Synchronization detection method and device for data acquisition of photovoltaic module

Abstract

The invention relates to the technical field of data processing, and discloses a synchronization detection method and device for photovoltaic module data acquisition. The method comprises the following steps: performing zero clearing based on 1PPS signals and 10MHz clock driving processing on an internal counter of slave station equipment to obtain a nanosecond timing system synchronized with a GPS timing module; performing high-speed sampling and timestamp embedding processing on the analog photovoltaic module output signal to obtain a time sequence data stream with microsecond resolution; performing block encryption and efficient transmission processing on the time sequence data stream to obtain a complete encrypted data packet in the master station equipment; carrying out decryption and spectrum analysis processing on the encrypted data packet to obtain a time-frequency characteristic index reflecting collection synchronism; and performing pattern recognition and anomaly detection processing on the time-frequency characteristic index to obtain a quantized synchronism evaluation index and a potential problem diagnosis result. According to the invention, the efficiency and accuracy of synchronization detection of photovoltaic module data acquisition are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a synchronization detection method and device for photovoltaic module data collection. Background Art

[0002] Photovoltaic module data acquisition system plays a vital role in the field of solar power generation. It is responsible for real-time monitoring and recording of various performance parameters of photovoltaic modules, such as voltage, current, power, etc. Traditional data acquisition systems usually adopt a distributed structure, with each photovoltaic module or string equipped with an independent acquisition device, and then transmitting data to the central control system through a communication network. This method can provide detailed component-level data, which helps to optimize system performance and detect faults in a timely manner. However, as the scale of photovoltaic power stations continues to expand, the number of acquisition points has increased sharply, and the requirements for data synchronization have also increased accordingly.

[0003] The data acquisition system in the prior art has some deficiencies in terms of synchronization. First, the acquisition devices distributed in different locations often use their own local clocks, and there is drift between these clocks, resulting in errors in the timestamps of the collected data. Secondly, delays and jitters in the data transmission process will also affect the synchronization of the data. In addition, traditional systems usually lack an effective synchronization evaluation mechanism, making it difficult to quantify and monitor the synchronization accuracy of data acquisition. These problems may lead to deviations in data analysis results and affect the accuracy of power plant performance evaluation and fault diagnosis. Summary of the invention

[0004] The present application provides a method and device for detecting synchronization of photovoltaic module data collection, which are used to improve the efficiency and accuracy of synchronization detection of photovoltaic module data collection.

[0005] In a first aspect, the present application provides a method for detecting synchronization of photovoltaic module data collection, the method comprising:

[0006] Perform phase comparison on the output signals of the two GPS timing modules to obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds.

[0007] The internal counter of the slave device is cleared based on the 1PPS signal and driven by the 10MHz clock to obtain a nanosecond timing system synchronized with the GPS timing module;

[0008] Perform high-speed sampling and time stamp embedding processing on the analog photovoltaic module output signal to obtain a time series data stream with microsecond resolution;

[0009] Perform block encryption and efficient transmission processing on the time series data stream to obtain a complete encrypted data packet in the master station device;

[0010] The encrypted data packets are decrypted and spectrum analyzed through Fourier transform and autocorrelation analysis to obtain time-frequency characteristic indicators reflecting the synchronization of acquisition;

[0011] The time-frequency characteristic indicators are processed by pattern recognition and anomaly detection to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results.

[0012] In a second aspect, the present application provides a synchronization detection device for photovoltaic module data collection, the synchronization detection device for photovoltaic module data collection comprising:

[0013] The comparison module is used to perform phase comparison processing on the output signals of the two GPS timing modules to obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds;

[0014] The driver module is used to perform zero clearing and 10MHz clock driving processing on the internal counter of the slave device based on the 1PPS signal, so as to obtain a nanosecond timing system synchronized with the GPS timing module;

[0015] An embedding module is used to perform high-speed sampling and time stamp embedding processing on the analog photovoltaic module output signal to obtain a time series data stream with microsecond resolution;

[0016] The transmission module is used to perform block encryption and efficient transmission processing on the time series data stream to obtain a complete encrypted data packet in the master station device;

[0017] An analysis module is used to decrypt and perform spectrum analysis on encrypted data packets through Fourier transform and autocorrelation analysis to obtain time-frequency characteristic indicators reflecting acquisition synchronization;

[0018] The detection module is used to perform pattern recognition and anomaly detection on time-frequency feature indicators to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results.

[0019] In the technical solution provided by the present application, by accurately comparing the output signals of the two GPS timing modules, a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds can be obtained, which lays a solid foundation for the time synchronization of the entire system and ensures the time consistency of data acquisition. Secondly, the internal counter of the slave device is cleared and driven by a 10MHz clock based on the 1PPS signal, realizing a nanosecond timing system synchronized with the GPS timing module, which greatly improves the accuracy of the timestamp and makes the time resolution of the collected data reach an unprecedented level. On this basis, the analog photovoltaic module output signal is sampled at high speed and the timestamp is embedded to obtain a time series data stream with microsecond resolution, which not only ensures the high time accuracy of the data, but also provides rich information for subsequent synchronization analysis. The time series data stream is encrypted in blocks and efficiently transmitted to obtain a complete encrypted data packet in the master station device, which not only ensures the security of data transmission, but also maintains the integrity and time consistency of the data. The encrypted data packet is decrypted and spectral analyzed by Fourier transform and autocorrelation analysis to obtain a time-frequency characteristic index reflecting the synchronization of acquisition. This method can not only effectively evaluate the synchronization of data, but also reveal potential synchronization problems. Finally, the time-frequency characteristic indicators are processed for pattern recognition and anomaly detection to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results, which provides intuitive and quantitative synchronization evaluation results for system managers and helps to timely discover and solve synchronization problems. In summary, this method achieves high-precision and high-reliability synchronization detection of photovoltaic module data collection through a series of innovative technical means. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 A schematic diagram of an embodiment of a method for detecting synchronization of photovoltaic module data collection in an embodiment of the present application;

[0022] Figure 2 Schematic diagram of an embodiment of a synchronization detection device for photovoltaic module data collection in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application provide a synchronization detection method and device for photovoltaic module data collection. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for detecting synchronization of photovoltaic module data collection includes:

[0025] Step S101, performing phase comparison processing on the output signals of the two GPS timing modules to obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds;

[0026] Step S102, clearing the internal counter of the slave device based on the 1PPS signal and driving the 10MHz clock to obtain a nanosecond timing system synchronized with the GPS timing module;

[0027] Step S103, performing high-speed sampling and time stamp embedding processing on the analog photovoltaic module output signal to obtain a time series data stream with microsecond resolution;

[0028] Step S104, performing block encryption and efficient transmission processing on the time series data stream to obtain a complete encrypted data packet in the master station device;

[0029] Step S105, decrypting and spectrum analyzing the encrypted data packet through Fourier transform and autocorrelation analysis to obtain time-frequency characteristic indicators reflecting acquisition synchronization;

[0030] Step S106: Perform pattern recognition and anomaly detection processing on the time-frequency feature indicators to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results.

