allocation strategy is adopted to achieve efficient data transmission and processing. By comprehensively evaluating the importance, timeliness, and size of data, communication resources can be rationally allocated, prioritizing the transmission of high-priority data, thereby improving the overall network response speed and throughput. This strategy not only improves data transmission efficiency but also ensures the timely transmission and processing of critical data, providing strong support for the intelligent management and operation and maintenance of photovoltaic systems. Furthermore, through topology analysis and multi-path planning, efficient transmission and aggregation of distributed data fragments were achieved, further improving communication efficiency. By optimizing the network topology and data transmission paths, data transmission latency and loss can be reduced, improving data transmission speed and accuracy. In terms of data storage, compression, encryption, and hierarchical storage methods were adopted, which not only improved data security but also reduced storage space usage and improved storage efficiency through compression technology. This multi-layered data processing strategy enables the photovoltaic optimizer network to better adapt to the needs of large-scale data transmission and storage, further enhancing the overall system's communication efficiency. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of one embodiment of the PLC-based photovoltaic optimizer communication method in this application.

[0016] Figure 2 This is a schematic diagram of one embodiment of the PLC-based photovoltaic optimizer communication system in this application. Detailed Implementation

[0017] This application provides a PLC-based photovoltaic optimizer communication method and system. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the PLC-based photovoltaic optimizer communication method in this application includes:

[0019] Step S101: Initialize and perform frequency testing on the MPPT optimizer to obtain the initial set of communication frequencies;

[0020] Step S102: Based on the initial set of communication frequencies, monitor and analyze the real-time communication quality indicators and environmental parameters to obtain the real-time communication frequencies;

[0021] Step S103: Based on the real-time communication frequency, classify, packetize, and prioritize the data to be transmitted to obtain a priority data packet sequence;

[0022] Step S104: Based on the priority data packet sequence, perform topology analysis and multi-path planning on the MPPT optimizer network to obtain a set of distributed data fragments;

[0023] Step S105: Based on the distributed data fragment set, aggregate and perform integrity checks on the multi-path transmission data to obtain the target verification data;

[0024] Step S106: Compress, encrypt, and store the target verification data in layers to obtain the target communication data.

[0025] It is understood that the executing entity of this application can be a PLC-based photovoltaic optimizer communication system, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0026] Specifically, the MPPT optimizer undergoes initialization and frequency testing to determine the initial communication frequency set. This process includes activating the PLC communication module, setting a preset frequency range, and conducting communication tests on multiple frequency points within that range. Through data cleaning, signal strength analysis, and bit error rate assessment, the optimal frequency points are selected, and stability tests are performed to ultimately obtain the initial communication frequency set. Based on this initial communication frequency set, real-time communication quality indicators and environmental parameters are continuously monitored. This includes noise filtering of real-time signal strength data, signal quality assessment, and consideration of environmental factors such as temperature, humidity, and electromagnetic interference. A comprehensive communication quality index is obtained through weighted fusion processing, and frequency adjustment suggestions are generated using an adaptive frequency adjustment algorithm. Combining this with the initial communication frequency set, the feasibility of the adjustment suggestions is evaluated, idle frequency bands are identified through spectrum scanning, and the optimal real-time communication frequency is finally selected.

[0027] Based on the real-time communication frequency, the data to be transmitted is classified, packetized, and prioritized. This process first involves scoring the data's importance and analyzing its timeliness, then calculating a priority label. Next, a dynamic packetization algorithm determines the appropriate packet size and performs data segmentation. After redundancy detection and header optimization, the packets are prioritized, and finally, a queue scheduling algorithm generates a priority packet sequence based on the real-time communication frequency. Based on this priority packet sequence, topology analysis and multi-path planning are performed on the MPPT optimizer network. This includes assessing the importance of network nodes, analyzing link reliability, filtering available links, and generating candidate transmission paths. Through path redundancy analysis and load balancing algorithms, transmission paths are optimized, and a load distribution scheme is developed. After feasibility simulation and path scoring, a path priority list is obtained, and finally, a distributed data fragment set is generated using a data fragmentation algorithm.

[0028] Based on a distributed set of data fragments, data transmitted via multiple paths is aggregated and its integrity is checked. This process includes consistency checks on data fragments, marking and repairing erroneous data, reassembling the data sequence, and verifying integrity. For missing data portions, retransmission requests are generated and prioritized. Using a data merging algorithm, the retransmitted data is merged with the original data, and finally, consistency checks and format normalization are performed to obtain the target verification data. Finally, the target verification data is compressed, encrypted, and stored in a tiered manner. An appropriate compression algorithm is selected to compress the data, the compression quality is evaluated, and a suitable encryption strategy is chosen based on the encryption strength evaluation results. The encrypted data is then tiered, and a storage scheme and data migration plan are developed. Finally, the data is stored in a tiered manner according to the plan, and a data index is generated to obtain the target communication data.

[0029] For example, in a photovoltaic array containing 100 MPPT optimizers, 20 frequency points in the range of 0.5MHz to 10MHz might be tested during initialization. Through signal strength analysis and bit error rate assessment, 3.5MHz, 4.2MHz, and 5.1MHz might be selected as candidate optimal frequencies. After stability testing, the system might ultimately choose 4.2MHz as the initial communication frequency. During real-time monitoring, if the signal-to-noise ratio (SNR) of the 4.2MHz band drops below 10dB, the system will re-evaluate and may switch to the 5.1MHz band. During data transmission, the system might divide real-time monitoring data into 100-byte packets and non-critical historical data into 500-byte packets, assigning different priorities. In network topology analysis, the system might identify three independent transmission paths and, based on the current network load, transmit 60% of the data through the main path, with the remaining 40% evenly distributed across the two backup paths. During data aggregation, if 5% of data packets are lost, the system will immediately send a retransmission request, prioritizing the retransmission of these lost packets. Finally, the system may use the LZMA algorithm to compress the data, reducing the data volume to 40% of the original, then use the AES-256 algorithm for encryption, and store 90% of the data on the local solid-state drive and 10% of the non-critical historical data in the cloud.

