A big data processing system for photovoltaic power generation prediction

Abstract

The present invention relates to the field of photovoltaic power generation technology, and specifically discloses a big data processing system for photovoltaic power generation power prediction, including: a processing module, which is used to obtain all-class influencing factor processing parameters of the photovoltaic power station at the corresponding moment based on all-class influencing factor parameters of each moment in a preset time period before the current moment; a judgment module, which is used to obtain all-class core influencing factor parameters of the photovoltaic power station at all corresponding moments based on all-class influencing factor processing parameters of the photovoltaic power station at all moments in the preset time period before the current moment; a construction module, which is used to obtain a parameter prediction matrix at the current moment based on all-class core influencing factor parameters of the photovoltaic power station at all moments in the preset time period before the current moment; and a prediction module, which is used to obtain a photovoltaic power generation power prediction result based on the parameter prediction matrix at the current moment. The present invention realizes a more accurate prediction of the power generation power of the photovoltaic power station at the predicted moment.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a big data processing system for photovoltaic power generation power prediction. Background Art

[0002] At present, as the proportion of renewable energy in the global energy structure continues to increase, photovoltaic power stations, as an important part of renewable energy, the stability and predictability of their power generation are crucial to the safe and stable operation of the power grid. Grid dispatching requires accurate prediction of the power generation of photovoltaic power stations in order to reasonably arrange the start and stop and output of traditional energy units, ensure the balance of supply and demand in the power grid, and avoid large-scale abandonment of light or power shortages.

[0003] However, the existing big data processing system for photovoltaic power generation prediction only performs data cleaning on the acquired real-time data and historical data, trains and generates the prediction model in the computing server, and deploys the trained prediction model in the server to realize the call of the prediction model. However, in the process of building the prediction model, the complex relationship between the power generation of the photovoltaic power station and various influencing factors is not deeply analyzed, especially all types of core influencing factor parameters that have a greater impact on the power generation of the photovoltaic power station are not screened out from all types of influencing factor processing parameters, and the impact value of each type of core influencing factor parameter on the power generation is not accurately quantified, resulting in low prediction accuracy of the prediction model, making it difficult to achieve a more accurate prediction of the power generation of the photovoltaic power station at the prediction time. For example, the patent with the publication number "CN115982125A" and the name "A big data processing system for photovoltaic power generation prediction" includes the following steps: data acquisition module, for real-time data servers, the data acquisition module acquires real-time data and various historical data; data communication module, including Flume system and distributed message queue Kafka, to realize transmission communication; data processing module, the data processing module cleans the acquired real-time data and historical data, and trains and generates prediction models in the computing server; data storage module, redundantly stores the real-time data and historical data that have been cleaned; data application module, used to deploy the prediction model generated by training in the server, realize the call of the prediction model, and report the prediction results obtained by the prediction model. The above patent has the advantages of real-time processing of big data of photovoltaic power generation. However, this patent only cleans the acquired real-time data and historical data, trains and generates the prediction model in the computing server, and deploys the trained prediction model in the server to call the prediction model. It does not accurately screen out all the core influencing factor parameters that have a greater impact on the power generation of the photovoltaic power station from all types of influencing factor processing parameters, and does not accurately quantify the impact value of each type of core influencing factor parameter on the power generation, making it difficult to achieve a more accurate prediction of the power generation of the photovoltaic power station at the prediction time.

[0004] Therefore, the present invention proposes a big data processing system for photovoltaic power generation prediction. Summary of the invention

[0005] The present invention provides a big data processing system for photovoltaic power generation prediction, which is used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment according to the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters, accurately screen out all types of core influencing factor parameters that have a greater degree of influence on the power generation of the photovoltaic power station from all types of influencing factor processing parameters, and then obtain the influence value of each type of core influencing factor parameter of the photovoltaic power station at all times in the preset time period before the current moment according to each type of core influencing factor parameter and power generation parameter of the photovoltaic power station at all times in the preset time period before the current moment, thereby realizing the prediction of the photovoltaic power station at the preset time period before the current moment. The degree of influence of each type of core influencing factor parameter at each moment in the preset time period before the current moment on the power generation power of the photovoltaic power station at the corresponding moment is quantified, and the parameter prediction matrix at the current moment is obtained according to the influence value of all types of core influencing factor parameters of the photovoltaic power station at all moments in the preset time period before the current moment, so as to facilitate the subsequent prediction of all types of core influencing factor parameters of the photovoltaic power station at the prediction moment, and obtain all types of core influencing factor parameters of the photovoltaic power station at the prediction moment according to the parameter prediction matrix at the current moment and the prediction moment, and obtain the photovoltaic power generation power prediction result according to all types of core influencing factor parameters of the photovoltaic power station at the prediction moment, thereby realizing a more accurate prediction of the power generation power of the photovoltaic power station at the prediction moment.

[0006] The present invention provides a big data processing system for photovoltaic power generation prediction, comprising:

[0007] A processing module, used to perform data processing on all influencing factor parameters of the photovoltaic power station at each moment in a preset time period before the current moment, and obtain all influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment;

[0008] A judgment module, used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters;

[0009] A construction module is used to obtain the influence value of each type of core influencing factor parameter of the photovoltaic power station at all times in the preset time period before the current moment based on each type of core influencing factor parameter and power generation parameter of the photovoltaic power station at all times in the preset time period before the current moment, and obtain the parameter prediction matrix at the current moment based on the influence value of all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment;

[0010] The prediction module is used to obtain all the core influencing factor parameters of the photovoltaic power station at the prediction time based on the parameter prediction matrix and the prediction time at the current moment, and obtain the photovoltaic power generation power prediction result based on all the core influencing factor parameters of the photovoltaic power station at the prediction time.

[0011] Preferably, the big data processing system for photovoltaic power generation prediction, the processing module, includes:

[0012] The first acquisition submodule is used to obtain all types of influencing factor parameters of the photovoltaic power station at each moment in a preset time period before the current moment, wherein all types of influencing factor parameters include solar irradiance parameters, ambient temperature parameters, ambient humidity parameters, atmospheric pressure parameters, and photovoltaic panel angle parameters;

[0013] The first processing submodule is used to process the parameters of all types of influencing factors of the photovoltaic power station at each moment in the preset time period before the current moment, and obtain the processing parameters of all types of influencing factors of the photovoltaic power station at each moment in the preset time period before the current moment.