[0031] It is understandable that the execution subject of the present application may be a synchronization detection device for photovoltaic module data collection, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0032] Specifically, the output signals of the two GPS timing modules are subjected to phase comparison processing, which involves the precise measurement and comparison of the 1PPS and 10MHz signals output by the GPS modules. The server uses a high-precision time interval counter to simultaneously capture the 1PPS rising edges of the two GPS modules and measure the time difference between them. Through multiple measurements and statistical analysis, the time deviation between the two GPS modules can be obtained. At the same time, the server also performs phase comparison on the 10MHz signal, and controls the phase difference of the two 10MHz signals within a very small range through digital phase-locked loop technology. After these processes, the server can obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds. Next, the server performs zeroing and 10MHz clock driving processing on the internal counter of the slave device based on the 1PPS signal. This step first uses the 1PPS signal output by the GPS module as a trigger source to clear the internal counter of the slave at the beginning of each second. At the same time, the server uses the 10MHz signal output by the GPS module as the driving clock of the internal counter of the slave. In this way, the internal counter of the slave device can be highly synchronized with the GPS timing module to achieve nanosecond timing accuracy.

[0033] Subsequently, the server performs high-speed sampling and timestamp embedding processing on the analog PV module output signal. This step uses a high-speed analog-to-digital converter to sample the output voltage and current of the PV module, and the sampling rate is usually set between tens of kilohertz and hundreds of kilohertz to capture the rapid changes in the output of the PV module. Each sampling point is assigned a precise timestamp, which comes from the previously synchronized nanosecond timing system. In this way, the server obtains a time series data stream with microsecond resolution, which not only contains the output information of the PV module, but also carries precise time information. In order to ensure data security and transmission efficiency, the server performs block encryption and efficient transmission processing on the time series data stream. First, the continuous time series data stream is divided into fixed-size data blocks, each of which usually contains a few seconds of sampling data. Then, the server encrypts each data block using a symmetric encryption algorithm (such as AES) and calculates the checksum of the data block. The encrypted data block and checksum information are packaged into data packets and then transmitted to the master device through a secure network protocol (such as TLS). After receiving these data packets, the master device decrypts and verifies the integrity, and finally obtains a complete encrypted data packet.

[0034] The server decrypts and performs spectrum analysis on the encrypted data packets through Fourier transform and autocorrelation analysis. First, the encrypted data packets are decrypted to restore the original time series data. Then, the server performs fast Fourier transform (FFT) on these data to convert the time domain signal into a frequency domain representation. By analyzing the characteristics of the spectrum, such as the amplitude and phase of the main frequency components, information about the periodicity and stability of the signal can be obtained. At the same time, the server also calculates the autocorrelation function of the data, which helps to discover repetitive patterns and long-term trends in the signal. By combining the analysis results of the frequency domain and time domain, the server generates time-frequency feature indicators that reflect the synchronization of the acquisition. Finally, the server performs pattern recognition and anomaly detection on the time-frequency feature indicators. This step involves the application of machine learning algorithms. The server first uses dimensionality reduction techniques (such as principal component analysis PCA) to reduce the dimension of the feature space, and then uses clustering algorithms (such as K-means) to classify the reduced features. By comparing the distribution patterns of current data with historical normal data, the server is able to identify abnormal synchronization patterns. In addition, the server also establishes a time series-based prediction model to detect long-term trends in synchronization. Through these analyses, the server ultimately generates quantitative synchronization evaluation indicators and potential problem diagnosis results.

[0035] For example, suppose a PV power plant has 100 PV modules, each collecting 10 data points per second. The server first ensures the synchronization accuracy of the GPS timing module. The measurement results show that the time deviation of the two GPS modules is stable within 8 nanoseconds. Then, the server synchronizes all slave devices, and the timing error of the internal counter is controlled within 50 nanoseconds. During the data collection process, the server processes 1000 data points per second, each data point contains a 64-bit sample value and a 64-bit timestamp. The data is divided into data blocks of 256 data points each, and is encrypted and transmitted to the master station. After receiving the data, the master station performs decryption and spectrum analysis. Through FFT analysis, the server finds that the data of all components have significant frequency peaks at 50Hz and 100Hz, which is consistent with the grid frequency. Autocorrelation analysis shows that the data has a strong 20ms periodicity, further confirming the synchronization of the collection. Finally, through the pattern recognition algorithm, the server detects that the data collection time of one of the components has a slight drift, and the time deviation shows a slowly increasing trend, increasing by about 1 microsecond per hour. Based on these analyses, the server gave a 98% synchronization score and recommended further inspection and calibration of the drifting components.

[0036] In the embodiment of the present application, by accurately comparing the output signals of the two GPS timing modules, a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds can be obtained, which lays a solid foundation for the time synchronization of the entire system and ensures the time consistency of data acquisition. Secondly, the internal counter of the slave device is cleared and driven by a 10MHz clock based on the 1PPS signal, and a nanosecond timing system synchronized with the GPS timing module is realized, which greatly improves the accuracy of the timestamp and makes the time resolution of the collected data reach an unprecedented level. On this basis, the analog photovoltaic module output signal is sampled at high speed and the timestamp is embedded to obtain a time series data stream with microsecond resolution, which not only ensures the high time accuracy of the data, but also provides rich information for subsequent synchronization analysis. The time series data stream is encrypted and efficiently transmitted in blocks to obtain a complete encrypted data packet in the master station device, which not only ensures the security of data transmission, but also maintains the integrity and time consistency of the data. The encrypted data packet is decrypted and spectral analyzed by Fourier transform and autocorrelation analysis to obtain a time-frequency characteristic index reflecting the synchronization of acquisition. This method can not only effectively evaluate the synchronization of data, but also reveal potential synchronization problems. Finally, the time-frequency characteristic indicators are processed for pattern recognition and anomaly detection to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results, which provide system managers with intuitive and quantitative synchronization evaluation results and help to timely discover and solve synchronization problems. This method achieves high-precision and high-reliability synchronization detection of photovoltaic module data collection through a series of innovative technical means.

[0037] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0038] (1) Receive and process satellite signals from two GPS timing modules to obtain raw satellite navigation data, and decode the raw satellite navigation data to obtain accurate time code information;

[0039] (2) performing time scale conversion processing on the time code information to obtain a unified Coordinated Universal Time time reference, and performing local clock calibration processing on the time reference to obtain an initial synchronized local clock signal;

[0040] (3) performing frequency stability evaluation processing on the local clock signal to obtain a frequency drift parameter, and performing compensation processing on the local clock signal according to the frequency drift parameter to obtain a frequency-corrected clock signal;

[0041] (4) performing phase noise analysis on the frequency-corrected clock signal to obtain phase jitter characteristics, and filtering the frequency-corrected clock signal according to the phase jitter characteristics to obtain a low-jitter accurate clock signal;

[0042] (5) Perform cross-correlation analysis on the precise clock signals output by the two GPS timing modules to obtain the time deviation between the signals, and perform statistical analysis on the time deviation to obtain the time deviation distribution characteristics;

[0043] (6) fine-tuning the precise clock signal according to the time deviation distribution characteristics to obtain a candidate synchronization time reference, and performing Allan variance analysis on the candidate synchronization time reference to obtain a time stability index;

[0044] (7) Screening candidate synchronization time references according to the time stability index to obtain the optimal synchronization time reference, and performing long-term drift prediction on the optimal synchronization time reference to obtain a drift compensation model;

[0045] (8) The optimal synchronization time reference is corrected in real time according to the drift compensation model to obtain a high-precision synchronization time reference with a time deviation of less than 15 nanoseconds.

