Photovoltaic data processing method and device based on edge calculation

Abstract

The invention relates to the technical field of data processing, and discloses a photovoltaic data processing method and device based on edge calculation. The method comprises the following steps: performing component state feature extraction processing on a preprocessed data packet to obtain component state feature data; mPPT parameter calculation processing is conducted on the component state characteristic data, MPPT parameter data are obtained, and the MPPT parameter data comprise controller duty ratio data and switching frequency data; performing data priority grading processing on the preprocessed data packet and the component state feature data to obtain graded data streams, the graded data streams including a real-time data stream, a monitoring data stream and a statistical data stream; and data time scale classification processing is performed on the hierarchical data stream to obtain a classification data result, and the classification data result comprises second-level control data, hour-level abnormal data and day-level statistical data. According to the invention, the efficiency and accuracy of photovoltaic data processing based on edge calculation are improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular, to a photovoltaic data processing method and device based on edge computing. Background Art

[0002] With the expansion of the scale of photovoltaic power stations, it has become increasingly important to monitor and analyze the voltage, current and other data of each photovoltaic module in real time to improve the overall efficiency of the system. Most traditional photovoltaic data collection and analysis adopt a centralized processing method, which transmits data from each photovoltaic module to a central server for processing. The advantage of the centralized method is that it has strong data storage and processing capabilities and can perform large-scale data analysis. However, due to the wide distribution of photovoltaic modules, the centralized processing mode is prone to problems such as data transmission delay, excessive bandwidth occupation and untimely response of processing results. In addition, the operating environment of the photovoltaic system is relatively complex, including lighting conditions, grid fluctuations, etc., which will affect the output state of the modules, and timely adjustment and feedback are required to ensure that each module always operates in the optimal state.

[0003] Existing centralized processing systems have deficiencies such as untimely response, data transmission bottlenecks and low processing efficiency, and are difficult to meet the requirements of photovoltaic for real-time and high precision. Especially for control data that requires quick response and short-cycle monitoring data, due to the large processing delay of the centralized system, it cannot effectively cope with frequent grid fluctuations and component output changes. In addition, the centralized processing mode fails to reasonably allocate data priorities and time scales, resulting in all data being transmitted and processed together, increasing data redundancy and computational burden. To address these problems, a photovoltaic data processing method based on edge computing has gradually become an effective solution, using edge devices to perform preprocessing, screening, grading and classification near the data source to achieve the timeliness and effectiveness of data processing. Summary of the Invention

[0004] This application provides a photovoltaic data processing method and device based on edge computing, which are used to improve the efficiency and accuracy of photovoltaic data processing based on edge computing.

[0005] In a first aspect, this application provides a photovoltaic data processing method based on edge computing, and the photovoltaic data processing method based on edge computing includes:

[0006] Synchronously collect and process the voltage and current data of each component in the photovoltaic string to obtain an original voltage and current data set;

[0007] Perform edge data preprocessing on the original voltage and current data set to obtain a preprocessing data packet, and the preprocessing data packet includes time-aligned data, power data and voltage deviation data;

[0008] Perform component status feature extraction processing on the preprocessed data packet to obtain component status feature data, where the component status feature data includes component matching degree data, power deviation data, and abnormal type data;

[0009] Perform MPPT parameter calculation processing on the component status feature data to obtain MPPT parameter data, where the MPPT parameter data includes controller duty cycle data and switching frequency data;

[0010] Perform data priority classification processing on the preprocessed data packet and the component status feature data to obtain a classified data stream, where the classified data stream includes a real-time data stream, a monitoring data stream, and a statistical data stream;

[0011] Perform data time scale classification processing on the classified data stream to obtain a classified data result, where the classified data result includes second-level control data, hourly abnormal data, and daily statistical data.

[0012] In a second aspect, the present application provides a photovoltaic data processing device based on edge computing, where the photovoltaic data processing device based on edge computing includes:

[0013] An acquisition module, configured to perform synchronous acquisition processing on the voltage and current data of each component in the photovoltaic string to obtain an original voltage-current data set;

[0014] A processing module, configured to perform edge data preprocessing on the original voltage-current data set to obtain a preprocessed data packet, where the preprocessed data packet includes time alignment data, power data, and voltage deviation data;

[0015] An extraction module, configured to perform component status feature extraction processing on the preprocessed data packet to obtain component status feature data, where the component status feature data includes component matching degree data, power deviation data, and abnormal type data;

[0016] A calculation module, configured to perform MPPT parameter calculation processing on the component status feature data to obtain MPPT parameter data, where the MPPT parameter data includes controller duty cycle data and switching frequency data;

[0017] A classification module, configured to perform data priority classification processing on the preprocessed data packet and the component status feature data to obtain a classified data stream, where the classified data stream includes a real-time data stream, a monitoring data stream, and a statistical data stream;

[0018] A classification module, configured to perform data time scale classification processing on the classified data stream to obtain a classified data result, where the classified data result includes second-level control data, hourly abnormal data, and daily statistical data.

[0019] In the technical solution provided by this application, through the mutual cooperation of multiple technical features, the efficient acquisition, intelligent preprocessing, real-time analysis, and hierarchical transmission of photovoltaic module data are realized, effectively improving the data processing ability and response speed of the photovoltaic system. First, the solution synchronously collects and processes the voltage and current data of each component in the photovoltaic string to generate an original data set containing the detailed working status of each component. The synchronous acquisition ensures data consistency, enabling subsequent analysis to be carried out under the same time reference, thus accurately reflecting the real-time operating status of the components. Next, the solution uses edge computing to preprocess the original data set to generate a preprocessing data packet containing time-aligned data, power data, and voltage deviation data. This step not only reduces data redundancy but also improves data quality through methods such as time alignment and data complementation, laying a foundation for the subsequent state feature analysis. Based on the preprocessed data, the solution further extracts component state features to generate component matching degree data, power deviation data, and abnormal type data, refining the monitoring of the status of each photovoltaic component. Through the deviation calculation of power data and the analysis of power attenuation trends, the system can quickly identify the operating deviations and potential faults of the components, monitor the health status of the components in real time, and improve the operation and maintenance efficiency and fault handling ability of the system. In addition, based on the state feature data, the system can automatically calculate the MPPT parameters applicable to each component, including the controller duty cycle and switching frequency, ensuring that each component always operates at the maximum power point. This adaptive MPPT control realizes precise power output regulation and improves the overall power generation efficiency of the photovoltaic system.