[0014] Preferably, the big data processing system for photovoltaic power generation prediction, the first processing submodule includes:

[0015] An obtaining unit, used to obtain the difference between the parameters of each type of influencing factor of the photovoltaic power station at each moment in a preset time period before the current moment and the mean value of the parameters of the corresponding type of influencing factor at all moments, as the judgment difference of the parameters of the corresponding type of influencing factor of the photovoltaic power station at each moment in the preset time period before the current moment;

[0016] The first judgment unit is used to judge whether the quotient between the judgment difference of each type of influencing factor parameter at each moment in a preset time period before the current moment of the photovoltaic power station and the standard deviation of the corresponding type of influencing factor parameter at all moments is greater than a preset quotient threshold value, and if so, the corresponding moment is regarded as an abnormal moment of the corresponding type of influencing factor parameter, otherwise, the corresponding moment is regarded as a non-abnormal moment of the corresponding type of influencing factor parameter;

[0017] The processing unit is used to treat the corresponding class of influencing factor parameters at the non-abnormal moment with the shortest time interval with each abnormal moment of the corresponding class of influencing factor parameters among all non-abnormal moments of each class of influencing factor parameters as the corresponding class of influencing factor processing parameters of the corresponding abnormal moment, and treat the corresponding class of influencing factor parameters at each non-abnormal moment of each class of influencing factor parameters as the corresponding class of influencing factor processing parameters of the corresponding non-abnormal moment, so as to obtain all class of influencing factor processing parameters of the photovoltaic power station at each moment in a preset time period before the current moment.

[0018] Preferably, the big data processing system for photovoltaic power generation prediction, the judgment module, includes:

[0019] The second acquisition submodule is used to obtain the power generation parameters of the photovoltaic power station at all times within a preset time period before the current time;

[0020] The judgment submodule is used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters.

[0021] Preferably, the big data processing system for photovoltaic power generation prediction, the judgment submodule, includes:

[0022] A parameter point acquisition unit is used to take all the moments of the photovoltaic power station in a preset time period before the current moment as the horizontal coordinate value, and the power generation parameters at the corresponding moments as the vertical coordinate value, to obtain all the power generation parameter points of the photovoltaic power station, and take all the moments of the photovoltaic power station in a preset time period before the current moment as the horizontal coordinate value, and the processing parameters of each type of influencing factors at the corresponding moments as the vertical coordinate value, to obtain the parameter points of each type of influencing factors of the photovoltaic power station;

[0023] A drawing unit is used to sequentially connect all power generation parameter points of the photovoltaic power station to obtain a power generation parameter diagram, and use the power generation parameter points corresponding to the peaks or troughs in the power generation parameter diagram as the judgment points of the power generation, and sequentially connect the parameter points of each type of influencing factors of the photovoltaic power station to obtain each type of influencing factor parameter diagram, and use the influencing factor parameter points corresponding to the peaks or troughs in each type of influencing factor parameter diagram as the judgment points of each type of influencing factors;

[0024] The second judgment unit is used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current time based on all judgment points of the generated power and all judgment points of all types of influencing factors.

[0025] Preferably, the big data processing system for photovoltaic power generation prediction, the second judgment unit includes:

[0026] A processing subunit is used to regard all judgment points of the corresponding type of influencing factors having the same horizontal coordinate value as the judgment point of the generated power among all judgment points of each type of influencing factors as core judgment points of the corresponding type of influencing factors;

[0027] The judgment subunit is used to take the quotient of the number of core judgment points and the number of judgment points of each type of influencing factor as the judgment value of the corresponding type of influencing factor. If the judgment value of each type of influencing factor is greater than the preset judgment threshold, the corresponding type of influencing factor is taken as the core influencing factor, and all types of core influencing factors are obtained. The influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment, which are classified as each type of core influencing factor, are taken as the corresponding type of core influencing factor parameters of the photovoltaic power station at the corresponding moment in the preset time period before the current moment.

[0028] Preferably, the big data processing system for photovoltaic power generation prediction, the building blocks include:

[0029] The first calculation submodule is used to take all moments except the start moment in the preset time period before the current moment of the photovoltaic power station as calculation moments, and obtain the influence value of each type of core influencing factor parameter of the photovoltaic power station at each calculation moment in the preset time period before the current moment based on each type of core influencing factor parameter and power generation power parameter of all moments in the preset time period before the current moment of the photovoltaic power station, that is:

[0030]

[0031] Among them, p i is the impact value of each type of core influencing factor parameter of the photovoltaic power station at the i-th calculation moment in the preset time period before the current moment, σ i is the power generation parameter of the photovoltaic power station at the i-th calculation time in the preset time period before the current time, S σ is the mean value of the power generation parameters of the photovoltaic power station at all times in the preset time period before the current time, μ i+1 is the core influencing factor parameter of the corresponding class at the moment after the i-th calculation moment in the preset time period before the current moment of the photovoltaic power station, p is the core influencing factor parameter of the corresponding class at the i-th calculation moment in the preset time period before the current moment of the photovoltaic power station, μ i-1 is the core influencing factor parameter of the corresponding class at the moment before the i-th calculation moment in the preset time period before the current moment of the photovoltaic power station, ln is the natural logarithm, and the value of the natural constant e is 2.718;

[0032] The second calculation submodule is used to take the average of the influence values ​​of each type of core influencing factor parameter of the photovoltaic power station at all calculation moments in the preset time period before the current moment as the influence value of each type of core influencing factor parameter of the photovoltaic power station at the starting moment in the preset time period before the current moment;

[0033] The submodule is constructed to obtain a parameter prediction matrix at the current moment based on the influence values ​​of all core influencing factor parameters of the photovoltaic power station at all moments in a preset time period before the current moment.