[0046] Specifically, the two GPS timing modules are processed to receive satellite signals and obtain raw satellite navigation data. This process involves receiving GPS signals in different frequency bands such as L1 and L2, and performing preliminary signal demodulation. Subsequently, these raw data are decoded and processed to extract accurate time code information. The time code information contains key information such as GPS system time and ephemeris data, providing a basis for subsequent time synchronization. The server performs time scale conversion on the time code information and converts the GPS system time into a unified Coordinated Universal Time (UTC) time reference. This step takes into account factors such as leap second correction and relativistic effects to ensure the accuracy of the time reference. Then, the server performs local clock calibration on this UTC time reference, compares and adjusts it with the output of the local oscillator, and obtains the initial synchronized local clock signal.

[0047] In order to further improve the accuracy of the clock signal, the server performs frequency stability evaluation on the local clock signal. This step involves long-term signal sampling and analysis to calculate the frequency deviation and drift rate of the signal. Through these calculations, the server obtains the frequency drift parameters and uses these parameters to compensate the local clock signal to obtain a frequency-corrected clock signal. The frequency-corrected clock signal also needs to be analyzed for phase noise. The server uses spectrum analysis technology to measure the phase noise level of the signal at different frequency deviations to obtain phase jitter characteristics. Based on these characteristics, the server designs and applies digital filters to filter the clock signal, ultimately obtaining a low-jitter, accurate clock signal.

[0048] In order to ensure the synchronization between the two GPS timing modules, the server performs cross-correlation analysis on the precise clock signals they output. By calculating the cross-correlation function of the two signals, the server can accurately measure the time deviation between the signals. These deviation values ​​are statistically analyzed, including calculating statistics such as the mean and standard deviation, to obtain the time deviation distribution characteristics. Based on the time deviation distribution characteristics, the server fine-tunes the precise clock signal to generate candidate synchronization time benchmarks. In order to evaluate the stability of these candidate benchmarks, the server performs Allan variance analysis on them. Allan variance is an important indicator for measuring frequency stability. By calculating the Allan variance at different sampling times, the server obtains a comprehensive time stability indicator.

[0049] According to the time stability index, the server screens the candidate synchronization time bases and selects the optimal synchronization time base. In order to cope with the drift that may occur in long-term operation, the server also performs long-term drift prediction processing on the optimal synchronization time base and establishes a drift compensation model. This model is usually based on trend analysis of historical data and can predict clock drift in the future. Finally, the server performs real-time correction processing on the optimal synchronization time base according to the drift compensation model. This step involves continuous monitoring and fine-tuning to ensure that the time base always maintains high accuracy. Through this series of complex processing steps, the server finally obtains a high-precision synchronization time base with a time deviation of less than 15 nanoseconds.

[0050] For example, in a data acquisition system of a large photovoltaic power station, two GPS timing modules received signals from eight satellites respectively. The decoded time code information showed that the initial time difference between the two modules was 100 nanoseconds. After UTC conversion and local clock calibration, this difference was reduced to 50 nanoseconds. Frequency stability evaluation showed that the frequency drift rates of the two modules were 1×10^(-11) and 1.2×10^(-11), respectively. Phase noise analysis found that at a frequency offset of 1Hz, the phase noise levels of the two modules were -130dBc / Hz and -128dBc / Hz, respectively. After filtering, this value was improved to -135dBc / Hz. Cross-correlation analysis showed that the mean time deviation of the two modules was 5 nanoseconds and the standard deviation was 2 nanoseconds. Allan variance analysis results showed that at a sampling time of 1000 seconds, the frequency stability of both modules reached the order of 1×10^(-13). Ultimately, through real-time correction, the system maintained a synchronization accuracy of 12 nanoseconds between the two modules, meeting the requirements of high-precision time synchronization and providing a reliable time reference for the accurate collection and analysis of PV module data.

[0051] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0052] (1) Perform rising edge detection on the 1PPS signal output by the GPS timing module to obtain an accurate pulse-per-second trigger point, and perform delay compensation on the pulse-per-second trigger point to obtain a corrected clear trigger signal;

[0053] (2) Performing a hardware reset process on the internal counter of the slave device to obtain a counter in an initialized state, and performing a synchronous zero reset process on the counter in the initialized state according to the corrected zero reset trigger signal to obtain an aligned counting starting point;

[0054] (3) Perform frequency division processing on the 10 MHz clock signal output by the GPS timing module to obtain a driving clock that matches the internal counter, and perform phase adjustment processing on the driving clock to obtain a driving pulse synchronized with the reset trigger signal;

[0055] (4) performing count increment processing on the internal counter according to the driving pulse to obtain the original count value, and performing time unit conversion processing on the original count value to obtain the time count with nanosecond resolution;

[0056] (5) Periodically calibrate the nanosecond resolution time count to obtain long-term stable timing data, and format the timing data to obtain a standard timestamp format;

[0057] (6) Perform GPS time synchronization processing on the standard timestamp format to obtain a nanosecond timing system synchronized with the GPS timing module.

[0058] Specifically, the rising edge detection process is performed on the 1PPS (pulse per second) signal output by the GPS timing module. This process uses a high-speed comparator and a precision time interval measurement circuit to capture the rising edge of the 1PPS signal and obtain an accurate pulse per second trigger point. Subsequently, the server performs delay compensation processing on this trigger point, taking into account factors such as the signal transmission path and circuit response time, and corrects it through the pre-measured delay value, and finally obtains the corrected reset trigger signal. The server performs a hardware reset process on the internal counter of the slave device. This step places the counter in a known initial state by sending a level signal to the reset pin of the counter. Then, based on the previously obtained corrected reset trigger signal, the server performs a synchronous reset process on the counter in the initialized state. This process ensures that the counters of all slave devices start counting at the same time, thereby obtaining an aligned counting starting point.

[0059] To provide accurate count increments, the server divides the 10MHz clock signal output by the GPS timing module. The 10MHz signal is divided into a drive clock that matches the internal counter, usually 1MHz or 100kHz, through a programmable divider. The server then phase-adjusts this drive clock to synchronize it with the clear trigger signal to obtain precisely aligned drive pulses. Based on these drive pulses, the server increments the internal counter. Each pulse causes the counter to add 1, resulting in the raw count value. To improve the time resolution, the server converts these raw count values ​​into time units. For example, if the drive clock is 1MHz, each count increment represents 1 microsecond, and the server converts it to 1000 nanoseconds, resulting in a time count with nanosecond resolution.