[0020] To optimize data transmission, the solution performs data priority classification on preprocessed data packets and status feature data, dividing them into real-time data streams, monitoring data streams, and statistical data streams according to the real-time nature and importance of different data. Through data classification, the system can give priority to transmitting data with high real-time nature, ensuring rapid response to critical control data in case of emergencies such as power grid fluctuations; for statistical data and monitoring data, they are transmitted over a longer period, reducing the occupancy of communication bandwidth. This priority classification mechanism makes data transmission more flexible, ensuring efficient operation even in the case of a large amount of information. In further time-scale classification processing, the solution sorts the classified data streams according to time scales of seconds, hours, and days, generating second-level control data, hourly abnormal data, and daily statistical data. This classification processing not only optimizes data storage and transmission efficiency, but also facilitates hierarchical analysis of data at different time scales by the backend, enabling more accurate system state prediction and optimized decision-making. For example, second-level data is used for real-time control, hourly data is used for abnormal monitoring and component status analysis, while daily data is used for long-term performance statistics and trend prediction. Through this hierarchical and classified data processing method, the system can comprehensively grasp the real-time state and long-term operation trend of photovoltaic modules, enhancing the overall data management ability and operation and maintenance intelligence level of the photovoltaic power station. It not only significantly improves the system performance in terms of the real-time nature of data acquisition and processing, but also optimizes data transmission and storage requirements through intelligent classification, greatly reducing the computing burden on the data center. Compared with traditional centralized processing methods, this solution has more advantages in terms of response speed, resource utilization, and data priority management, providing strong support for the efficient operation of photovoltaics in a complex power grid environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a schematic diagram of an embodiment of the photovoltaic data processing method based on edge computing in the embodiments of the present application;

[0023] Figure 2 It is a schematic diagram of an embodiment of the photovoltaic data processing device based on edge computing in the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The embodiments of the present application provide a photovoltaic data processing method and device based on edge computing. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "including" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the photovoltaic data processing method based on edge computing in the embodiments of the present application includes:

[0026] Step S101: Synchronously collect and process the voltage and current data of each component in the photovoltaic string to obtain an original voltage and current data set;

[0027] Step S102: Perform edge data preprocessing on the original voltage and current data set to obtain a preprocessed data packet. The preprocessed data packet includes time-aligned data, power data, and voltage deviation data;

[0028] Step S103: Perform component status feature extraction processing on the preprocessed data packet to obtain component status feature data. The component status feature data includes component matching degree data, power deviation data, and abnormal type data;

[0029] Step S104: Perform MPPT parameter calculation processing on the component status feature data to obtain MPPT parameter data. The MPPT parameter data includes controller duty cycle data and switching frequency data;

[0030] Step S105: Perform data priority grading processing on the preprocessed data packet and the component status feature data to obtain a graded data stream. The graded data stream includes a real-time data stream, a monitoring data stream, and a statistical data stream;

[0031] Step S106: Perform data time scale classification processing on the graded data stream to obtain a classified data result. The classified data result includes second-level control data, hourly abnormal data, and daily statistical data.

[0032] It can be understood that the execution entity of this application can be a photovoltaic data processing device based on edge computing, or it can also be a terminal or a server, and specific details are not limited here. In this embodiment of the application, the server is taken as an example of the execution entity for illustration.

[0033] Specifically, first, the voltage and current data of each component in the photovoltaic string are synchronously collected to generate an accurate original voltage-current data set. The core of synchronous collection lies in ensuring that the data of all components are recorded based on the same time reference, and this data consistency is the basis for the accuracy of subsequent analysis and calculation. In actual operation, by setting synchronous broadcast instructions to trigger the collection modules of all components, high-precision sampling of the voltage and current signals of each component is achieved. Then, the time-series power data of each component are gradually transmitted to the edge device through data transmission packets to form an original voltage-current data set, which provides a full and accurate data input for subsequent data analysis. After obtaining the original data set, the system enters the edge data preprocessing stage to generate a preprocessing data packet containing time-aligned data, power data, and voltage deviation data. Time alignment is the primary step in data preprocessing. By sorting and windowing the timestamps of each data, it is ensured that each record in the data set has accurate time synchronization. In the data integrity check, the linear interpolation algorithm is used to fill in any data missing points caused by acquisition delays or interference, and the completed data can truly reflect the operating state of the photovoltaic components. Subsequently, the power data is obtained by performing a product operation on the voltage and current data, and the power conversion efficiency is further calculated to evaluate the energy utilization of each component. For the voltage data, the voltage deviation data is generated by calculating the difference between the voltage of each component and the average voltage of the string. The voltage deviation data reflects the voltage consistency between each component and the overall string, ensuring that the system can promptly identify and correct components with large voltage deviations. Finally, all processed data are organized into a preprocessing data packet with a unified format, providing high-quality data input for subsequent state feature extraction.

[0034] After the data preprocessing is completed, the system extracts the component status features based on the preprocessed data packets, generating component status feature data that includes component matching degree data, power deviation data, and abnormal type data. The calculation of the component matching degree is performed by comparing the cumulative value of the voltage deviation data with a threshold, reflecting the matching status of the photovoltaic components in the string. Components with a lower matching degree usually need to be debugged or replaced. In addition, the power deviation data is generated by calculating the ratio of the actual power to the theoretical power, which indicates the actual output deviation degree of the component within the theoretical power range, ensuring that the output power of the component is always within a reasonable range. By analyzing the change trend of the power deviation data through the moving average algorithm, the power attenuation trend can be obtained, and the component status can be classified in combination with the fluctuation characteristics to identify and classify the abnormal types of the components. The data of the abnormal types is further refined through feature classification and abnormal level division, enabling the system to implement hierarchical management of the faulty components and arrange corresponding maintenance measures. Next, based on the component status feature data, the system calculates the MPPT parameters to obtain accurate data of the controller duty cycle and switching frequency, thereby achieving efficient tracking of the maximum power point of the photovoltaic components. The core of the MPPT parameter calculation is to determine the current working mode of the component according to its status features and generate the target voltage value based on the maximum power point positioning of the power curve. The target voltage value controls the output voltage of the component through duty cycle calculation, enabling it to reach the maximum power output state. At the same time, by adaptively adjusting the step size of the controller duty cycle, the system can maintain real-time tracking of the maximum power point when the output environment changes, and then obtain the adjustment step size data and the corrected duty cycle data. The corrected duty cycle data is further converted into the switching frequency to generate the final MPPT control parameters, ensuring optimal power output under changing environmental conditions.

[0035] To improve data transmission efficiency, the preprocessed data packets and component status feature data are processed according to data priority levels, generating three hierarchical data streams: real-time data stream, monitoring data stream, and statistical data stream. By analyzing the real-time requirements of MPPT parameter data, the system assigns different priorities to each data stream to ensure the priority transmission of critical control data. At the same time, the preprocessed data is classified according to the importance index of the data, achieving effective transmission under the condition of limited data transmission bandwidth. The real-time data stream mainly covers high-priority control data to ensure rapid response in the case of grid fluctuations; the monitoring data stream transmits status feature information at a lower transmission frequency to ensure the timeliness of operation and maintenance monitoring; the statistical data stream is mainly used for long-term operation statistics for trend analysis and performance evaluation at the back end. Finally, to achieve effective classification of data on different time scales, the hierarchical data streams are sorted according to second-level, hourly-level, and daily-level time scales, generating second-level control data, hourly-level abnormal data, and daily-level statistical data. The second-level data block is specifically used for real-time monitoring of control parameters such as voltage and current to ensure immediate response in the event of grid fluctuations; the hourly-level data block is used to monitor the abnormal conditions of components to promptly detect power or voltage deviations and guide operation and maintenance operations; the daily-level data block covers the operation data over a long period, providing a historical perspective of the overall system state, enabling managers to optimize system design through trend analysis.