[0030] In this embodiment, the communication efficiency of the PLC-based photovoltaic optimizer is improved.

[0031] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0032] (1) Activate the PLC communication module of the MPPT optimizer to obtain an adjustable frequency PLC communication module;

[0033] (2) The frequency range setting process of the adjustable frequency PLC communication module is performed to obtain the preset frequency range, and the communication test process of multiple frequency points within the preset frequency range is performed to obtain the frequency point communication quality dataset.

[0034] (3) Perform data cleaning on the frequency point communication quality dataset to obtain effective communication quality data, and process the effective communication quality data through the signal strength analysis algorithm to obtain the frequency point signal strength ranking result;

[0035] (4) The frequency point signal strength sorting results are subjected to threshold screening to obtain the candidate optimal frequency set, and the candidate optimal frequency set is processed by the bit error rate evaluation algorithm to obtain the frequency point with the lowest bit error rate.

[0036] (5) Perform stability testing on the frequency point with the lowest bit error rate to obtain frequency stability data, and process the frequency stability data through time series analysis algorithm to obtain frequency stability score;

[0037] (6) The frequency stability score is compared with the threshold to obtain a set of stable frequencies. The set of stable frequencies is then evaluated by a multi-objective optimization algorithm to obtain an initial set of communication frequencies.

[0038] Specifically, the MPPT optimizer undergoes PLC communication module activation. This process involves sending a specific activation command to the MPPT optimizer, enabling its built-in PLC communication module to enter a frequency-adjustable state. The activated PLC communication module then gains the ability to communicate at different frequencies, laying the foundation for subsequent frequency testing and selection. Next, the frequency range of the frequency-adjustable PLC communication module is set. Based on the actual environment of the photovoltaic system and the characteristics of the power lines, a suitable frequency test range is set, typically between 0.1MHz and 30MHz. For example, a frequency range of 1MHz to 10MHz can be set, and multiple frequency points can be selected within this range for testing, such as one frequency point every 1MHz. Communication tests are then performed on these preset frequency points, sending test data packets and recording the reception results to obtain a frequency point communication quality dataset. This dataset contains key indicators such as signal strength, signal-to-noise ratio, and bit error rate for each frequency point.

[0039] The obtained frequency point communication quality dataset undergoes data cleaning to remove outliers and invalid data. For example, data points with excessively low signal strength (e.g., below -90dBm) or excessively high bit error rates (e.g., exceeding 10%) are removed. The cleaned data more accurately reflects the actual communication performance of each frequency point. Next, the effective communication quality data is processed using a signal strength analysis algorithm. This algorithm may employ a moving average method to smooth the data, calculate the average signal strength for each frequency point, and sort the frequencies from highest to lowest signal strength. A threshold filtering process is then applied to the sorted frequency points, selecting those with signal strengths above a preset threshold (e.g., -70dBm) to form a candidate optimal frequency set. This step effectively reduces the amount of data processed subsequently, improving efficiency. Finally, the candidate optimal frequency set is processed using a bit error rate (BER) evaluation algorithm. This algorithm calculates the average BER for each candidate frequency point and selects the frequency point with the lowest BER. For example, if the bit error rate of 3MHz is 0.01%, 5MHz is 0.005%, and 7MHz is 0.02% in the candidate frequency set, then 5MHz is selected as the frequency with the lowest bit error rate.

[0040] Stability testing is performed on the frequency points with the lowest bit error rate. The communication quality of these frequencies is continuously monitored over a certain time period (e.g., 24 hours), recording changes in signal strength and bit error rate to obtain frequency stability data. Time series analysis algorithms, such as the Autoregressive Integral Moving Average (ARIMA) model, are used to process this data. This algorithm analyzes the trends, periodicity, and random fluctuations of the data to calculate a frequency stability index, resulting in a frequency stability score. Finally, a threshold comparison is performed on the frequency stability scores, selecting frequency points with stability scores higher than a preset threshold (e.g., 0.8, out of 1) to form a stable frequency set. A multi-objective optimization algorithm, such as the Pareto optimization method, is then used to comprehensively evaluate the stable frequency set. This algorithm simultaneously considers three objectives: signal strength, bit error rate, and stability, seeking the optimal balance point to ultimately obtain the initial communication frequency set.

[0041] For example, in a photovoltaic array containing 50 MPPT optimizers, the initialization process sets the frequency range to 1MHz to 10MHz, with one frequency point tested every 1MHz. After data cleaning, data from 9 valid frequency points are obtained. Signal strength analysis results show that the signal strengths at 3MHz, 5MHz, and 7MHz are -65dBm, -62dBm, and -68dBm, respectively, forming a candidate optimal frequency set. Bit error rate (BER) assessment shows that the BERs at these three frequency points are 0.02%, 0.01%, and 0.03%, respectively, with 5MHz selected as the point with the lowest BER. After 24 hours of stability testing, the stability score for the 5MHz frequency is 0.85. After multi-objective optimization, 5MHz is finally selected as the initial communication frequency, with 3MHz and 7MHz as backup frequencies, forming the initial communication frequency set.

[0042] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0043] (1) Noise filtering is applied to the real-time signal strength data to obtain filtered signal strength data. The filtered signal strength data is then processed using a signal quality assessment algorithm to obtain a signal quality score.

[0044] (2) Normalize the temperature, humidity and electromagnetic interference data collected by the environmental sensors to obtain standardized environmental parameters, and process the standardized environmental parameters through the environmental impact model to obtain environmental impact factors;

[0045] (3) The signal quality score and environmental impact factor are weighted and fused to obtain the comprehensive communication quality index. The comprehensive communication quality index is then processed by the adaptive frequency adjustment algorithm to obtain frequency adjustment suggestions.