[0060] To maintain long-term stability, the server periodically calibrates the nanosecond resolution time counts. This process involves periodically comparing with GPS time, calculating the accumulated error, and compensating for it. In this way, the server obtains long-term stable timing data. The server then formats the timing data using time codes, converting the nanosecond counts into a standard timestamp format of year, month, day, hour, minute, second, and nanosecond. Finally, the server synchronizes the standard timestamp format with GPS time. This step involves comparing and adjusting the local timestamp with the precise time information provided by the GPS timing module, taking into account factors such as leap seconds and time zones, and ultimately obtaining a nanosecond timing system that is fully synchronized with the GPS timing module.

[0061] For example, in a data acquisition system of a large photovoltaic power station, the 1PPS signal output by the GPS timing module is detected by the rising edge, and the signal rise time is measured to be 5 nanoseconds. Considering that the signal transmission delay is 15 nanoseconds, the server performs a 20 nanosecond delay compensation on the trigger point. After the hardware reset, the internal counter of the slave device is immediately cleared synchronously according to the compensated trigger signal. The 10MHz clock signal of the GPS module is divided by 8 to obtain a 1.25MHz driving clock. The server aligns its phase with the clearing trigger signal through the phase-locked loop technology, and the phase error is controlled within 1 nanosecond. The internal counter counts increments at a frequency of 1.25MHz, and every 800 counts represent 1 millisecond. The server converts this count value into nanoseconds to obtain a time count with nanosecond resolution. By comparing and calibrating with the GPS time once every hour, the server controls the timing error within 50 nanoseconds / hour. After the time code is formatted, a standard timestamp such as "2023-04-15 14:30:25.123456789" is generated. Finally, by synchronizing with GPS time, the server adjusts the timestamp accordingly, taking into account the current 18-second leap second, and finally obtains a GPS time synchronization timing system accurate to nanoseconds. Through this series of processing, the server achieves high-precision time marking of photovoltaic module output data, providing a reliable time benchmark for subsequent synchronization analysis and performance evaluation.

[0062] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0063] (1) performing signal conditioning processing on the analog photovoltaic module output signal to obtain a standardized signal suitable for sampling, and performing anti-aliasing filtering processing on the standardized signal to obtain a band-limited signal;

[0064] (2) performing multi-bit ADC conversion processing on the band-limited signal to obtain a high-precision digital sampling value, and performing quantization error compensation processing on the high-precision digital sampling value to obtain a corrected digital signal;

[0065] (3) performing time stamp processing on the corrected digital signal according to the nanosecond timing system to obtain sampling points with precise time information, and performing data packaging processing on the sampling points with precise time information to obtain time-synchronized data frames;

[0066] (4) performing data compression processing on the time-synchronized data frame to obtain a data stream in an efficient storage format, and performing cache management processing on the data stream in the efficient storage format to obtain a data buffer updated in real time;

[0067] (5) Performing data integrity check on the real-time updated data buffer to obtain a verified data sequence, and performing time alignment on the verified data sequence to obtain a time series data stream with microsecond resolution.

[0068] Specifically, the analog photovoltaic module output signal is subjected to signal conditioning. This process includes using a precision operational amplifier for signal amplification, adjusting the typical 0-50V voltage signal to the 0-5V range, and using a high-precision reference voltage source for level biasing to ensure that the signal fully utilizes the input range of the ADC. Next, the conditioned signal is subjected to anti-aliasing filtering, using a Butterworth low-pass filter with a cutoff frequency set to 1 / 2.56 of the sampling frequency to effectively suppress high-frequency noise and aliasing effects, and obtain a band-limited signal suitable for sampling. Subsequently, the server performs multi-bit ADC conversion processing on the band-limited signal. A 24-bit sigma-delta ADC is used with a sampling rate set to 100kHz to achieve high-precision digitization. The raw data output by the ADC is subjected to digital filtering and jitter shaping to further increase the number of effective bits. To compensate for the nonlinear error of the ADC, the server performs quantization error compensation processing on the high-precision digital sampled values. This step uses a lookup table (LUT) method to fine-tune each sampled value according to a pre-calibrated ADC error curve to obtain a corrected digital signal.

[0069] Next, the server timestamps the corrected digital signal according to the nanosecond timing system. Each sampling point is assigned a timestamp accurate to nanoseconds, which comes from the previously synchronized GPS time system. The timestamp and the sampling value are packaged together into a data frame, each of which contains a fixed number of sampling points (such as 1000) and the corresponding time information to form a time-synchronized data frame. In order to improve data transmission and storage efficiency, the server performs data compression on the time-synchronized data frame. Using lossless compression algorithms, such as differential coding combined with Huffman coding, the original data is compressed to 50-60% of the original size to obtain a data stream in an efficient storage format. These compressed data streams are stored in a circular buffer to achieve real-time updated data buffer management. The buffer size is dynamically adjusted according to system latency and data processing speed, and usually maintains a data volume of 10-30 seconds.

[0070] Finally, the server performs a data integrity check on the real-time updated data buffer. Using the cyclic redundancy check (CRC) algorithm, the check value of each data frame is calculated and compared with the check value attached during transmission to ensure that the data has not been tampered with or lost during transmission. For the data sequence that has passed the verification, the server performs time alignment. This step involves an interpolation algorithm to align the data of different acquisition devices to a unified time point, and finally obtain a time series data stream with microsecond resolution. For example, in a data acquisition system of a 100kW photovoltaic power station, the output voltage of a photovoltaic module fluctuates in the range of 30-40V. The signal conditioning circuit converts it into a standard signal of 1-3V, which is filtered by an 8th-order Butterworth filter (cutoff frequency 19.5kHz) and sent to a 24-bit ADC for sampling. The ADC samples at a frequency of 100kHz, and each sampled value occupies 32 bits (24 effective bits + 8 status bits). Quantization error compensation increases the effective number of bits of the ADC from 21.5 bits to 22.8 bits. The timestamp accuracy reaches 10 nanoseconds, and each data frame contains 1000 sampling points and a 64-bit timestamp, totaling 32064 bits. Data compression compresses each data frame to 19238 bits, with a compression rate of 60%. The real-time updated data buffer maintains 20 seconds of data, about 400 data frames. The data integrity check uses the CRC-32 algorithm, and a 32-bit check value is added to each data frame. Time alignment processing interpolates the data of different devices to one point every 10 microseconds to form a unified timing data stream.

