A device and method suitable for photovoltaic data analysis and processing

Abstract

The present invention discloses a device and method for photovoltaic data parsing and processing, comprising the following steps: Step 1, sending an acquisition frame to a photovoltaic device in a periodic polling manner, and simultaneously receiving a response frame returned by the photovoltaic device; Step 2, constructing a computing model based on the integration of data parsing and processing, parsing photovoltaic data of unknown format and storage method returned by the response frame through the computing model, and then converting the photovoltaic data into a unified type of data; Step 3, establishing a data association strategy, and performing strategy association on the converted photovoltaic data to provide data support for the operation of the photovoltaic power generation system. By constructing a computing model based on the integration of data parsing and processing, the present invention can convert photovoltaic data from different sources and formats into a unified type of data, eliminating incompatibilities and differences between the data, and making the data easier to associate, analyze, and apply.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic data acquisition and control, and in particular to a device and method suitable for photovoltaic data analysis and processing. Background Art

[0002] Photovoltaic power generation system data primarily includes voltage, current, power generation, active power, reactive power, and operating status. With the increasing installed capacity of photovoltaic power in my country, the traditional passive power grid has become an active one. Therefore, collecting and regulating photovoltaic data is crucial for power grid security. The importance of photovoltaic data lies in several aspects: Parsing and analyzing this data allows for the timely detection and resolution of component failures and losses, improving the system's power generation efficiency and reliability. Analyzing historical data allows for the development of power generation forecasting models to predict future power generation. Furthermore, it allows for the optimization of photovoltaic power generation system operating strategies, increasing power generation and economic benefits. The larger power grid requires the rational management and scheduling of photovoltaic power generation systems, ensuring the safe operation of the entire grid while maximizing the utilization of solar resources for power generation.

[0003] However, there are numerous manufacturers of photovoltaic power generation systems, and their communication protocols and data parsing methods vary widely. Differences in endian and endian formats, data types, and other factors hinder the ability to parse and process all photovoltaic data. Furthermore, these differences hinder the integration of compatible photovoltaic data, and consequently, photovoltaic power generation systems lack the ability to process and analyze the entire photovoltaic data. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology in photovoltaic data analysis, which involves differences in big-endian and small-endian modes, data types, etc., and cannot parse and process all photovoltaic data. The differences in photovoltaic data also lead to the inability to better integrate photovoltaic data, and thus the photovoltaic power generation system lacks the ability to process and analyze photovoltaic data as a whole. A device and method suitable for photovoltaic data analysis and processing are provided. Through this device and method, all photovoltaic data can be analyzed and integrated, providing effective data support for the stable operation of the photovoltaic power generation system.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A method for analyzing and processing photovoltaic data includes the following steps:

[0007] Step 1: Sending acquisition frames to photovoltaic devices in a periodic polling manner, and receiving response frames returned by the photovoltaic devices;

[0008] Step 2: Build a computing model based on the integration of data analysis and processing. Use the computing model to parse the photovoltaic data of unknown format and storage method returned by the response frame, and then convert the photovoltaic data into a unified type of data.

[0009] Step 3: Establish a data association strategy to associate the converted photovoltaic data with the strategy to provide data support for the operation of the photovoltaic power generation system.

[0010] In this solution, photovoltaic data from photovoltaic devices is first acquired through acquisition frames and response frames, and then the data is parsed and processed. The received photovoltaic data includes information such as data type and storage method. The computational model converts different content according to preset rules, matching and parsing the specific data content based on the data type and storage method to obtain data in a unified standard format. Compared with existing photovoltaic data parsing and processing methods, which require the establishment of corresponding parsing and processing devices for different data types, the parsed photovoltaic data cannot be formatted in a unified format, making it difficult to establish strategic associations based on photovoltaic data. The solution of the present invention facilitates and effectively establishes strategic associations for all photovoltaic data, thereby providing data support for the operation of photovoltaic power generation systems.

