Communication method and platform for distributed photovoltaic data acquisition control rod

Abstract

The invention provides a communication method and platform for a distributed photovoltaic data acquisition control rod. The method comprises the following steps: acquiring operation data of a target photovoltaic module in the distributed photovoltaic system at each moment through a data acquisition control rod so as to form a time domain operation data sequence, then determining operation compression data according to the time domain operation data sequence by using a preset data compression model, and performing data compression according to the operation compression data. And the operation compressed data is sent to the photovoltaic data management platform, so that the photovoltaic data management platform generates operation decompressed data according to the operation compressed data by using a preset data decompression model, and the operation decompressed data is stored in an operation data list corresponding to the target photovoltaic module. Therefore, only part of data having important significance on operation analysis of the photovoltaic system is reserved, other redundant or secondary information is ignored or simplified, and the targeted data compression mode not only ensures the accuracy of the data, but also realizes efficient transmission of the data.

Description

Technical Field

[0001] The present application relates to data processing technology, and more particularly to a communication method and platform for distributed photovoltaic data acquisition control rods. Background Art

[0002] With the widespread application of distributed photovoltaic systems, the collection, transmission and analysis of photovoltaic module operation data have become particularly important. The operation data of photovoltaic modules covers multiple key parameters, such as power generation, voltage, current, temperature, etc. These data are of great significance for real-time monitoring, fault diagnosis, performance optimization and long-term operation and maintenance of the system. However, since distributed photovoltaic systems usually contain a large number of photovoltaic modules, and the operation data of each module at different times will generate a large number of data points, the total amount of data is huge and complex.

[0003] In the data transmission process, if the original time domain operation data sequence is directly transmitted, it will not only occupy a large amount of network bandwidth resources and increase the transmission cost, but also may cause instability and unreliability of data transmission due to network delays and packet loss. In addition, the original data often contains a lot of redundant or secondary information, which is not necessary for the operation analysis of the photovoltaic system, but increases the complexity and time cost of data processing. Summary of the invention

[0004] The present application provides a communication method and platform for distributed photovoltaic data acquisition control rods, which are used to ensure the accuracy of data and achieve efficient data transmission.

[0005] In a first aspect, the present application provides a communication method for a distributed photovoltaic data acquisition control rod, comprising:

[0006] The operation data of the target photovoltaic components in the distributed photovoltaic system at each time is obtained through the data acquisition control rod to form a time domain operation data sequence;

[0007] The data acquisition control rod uses a preset data compression model and determines the operation compression data according to the time domain operation data sequence, and sends the operation compression data to the photovoltaic data management platform. The preset data compression model is used to extract part of the data of the time domain operation data sequence in the target frequency space;

[0008] The photovoltaic data management platform utilizes a preset data decompression model and generates operation decompression data based on the operation compression data, so as to store the operation decompressed data in an operation data list corresponding to the target photovoltaic component, wherein the preset data decompression model is an inverse transformation model of the preset data compression model.

[0009] In the above scheme, the operation data of the target photovoltaic components in the distributed photovoltaic system at each moment can be obtained in real time through the data acquisition control rod. These data cover the key parameters of the photovoltaic components such as power output, voltage, and current, and are the basis for evaluating the performance of the photovoltaic system, conducting fault diagnosis, and optimizing the operation strategy. The data acquisition control rod continuously collects data at a certain time interval to form a time domain operation data sequence. This sequence retains the time series information of the operation status of the photovoltaic components and provides rich time dimension data for subsequent data analysis and processing. Then, the data acquisition control rod uses a preset data compression model to compress the time domain operation data sequence to generate operation compressed data. This step significantly reduces the volume of data, reduces the bandwidth requirement for data transmission, and improves the efficiency of data transmission. The preset data compression model is based on the principle of data feature extraction in the target frequency space, and only retains part of the data that is important for the operation analysis of the photovoltaic system, while ignoring or simplifying the remaining redundant or secondary information. This targeted data compression method not only ensures the accuracy of the data, but also realizes efficient data transmission. After receiving the operation compressed data, the photovoltaic data management platform uses a preset data decompression model to decompress it and generate operation decompressed data. These data are then stored in the operating data list corresponding to the target photovoltaic module, providing an accurate and complete data basis for subsequent data analysis, fault prediction and system optimization. Among them, the preset data decompression model, as the inverse transformation model of the preset data compression model, can accurately restore the compressed data information. Through this decompression process, the consistency of the operating decompressed data and the original time domain operating data sequence is ensured, and the accuracy and reliability of data analysis are guaranteed. In addition, the above scheme effectively improves the data transmission efficiency and data storage efficiency of the distributed photovoltaic system through the combined application of data compression and decompression technology. At the same time, due to the reduction in data transmission volume, the risk of network congestion is reduced, and the overall reliability and stability of the system are improved. In addition, the collaborative work of the data acquisition control rod, the preset data compression model and the photovoltaic data management platform constitutes a closed-loop data acquisition, transmission, storage and processing system, which can automatically and efficiently process a large amount of photovoltaic operation data.

[0010] Optionally, the data acquisition control rod uses a preset data compression model and determines the operation compression data according to the time domain operation data sequence, including:

[0011] Using discrete Fourier transform, the time domain operation data sequence is converted into a frequency domain operation data sequence, and the frequency domain operation data sequence is normalized using the absolute value of the maximum frequency in the frequency domain operation data sequence to generate a frequency domain normalized data sequence;

[0012] Extracting a target feature operation data sequence from the frequency domain operation data sequence according to a preset acquisition frequency index range and the frequency domain normalized data sequence;

[0013] Performing wavelet transform on the target feature operation data sequence to determine the target feature wavelet coefficient matrix;

[0014] The target characteristic wavelet coefficient matrix is ​​entropy encoded by using Huffman coding to determine the running compressed data.

[0015] In the above scheme, the data acquisition control rod first uses discrete Fourier transform to convert the time domain operation data sequence into the frequency domain operation data sequence. This process converts the data from the time domain to the frequency domain, which facilitates the subsequent analysis and processing of the frequency characteristics of the data. Then, the frequency domain operation data sequence is normalized using the absolute value of the maximum frequency in the frequency domain operation data sequence to generate a frequency domain normalized data sequence. Through discrete Fourier transform, the frequency components of the photovoltaic module operation data can be clearly observed, which helps to identify periodic changes or abnormal fluctuations in the data. Normalization allows data of different dimensions to be compared and analyzed at the same scale, improving the accuracy and efficiency of subsequent data processing. Then, according to the preset acquisition frequency index range and the frequency domain normalized data sequence, the target feature operation data sequence is extracted from the frequency domain operation data sequence. This step is based on the understanding of the frequency characteristics of the data, and selects part of the data that is important for the operation analysis of the photovoltaic system as feature data. Then, by extracting the target feature operation data sequence, the dimension of the data is effectively reduced, and the amount of calculation for subsequent data processing is reduced. The feature extraction process ensures that the key information in the operation data of the photovoltaic module is retained, providing strong support for subsequent data analysis and decision-making. Then, the extracted target feature operation data sequence is subjected to wavelet transform to determine the target feature wavelet coefficient matrix. Wavelet transform is a multi-resolution analysis method that can decompose data into a superposition of different frequency components, thereby more finely characterizing the local characteristics of the data. Wavelet transform provides a multi-resolution representation of the data, allowing the operation status of the photovoltaic module to be observed at different scales. In addition, through wavelet transform, local features in the operation data of the photovoltaic module, such as mutations and spikes, can be more accurately extracted, which are of great significance for fault diagnosis and performance optimization. Then, Huffman coding is used to entropy encode the target feature wavelet coefficient matrix to determine the operation compression data. Huffman coding is a lossless data compression algorithm that effectively compresses data by assigning shorter codes to symbols with higher frequency and longer codes to symbols with lower frequency. Huffman coding can significantly reduce the volume of data and improve the efficiency of data transmission and storage. Moreover, as a lossless data compression algorithm, Huffman coding does not lose any information while compressing data, thus ensuring the integrity and accuracy of the data.

