Photovoltaic generating capacity evaluation method and system

Abstract

According to the photovoltaic power generation capacity evaluation method and system provided by the invention, the photovoltaic power generation capacity interaction event evaluation condition of the target photovoltaic power generation capacity interaction data is obtained through the debugged photovoltaic power generation capacity interaction data representation. Therefore, the photovoltaic generating capacity interaction data representation is debugged through the interaction event correlation degree, so that the photovoltaic generating capacity interaction data representation bound with the photovoltaic generating capacity interaction data of the consistent photovoltaic generating capacity interaction data interaction event is close to similarity; and deleting the photovoltaic power generation interaction data representation bound with the photovoltaic power generation interaction data of the photovoltaic power generation interaction data interaction events with differences, so that the stability of the photovoltaic power generation interaction data representation can be improved; and the photovoltaic generating capacity interaction event evaluation condition represented by the photovoltaic generating capacity interaction data can be collected, so that the accuracy of the photovoltaic generating capacity interaction data interaction event evaluation can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data evaluation, and in particular to a photovoltaic power generation evaluation method and system. Background Art

[0002] Photovoltaic power generation is a technology that uses the photovoltaic effect of semiconductor interfaces to directly convert light energy into electrical energy. It is mainly composed of three parts: solar panels (modules), controllers and inverters, and the main components are composed of electronic components. After solar cells are connected in series and packaged for protection, they can form large-area solar cell modules, which are then combined with power controllers and other components to form photovoltaic power generation devices.

[0003] Power generation is a very important indicator. It can evaluate the performance of the equipment and the economic benefits it brings. Based on this information, it can be determined how to debug or replace photovoltaic power generation components to increase the amount of power generation. However, how to conduct an accurate evaluation is a technical problem that is difficult to solve at present. Summary of the invention

[0004] In view of this, the present application provides a photovoltaic power generation evaluation method and system.

[0005] In a first aspect, a photovoltaic power generation evaluation method is provided, including: obtaining photovoltaic power generation interaction data representations of multiple groups of photovoltaic power generation interaction data and the degree of interaction event correlation of at least one group of photovoltaic power generation interaction data binary groups, wherein the multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data, and every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary groups, and the degree of interaction event correlation indicates the possibility that the photovoltaic power generation interaction data binary groups belong to consistent photovoltaic power generation interaction data interaction events; through the degree of interaction event correlation, debugging the photovoltaic power generation interaction data representations of the multiple groups of photovoltaic power generation interaction data; and obtaining the photovoltaic power generation interaction event evaluation status of the target photovoltaic power generation interaction data through the debugged photovoltaic power generation interaction data representations.

[0006] It should be understood that the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data and the degree of interaction event correlation of at least one group of photovoltaic power generation interaction data binary are obtained, and the multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data, and every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary, and the degree of interaction event correlation indicates the possibility that the photovoltaic power generation interaction data binary belongs to a consistent photovoltaic power generation interaction data interaction event, and through the degree of interaction event correlation, the photovoltaic power generation interaction data representation is debugged, so that the photovoltaic power generation interaction event evaluation of the target photovoltaic power generation interaction data is obtained through the debugged photovoltaic power generation interaction data representation. Therefore, by debugging the photovoltaic power generation interaction data representation through the correlation degree of interaction events, the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of consistent photovoltaic power generation interaction data interaction events can be made close to similar, and the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of photovoltaic power generation interaction data interaction events with differences can be deleted, thereby improving the stability of the photovoltaic power generation interaction data representation, and facilitating the collection of photovoltaic power generation interaction event evaluation conditions of the photovoltaic power generation interaction data representation, thereby improving the accuracy of the photovoltaic power generation interaction data event evaluation.

[0007] In an independently implemented embodiment, a photovoltaic power generation interaction event evaluation situation of target photovoltaic power generation interaction data is determined through the debugged photovoltaic power generation interaction data representation, including: performing power generation prediction processing through the debugged photovoltaic power generation interaction data representation to obtain a power generation prediction result, wherein the power generation prediction result includes a first power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event, and the target interaction event is a photovoltaic power generation interaction data interaction event to which the target photovoltaic power generation interaction data belongs; based on the first power generation description result, an evaluation situation of the photovoltaic power generation interaction event is obtained; wherein the photovoltaic power generation interaction event evaluation situation is used to characterize the photovoltaic power generation interaction data interaction event to which the target photovoltaic power generation interaction data belongs.