[0034] Preferably, a big data processing system for photovoltaic power generation prediction constructs a submodule based on the influence values ​​of all core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current time, and obtains a parameter prediction matrix at the current time, including:

[0035]

[0036] Among them, R is the parameter prediction matrix at the current moment, A 11 is the impact value of the first type of core influencing factor parameters of the photovoltaic power station at the first moment in the preset time period before the current moment, A 12 is the impact value of the first type of core influencing factor parameters of the photovoltaic power station at the second moment in the preset time period before the current moment, A 1m is the impact value of the first type of core influencing factor parameter of the photovoltaic power station at the mth moment in the preset time period before the current moment, A 21 is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the first moment in the preset time period before the current moment, A 22 is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the second moment in the preset time period before the current moment, A 2m is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the mth moment in the preset time period before the current moment, A n1 A is the impact value of the nth type of core influencing factor parameter of the photovoltaic power station at the first moment in the preset time period before the current moment, n2 A is the impact value of the nth type of core influencing factor parameter of the photovoltaic power station at the second moment in the preset time period before the current moment, nm is the influence value of the nth type of core influencing factor parameter of the photovoltaic power station at the mth moment in the preset time period before the current moment, m is the total number of moments of the photovoltaic power station in the preset time period before the current moment, and n is the number of types of core influencing factors.

[0037] Preferably, the big data processing system for photovoltaic power generation prediction, the prediction module, includes:

[0038] The second processing submodule is used to obtain the prediction time interval based on the prediction time and the current time, and take any two matrix elements in each row matrix of the parameter prediction matrix at the current time as a matrix element group of the corresponding row matrix, obtain all the matrix element groups of all the row matrices, take the matrix element group with the same difference between the moments corresponding to the two matrix elements contained in the matrix element group and the prediction time interval in all the matrix element groups of each row matrix as the prediction matrix element group of the corresponding row matrix, and take the difference between the core influencing factor parameters corresponding to the two matrix elements contained in each prediction matrix element group of each row matrix as the parameter difference of the corresponding prediction matrix element group of the corresponding row matrix;

[0039] The first prediction submodule is used to take the mean of the parameter difference values ​​of all prediction matrix element groups of each row matrix of the parameter prediction matrix at the current moment as the prediction parameter difference value of the corresponding row matrix, and take the sum of the core influencing factor parameter corresponding to the last matrix element of each row matrix and the prediction parameter difference value of the corresponding row matrix as the corresponding class core influencing factor parameter of the photovoltaic power station at the prediction moment;

[0040] The second prediction submodule is used to obtain the photovoltaic power generation power prediction result based on all core influencing factor parameters of the photovoltaic power station at the prediction time.

[0041] Preferably, in a big data processing system for photovoltaic power generation prediction, the second prediction submodule obtains a method for photovoltaic power generation prediction results based on all core influencing factor parameters of the photovoltaic power station at the prediction time, including:

[0042] Based on all the core influencing factors and the preset model library, the optimal power prediction model of the photovoltaic power station at the current moment is obtained. Based on all the core influencing factor parameters of the photovoltaic power station at the prediction moment and the optimal power prediction model of the photovoltaic power station at the current moment, the photovoltaic power generation power prediction result is obtained.

[0043] The beneficial effects of the present invention compared to the prior art are as follows: based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters, all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment are obtained, and all types of core influencing factor parameters that have a greater impact on the power generation power of the photovoltaic power station are accurately screened out from all types of influencing factor processing parameters, and then based on each type of core influencing factor parameters and power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment, the influence value of each type of core influencing factor parameter of the photovoltaic power station at all times in the preset time period before the current moment is obtained, thereby achieving the effect of the photovoltaic power station at the current moment. The influence degree of each type of core influencing factor parameter at each moment in the preset time period before the current moment on the power generation power of the photovoltaic power station at the corresponding moment is quantified, and the parameter prediction matrix at the current moment is obtained according to the influence value of all types of core influencing factor parameters of the photovoltaic power station at all moments in the preset time period before the current moment, so as to facilitate the subsequent prediction of all types of core influencing factor parameters of the photovoltaic power station at the prediction moment, and obtain all types of core influencing factor parameters of the photovoltaic power station at the prediction moment according to the parameter prediction matrix at the current moment and the prediction moment, and obtain the photovoltaic power generation power prediction result according to all types of core influencing factor parameters of the photovoltaic power station at the prediction moment, so as to realize a more accurate prediction of the power generation power of the photovoltaic power station at the prediction moment.

[0044] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structure specifically pointed out in the written application document.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 Schematic diagram of a big data processing system for photovoltaic power generation prediction in an embodiment of the present invention;

[0048] Figure 2 It is a specific schematic diagram of the processing module in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0050] Embodiment 1:

[0051] The present invention provides a big data processing system for photovoltaic power generation prediction. Figure 1 ,include:

[0052] A processing module, used to perform data processing on all influencing factor parameters of the photovoltaic power station at each moment in a preset time period before the current moment, and obtain all influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment;

[0053] A judgment module, used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters;

[0054] A construction module is used to obtain the influence value of each type of core influencing factor parameter of the photovoltaic power station at all times in the preset time period before the current moment based on each type of core influencing factor parameter and power generation parameter of the photovoltaic power station at all times in the preset time period before the current moment, and obtain the parameter prediction matrix at the current moment based on the influence value of all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment;

[0055] The prediction module is used to obtain all the core influencing factor parameters of the photovoltaic power station at the prediction time based on the parameter prediction matrix and the prediction time at the current moment, and obtain the photovoltaic power generation power prediction result based on all the core influencing factor parameters of the photovoltaic power station at the prediction time.

[0056] In this embodiment, the photovoltaic power station is a single photovoltaic power station to which a big data processing system for photovoltaic power generation prediction is applied.

[0057] In this embodiment, the preset time period is a preset time period for obtaining parameters of all types of influencing factors, such as 24 hours.

[0058] In this embodiment, each moment is a moment selected from a preset time period before the current moment, and the time length between each adjacent moment is the same.

[0059] In this embodiment, data processing is a process of obtaining all types of influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment based on all types of influencing factor parameters of the photovoltaic power station at each moment in the preset time period before the current moment.

[0060] In this embodiment, the influencing factor processing parameter is obtained based on all types of influencing factor parameters of the photovoltaic power station at each moment in the preset time period before the current moment, and can truly reflect the influencing factor processing parameter of the photovoltaic power station at each moment in the preset time period before the current moment.