[0016] Optionally, performing a wavelet transform on the target feature running data sequence to determine a target feature wavelet coefficient matrix includes:

[0017] Using a first wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a first wavelet coefficient matrix;

[0018] Using a second wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a second wavelet coefficient matrix;

[0019] The target feature wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix.

[0020] In the above scheme, the first wavelet coefficient matrix can be determined by performing wavelet decomposition on the target feature running data sequence through the first wavelet transform tree. This step utilizes the multi-resolution analysis capability of the wavelet transform, effectively extracts the high-frequency and low-frequency components in the data, and provides a more refined data representation for subsequent data processing. The generation of the first wavelet coefficient matrix is ​​based on a specific mother wavelet function, which ensures the stability and accuracy of the data transformation process and helps to improve the accuracy of data processing. Similar to the first wavelet transform tree, the second wavelet transform tree is also used to perform wavelet decomposition on the target feature running data sequence, but uses a different mother wavelet function to generate a second wavelet coefficient matrix. By introducing different mother wavelet functions, the second wavelet transform tree can capture different features in the data, further enrich the representation of the data, and improve the flexibility of data processing. Combining the first wavelet coefficient matrix and the second wavelet coefficient matrix, the target feature wavelet coefficient matrix can be constructed. This step not only utilizes the real part information of the data (obtained through the first wavelet transform tree), but also incorporates the imaginary part information of the data (obtained through the second wavelet transform tree), thereby achieving a comprehensive and in-depth representation of the data. The construction of the target characteristic wavelet coefficient matrix provides a richer and more effective data foundation for subsequent data compression, transmission and decompression, which helps to improve the performance and reliability of the entire communication system. In the subsequent data compression process (such as entropy encoding the target characteristic wavelet coefficient matrix using Huffman coding), since the target characteristic wavelet coefficient matrix already contains the real and imaginary information of the data and has been optimized by the wavelet transform, better data compression effects can be expected. Better data compression effects mean that when transmitting the same amount of data, the required bandwidth and transmission time can be reduced, thereby reducing communication costs and improving communication efficiency.

[0021] Optionally, the using the first wavelet transform tree to perform wavelet decomposition on the target feature operation data sequence to determine a first wavelet coefficient matrix includes:

[0022] The first wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the first mother wavelet function of the first wavelet transform tree, wherein the first mother wavelet function is a Daubechies wavelet basis function.

[0023] In the above scheme, the compact support of Daubechies wavelet basis function makes the calculation process of wavelet transform more efficient. In the distributed photovoltaic data collection scenario, this efficiency is crucial for real-time processing and transmission of large amounts of data. Orthogonality ensures the independence between wavelet coefficients, which helps to retain more information during data compression, while reducing redundancy and improving compression efficiency. Using Daubechies wavelet basis function to perform wavelet decomposition on the target feature operation data sequence can more finely capture the high-frequency and low-frequency components in the data. The diversity of Daubechies wavelet basis function (such as different vanishing moment orders) allows us to select appropriate wavelet basis function according to specific needs to achieve more accurate extraction of data features. The high-frequency capture ability of Daubechies wavelet basis function enables us to more accurately identify mutations and detail information in the data, which is of great significance for monitoring the instantaneous operation status of photovoltaic modules. At the same time, Daubechies wavelet basis function can also effectively extract low-frequency components in the data, which usually represent the overall trend and periodic changes of the data, which is crucial for long-term monitoring and analysis of the performance of photovoltaic modules. The first wavelet coefficient matrix obtained by wavelet decomposition using Daubechies wavelet basis functions performs well in the subsequent data compression and reconstruction process. Since the wavelet coefficient matrix is ​​usually sparse (i.e., most of the coefficients are close to zero), this is conducive to the application of data compression algorithms (such as Huffman coding), thereby significantly reducing the amount of data transmission. At the same time, due to the orthogonality of the Daubechies wavelet basis function, the reconstructed data can better retain the characteristic information of the original data. The sparsity of the wavelet coefficient matrix enables the data compression algorithm to more effectively discard coefficients close to zero, thereby reducing the amount of data transmission and reducing communication costs. The orthogonality of the Daubechies wavelet basis function ensures that the reconstructed data can better retain the characteristic information of the original data, which is crucial to ensuring the accuracy and integrity of the data during transmission.

[0024] Optionally, the using the second wavelet transform tree to perform wavelet decomposition on the target feature operation data sequence to determine a second wavelet coefficient matrix includes:

[0025] The second wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the second mother wavelet function of the second wavelet transform tree, wherein the second mother wavelet function is a Symlet wavelet basis function.

[0026] In the above scheme, by combining the first wavelet transform tree (using Daubechies wavelet basis function) and the second wavelet transform tree (using Symlet wavelet basis function), a more comprehensive wavelet decomposition of the target feature running data sequence can be performed. Daubechies wavelet basis function is good at extracting low-frequency components of the signal, while Symlet wavelet basis function has an advantage in extracting high-frequency components due to its symmetry. This combination makes the results of wavelet decomposition more complementary and can more accurately reflect the overall picture of the signal. The first wavelet transform tree uses the Daubechies wavelet basis function to effectively extract the low-frequency components in the signal, which represent the overall trend and periodic changes of the signal. The second wavelet transform tree uses the Symlet wavelet basis function to further capture the high-frequency components in the signal, and because of its symmetry, it better maintains the phase information of these high-frequency components, thereby improving the accuracy of signal reconstruction. The second wavelet coefficient matrix obtained by wavelet decomposition using the Symlet wavelet basis function performs well in the subsequent data compression and reconstruction process. Due to the symmetry and compact support of the Symlet wavelet basis function, the wavelet coefficient matrix usually has better sparsity and energy concentration, which is conducive to the application of data compression algorithms (such as Huffman coding), thereby further reducing the amount of data transmission. At the same time, during the reconstruction process, since the Symlet wavelet basis function can better maintain the phase information of the signal, the reconstructed data can more accurately restore the characteristics of the original signal. The symmetry and compact support of the Symlet wavelet basis function make the wavelet coefficient matrix have better sparsity and energy concentration, which is conducive to the application of data compression algorithms and reduces the amount of data transmission. Since the Symlet wavelet basis function can better maintain the phase information of the signal, the reconstructed data can more accurately restore the characteristics of the original signal and improve the accuracy of data reconstruction. For the communication method of distributed photovoltaic data acquisition control rods, this means that the operating status of photovoltaic components can be transmitted and monitored more accurately.

[0027] Optionally, determining the target feature wavelet coefficient matrix according to the first wavelet coefficient matrix and the second wavelet coefficient matrix includes:

[0028] The target characteristic wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix, wherein the first wavelet coefficient matrix is ​​used to construct the real part of each element in the target characteristic wavelet coefficient matrix, and the second wavelet coefficient matrix is ​​used to construct the imaginary part of each element in the target characteristic wavelet coefficient matrix.