[0008] It should be understood that, by performing power generation prediction processing on the debugged photovoltaic power generation interaction data representation, a power generation prediction result is obtained, and the power generation prediction result includes a first power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event, so that based on the first power generation description result, a photovoltaic power generation interaction event evaluation situation is obtained, and the photovoltaic power generation interaction event evaluation situation is used to characterize the photovoltaic power generation interaction data interaction event to which the target photovoltaic power generation interaction data belongs, and then power generation prediction can be performed on the basis of the photovoltaic power generation interaction data representation after debugging through the degree of interaction event correlation, and the first power generation description result that the target photovoltaic power generation interaction data belongs to at least one photovoltaic power generation interaction data interaction event is obtained, which can improve the accuracy of power generation prediction.

[0009] In an independently implemented embodiment, the power generation prediction result also includes a second power generation description result in which the target photovoltaic power generation interaction data belongs to at least one target interaction event; before obtaining the evaluation of the photovoltaic power generation interaction event based on the first power generation description result, the method also includes: debugging the degree of correlation of the interaction event through the power generation prediction result on the basis that the predicted value of the power generation prediction processing meets the previously configured threshold; and again executing the step of debugging the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data through the degree of correlation of the interaction event, and obtaining the evaluation of the photovoltaic power generation interaction event based on the first power generation description result on the basis that the predicted value of the power generation prediction processing does not meet the previously configured threshold.

[0010] It should be understood that by configuring the power generation prediction result to also include a second power generation description result in which the target photovoltaic power generation interaction data belongs to at least one target interaction event, and before obtaining the photovoltaic power generation interaction event evaluation situation based on the first power generation description result, further on the basis that the predicted value of the power generation prediction processing meets the previously configured threshold, the power generation prediction result is used to debug the degree of association of the interaction event, and the step of debugging the photovoltaic power generation interaction data representation through the degree of association of the interaction event is performed again, and on the basis that the predicted value of the power generation prediction processing does not meet the previously configured threshold, the photovoltaic power generation interaction event evaluation situation is obtained based on the first power generation description result. Therefore, on the basis that the predicted value of the power generation prediction processing meets the previously configured threshold, the degree of association of the interaction events can be debugged through the first power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event and the second power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event, thereby improving the stability of the degree of association of the interaction events, and continue to debug the photovoltaic power generation interaction data representation through the debugged degree of association of the interaction events, thereby further improving the stability of the photovoltaic power generation interaction data representation, and then the degree of association of the interaction events and the photovoltaic power generation interaction data representation can complement each other, and further, and on the basis that the predicted value of the power generation prediction processing does not meet the previously configured threshold, according to the first power generation description result, the evaluation situation of the photovoltaic power generation interaction event is obtained, thereby being able to further improve the accuracy of the evaluation of the photovoltaic power generation interaction data interaction event.

[0011] In an independently implemented embodiment, the degree of association of the interaction event includes: each group of photovoltaic power generation interaction data binary group belongs to the target power generation description result of the consistent photovoltaic power generation interaction data interaction event; through the power generation prediction result, the degree of association of the interaction event is debugged, including: each group of photovoltaic power generation interaction data in multiple groups of photovoltaic power generation interaction data is determined as the current photovoltaic power generation interaction data, and the photovoltaic power generation interaction data binary group containing the current photovoltaic power generation interaction data is determined as the current photovoltaic power generation interaction data binary group; the splicing of the target power generation description results of all the current photovoltaic power generation interaction data binary groups of the current photovoltaic power generation interaction data is obtained, and determined as the function processing result of the current photovoltaic power generation interaction data; and through the first power generation description result and the second power generation description result, the target power generation description result of each group of current photovoltaic power generation interaction data binary group belonging to the consistent photovoltaic power generation interaction data interaction event is obtained respectively; and the target power generation description result of each group of current photovoltaic power generation interaction data binary group is corrected through the function processing result and the target power generation description result respectively.