[0046] (4) Based on the initial communication frequency set, the feasibility of the frequency adjustment proposal is evaluated to obtain the adjustable frequency range. The adjustable frequency range is then processed by the spectrum scanning algorithm to obtain the list of idle frequency bands.

[0047] (5) Priority sorting is performed on the list of idle frequency bands to obtain a candidate frequency sequence, and the candidate frequency sequence is processed by a dynamic frequency selection algorithm to obtain the real-time communication frequency.

[0048] Specifically, noise filtering is performed on real-time signal strength data. A moving average filtering algorithm is used to remove high-frequency noise, resulting in filtered signal strength data. The moving average filtering algorithm smooths the data by calculating the average value within a certain window, effectively reducing the impact of instantaneous fluctuations. Then, a signal quality assessment algorithm is used to process the filtered signal strength data to obtain a signal quality score. This algorithm comprehensively considers factors such as signal strength, signal-to-noise ratio, and stability, normalizing each indicator and then weighting and summing them to obtain a score between 0 and 100. Temperature, humidity, and electromagnetic interference data collected by environmental sensors are normalized, unifying data with different dimensions into a range of 0 to 1, resulting in standardized environmental parameters. The normalization process uses a maximum-minimum normalization method to ensure fair comparison of different environmental parameters. Next, the standardized environmental parameters are processed using an environmental impact model to obtain environmental impact factors. The environmental impact model is a machine learning model trained on historical data, such as a support vector regression (SVR) model, which can predict the degree of impact of environmental parameters on communication quality.

[0049] A weighted fusion process is performed on signal quality scores and environmental impact factors to obtain a comprehensive communication quality index. The weighted fusion process considers the relative importance of signal quality and environmental factors, calculating an index reflecting the overall communication status by setting appropriate weighting coefficients. Next, an adaptive frequency adjustment algorithm is used to process the comprehensive communication quality index to obtain frequency adjustment suggestions. This algorithm, based on fuzzy logic control, dynamically adjusts the direction and magnitude of frequency adjustments according to the changing trend and magnitude of the comprehensive communication quality index. Based on the initial communication frequency set, a feasibility assessment is performed on the frequency adjustment suggestions to obtain an adjustable frequency range. The feasibility assessment considers hardware limitations, regulatory requirements, and the distribution of the initial frequency set to ensure that the adjusted frequencies remain within a reasonable range. Then, a spectrum scanning algorithm is used to process the adjustable frequency range to obtain a list of idle frequency bands. The spectrum scanning algorithm uses Fast Fourier Transform (FFT) to analyze the spectral energy distribution and identify idle frequency bands with lower energy.

[0050] Finally, the list of available frequency bands is prioritized to obtain a candidate frequency sequence. The prioritization criteria include the bandwidth of the frequency band and the proximity of its center frequency to the currently used frequency. A dynamic frequency selection algorithm is then used to process the candidate frequency sequence to obtain the real-time communication frequency. This algorithm comprehensively considers the priority, historical performance, and current network load of the candidate frequencies to select the most suitable frequency as the new communication frequency.

[0051] For example, in a photovoltaic array containing 100 MPPT optimizers, the current communication frequency is 5MHz. Real-time monitoring shows that the filtered signal strength is -65dBm, the signal-to-noise ratio is 15dB, and the signal quality score is calculated to be 85. Environmental sensors record a temperature of 30℃, humidity of 60%, and electromagnetic interference intensity of 2mV / m, which, after normalization, are 0.6, 0.4, and 0.3, respectively. The environmental impact model predicts an impact factor of 0.8, indicating that environmental conditions have a slight negative impact on communication quality. The weighted fusion comprehensive communication quality index is 78, which is lower than the preset threshold of 80, triggering frequency adjustment. The adaptive frequency adjustment algorithm suggests increasing the frequency by 0.5MHz. Feasibility assessment determines that the adjustable frequency range is 4.5MHz to 6MHz. Spectrum scanning reveals three relatively idle frequency bands: 5.2MHz, 5.5MHz, and 5.8MHz. After priority ranking, the candidate frequency sequence is 5.5MHz, 5.2MHz, and 5.8MHz. After comprehensively considering various factors, the dynamic frequency selection algorithm ultimately chose 5.5MHz as the new real-time communication frequency. This process demonstrates how to dynamically adjust the PLC communication frequency based on real-time monitoring data and environmental factors to maintain the communication quality and stability of the photovoltaic optimizer network.

[0052] In one specific embodiment, the process of performing step S103 may specifically include the following steps:

[0053] (1) Threshold classification processing is performed on the data importance score to obtain the initial priority label, and the initial priority label is processed by the timeliness analysis algorithm to obtain the timeliness adjustment coefficient;

[0054] (2) The initial priority mark and the timeliness adjustment coefficient are comprehensively calculated and processed to obtain the priority mark, and the priority mark is processed by the dynamic packet splitting algorithm to obtain the data packet size suggestion;

[0055] (3) Perform feasibility verification on the data packet size suggestion to obtain the verified packet size, and process the verified packet size through a data segmentation algorithm to obtain the initial data packet set;

[0056] (4) Redundancy detection is performed on the initial data packet set to obtain a non-redundant data packet set, and the non-redundant data packet set is processed by the packet header optimization algorithm to obtain the optimized data packets;

[0057] (5) Priority encoding is performed on the optimized data packets to obtain data packets with priority information. Based on the real-time communication frequency, the data packets with priority information are processed by the queue scheduling algorithm to obtain a priority data packet sequence.

[0058] Specifically, the data importance score is threshold-classified to obtain initial priority labels. In this process, the data importance score is divided into multiple levels; for example, the score range of 0-100 is divided into high, medium, and low priorities. Specifically, a score of 90-100 is high priority and labeled 3; 70-89 is medium priority and labeled 2; and 0-69 is low priority and labeled 1. Next, the initial priority labels are processed using a timeliness analysis algorithm to obtain a timeliness adjustment coefficient. The timeliness analysis algorithm considers the data's generation time and validity period to calculate the data's urgency. For example, for real-time monitoring data, the timeliness adjustment coefficient might be 1.5; for daily operation logs, the coefficient might be 1.0; and for long-term stored historical data, the coefficient might be 0.8.