[0029] In the above scheme, the real part of the first wavelet coefficient matrix is ​​combined with the imaginary part of the second wavelet coefficient matrix to form the target feature wavelet coefficient matrix, so as to represent the signal in complex form and capture the characteristics of the signal more comprehensively. The complex signal representation method can simultaneously contain the amplitude and phase information of the signal, and has richer information expression capabilities than the real signal representation method. The operation data of photovoltaic modules in distributed photovoltaic systems often have non-stationary characteristics. The complex wavelet coefficient matrix can better adapt to this change and extract more accurate signal characteristics. The first wavelet coefficient matrix (based on Daubechies wavelet basis function) is used to extract the low-frequency components of the signal as the real part, and the second wavelet coefficient matrix (based on Symlet wavelet basis function) is used to extract the high-frequency components of the signal as the imaginary part. This combination method realizes the accurate extraction of signal characteristics. The low-frequency components represent the overall trend and periodic changes of the signal, while the high-frequency components contain the details and mutation information of the signal. Combining the two can more comprehensively reflect the characteristics of the signal. By using wavelet basis functions suitable for extracting low-frequency and high-frequency components respectively, and combining their real and imaginary parts, the accuracy of feature extraction can be significantly improved, providing a reliable basis for subsequent data analysis and processing. The formation of the target feature wavelet coefficient matrix not only helps to accurately extract signal features, but also improves data compression efficiency through its sparsity and energy concentration, thereby reducing the amount of data transmission and improving communication efficiency. Due to the characteristics of wavelet transform, the target feature wavelet coefficient matrix is ​​usually sparse, that is, most of the coefficients are close to zero. This sparsity enables data compression algorithms (such as Huffman coding) to work more effectively and reduce the amount of data transmission. By reducing the amount of data transmission, the communication cost can be reduced and the communication efficiency can be improved. For the communication method of distributed photovoltaic data acquisition control rods, this means that the operation data of photovoltaic modules can be transmitted more quickly and accurately, providing strong support for real-time monitoring and efficient management of the system. In the process of data reconstruction, since the target feature wavelet coefficient matrix contains the real and imaginary information of the signal, it can more accurately restore the characteristics of the original signal and ensure the quality of the reconstructed signal. In the reconstruction process, combining the real and imaginary information can more comprehensively restore the amplitude and phase information of the signal, reducing the errors introduced by information loss or distortion. By ensuring the quality of the reconstructed signal, it can ensure that the data transmitted by the distributed photovoltaic data acquisition control rod can be accurately analyzed and processed at the receiving end, providing a strong guarantee for the stable operation of the system.

[0030] Optionally, the photovoltaic data management platform uses a preset data decompression model and generates operation decompression data according to the operation compression data, including:

[0031] Performing inverse Huffman coding on the running compressed data to obtain the target feature running data sequence;

[0032] The real part of each element in the target feature operation data sequence is subjected to a first wavelet inverse transform based on the first inverse transform function corresponding to the first wavelet transform tree, and the imaginary part of each element in the target feature operation data sequence is subjected to a second wavelet inverse transform based on the second inverse transform function corresponding to the second wavelet transform tree, so as to generate the operation decompressed data.

[0033] In the above scheme, the photovoltaic data management platform first performs inverse Huffman coding on the received running compressed data to obtain the target feature running data sequence. As the inverse process of Huffman coding, inverse Huffman coding can effectively decompress the data and restore the original data sequence. This step significantly improves the efficiency of data decompression, so that the photovoltaic data management platform can quickly obtain the target feature running data sequence. Huffman coding is a lossless data compression algorithm, and its inverse process inverse Huffman coding can accurately restore the original data, ensuring the integrity and accuracy of the data during transmission. The inverse Huffman coding process is simple and efficient, and can quickly decompress the data, reduce data processing time, and improve the response speed of the photovoltaic data management platform. After obtaining the target feature running data sequence, the photovoltaic data management platform performs wavelet inverse transform processing on the real and imaginary parts of each element. The real part performs the first wavelet inverse transform based on the first inverse transform function corresponding to the first wavelet transform tree, and the imaginary part performs the second wavelet inverse transform based on the second inverse transform function corresponding to the second wavelet transform tree. This step realizes the accurate reconstruction of the signal and restores the time domain characteristics of the original signal. Wavelet transform decomposes the signal into wavelet coefficient matrices, while inverse wavelet transform reconstructs the original signal based on these coefficient matrices. The two complement each other and together constitute the complete process of signal processing. By performing inverse wavelet transform processing on the real part and the imaginary part respectively, the time domain characteristics of the original signal can be restored more accurately. This step ensures the consistency and accuracy of the signal during transmission and processing. The combination of inverse Huffman coding processing and inverse wavelet transform not only improves the efficiency of data decompression, but also ensures the accuracy of signal reconstruction. The entire data decompression and reconstruction process is efficient and reliable, providing strong technical support for the communication method of distributed photovoltaic data acquisition control rods. The two steps of inverse Huffman coding processing and inverse wavelet transform are closely linked, forming a complete process of data decompression and reconstruction. The coherence of this process ensures the efficiency and accuracy of data processing. By optimizing the technical implementation of each processing step, the efficiency of the entire data decompression and reconstruction process has been significantly improved. This not only reduces the data processing time, but also reduces the resource consumption and cost of the system. Efficient data decompression and accurate signal reconstruction have a positive impact on the management of distributed photovoltaic systems. The photovoltaic data management platform can obtain the operating data of photovoltaic modules more quickly and accurately, providing strong support for real-time monitoring, fault diagnosis and performance optimization of the system. Efficient data decompression and reconstruction enable the photovoltaic data management platform to obtain the operating data of photovoltaic modules in real time, improving the real-time monitoring capability of the system. Accurate data provides a reliable basis for fault diagnosis and performance optimization. Through in-depth analysis of operating data, potential problems in the system can be discovered in time and optimized and adjusted to improve the stability and efficiency of the system.

[0034] In a second aspect, the present application provides a photovoltaic data management platform, including:

[0035] An acquisition module is used to acquire the operation data of the target photovoltaic components in the distributed photovoltaic system at various times through a data acquisition control rod to form a time domain operation data sequence;

[0036] a processing module, configured to determine operation compression data by using a preset data compression model using the data acquisition control rod and according to the time domain operation data sequence, wherein the preset data compression model is used to extract partial data of the time domain operation data sequence in a target frequency space;

[0037] The processing module is also used to use a preset data decompression model to generate operation decompression data according to the operation compression data, so as to store the operation decompressed data in an operation data list corresponding to the target photovoltaic component, wherein the preset data decompression model is an inverse transformation model of the preset data compression model.

[0038] Optionally, the processing module is specifically used to:

[0039] Using discrete Fourier transform, the time domain operation data sequence is converted into a frequency domain operation data sequence, and the frequency domain operation data sequence is normalized using the absolute value of the maximum frequency in the frequency domain operation data sequence to generate a frequency domain normalized data sequence;

[0040] Extracting a target feature operation data sequence from the frequency domain operation data sequence according to a preset acquisition frequency index range and the frequency domain normalized data sequence;

[0041] Performing wavelet transform on the target feature operation data sequence to determine the target feature wavelet coefficient matrix;

[0042] The target characteristic wavelet coefficient matrix is ​​entropy encoded by using Huffman coding to determine the running compressed data.

[0043] Optionally, the processing module is specifically used to:

[0044] Using a first wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a first wavelet coefficient matrix;

[0045] Using a second wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a second wavelet coefficient matrix;

[0046] The target feature wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix.

[0047] Optionally, the processing module is specifically used to:

[0048] The first wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the first mother wavelet function of the first wavelet transform tree, wherein the first mother wavelet function is a Daubechies wavelet basis function.

[0049] Optionally, the processing module is specifically used to:

[0050] The second wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the second mother wavelet function of the second wavelet transform tree, wherein the second mother wavelet function is a Symlet wavelet basis function.

[0051] Optionally, the processing module is specifically used to:

[0052] The target characteristic wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix, wherein the first wavelet coefficient matrix is ​​used to construct the real part of each element in the target characteristic wavelet coefficient matrix, and the second wavelet coefficient matrix is ​​used to construct the imaginary part of each element in the target characteristic wavelet coefficient matrix.

[0053] Optionally, the processing module is specifically used to:

[0054] Performing inverse Huffman coding on the running compressed data to obtain the target feature running data sequence;

[0055] The real part of each element in the target feature operation data sequence is subjected to a first wavelet inverse transform based on the first inverse transform function corresponding to the first wavelet transform tree, and the imaginary part of each element in the target feature operation data sequence is subjected to a second wavelet inverse transform based on the second inverse transform function corresponding to the second wavelet transform tree, so as to generate the operation decompressed data.