[0012] It should be understood that the degree of association of the interaction events is configured to include the target power generation description results of each group of photovoltaic power generation interaction data binary group belonging to the consistent photovoltaic power generation interaction data interaction event, and each group of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data is determined as the current photovoltaic power generation interaction data, and the photovoltaic power generation interaction data binary group containing the current photovoltaic power generation interaction data is determined as the current photovoltaic power generation interaction data binary group, thereby obtaining the target power generation description results of all current photovoltaic power generation interaction data binary groups of the current photovoltaic power generation interaction data, and determining them as the function processing results of the current photovoltaic power generation interaction data, and through the first power generation description result and the second power generation description result, respectively obtain the target power generation description results of each group of photovoltaic power generation interaction data binary group belonging to the consistent photovoltaic power generation interaction data interaction event, and then correct the target power generation description results of each group of current photovoltaic power generation interaction data binary group through the function processing results and the target power generation description results. Therefore, the degree of correlation of interaction events can be debugged by using the target power generation description results of each set of current photovoltaic power generation interaction data tuples belonging to the consistent photovoltaic power generation interaction data interaction events, thereby improving the photovoltaic power generation interaction data interaction events to which the spliced ​​photovoltaic power generation interaction data belongs and improving the accuracy of the degree of correlation of interaction events.

[0013] In an independently implemented embodiment, power generation prediction processing is performed through the debugged photovoltaic power generation interaction data representation to obtain a power generation prediction result, including: through the debugged photovoltaic power generation interaction data representation, power generation prediction target photovoltaic power generation interaction data and the power generation prediction interaction event to which the target photovoltaic power generation interaction data belongs, wherein the power generation prediction interaction event belongs to at least one target interaction event; for each group of photovoltaic power generation interaction data binary groups, the interaction event comparison and description matching degree of the photovoltaic power generation interaction data binary group are obtained, and the first correlation degree between the interaction event comparison and description matching degree of the photovoltaic power generation interaction data binary group is obtained, wherein the interaction event comparison indicates whether the power generation prediction interaction events to which the photovoltaic power generation interaction data binary group belongs are consistent, and the description matching degree indicates the correlation degree between the photovoltaic power generation interaction data representations of the photovoltaic power generation interaction data binary group; and, based on the power generation prediction interaction event to which the target photovoltaic power generation interaction data belongs and the target interaction event, a second correlation degree between the target photovoltaic power generation interaction data and the power generation prediction interaction event and the target interaction event is obtained; and the power generation prediction result is obtained through the first correlation degree and the second correlation degree.

[0014] It should be understood that, through the debugged photovoltaic power generation interaction data representation, the power generation prediction target photovoltaic power generation interaction data and the power generation prediction interaction event to which the target photovoltaic power generation interaction data belongs, and the power generation prediction interaction event belongs to at least one target interaction event, so that for each group of photovoltaic power generation interaction data tuples, the interaction event comparison situation and the description matching degree of the photovoltaic power generation interaction data tuple are obtained, and the first correlation degree between the interaction event comparison situation and the description matching degree of the photovoltaic power generation interaction data tuple is obtained, and the interaction event comparison situation indicates whether the power generation prediction interaction events to which the photovoltaic power generation interaction data tuple belongs are consistent, and the description matching degree indicates the correlation degree between the photovoltaic power generation interaction data representations of the photovoltaic power generation interaction data tuple, and based on the power generation prediction interaction event and the target interaction event to which the target photovoltaic power generation interaction data belongs, the second correlation degree of the target photovoltaic power generation interaction data regarding the power generation prediction interaction event and the target interaction event is obtained, and then the power generation prediction result is obtained through the first correlation degree and the second correlation degree. Therefore, by obtaining the first correlation degree of the comparison of interaction events and the correlation degree of the photovoltaic power generation interaction data binary group, it is possible to characterize the reliability of the interaction event evaluation of the photovoltaic power generation interaction data from the dimension of any photovoltaic power generation interaction data binary group based on the comparison of interaction events of the power generation prediction interaction events and the correlation between the description matching degrees, and by obtaining the second correlation degree of the target photovoltaic power generation interaction data about the power generation prediction interaction event and the target interaction event, it is possible to characterize the reliability of the interaction event evaluation of the photovoltaic power generation interaction data from the dimension of one photovoltaic power generation interaction data based on the correlation between the power generation prediction interaction event and the target interaction event, and by combining the two dimensions of any two photovoltaic power generation interaction data and one photovoltaic power generation interaction data, the power generation prediction result can be obtained, which can improve the power generation prediction accuracy of the power generation prediction result.