[0059] Then, the initial priority label and the timeliness adjustment coefficient are comprehensively calculated to obtain the final priority label. This step multiplies the initial priority by the timeliness adjustment coefficient to obtain a more accurate priority assessment. For example, a data point with an initial priority of 2 and a timeliness adjustment coefficient of 1.5 will have a final priority label of 3 (2 × 1.5 = 3).

[0060] A dynamic packet segmentation algorithm is used to process priority markers to obtain a suggested packet size. This algorithm dynamically adjusts the packet size based on current network conditions and data priority. For high-priority data, a smaller packet size (e.g., 512 bytes) might be suggested to ensure fast transmission; for low-priority data, a larger packet size (e.g., 2048 bytes) might be suggested to improve transmission efficiency. Subsequently, the suggested packet size undergoes feasibility verification to obtain the verified packet size. This step considers factors such as network bandwidth and device processing capabilities to ensure the suggested packet size is feasible in the actual environment. For example, if network bandwidth is limited, the originally suggested 2048-byte packet size might be adjusted to 1024 bytes.

[0061] The verified packet size is processed using a data segmentation algorithm to obtain an initial set of data packets. The algorithm divides large blocks of data into packets according to their verified size, adding necessary sequence numbers and checksum information. For example, a 10KB data file, using 1KB packets, will be divided into 10 packets, each containing a sequence number and a CRC checksum. Redundancy detection is then performed on the initial set of data packets to obtain a non-redundant set. The redundancy detection algorithm compares the contents of adjacent packets, removing duplicate information and reducing the amount of data transmitted. For example, if consecutive status report packets only have a changed timestamp while other information remains the same, only the changed portion is retained.

[0062] The non-redundant data packet set is processed by a header optimization algorithm to obtain optimized data packets. This algorithm compresses header information, for example, by using differential encoding to record timestamps and bitmaps to represent changing fields, thereby reducing header overhead. The optimized data packets are then subjected to priority encoding to obtain data packets with priority information. Priority encoding embeds the previously calculated priority markers into the data packet header, facilitating identification and processing by network devices.

[0063] Finally, based on the real-time communication frequency, the data packets with priority information are processed using a queue scheduling algorithm to obtain a priority data packet sequence. The queue scheduling algorithm determines the transmission order of data packets based on their priority and the transmission capacity of the current communication frequency. For example, at a 5MHz communication frequency, the algorithm might decide to send one medium-priority packet and one low-priority packet after sending three high-priority packets.

[0064] For example, in a 100kW photovoltaic power plant, the MPPT optimizer needs to transmit data including real-time power data (score 95, high priority), temperature alarms (score 85, medium priority), and historical power generation (score 60, low priority). After timeliness analysis, the adjustment factor for real-time power data is 1.5, for temperature alarms it is 1.2, and for historical power generation it is 0.9. The final priority labels are 4.5, 2.4, and 0.9, respectively. The dynamic packet splitting algorithm suggests using 256-byte packets for real-time power data, 512-byte packets for temperature alarms, and 1024-byte packets for historical power generation. After feasibility verification and data segmentation, a 10KB data stream is divided into packets of different sizes. Redundancy detection removes 20% of duplicate information, and packet header optimization reduces the packet header size from 32 bytes to 16 bytes. Finally, the queue scheduling algorithm, at a 5MHz communication frequency, schedules a sequence of sending one temperature alarm packet and one historical power generation packet after every four real-time power data packets.

[0065] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0066] (1) Based on the priority data packet sequence, the node importance of the network topology diagram is evaluated to obtain the node weight distribution. The node weight distribution is then processed by the link quality evaluation algorithm to obtain the link reliability score.

[0067] (2) Threshold screening is performed on the link reliability score to obtain the set of available links, and the set of available links is processed by the multi-path generation algorithm to obtain the set of candidate transmission paths;

[0068] (3) Perform path redundancy analysis on the candidate transmission path set to obtain the optimized path set, and process the optimized path set through the load balancing algorithm to obtain the load distribution scheme.

[0069] (4) Perform feasibility simulation on the load allocation scheme to obtain theoretical transmission efficiency data, and process the theoretical transmission efficiency data through the path scoring algorithm to obtain the path priority list;

[0070] (5) The path priority list is dynamically adjusted to obtain the transmission path, and the transmission path is processed by the data fragmentation algorithm to obtain a set of distributed data fragments.

[0071] Specifically, based on priority packet sequences, the network topology graph is processed to assess node importance, resulting in a node weight distribution. This process considers the location, connectivity, and data traffic of each MPPT optimizer node in the network, calculating its importance score. For example, nodes connecting multiple sub-networks or processing more high-priority packets will receive higher weights. The node weight distribution is then processed using a link quality assessment algorithm to obtain a link reliability score. This algorithm comprehensively considers factors such as signal strength, bit error rate, and latency, assigning a reliability score to each link. For example, a link with a signal strength of -60dBm, a bit error rate of 0.01%, and a latency of 10ms might receive a reliability score of 90.

[0072] Next, the link reliability scores are thresholded to obtain a set of available links. This step sets a minimum reliability threshold, such as 75 points; only links with scores higher than this threshold are included in the set of available links. Then, a multi-path generation algorithm is used to process the set of available links to obtain a set of candidate transmission paths. The multi-path generation algorithm uses an improved Dijkstra algorithm, considering not only path length but also link reliability, generating multiple candidate paths. Path redundancy analysis is performed on the candidate transmission path set to obtain an optimized path set. Path redundancy analysis identifies and removes overly similar paths to ensure the diversity of the path set. For example, if two paths overlap by more than 80%, only the one with the higher score is retained. Subsequently, a load balancing algorithm is used to process the optimized path set to obtain a load distribution scheme. The load balancing algorithm considers the capacity and current load of each path to reasonably allocate data traffic, avoiding overload on some paths while other paths are idle.