[0056] In a third aspect, the present application provides an electronic device, including:

[0057] processor; and,

[0058] A memory, configured to store executable instructions of the processor;

[0059] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0060] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0061] The present application provides a communication method and platform for a distributed photovoltaic data acquisition control rod, which obtains the operating data of a target photovoltaic component in a distributed photovoltaic system at various times through a data acquisition control rod to form a time domain operating data sequence, and then uses a preset data compression model to determine the operating compressed data according to the time domain operating data sequence, and sends the operating compressed data to a photovoltaic data management platform, so that the photovoltaic data management platform uses a preset data decompression model and generates operating decompressed data according to the operating compressed data, so as to store the operating decompressed data in an operating data list corresponding to the target photovoltaic component, thereby retaining only part of the data that is important for the operation analysis of the photovoltaic system, while ignoring or simplifying the remaining redundant or secondary information. This targeted data compression method not only ensures the accuracy of the data, but also realizes efficient data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0063] Figure 1 is a flow chart of a communication method for distributed photovoltaic data acquisition control rods according to an exemplary embodiment of the present application;

[0064] Figure 2 is a flow chart of a communication method for distributed photovoltaic data acquisition control rods according to another exemplary embodiment of the present application;

[0065] Figure 3 is a schematic diagram of the structure of a photovoltaic data management platform according to an exemplary embodiment of the present application;

[0066] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0067] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0068] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0069] With the widespread application of distributed photovoltaic systems, the collection, transmission and analysis of photovoltaic module operation data have become increasingly important. These data cover key parameters such as power generation, voltage, current, temperature, etc., which are of great significance for real-time monitoring, fault diagnosis, performance optimization and long-term operation and maintenance of the system. However, distributed photovoltaic systems usually contain a large number of photovoltaic modules, and the operation data of each module at different times will generate a large number of data points, resulting in a large and complex total amount of data.

[0070] In the prior art, there are many problems with directly transmitting the original time-domain operation data sequence. First, the original data contains a lot of redundant or secondary information, which is not necessary for the operation analysis of the photovoltaic system, but increases the complexity and time cost of data processing. Second, directly transmitting the original data will occupy a lot of network bandwidth resources, increase transmission costs, and may cause instability and unreliability of data transmission due to problems such as network delays and packet loss.

[0071] In view of the above problems, the core concept of the present invention is to provide a communication method and platform for distributed photovoltaic data acquisition control rods, aiming to achieve efficient data transmission and storage while ensuring data accuracy through efficient data compression and decompression technology. The embodiments provided in this application are specifically conceived as follows:

[0072] Data acquisition and preprocessing: The data acquisition control rod is used to obtain the operating data of the target photovoltaic components in the distributed photovoltaic system at various times in real time to form a time domain operating data sequence. The time domain operating data sequence is converted into a frequency domain operating data sequence through discrete Fourier transform, and the absolute value of the maximum frequency in the frequency domain operating data sequence is used for normalization to generate a frequency domain normalized data sequence. This process helps to eliminate dimensional differences and improve the stability and accuracy of subsequent processing.

[0073] Data compression: Based on the principle of data feature extraction in the target frequency space, according to the preset acquisition frequency index range and the frequency domain normalized data sequence, the target feature running data sequence is extracted from the frequency domain running data sequence. The target feature running data sequence is processed by wavelet transform, and the first wavelet transform tree (based on Daubechies wavelet basis function) and the second wavelet transform tree (based on Symlet wavelet basis function) are used for wavelet decomposition to obtain the first wavelet coefficient matrix and the second wavelet coefficient matrix. Combining these two matrices, the target feature wavelet coefficient matrix is ​​formed, which represents the signal in complex form and fully captures the signal characteristics. Huffman coding is used to entropy encode the target feature wavelet coefficient matrix to generate running compressed data. Huffman coding is a lossless data compression algorithm that can assign codewords of different lengths according to the probability of symbol occurrence, thereby achieving effective data compression.

[0074] Data transmission and decompression: The data acquisition control rod sends the compressed operation data to the photovoltaic data management platform through the communication network. After receiving the compressed operation data, the photovoltaic data management platform uses the preset data decompression model (i.e., the inverse transformation model of the preset data compression model) for decompression processing. First, inverse Huffman coding is performed to obtain the target feature operation data sequence; then the real and imaginary parts of each element in the target feature operation data sequence are respectively subjected to wavelet inverse transformation processing to restore the original time domain operation data sequence. The restored time domain operation data sequence (i.e., operation decompressed data) is stored in the operation data list corresponding to the target photovoltaic module, providing an accurate and complete data basis for subsequent data analysis, fault prediction and system optimization.

[0075] Through the above conception, the embodiment of the present application realizes the efficient collection, compression, transmission and decompression of photovoltaic module operation data in distributed photovoltaic systems. This targeted data compression method not only ensures the accuracy of the data, but also significantly reduces the amount of data transmission, and improves the efficiency and stability of data transmission. At the same time, through the collaborative work of the data acquisition control rod, the preset data compression model and the photovoltaic data management platform, a closed-loop data acquisition, transmission, storage and processing system is formed, which can automatically and efficiently process a large amount of photovoltaic operation data, and provide strong support for the monitoring, management and optimization of distributed photovoltaic systems.

[0076] Figure 1 FIG. 1 is a flow chart of a communication method for distributed photovoltaic data acquisition control rods according to an exemplary embodiment of the present application. Figure 1 As shown, the method provided in this embodiment includes:

[0077] S101. Acquire the operating data of a target photovoltaic module in a distributed photovoltaic system at each time through a data acquisition control rod.

[0078] In the step, the operation data of the target photovoltaic components in the distributed photovoltaic system at various times are acquired through a data acquisition control rod to form a time domain operation data sequence.

[0079] Specifically, in a distributed photovoltaic system, the data acquisition control rod is deployed near the target photovoltaic module and is responsible for collecting the operating data of the photovoltaic module in real time. These data include but are not limited to key parameters such as power generation, voltage, current, and temperature. The data acquisition control rod has built-in high-precision sensors and analog-to-digital converters, which can convert analog signals into digital signals for subsequent processing.

[0080] The data acquisition control rod continuously collects the operating data of the photovoltaic components at various times according to the preset sampling frequency (such as sampling once per second), or dynamically adjusts the sampling frequency based on the operating conditions. These data are arranged in chronological order to form a time domain operation data sequence. The time domain operation data sequence can fully reflect the operating status of the photovoltaic components at different time points, providing a basis for subsequent data analysis and processing.

[0081] S102. The data acquisition control rod uses a preset data compression model and determines operation compression data according to a time domain operation data sequence.

[0082] In this step, the data acquisition control rod uses a preset data compression model to determine the operation compression data based on the time domain operation data sequence, and sends the operation compression data to the photovoltaic data management platform. The preset data compression model is used to extract part of the data of the time domain operation data sequence in the target frequency space.

[0083] Specifically, in order to reduce the amount of data transmission and improve communication efficiency, the data acquisition control rod uses a preset data compression model to process the time domain operation data sequence. The preset data compression model is based on advanced algorithms such as wavelet transform and Huffman coding, which can extract part of the data of the time domain operation data sequence in the target frequency space while maintaining the main features of the data.

[0084] Specifically, the data acquisition control rod first performs a discrete Fourier transform on the time domain operation data sequence to convert it into a frequency domain operation data sequence. Then, the frequency domain operation data sequence is normalized using the absolute value of the maximum frequency in the frequency domain operation data sequence to generate a frequency domain normalized data sequence. Next, according to the preset acquisition frequency index range and the frequency domain normalized data sequence, the target feature operation data sequence is extracted from the frequency domain operation data sequence.

[0085] The target feature running data sequence is subjected to wavelet transform processing, and the first wavelet transform tree and the second wavelet transform tree are used to perform wavelet decomposition respectively to obtain the first wavelet coefficient matrix and the second wavelet coefficient matrix. Then, the target feature wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix, wherein the first wavelet coefficient matrix is ​​used to construct the real part of each element in the target feature wavelet coefficient matrix, and the second wavelet coefficient matrix is ​​used to construct its imaginary part.

