Intelligent photovoltaic power station management system and method

Abstract

The invention relates to the technical field of photovoltaic power stations, and discloses an intelligent photovoltaic power station management system and method.The method is characterized in that a sequence establishment module collects operation data of a photovoltaic power station with a preset time period as the collection frequency, and an operation data time sequence T is established; a sequence processing module obtains standard operation data of each operation data, and processes the operation data time sequence T to obtain a sub-operation data sequence; the first management module manages operation data in the risk operation data sequence; the sequence calculation module analyzes the suspected operation data sequence and calculates suspected data factors; and the second management module judges whether to manage the operation data in the suspected operation data sequence according to the suspected data factor, so that the actual situation and safety management requirements of the photovoltaic power station can be comprehensively considered, and the functions of real-time monitoring, remote control, scheduling and the like of the operation state of the photovoltaic power station equipment are realized. Therefore, the safety and reliability of the photovoltaic power station are improved, and the occurrence rate of safety accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power plants, and in particular to an intelligent photovoltaic power plant management system and method. Background Art

[0002] As the global demand for renewable energy continues to increase, photovoltaic power generation has been widely used as a clean and renewable energy form. Photovoltaic power stations provide a green and sustainable energy solution for mankind by converting solar energy into electrical energy. However, as the number and scale of photovoltaic power stations continue to expand, their safety issues have become increasingly prominent, becoming one of the important factors restricting the healthy development of the photovoltaic industry.

[0003] There are many challenges and problems in the safe operation and management of photovoltaic power stations. Photovoltaic power stations involve a variety of key equipment such as high-voltage electrical equipment, inverters, transformers, etc. The failure of any equipment may cause a safety accident, resulting in casualties and property losses. In addition, the traditional safe operation and management of photovoltaic power stations mainly relies on manual inspections and regular maintenance. This method is not only inefficient, but also difficult to detect and deal with potential safety hazards in a timely manner. At the same time, due to the lack of effective monitoring methods and early warning mechanisms, once a safety accident occurs, it is often difficult to respond and deal with it quickly, resulting in the expansion of the accident. Summary of the invention

[0004] The embodiment of the present invention provides an intelligent photovoltaic power station management system and method, which can comprehensively consider the actual situation and safety management requirements of the photovoltaic power station, realize real-time monitoring, remote control and scheduling of the operating status of photovoltaic power station equipment, thereby improving the safety and reliability of the photovoltaic power station and reducing the occurrence rate of safety accidents.

[0005] In order to achieve the above object, the present invention provides an intelligent photovoltaic power station management system, comprising:

[0006] A sequence establishment module is used to collect the operation data of the photovoltaic power station at a preset time period as the collection frequency, and establish an operation data time series T, where T = {et|t = t0, t1, t2, ..., tn}, et is the operation data collected at time t, and n is the number of time points corresponding to the preset time period;

[0007] A sequence processing module, used for obtaining standard operation data corresponding to each operation data, and processing the operation data time series T based on the standard operation data to obtain a sub-operation data sequence, wherein the sub-operation data sequence includes a standard operation data sequence, a risk operation data sequence, and a suspected operation data sequence;

[0008] A first management module, used for managing the operation data in the risk operation data sequence;

[0009] A sequence calculation module, used for analyzing the suspected operation data sequence and calculating the suspected data factor of the suspected operation data sequence based on the analysis result;

[0010] The second management module is used to determine whether to manage the operation data in the suspected operation data sequence according to the suspected data factor.

[0011] Furthermore, the sequence establishment module is used to:

[0012] The sequence establishment module is used to analyze the operation data, determine whether there is duplicate data in the operation data based on the analysis result, and if so, generate duplicate data identifiers for all duplicate data;

[0013] The sequence establishment module is used to retain the running data corresponding to one duplicate data identifier and delete the data corresponding to the remaining duplicate data identifiers;

[0014] The sequence establishment module is used to output the data entropy value corresponding to each running data based on the pre-trained data entropy value model, and obtain the pre-set data entropy value threshold;

[0015] The sequence establishment module is used to delete all operating data less than the data entropy value threshold, and establish an operating data time series T based on all operating data greater than or equal to the data entropy value threshold.