[0061] In this embodiment, the power generation parameter is a value of the power generation of the photovoltaic power station.

[0062] In this embodiment, the core influencing factor parameters are some influencing factor processing parameters of all influencing factor processing parameters that have a greater impact on the power generation of the photovoltaic power station.

[0063] In this embodiment, the influence value of each type of core influencing factor parameter is obtained based on each type of core influencing factor parameter and the power generation power parameter of the photovoltaic power station at all times within the preset time period before the current moment, and can characterize the degree of influence of the corresponding type of core influencing factor parameter of the photovoltaic power station at each moment within the preset time period before the current moment on the power generation power of the photovoltaic power station at the corresponding moment. When the influence value is a negative number, it means that the influence of the core influencing factor parameter on the power generation power of the photovoltaic power station is a negative impact, otherwise, it is a positive impact.

[0064] In this embodiment, the parameter prediction matrix at the current moment is a matrix constructed by using the influence values ​​of all core influencing factor parameters of the photovoltaic power station at all moments in a preset time period before the current moment as matrix elements, and the obtained matrix can be used to predict all core influencing factor parameters of the photovoltaic power station at future moments.

[0065] In this embodiment, the prediction time is the future time at which the power generation power of the photovoltaic power station is predicted, and the time interval between the prediction time and the current time cannot exceed the time length of the preset time period.

[0066] In this embodiment, the photovoltaic power generation prediction result is a predicted value of the photovoltaic power generation of the photovoltaic power station at the prediction time, which is obtained by taking all the core influencing factor parameters of the photovoltaic power station at the prediction time as the input of the model and outputting the model.

[0067] The beneficial effects of the above technology are as follows: according to the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and the processing parameters of all types of influencing factors, all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment are obtained, and all types of core influencing factor parameters that have a greater degree of influence on the power generation power of the photovoltaic power station are accurately screened out from the processing parameters of all types of influencing factors. Then, according to each type of core influencing factor parameters and power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment, the influence value of each type of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment is obtained, so as to realize the preset time period before the current moment of the photovoltaic power station. The influence degree of each type of core influencing factor parameter at each moment in the interval on the power generation power of the photovoltaic power station at the corresponding moment is quantified, and the parameter prediction matrix at the current moment is obtained according to the influence value of all types of core influencing factor parameters of the photovoltaic power station at all moments in the preset time period before the current moment, so as to facilitate the subsequent prediction of all types of core influencing factor parameters of the photovoltaic power station at the prediction moment, and obtain all types of core influencing factor parameters of the photovoltaic power station at the prediction moment according to the parameter prediction matrix at the current moment and the prediction moment, and obtain the photovoltaic power generation power prediction result according to all types of core influencing factor parameters of the photovoltaic power station at the prediction moment, so as to realize a more accurate prediction of the power generation power of the photovoltaic power station at the prediction moment.

[0068] Embodiment 2:

[0069] Based on Example 1, a big data processing system for photovoltaic power generation prediction, a processing module, and a reference Figure 2 ,include:

[0070] The first acquisition submodule is used to obtain all types of influencing factor parameters of the photovoltaic power station at each moment in a preset time period before the current moment, wherein all types of influencing factor parameters include solar irradiance parameters, ambient temperature parameters, ambient humidity parameters, atmospheric pressure parameters, and photovoltaic panel angle parameters;

[0071] The first processing submodule is used to process the parameters of all types of influencing factors of the photovoltaic power station at each moment in the preset time period before the current moment, and obtain the processing parameters of all types of influencing factors of the photovoltaic power station at each moment in the preset time period before the current moment.

[0072] In this embodiment, the solar irradiance parameter is a value of the solar irradiance measured in real time based on a radiometer pre-set in the photovoltaic power station.

[0073] In this embodiment, the ambient temperature parameter is a value of the ambient temperature measured in real time by a temperature sensor pre-set in the photovoltaic power station.

[0074] In this embodiment, the ambient humidity parameter is a value of the ambient humidity measured in real time by a humidity sensor pre-set in the photovoltaic power station.

[0075] In this embodiment, the atmospheric pressure parameter is a value of the ambient humidity measured in real time by a barometer pre-set in the photovoltaic power station.

[0076] In this embodiment, the photovoltaic panel angle parameter is the value of the angle between the photovoltaic panel and the ground measured in real time by a tilt angle meter pre-set on the photovoltaic panel in the photovoltaic power station, wherein the values ​​of the angles between all photovoltaic panels in the photovoltaic power station and the ground remain consistent at any time.

[0077] The beneficial effects of the above technology are: clarifying the specific parameter items of all types of influencing factor parameters of the photovoltaic power station at each moment in the preset time period before the current moment, and performing data processing on all types of influencing factor parameters of the photovoltaic power station at each moment in the preset time period before the current moment, thereby realizing the correction of the parameter values ​​of each type of influencing factor parameters at abnormal moments.

[0078] Embodiment 3:

[0079] On the basis of Example 2, the big data processing system for photovoltaic power generation prediction, the first processing submodule includes:

[0080] An obtaining unit, used to obtain the difference between the parameters of each type of influencing factor of the photovoltaic power station at each moment in a preset time period before the current moment and the mean value of the parameters of the corresponding type of influencing factor at all moments, as the judgment difference of the parameters of the corresponding type of influencing factor of the photovoltaic power station at each moment in the preset time period before the current moment;

[0081] The first judgment unit is used to judge whether the quotient between the judgment difference of each type of influencing factor parameter at each moment in a preset time period before the current moment of the photovoltaic power station and the standard deviation of the corresponding type of influencing factor parameter at all moments is greater than a preset quotient threshold value, and if so, the corresponding moment is regarded as an abnormal moment of the corresponding type of influencing factor parameter, otherwise, the corresponding moment is regarded as a non-abnormal moment of the corresponding type of influencing factor parameter;

[0082] The processing unit is used to treat the corresponding class of influencing factor parameters at the non-abnormal moment with the shortest time interval with each abnormal moment of the corresponding class of influencing factor parameters among all non-abnormal moments of each class of influencing factor parameters as the corresponding class of influencing factor processing parameters of the corresponding abnormal moment, and treat the corresponding class of influencing factor parameters at each non-abnormal moment of each class of influencing factor parameters as the corresponding class of influencing factor processing parameters of the corresponding non-abnormal moment, so as to obtain all class of influencing factor processing parameters of the photovoltaic power station at each moment in a preset time period before the current moment.