[0086] Finally, the target characteristic wavelet coefficient matrix is ​​processed by Huffman coding to generate running compressed data. The running compressed data has a smaller data volume than the original time domain running data sequence, but retains the main features of the data. The data acquisition control rod sends the running compressed data to the photovoltaic data management platform through the wireless communication network.

[0087] S103. The photovoltaic data management platform uses a preset data decompression model and generates operation decompression data according to the operation compression data.

[0088] In this step, the photovoltaic data management platform uses a preset data decompression model and generates operation decompression data based on the operation compression data to store the operation decompressed data in the operation data list corresponding to the target photovoltaic component, wherein the preset data decompression model is an inverse transformation model of the preset data compression model.

[0089] Specifically, after receiving the operation compressed data sent by the data acquisition control rod, the photovoltaic data management platform uses a preset data decompression model to process it. The preset data decompression model is an inverse transformation model of the preset data compression model, which can accurately restore the original time domain operation data sequence.

[0090] Specifically, the photovoltaic data management platform first performs inverse Huffman coding on the running compressed data to obtain the target feature running data sequence. Then, the real part of each element in the target feature running data sequence is processed by the first wavelet inverse transform based on the first inverse transform function corresponding to the first wavelet transform tree, and the imaginary part is processed by the second wavelet inverse transform based on the second inverse transform function corresponding to the second wavelet transform tree, and the inverse discrete Fourier transform is performed. Through this process, the photovoltaic data management platform can accurately restore the original time domain running data sequence.

[0091] Finally, the photovoltaic data management platform stores the restored time domain operation data sequence (i.e., operation decompressed data) in the operation data list corresponding to the target photovoltaic module. The operation data list records the operation status of the photovoltaic module at each moment in chronological order, which facilitates subsequent data analysis and processing. At the same time, the photovoltaic data management platform can also perform real-time monitoring, fault diagnosis, and performance optimization of photovoltaic modules based on the operation data list, thereby improving the overall efficiency and stability of the distributed photovoltaic system.

[0092] In this embodiment, the operation data of the target photovoltaic component in the distributed photovoltaic system at each time is obtained through the data acquisition control rod to form a time domain operation data sequence. Then, the preset data compression model is used to determine the operation compression data according to the time domain operation data sequence, and the operation compression data is sent to the photovoltaic data management platform, so that the photovoltaic data management platform uses the preset data decompression model and generates operation decompressed data according to the operation compression data to store the operation decompressed data in the operation data list corresponding to the target photovoltaic component, so as to retain only part of the data that is important for the operation analysis of the photovoltaic system, while ignoring or simplifying the remaining redundant or secondary information. This targeted data compression method not only ensures the accuracy of the data, but also realizes the efficient transmission of the data, and provides an accurate and complete data basis for subsequent data analysis, fault prediction and system optimization.

[0093] Specifically, the preset data compression model is based on the principle of data feature extraction in the target frequency space, and only retains part of the data that is important for the operation analysis of the photovoltaic system, while ignoring or simplifying the remaining redundant or secondary information. This targeted data compression method not only ensures the accuracy of the data, but also realizes efficient data transmission. Based on the frequency domain analysis, the preset data compression model can identify which frequency components have important data changes that have an important impact on the operation analysis of the photovoltaic system. For example, some low-frequency components may reflect the long-term performance change trend of photovoltaic components, while some high-frequency components may reflect instantaneous disturbances or fault information. The model will only retain these parts of the data that are important for operation analysis according to the preset rules or algorithms, while ignoring or simplifying the remaining redundant or secondary information. This selective data retention method not only reduces the amount of data, but also ensures the analytical value of the data. By retaining only key data, the preset data compression model significantly reduces the bandwidth requirements for data transmission. In distributed photovoltaic systems, data transmission between photovoltaic components and data management platforms is usually required through wireless networks. Since wireless network bandwidth is limited and may be interfered by various factors, reducing the amount of data transmission is of great significance to improving transmission efficiency and reliability. After adopting the preset data compression model, the amount of data transmitted is greatly reduced, so the speed and stability of data transmission can be significantly improved, and the occurrence of network delay and packet loss can be reduced. Although the preset data compression model compresses the original data, it can still accurately restore the main features of the original data. This is because the model retains enough information during the compression process to support subsequent data analysis and processing.

[0094] After receiving the compressed operation data, the photovoltaic data management platform uses the preset data decompression model to decompress it and generate the decompressed operation data. These data are then stored in the operation data list corresponding to the target photovoltaic module, providing an accurate and complete data basis for subsequent data analysis, fault prediction and system optimization. Among them, the preset data decompression model, as an inverse transformation model of the preset data compression model, can accurately restore the compressed data information. Through this decompression process, the consistency of the operation decompressed data and the original time domain operation data sequence is ensured, and the accuracy and reliability of data analysis are guaranteed. In addition, the above scheme effectively improves the data transmission efficiency and data storage efficiency of the distributed photovoltaic system through the combined application of data compression and decompression technology. At the same time, due to the reduction in the amount of data transmission, the risk of network congestion is reduced, and the overall reliability and stability of the system are improved. In addition, the collaborative work of the data acquisition control rod, the preset data compression model and the photovoltaic data management platform constitutes a closed-loop data acquisition, transmission, storage and processing system, which can automatically and efficiently process a large amount of photovoltaic operation data.

[0095] Figure 2 FIG. 1 is a flow chart of a communication method for distributed photovoltaic data acquisition control rods according to another exemplary embodiment of the present application. Figure 2 As shown, the method provided in this embodiment includes:

[0096] S201. Acquire the operating data of the target photovoltaic module in the distributed photovoltaic system at each time through a data acquisition control rod.

[0097] In the step, the operation data of the target photovoltaic components in the distributed photovoltaic system at various times are acquired through a data acquisition control rod to form a time domain operation data sequence.

[0098] Specifically, in a distributed photovoltaic system, the data acquisition control rod is deployed near the target photovoltaic module and is responsible for collecting the operating data of the photovoltaic module in real time. These data include but are not limited to key parameters such as power generation, voltage, current, and temperature. The data acquisition control rod has built-in high-precision sensors and analog-to-digital converters, which can convert analog signals into digital signals for subsequent processing.

[0099] The data acquisition control rod can dynamically adjust the sampling frequency based on the operating conditions and continuously collect the operating data of the photovoltaic components at various times. Specifically, the current power generation data of the target photovoltaic component in the current period can be obtained, and the current power generation data includes the current power generation power and the current power generation power phase angle. The expected data collection frequency of the next period is determined based on the current power generation data, historical power generation data, and the current data collection frequency in the current period, so that the target photovoltaic component can collect power generation data according to the expected data collection frequency in the next period.

[0100] In the above scheme, the current power generation data of the target photovoltaic module in the current period is first obtained through the data acquisition control rod, which includes the current power generation and the current power generation phase angle. These two parameters are key indicators for evaluating the operating status of photovoltaic modules, and directly reflect the power generation efficiency and power quality of photovoltaic modules in the current period. By obtaining these parameters in real time, refined monitoring of the operating status of photovoltaic modules can be achieved.

[0101] On the basis of obtaining the current power generation data, the expected data collection frequency for the next period is intelligently determined according to the current power generation data, historical power generation data and the current data collection frequency in the current period. It reflects the dynamic adjustment and optimization of the data collection frequency.

[0102] Specifically, by comparing the current power generation data with the historical power generation data, the changing trend of the power generation status of the photovoltaic module can be identified. If the power generation status is relatively stable, that is, the current power generation data is not much different from the historical data, the data collection frequency of the next period can be appropriately reduced to reduce the amount of data transmission and processing burden. On the contrary, if there is a significant change in the power generation status, such as a sudden increase or decrease in power generation, or a significant shift in the phase angle, it may be necessary to increase the data collection frequency to more accurately capture these changes and ensure the integrity and accuracy of the data.