[0011] Preferably, in step 2, the photovoltaic data of unknown format and storage mode returned by the response frame is parsed by a computational model, and then the photovoltaic data is converted into uniform type data, specifically:

[0012] Step 2a, the calculation model extracts characteristic information of photovoltaic data;

[0013] Step 2b: parse the feature information according to the rules. For multiple feature information under the same photovoltaic data, determine whether the feature information parsing is reasonable based on the correlation between the feature information. If reasonable, jump to step 2c. If unreasonable, parse the unreasonable feature information according to other rules until all feature information parsing is reasonable.

[0014] Step 2c: convert the characteristic information according to the conversion rules of the information template. After all characteristic information is converted, unified type data of photovoltaic information is formed.

[0015] In this scheme, the characteristic information of photovoltaic data is first extracted and the type of characteristic information is obtained. Since the format and storage type of photovoltaic data are unknown, in this scheme, any format and any storage type are randomly selected to parse the characteristic information. Then, the correlation between the features is used to determine whether the analysis is reasonable. If it is unreasonable, another format and storage type are selected to parse the characteristic information until all the characteristic information is analyzed reasonably.

[0016] Preferably, the determination of whether the feature information analysis is reasonable based on the correlation between the feature information is as follows:

[0017] Historical photovoltaic data is obtained and several correlation functions are constructed. The correlation functions are used to associate at least two feature information. The correlation functions are recorded and stored. For multiple feature information under the same photovoltaic data, matching correlation functions are found and obtained. The correlation functions are used to determine whether the feature information parsed according to the rules is reasonable.

[0018] Preferably, in step 2, the photovoltaic data of unknown format and storage mode returned by the response frame is parsed by a computational model, and then the photovoltaic data is converted into uniform type data, specifically:

[0019] Step 2a, the calculation model extracts characteristic information of photovoltaic data;

[0020] Step 2b: Compare the feature information with the stored information templates to determine the similarity of the feature information. If the similarity between the feature information and any information template is greater than or equal to a set threshold, the feature information and the information template are determined to be of the same type.

[0021] Step 2c: convert the characteristic information according to the conversion rules of the information template. After all characteristic information is converted, unified type data of photovoltaic information is formed.

[0022] The design of this solution first extracts the characteristic information of photovoltaic data. The characteristic information may include timestamp, device status information, its own parameter information and environmental parameter information. Since these characteristic information are returned by different types of photovoltaic devices, the specific attributes are unknown. In addition, due to the influence of the storage method of the received data, for example, different data storage methods may have multiple specific different values ​​that are expressions of the same parameter, or the same value is an expression of different parameters, resulting in the acquired characteristic information being unknown. Therefore, a similarity comparison is performed between the characteristic information and the saved information template to determine the similarity of the characteristic information. Since the information template can be preset, as long as it is characteristic information that can be matched by the information template, it can be parsed out. The information template with the highest similarity and a similarity better than the set threshold is selected. It can be determined that the characteristic information and the information template belong to the same type of information. The information template has preset conversion rules. After all characteristic information is converted, it constitutes a unified type of data for photovoltaic information.

[0023] Preferably, in step 2b, the feature information is compared with the stored information template for similarity, and the similarity of the feature information is determined, specifically:

[0024] First, determine whether the feature information is the same as a certain information template. If they are the same, the similarity between the feature information and the information template is determined to be 100%. If they are not the same, calculate the number of bits with the same values ​​between the feature information and the information template, and then calculate the numerical difference between the bits with different values ​​between the feature information and the information template. The similarity of the feature information is determined based on the size of the number of bits with the same values ​​and the numerical difference.

[0025] This design improves the accuracy of similarity judgments by first determining whether the feature information is identical to the information template. If so, the similarity is 100%. This method ensures accuracy when the feature information and the template are completely consistent. When the feature information and the template are not identical, similarity is determined by calculating the number of digits with the same value and the magnitude of the difference in the values. This method is more sophisticated than simply matching all or not matching all and can more accurately reflect the degree of similarity between the feature information and the template. This design improves the adaptability of the judgment. Even if the format or accuracy of the photovoltaic data changes, as long as the main part of the feature information remains similar to the template, this method can accurately determine the similarity.