[0073] Feasibility simulations are performed on the load distribution scheme to obtain theoretical transmission efficiency data. The feasibility simulation uses discrete event simulation technology to simulate network performance under different load conditions and calculate the theoretical transmission efficiency of each path. Then, a path scoring algorithm is used to process the theoretical transmission efficiency data to obtain a path priority list. The path scoring algorithm comprehensively considers transmission efficiency, reliability, and load balancing, calculating a comprehensive score for each path and sorting them according to their scores. Finally, the path priority list is dynamically adjusted to obtain the final transmission paths. The dynamic adjustment process considers real-time network conditions and may temporarily increase or decrease the priority of some paths. For example, if a sudden performance drop in a high-priority path is detected, its priority will be immediately reduced and an alternative path will be selected. After determining the transmission paths, a data fragmentation algorithm is used to process the transmission paths to obtain a distributed set of data fragments. The data fragmentation algorithm divides the data into appropriately sized fragments based on path characteristics and packet size, and assigns a unique identifier and sequence number to each fragment.

[0074] For example, in a photovoltaic array containing 50 MPPT optimizers, network topology analysis identified five critical nodes with importance scores of 95, 88, 82, 79, and 75, respectively. Link quality assessment found 15 high-quality links with reliability scores ranging from 80 to 95. After threshold filtering (set to 85), 10 links were retained. A multipath generation algorithm generated eight candidate transmission paths based on these 10 links. Path redundancy analysis removed two highly redundant paths, leaving six optimized paths. A load balancing algorithm allocated the total data traffic (assumed to be 100 Mbps) according to the capacity of each path as follows: Path 1 (30 Mbps), Path 2 (25 Mbps), Path 3 (20 Mbps), Path 4 (15 Mbps), and Paths 5 and 6 each at 5 Mbps. Feasibility simulations showed that under this load allocation, the theoretical transmission efficiencies of the six paths were 95%, 92%, 90%, 88%, 85%, and 84%, respectively. The path scoring algorithm comprehensively considers these efficiency data, as well as path reliability and load balancing, to arrive at the following final scores: Path 1 (93 points), Path 2 (90 points), Path 3 (88 points), Path 4 (86 points), Path 5 (83 points), and Path 6 (82 points). During actual transmission, if the performance of Path 1 suddenly drops to 75%, a dynamic adjustment mechanism will shift the majority of traffic to Paths 2 and 3. Finally, for a 10MB data file, the data fragmentation algorithm may divide it into 100 100KB data fragments, each with a unique identifier and a sequence number from 1 to 100. These fragments will be transmitted in parallel through multiple selected paths, significantly improving the efficiency and reliability of data transmission.

[0075] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0076] (1) Error marking is performed on the consistency check results to obtain a set of data fragments to be corrected, and the set of data fragments to be corrected is processed by the data repair algorithm to obtain the repaired data fragments;

[0077] (2) Perform sequence recombination on the repaired data fragments to obtain preliminary aggregated data, and process the preliminary aggregated data through a data integrity verification algorithm to obtain integrity verification results;

[0078] (3) The missing parts of the integrity verification results are identified and processed to obtain a list of data completion requirements. The list of data completion requirements is then processed by the data retransmission request algorithm to obtain a retransmission request.

[0079] (4) Based on the set of distributed data fragments, the retransmission requests are prioritized to obtain a priority retransmission queue. The priority retransmission queue is then processed by a data merging algorithm to obtain the merged complete data.

[0080] (5) Perform consistency verification on the merged complete data to obtain the verified data, and process the verified data through the data format normalization algorithm to obtain the target verification data.

[0081] Specifically, the consistency check results are processed by error marking to obtain a set of data segments to be corrected. During this process, received data segments are compared with the expected data structure, and segments that do not conform to the expectations are marked. For example, in a transmission containing 100 data segments, if the checksums of segments 15, 37, and 82 do not match, these segments are marked as needing correction. Subsequently, the set of data segments to be corrected is processed by a data repair algorithm to obtain the repaired data segments. The data repair algorithm uses forward error correction (FEC) technology to reconstruct the corrupted data using redundant information. Data segments that cannot be repaired by FEC are marked as needing retransmission. For example, for the three erroneous segments mentioned above, if segments 15 and 82 can be repaired by FEC, but segment 37 cannot be repaired, then segment 37 is marked as needing retransmission.

[0082] Next, the repaired data fragments undergo sequence reconstruction to obtain preliminary aggregated data. The sequence reconstruction process assembles all received and repaired data fragments in the correct order according to their sequence numbers. Then, the preliminary aggregated data is processed by a data integrity verification algorithm to obtain integrity verification results. This algorithm checks the integrity of the beginning and end of the data, whether there are any missing parts in the middle, and whether the overall data structure meets expectations. Missing parts are identified in the integrity verification results, resulting in a data completion request list. This step precisely locates the missing parts in the data; for example, it might find that data fragment number 37 is still missing, and a series of fragments with sequence numbers 90-95 have also not been received. Subsequently, the data completion request list is processed by a data retransmission request algorithm to obtain retransmission requests. This algorithm generates optimized retransmission requests based on the importance of the missing data and network conditions, for example, merging requests into "retransmit fragment number 37 and fragments 90-95". Based on the distributed data fragment set, the retransmission requests are prioritized to obtain a priority retransmission queue. Prioritization considers data importance, timeliness, and network resource utilization efficiency. For example, a single segment 37 might be given a higher priority and retransmitted immediately, while consecutive segments 90-95 might be scheduled for retransmission when network load is lighter. Then, a data merging algorithm processes the priority retransmission queue to obtain the merged complete data. The data merging algorithm inserts the retransmitted data segments into their correct positions, forming a complete dataset.