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

[0072] (1) Segment the time series data stream to obtain data blocks of fixed size, and perform checksum calculation on the data blocks to obtain data integrity verification information;

[0073] (2) The data block and the data integrity verification information are combined to obtain a data packet to be encrypted, and the data packet to be encrypted is processed by a symmetric encryption algorithm to obtain preliminary encrypted data;

[0074] (3) performing digital signature processing on the initially encrypted data to obtain encrypted data with identity authentication function, and performing session key encryption processing on the encrypted data with identity authentication function to obtain a double-encrypted security data packet;

[0075] (4) performing network protocol encapsulation processing on the double-encrypted security data packets to obtain transmittable data frames, and performing communication channel allocation processing on the transmittable data frames to obtain an optimized transmission path;

[0076] (5) Performing data transmission processing on the transmittable data frame according to the optimized transmission path to obtain the original data packet received by the master station, and performing data decapsulation processing on the original data packet to obtain a complete encrypted data packet in the master station device.

[0077] Specifically, the time series data stream is segmented, and the continuous data stream is cut into data blocks of fixed size. Usually, each data block contains 1 second or 0.1 second of sampling data. This segmentation method facilitates subsequent data processing and transmission management. Each data block is checked and calculated, and a 32-bit check code is generated using the CRC-32 algorithm as data integrity verification information. The CRC-32 algorithm can effectively detect errors in the data transmission process and provide high-reliability data integrity protection. The server combines the data block and the corresponding checksum to form a data packet to be encrypted. The structure of the data packet includes metadata such as data block, checksum, timestamp and sequence number. The data packet to be encrypted is processed by a symmetric encryption algorithm and encrypted using the AES-256 algorithm. AES-256 uses a 256-bit key to provide high-intensity data encryption protection. The encryption process is fast and efficient, and is suitable for real-time encryption of large amounts of data. After encryption, the preliminary encrypted data is obtained, and this step ensures the confidentiality of the data.

[0078] To further enhance security, the server performs digital signature processing on the initially encrypted data. The digital signature is generated using the RSA-2048 algorithm. First, the hash value of the data (such as SHA-256) is calculated, and then the hash value is encrypted with the sender's private key to generate a digital signature. This process not only ensures the integrity of the data, but also provides identity authentication to ensure the reliability of the data source. Subsequently, the server performs session key encryption processing on the encrypted data with identity authentication. The Diffie-Hellman key exchange protocol is used to generate a temporary session key, and this session key is used to encrypt the entire data packet twice to form a doubly encrypted secure data packet. This double encryption mechanism greatly improves the security of data transmission. The server then performs network protocol encapsulation processing on the doubly encrypted secure data packet. According to the transmission requirements, a suitable network protocol (such as TCP / IP or UDP) is selected to encapsulate the secure data packet into a transmittable data frame. The encapsulation process includes adding a protocol header, fragmentation processing (if the data packet exceeds the MTU size), etc. The server then performs communication channel allocation processing on the transmittable data frame. According to the network topology and current network conditions, a dynamic routing algorithm (such as OSPF or BGP) is used to calculate the optimal transmission path. This process takes into account factors such as network latency, bandwidth, and load balancing to ensure the efficiency and reliability of data transmission.

[0079] Finally, the server performs data transmission processing on the transmittable data frames according to the optimized transmission path. The data frames are sent to the master device using a network transmission protocol (such as TCP / IP). After receiving the original data packet, the master device first performs data decapsulation processing. This process includes removing the transmission protocol header, reassembling the fragmented data packet, etc. After these processes, the master device finally obtains a complete encrypted data packet, ready for subsequent decryption and data analysis. For example, in a data acquisition system of a large photovoltaic power station, a photovoltaic module generates 100 sampling points per second, and each sampling point contains 64 bits of data (32-bit voltage value + 32-bit current value). The server takes 1 second of data (6400 bits) as a data block and calculates a 32-bit CRC checksum. The data block and checksum are combined into a 6432-bit data packet to be encrypted. After encryption with AES-256, the data packet size increases to 6528 bits (because AES encrypts in 128-bit blocks and requires padding). The RSA-2048 digital signature adds 256 bytes (2048 bits) of signature data, and the data packet increases to 8576 bits. Session key encryption does not change the size of the data packet. The network protocol encapsulation adds a 40-byte (320-bit) TCP / IP header, ultimately forming an 8896-bit transmittable data frame. Through the optimized network path, this data frame is transmitted from the PV module collection point to the master station, and the entire process takes about 50 milliseconds. After the master station receives and decapsulates it, it obtains a complete 8576-bit encrypted data packet, ready for subsequent decryption and data analysis.

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

[0081] (1) Perform session key decryption on the complete encrypted data packet to obtain single-layer encrypted data, and perform digital signature verification on the single-layer encrypted data to obtain encrypted content after identity authentication;

[0082] (2) Performing symmetric decryption algorithm processing on the encrypted content after identity authentication to obtain the original time series data stream, and performing data integrity verification processing on the original time series data stream to obtain verified valid data;

[0083] (3) Perform fast Fourier transform processing on the verified valid data to obtain the frequency domain representation of the signal, and perform power spectrum density estimation processing on the frequency domain representation to obtain the frequency distribution characteristics of the signal;

[0084] (4) performing autocorrelation function calculation on the verified valid data to obtain the time domain correlation of the signal, and performing periodic analysis on the time domain correlation to obtain the repetitive pattern characteristics of the signal;

[0085] (5) Feature fusion processing is performed based on the frequency distribution characteristics and the repetitive pattern characteristics to obtain comprehensive time-frequency characteristics, and the comprehensive time-frequency characteristics are normalized to obtain time-frequency feature indicators that reflect the synchronization of acquisition.

[0086] Specifically, the complete encrypted data packet is decrypted using the session key, and the pre-negotiated session key and AES-256 algorithm are used to decrypt the data to obtain the single-layer encrypted data. Subsequently, the single-layer encrypted data is digitally signed and verified using the sender's public key to ensure that the data has not been tampered with and that the source is credible, thereby obtaining the encrypted content after identity authentication. Next, the server performs a symmetric decryption algorithm on the encrypted content after identity authentication, and decrypts it again using the AES-256 algorithm and the preset key to obtain the original sequential data stream. The original sequential data stream is subjected to data integrity verification, and the checksum is recalculated using the CRC-32 algorithm and compared with the checksum carried in the data packet to ensure that no errors or losses occur during data transmission, and finally valid data is obtained after verification.

[0087] The server then performs fast Fourier transform (FFT) processing on the validated data. The FFT algorithm converts the time domain signal into a frequency domain representation. The calculation process includes decomposing the signal into even and odd parts, recursively calculating the FFT of these parts, and then merging the results. This step reveals the frequency composition of the signal, which is crucial for the periodic analysis of the output signal of the photovoltaic module. After obtaining the frequency domain representation, the server performs power spectral density estimation processing on it. Using the Welch method, the signal is divided into several overlapping segments, each segment is FFTed, and then the results are averaged to obtain the frequency distribution characteristics of the signal. This method effectively reduces the impact of noise and improves the accuracy of spectrum estimation. At the same time, the server performs autocorrelation function calculation processing on the validated data. The autocorrelation function reflects the similarity of the signal with its time-shifted version. The calculation process includes convolving the signal with different time-delayed versions of itself. This step obtains the time domain correlation of the signal, revealing the periodicity and repetitive pattern of the signal. Subsequently, the time domain correlation is subjected to periodic analysis processing. By finding the peak position of the autocorrelation function, the main periodic components of the signal are determined, thereby obtaining the repetitive pattern characteristics of the signal.