[0026] Preferably, the feature information is converted according to the conversion rules of the information template, specifically:

[0027] Set data processing conversion rules, which include shift, reversal, combination and mathematical calculation. Each information template has its own data processing conversion rules, and feature information is converted according to the data processing conversion rules.

[0028] Preferably, the method for photovoltaic data analysis and processing further establishes several analysis and processing channels, and further analyzes the type of photovoltaic data based on the calculation model integrating data analysis and processing, and enters different analysis and processing channels according to the type of photovoltaic data.

[0029] Preferably, the types of photovoltaic data include telesignaling, telemetering, remote control, and remote adjustment. If it is telesignaling, it will enter the telesignaling data parsing and processing channel, and the data finally calculated will be one-bit, double-bit or multi-bit; if it is telemetering, it will enter the telemetering data parsing and processing channel, and the data finally calculated will be integer, floating-point or character; if it is remote control, it will enter the remote control data parsing and processing channel, and the data finally calculated will be one-bit, double-bit or multi-bit; if it is remote adjustment, it will enter the remote adjustment data parsing and processing channel, and the data finally calculated will be integer, floating-point or character; if it is a custom type, it will enter the special data parsing and processing channel, and will eventually match the custom rules, and the calculated data will be data related to the custom type.

[0030] Preferably, in step 3, the data association strategy includes:

[0031] Time series association strategy, which arranges and associates PV data in chronological order to form time series data;

[0032] The inter-device association strategy associates data from different photovoltaic devices to obtain the operating status and collaborative working effect of the photovoltaic system;

[0033] Environmental factor correlation strategy, which correlates photovoltaic data with external environmental factors to analyze the impact of environmental factors on the power generation efficiency of photovoltaic systems;

[0034] Performance indicator correlation strategy, which correlates various performance indicators of the photovoltaic system to evaluate the overall performance of the photovoltaic system;

[0035] The fault diagnosis correlation strategy associates the operating data of the photovoltaic system with the fault information to timely detect and diagnose photovoltaic system faults.

[0036] Preferably, in step 2b, if the similarity between the feature information and any information template is less than a set threshold, the feature information is judged to not match the information template, and the calculation model performs a separate conversion step on the feature information, specifically:

[0037] Obtain whether other characteristic information in the photovoltaic data corresponding to the characteristic information matches the information template. If all other characteristic information matches the information template or the number of characteristic information that matches the information template is greater than a set value, obtain the standard photovoltaic data corresponding to these matching information template combinations, find the information template corresponding to the characteristic information in the standard photovoltaic data, and convert the characteristic information according to the conversion rule corresponding to the information template.

[0038] This design considers two scenarios. One is the integration of new photovoltaic equipment. Some of the characteristic information in the photovoltaic data may be of a new data type, with no existing information templates to match it well. However, new photovoltaic data generally does not significantly change the data format or storage method. Therefore, as long as the majority of the characteristic information in the photovoltaic data matches, it can be determined that the photovoltaic data matches the existing standard photovoltaic data (standard photovoltaic data contains several information templates). Characteristic information that does not match the existing information templates can also be matched using the corresponding information templates in the matching standard photovoltaic data. Another scenario is that in actual applications, photovoltaic data may be abnormal or missing due to various reasons such as equipment failure and transmission errors. This design tolerates data anomalies and missing data to a certain extent, thereby improving the robustness of data processing.

[0039] Preferably, the method suitable for photovoltaic data analysis and processing also verifies the data after the characteristic information is converted according to the conversion rules corresponding to the information template, and judges the rationality of the converted data. If the converted data is unreasonable, the characteristic information is re-searched for an information template for conversion until the converted data is reasonable.