[0083] Finally, the merged complete data undergoes a consistency check to obtain the verified data. The consistency check ensures that all data segments are correctly assembled without duplication or omission. Subsequently, the verified data is processed using a data format normalization algorithm to obtain the target verification data. Data format normalization ensures that the final data conforms to a predefined format standard, facilitating subsequent processing and storage. For example, in a 100kW photovoltaic power plant, the MPPT optimizer needs to transmit a file containing 24 hours of power generation data, with a total size of 10MB. This file is divided into 1000 10KB data segments for transmission. At the receiving end, a consistency check finds problems with 50 data segments. Using FEC technology, 40 segments are successfully repaired, and the remaining 10 segments (sequence numbers 37, 145-150, 782, 968, and 969) are marked for retransmission. After sequence reassembly, it is found that 6 segments with sequence numbers 500-505 are still missing (possibly due to network failure and not received at all).

[0084] The data retransmission request algorithm generates two retransmission requests: one for individual segments 37, 782, 968, and 969, and the other for consecutive segments 145-150 and 500-505. Priority sorting places the retransmission request for individual segments at the front of the queue and requests for consecutive segments at the back. After retransmission, the data merging algorithm inserts these 16 retransmitted segments into the correct positions, forming a complete 10MB file. A final consistency check confirms that all 1000 segments have been correctly received and assembled, and data format normalization converts the file to standard CSV format for easier subsequent data analysis and storage.

[0085] In one specific embodiment, the process of executing step S106 may specifically include the following steps:

[0086] (1) Apply the optimal compression algorithm to compress the target verification data to obtain compressed data, and process the compressed data through the compression efficiency evaluation algorithm to obtain a compression quality report;

[0087] (2) The compression quality report is subjected to threshold judgment processing to obtain qualified compressed data, and the qualified compressed data is processed by the encryption strength evaluation algorithm to obtain encryption strategy suggestions;

[0088] (3) Apply encryption strategy to qualified compressed data to encrypt it, obtain encrypted data, and process the encrypted data through data classification algorithm to obtain a hierarchical storage scheme.

[0089] (4) Perform storage medium matching processing on the hierarchical storage scheme to obtain the storage allocation strategy, and process the storage allocation strategy through the data lifecycle management algorithm to obtain the data migration plan;

[0090] (5) The encrypted data is processed in layers according to the data migration plan to obtain distributed storage data, and the distributed storage data is processed by the data index generation algorithm to obtain the target communication data.

[0091] Specifically, the target verification data is compressed using the optimal compression algorithm to obtain compressed data. The selection of the optimal compression algorithm is based on the data type and characteristics; for example, differential coding combined with Huffman coding might be used for numerical data, while the LZW algorithm might be used for text data. During compression, the algorithm analyzes data patterns and removes redundant information, thereby reducing data volume. Subsequently, the compressed data is processed by a compression efficiency evaluation algorithm to obtain a compression quality report. The compression efficiency evaluation algorithm calculates metrics such as compression ratio, compression speed, and decompression speed to comprehensively evaluate the compression effect. The compression quality report includes the specific values ​​of these metrics and the comparison results with the preset target. Next, the compression quality report undergoes threshold judgment processing to obtain qualified compressed data. During the threshold judgment process, it is checked whether the compression ratio reaches the preset target (e.g., 50%) and whether the compression and decompression speeds are within acceptable ranges. Only compressed data that simultaneously meets these conditions is considered qualified.

[0092] Then, the qualified compressed data is processed using an encryption strength evaluation algorithm to obtain encryption strategy recommendations. The encryption strength evaluation algorithm analyzes the sensitivity and security requirements of the data, and, considering existing computing resources, recommends appropriate encryption algorithms and key lengths. For example, AES-256 encryption might be recommended for highly sensitive data, while AES-128 might be recommended for ordinary data. The encryption strategy recommendations are then applied to the qualified compressed data to obtain encrypted data. The encryption process strictly follows the recommended strategy to ensure data confidentiality. Subsequently, the encrypted data is processed using a data grading algorithm to obtain a tiered storage scheme. Based on factors such as data importance, access frequency, and timeliness, the data grading algorithm divides the data into different storage levels, such as hot data, warm data, and cold data.

[0093] The tiered storage scheme undergoes storage media matching to derive a storage allocation strategy. This matching process considers the characteristics of data at each level and the performance and cost of different storage media (such as cache, SSDs, HDDs, and cloud storage) to determine the optimal allocation strategy. Next, a data lifecycle management algorithm processes this strategy to generate a data migration plan. This algorithm predicts data usage patterns and value changes, developing a long-term data migration and archiving plan. Finally, encrypted data is tiered and stored according to the migration plan to obtain distributed storage data. This tiered storage process physically stores the data in designated locations and establishes necessary metadata records. Subsequently, a data indexing algorithm processes the distributed storage data to obtain the target communication data. This algorithm creates an efficient index structure, facilitating rapid data retrieval and access.

[0094] For example, in a 100MW photovoltaic power plant, the MPPT optimizer generates 100GB of raw data daily. This data includes records of parameters such as voltage, current, and temperature every 5 minutes. First, this 100GB of target verification data is compressed using the LZMA2 compression algorithm, resulting in 40GB of compressed data. Compression efficiency evaluation shows a compression ratio of 60%, a compression speed of 100MB / s, and a decompression speed of 200MB / s. These indicators all exceed preset thresholds (compression ratio > 50%, compression speed > 80MB / s, decompression speed > 150MB / s), therefore the compressed data is deemed acceptable. After encryption strength evaluation algorithm analysis, it is recommended to encrypt 10GB of highly sensitive data (such as detailed power generation efficiency data) using AES-256, and the remaining 30GB of ordinary data using AES-128. After encryption, the data grading algorithm divides the encrypted data into three levels: 5GB of hot data (data from the current day), 15GB of warm data (data from the past week), and 20GB of cold data (historical data).