[0088] Finally, the server performs feature fusion processing based on the frequency distribution features and repeated pattern features. This process involves the combination and weight assignment of multiple features, such as using the principal component analysis (PCA) method to combine frequency domain and time domain features into a comprehensive feature vector. The obtained comprehensive time-frequency features are normalized, and each feature component is scaled to the same numerical range (such as 0 to 1), eliminating the dimensional differences between different features, and finally obtaining the time-frequency feature index reflecting the synchronization of acquisition.

[0089] For example, in a data acquisition system for a 10MW photovoltaic power plant, the server receives 100 encrypted data packets per second, each containing voltage and current data of 1024 sampling points. The decryption process first uses a 128-bit session key to decrypt the outer layer, then verifies the 2048-bit RSA digital signature, and finally uses a 256-bit AES key to decrypt the inner layer to obtain the original data stream. The data integrity check shows that 99.98% of the data packets pass the verification. A typical 1-second data is processed by FFT, using a 2048-point FFT to obtain a spectrum in the range of 0-500Hz. The power spectral density estimation shows that there are significant peaks at 50Hz and 100Hz, which are -20dB and -40dB respectively, reflecting the influence of the grid fundamental frequency and second harmonics. The autocorrelation function calculation uses a 1024-point delay, and the results show that there is an obvious periodic peak at 20ms (corresponding to 50Hz), with a correlation coefficient of 0.95, confirming the strong periodicity of the signal.

[0090] During the feature fusion process, the first 10 main frequency components in the frequency domain and the first 5 autocorrelation peaks in the time domain are used as feature vectors. The PCA algorithm is used to reduce these 15-dimensional features to 5 dimensions, explaining 95% of the variance. The normalization process scales each feature to the range of 0-1. The final time-frequency feature indicators are [0.98, 0.85, 0.76, 0.62, 0.57], among which the first component 0.98 mainly reflects the strength and stability of the 50Hz component, indicating that the acquisition system is highly synchronized with the power grid frequency.

[0091] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0092] (1) Performing feature dimensionality reduction processing on the time-frequency feature indicators to obtain low-dimensional feature representation, and performing cluster analysis on the low-dimensional feature representation to obtain the intrinsic distribution pattern of the data;

[0093] (2) performing normal mode modeling on the low-dimensional feature representation according to the intrinsic distribution pattern to obtain a standard model of the synchronization state, and performing deviation calculation on the low-dimensional feature representation and the standard model to obtain the synchronization deviation;

[0094] (3) Performing statistical distribution analysis on the synchronization deviation to obtain the probability distribution characteristics of the deviation, and performing threshold setting processing based on the probability distribution characteristics to obtain the judgment standard for abnormality detection;

[0095] (4) Perform sliding window analysis on the low-dimensional feature representation to obtain a time-varying feature sequence, and perform trend analysis on the time-varying feature sequence to obtain a synchronous change trend;

[0096] (5) Conducting a comprehensive evaluation based on the judgment criteria and the synchronization change trend to obtain a quantitative synchronization evaluation index, and then classifying the quantitative synchronization evaluation index to obtain a synchronization grade evaluation result;

[0097] (6) Conduct in-depth analysis and processing on data whose synchronization level assessment results are lower than the preset standard to obtain the characteristic pattern of potential problems, and perform fault tree analysis and processing based on the characteristic pattern to obtain the diagnosis results of potential problems.

[0098] Specifically, the time-frequency feature indicators are processed for feature dimensionality reduction, and the principal component analysis (PCA) method is used to compress the high-dimensional feature space into a low-dimensional representation. The PCA process includes calculating the feature covariance matrix, solving the eigenvalues ​​and eigenvectors, and selecting the first few principal components as the new feature basis. This step effectively reduces the redundancy of the data and improves the efficiency of subsequent analysis. After obtaining the low-dimensional feature representation, the server performs cluster analysis on it and uses the K-means algorithm to group the data points into K clusters. K-means iteratively calculates the cluster center and redistributes the data points until convergence, and finally obtains the intrinsic distribution pattern of the data, revealing the natural grouping of photovoltaic module data. According to the intrinsic distribution pattern, the server performs normal mode modeling on the low-dimensional feature representation. This step uses the Gaussian mixture model (GMM) to describe the data distribution under normal operating conditions. GMM combines multiple Gaussian distributions, estimates the model parameters through the expectation maximization (EM) algorithm, and obtains the standard model of the synchronization state. Subsequently, the deviation between the low-dimensional feature representation and the standard model is calculated, and the Mahalanobis distance from each data point to the center of the nearest Gaussian distribution is calculated to obtain the synchronization deviation, which quantifies the degree of difference between the current data and the normal pattern.

[0099] The server performs statistical distribution analysis on the synchronization deviation. By calculating the mean, variance, skewness, and kurtosis of the deviation, the probability distribution characteristics of the deviation are obtained. Based on these characteristics, the server uses the triple standard deviation method or the quantile-based method to set the threshold and determine the judgment criteria for anomaly detection. This standard will be used to distinguish normal data from potential abnormal data. In order to capture the dynamic changes in synchronization, the server performs sliding window analysis on the low-dimensional feature representation. Select an appropriate window size (such as 5 minutes or 10 minutes) and slide it on the time axis to obtain a time-varying feature sequence. Perform trend analysis on this feature sequence, use methods such as exponentially weighted moving average (EWMA) to calculate short-term and long-term trends, and thus obtain the synchronization change trend, which helps predict potential synchronization problems.

[0100] According to the judgment criteria and synchronization change trends obtained above, the server performs comprehensive evaluation. Compare the current synchronization deviation with the judgment criteria, and consider the slope of the change trend to obtain a quantitative synchronization evaluation index. This index is graded and may be divided into "excellent", "good", "general", "need to pay attention" and "abnormal" levels to obtain the synchronization grade assessment results. Finally, the data whose synchronization grade assessment results are lower than the preset standard are deeply analyzed and processed. This step involves feature extraction of abnormal data, such as frequency offset, phase jump or amplitude abnormality, to obtain the characteristic pattern of potential problems. Based on these characteristic patterns, the server performs fault tree analysis. The fault tree starts from the top-level event (synchronization abnormality), decomposes downward into intermediate events and basic events, and connects through logic gates to finally obtain the potential problem diagnosis results, providing specific fault causes and location guidance for operation and maintenance personnel.