[0015] In an independently implemented embodiment, on the basis that the comparison of interaction events is that the power generation prediction interaction event is consistent, the description of the matching degree is related to the first degree of association, and on the basis that the comparison of interaction events is that the power generation prediction interaction event is different, the description of the matching degree is not related to the first degree of association, and the second degree of association when the power generation prediction interaction event is consistent with the target interaction event exceeds the second degree of association when the power generation prediction interaction event is different from the target interaction event.

[0016] It should be understood that, on the basis that the interactive event comparison situation is consistent with the power generation prediction interactive event, the description matching degree is configured to be related to the first correlation degree, and on the basis that the interactive event comparison situation is different from the power generation prediction interactive event, the description matching degree is configured to have no relationship with the first correlation degree, so that when the interactive event comparison situation is consistent with the power generation prediction interactive event, the greater the description matching degree, the greater the first correlation degree with the interactive event comparison situation, that is, the more the description matching degree is related to the interactive event comparison situation, and when the interactive event comparison situation is different from the power generation prediction interactive event, the greater the description matching degree, the smaller the first correlation degree with the interactive event comparison situation, that is, the description matching degree is more related to the interactive event comparison situation. The more the degree of correlation is compared with the interaction event, the less correlation there is, thereby increasing the possibility of collecting consistent interaction events of photovoltaic power generation interaction data between any two photovoltaic power generation interaction data in the power generation prediction process of the subsequent power generation prediction result, thereby improving the accuracy of power generation prediction of the power generation prediction result. In addition, since the second correlation degree when the power generation prediction interaction event is consistent with the target interaction event exceeds the second correlation degree when the power generation prediction interaction event is different from the target interaction event, it is beneficial to collect the accuracy of the photovoltaic power generation interaction data representation of one photovoltaic power generation interaction data in the power generation prediction process of the subsequent power generation prediction result, thereby improving the accuracy of power generation prediction of the power generation prediction result.

[0017] In an independently implemented embodiment, the photovoltaic power generation interaction data after debugging represents the power generation prediction interaction event to which the photovoltaic power generation interaction data belongs, including: based on the score evaluation thread, the photovoltaic power generation interaction data after debugging represents the power generation prediction interaction event to which the photovoltaic power generation interaction data belongs.

[0018] It should be understood that by evaluating the thread based on the score, represented by the debugged photovoltaic power generation interaction data, the power generation prediction target photovoltaic power generation interaction data and the power generation prediction interaction events to which the target photovoltaic power generation interaction data belongs, the accuracy and effect of power generation prediction can be improved.

[0019] In an independently implemented embodiment, obtaining a power generation prediction result through a first correlation degree and a second correlation degree includes: obtaining a power generation prediction result through a first correlation degree and a second correlation degree according to a decision function.

[0020] It should be understood that, according to the decision function, the power generation prediction result is obtained through the first correlation degree and the second correlation degree, which can improve the accuracy of the power generation prediction result.

[0021] In an independently implemented embodiment, the previously configured threshold value includes: a predicted value of executing the power generation prediction process does not meet a previously configured determination value.

[0022] It should be understood that configuring the previously configured threshold as follows: the predicted value for executing the power generation prediction processing does not meet the previously configured judgment value. This can improve the accuracy of the evaluation of photovoltaic power generation interaction data interaction events by optimizing the predicted value of the previously configured judgment value. This can fully collect the correlation between interaction events between photovoltaic power generation interaction data, thereby improving the accuracy of the evaluation of photovoltaic power generation interaction data interaction events.

[0023] In an independently implemented embodiment, the step of debugging the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data according to the degree of association of the interaction events is performed by an artificial intelligence thread.

[0024] It should be understood that the effect of debugging the interactive data representation of photovoltaic power generation can be improved by executing the above-mentioned steps of debugging the interactive data representation of photovoltaic power generation through the degree of correlation of interactive events through the artificial intelligence thread.