[0095] Storage media matching results showed that hot data should be stored on solid-state drives (SSDs), warm data on high-capacity hard disk drives (HDDs), and cold data on cloud storage. A data lifecycle management algorithm devised a 30-day data migration plan: hot data was converted to warm data after 3 days, and warm data to cold data after 30 days. Following this plan, data was tiered and stored on the appropriate storage media. Finally, a data indexing algorithm created a B+ tree-based index structure for this distributed storage, ensuring that data at any point in time could be retrieved within milliseconds.

[0096] The PLC-based photovoltaic optimizer communication method in the embodiments of this application has been described above. The PLC-based photovoltaic optimizer communication system in the embodiments of this application is described below. Please refer to [link / reference]. Figure 2 One embodiment of the PLC-based photovoltaic optimizer communication system in this application includes:

[0097] Test module 201 is used to initialize the MPPT optimizer and perform frequency testing to obtain an initial set of communication frequencies;

[0098] Analysis module 202 is used to monitor and analyze real-time communication quality indicators and environmental parameters based on the initial set of communication frequencies, and obtain the real-time communication frequencies.

[0099] The allocation module 203 is used to classify, packetize and prioritize the data to be transmitted according to the real-time communication frequency to obtain a priority data packet sequence.

[0100] Planning module 204 is used to perform topology analysis and multi-path planning on the MPPT optimizer network based on priority data packet sequences to obtain a set of distributed data fragments;

[0101] The inspection module 205 is used to aggregate and perform integrity checks on the multi-path transmission data based on the distributed data fragment set to obtain target verification data;

[0102] Compression module 206 is used to compress, encrypt and store the target verification data in layers to obtain target communication data.

[0103] Through the collaborative efforts of the aforementioned components, and through initialization and frequency testing, a high-quality initial set of communication frequencies was precisely determined for the MPPT (Maximum Power Point Tracking) optimizer. This step not only considered the signal strength, bit error rate, and stability of the communication frequencies but also conducted a comprehensive evaluation using a multi-objective optimization algorithm, ensuring that the selected frequencies maintain efficient and stable communication in complex and ever-changing communication environments. By monitoring and analyzing real-time communication quality indicators and environmental parameters, the communication frequencies can be dynamically adjusted to the optimal state, further improving communication efficiency. This adaptive capability allows the photovoltaic optimizer network to flexibly adjust communication parameters according to current environmental conditions and communication needs, ensuring smooth and efficient communication. In terms of data transmission, a classification, packetization, and priority allocation strategy was adopted, achieving efficient data transmission and processing. By comprehensively evaluating the importance, timeliness, and size of data, communication resources can be rationally allocated, prioritizing the transmission of high-priority data, thereby improving the overall network response speed and throughput. This strategy not only improves data transmission efficiency but also ensures the timely transmission and processing of critical data, providing strong support for the intelligent management and operation and maintenance of photovoltaic systems. Furthermore, through topology analysis and multi-path planning, efficient transmission and aggregation of distributed data fragments were achieved, further improving communication efficiency. By optimizing the network topology and data transmission paths, data transmission latency and loss can be reduced, improving data transmission speed and accuracy. In terms of data storage, compression, encryption, and hierarchical storage methods were adopted, which not only improved data security but also reduced storage space usage and improved storage efficiency through compression technology. This multi-layered data processing strategy enables the photovoltaic optimizer network to better adapt to the needs of large-scale data transmission and storage, further enhancing the overall system's communication efficiency.

[0104] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A PLC-based photovoltaic optimizer communication method, characterized in that, The PLC-based photovoltaic optimizer communication method includes: The MPPT optimizer is initialized and frequency testing is performed to obtain the initial set of communication frequencies; Based on the initial set of communication frequencies, real-time communication quality indicators and environmental parameters are monitored and analyzed to obtain the real-time communication frequencies. Based on the real-time communication frequency, the data to be transmitted is classified, packetized, and prioritized to obtain a priority data packet sequence. Based on the priority data packet sequence, topology analysis and multi-path planning are performed on the MPPT optimizer network to obtain a set of distributed data fragments. Based on the distributed data fragment set, the multi-path transmission data is aggregated and integrity checked to obtain the target verification data. The target verification data is compressed, encrypted, and stored in layers to obtain target communication data.

2. The PLC-based photovoltaic optimizer communication method according to claim 1, characterized in that, The initialization and frequency testing of the MPPT optimizer yields an initial set of communication frequencies, including: The MPPT optimizer is activated by the PLC communication module to obtain an adjustable frequency PLC communication module. The frequency range of the adjustable frequency PLC communication module is set to obtain a preset frequency range. Communication tests are then performed on multiple frequency points within the preset frequency range to obtain a frequency point communication quality dataset. The frequency point communication quality dataset is cleaned to obtain effective communication quality data. The effective communication quality data is then processed by a signal strength analysis algorithm to obtain the frequency point signal strength ranking results. The frequency point signal strength ranking results are subjected to threshold filtering to obtain a candidate optimal frequency set. The candidate optimal frequency set is then processed by a bit error rate evaluation algorithm to obtain the frequency point with the lowest bit error rate. Stability testing was performed on the frequency points with the lowest bit error rate to obtain frequency stability data. The frequency stability data was then processed using a time series analysis algorithm to obtain a frequency stability score. The frequency stability scores are compared using a threshold to obtain a set of stable frequencies. The stable frequency set is then comprehensively evaluated using a multi-objective optimization algorithm to obtain the initial communication frequency set.