[0036] For example, during a daily operation, the system synchronously collects voltage and current data from photovoltaic components and completes preprocessing through edge devices to obtain the power data and voltage deviation data of each component. The system detects that the power deviation of a certain component is gradually increasing and identifies the abnormal type of this component as slight power attenuation through state feature extraction. In the MPPT parameter calculation, the system sets a slightly increased duty cycle for this component to increase the output power. At the same time, the data of this component is marked as the monitoring data stream, and the hourly-level abnormal data shows that there is a certain power fluctuation in this component. The back-end operation and maintenance team thus learns about the potential failure risk of this component and arranges maintenance operations in advance. This data processing method not only achieves precise control of the photovoltaic system but also improves the overall response speed and maintenance efficiency of the system.

[0037] In the embodiments of the present application, through the mutual cooperation of multiple technical features, the efficient acquisition, intelligent preprocessing, real-time analysis and hierarchical transmission of photovoltaic module data are realized, effectively improving the data processing ability and response speed of the photovoltaic system. First, the solution synchronously collects and processes the voltage and current data of each component in the photovoltaic string to generate an original data set containing the detailed working status of each component. The synchronous acquisition ensures the consistency of the data, enabling subsequent analysis to be carried out on the same time basis, thus accurately reflecting the real-time operating status of the components. Next, the solution uses edge computing to preprocess the original data set to generate a preprocessed data packet containing time-aligned data, power data and voltage deviation data. This step not only reduces data redundancy, but also improves data quality through methods such as time alignment and data complementation, laying a foundation for the subsequent state feature analysis. Based on the preprocessed data, the solution further extracts the component state features to generate component matching degree data, power deviation data and abnormal type data, refining the monitoring of the status of each photovoltaic component. Through the deviation calculation of power data and the analysis of power attenuation trend, the system can quickly identify the operating deviation and potential faults of the components, monitor the health status of the components in real time, and improve the operation and maintenance efficiency and fault handling ability of the system. In addition, based on the state feature data, the system can automatically calculate the MPPT parameters applicable to each component, including the controller duty cycle and switching frequency, ensuring that each component always operates at the maximum power point. This adaptive MPPT control realizes precise power output regulation and improves the overall power generation efficiency of the photovoltaic system.

[0038] To achieve optimized data transmission, the solution performs data priority classification on preprocessed data packets and status feature data, dividing them into real-time data streams, monitoring data streams, and statistical data streams according to the real-time nature and importance of different data. Through data classification, the system can give priority to transmitting data with high real-time nature, ensuring rapid response to critical control data in the event of emergencies such as power grid fluctuations; for statistical data and monitoring data, they are transmitted over a longer period, reducing the occupancy of communication bandwidth. This priority classification mechanism makes data transmission more flexible, ensuring efficient operation even in the case of large amounts of information. In further time-scale classification processing, the solution sorts the classified data streams according to time scales of seconds, hours, and days, generating second-level control data, hourly abnormal data, and daily statistical data. This classification processing not only optimizes data storage and transmission efficiency but also facilitates hierarchical analysis of data at different time scales by the backend, enabling more accurate system state prediction and optimized decision-making. For example, second-level data is used for real-time control, hourly data is used for abnormal monitoring and component status analysis, and daily data is used for long-term performance statistics and trend prediction. Through this hierarchical and classified data processing method, the system can comprehensively grasp the real-time status and long-term operation trend of photovoltaic modules, enhancing the overall data management ability and operation and maintenance intelligence level of the photovoltaic power station. It not only significantly improves the system performance in terms of the real-time nature of data acquisition and processing but also optimizes data transmission and storage requirements through intelligent classification and hierarchical processing, greatly reducing the computational burden on the data center. Compared with traditional centralized processing methods, this solution has more advantages in terms of response speed, resource utilization, and data priority management, providing strong support for the efficient operation of photovoltaics in complex power grid environments.

[0039] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0040] (1) Perform power supply parameter setting processing on the POWERBUS bus of each acquisition module in the photovoltaic string to obtain bus power supply parameters, and perform electrical isolation processing on the bus power supply parameters to obtain an isolated power supply signal;

[0041] (2) Perform bus communication protocol configuration processing on the isolated power supply signal to obtain bus communication protocol parameters, and perform master-slave station address allocation processing on the bus communication protocol parameters to obtain site address data;

[0042] (3) Perform synchronous acquisition instruction generation processing on the site address data to obtain a synchronous broadcast instruction, and perform data distribution processing on the synchronous broadcast instruction through the POWERBUS bus to obtain an acquisition trigger signal;

[0043] (4) Sample and process the voltage signals of each component through a high-precision analog-to-digital converter to obtain voltage sampling data, and sample and process the current signals of each component through a shunt monitor to obtain current sampling data;

[0044] (5) Perform data splicing processing on the voltage sampling data and current sampling data to obtain component power data, and perform timestamp marking processing on the component power data to obtain time-series power data;

[0045] (6) Perform cascade data packing processing on the time-series power data to obtain a data transmission packet, and perform step-by-step transfer processing on the data transmission packet through a handshake mechanism to obtain an original voltage and current data set.

[0046] Specifically, through the gradual configuration and processing of the POWERBUS bus and the acquisition module, the system can accurately and stably collect and transmit the voltage and current data of photovoltaic components, forming a complete original data set. First, set the power supply parameters of the POWERBUS bus of each acquisition module in the photovoltaic string to ensure that the bus operates within a stable and appropriate voltage and current range. The setting of power supply parameters not only provides sufficient power support for each acquisition module but also avoids unstable data acquisition caused by overload or underload. To enhance electrical safety, perform electrical isolation processing on the bus power supply parameters to form an isolated power supply signal. The isolated power supply signal effectively prevents electrical interference and improves the anti-interference ability in a complex electromagnetic environment, thus ensuring the stability of data transmission. After the isolation processing is completed, the system further configures the bus communication protocol for the isolated power supply signal to generate bus communication protocol parameters. The configuration process of the communication protocol includes the setting of details such as data transmission format, transmission rate, and data verification to ensure the accuracy and consistency of subsequent data acquisition. Based on the setting of the bus communication protocol parameters, the system performs master-slave station address allocation to generate site address data. The site address data is the unique identifier of the photovoltaic component in the acquisition network, facilitating the system to accurately manage each acquisition module and realizing data acquisition and status monitoring through the interaction between the master station and each slave station.

[0047] Next, the system generates a synchronous acquisition instruction based on the site address data, and sends the synchronous broadcast instruction to all acquisition modules through the POWERBUS bus, thereby generating an acquisition trigger signal. The role of the synchronous broadcast instruction is to ensure that all acquisition modules start data acquisition at the same time, avoiding the problem of data time asynchrony caused by the difference in startup time of different modules. In this way, the system achieves high-precision synchronous acquisition, ensuring that the voltage and current data of each component are collected under the same time reference, which is convenient for subsequent power calculation and status monitoring. On the basis of synchronous acquisition, the system uses a high-precision analog-to-digital converter to sample the voltage signal of each component and generate voltage sampling data. At the same time, the current signal of each component is sampled through the shunt monitor to form current sampling data. The analog-to-digital converter ensures the acquisition accuracy of the voltage signal, enabling the system to record subtle voltage fluctuations; the shunt monitor provides a reliable data source for current sampling, ensuring the accuracy of current data. These high-precision sampling devices together provide high-quality data input for the system.