[0016] Furthermore, the sequence processing module is used to:

[0017] The sequence processing module is used to obtain standard operating data corresponding to each operating data, wherein the standard operating data includes first standard operating data and second standard operating data, and the first standard operating data is smaller than the second standard operating data;

[0018] The sequence processing module is used to classify all the operation data that are greater than or equal to the first standard operation data and less than or equal to the second standard operation data into a standard operation data sequence;

[0019] The sequence processing module is used to obtain the last historical safe operation data of the photovoltaic power station, compare the last historical safe operation data with the standard operation data, and update and optimize the first standard operation data and the second standard operation data based on the comparison result to obtain updated first standard operation data and updated second standard operation data;

[0020] The sequence processing module is used to extract the remaining operation data, and classify all the operation data that are smaller than the updated first standard operation data and all the operation data that are larger than the updated second standard operation data into a risk operation data sequence;

[0021] The sequence processing module is used to extract the remaining operation data and divide the remaining operation data into suspected operation data sequences.

[0022] Furthermore, the sequence calculation module is used for:

[0023] The sequence calculation module is used to collect a plurality of historical operation data corresponding to the suspected operation data sequence;

[0024] The sequence calculation module is used to calculate the operation data difference between each historical operation data and the corresponding operation data in the suspected operation data sequence;

[0025] The sequence calculation module is used to analyze all the running data differences to determine the number of positive running data differences and the number of negative running data differences;

[0026] The sequence calculation module is used to calculate the future prediction factor of the operation data in the suspected operation data sequence according to the number of positive operation data difference values, the number of negative operation data difference values ​​and the operation data difference value;

[0027] The sequence calculation module is used to calculate the suspected data factors of the suspected operation data sequence based on all future prediction factors.

[0028] Furthermore, the sequence calculation module is used for:

[0029] The sequence calculation module is used to calculate the future prediction factor of the operation data in the suspected operation data sequence according to the following formula:

[0030]

[0031] Where r is the future prediction factor, n1 is the number of positive running data differences, n2 is the number of negative running data differences, and f j is the jth positive running data difference, and fmax is the maximum positive running data difference.

[0032] Furthermore, the sequence calculation module is used for:

[0033] The sequence calculation module is used to calculate the average value of all future prediction factors;

[0034] The sequence calculation module is used to generate a low processing set according to all future prediction factors that are less than the average value;

[0035] The sequence calculation module is used to generate an equal processing set according to all future prediction factors equal to the average value;

[0036] The sequence calculation module is used to generate a high processing set according to all future prediction factors greater than the average value;

[0037] The sequence calculation module is used to calculate a first mean value and a first standard deviation of the low processing set, and generate a first inner set mark for future prediction factors in the low processing set that are greater than the first mean value and less than the first standard deviation;

[0038] The sequence calculation module is used to calculate a second mean value and a second standard deviation of the equal processing set, and generate a second inner set mark for future prediction factors in the equal processing set that are greater than the second mean value and less than the second standard deviation;

[0039] The sequence calculation module is used to calculate a third mean value and a third standard deviation of the high processing set, and generate a third internal set mark for future prediction factors in the high processing set that are greater than the third mean value and less than the third standard deviation;

[0040] The sequence calculation module is used to calculate the suspected data factor of the suspected operation data sequence according to the first inner set mark, the second inner set mark and the third inner set mark.

[0041] Furthermore, the sequence calculation module is used for:

[0042] The sequence calculation module is used to count a first number of the first inner set markers, a second number of the second inner set markers, and a third number of the third inner set markers;

[0043] The suspected data factor of the suspected running data sequence is calculated according to the following formula:

[0044]

[0045] Wherein, W is the suspected data factor of the suspected running data sequence, B1 is the first number, B2 is the second number, and B3 is the third number.