[0083] In this embodiment, the preset quotient threshold is a preset quotient threshold used to obtain all abnormal moments of each type of influencing factor parameter.

[0084] In this embodiment, the abnormal moment of each type of influencing factor parameter is the moment corresponding to when each type of influencing factor parameter is abnormal within a preset time period before the current moment of the photovoltaic power station.

[0085] In this embodiment, the non-abnormal moment of each type of influencing factor parameter is the moment corresponding to when each type of influencing factor parameter is non-abnormal within a preset time period before the current moment of the photovoltaic power station.

[0086] The beneficial effects of the above technology are: according to the parameters of each type of influencing factor at each moment in the preset time period before the current moment of the photovoltaic power station, all abnormal moments and all non-abnormal moments of each type of influencing factor parameters are obtained, and according to all abnormal moments and all non-abnormal moments of each type of influencing factor parameters, all types of influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment are obtained. This embodiment gives in detail a specific method for performing data processing on all types of influencing factor parameters of the photovoltaic power station at each moment in the preset time period before the current moment, and obtaining all types of influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment.

[0087] Embodiment 4:

[0088] Based on Example 1, the big data processing system for photovoltaic power generation prediction, the judgment module, includes:

[0089] The second acquisition submodule is used to obtain the power generation parameters of the photovoltaic power station at all times within a preset time period before the current time;

[0090] The judgment submodule is used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters.

[0091] The beneficial effects of the above technology are: based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters, all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment are obtained, and all types of core influencing factor parameters that have a greater impact on the power generation of the photovoltaic power station are accurately screened out from all types of influencing factor processing parameters, which is convenient for the subsequent construction of the parameter prediction matrix.

[0092] Embodiment 5:

[0093] On the basis of Example 4, the big data processing system for photovoltaic power generation prediction, the judgment submodule, includes:

[0094] A parameter point acquisition unit is used to take all the moments of the photovoltaic power station in a preset time period before the current moment as the horizontal coordinate value, and the power generation parameters at the corresponding moments as the vertical coordinate value, to obtain all the power generation parameter points of the photovoltaic power station, and take all the moments of the photovoltaic power station in a preset time period before the current moment as the horizontal coordinate value, and the processing parameters of each type of influencing factors at the corresponding moments as the vertical coordinate value, to obtain the parameter points of each type of influencing factors of the photovoltaic power station;

[0095] A drawing unit is used to sequentially connect all power generation parameter points of the photovoltaic power station to obtain a power generation parameter diagram, and use the power generation parameter points corresponding to the peaks or troughs in the power generation parameter diagram as the judgment points of the power generation, and sequentially connect the parameter points of each type of influencing factors of the photovoltaic power station to obtain each type of influencing factor parameter diagram, and use the influencing factor parameter points corresponding to the peaks or troughs in each type of influencing factor parameter diagram as the judgment points of each type of influencing factors;

[0096] The second judgment unit is used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current time based on all judgment points of the generated power and all judgment points of all types of influencing factors.

[0097] In this embodiment, the power generation parameter diagram is a diagram obtained by sequentially connecting all power generation parameter points of the photovoltaic power station, reflecting the power generation parameter changes of the photovoltaic power station at all times within a preset time period before the current time.

[0098] In this embodiment, each type of influencing factor parameter graph is a graph obtained by sequentially connecting each type of influencing factor parameter points of the photovoltaic power station, reflecting the changes in the corresponding type of influencing factor processing parameters of the photovoltaic power station at all times within a preset time period before the current time.

[0099] The beneficial effects of the above technology are as follows: according to the power generation parameters of the photovoltaic power station at all times within a preset time period before the current moment and the processing parameters of all types of influencing factors, all power generation parameter points and parameter points of each type of influencing factors of the photovoltaic power station are obtained; according to all power generation parameter points of the photovoltaic power station and parameter points of each type of influencing factors, all judgment points of the power generation and all judgment points of all types of influencing factors are obtained; according to all judgment points of the power generation and all judgment points of all types of influencing factors, all types of core influencing factor parameters of the photovoltaic power station at all times within the preset time period before the current moment are obtained. This embodiment gives in detail a specific method for obtaining all types of core influencing factor parameters of the photovoltaic power station at all times within the preset time period before the current moment according to the power generation parameters of the photovoltaic power station at all times within the preset time period before the current moment and the processing parameters of all types of influencing factors.

[0100] Embodiment 6:

[0101] On the basis of Embodiment 5, the big data processing system for photovoltaic power generation prediction, the second judgment unit includes:

[0102] A processing subunit is used to regard all judgment points of the corresponding type of influencing factors having the same horizontal coordinate value as the judgment point of the generated power among all judgment points of each type of influencing factors as core judgment points of the corresponding type of influencing factors;

[0103] The judgment subunit is used to take the quotient of the number of core judgment points and the number of judgment points of each type of influencing factor as the judgment value of the corresponding type of influencing factor. If the judgment value of each type of influencing factor is greater than the preset judgment threshold, the corresponding type of influencing factor is taken as the core influencing factor, and all types of core influencing factors are obtained. The influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment, which are classified as each type of core influencing factor, are taken as the corresponding type of core influencing factor parameters of the photovoltaic power station at the corresponding moment in the preset time period before the current moment.

[0104] In this embodiment, the preset judgment threshold is a judgment value threshold that is preset to obtain the judgment value of each type of influencing factor.

[0105] The beneficial effects of the above technology are: based on all judgment points of the power generation power and all judgment points of each type of influencing factors, the core judgment points of each type of influencing factors are obtained; based on the core judgment points of each type of influencing factors, all types of core influencing factors are obtained; based on all types of core influencing factors, all types of core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current moment are obtained. This embodiment gives in detail a specific method for obtaining all types of core influencing factors based on all judgment points of the power generation power and all judgment points of each type of influencing factors.