[0040] A device for photovoltaic data analysis and processing, and a method for photovoltaic data analysis and processing, comprising:

[0041] A data acquisition unit, configured to receive photovoltaic data information in a response frame returned by the photovoltaic device;

[0042] The data analysis unit includes a computing model based on the integration of data analysis and processing, which analyzes the photovoltaic data returned by the response frame through the computing model and converts the photovoltaic data into a unified type of data;

[0043] A strategy generation unit is used to establish a data association strategy and associate the converted photovoltaic data with the strategy;

[0044] The data sending unit is used to send the photovoltaic data associated with the strategy to the photovoltaic power generation system, providing data support for the operation of the photovoltaic power generation system.

[0045] The beneficial effects of the present invention are as follows: By constructing a computational model based on the integration of data parsing and processing, the present invention can convert photovoltaic data from different sources and formats into a unified type of data, eliminating incompatibilities and differences between the data, making it easier to associate, analyze, and apply the data, thereby providing data support for the operation of photovoltaic power generation systems. Furthermore, the present invention's solution can effectively parse and process data when new photovoltaic equipment is connected, improving the system's scalability; it can tolerate data anomalies or omissions to a certain extent, thereby improving the robustness of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0048] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0049] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0050] Example 1:

[0051] A method suitable for photovoltaic data analysis and processing, such as Figure 1 As shown, the following steps are included:

[0052] Step 1: Sending acquisition frames to photovoltaic devices in a periodic polling manner, and receiving response frames returned by the photovoltaic devices;

[0053] Step 2: Build a computing model based on the integration of data analysis and processing. Use the computing model to parse the photovoltaic data of unknown format and storage method returned by the response frame, and then convert the photovoltaic data into a unified type of data.

[0054] Step 3: Establish a data association strategy to associate the converted photovoltaic data with the strategy to provide data support for the operation of the photovoltaic power generation system.

[0055] In this solution, photovoltaic data from photovoltaic devices is first acquired through acquisition frames and response frames, and then the data is parsed and processed. The received photovoltaic data includes information such as data type and storage method. The computational model converts different content according to preset rules, matching and parsing the specific data content based on the data type and storage method to obtain data in a unified standard format. Compared with existing photovoltaic data parsing and processing methods, which require the establishment of corresponding parsing and processing devices for different data types, the parsed photovoltaic data cannot be formatted in a unified format, making it difficult to establish strategic associations based on photovoltaic data. The solution of the present invention facilitates and effectively establishes strategic associations for all photovoltaic data, thereby providing data support for the operation of photovoltaic power generation systems.

[0056] In step 2, the photovoltaic data of unknown format and storage mode returned by the response frame is parsed by the calculation model, and then the photovoltaic data is converted into unified type data as follows:

[0057] Step 2a, the calculation model extracts characteristic information of photovoltaic data;

[0058] Step 2b: parse the feature information according to the rules. For multiple feature information under the same photovoltaic data, determine whether the feature information parsing is reasonable based on the correlation between the feature information. If reasonable, jump to step 2c. If unreasonable, parse the unreasonable feature information according to other rules until all feature information parsing is reasonable.

[0059] Step 2c: convert the characteristic information according to the conversion rules of the information template. After all characteristic information is converted, unified type data of photovoltaic information is formed.

[0060] In this scheme, the characteristic information of photovoltaic data is first extracted and the type of characteristic information is obtained. Since the format and storage type of photovoltaic data are unknown, in this scheme, any format and any storage type are randomly selected to parse the characteristic information. Then, the correlation between the features is used to determine whether the analysis is reasonable. If it is unreasonable, another format and storage type are selected to parse the characteristic information until all the characteristic information is analyzed reasonably.

[0061] The determination of whether the feature information analysis is reasonable based on the correlation between the feature information is as follows:

[0062] Historical photovoltaic data is obtained and several correlation functions are constructed. The correlation functions are used to associate at least two feature information. The correlation functions are recorded and stored. For multiple feature information under the same photovoltaic data, matching correlation functions are found and obtained. The correlation functions are used to determine whether the feature information parsed according to the rules is reasonable.