[0046] Furthermore, the second management module is used to:

[0047] The second management module is used to obtain a preset suspected data factor, and determine whether to manage the operation data in the suspected operation data sequence according to the relationship between the suspected data factor and the preset suspected data factor;

[0048] The second management module is used for determining not to manage the operation data in the suspected operation data sequence when the suspected data factor is less than the preset suspected data factor;

[0049] The second management module is used to determine whether to manage the operation data in the suspected operation data sequence when the suspected data factor is greater than or equal to the preset suspected data factor.

[0050] In order to achieve the above object, the present invention also provides a smart photovoltaic power station management method, comprising:

[0051] The operation data of the photovoltaic power station is collected at a preset time period as the collection frequency, and an operation data time series T is established, where T = {et|t = t0, t1, t2, ..., tn}, et is the operation data collected at time t, and n is the number of time points corresponding to the preset time period;

[0052] Acquire standard operating data corresponding to each operating data, and process the operating data time series T based on the standard operating data to obtain a sub-operating data sequence, wherein the sub-operating data sequence includes a standard operating data sequence, a risk operating data sequence, and a suspected operating data sequence;

[0053] Managing the operation data in the risk operation data sequence;

[0054] Analyzing the suspected operation data sequence, and calculating the suspected data factor of the suspected operation data sequence based on the analysis result;

[0055] Whether to manage the operation data in the suspected operation data sequence is determined according to the suspected data factor.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention discloses an intelligent photovoltaic power station management system and method. A sequence establishment module collects operating data of a photovoltaic power station with a preset time period as the collection frequency, and establishes an operating data time series T; a sequence processing module obtains standard operating data of each operating data, and processes the operating data time series T to obtain a sub-operating data sequence; a first management module manages the operating data in a risk operating data sequence; a sequence calculation module analyzes a suspected operating data sequence and calculates a suspected data factor; a second management module determines whether to manage the operating data in the suspected operating data sequence according to the suspected data factor, which can comprehensively consider the actual situation and safety management requirements of the photovoltaic power station, and realize functions such as real-time monitoring, remote control and scheduling of the operating status of photovoltaic power station equipment, thereby improving the safety and reliability of the photovoltaic power station and reducing the occurrence rate of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0059] Figure 1 A schematic diagram of the structure of an intelligent photovoltaic power station management system in an embodiment of the present invention is shown;

[0060] Figure 2 A schematic diagram of the process of the intelligent photovoltaic power station management method in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0061] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0062] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0063] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0064] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0065] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0066] like Figure 1 As shown, an embodiment of the present invention discloses an intelligent photovoltaic power station management system, including:

[0067] A sequence establishment module is used to collect the operation data of the photovoltaic power station at a preset time period as the collection frequency, and establish an operation data time series T, where T = {et|t = t0, t1, t2, ..., tn}, et is the operation data collected at time t, and n is the number of time points corresponding to the preset time period;

[0068] A sequence processing module, used for obtaining standard operation data corresponding to each operation data, and processing the operation data time series T based on the standard operation data to obtain a sub-operation data sequence, wherein the sub-operation data sequence includes a standard operation data sequence, a risk operation data sequence, and a suspected operation data sequence;

[0069] A first management module, used for managing the operation data in the risk operation data sequence;

[0070] A sequence calculation module, used for analyzing the suspected operation data sequence and calculating the suspected data factor of the suspected operation data sequence based on the analysis result;

[0071] The second management module is used to determine whether to manage the operation data in the suspected operation data sequence according to the suspected data factor.

[0072] In this embodiment, the preset time period can be set to 10 minutes or 20 minutes, etc. Time t represents minutes, that is, the operation data is collected once every one minute.