[0101] For example, in a data acquisition system for a 50MW photovoltaic power plant, the server processes 1,000 data points per minute, each of which contains 20 original time-frequency features. PCA dimensionality reduction compresses the 20-dimensional features to 5 dimensions, retaining 95% of the variance information. K-means clustering (K=3) divides the data into three categories, representing different operating states. GMM modeling uses three Gaussian components to describe the probability distribution of the normal synchronization state. The calculated synchronization deviation has a mean of 0.5 and a standard deviation of 0.2. The anomaly detection threshold is set to the mean plus three times the standard deviation, i.e. 1.1. The sliding window analysis uses a 10-minute window, and the short-term trend slope calculated by EWMA is 0.01 / minute, indicating a slight decrease in synchronization. The comprehensive evaluation divides the synchronization index into five levels, and the evaluation result of the current data is "good", with a score of 85 (out of 100).

[0102] During continuous monitoring, it was found that the synchronization score of a PV module suddenly dropped to 65 points, triggering in-depth analysis. The characteristic pattern showed that the output frequency of the module had a 0.1Hz offset. Fault tree analysis traced the problem to the inverter clock drift of the module. This result prompted maintenance personnel to check and calibrate the inverter in time, preventing potential large-scale synchronization problems. Through this series of processing, the server not only achieved high-precision synchronization detection of PV module data collection, but also provided timely and effective abnormal diagnosis, greatly improving the operating efficiency and reliability of PV power plants.

[0103] The above describes the synchronization detection method of photovoltaic module data collection in the embodiment of the present application. The following describes the synchronization detection device of photovoltaic module data collection in the embodiment of the present application. Figure 2 In one embodiment of the present application, a synchronization detection device for photovoltaic module data collection includes:

[0104] Comparison module 201, used for performing phase comparison processing on the output signals of two GPS timing modules to obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds;

[0105] The driving module 202 is used to perform zero clearing and 10MHz clock driving processing on the internal counter of the slave device based on the 1PPS signal, so as to obtain a nanosecond timing system synchronized with the GPS timing module;

[0106] The embedding module 203 is used to perform high-speed sampling and time stamp embedding processing on the analog photovoltaic module output signal to obtain a time series data stream with a microsecond resolution;

[0107] The transmission module 204 is used to perform block encryption and efficient transmission processing on the time series data stream to obtain a complete encrypted data packet in the master station device;

[0108] The analysis module 205 is used to decrypt and perform spectrum analysis on the encrypted data packet through Fourier transform and autocorrelation analysis to obtain time-frequency characteristic indicators reflecting acquisition synchronization;

[0109] The detection module 206 is used to perform pattern recognition and anomaly detection processing on the time-frequency feature indicators to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results.

[0110] Through the cooperation of the above components, by accurately comparing the output signals of the two GPS timing modules, a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds can be obtained, which lays a solid foundation for the time synchronization of the entire system and ensures the time consistency of data acquisition. Secondly, the internal counter of the slave device is cleared based on the 1PPS signal and driven by the 10MHz clock, realizing a nanosecond timing system synchronized with the GPS timing module, which greatly improves the accuracy of the timestamp and makes the time resolution of the collected data reach an unprecedented level. On this basis, the analog photovoltaic module output signal is sampled at high speed and the timestamp is embedded to obtain a time series data stream with microsecond resolution, which not only ensures the high time accuracy of the data, but also provides rich information for subsequent synchronization analysis. The time series data stream is encrypted in blocks and efficiently transmitted to obtain a complete encrypted data packet in the master device, which not only ensures the security of data transmission, but also maintains the integrity and time consistency of the data. The encrypted data packet is decrypted and spectrally analyzed by Fourier transform and autocorrelation analysis to obtain the time-frequency characteristic indicators reflecting the synchronization of acquisition. This method can not only effectively evaluate the synchronization of data, but also reveal potential synchronization problems. Finally, the time-frequency characteristic indicators are processed for pattern recognition and anomaly detection to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results, which provides intuitive and quantitative synchronization evaluation results for system managers and helps to timely discover and solve synchronization problems. In summary, this method achieves high-precision and high-reliability synchronization detection of photovoltaic module data collection through a series of innovative technical means.

[0111] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting synchronization of photovoltaic module data collection, characterized in that: The synchronization detection method for photovoltaic module data collection includes: Perform phase comparison on the output signals of the two GPS timing modules to obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds. The internal counter of the slave device is cleared based on the 1PPS signal and driven by the 10MHz clock to obtain a nanosecond timing system synchronized with the GPS timing module; Perform high-speed sampling and time stamp embedding processing on the analog photovoltaic module output signal to obtain a time series data stream with microsecond resolution; Perform block encryption and efficient transmission processing on the time series data stream to obtain a complete encrypted data packet in the master station device; The encrypted data packets are decrypted and spectrum analyzed through Fourier transform and autocorrelation analysis to obtain time-frequency characteristic indicators reflecting the synchronization of acquisition; The time-frequency characteristic indicators are processed by pattern recognition and anomaly detection to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results.

2. The synchronization detection method for photovoltaic module data collection according to claim 1 is characterized in that: The phase comparison process of the output signals of the two GPS timing modules is performed to obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds, including: Perform satellite signal reception processing on the two GPS timing modules to obtain original satellite navigation data, and perform decoding processing on the original satellite navigation data to obtain accurate time code information; Performing time scale conversion processing on the time code information to obtain a unified coordinated universal time reference, and performing local clock calibration processing on the time reference to obtain an initial synchronized local clock signal; Performing frequency stability evaluation processing on the local clock signal to obtain a frequency drift parameter, and performing compensation processing on the local clock signal according to the frequency drift parameter to obtain a frequency-corrected clock signal; Performing phase noise analysis processing on the frequency-corrected clock signal to obtain a phase jitter characteristic, and performing filtering processing on the frequency-corrected clock signal according to the phase jitter characteristic to obtain a low-jitter accurate clock signal; Performing cross-correlation analysis on the precise clock signals output by the two GPS timing modules to obtain a time deviation value between the signals, and performing statistical analysis on the time deviation value to obtain a time deviation distribution feature; Fine-tune the precise clock signal according to the time deviation distribution characteristics to obtain a candidate synchronization time reference, and perform Allan variance analysis on the candidate synchronization time reference to obtain a time stability index; Screening the candidate synchronization time references according to the time stability index to obtain an optimal synchronization time reference, and performing long-term drift prediction on the optimal synchronization time reference to obtain a drift compensation model; The optimal synchronization time reference is corrected in real time according to the drift compensation model to obtain a high-precision synchronization time reference with a time deviation less than 15 nanoseconds.