[0063] Specifically, in this solution, the type of characteristic information can be timestamp, photovoltaic panel temperature, light intensity, voltage and current, power output, weather conditions, geographic location information, and device location information. The format of photovoltaic data can be text format, json format, MXL format, or binary format. The text can be a CSV file, which is used to store tabular data. The storage type can be big-endian storage or little-endian storage. The correlation function can be the relationship between light intensity and power output, or the relationship between voltage, current, and power data. Weather conditions, geographic location information, and device location information can also be used as parameters of the correlation function to affect the output of the function. In actual construction of the correlation function, several correlation functions can be set to correlate the characteristic information in the photovoltaic data separately to ensure that all characteristic information has a correlation relationship. Alternatively, one correlation function can be set to correlate all characteristic information as a whole. Taking the correlation function between light intensity and power output as an example, the light intensity and power output are first parsed according to text format and big-endian mode storage respectively. The rationality of the parsed data is judged by the correlation function, that is, whether the power output data obtained by using light intensity as a parameter is consistent with the parsed power output data or the error is within a reasonable range. If so, it is judged to be reasonable. Otherwise, the light intensity and power output are re-parsed according to other types and storage modes until it is judged to be reasonable.

[0064] In step 2c, the characteristic information is converted according to the conversion rules of the information template, specifically:

[0065] Set data processing conversion rules, which include shift, reversal, combination and mathematical calculation. Each information template has its own data processing conversion rules, and feature information is converted according to the data processing conversion rules.

[0066] In this embodiment, the characteristic information is converted according to the conversion rules of the information template. After all characteristic information is converted, it forms a unified type of photovoltaic information data. The conversion rules are determined by the characteristics of the information template. For example, if all data stored in big-endian mode needs to be converted to little-endian storage, it needs to be sorted in reverse order; different characteristic information has different units, which requires unified units for unit conversion, such as watts and kilowatts; some photovoltaic data contains voltage and current values, while some photovoltaic data contains power values. These photovoltaic data can also be converted into unified data, and the power value can be calculated from the voltage and current values. Furthermore, if a certain photovoltaic data needs to be uniformly processed with data expressed in different units, such as converting power values ​​into power generation.

[0067] The method applicable to photovoltaic data analysis and processing also establishes several analysis and processing channels. The calculation model based on the integration of data analysis and processing also analyzes the type of photovoltaic data and enters different analysis and processing channels according to the type of photovoltaic data.

[0068] The photovoltaic data types include telesignaling, telemetering, remote control, and remote adjustment. If it is telesignaling, it will enter the telesignaling data parsing and processing channel, and the data finally calculated is bit, double bit or multiple bits; if it is telemetering, it will enter the telemetering data parsing and processing channel, and the data finally calculated is integer, floating point or character type; if it is remote control, it will enter the remote control data parsing and processing channel, and the data finally calculated is bit, double bit or multiple bits; if it is remote adjustment, it will enter the remote adjustment data parsing and processing channel, and the data finally calculated is integer, floating point or character type; if it is a custom type, it will enter the special data parsing and processing channel, and finally match the custom rules, and the calculated data is data related to the custom type.

[0069] Different parsing processing channels reduce the burden of computational model parsing and ensure that data within the same processing channel is identical or similar. Consequently, the characteristic information of the photovoltaic data is also identical or similar. When parsing the computational model, only a few information templates need to be referenced for the content within the same channel. Specifically, in this embodiment, telesignaling is located in the northbound channel and primarily handles status information data; telemetering is located in the northbound channel and primarily handles real-time data or fixed-value parameters; remote control is located in the southbound channel and primarily handles status information data; and remote adjustment is located in the southbound channel and primarily handles real-time data or fixed-value parameters.