[0073] In this embodiment, the operating data includes operating temperature, operating voltage, operating current, and the like.

[0074] The beneficial effect of the above technical solution is that the present invention can comprehensively consider the actual situation and safety management requirements of the photovoltaic power station, realize the functions of real-time monitoring, remote control and scheduling of the operating status of the photovoltaic power station equipment, thereby improving the safety and reliability of the photovoltaic power station and reducing the occurrence rate of safety accidents.

[0075] In some embodiments of the present application, the sequence establishment module is used to:

[0076] The sequence establishment module is used to analyze the operation data, determine whether there is duplicate data in the operation data based on the analysis result, and if so, generate duplicate data identifiers for all duplicate data;

[0077] The sequence establishment module is used to retain the running data corresponding to one duplicate data identifier and delete the data corresponding to the remaining duplicate data identifiers;

[0078] The sequence establishment module is used to output the data entropy value corresponding to each running data based on the pre-trained data entropy value model, and obtain the pre-set data entropy value threshold;

[0079] The sequence establishment module is used to delete all operating data less than the data entropy value threshold, and establish an operating data time series T based on all operating data greater than or equal to the data entropy value threshold.

[0080] In this embodiment, the data entropy value model is pre-trained and can output the data entropy value corresponding to each running data.

[0081] In this embodiment, the data entropy value threshold is preferably 0.8.

[0082] The beneficial effect of the above technical solution is that the present invention can lay a foundation for the safety management of the smart photovoltaic power station and provide a management basis by establishing the operation data time series T.

[0083] In some embodiments of the present application, the sequence processing module is used to:

[0084] The sequence processing module is used to obtain standard operating data corresponding to each operating data, wherein the standard operating data includes first standard operating data and second standard operating data, and the first standard operating data is smaller than the second standard operating data;

[0085] The sequence processing module is used to classify all the operation data that are greater than or equal to the first standard operation data and less than or equal to the second standard operation data into a standard operation data sequence;

[0086] The sequence processing module is used to obtain the last historical safe operation data of the photovoltaic power station, compare the last historical safe operation data with the standard operation data, and update and optimize the first standard operation data and the second standard operation data based on the comparison result to obtain updated first standard operation data and updated second standard operation data;

[0087] The sequence processing module is used to extract the remaining operation data, and classify all the operation data that are smaller than the updated first standard operation data and all the operation data that are larger than the updated second standard operation data into a risk operation data sequence;

[0088] The sequence processing module is used to extract the remaining operation data and divide the remaining operation data into suspected operation data sequences.

[0089] In this embodiment, the first standard operating data and the second standard operating data can constitute a range. For example, if the operating data is the operating temperature, the standard operating data is 25°C-28°C, 25°C is the first standard operating data, 28°C is the second standard operating data, and the rest are not shown one by one.

[0090] In this embodiment, if the previous historical safety operation data is the operation temperature, and specifically is 29°C, the second standard operation data is updated to 29°C, and the standard operation data is 25°C-29°C.

[0091] The beneficial effect of the above technical solution is that the present invention can accurately divide the operation data into a standard operation data sequence, a risky operation data sequence, and a suspected operation data sequence, thereby ensuring the calculation accuracy of the suspected data factor.

[0092] In some embodiments of the present application, the sequence calculation module is used to:

[0093] The sequence calculation module is used to collect a plurality of historical operation data corresponding to the suspected operation data sequence;

[0094] The sequence calculation module is used to calculate the operation data difference between each historical operation data and the corresponding operation data in the suspected operation data sequence;

[0095] The sequence calculation module is used to analyze all the running data differences to determine the number of positive running data differences and the number of negative running data differences;

[0096] The sequence calculation module is used to calculate the future prediction factor of the operation data in the suspected operation data sequence according to the number of positive operation data difference values, the number of negative operation data difference values ​​and the operation data difference value;

[0097] The sequence calculation module is used to calculate the suspected data factors of the suspected operation data sequence based on all future prediction factors.