[0048] After obtaining the voltage and current sampling data, these two types of data are spliced to generate the power data of each component. The power data is calculated by multiplying the voltage and current, and it is a key indicator for evaluating the working status of photovoltaic components. In order to ensure the time consistency of the power data, a timestamp is added to each set of power data to generate time-series power data. The timestamp mark is a key step in achieving time alignment. Through this mark, the system can accurately track the acquisition time of each data point, so as to perform synchronous trend comparison and fault detection in subsequent analysis. Finally, the time-series power data is packaged in cascade to generate a data transmission package. Data packaging is to optimize the data transmission efficiency and reduce the risk of data delay and loss during the transmission process. The packaged data transmission package is transmitted step by step through the handshake mechanism to ensure the complete transmission of each data packet. The handshake mechanism is a two-way confirmation transmission method. Through the confirmation signal of the data sender and receiver, the system can timely know the data transmission status and retransmit it when necessary to ensure the integrity and accuracy of the data. After completing the data transmission, the system finally forms an original voltage and current data set containing the voltage and current information of each component, which provides detailed basic data for subsequent status analysis and fault detection.

[0049] For example, in a data acquisition task, the system first sets the POWERBUS bus power supply parameter to a stable 24V voltage and completes the isolation process to ensure safety. After configuring the bus communication protocol parameters, the site addresses are sequentially assigned to 10 photovoltaic module acquisition modules. Under the action of the synchronous broadcast instruction issued by the system, all acquisition modules start collecting at the same moment. Each module uses an analog-to-digital converter and a shunt monitor to obtain real-time data of 30V voltage and 8A current respectively, and calculates the component power data of 240W through data splicing. Subsequently, a timestamp is marked on this power data and the data is packed, and the data transmission is completed through a handshake mechanism, and finally a complete original data set containing 10 components is generated. This process ensures the real-time monitoring of the photovoltaic system and the integrity and accuracy of the data, supporting subsequent performance evaluation and system optimization.

[0050] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0051] (1) Perform data sorting processing on the timestamp information in the original voltage and current data set to obtain time series data, and perform time window partitioning processing on the time series data to obtain time window data;

[0052] (2) Perform data integrity check processing on the time window data to obtain data missing position information, and perform data filling processing on the data missing position information through a linear interpolation algorithm to obtain filled data;

[0053] (3) Perform threshold range detection processing on the filled data to obtain abnormal data marks, and perform data filtering processing on the abnormal data marks to obtain time-aligned data;

[0054] (4) Perform product operation processing on the voltage and current data in the time-aligned data to obtain power data, and perform power conversion efficiency calculation processing on the power data to obtain efficiency data;

[0055] (5) Perform mean calculation processing on the voltage data of each component in the same string to obtain the string voltage mean value, and perform difference calculation processing on the voltage of each component and the string voltage mean value to obtain voltage deviation data;

[0056] (6) Perform data formatting processing on the time-aligned data, power data and voltage deviation data to obtain a preprocessed data packet.

[0057] Specifically, multi-step preprocessing is performed on the original voltage and current dataset to generate high-quality preprocessed data packets. First, the timestamp information in the original voltage and current dataset is sorted to form time series data. This sorting process ensures that all data points are arranged in chronological order, facilitating subsequent trend analysis and real-time monitoring. After the time sorting is completed, the time series data is divided into time windows, and the data is segmented according to the set time period to form time window data. The division of time windows facilitates data analysis on different time scales, especially suitable for processing voltage and current data collected at high frequencies.

[0058] Based on the time window data, the system performs data integrity checks to identify any potentially missing data points, thereby obtaining data missing location information. In a photovoltaic system, data loss may occur due to signal interference, transmission delays, etc. Accurately marking the data missing location information is crucial for data preprocessing. To fill in the missing data, the system uses a linear interpolation algorithm to estimate the missing points. Linear interpolation is an algorithm that estimates the missing data in the middle based on adjacent data points, which can quickly and reliably fill the data gaps and form a continuous and complete time series data. This data filling ensures the accuracy of subsequent calculations and avoids abnormal deviations caused by data loss. After generating the filled data, threshold range detection is performed on it to identify and mark any abnormal data points. Threshold range detection is to compare the data with the preset normal operating range, and if the data exceeds the reasonable range, it is regarded as abnormal. For the marked abnormal data, the system will filter it out to avoid the impact of abnormal data on the overall analysis results. The data after the filtering process forms time-aligned data, ensuring that all data points within the same time window meet the working standards, facilitating subsequent power calculation and efficiency analysis.

[0059] Next, perform a multiplication operation on the voltage and current data in the time-aligned data to obtain power data. The power data is a direct reflection of the operating state of the photovoltaic module. By multiplying the voltage and current, the system can calculate the output power of each module in real time. To further evaluate the energy conversion effect of the system, calculate the power conversion efficiency of the power data. The power conversion efficiency is calculated by taking the ratio of the actual output power to the theoretical maximum power, which reflects the energy utilization rate of the module. This efficiency data helps to monitor whether the module is operating within the optimal efficiency range and to identify inefficient modules in a timely manner. Based on the power data, calculate the mean value of the voltage data of each module in the same string to obtain the string voltage mean value. The string voltage mean value represents the overall voltage level of the current string. To evaluate the consistency of each module in the string, the system calculates the difference between the voltage of each module and the string voltage mean value to form voltage deviation data. The voltage deviation data can help identify the modules that deviate from the string average level. Usually, the modules with large voltage deviations may have abnormal output or reduced operating efficiency.

[0060] Finally, format the time-aligned data, power data, and voltage deviation data to generate a preprocessed data packet. Data formatting is to organize different types of data uniformly so that they have a consistent structure and format, which is convenient for subsequent storage, transmission, and analysis. The preprocessed data packet, as a complete data set, can be directly used for subsequent state feature extraction, MPPT parameter calculation, and other real-time monitoring and analysis tasks. For example, in a photovoltaic data processing task, the system first sorts the original voltage and current data set according to the timestamp to obtain continuous time series data. Then, divide these data into 5-minute time windows and check the data integrity. It is found that 2 voltage data points are missing in a certain time window. The system uses a linear interpolation algorithm to complete the missing data points to generate complete complemented data. Subsequently, perform a threshold range detection on the complemented data. It is found that a certain current data point exceeds the normal operating range, mark and filter it to form time-aligned data. The system calculates the power data and efficiency data of the time-aligned data, and further calculates the string voltage mean value, and then obtains the voltage deviation data of each module. Finally, organize the time-aligned data, power data, and voltage deviation data into a preprocessed data packet with a unified format for subsequent component state analysis and real-time monitoring. This data processing flow ensures the high precision and real-time performance of the photovoltaic system data, providing reliable data support for the stable operation of the system.

[0061] In a specific embodiment, the process of performing step S103 may specifically include the following steps:

[0062] (1) Perform a time-series cumulative process on the voltage deviation data to obtain a voltage deviation cumulative value, and perform a threshold comparison process on the voltage deviation cumulative value to obtain component matching degree data;

[0063] (2) Perform theoretical power calculation and processing on the power data to obtain the theoretical power value, and perform ratio calculation and processing on the power data and the theoretical power value to obtain the power deviation data;

[0064] (3) Perform trend analysis and processing on the power deviation data through the moving average algorithm to obtain the power attenuation trend, and perform fluctuation feature extraction and processing on the power attenuation trend to obtain the fluctuation feature data;

[0065] (4) Perform feature classification and processing on the fluctuation feature data to obtain the abnormal type data, and perform abnormal level classification and processing on the abnormal type data to obtain the abnormal level index;

[0066] (5) Perform feature fusion and processing on the component matching degree data, the power deviation data, and the abnormal type data to obtain the component state feature data.