[0025] In an independently implemented embodiment, photovoltaic power generation interaction data representations of multiple groups of photovoltaic power generation interaction data are debugged through the degree of association of interaction events, including: obtaining a first photovoltaic power generation interaction data representation and a second photovoltaic power generation interaction data representation through the degree of association of interaction events and the photovoltaic power generation interaction data representation; optimizing the first photovoltaic power generation interaction data representation and the second photovoltaic power generation interaction data representation to obtain a debugged photovoltaic power generation interaction data representation.

[0026] It should be understood that the first photovoltaic power generation interaction data representation and the second photovoltaic power generation interaction data representation are obtained by the degree of correlation between the interaction events and the photovoltaic power generation interaction data representation, and the first photovoltaic power generation interaction data representation and the second photovoltaic power generation interaction data representation are combined to perform optimization in two dimensions to obtain the debugged photovoltaic power generation interaction data representation, which can improve the accuracy of the debugging of the photovoltaic power generation interaction data representation.

[0027] In an independently implemented embodiment, the photovoltaic power generation interaction data evaluation method also includes: on the basis that the photovoltaic power generation interaction data tuple belongs to a consistent photovoltaic power generation interaction data interaction event, determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as a previously configured first limit value; on the basis that the photovoltaic power generation interaction data tuple belongs to a photovoltaic power generation interaction data interaction event with differences, determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as a previously configured second limit value; on the basis that at least one of the photovoltaic power generation interaction data tuples is the target photovoltaic power generation interaction data, determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as a previously configured third limit value between the previously configured second limit value and the previously configured first limit value.

[0028] It should be understood that, by determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as the previously configured first limit value on the basis that the photovoltaic power generation interaction data tuple belongs to a consistent photovoltaic power generation interaction data interaction event, and determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as the previously configured second limit value when the photovoltaic power generation interaction data tuple belongs to a photovoltaic power generation interaction data interaction event with differences, and determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as the previously configured third limit value between the previously configured second limit value and the previously configured first limit value on the basis that at least one of the photovoltaic power generation interaction data tuples is the target photovoltaic power generation interaction data, so that the possibility of consistency of the photovoltaic power generation interaction data interaction events of the photovoltaic power generation interaction data tuple can be characterized by the above-mentioned previously configured first limit value, the previously configured second limit value and the previously configured third limit value, so as to facilitate subsequent processing, thereby improving the accuracy of characterizing the correlation degree of the interaction events.

[0029] In a second aspect, a photovoltaic power generation evaluation system is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to retrieve a computer program from the memory and implement the above method by running the computer program.

[0030] A photovoltaic power generation evaluation method and system provided in an embodiment of the present application obtain photovoltaic power generation interaction data representations of multiple groups of photovoltaic power generation interaction data and the degree of interaction event correlation of at least one group of photovoltaic power generation interaction data binary groups, and the multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data, and every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary groups, and the degree of interaction event correlation indicates the possibility that the photovoltaic power generation interaction data binary groups belong to consistent photovoltaic power generation interaction data interaction events, and the photovoltaic power generation interaction data representation is debugged through the degree of interaction event correlation, so as to obtain the photovoltaic power generation interaction event evaluation status of the target photovoltaic power generation interaction data through the debugged photovoltaic power generation interaction data representation. Therefore, by debugging the photovoltaic power generation interaction data representation through the correlation degree of interaction events, the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of consistent photovoltaic power generation interaction data interaction events can be made close to similar, and the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of photovoltaic power generation interaction data interaction events with differences can be deleted, thereby improving the stability of the photovoltaic power generation interaction data representation, and facilitating the collection of photovoltaic power generation interaction event evaluation conditions of the photovoltaic power generation interaction data representation, thereby improving the accuracy of the photovoltaic power generation interaction data event evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A flow chart of a photovoltaic power generation evaluation method provided in an embodiment of the present application.

[0033] Figure 2 A block diagram of a photovoltaic power generation evaluation device provided in an embodiment of the present application.

[0034] Figure 3 This is an architecture diagram of a photovoltaic power generation assessment system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0036] See also Figure 1 , shows a method for evaluating photovoltaic power generation, which may include the technical solutions described in the following steps 11-13.

[0037] step11: obtain the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data and the degree of association of interaction events of at least one group of photovoltaic power generation interaction data binary groups.