[0106] Embodiment 7:

[0107] Based on Example 1, a big data processing system for photovoltaic power generation prediction is constructed with modules including:

[0108] The first calculation submodule is used to take all moments except the start moment in the preset time period before the current moment of the photovoltaic power station as calculation moments, and obtain the influence value of each type of core influencing factor parameter of the photovoltaic power station at each calculation moment in the preset time period before the current moment based on each type of core influencing factor parameter and power generation power parameter of all moments in the preset time period before the current moment of the photovoltaic power station, that is:

[0109]

[0110] Among them, ρ iis the impact value of each type of core influencing factor parameter of the photovoltaic power station at the i-th calculation moment in the preset time period before the current moment, σ i is the power generation parameter of the photovoltaic power station at the i-th calculation time in the preset time period before the current time, S σ is the mean value of the power generation parameters of the photovoltaic power station at all times in the preset time period before the current time, μ i+1 is the core influencing factor parameter of the corresponding class at the moment after the ith calculation moment in the preset time period before the current moment of the photovoltaic power station, μ is the core influencing factor parameter of the corresponding class at the ith calculation moment in the preset time period before the current moment of the photovoltaic power station, μ i-1 is the core influencing factor parameter of the corresponding class at the moment before the i-th calculation moment in the preset time period before the current moment of the photovoltaic power station, ln is the natural logarithm, and the value of the natural constant e is 2.718;

[0111] The second calculation submodule is used to take the average of the influence values ​​of each type of core influencing factor parameter of the photovoltaic power station at all calculation moments in the preset time period before the current moment as the influence value of each type of core influencing factor parameter of the photovoltaic power station at the starting moment in the preset time period before the current moment;

[0112] The submodule is constructed to obtain a parameter prediction matrix at the current moment based on the influence values ​​of all core influencing factor parameters of the photovoltaic power station at all moments in a preset time period before the current moment.

[0113] In this embodiment, the start time is the first time and the last time of the photovoltaic power station within a preset time period before the current time.

[0114] The beneficial effects of the above technology are: according to each type of core influencing factor parameters and power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment, the influence value of each type of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment is obtained, and the influence degree of each type of core influencing factor parameters of the photovoltaic power station at each moment in the preset time period before the current moment on the power generation power of the photovoltaic power station at the corresponding moment is quantified, which is convenient for the construction of the subsequent parameter prediction matrix.

[0115] Embodiment 8:

[0116] On the basis of Example 7, a big data processing system for photovoltaic power generation prediction is constructed, and a submodule is constructed based on the influence values ​​of all core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current moment to obtain a parameter prediction matrix at the current moment, including:

[0117]

[0118] Among them, R is the parameter prediction matrix at the current moment, A11 is the impact value of the first type of core influencing factor parameters of the photovoltaic power station at the first moment in the preset time period before the current moment, A 12 is the impact value of the first type of core influencing factor parameters of the photovoltaic power station at the second moment in the preset time period before the current moment, A 1m is the impact value of the first type of core influencing factor parameter of the photovoltaic power station at the mth moment in the preset time period before the current moment, A 21 is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the first moment in the preset time period before the current moment, A 22 is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the second moment in the preset time period before the current moment, A 2m is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the mth moment in the preset time period before the current moment, A n1 A is the impact value of the nth type of core influencing factor parameter of the photovoltaic power station at the first moment in the preset time period before the current moment, n2 A is the impact value of the nth type of core influencing factor parameter of the photovoltaic power station at the second moment in the preset time period before the current moment, nm is the influence value of the nth type of core influencing factor parameter of the photovoltaic power station at the mth moment in the preset time period before the current moment, m is the total number of moments of the photovoltaic power station in the preset time period before the current moment, and n is the number of types of core influencing factors.

[0119] The beneficial effect of the above technology is: according to the influence values ​​of all types of core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current moment, the parameter prediction matrix at the current moment is obtained, which is convenient for the subsequent prediction of all types of core influencing factor parameters of the photovoltaic power station at the predicted moment. This embodiment gives in detail a specific method for obtaining the parameter prediction matrix at the current moment according to the influence values ​​of all types of core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current moment.

[0120] Embodiment 9:

[0121] Based on Example 1, a big data processing system for photovoltaic power generation prediction, a prediction module, includes:

[0122] The second processing submodule is used to obtain the prediction time interval based on the prediction time and the current time, and take any two matrix elements in each row matrix of the parameter prediction matrix at the current time as a matrix element group of the corresponding row matrix, obtain all the matrix element groups of all the row matrices, take the matrix element group with the same difference between the moments corresponding to the two matrix elements contained in the matrix element group and the prediction time interval in all the matrix element groups of each row matrix as the prediction matrix element group of the corresponding row matrix, and take the difference between the core influencing factor parameters corresponding to the two matrix elements contained in each prediction matrix element group of each row matrix as the parameter difference of the corresponding prediction matrix element group of the corresponding row matrix;

[0123] The first prediction submodule is used to take the mean of the parameter difference values ​​of all prediction matrix element groups of each row matrix of the parameter prediction matrix at the current moment as the prediction parameter difference value of the corresponding row matrix, and take the sum of the core influencing factor parameter corresponding to the last matrix element of each row matrix and the prediction parameter difference value of the corresponding row matrix as the corresponding class core influencing factor parameter of the photovoltaic power station at the prediction moment;

[0124] The second prediction submodule is used to obtain the photovoltaic power generation power prediction result based on all core influencing factor parameters of the photovoltaic power station at the prediction time.

[0125] In this embodiment, the predicted time interval is the time interval between the predicted time and the current time.

[0126] In this embodiment, the row matrix is ​​a single-row matrix constructed by all matrix elements of each row in the parameter prediction matrix at the current moment.

[0127] The beneficial effects of the above technology are: according to the parameter prediction matrix and the prediction time at the current moment, all matrix element groups of all row matrices are obtained, and then according to all matrix element groups of all row matrices, the prediction matrix element groups of each row matrix are obtained, according to all prediction matrix element groups of each row matrix, the prediction parameter difference of each row matrix is ​​obtained, according to the prediction parameter difference of all row matrices, all core influencing factor parameters of the photovoltaic power station at the prediction moment are obtained, according to all core influencing factor parameters of the photovoltaic power station at the prediction moment, the photovoltaic power generation power prediction result is obtained, and a more accurate prediction of the power generation power of the photovoltaic power station at the prediction moment is achieved. This embodiment gives in detail a specific method for obtaining all core influencing factor parameters of the photovoltaic power station at the prediction moment according to the parameter prediction matrix and the prediction moment at the current moment.