[0067] Specifically, in-depth analysis is performed on multi-dimensional data such as voltage deviation, power deviation, and trend characteristics to generate component state feature data. First, perform time-series cumulative processing on the voltage deviation data to obtain the cumulative value of the voltage deviation. Time-series cumulative processing is to accumulate or integrate the voltage deviation data over a period of time to observe the long-term change trend of the voltage deviation. The cumulative value of the voltage deviation reflects the consistency of the component voltage in the string. By comparing this value with a preset threshold, the system can determine the component matching degree data. The matching degree data quantifies the matching degree of the component with other components in the string in terms of voltage output. Components with low matching degrees may have operating state deviations and need further inspection. After the voltage deviation analysis is completed, theoretical power calculation is performed on the power data to obtain the theoretical power value. The theoretical power value is a reference value calculated based on the ideal output conditions of the component (such as standard solar radiation intensity, component temperature, etc.). By performing ratio calculation on the actual power data and the theoretical power value, the power deviation data can be obtained. The power deviation data reflects the deviation degree between the actual output and the ideal output and is an important indicator for evaluating the performance of the component. A large power deviation may indicate a decrease in component efficiency or problems such as shading and aging.

[0068] To further understand the changing trend of power deviation, the system conducts trend analysis on power deviation data through the moving average algorithm. Moving average is a commonly used time series smoothing method that calculates the average value of data within a certain time window to eliminate short-term fluctuations and thus reveals the long-term changing trend of the data. In the trend analysis of power deviation data, the system can observe the power decay trend to understand whether the component performance shows a downward trend. The obviousness of the power decay trend can help the system identify component aging or light attenuation in advance. After obtaining the power decay trend, the system further extracts its fluctuation characteristics to identify abnormal fluctuations in the trend. The fluctuation characteristic data is used to identify abnormal fluctuation patterns of power output, such as periodic fluctuations, sudden fluctuations, etc., and these characteristics may be associated with environmental factors or equipment failures. Then, the fluctuation characteristic data is subjected to feature classification processing to classify different fluctuation patterns into specific abnormal types. The abnormal type data can help the system quickly identify the cause of the fluctuation through feature classification. For example, power drop may be related to occlusion, while periodic fluctuations may be related to grid interference or environmental factors. For different abnormal types, the system will divide the abnormal levels according to the degree of influence to generate abnormal level indicators. The abnormal level indicators distinguish abnormal situations into levels such as minor, moderate, and severe, so as to conduct hierarchical management of faults to be prioritized during operation and maintenance.

[0069] After the above multi-level data analysis, feature fusion is performed on the component matching degree data, power deviation data, and abnormal type data to generate component status feature data. Feature fusion is to integrate multiple analysis results into a comprehensive data representation, enabling the system to comprehensively grasp the status of photovoltaic components. The component status feature data can accurately describe the voltage, power output consistency, working deviation, and abnormal level of the components, providing reliable data support for the operation and maintenance and performance optimization of photovoltaic power plants.

[0070] For example, in actual operation, the time series accumulation of the voltage deviation data of a certain component is carried out, and the accumulated value exceeds the threshold, indicating that the matching degree of this component is low and there may be abnormal voltage output. At the same time, the system calculates that the theoretical power value of this component is 200W, while the actual power data is 180W, and the power deviation data is 0.9 (i.e., an output rate of 90%). Further observation through the moving average shows that the power deviation data of this component shows a downward trend, and the extracted fluctuation characteristics show periodic fluctuations. It is initially judged that the reason may be affected by daytime occlusion. The abnormal type of this component is marked as "affected by occlusion" and rated as a "moderate" abnormal level. Finally, the low matching degree, 90% power output rate, and occlusion abnormal level of this component are subjected to feature fusion to generate component status feature data. This component status feature data provides a comprehensive description of the operating conditions of this component, facilitating subsequent maintenance and performance optimization operations.

[0071] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0072] (1) Classify the component status characteristic data according to the working status to obtain status label data, and determine the working mode of the component according to the status label data to obtain working mode parameters;

[0073] (2) Extract the power curve characteristics of the power deviation data to obtain power curve data, and locate the maximum power point of the power curve data to obtain target voltage data;

[0074] (3) Calculate the duty cycle of the controller based on the target voltage data to obtain controller duty cycle data, and adaptively adjust the step size of the controller duty cycle data to obtain adjusted step size data;

[0075] (4) Correct the controller duty cycle data according to the abnormal type data to obtain corrected duty cycle data, and calculate the switching frequency of the corrected duty cycle data to obtain switching frequency data;

[0076] (5) Combine the controller duty cycle data and the switching frequency data to obtain MPPT parameter data.

[0077] Specifically, in the edge computing data processing flow of the photovoltaic system, the component status characteristic data undergoes multiple layers of analysis and processing to generate accurate MPPT (maximum power point tracking) parameters, thereby ensuring that the photovoltaic components maintain the highest power output under different environmental conditions. First, classify the component status characteristic data according to the working status to generate status label data. The working status classification is based on indicators such as the voltage, power deviation, and abnormal conditions of the component, and the component is classified into status labels such as "normal operation", "low-efficiency output", and "abnormal fluctuation". According to the status label data, the system determines the working mode of each component and generates corresponding working mode parameters. The working mode parameters reflect the operation strategy of the component in a specific state. For example, a low-efficiency output component can enter the power optimization mode, while an abnormally fluctuating component can enter the protection mode to ensure safe operation under adverse conditions. After clarifying the working status and mode of the component, extract the power curve characteristics of the power deviation data to obtain the power curve data of the component under the actual output conditions. The power curve data is the performance of the component output power relative to the input voltage change, and the best point of power output can be identified by analyzing the curve trend. The system further locates the maximum power point of the power curve data to obtain the target voltage data. The maximum power point is the voltage value at which the component achieves the highest power output under a specific environment, and determining this point is crucial for maintaining the efficient operation of the component.

[0078] After obtaining the target voltage data, the duty cycle is calculated for it to generate the duty cycle data of the controller. The duty cycle refers to the proportion of time when the signal is high within a cycle. By adjusting the duty cycle, precise control of the output voltage can be achieved, keeping the component near the target voltage. However, since environmental conditions may change over time, a fixed setting of the duty cycle may lead to power fluctuations. Therefore, the duty cycle data is adaptively adjusted in steps to generate the adjustment step data. Adaptive adjustment is a process of automatically adjusting the amplitude of the duty cycle change based on the current output state. The step size is larger when the component is far from the maximum power point to accelerate the adjustment, and the step size decreases when approaching the maximum power point to ensure more precise control. In addition, the system corrects the controller duty cycle data according to the abnormal type data. The abnormal type data marks the abnormal conditions during the operation of the component, such as shadow occlusion, overheating, etc. These abnormal factors will affect the actual output power of the component. Therefore, according to different abnormal types, the duty cycle is adaptively corrected to compensate for the output deviation caused by abnormal conditions. The corrected duty cycle data is further used for the calculation of the switching frequency. The switching frequency determines the response speed of the controller to the voltage. A high switching frequency can quickly adjust the voltage to cope with the fluctuations of the power grid and the environment, ensuring that the component can approach the maximum power output in real time.