[0098] In this embodiment, if the suspected operation data sequence includes the operation voltage, the historical operation data is also the operation voltage.

[0099] In this embodiment, the difference between the operating voltage in the suspected operating data sequence and the historical operating voltage is calculated. When the difference is positive, it is determined as a positive operating data difference, and when the difference is negative, it is determined as a negative operating data difference.

[0100] The beneficial effect of the above technical solution is: the present invention calculates the future prediction factor of the operating data in the suspected operating data sequence according to the number of positive operating data differences, the number of negative operating data differences and the operating data differences, which can provide data support for calculating the suspected data factor of the suspected operating data sequence and ensure the calculation accuracy of the suspected data factor.

[0101] In some embodiments of the present application, the sequence calculation module is used to:

[0102] The sequence calculation module is used to calculate the future prediction factor of the operation data in the suspected operation data sequence according to the following formula:

[0103]

[0104] Where r is the future prediction factor, n1 is the number of positive running data differences, n2 is the number of negative running data differences, and f j is the jth positive running data difference, and fmax is the maximum positive running data difference.

[0105] In some embodiments of the present application, the sequence calculation module is used to:

[0106] The sequence calculation module is used to calculate the average value of all future prediction factors;

[0107] The sequence calculation module is used to generate a low processing set according to all future prediction factors that are less than the average value;

[0108] The sequence calculation module is used to generate an equal processing set according to all future prediction factors equal to the average value;

[0109] The sequence calculation module is used to generate a high processing set according to all future prediction factors greater than the average value;

[0110] The sequence calculation module is used to calculate a first mean value and a first standard deviation of the low processing set, and generate a first inner set mark for future prediction factors in the low processing set that are greater than the first mean value and less than the first standard deviation;

[0111] The sequence calculation module is used to calculate a second mean value and a second standard deviation of the equal processing set, and generate a second inner set mark for future prediction factors in the equal processing set that are greater than the second mean value and less than the second standard deviation;

[0112] The sequence calculation module is used to calculate a third mean value and a third standard deviation of the high processing set, and generate a third internal set mark for future prediction factors in the high processing set that are greater than the third mean value and less than the third standard deviation;

[0113] The sequence calculation module is used to calculate the suspected data factor of the suspected operation data sequence according to the first inner set mark, the second inner set mark and the third inner set mark.

[0114] The beneficial effect of the above technical solution is: the suspected data factors of the suspected running data sequence are calculated according to the first inner set mark, the second inner set mark and the third inner set mark, which ensures the accurate calculation of the suspected data factors, avoids errors caused by human participation, and improves the calculation efficiency.

[0115] In some embodiments of the present application, the sequence calculation module is used to:

[0116] The sequence calculation module is used to count a first number of the first inner set markers, a second number of the second inner set markers, and a third number of the third inner set markers;

[0117] The suspected data factor of the suspected running data sequence is calculated according to the following formula:

[0118]

[0119] Wherein, W is the suspected data factor of the suspected running data sequence, B1 is the first number, B2 is the second number, and B3 is the third number.

[0120] In some embodiments of the present application, the second management module is used to:

[0121] The second management module is used to obtain a preset suspected data factor, and determine whether to manage the operation data in the suspected operation data sequence according to the relationship between the suspected data factor and the preset suspected data factor;

[0122] The second management module is used for determining not to manage the operation data in the suspected operation data sequence when the suspected data factor is less than the preset suspected data factor;

[0123] The second management module is used to determine whether to manage the operation data in the suspected operation data sequence when the suspected data factor is greater than or equal to the preset suspected data factor.

[0124] In this embodiment, the preset suspected data factor is 0.75, and can be adjusted according to actual conditions.

[0125] The beneficial effect of the above technical solution is that the present invention can comprehensively consider the actual situation and safety management requirements of the photovoltaic power station, realize the functions of real-time monitoring, remote control and scheduling of the operating status of the photovoltaic power station equipment, thereby improving the safety and reliability of the photovoltaic power station and reducing the occurrence rate of safety accidents.