3. The synchronization detection method for photovoltaic module data collection according to claim 1, characterized in that: The internal counter of the slave device is cleared based on the 1PPS signal and driven by the 10MHz clock to obtain a nanosecond timing system synchronized with the GPS timing module, including: Perform rising edge detection processing on the 1PPS signal output by the GPS timing module to obtain an accurate second pulse trigger point, and perform delay compensation processing on the second pulse trigger point to obtain a corrected clearing trigger signal; Performing a hardware reset process on the internal counter of the slave device to obtain a counter in an initialized state, and performing a synchronous reset process on the counter in the initialized state according to the corrected reset trigger signal to obtain an aligned counting starting point; Performing frequency division processing on the 10MHz clock signal output by the GPS timing module to obtain a driving clock that matches the internal counter, and performing phase adjustment processing on the driving clock to obtain a driving pulse synchronized with the reset trigger signal; Performing count increment processing on the internal counter according to the driving pulse to obtain an original count value, and performing time unit conversion processing on the original count value to obtain a time count with nanosecond resolution; Performing periodic calibration processing on the nanosecond resolution time count to obtain long-term stable timing data, and performing time code formatting processing on the timing data to obtain a standard timestamp format; The standard timestamp format is subjected to GPS time synchronization processing to obtain a nanosecond-level timing system synchronized with the GPS timing module.

4. The synchronization detection method for photovoltaic module data collection according to claim 1, characterized in that: The high-speed sampling and time stamp embedding processing of the analog photovoltaic module output signal to obtain a time series data stream with a microsecond resolution includes: Performing signal conditioning processing on the analog photovoltaic module output signal to obtain a standardized signal suitable for sampling, and performing anti-aliasing filtering processing on the standardized signal to obtain a band-limited signal; Performing multi-bit ADC conversion processing on the band-limited signal to obtain a high-precision digital sampling value, and performing quantization error compensation processing on the high-precision digital sampling value to obtain a corrected digital signal; Performing time stamp processing on the corrected digital signal according to the nanosecond timing system to obtain sampling points with precise time information, and performing data packaging processing on the sampling points with precise time information to obtain time-synchronized data frames; Performing data compression processing on the time-synchronized data frame to obtain a data stream in an efficient storage format, and performing cache management processing on the data stream in the efficient storage format to obtain a data buffer updated in real time; A data integrity check process is performed on the real-time updated data buffer to obtain a verified data sequence, and a time alignment process is performed on the verified data sequence to obtain a time series data stream with a microsecond resolution.

5. The synchronization detection method for photovoltaic module data collection according to claim 1, characterized in that: The block encryption and efficient transmission processing of the time series data stream to obtain a complete encrypted data packet in the master station device includes: Performing data segmentation processing on the time series data stream to obtain data blocks of fixed size, and performing verification and calculation processing on the data blocks to obtain data integrity verification information; Combining the data block and the data integrity verification information to obtain a data packet to be encrypted, and performing symmetric encryption algorithm processing on the data packet to be encrypted to obtain preliminarily encrypted data; Performing digital signature processing on the initially encrypted data to obtain encrypted data with identity authentication function, and performing session key encryption processing on the encrypted data with identity authentication function to obtain a double-encrypted security data packet; Performing network protocol encapsulation processing on the double-encrypted security data packet to obtain a transmittable data frame, and performing communication channel allocation processing on the transmittable data frame to obtain an optimized transmission path; The transmittable data frame is subjected to data transmission processing according to the optimized transmission path to obtain the original data packet received by the main station, and the original data packet is subjected to data decapsulation processing to obtain a complete encrypted data packet in the main station device.

6. The synchronization detection method for photovoltaic module data collection according to claim 1, characterized in that: The encrypted data packets are decrypted and spectrum analyzed by Fourier transform and autocorrelation analysis to obtain time-frequency characteristic indicators reflecting acquisition synchronization, including: Performing session key decryption processing on the complete encrypted data packet to obtain single-layer encrypted data, and performing digital signature verification processing on the single-layer encrypted data to obtain encrypted content after identity authentication; Performing symmetric decryption algorithm processing on the encrypted content after identity authentication to obtain an original time series data stream, and performing data integrity verification processing on the original time series data stream to obtain verified valid data; Performing fast Fourier transform processing on the verified valid data to obtain a frequency domain representation of the signal, and performing power spectrum density estimation processing on the frequency domain representation to obtain a frequency distribution feature of the signal; Performing autocorrelation function calculation processing on the verified valid data to obtain the time domain correlation of the signal, and performing periodic analysis processing on the time domain correlation to obtain the repetitive pattern characteristics of the signal; Feature fusion processing is performed according to the frequency distribution feature and the repetitive pattern feature to obtain a comprehensive time-frequency feature, and the comprehensive time-frequency feature is normalized to obtain a time-frequency feature index reflecting acquisition synchronization.

7. The method for detecting synchronization of photovoltaic module data collection according to claim 1, characterized in that: The pattern recognition and anomaly detection processing of the time-frequency feature indicators is performed to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results, including: Performing feature dimensionality reduction processing on the time-frequency feature indicators to obtain low-dimensional feature representations, and performing cluster analysis processing on the low-dimensional feature representations to obtain an intrinsic distribution pattern of the data; Performing normal mode modeling processing on the low-dimensional feature representation according to the intrinsic distribution mode to obtain a standard model of the synchronization state, and performing deviation calculation processing on the low-dimensional feature representation and the standard model to obtain a synchronization deviation amount; Performing statistical distribution analysis on the synchronization deviation to obtain probability distribution characteristics of the deviation, and performing threshold setting processing according to the probability distribution characteristics to obtain a judgment standard for abnormality detection; Performing sliding window analysis on the low-dimensional feature representation to obtain a time-varying feature sequence, and performing trend analysis on the time-varying feature sequence to obtain a synchronous change trend; Performing a comprehensive evaluation process according to the judgment criteria and the synchronization change trend to obtain a quantitative synchronization evaluation index, and performing a grade classification process on the quantitative synchronization evaluation index to obtain a synchronization grade evaluation result; The data whose synchronization level assessment results are lower than the preset standard are deeply analyzed and processed to obtain the characteristic pattern of the potential problem, and a fault tree analysis is performed based on the characteristic pattern to obtain the potential problem diagnosis result.

8. A synchronization detection device for photovoltaic module data collection, used to implement the synchronization detection method for photovoltaic module data collection as described in any one of claims 1 to 7, characterized in that: The synchronization detection device for photovoltaic module data collection includes: The comparison module is used to perform phase comparison processing on the output signals of the two GPS timing modules to obtain a high-precision synchronous time reference with a time deviation of less than 15 nanoseconds; The driver module is used to perform zero clearing and 10MHz clock driving processing on the internal counter of the slave device based on the 1PPS signal, so as to obtain a nanosecond timing system synchronized with the GPS timing module; An embedding module is used to perform high-speed sampling and time stamp embedding processing on the analog photovoltaic module output signal to obtain a time series data stream with microsecond resolution; The transmission module is used to perform block encryption and efficient transmission processing on the time series data stream to obtain a complete encrypted data packet in the master station device; An analysis module is used to decrypt and perform spectrum analysis on encrypted data packets through Fourier transform and autocorrelation analysis to obtain time-frequency characteristic indicators reflecting acquisition synchronization; The detection module is used to perform pattern recognition and anomaly detection on time-frequency feature indicators to obtain quantitative synchronization evaluation indicators and potential problem diagnosis results.

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