[0079] Finally, the controller duty cycle data and the switching frequency data are combined in parameters to generate the complete MPPT parameter data. The MPPT parameter data contains the optimal setting values of the duty cycle and the switching frequency, guiding the photovoltaic component to always operate near the maximum power point in a dynamic environment. These parameter combinations can provide efficient power conversion and improve the overall power generation efficiency of the photovoltaic system. For example, in actual operation, the system detects that the status label of a certain component is "low-efficiency output" and enters the power optimization mode. Through power curve feature extraction, the system locates the target voltage corresponding to the maximum power point of this component as 32V. The system calculates the duty cycle to be 60% based on this target voltage and sets the initial step size to 5%. However, due to slight occlusion of the component, the system corrects the duty cycle according to the abnormal type data, increasing it to 62%, and adjusts the switching frequency to 500Hz to quickly respond to voltage changes. Finally, these parameter combinations are the MPPT parameter data, which successfully keeps the component near the maximum power output state in actual operation, ensuring the power generation efficiency. This process effectively realizes power optimization and provides real-time control support for the component.

[0080] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0081] (1) Analyze and process the real-time requirements of the MPPT parameter data to obtain the data timeliness index, and perform priority classification processing on the data timeliness index to obtain the priority mark;

[0082] (2) Evaluate the importance of the preprocessed data packets to obtain importance metrics, and classify the data according to the importance metrics to obtain data level labels;

[0083] (3) Generate real-time data streams for the data with priority markings, and generate monitoring data streams for the data with data level labels;

[0084] (4) Generate statistical data streams for the remaining data, and merge the real-time data streams, monitoring data streams, and statistical data streams to obtain hierarchical data streams.

[0085] Specifically, analyze the real-time requirements of MPPT parameter data to obtain timeliness metrics for the data. MPPT parameter data are key control data in the maximum power point tracking process, and their real-time nature directly affects whether the photovoltaic modules can maintain the optimal working state. Therefore, analyzing the timeliness metrics for these data ensures that high-priority data can be processed in a timely manner to achieve fast response. By grading the timeliness metrics for priority, the system adds priority markings to different MPPT data to ensure that data with high real-time requirements are given priority during transmission and processing. Next, evaluate the importance of the preprocessed data packets to generate importance metrics for each data. The preprocessed data packets contain various basic operation data such as voltage, current, and power. The importance of different data has different impacts on system status monitoring and fault detection. By evaluating the importance of the data, the system can identify the most valuable data at different times and operating environments. For example, voltage deviation and power attenuation trends have high importance in daily monitoring because they can reflect the health status and efficiency of the modules. Based on the importance metrics, classify the data to generate data level labels for subsequent hierarchical processing.

[0086] After determining the data priority and importance levels, the system further processes the data with priority tags to generate real-time data streams. The real-time data streams cover data with high timeliness, ensuring that this data can be immediately transmitted and processed, and are applicable to control tasks that require quick responses. In addition, the data with data level tags is used to generate monitoring data streams. The monitoring data streams contain monitoring data with relatively lower but still important priorities, which are used for system status monitoring and regular detection, such as power and voltage data for periodic analysis, and these data play a key role in the system's health management and trend analysis. For the remaining data with lower importance or less stringent timeliness requirements, the system generates statistical data streams. The statistical data streams mainly include data accumulated over a long period, such as the total daily power generation and historical data on component efficiency, etc., and these data do not require immediate processing and can be batch-analyzed at the backend. The generation of statistical data streams can greatly reduce the transmission load and ensure that high-priority data is always guaranteed in the case of limited bandwidth resources.

[0087] Finally, the real-time data streams, monitoring data streams, and statistical data streams are merged to form hierarchical data streams. The hierarchical data streams integrate all types of data and are dynamically allocated according to priority and importance. In this way, high-priority data can be quickly transmitted to the control system for real-time regulation, medium-priority data supports regular monitoring and status assessment, and low-priority data is reserved for long-term trend analysis.

[0088] For example, in the daily operation of a photovoltaic system, a timeliness analysis is performed on the real-time collected MPPT parameter data, and it is determined that the data has a high priority and is marked as "real-time". At the same time, an importance assessment is carried out on the preprocessed data packets, and it is found that the importance of the power deviation data is "high" and it is marked as "monitoring data". The remaining current, voltage, etc. data are evaluated as having lower importance and are marked as "statistical data". The system generates real-time data streams, monitoring data streams, and statistical data streams based on these priority and importance tags. The MPPT parameter data in the real-time data stream is immediately transmitted to the control port for dynamic adjustment; the power deviation data in the monitoring data stream is used for system status assessment and is regularly fed back to the operation and maintenance platform; the statistical data stream is stored at the backend for long-term data analysis and performance prediction. Through this hierarchical data stream processing, while ensuring the timeliness of high-priority data, the data transmission burden is significantly reduced, and the operation and maintenance efficiency of the photovoltaic system is optimized.

[0089] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0090] (1) Perform second-level time window processing on the real-time data stream to obtain second-level data blocks, and perform control data extraction processing on the second-level data blocks to obtain second-level control data;

[0091] (2) Perform hourly time window processing on the monitored data stream to obtain hourly data blocks, and perform abnormal data extraction processing on the hourly data blocks to obtain hourly abnormal data;

[0092] (3) Perform daily time window processing on the statistical data stream to obtain daily data blocks, and perform statistical data extraction processing on the daily data blocks to obtain daily statistical data;

[0093] (4) Perform data integration processing on the second-level control data, hourly abnormal data, and daily statistical data to obtain classified data results.

[0094] Specifically, further classify the hierarchical data stream by time scale to perform different time window processing on real-time, monitored, and statistical data and integrate them into the final classified data results. First, perform second-level time window processing on the real-time data stream and divide it into second-level data blocks. The real-time data stream mainly contains data that requires immediate control and adjustment. Through second-level time window processing, the system can obtain a complete data snapshot per second, ensuring high-frequency update of the control data. In the second-level data blocks, the system performs control data extraction to generate second-level control data. The second-level control data is mainly used to quickly respond to and adjust the operating state of the photovoltaic modules. For example, real-time adjustment of the MPPT parameters to ensure that the photovoltaic system can continuously maintain maximum power output in a dynamic grid environment. For the monitored data stream, the system uses an hourly time window for division to generate hourly data blocks. The monitored data stream mainly includes data for periodic status monitoring and anomaly detection. Through hourly time scale division, the system can track the short-term change trends of the components and identify abnormal situations. Each hourly data block contains multiple data points, and abnormal data extraction is performed on these data to generate hourly abnormal data. The hourly abnormal data can indicate state deviations, performance degradation, or other abnormal events of the components, providing a reference basis for regular detection by the operation and maintenance team. For example, the hourly abnormal data can be used to detect problems such as voltage fluctuations and power deviations and trigger corresponding maintenance or adjustment measures to ensure the continuous and stable operation of the system.