[0126] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0127] Correspondingly, such as Figure 2As shown, the present application also provides a smart photovoltaic power station management method, including:

[0128] The operation data of the photovoltaic power station is collected at a preset time period as the collection frequency, and an operation data time series T is established, where T = {et|t = t0, t1, t2, ..., tn}, et is the operation data collected at time t, and n is the number of time points corresponding to the preset time period;

[0129] Acquire standard operating data corresponding to each operating data, and process the operating data time series T based on the standard operating data to obtain a sub-operating data sequence, wherein the sub-operating data sequence includes a standard operating data sequence, a risk operating data sequence, and a suspected operating data sequence;

[0130] Managing the operation data in the risk operation data sequence;

[0131] Analyzing the suspected operation data sequence, and calculating the suspected data factor of the suspected operation data sequence based on the analysis result;

[0132] Whether to manage the operation data in the suspected operation data sequence is determined according to the suspected data factor.

[0133] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0134] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0135] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent photovoltaic power station management system, characterized in that: include: A sequence establishment module is used to collect the operation data of the photovoltaic power station at a preset time period as the collection frequency, and establish an operation data time series T, where T = {et|t = t0, t1, t2, ..., tn}, et is the operation data collected at time t, and n is the number of time points corresponding to the preset time period; A sequence processing module, used for obtaining standard operation data corresponding to each operation data, and processing the operation data time series T based on the standard operation data to obtain a sub-operation data sequence, wherein the sub-operation data sequence includes a standard operation data sequence, a risk operation data sequence, and a suspected operation data sequence; A first management module, used for managing the operation data in the risk operation data sequence; A sequence calculation module, used for analyzing the suspected operation data sequence and calculating the suspected data factor of the suspected operation data sequence based on the analysis result; The second management module is used to determine whether to manage the operation data in the suspected operation data sequence according to the suspected data factor.

2. The intelligent photovoltaic power station management system according to claim 1, characterized in that: The sequence establishment module is used for: The sequence establishment module is used to analyze the operation data, determine whether there is duplicate data in the operation data based on the analysis result, and if so, generate duplicate data identifiers for all duplicate data; The sequence establishment module is used to retain the running data corresponding to one duplicate data identifier and delete the data corresponding to the remaining duplicate data identifiers; The sequence establishment module is used to output the data entropy value corresponding to each running data based on the pre-trained data entropy value model, and obtain the pre-set data entropy value threshold; The sequence establishment module is used to delete all operating data less than the data entropy value threshold, and establish an operating data time series T based on all operating data greater than or equal to the data entropy value threshold.

3. The intelligent photovoltaic power station management system according to claim 1, characterized in that: The sequence processing module is used for: The sequence processing module is used to obtain standard operating data corresponding to each operating data, wherein the standard operating data includes first standard operating data and second standard operating data, and the first standard operating data is smaller than the second standard operating data; The sequence processing module is used to classify all the operation data that are greater than or equal to the first standard operation data and less than or equal to the second standard operation data into a standard operation data sequence; The sequence processing module is used to obtain the last historical safe operation data of the photovoltaic power station, compare the last historical safe operation data with the standard operation data, and update and optimize the first standard operation data and the second standard operation data based on the comparison result to obtain updated first standard operation data and updated second standard operation data; The sequence processing module is used to extract the remaining operation data, and classify all the operation data that are smaller than the updated first standard operation data and all the operation data that are larger than the updated second standard operation data into a risk operation data sequence; The sequence processing module is used to extract the remaining operation data and divide the remaining operation data into suspected operation data sequences.