[0128] Embodiment 10:

[0129] On the basis of Example 9, in a big data processing system for photovoltaic power generation prediction, a method for obtaining a photovoltaic power generation prediction result based on all core influencing factor parameters of the photovoltaic power station at the prediction time by the second prediction submodule includes:

[0130] Based on all the core influencing factors and the preset model library, the optimal power prediction model of the photovoltaic power station at the current moment is obtained. Based on all the core influencing factor parameters of the photovoltaic power station at the prediction moment and the optimal power prediction model of the photovoltaic power station at the current moment, the photovoltaic power generation power prediction result is obtained.

[0131] In this embodiment, the preset model library is a pre-set database composed of power prediction models corresponding to all groups of training parameter groups (at least one type of influencing factor parameters is selected from all types of influencing factor parameters as a group of training parameter groups, all groups of training parameter groups are obtained, and each group of training parameter groups is used as model input, and the corresponding power generation power parameters are used as model output, and the power prediction model corresponding to each group of training parameter groups is trained).

[0132] In this embodiment, the optimal power prediction model of the photovoltaic power station at the current moment is a power prediction model when the category of the influencing factor parameter included in the corresponding training parameter group among all power prediction models in the preset model library is a core influencing factor.

[0133] The beneficial effects of the above technology are: based on the parameters of all types of core influencing factors of the photovoltaic power station at the prediction time, the optimal power prediction model of the photovoltaic power station at the current moment is obtained; based on the optimal power prediction model of the photovoltaic power station at the current moment, the photovoltaic power generation power prediction result is obtained, thereby achieving a more accurate prediction of the power generation power of the photovoltaic power station at the prediction time.

[0134] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention, and the present invention is also intended to include these changes and modifications.

Claims

1. A big data processing system for photovoltaic power generation prediction, characterized in that: include: A processing module, used to perform data processing on all influencing factor parameters of the photovoltaic power station at each moment in a preset time period before the current moment, and obtain all influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment; A judgment module, used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters; A construction module is used to obtain the influence value of each type of core influencing factor parameter of the photovoltaic power station at all times in the preset time period before the current moment based on each type of core influencing factor parameter and power generation parameter of the photovoltaic power station at all times in the preset time period before the current moment, and obtain the parameter prediction matrix at the current moment based on the influence value of all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment; A prediction module is used to obtain all core influencing factor parameters of the photovoltaic power station at the prediction time based on the parameter prediction matrix at the current time and the prediction time, and obtain the photovoltaic power generation power prediction result based on all core influencing factor parameters of the photovoltaic power station at the prediction time; The judgment module includes: The second acquisition submodule is used to obtain the power generation parameters of the photovoltaic power station at all times within a preset time period before the current time; A judgment submodule, used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times in the preset time period before the current moment based on the power generation parameters of the photovoltaic power station at all times in the preset time period before the current moment and all types of influencing factor processing parameters; Among them, the judgment submodule includes: A parameter point acquisition unit is used to take all the moments of the photovoltaic power station in a preset time period before the current moment as the horizontal coordinate value, and the power generation parameters at the corresponding moments as the vertical coordinate value, to obtain all the power generation parameter points of the photovoltaic power station, and take all the moments of the photovoltaic power station in a preset time period before the current moment as the horizontal coordinate value, and the processing parameters of each type of influencing factors at the corresponding moments as the vertical coordinate value, to obtain the parameter points of each type of influencing factors of the photovoltaic power station; A drawing unit is used to sequentially connect all power generation parameter points of the photovoltaic power station to obtain a power generation parameter diagram, and use the power generation parameter points corresponding to the peaks or troughs in the power generation parameter diagram as the judgment points of the power generation, and sequentially connect the parameter points of each type of influencing factors of the photovoltaic power station to obtain each type of influencing factor parameter diagram, and use the influencing factor parameter points corresponding to the peaks or troughs in each type of influencing factor parameter diagram as the judgment points of each type of influencing factors; A second judgment unit is used to obtain all types of core influencing factor parameters of the photovoltaic power station at all times within a preset time period before the current time based on all judgment points of the power generation and all judgment points of all types of influencing factors; Among them, the building blocks include: The first calculation submodule is used to take all moments except the start moment in the preset time period before the current moment of the photovoltaic power station as calculation moments, and obtain the influence value of each type of core influencing factor parameter of the photovoltaic power station at each calculation moment in the preset time period before the current moment based on each type of core influencing factor parameter and power generation power parameter of all moments in the preset time period before the current moment of the photovoltaic power station, that is: Among them, ρ i is the impact value of each type of core influencing factor parameter of the photovoltaic power station at the i-th calculation moment in the preset time period before the current moment, σ i is the power generation parameter of the photovoltaic power station at the i-th calculation time in the preset time period before the current time, S σ is the mean value of the power generation parameters of the photovoltaic power station at all times in the preset time period before the current time, μ i+1 is the core influencing factor parameter of the corresponding class at the moment after the i-th calculation moment in the preset time period before the current moment of the photovoltaic power station, μ i is the core influencing factor parameter of the corresponding class at the i-th calculation moment in the preset time period before the current moment of the photovoltaic power station, μ i-1 is the core influencing factor parameter of the corresponding class at the moment before the i-th calculation moment in the preset time period before the current moment of the photovoltaic power station, ln is the natural logarithm, and the value of the natural constant e is 2.718; The second calculation submodule is used to take the average of the influence values ​​of each type of core influencing factor parameter of the photovoltaic power station at all calculation moments in the preset time period before the current moment as the influence value of each type of core influencing factor parameter of the photovoltaic power station at the starting moment in the preset time period before the current moment; The submodule is constructed to obtain a parameter prediction matrix at the current moment based on the influence values ​​of all core influencing factor parameters of the photovoltaic power station at all moments in a preset time period before the current moment.