[0095] Meanwhile, for the statistical data stream, the system performs daily time window processing and divides it into daily data blocks. The statistical data stream mainly includes long-term trend data and historical data, such as daily power generation, component efficiency statistics, etc. The division of the daily time window enables the data to reflect the performance of the photovoltaic system over a longer time period. Statistical data is extracted from each daily data block to generate daily statistical data. The daily statistical data is used for historical trend analysis and performance evaluation of the system. By analyzing the long-term data, the operation and maintenance team can optimize the design and maintenance strategies of the photovoltaic system. Finally, the second-level control data, hourly anomaly data, and daily statistical data are integrated to generate classified data results. The classified data results contain key information on the system operation at different time scales, providing a comprehensive reference framework for system managers. The second-level control data reflects the immediate response status of the system, the hourly anomaly data reveals short-term anomaly trends, and the daily statistical data shows the long-term performance of the system. Through this classification and integration, the operation and maintenance team of the photovoltaic system can quickly locate the time and data type of problems and adopt real-time adjustment, short-term monitoring, and long-term optimization strategies respectively to achieve comprehensive and accurate photovoltaic system data management and performance improvement.

[0096] For example, during a day's operation, the system's real-time data stream generates a batch of second-level control data every second, which is used to immediately adjust the MPPT parameters of the components to cope with frequency fluctuations and environmental changes. At the same time, the system generates hourly anomaly data for the monitoring data stream every hour, and detects a gradually increasing power deviation in an hourly data block in the afternoon, indicating a possible component performance degradation problem. The operation and maintenance team then arranges an inspection. In addition, daily statistical data is generated in the statistical data stream every night, recording the total daily power generation and efficiency performance, and comparing and analyzing it with the statistical data of the previous week. These classified data results provide a comprehensive decision-making basis for the immediate adjustment, anomaly detection, and long-term optimization of the system, significantly improving the operation efficiency and stability of the photovoltaic system.

[0097] The above describes the photovoltaic data processing method based on edge computing in the embodiments of the present application. Next, the photovoltaic data processing device based on edge computing in the embodiments of the present application will be described. Please refer to Figure 2 One embodiment of the photovoltaic data processing device based on edge computing in the embodiments of the present application includes:

[0098] An acquisition module 201, configured to synchronously acquire and process the voltage and current data of each component in the photovoltaic string to obtain an original voltage-current data set;

[0099] A processing module 202, configured to perform edge data preprocessing on the original voltage-current data set to obtain a preprocessing data packet, where the preprocessing data packet includes time-aligned data, power data, and voltage deviation data;

[0100] An extraction module 203 is configured to perform component status feature extraction processing on the preprocessed data packet to obtain component status feature data, where the component status feature data includes component matching degree data, power deviation data, and abnormal type data;

[0101] A calculation module 204 is configured to perform MPPT parameter calculation processing on the component status feature data to obtain MPPT parameter data, where the MPPT parameter data includes controller duty cycle data and switching frequency data;

[0102] A grading module 205 is configured to perform data priority grading processing on the preprocessed data packet and the component status feature data to obtain a graded data stream, where the graded data stream includes a real-time data stream, a monitoring data stream, and a statistical data stream;

[0103] A classification module 206 is configured to perform data time scale classification processing on the graded data stream to obtain a classified data result, where the classified data result includes second-level control data, hourly abnormal data, and daily statistical data.

[0104] Through the collaborative cooperation of the above-mentioned various components and the mutual cooperation of multiple technical features, the efficient acquisition, intelligent preprocessing, real-time analysis, and graded transmission of photovoltaic component data are realized, effectively improving the data processing ability and response speed of the photovoltaic system. First, the solution generates an original data set containing the detailed working status of each component by synchronously collecting and processing the voltage and current data of each component in the photovoltaic string. The synchronous collection ensures data consistency, enabling subsequent analysis to be carried out under the same time reference, thus accurately reflecting the real-time operating status of the components. Next, the solution uses edge computing to preprocess the original data set to generate a preprocessed data packet containing time-aligned data, power data, and voltage deviation data. This step not only reduces data redundancy but also improves data quality through methods such as time alignment and data complementation, laying a foundation for subsequent status feature analysis. Based on the preprocessed data, the solution further extracts component status features to generate component matching degree data, power deviation data, and abnormal type data, refining the monitoring of the status of each photovoltaic component. Through deviation calculation of power data and analysis of power attenuation trends, the system can quickly identify operating deviations and potential faults of the components, monitor the health status of the components in real time, and improve the system's operation and maintenance efficiency and fault handling ability. In addition, based on the status feature data, the system can automatically calculate MPPT parameters applicable to each component, including the controller duty cycle and switching frequency, ensuring that each component always operates at the maximum power point. This adaptive MPPT control realizes precise power output regulation and improves the overall power generation efficiency of the photovoltaic system.

[0105] To achieve the optimization of data transmission, the solution performs data priority classification on preprocessed data packets and status feature data, and divides them into real-time data streams, monitoring data streams, and statistical data streams according to the real-time nature and importance of different data. Through data classification, the system can give priority to transmitting data with high real-time nature, ensuring rapid response to key control data in case of emergencies such as power grid fluctuations; for statistical data and monitoring data, they are transmitted over a longer period, reducing the occupancy of communication bandwidth. This priority classification mechanism makes data transmission more flexible and ensures efficient operation even in the case of a large amount of information. In further time-scale classification processing, the solution sorts the classified data streams according to time scales of seconds, hours, and days, generating second-level control data, hourly abnormal data, and daily statistical data. This classification processing not only optimizes data storage and transmission efficiency, but also facilitates hierarchical analysis of data at different time scales by the backend, enabling more accurate system state prediction and optimized decision-making. For example, second-level data is used for real-time control, hourly data is used for abnormal monitoring and component status analysis, and daily data is used for long-term performance statistics and trend prediction. Through this hierarchical and classified data processing method, the system can comprehensively grasp the real-time status and long-term operation trend of photovoltaic modules, enhancing the overall data management ability and operation and maintenance intelligence level of photovoltaic power plants. It not only greatly improves the system performance in terms of the real-time nature of data acquisition and processing, but also optimizes data transmission and storage requirements through intelligent classification and grading, greatly reducing the computing burden on the data center. Compared with the traditional centralized processing method, this solution has more advantages in terms of response speed, resource utilization, and data priority management, providing strong support for the efficient operation of photovoltaics in a complex power grid environment.

[0106] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A photovoltaic data processing method based on edge computing, characterized in that, The photovoltaic data processing method based on edge computing includes: Synchronously collecting and processing the voltage and current data of each component in the photovoltaic string to obtain an original voltage-current data set; Performing edge data preprocessing on the original voltage-current data set to obtain a preprocessing data packet, where the preprocessing data packet includes time-aligned data, power data, and voltage deviation data; Performing component status feature extraction processing on the preprocessing data packet to obtain component status feature data, where the component status feature data includes component matching degree data, power deviation data, and abnormal type data; Performing MPPT parameter calculation processing on the component status feature data to obtain MPPT parameter data, where the MPPT parameter data includes controller duty cycle data and switching frequency data; Performing data priority grading processing on the preprocessing data packet and the component status feature data to obtain a graded data stream, where the graded data stream includes a real-time data stream, a monitoring data stream, and a statistical data stream; Performing data time scale classification processing on the graded data stream to obtain a classified data result, where the classified data result includes second-level control data, hourly abnormal data, and daily statistical data.