[0038] In this embodiment, the plurality of groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data. Further, the target photovoltaic power generation interaction data is photovoltaic power generation interaction data for which the photovoltaic power generation interaction data interaction event is unknown, and the target photovoltaic power generation interaction data is photovoltaic power generation interaction data for which the photovoltaic power generation interaction data interaction event is known.

[0039] In this embodiment, every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a photovoltaic power generation interaction data binary group. For example, the multiple groups of photovoltaic power generation interaction data include the first target photovoltaic power generation interaction data, the second target photovoltaic power generation interaction data and the third target photovoltaic power generation interaction data, then the photovoltaic power generation interaction data binary group may include: the first target photovoltaic power generation interaction data and the third target photovoltaic power generation interaction data, the second target photovoltaic power generation interaction data and the third target photovoltaic power generation interaction data, and the first target photovoltaic power generation interaction data and the third target photovoltaic power generation interaction data.

[0040] In a possible implementation, the degree of association of the possibility that the photovoltaic power generation interaction data tuple belongs to the consistent photovoltaic power generation interaction data interaction event may specifically include: the photovoltaic power generation interaction data tuple belongs to the target power generation description result of the consistent photovoltaic power generation interaction data interaction event. For example, when the target power generation description result is 3.6, it can be understood that the possibility that the photovoltaic power generation interaction data tuple belongs to the consistent photovoltaic power generation interaction data interaction event is large; or, when the target power generation description result is 0.4, it can be understood that the possibility that the photovoltaic power generation interaction data tuple belongs to the consistent photovoltaic power generation interaction data interaction event is small; or, when the target power generation description result is 2, it can be understood that the possibility that the photovoltaic power generation interaction data tuple belongs to the function processing result of the consistent photovoltaic power generation interaction data interaction event belongs to the photovoltaic power generation interaction data interaction event with differences is equal.

[0041] In a possible implementation, when starting to execute the steps in this embodiment, the degree of association of the interaction events of the photovoltaic power generation interaction data tuple belonging to the consistent photovoltaic power generation interaction data interaction event can be initialized. Further, on the basis that the photovoltaic power generation interaction data tuple belongs to the consistent photovoltaic power generation interaction data interaction event, the initial degree of association of the interaction event of the photovoltaic power generation interaction data tuple can be determined as the first limit value configured in advance. For example, when the degree of association of the interaction event is represented by the above-mentioned target power generation description result, the first limit value configured in advance can be configured as 4; in addition, on the basis that the photovoltaic power generation interaction data tuple belongs to the photovoltaic power generation interaction data interaction event with differences, the initial degree of association of the interaction event of the photovoltaic power generation interaction data tuple is determined as the second limit value configured in advance. For example, when the degree of association of the interaction event is represented by the above-mentioned target power generation description result, the second limit value configured in advance can be configured as 0; in addition, by The target photovoltaic power generation interaction data is the photovoltaic power generation interaction data to be evaluated. Therefore, when at least one of the photovoltaic power generation interaction data tuples is the target photovoltaic power generation interaction data, the degree of correlation of the interaction events of the photovoltaic power generation interaction data tuples belonging to the consistent photovoltaic power generation interaction data interaction events cannot be determined. In order to improve the stability of the initialization interaction event correlation degree, the interaction event correlation degree can be determined as a previously configured third limit value between a previously configured second limit value and a previously configured first limit value. For example, when the interaction event correlation degree is represented by the above-mentioned target power generation description result, the previously configured third limit value can be configured to 2. Of course, it can also be configured to 1.6, 2.4, 2.8 as needed, and again no one-to-one limitation is performed.

[0042] In a possible embodiment, in order to further limit, when the degree of correlation of the interaction event is represented by the target power generation description result, the target photovoltaic power generation interaction data and the target photovoltaic power generation interaction data can be recorded as the target photovoltaic power generation interaction data of the x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data. In addition, there are M types of photovoltaic power generation interaction data interaction events, and each photovoltaic power generation interaction data interaction event corresponds to L target photovoltaic power generation interaction data. Then, when the 1st to ML-th photovoltaic power generation interaction data are the target photovoltaic power generation interaction data, the photovoltaic power generation interaction data interaction events recorded by the x-th target photovoltaic power generation interaction data and the y-th target photovoltaic power generation interaction data can be recorded as ax, ay respectively. Then the target power generation description result of the initialization of the photovoltaic power generation interaction data tuple belonging to the consistent photovoltaic power generation interaction data interaction event can be recorded as can be expressed by the following content.