2. The big data processing system for photovoltaic power generation prediction according to claim 1, characterized in that: Processing modules, including: The first acquisition submodule is used to obtain all types of influencing factor parameters of the photovoltaic power station at each moment in a preset time period before the current moment, wherein all types of influencing factor parameters include solar irradiance parameters, ambient temperature parameters, ambient humidity parameters, atmospheric pressure parameters, and photovoltaic panel angle parameters; The first processing submodule is used to process the parameters of all types of influencing factors of the photovoltaic power station at each moment in the preset time period before the current moment, and obtain the processing parameters of all types of influencing factors of the photovoltaic power station at each moment in the preset time period before the current moment.

3. The big data processing system for photovoltaic power generation prediction according to claim 2, characterized in that: The first processing submodule includes: An obtaining unit, used to obtain the difference between the parameters of each type of influencing factor of the photovoltaic power station at each moment in a preset time period before the current moment and the mean value of the parameters of the corresponding type of influencing factor at all moments, as the judgment difference of the parameters of the corresponding type of influencing factor of the photovoltaic power station at each moment in the preset time period before the current moment; The first judgment unit is used to judge whether the quotient between the judgment difference of each type of influencing factor parameter at each moment in a preset time period before the current moment of the photovoltaic power station and the standard deviation of the corresponding type of influencing factor parameter at all moments is greater than a preset quotient threshold value, and if so, the corresponding moment is regarded as an abnormal moment of the corresponding type of influencing factor parameter, otherwise, the corresponding moment is regarded as a non-abnormal moment of the corresponding type of influencing factor parameter; The processing unit is used to treat the corresponding class of influencing factor parameters at the non-abnormal moment with the shortest time interval with each abnormal moment of the corresponding class of influencing factor parameters among all non-abnormal moments of each class of influencing factor parameters as the corresponding class of influencing factor processing parameters of the corresponding abnormal moment, and treat the corresponding class of influencing factor parameters at each non-abnormal moment of each class of influencing factor parameters as the corresponding class of influencing factor processing parameters of the corresponding non-abnormal moment, so as to obtain all class of influencing factor processing parameters of the photovoltaic power station at each moment in a preset time period before the current moment.

4. The big data processing system for photovoltaic power generation prediction according to claim 1, characterized in that: The second judging unit comprises: A processing subunit is used to regard all judgment points of the corresponding type of influencing factors having the same horizontal coordinate value as the judgment point of the generated power among all judgment points of each type of influencing factors as core judgment points of the corresponding type of influencing factors; The judgment subunit is used to take the quotient of the number of core judgment points and the number of judgment points of each type of influencing factor as the judgment value of the corresponding type of influencing factor. If the judgment value of each type of influencing factor is greater than the preset judgment threshold, the corresponding type of influencing factor is taken as the core influencing factor, and all types of core influencing factors are obtained. The influencing factor processing parameters of the photovoltaic power station at each moment in the preset time period before the current moment, which are classified as each type of core influencing factor, are taken as the corresponding type of core influencing factor parameters of the photovoltaic power station at the corresponding moment in the preset time period before the current moment.

5. The big data processing system for photovoltaic power generation prediction according to claim 1, characterized in that: The method of constructing a submodule to obtain a parameter prediction matrix at the current moment based on the influence values ​​of all core influencing factor parameters of the photovoltaic power station at all moments in a preset time period before the current moment includes: Among them, R is the parameter prediction matrix at the current moment, A 11 is the impact value of the first type of core influencing factor parameters of the photovoltaic power station at the first moment in the preset time period before the current moment, A 12 is the impact value of the first type of core influencing factor parameters of the photovoltaic power station at the second moment in the preset time period before the current moment, A 1m is the impact value of the first type of core influencing factor parameter of the photovoltaic power station at the mth moment in the preset time period before the current moment, A 21 is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the first moment in the preset time period before the current moment, A 22 is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the second moment in the preset time period before the current moment, A 2m is the impact value of the second type of core influencing factor parameters of the photovoltaic power station at the mth moment in the preset time period before the current moment, A n1 A is the impact value of the nth type of core influencing factor parameter of the photovoltaic power station at the first moment in the preset time period before the current moment, n2 A is the impact value of the nth type of core influencing factor parameter of the photovoltaic power station at the second moment in the preset time period before the current moment, nm is the influence value of the nth type of core influencing factor parameter of the photovoltaic power station at the mth moment in the preset time period before the current moment, m is the total number of moments of the photovoltaic power station in the preset time period before the current moment, and n is the number of types of core influencing factors.

6. The big data processing system for photovoltaic power generation prediction according to claim 1, characterized in that: Prediction module, including: The second processing submodule is used to obtain the prediction time interval based on the prediction time and the current time, and take any two matrix elements in each row matrix of the parameter prediction matrix at the current time as a matrix element group of the corresponding row matrix, obtain all the matrix element groups of all the row matrices, take the matrix element group with the same difference between the moments corresponding to the two matrix elements contained in the matrix element group and the prediction time interval in all the matrix element groups of each row matrix as the prediction matrix element group of the corresponding row matrix, and take the difference between the core influencing factor parameters corresponding to the two matrix elements contained in each prediction matrix element group of each row matrix as the parameter difference of the corresponding prediction matrix element group of the corresponding row matrix; The first prediction submodule is used to take the mean of the parameter difference values ​​of all prediction matrix element groups of each row matrix of the parameter prediction matrix at the current moment as the prediction parameter difference value of the corresponding row matrix, and take the sum of the core influencing factor parameter corresponding to the last matrix element of each row matrix and the prediction parameter difference value of the corresponding row matrix as the corresponding class core influencing factor parameter of the photovoltaic power station at the prediction moment; The second prediction submodule is used to obtain the photovoltaic power generation power prediction result based on all core influencing factor parameters of the photovoltaic power station at the prediction time.

7. A big data processing system for photovoltaic power generation prediction according to claim 6, characterized in that: The second prediction submodule obtains the photovoltaic power generation power prediction result based on all core influencing factor parameters of the photovoltaic power station at the prediction time, including: Based on all the core influencing factors and the preset model library, the optimal power prediction model of the photovoltaic power station at the current moment is obtained. Based on all the core influencing factor parameters of the photovoltaic power station at the prediction moment and the optimal power prediction model of the photovoltaic power station at the current moment, the photovoltaic power generation power prediction result is obtained.

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