2. The photovoltaic data processing method based on edge computing according to claim 1, wherein The synchronously collecting and processing the voltage and current data of each component in the photovoltaic string to obtain an original voltage-current data set includes: Performing power supply parameter setting processing on the POWERBUS bus of each acquisition module in the photovoltaic string to obtain bus power supply parameters, and performing electrical isolation processing on the bus power supply parameters to obtain an isolated power supply signal; Performing bus communication protocol configuration processing on the isolated power supply signal to obtain bus communication protocol parameters, and performing master-slave station address allocation processing on the bus communication protocol parameters to obtain site address data; Performing synchronous acquisition instruction generation processing on the site address data to obtain a synchronous broadcast instruction, and performing data distribution processing on the synchronous broadcast instruction through the POWERBUS bus to obtain an acquisition trigger signal; Sampling the voltage signal of each component through a high-precision analog-to-digital converter to obtain voltage sampling data, and sampling the current signal of each component through a shunt monitor to obtain current sampling data; Performing data splicing processing on the voltage sampling data and the current sampling data to obtain component power data, and performing timestamp marking processing on the component power data to obtain time-series power data; Performing cascade data packaging processing on the time-series power data to obtain a data transmission packet, and performing step-by-step transmission processing on the data transmission packet through a handshake mechanism to obtain an original voltage-current data set.

3. The photovoltaic data processing method based on edge computing according to claim 1, wherein The performing edge data preprocessing on the original voltage-current data set to obtain a preprocessing data packet, where the preprocessing data packet includes time-aligned data, power data, and voltage deviation data, includes: Performing data sorting processing on the timestamp information in the original voltage-current data set to obtain time-series data, and performing time window division processing on the time-series data to obtain time window data; Perform data integrity check processing on the time window data to obtain data missing position information, and perform data completion processing on the data missing position information through a linear interpolation algorithm to obtain completed data; Perform threshold range detection processing on the completed data to obtain abnormal data marks, and perform data filtering processing on the abnormal data marks to obtain time-aligned data; Perform product operation processing on the voltage and current data in the time-aligned data to obtain power data, and perform power conversion efficiency calculation processing on the power data to obtain efficiency data; Perform mean calculation processing on the voltage data of each component in the same string to obtain the string voltage mean value, and perform difference calculation processing on the voltage of each component and the string voltage mean value to obtain voltage deviation data; Perform data formatting processing on the time-aligned data, the power data, and the voltage deviation data to obtain a preprocessing data packet.

4. The photovoltaic data processing method based on edge computing according to claim 1, wherein Perform component status feature extraction processing on the preprocessing data packet to obtain component status feature data, and the component status feature data includes component matching degree data, power deviation data, and abnormal type data, including: Perform time-series cumulative processing on the voltage deviation data to obtain a voltage deviation cumulative value, and perform threshold comparison processing on the voltage deviation cumulative value to obtain component matching degree data; Perform theoretical power calculation processing on the power data to obtain a theoretical power value, and perform ratio calculation processing on the power data and the theoretical power value to obtain power deviation data; Perform trend analysis processing on the power deviation data through a moving average algorithm to obtain a power decay trend, and perform fluctuation feature extraction processing on the power decay trend to obtain fluctuation feature data; Perform feature classification processing on the fluctuation feature data to obtain abnormal type data, and perform abnormal level division processing on the abnormal type data to obtain an abnormal level index; Perform feature fusion processing on the component matching degree data, the power deviation data, and the abnormal type data to obtain component status feature data.

5. The photovoltaic data processing method based on edge computing according to claim 1, wherein Perform MPPT parameter calculation processing on the component status feature data to obtain MPPT parameter data, and the MPPT parameter data includes controller duty cycle data and switching frequency data, including: Perform working state classification processing on the component status feature data to obtain status label data, and perform working mode determination processing on the component according to the status label data to obtain working mode parameters; Perform power curve feature extraction processing on the power deviation data to obtain power curve data, and perform maximum power point positioning processing on the power curve data to obtain target voltage data; Perform duty cycle calculation processing on the target voltage data to obtain controller duty cycle data, and perform step size adaptive adjustment processing on the controller duty cycle data to obtain adjustment step size data; Perform correction processing on the controller duty cycle data according to the abnormal type data to obtain corrected duty cycle data, and perform switching frequency calculation processing on the corrected duty cycle data to obtain switching frequency data; Perform parameter combination processing on the duty cycle data and the switching frequency data of the controller to obtain MPPT parameter data.

6. The photovoltaic data processing method based on edge computing according to claim 1, characterized in that Perform data priority classification processing on the preprocessed data packet and the component status feature data to obtain a classified data stream. The classified data stream includes a real-time data stream, a monitoring data stream, and a statistical data stream, and includes: Perform real-time requirement analysis processing on the MPPT parameter data to obtain a data timeliness index, and perform priority classification processing on the data timeliness index to obtain a priority tag; Perform data importance evaluation processing on the preprocessed data packet to obtain an importance index, and perform level division processing on the data according to the importance index to obtain a data level tag; Perform real-time data stream generation processing on the data with the priority tag to obtain a real-time data stream, and perform monitoring data stream generation processing on the data with the data level tag to obtain a monitoring data stream; Perform statistical data stream generation processing on the remaining data to obtain a statistical data stream, and perform data stream merging processing on the real-time data stream, the monitoring data stream, and the statistical data stream to obtain a classified data stream.

7. The photovoltaic data processing method based on edge computing according to claim 1, wherein Perform data time scale classification processing on the classified data stream to obtain a classified data result. The classified data result includes second-level control data, hourly abnormal data, and daily statistical data, and includes: Perform second-level time window processing on the real-time data stream to obtain second-level data blocks, and perform control data extraction processing on the second-level data blocks to obtain second-level control data; Perform hourly time window processing on the monitoring data stream to obtain hourly data blocks, and perform abnormal data extraction processing on the hourly data blocks to obtain hourly abnormal data; Perform daily time window processing on the statistical data stream to obtain daily data blocks, and perform statistical data extraction processing on the daily data blocks to obtain daily statistical data; Perform data integration processing on the second-level control data, the hourly abnormal data, and the daily statistical data to obtain a classified data result.

8. A photovoltaic data processing device based on edge computing, which is used to implement the photovoltaic data processing method based on edge computing as described in any one of claims 1-7, and is characterized in that, The photovoltaic data processing device based on edge computing includes: An acquisition module for synchronously acquiring voltage and current data of each component in a photovoltaic string to obtain an original voltage-current data set; A processing module for performing edge data preprocessing on the original voltage-current data set to obtain a preprocessed data packet. The preprocessed data packet includes time alignment data, power data, and voltage deviation data; An extraction module for performing component status feature extraction processing on the preprocessed data packet to obtain component status feature data. The component status feature data includes component matching degree data, power deviation data, and abnormal type data; A calculation module for performing MPPT parameter calculation processing on the component status feature data to obtain MPPT parameter data. The MPPT parameter data includes controller duty cycle data and switching frequency data; A grading module, configured to perform data priority grading processing on the preprocessed data packet and the component status feature data to obtain a graded data stream, where the graded data stream includes a real-time data stream, a monitoring data stream, and a statistical data stream; A classification module, configured to perform data time scale classification processing on the graded data stream to obtain a classified data result, where the classified data result includes second-level control data, hourly abnormal data, and daily statistical data.

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