[0070] In step 3, the data association strategy includes:

[0071] Time series association strategy, which arranges and associates PV data in chronological order to form time series data;

[0072] The inter-device association strategy associates data from different photovoltaic devices to obtain the operating status and collaborative working effect of the photovoltaic system;

[0073] Environmental factor correlation strategy, which correlates photovoltaic data with external environmental factors to analyze the impact of environmental factors on the power generation efficiency of photovoltaic systems;

[0074] Performance indicator correlation strategy, which correlates various performance indicators of the photovoltaic system to evaluate the overall performance of the photovoltaic system;

[0075] The fault diagnosis correlation strategy associates the operating data of the photovoltaic system with the fault information to timely detect and diagnose photovoltaic system faults.

[0076] A device for photovoltaic data analysis and processing, and a method for photovoltaic data analysis and processing, comprising:

[0077] A data acquisition unit, configured to receive photovoltaic data information in a response frame returned by the photovoltaic device;

[0078] The data analysis unit includes a computing model based on the integration of data analysis and processing, which analyzes the photovoltaic data returned by the response frame through the computing model and converts the photovoltaic data into a unified type of data;

[0079] A strategy generation unit is used to establish a data association strategy and associate the converted photovoltaic data with the strategy;

[0080] The data sending unit is used to send the photovoltaic data associated with the strategy to the photovoltaic power generation system, providing data support for the operation of the photovoltaic power generation system.

[0081] Example 2: A device and method for photovoltaic data parsing and processing. The principle and implementation method thereof differ from those of Example 1 in that: in step 2, the photovoltaic data of unknown format and storage method returned by the response frame is parsed by a computational model, and then the photovoltaic data is converted into uniform type data. Specifically,

[0082] Step 2a, the calculation model extracts characteristic information of photovoltaic data;

[0083] Step 2b: Compare the feature information with the stored information templates to determine the similarity of the feature information. If the similarity between the feature information and any information template is greater than or equal to a set threshold, the feature information and the information template are determined to be of the same type.

[0084] Step 2c: convert the characteristic information according to the conversion rules of the information template. After all characteristic information is converted, unified type data of photovoltaic information is formed.

[0085] The design of this solution first extracts the characteristic information of photovoltaic data. The characteristic information may include timestamp, device status information, its own parameter information and environmental parameter information. Since these characteristic information are returned by different types of photovoltaic devices, the specific attributes are unknown. In addition, due to the influence of the storage method of the received data, for example, different data storage methods may have multiple specific different values ​​that are expressions of the same parameter, or the same value is an expression of different parameters, resulting in the acquired characteristic information being unknown. Therefore, a similarity comparison is performed between the characteristic information and the saved information template to determine the similarity of the characteristic information. Since the information template can be preset, as long as it is characteristic information that can be matched by the information template, it can be parsed out. The information template with the highest similarity and a similarity better than the set threshold is selected. It can be determined that the characteristic information and the information template belong to the same type of information. The information template has preset conversion rules. After all characteristic information is converted, it constitutes a unified type of data for photovoltaic information.

[0086] Specifically, in this embodiment, the received photovoltaic data may include multiple formats, such as text format, JSON format, MXL format, or binary format, where the text may be a CSV file, which is used to store tabular data. At the same time, the characteristic information may also include multiple types, specifically timestamp, photovoltaic panel temperature, light intensity, voltage and current, power output, weather conditions, geographic location information, and device location information. The data storage method also includes big-endian and little-endian modes. In big-endian mode, the most significant byte of the data is stored at the lowest memory address, and the least significant byte is stored at the highest memory address; in little-endian mode, the least significant byte of the data is stored at the lowest memory address, and the most significant byte is stored at the highest memory address. For example, the integer 0x12345678 has a memory arrangement of 12 34 5678 in big-endian mode, and 78 56 34 12 in little-endian mode. In summary, the format of the received photovoltaic data, the type of characteristic information contained therein, and the storage method may not be completely clear, or one or more of the characteristics may be known. Therefore, it is necessary to match the characteristic information. For example, the value 05132024 is likely to be characteristic data of the timestamp, arranged in the order of MM / DD / YY. Other characteristic information also has its own characteristics. Therefore, in this embodiment, multiple information templates are set. As long as the similarity between the characteristic information and any information template is greater than or equal to a set threshold, the characteristic information and the information template are judged to belong to the same type of information. At the same time, the rationality of the parsed value must also be considered. For example, the temperature of the photovoltaic panel is roughly between 15°C and 50°C. If the parsed data deviates significantly from this range, it is necessary to consider whether it is a difference between big-endian and small-endian storage data. Therefore, trying to parse the data using the big-endian and small-endian storage methods can also determine the data storage method.