[0103] The technical effect of intelligently adjusting the data collection frequency is not only reflected in the accuracy of data collection, but also directly related to the quality and transmission efficiency of the data. By dynamically adjusting the collection frequency, it can be ensured that when the operating status of the photovoltaic module changes significantly, enough data points are collected to accurately reflect its changing trend; when the status is relatively stable, unnecessary data collection is reduced to avoid data redundancy. This targeted data collection strategy not only ensures the integrity and accuracy of the data, but also effectively reduces the amount of data transmission and improves the transmission efficiency. This is particularly important for distributed photovoltaic systems, because photovoltaic modules are usually distributed over a wide geographical area, and the bandwidth and delay of data transmission are one of the key factors restricting system performance.

[0104] In addition, intelligent adjustment of data acquisition frequency can also help optimize the allocation of system resources. In distributed photovoltaic systems, components such as data acquisition control rods, data transmission networks, and data management platforms all consume certain resources (such as computing resources, storage resources, network bandwidth, etc.). By dynamically adjusting the data acquisition frequency, these resources can be reasonably allocated according to actual needs, avoiding resource bottlenecks or waste during data acquisition and processing.

[0105] Furthermore, Formula 1 can be used, and according to the current power generation data, the historical power generation data and the current data collection frequency F t Determine the expected data collection frequency Ft+1 , expected data acquisition frequency F t+1 It is positively correlated with the number of running data in the time domain running data sequence, where formula 1 is:

[0106] F t+1 =F t (1+α·(P t -P t-1 )·sin(θ t -θ t-1 )·e -β·ΔT )

[0107] Among them, P t is the current power generation, P t-1 is the historical power generation in the previous period, θ t is the current power generation phase angle, θ t-1 is the historical power generation phase angle of the previous period, ΔT is the preset monitoring period, α is the preset data collection adjustment coefficient, and β is the preset time difference influence coefficient.

[0108] In the above scheme, by taking the difference between the current power generation and the historical power generation as one of the adjustment factors, Formula 1 can capture the real-time change trend of the output power of the photovoltaic power generation system. This dynamic adaptability ensures that when the power generation state of the system changes significantly, the data acquisition frequency can respond in time to adapt to the new power generation mode. The introduction of the phase angle of the power generation not only considers the change in the size of the power, but also pays attention to the phase information of the power fluctuation. This is of great significance for understanding the periodic behavior and potential fault warning of the photovoltaic power generation system, because the change in the phase angle can often reflect the subtle adjustment of the internal state of the system. The synergy of the preset monitoring cycle, the data acquisition adjustment coefficient and the time difference influence coefficient enables Formula 1 to intelligently adjust the growth or decay speed of the data acquisition frequency according to the length of the time interval. This fine-tuning mechanism helps to effectively balance the system load and data storage requirements while maintaining the integrity of data acquisition. It is expected that the data acquisition frequency is positively correlated with the number of operating data in the time domain operating data sequence, which means that as the system operating state changes, the data acquisition frequency can be automatically adjusted to adapt to higher data processing requirements. This not only improves the timeliness of data, but also optimizes the overall efficiency of data processing and reduces unnecessary overhead caused by frequent or sparse collection. Through the above mechanism, the system can dynamically adjust the frequency of data collection while ensuring the quality of data collection, thereby reducing unnecessary data collection from the source on the basis of data compression, thereby effectively avoiding the waste of resources and energy consumption caused by excessive collection.

[0109] S202. Using discrete Fourier transform, convert the time domain operation data sequence into a frequency domain operation data sequence, and use the absolute value of the maximum frequency in the frequency domain operation data sequence to normalize the frequency domain operation data sequence.

[0110] In this step, the time domain operation data sequence is converted into a frequency domain operation data sequence using discrete Fourier transform, and the frequency domain operation data sequence is normalized using the absolute value of the maximum frequency in the frequency domain operation data sequence to generate a frequency domain normalized data sequence.

[0111] Specifically, first, the data acquisition control rod receives the time domain operation data sequence from the photovoltaic module. This sequence contains the key operating parameters of the photovoltaic system at different time points, such as voltage, current, and power. Then, the discrete Fourier transform algorithm is used to convert the time domain operation data sequence from the time domain to the frequency domain to obtain the frequency domain operation data sequence. The discrete Fourier transform algorithm reveals the component distribution of the signal at different frequencies by calculating the inner product of the original signal and a series of complex exponential functions.

[0112] In order to eliminate the dimensional differences in the frequency domain operation data sequence and improve the stability and accuracy of subsequent processing, the present invention uses the absolute value of the maximum frequency in the frequency domain operation data sequence to normalize the frequency domain operation data sequence. Specifically, each frequency component is divided by the absolute value of the maximum frequency to obtain a frequency domain normalized data sequence. This step ensures that all frequency components are within the range of [-1,1], which is convenient for subsequent feature extraction and data processing.

[0113] S203, extracting a target feature running data sequence from the frequency domain running data sequence according to a preset acquisition frequency index range and a frequency domain normalized data sequence.

[0114] Specifically, according to the characteristics and monitoring requirements of the photovoltaic system, a collection frequency index range is pre-set. This range covers the most critical and sensitive frequency components in the photovoltaic system operation data. Based on the preset collection frequency index range and the frequency domain normalized data sequence, the target feature operation data sequence is extracted from the frequency domain operation data sequence. This step provides a more refined data basis for the subsequent wavelet transform by screening out the frequency components closely related to the operation status of the photovoltaic system.

[0115] S204, performing wavelet transform on the target feature operation data sequence to determine the target feature wavelet coefficient matrix.

[0116] Specifically, the extracted target feature operation data sequence is subjected to wavelet transform. Wavelet transform is a multi-scale analysis method that can provide localized information of the signal in both the time domain and the frequency domain. By selecting appropriate wavelet basis functions and decomposition layers, the target feature operation data sequence is decomposed into wavelet coefficients at different scales. The wavelet coefficients obtained by wavelet transform are organized into a target feature wavelet coefficient matrix. This matrix reflects the detailed information of the target feature operation data sequence at different scales and positions, and provides rich feature representation for subsequent data compression.

[0117] Specifically, using the first wavelet transform tree, the data sequence D = {d 1 ,d 2 ,…,d i ,…,d n} Perform wavelet decomposition to determine the first wavelet coefficient matrix W 1 ;

[0118] Using the second wavelet transform tree, the data sequence D = {d 1 ,d 2 ,…,d i ,…,d n} Perform wavelet decomposition to determine the second wavelet coefficient matrix W 2 ;

[0119] According to the first wavelet coefficient matrix W 1 And the second wavelet coefficient matrix W 2 Determine the target characteristic wavelet coefficient matrix, where the first wavelet coefficient matrix W 1 Used to construct the real part of each element in the target feature wavelet coefficient matrix, the second wavelet coefficient matrix W 2 Used to construct the imaginary part of each element in the target feature wavelet coefficient matrix.

[0120] Further, using the first wavelet transform tree, the data sequence D = {d 1 ,d 2 ,…,d i ,…,d n} Perform wavelet decomposition to determine the first wavelet coefficient matrix W 1 ,include:

[0121] Using formula 2, and running the data sequence D and the first mother wavelet function ψ of the first wavelet transform tree according to the target feature 1 Determine the first wavelet coefficient matrix W 1 , where Formula 2 is:

[0122]

[0123] in, is the first wavelet coefficient matrix W 1 The i-th element in , n is the number of running data in the target feature running data sequence D, d i is the i-th running data in the target feature running data sequence D, h(k) is the first preset filter coefficient, is the Daubechies wavelet basis function, and M is the length of the first preset filter.

[0124] In the above scheme, the target feature running data sequence is transformed using the first mother wavelet function of the first wavelet transform tree through formula 2, and the first wavelet coefficient matrix is ​​calculated. This process is essentially an efficient feature extraction of the original data sequence. Due to its multi-scale analysis capability, the wavelet transform can capture the local features of the data in different frequency ranges, thereby effectively reducing the dimension of the data while retaining key information. For the distributed photovoltaic data acquisition system, this means that while maintaining data accuracy, the burden of data transmission and storage can be significantly reduced, thereby improving the overall efficiency of the system.