3. The PLC-based photovoltaic optimizer communication method according to claim 1, characterized in that, The process of monitoring and analyzing real-time communication quality indicators and environmental parameters based on the initial set of communication frequencies to obtain real-time communication frequencies includes: Noise filtering is performed on real-time signal strength data to obtain filtered signal strength data. Then, the filtered signal strength data is processed by a signal quality assessment algorithm to obtain a signal quality score. The temperature, humidity and electromagnetic interference data collected by environmental sensors are normalized to obtain standardized environmental parameters. The standardized environmental parameters are then processed by an environmental impact model to obtain environmental impact factors. The signal quality score and environmental impact factor are weighted and fused to obtain a comprehensive communication quality index. The comprehensive communication quality index is then processed by an adaptive frequency adjustment algorithm to obtain frequency adjustment suggestions. Based on the initial set of communication frequencies, a feasibility assessment is performed on the frequency adjustment suggestions to obtain an adjustable frequency range. The adjustable frequency range is then processed using a spectrum scanning algorithm to obtain a list of idle frequency bands. The list of idle frequency bands is sorted by priority to obtain a candidate frequency sequence, and the candidate frequency sequence is processed by a dynamic frequency selection algorithm to obtain the real-time communication frequency.

4. The PLC-based photovoltaic optimizer communication method according to claim 1, characterized in that, The step of classifying, packetizing, and prioritizing the data to be transmitted based on the real-time communication frequency to obtain a priority data packet sequence includes: The importance scores of the data are classified by threshold to obtain the initial priority labels, and the initial priority labels are processed by the timeliness analysis algorithm to obtain the timeliness adjustment coefficient. The initial priority mark and the timeliness adjustment coefficient are comprehensively calculated to obtain the priority mark, and the priority mark is processed by the dynamic packet splitting algorithm to obtain the data packet size suggestion; The proposed data packet size is verified to obtain the verified packet size. The verified packet size is then processed using a data segmentation algorithm to obtain the initial data packet set. Redundancy detection is performed on the initial data packet set to obtain a non-redundant data packet set. Then, the non-redundant data packet set is processed by the packet header optimization algorithm to obtain the optimized data packets. The optimized data packets are subjected to priority encoding to obtain data packets with priority information. Based on the real-time communication frequency, the data packets with priority information are processed by a queue scheduling algorithm to obtain the priority data packet sequence.

5. The PLC-based photovoltaic optimizer communication method according to claim 1, characterized in that, Based on the priority data packet sequence, the MPPT optimizer network is subjected to topology analysis and multi-path planning to obtain a distributed data fragment set, including: Based on the priority data packet sequence, the network topology diagram is processed to evaluate the importance of nodes, and the node weight distribution is obtained. The node weight distribution is then processed by a link quality evaluation algorithm to obtain a link reliability score. The link reliability score is subjected to threshold filtering to obtain a set of available links. The set of available links is then processed by a multi-path generation algorithm to obtain a set of candidate transmission paths. The candidate transmission path set is subjected to path redundancy analysis to obtain an optimized path set. The optimized path set is then processed by a load balancing algorithm to obtain a load distribution scheme. Feasibility simulation of the load allocation scheme is performed to obtain theoretical transmission efficiency data, and the theoretical transmission efficiency data is processed by the path scoring algorithm to obtain a path priority list. The path priority list is dynamically adjusted to obtain the transmission path, and the transmission path is processed by a data fragmentation algorithm to obtain the distributed data fragment set.

6. The PLC-based photovoltaic optimizer communication method according to claim 1, characterized in that, The step of aggregating and performing integrity checks on multi-path transmitted data based on the distributed data fragment set to obtain target verification data includes: Error marking is performed on the consistency check results to obtain a set of data fragments to be corrected. The data fragments to be corrected are then processed by a data repair algorithm to obtain the repaired data fragments. The repaired data fragments are subjected to sequence recombination to obtain preliminary aggregated data. The preliminary aggregated data is then processed by a data integrity verification algorithm to obtain integrity verification results. The missing parts of the integrity verification results are identified to obtain a list of data completion requirements. The list of data completion requirements is then processed by a data retransmission request algorithm to obtain retransmission requests. Based on the distributed data fragment set, the retransmission requests are prioritized to obtain a priority retransmission queue, and the priority retransmission queue is processed by a data merging algorithm to obtain the merged complete data. The merged complete data undergoes consistency verification to obtain verified data. The verified data is then processed using a data format normalization algorithm to obtain the target verification data.

7. The PLC-based photovoltaic optimizer communication method according to claim 6, characterized in that, The process of compressing, encrypting, and storing the target verification data in layers to obtain target communication data includes: The target verification data is compressed using the optimal compression algorithm to obtain compressed data. The compressed data is then processed using a compression efficiency evaluation algorithm to obtain a compression quality report. The compression quality report is subjected to threshold judgment to obtain qualified compressed data. The qualified compressed data is then processed by an encryption strength evaluation algorithm to obtain encryption strategy suggestions. It is recommended to apply encryption strategies to qualified compressed data to obtain encrypted data, and then process the encrypted data through a data grading algorithm to obtain a tiered storage scheme. The tiered storage scheme is matched with storage media to obtain a storage allocation strategy, and the storage allocation strategy is processed by a data lifecycle management algorithm to obtain a data migration plan. The encrypted data is processed into distributed storage data according to the data migration plan, and then the distributed storage data is processed by the data index generation algorithm to obtain the target communication data.

8. A PLC-based photovoltaic optimizer communication system, characterized in that, For executing the PLC-based photovoltaic optimizer communication method as described in any one of claims 1-7, the PLC-based photovoltaic optimizer communication system comprises: The test module is used to initialize the MPPT optimizer and perform frequency testing to obtain the initial set of communication frequencies. The analysis module is used to monitor and analyze real-time communication quality indicators and environmental parameters based on the initial set of communication frequencies, and to obtain the real-time communication frequencies. The allocation module is used to classify, packetize, and prioritize the data to be transmitted according to the real-time communication frequency to obtain a priority data packet sequence. The planning module is used to perform topology analysis and multi-path planning on the MPPT optimizer network based on the priority data packet sequence to obtain a set of distributed data fragments. The inspection module is used to aggregate and perform integrity checks on the multi-path transmission data based on the distributed data fragment set to obtain target verification data; The compression module is used to compress, encrypt, and perform layered storage processing on the target verification data to obtain target communication data.

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