[0087] In step 2b, the feature information is compared with the stored information template to determine the similarity of the feature information, specifically:

[0088] First, determine whether the feature information is the same as a certain information template. If they are the same, the similarity between the feature information and the information template is determined to be 100%. If they are not the same, calculate the number of bits with the same values ​​between the feature information and the information template, and then calculate the numerical difference between the bits with different values ​​between the feature information and the information template. The similarity of the feature information is determined based on the size of the number of bits with the same values ​​and the numerical difference.

[0089] This design improves the accuracy of similarity judgments by first determining whether the feature information is identical to the information template. If so, the similarity is 100%. This method ensures accuracy when the feature information and the template are completely consistent. When the feature information and the template are not identical, similarity is determined by calculating the number of digits with the same value and the magnitude of the difference in the values. This method is more sophisticated than simply matching all or not matching all and can more accurately reflect the degree of similarity between the feature information and the template. This design improves the adaptability of the judgment. Even if the format or accuracy of the photovoltaic data changes, as long as the main part of the feature information remains similar to the template, this method can accurately determine the similarity.

[0090] In step 2b, if the similarity between the feature information and any information template is less than a set threshold, the feature information is judged to be mismatched with the information template, and the calculation model performs a separate conversion step on the feature information, specifically:

[0091] Obtain whether other characteristic information in the photovoltaic data corresponding to the characteristic information matches the information template. If all other characteristic information matches the information template or the number of characteristic information that matches the information template is greater than a set value, obtain the standard photovoltaic data corresponding to these matching information template combinations, find the information template corresponding to the characteristic information in the standard photovoltaic data, and convert the characteristic information according to the conversion rule corresponding to the information template.

[0092] This design considers two scenarios. One is the integration of new photovoltaic equipment. Some of the characteristic information in the photovoltaic data may be of a new data type, with no existing information templates to match it well. However, new photovoltaic data generally does not significantly change the data format or storage method. Therefore, as long as the majority of the characteristic information in the photovoltaic data matches, it can be determined that the photovoltaic data matches the existing standard photovoltaic data (standard photovoltaic data contains several information templates). Characteristic information that does not match the existing information templates can also be matched using the corresponding information templates in the matching standard photovoltaic data. Another scenario is that in actual applications, photovoltaic data may be abnormal or missing due to various reasons such as equipment failure and transmission errors. This design tolerates data anomalies and missing data to a certain extent, thereby improving the robustness of data processing.

[0093] The method suitable for photovoltaic data analysis and processing also verifies the data after the characteristic information is converted according to the conversion rules corresponding to the information template, and judges the rationality of the converted data. If the converted data is unreasonable, the characteristic information is re-searched for an information template for conversion until the converted data is reasonable.