[0125] The first mother wavelet function in formula 2 is constructed based on the Daubechies wavelet basis function. By presetting the linear combination of the filter coefficients and the basis function, a refined representation of the data is achieved. Due to its compact support and orthogonality, the Daubechies wavelet can provide good time-frequency localization characteristics, so that the wavelet coefficient matrix can accurately reflect the detailed characteristics of the original data at different scales. This provides a richer and more accurate information basis for subsequent data analysis and processing, such as anomaly detection and trend prediction.

[0126] Wavelet transform performs well in processing non-stationary signals and can effectively suppress noise interference. In the process of distributed photovoltaic data collection, the collected data often contains noise due to various reasons such as environmental factors and equipment status. Through wavelet decomposition, the signal and noise can be separated at different scales, and the high-frequency components in the wavelet coefficient matrix can be used to identify and remove noise, thereby enhancing the robustness and noise resistance of the data. This is of great significance for improving the accuracy and reliability of data collection. Further processing and analysis of the first wavelet coefficient matrix, such as multi-layer wavelet decomposition and reconstruction, can deeply explore the inherent laws and trends of the data, and provide scientific basis and decision-making support for the operation optimization, fault diagnosis, and energy scheduling of distributed photovoltaic systems.

[0127] Similarly, using the second wavelet transform tree, the data sequence D = {d 1 ,d 2 ,…,d i ,…,d n} Perform wavelet decomposition to determine the second wavelet coefficient matrix W2 ,include:

[0128] Using formula 3, and running the data sequence D and the second mother wavelet function ψ of the second wavelet transform tree according to the target feature 2 Determine the second wavelet coefficient matrix W 2 , where Formula 3 is:

[0129]

[0130] in, is the second wavelet coefficient matrix W 2 The i-th element in , g(k) is the second preset filter coefficient, δ is the Symlet wavelet basis function, L is the length of the second preset filter, and θ is the preset phase angle.

[0131] In the above scheme, by using formula 3 and the second mother wavelet function of the second wavelet transform tree, the target feature running data sequence can be efficiently decomposed by wavelet. This process not only retains the key information in the data, but also realizes the dimensionality reduction of the data. As an effective time-frequency analysis tool, wavelet transform can capture the local characteristics of data at different scales, which is helpful for subsequent data processing and analysis.

[0132] The calculation process in formula 3, especially through phase adjustment, ensures the accurate calculation of the second wavelet coefficient matrix. This accurate calculation method is crucial for subsequent steps such as data reconstruction, feature recognition and anomaly detection, and helps to improve the accuracy and reliability of the entire communication method. In the definition of the second mother wavelet function, the convolution form of the Symlet wavelet basis function δ and the preset filter coefficient is adopted, which gives the mother wavelet function great flexibility. As a compactly supported wavelet function, the Symlet wavelet has good time-frequency localization characteristics and symmetry, which helps to reduce reconstruction errors and improve signal processing efficiency. At the same time, by adjusting the preset filter coefficients and filter length, the effect of wavelet decomposition can be further optimized to meet the needs of different application scenarios. The preset phase angle introduced in formula 3 makes the wavelet coefficients contain not only the amplitude information of the data, but also the phase information. This is particularly important for processing complex signals in distributed photovoltaic data acquisition, because the phase information often contains the relative relationship and timing characteristics between signals. By retaining the phase information, the dynamic changes and potential laws in the data can be better captured, providing strong support for subsequent communication control.

[0133] Furthermore, according to the first wavelet coefficient matrix W 1 And the second wavelet coefficient matrix W 2 Determine the target characteristic wavelet coefficient matrix, including:

[0134] Using formula 4, and according to the first wavelet coefficient matrix W 1 And the second wavelet coefficient matrix W 2 Determine the target characteristic wavelet coefficient matrix W, where Formula 4 is:

[0135]

[0136] Among them, W i is the i-th element in the target feature wavelet coefficient matrix W.

[0137] In the above scheme, the i-th element of the target characteristic wavelet coefficient matrix in formula 4 is obtained by complex number operation of the first wavelet coefficient matrix and the second wavelet coefficient matrix. This complex number addition operation not only integrates the information from two different matrices, but also realizes the multi-dimensional representation of information by introducing the imaginary unit j, thereby enhancing the expressiveness and robustness of the data to a certain extent. In the scenario of distributed photovoltaic data acquisition, the data often contains a lot of noise and redundant information, which poses a challenge to subsequent data processing and analysis. By calculating the target characteristic wavelet coefficient matrix, the multi-scale analysis ability of the wavelet transform can be used to effectively extract the key frequency features from the original data. More importantly, due to the combination of information from two different matrices, the target characteristic wavelet coefficient matrix can better reflect the essential characteristics of the data, providing a more accurate and rich information basis for subsequent data processing. In the communication process, the amount of data transmitted is one of the key factors affecting the communication efficiency. By calculating the target characteristic wavelet coefficient matrix, the amount of data to be transmitted can be reduced while ensuring the data quality. This is because the target characteristic wavelet coefficient matrix not only contains the main features of the original data, but also realizes the compression and integration of information through complex number operations, thereby reducing the complexity of data transmission and improving communication efficiency. In the distributed photovoltaic data acquisition system, the accuracy and integrity of the data are crucial to the stable operation of the system. By calculating the target characteristic wavelet coefficient matrix, the data distortion caused by noise interference and packet loss during data transmission can be reduced to a certain extent. This is because the target characteristic wavelet coefficient matrix combines information from two different matrices and has strong anti-interference ability. It can restore or compensate for the data lost or distorted due to noise interference to a certain extent, thereby improving the reliability of the system.

[0138] S205, using Huffman coding, entropy coding the target characteristic wavelet coefficient matrix to determine running compressed data.

[0139] Specifically, the target characteristic wavelet coefficient matrix is ​​entropy encoded using the Huffman coding algorithm. Huffman coding is a lossless data compression method based on probability statistics. It assigns codewords of different lengths according to the probability of symbol occurrence, so that high-frequency symbols use shorter codewords and low-frequency symbols use longer codewords, thereby achieving data compression. After Huffman coding, the operation compressed data is obtained. This data represents the operation characteristics of the photovoltaic system in a more compact form, effectively reducing the amount of data transmission. The data acquisition control rod then sends the operation compressed data to the central monitoring system through the communication network for subsequent data analysis and processing.

[0140] S206. The photovoltaic data management platform uses a preset data decompression model and generates operation decompression data according to the operation compression data.

[0141] In this step, the photovoltaic data management platform uses a preset data decompression model and generates operation decompression data based on the operation compression data to store the operation decompressed data in the operation data list corresponding to the target photovoltaic component, wherein the preset data decompression model is an inverse transformation model of the preset data compression model.

[0142] Specifically, after receiving the operation compressed data sent by the data acquisition control rod, the photovoltaic data management platform uses a preset data decompression model to process it. The preset data decompression model is an inverse transformation model of the preset data compression model, which can accurately restore the original time domain operation data sequence.

[0143] Specifically, the photovoltaic data management platform first performs inverse Huffman coding on the running compressed data to obtain the target feature running data sequence. Then, the real part of each element in the target feature running data sequence is processed by the first wavelet inverse transform based on the first inverse transform function corresponding to the first wavelet transform tree, and the imaginary part is processed by the second wavelet inverse transform based on the second inverse transform function corresponding to the second wavelet transform tree, and the inverse discrete Fourier transform is performed. Through this process, the photovoltaic data management platform can accurately restore the original time domain running data sequence.

[0144] Finally, the photovoltaic data management platform stores the restored time domain operation data sequence (i.e., operation decompressed data) in the operation data list corresponding to the target photovoltaic module. The operation data list records the operation status of the photovoltaic module at each moment in chronological order, which facilitates subsequent data analysis and processing. At the same time, the photovoltaic data management platform can also perform real-time monitoring, fault diagnosis, and performance optimization of photovoltaic modules based on the operation data list, thereby improving the overall efficiency and stability of the distributed photovoltaic system.