4. The intelligent photovoltaic power station management system according to claim 1, characterized in that: The sequence calculation module is used for: The sequence calculation module is used to collect a plurality of historical operation data corresponding to the suspected operation data sequence; The sequence calculation module is used to calculate the operation data difference between each historical operation data and the corresponding operation data in the suspected operation data sequence; The sequence calculation module is used to analyze all the running data differences to determine the number of positive running data differences and the number of negative running data differences; The sequence calculation module is used to calculate the future prediction factor of the operation data in the suspected operation data sequence according to the number of positive operation data difference values, the number of negative operation data difference values ​​and the operation data difference value; The sequence calculation module is used to calculate the suspected data factors of the suspected operation data sequence based on all future prediction factors.

5. The intelligent photovoltaic power station management system according to claim 4, characterized in that: The sequence calculation module is used for: The sequence calculation module is used to calculate the future prediction factor of the operation data in the suspected operation data sequence according to the following formula: Where r is the future prediction factor, n1 is the number of positive running data differences, n2 is the number of negative running data differences, and f j is the jth positive running data difference, and fmax is the maximum positive running data difference.

6. The intelligent photovoltaic power station management system according to claim 1, characterized in that: The sequence calculation module is used for: The sequence calculation module is used to calculate the average value of all future prediction factors; The sequence calculation module is used to generate a low processing set according to all future prediction factors that are less than the average value; The sequence calculation module is used to generate an equal processing set according to all future prediction factors equal to the average value; The sequence calculation module is used to generate a high processing set according to all future prediction factors greater than the average value; The sequence calculation module is used to calculate a first mean value and a first standard deviation of the low processing set, and generate a first inner set mark for future prediction factors in the low processing set that are greater than the first mean value and less than the first standard deviation; The sequence calculation module is used to calculate a second mean value and a second standard deviation of the equal processing set, and generate a second inner set mark for future prediction factors in the equal processing set that are greater than the second mean value and less than the second standard deviation; The sequence calculation module is used to calculate a third mean value and a third standard deviation of the high processing set, and generate a third internal set mark for future prediction factors in the high processing set that are greater than the third mean value and less than the third standard deviation; The sequence calculation module is used to calculate the suspected data factor of the suspected operation data sequence according to the first inner set mark, the second inner set mark and the third inner set mark.

7. The intelligent photovoltaic power station management system according to claim 6, characterized in that: The sequence calculation module is used for: The sequence calculation module is used to count a first number of the first inner set markers, a second number of the second inner set markers, and a third number of the third inner set markers; The suspected data factor of the suspected running data sequence is calculated according to the following formula: Wherein, W is the suspected data factor of the suspected running data sequence, B1 is the first number, B2 is the second number, and B3 is the third number.

8. The intelligent photovoltaic power station management system according to claim 1, characterized in that: The second management module is used for: The second management module is used to obtain a preset suspected data factor, and determine whether to manage the operation data in the suspected operation data sequence according to the relationship between the suspected data factor and the preset suspected data factor; The second management module is used for determining not to manage the operation data in the suspected operation data sequence when the suspected data factor is less than the preset suspected data factor; The second management module is used to determine whether to manage the operation data in the suspected operation data sequence when the suspected data factor is greater than or equal to the preset suspected data factor.

9. A method for managing an intelligent photovoltaic power station, characterized in that: include: The operation data of the photovoltaic power station is collected at a preset time period as the collection frequency, and an operation data time series T is established, where T = {et|t = t0, t1, t2, ..., tn}, et is the operation data collected at time t, and n is the number of time points corresponding to the preset time period; Acquire standard operating data corresponding to each operating data, and process the operating data time series T based on the standard operating data to obtain a sub-operating data sequence, wherein the sub-operating data sequence includes a standard operating data sequence, a risk operating data sequence, and a suspected operating data sequence; Managing the operation data in the risk operation data sequence; Analyzing the suspected operation data sequence, and calculating the suspected data factor of the suspected operation data sequence based on the analysis result; Whether to manage the operation data in the suspected operation data sequence is determined according to the suspected data factor.

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