[0094] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0095] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0096] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0097] It should be understood that the present application is not limited to the exact structures 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 method for analyzing and processing photovoltaic data, characterized in that: The following steps are involved: Step 1: Sending acquisition frames to photovoltaic devices in a periodic polling manner, and receiving response frames returned by the photovoltaic devices; Step 2: Build a computing model based on the integration of data analysis and processing. Use the computing model to parse the photovoltaic data of unknown format and storage method returned by the response frame, and then convert the photovoltaic data into a unified type of data. Step 3: Establish a data association strategy to associate the converted photovoltaic data with the strategy to provide data support for the operation of the photovoltaic power generation system; In step 2, the photovoltaic data of unknown format and storage mode returned by the response frame is parsed by the calculation model, and then the photovoltaic data is converted into unified type data as follows: Step 2a, the calculation model extracts characteristic information of photovoltaic data; Step 2b: parse the feature information according to the rules. For multiple feature information under the same photovoltaic data, determine whether the feature information parsing is reasonable based on the correlation between the feature information. If reasonable, jump to step 2c. If unreasonable, parse the unreasonable feature information according to other rules until all feature information parsing is reasonable. Step 2c: convert the characteristic information according to the conversion rules of the information template. After all characteristic information is converted, it constitutes unified type data of photovoltaic information. The determination of whether the feature information analysis is reasonable based on the correlation between the feature information is as follows: Historical photovoltaic data is obtained and several correlation functions are constructed. The correlation functions are used to associate at least two feature information. The correlation functions are recorded and stored. For multiple feature information under the same photovoltaic data, matching correlation functions are found and obtained. The correlation functions are used to determine whether the feature information parsed according to the rules is reasonable.

2. A method for photovoltaic data analysis and processing according to claim 1, characterized in that: The characteristic information is converted according to the conversion rules of the information template, specifically: Set data processing conversion rules, which include shift, reversal, combination and mathematical calculation. Each information template has its own data processing conversion rules, and feature information is converted according to the data processing conversion rules.

3. The method for photovoltaic data analysis and processing according to claim 1, characterized in that: Several analysis and processing channels are also established. Based on the calculation model that integrates data analysis and processing, the type of photovoltaic data is analyzed, and different analysis and processing channels are entered according to the type of photovoltaic data.

4. A method for analyzing and processing photovoltaic data according to claim 3, characterized in that: The photovoltaic data types include telesignaling, telemetering, remote control, and remote adjustment. If it is telesignaling, it will enter the telesignaling data parsing and processing channel, and the final calculated data is single-digit, double-digit, or multi-digit; if it is telemetering, it will enter the telemetering data parsing and processing channel, and the final calculated data is integer, floating point, or character type; If it is remote control, it will enter the remote control data parsing and processing channel, and the final calculated data is single-digit, double-digit or multi-digit; if it is remote adjustment, it will enter the remote adjustment data parsing and processing channel, and the final calculated data is integer, floating-point or character; if it is a custom type, it will enter the special data parsing and processing channel, and will eventually match the custom rules, and the calculated data is data related to the custom type.

5. The method for photovoltaic data analysis and processing according to claim 1, characterized in that: In step 3, the data association strategy includes: Time series association strategy, which arranges and associates PV data in chronological order to form time series data; The inter-device association strategy associates data from different photovoltaic devices to obtain the operating status and collaborative working effect of the photovoltaic system; Environmental factor correlation strategy, which correlates photovoltaic data with external environmental factors to analyze the impact of environmental factors on the power generation efficiency of photovoltaic systems; Performance indicator correlation strategy, which correlates various performance indicators of the photovoltaic system to evaluate the overall performance of the photovoltaic system; The fault diagnosis correlation strategy associates the operating data of the photovoltaic system with the fault information to timely detect and diagnose photovoltaic system faults.

6. A device for photovoltaic data analysis and processing, suitable for a method for photovoltaic data analysis and processing as claimed in any one of claims 1 to 5, characterized in that: include: A data acquisition unit, configured to receive photovoltaic data information in a response frame returned by the photovoltaic device; The data analysis unit includes a computing model based on the integration of data analysis and processing, which analyzes the photovoltaic data returned by the response frame through the computing model and converts the photovoltaic data into a unified type of data; A strategy generation unit is used to establish a data association strategy and associate the converted photovoltaic data with the strategy; The data sending unit is used to send the photovoltaic data associated with the strategy to the photovoltaic power generation system, providing data support for the operation of the photovoltaic power generation system.

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