[0145] Figure 3 It is a schematic diagram of the structure of a photovoltaic data management platform according to an example embodiment of the present application.

[0146] like Figure 3 As shown, the photovoltaic data management platform 300 provided in this embodiment includes:

[0147] The acquisition module 310 is used to acquire the operation data of the target photovoltaic component in the distributed photovoltaic system at each time through the data acquisition control rod to form a time domain operation data sequence;

[0148] A processing module 320 is used to determine operation compression data by using a preset data compression model using the data acquisition control rod and according to the time domain operation data sequence, wherein the preset data compression model is used to extract partial data of the time domain operation data sequence in a target frequency space;

[0149] The processing module 320 is further used to utilize a preset data decompression model and generate operation decompression data according to the operation compression data, so as to store the operation decompressed data in an operation data list corresponding to the target photovoltaic component, wherein the preset data decompression model is an inverse transformation model of the preset data compression model.

[0150] Optionally, the processing module 320 is specifically configured to:

[0151] Using discrete Fourier transform, the time domain operation data sequence is converted into a frequency domain operation data sequence, and the frequency domain operation data sequence is normalized using the absolute value of the maximum frequency in the frequency domain operation data sequence to generate a frequency domain normalized data sequence;

[0152] Extracting a target feature operation data sequence from the frequency domain operation data sequence according to a preset acquisition frequency index range and the frequency domain normalized data sequence;

[0153] Performing wavelet transform on the target feature operation data sequence to determine the target feature wavelet coefficient matrix;

[0154] The target characteristic wavelet coefficient matrix is ​​entropy encoded by using Huffman coding to determine the running compressed data.

[0155] Optionally, the processing module 320 is specifically configured to:

[0156] Using a first wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a first wavelet coefficient matrix;

[0157] Using a second wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a second wavelet coefficient matrix;

[0158] The target feature wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix.

[0159] Optionally, the processing module 320 is specifically configured to:

[0160] The first wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the first mother wavelet function of the first wavelet transform tree, wherein the first mother wavelet function is a Daubechies wavelet basis function.

[0161] Optionally, the processing module 320 is specifically configured to:

[0162] The second wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the second mother wavelet function of the second wavelet transform tree, wherein the second mother wavelet function is a Symlet wavelet basis function.

[0163] Optionally, the processing module 320 is specifically configured to:

[0164] The target characteristic wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix, wherein the first wavelet coefficient matrix is ​​used to construct the real part of each element in the target characteristic wavelet coefficient matrix, and the second wavelet coefficient matrix is ​​used to construct the imaginary part of each element in the target characteristic wavelet coefficient matrix.

[0165] Optionally, the processing module 320 is specifically configured to:

[0166] Performing inverse Huffman coding on the running compressed data to obtain the target feature running data sequence;

[0167] The real part of each element in the target feature operation data sequence is subjected to a first wavelet inverse transform based on the first inverse transform function corresponding to the first wavelet transform tree, and the imaginary part of each element in the target feature operation data sequence is subjected to a second wavelet inverse transform based on the second inverse transform function corresponding to the second wavelet transform tree, so as to generate the operation decompressed data.

[0168] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0169] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0170] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0171] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0172] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0173] The bus 403 is used to connect the memory 402 and the processor 401 .

[0174] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0175] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0176] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0177] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A communication method for distributed photovoltaic data acquisition control rods, characterized in that: include: The operation data of the target photovoltaic components in the distributed photovoltaic system at each time is obtained through the data acquisition control rod to form a time domain operation data sequence; The data acquisition control rod uses a preset data compression model and determines the operation compression data according to the time domain operation data sequence, and sends the operation compression data to the photovoltaic data management platform. The preset data compression model is used to extract part of the data of the time domain operation data sequence in the target frequency space; The photovoltaic data management platform utilizes a preset data decompression model and generates operation decompression data based on the operation compression data, so as to store the operation decompressed data in an operation data list corresponding to the target photovoltaic component, wherein the preset data decompression model is an inverse transformation model of the preset data compression model.

2. The communication method for distributed photovoltaic data acquisition control rod according to claim 1, characterized in that: The data acquisition control rod uses a preset data compression model and determines the operation compression data according to the time domain operation data sequence, including: Using discrete Fourier transform, the time domain operation data sequence is converted into a frequency domain operation data sequence, and the frequency domain operation data sequence is normalized using the absolute value of the maximum frequency in the frequency domain operation data sequence to generate a frequency domain normalized data sequence; Extracting a target feature operation data sequence from the frequency domain operation data sequence according to a preset acquisition frequency index range and the frequency domain normalized data sequence; Performing wavelet transform on the target feature operation data sequence to determine the target feature wavelet coefficient matrix; The target characteristic wavelet coefficient matrix is ​​entropy encoded by using Huffman coding to determine the running compressed data.

3. The communication method for distributed photovoltaic data acquisition control rod according to claim 2, characterized in that: The step of performing wavelet transform on the target feature operation data sequence to determine the target feature wavelet coefficient matrix includes: Using a first wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a first wavelet coefficient matrix; Using a second wavelet transform tree, performing wavelet decomposition on the target feature operation data sequence to determine a second wavelet coefficient matrix; The target feature wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix.

4. The communication method for distributed photovoltaic data acquisition control rod according to claim 3, characterized in that: The step of using the first wavelet transform tree to perform wavelet decomposition on the target feature operation data sequence to determine a first wavelet coefficient matrix includes: The first wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the first mother wavelet function of the first wavelet transform tree, wherein the first mother wavelet function is a Daubechies wavelet basis function.

5. The communication method for distributed photovoltaic data acquisition control rod according to claim 4, characterized in that: The step of using the second wavelet transform tree to perform wavelet decomposition on the target feature operation data sequence to determine a second wavelet coefficient matrix includes: The second wavelet coefficient matrix is ​​determined according to the target feature running data sequence and the second mother wavelet function of the second wavelet transform tree, wherein the second mother wavelet function is a Symlet wavelet basis function.

6. The communication method for distributed photovoltaic data acquisition control rod according to claim 5, characterized in that: The determining the target feature wavelet coefficient matrix according to the first wavelet coefficient matrix and the second wavelet coefficient matrix comprises: The target characteristic wavelet coefficient matrix is ​​determined according to the first wavelet coefficient matrix and the second wavelet coefficient matrix, wherein the first wavelet coefficient matrix is ​​used to construct the real part of each element in the target characteristic wavelet coefficient matrix, and the second wavelet coefficient matrix is ​​used to construct the imaginary part of each element in the target characteristic wavelet coefficient matrix.

7. The communication method for distributed photovoltaic data acquisition control rod according to claim 6, characterized in that: The photovoltaic data management platform uses a preset data decompression model and generates operation decompression data according to the operation compression data, including: Performing inverse Huffman coding on the running compressed data to obtain the target feature running data sequence; The real part of each element in the target feature operation data sequence is subjected to a first wavelet inverse transform based on the first inverse transform function corresponding to the first wavelet transform tree, and the imaginary part of each element in the target feature operation data sequence is subjected to a second wavelet inverse transform based on the second inverse transform function corresponding to the second wavelet transform tree, so as to generate the operation decompressed data.

8. A photovoltaic data management platform, characterized in that: include: An acquisition module is used to acquire the operation data of the target photovoltaic components in the distributed photovoltaic system at various times through a data acquisition control rod to form a time domain operation data sequence; a processing module, configured to determine operation compression data by using a preset data compression model using the data acquisition control rod and according to the time domain operation data sequence, wherein the preset data compression model is used to extract partial data of the time domain operation data sequence in a target frequency space; The processing module is also used to use a preset data decompression model to generate operation decompression data according to the operation compression data, so as to store the operation decompressed data in an operation data list corresponding to the target photovoltaic component, wherein the preset data decompression model is an inverse transformation model of the preset data compression model.

9. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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