[0043] Therefore, when there are H target photovoltaic power generation interaction data, that is, when the ML+2th to ML+Hth photovoltaic power generation interaction data are the target photovoltaic power generation interaction data, the degree of association of the interaction events of the photovoltaic power generation interaction data tuples can be expressed as a queue of (ML+H) x (ML+H).

[0044] In a possible implementation example, the photovoltaic power generation interactive data interactive event can be specifically configured according to the current usage scenario.

[0045] In a possible implementation embodiment, according to the above content, there may be a total of M types of target photovoltaic power generation interaction data for photovoltaic power generation interaction data interaction events, and each type of photovoltaic power generation interaction data interaction event corresponds to L target photovoltaic power generation interaction data, M is an integer not less than 1, and L is an integer not less than 1.

[0046] Step 12: Debug the photovoltaic power generation interactive data representation of multiple groups of photovoltaic power generation interactive data according to the degree of correlation of interactive events.

[0047] In a possible implementation, in order to improve the effect of debugging the photovoltaic power generation interactive data representation, according to the above content, a photovoltaic power generation interactive data evaluation thread can be preconfigured, and the photovoltaic power generation interactive data evaluation thread further includes an artificial intelligence thread. On this basis, the photovoltaic power generation interactive data representation of each photovoltaic power generation interactive data can be determined as a node loaded into the photovoltaic power generation interactive data of the artificial intelligence thread. In order to further define, the initialized photovoltaic power generation interactive data representation can be recorded as and the degree of association of the interactive event of the random photovoltaic power generation interactive data tuple is determined as the edge between the nodes, in order to further define.

[0048] In a possible implementation, in order to improve the accuracy of the photovoltaic power generation interactive data representation, the first photovoltaic power generation interactive data representation and the second photovoltaic power generation interactive data representation can be obtained through the degree of association of interactive events and the photovoltaic power generation interactive data representation, wherein the first photovoltaic power generation interactive data representation is the photovoltaic power generation interactive data representation obtained by first splicing the photovoltaic power generation interactive data representation through the degree of association of interactive events, and the second photovoltaic power generation interactive data representation is the photovoltaic power generation interactive data representation obtained by second splicing the photovoltaic power generation interactive data representation through the degree of association of interactive events. For the purpose of unified description, the photovoltaic power generation interactive data representation obtained by initialization is still represented, and the degree of association of interactive events is obtained. Then, the first photovoltaic power generation interactive data representation can be understood as the second photovoltaic power generation interactive data representation can be understood as after obtaining the first photovoltaic power generation interactive data representation and the second photovoltaic power generation interactive data representation, the first photovoltaic power generation interactive data representation and the second photovoltaic power generation interactive data representation can be optimized to obtain the debugged photovoltaic power generation interactive data representation.

[0049] Step 13: The photovoltaic power generation interaction event evaluation situation of the target photovoltaic power generation interaction data is obtained through the photovoltaic power generation interaction data after debugging.

[0050] In a possible implementation example, the photovoltaic power generation interaction event evaluation situation may be used to characterize the photovoltaic power generation interaction data interaction event to which the target photovoltaic power generation interaction data belongs.

[0051] In a possible implementation, after obtaining the debugged photovoltaic power generation interactive data representation, the debugged photovoltaic power generation interactive data representation can be used to perform power generation prediction processing to obtain a power generation prediction result, and the power generation prediction result includes a first power generation description result that the target photovoltaic power generation interactive data belongs to at least one target interaction event, so that the photovoltaic power generation interactive event evaluation situation can be obtained based on the first power generation description result. Furthermore, the target interaction event is a photovoltaic power generation interactive data interaction event to which the target photovoltaic power generation interactive data belongs.

[0052] In an alternative embodiment, power generation prediction processing is performed by debugging the photovoltaic power generation interaction data representation to obtain a power generation prediction result, and the power generation prediction result includes a first power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event and a second power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event. Then, on the basis that the predicted value of the power generation prediction processing meets the previously configured threshold, the power generation prediction result can be used to debug the degree of association of the interaction events of multiple groups of photovoltaic power generation interaction data, and the above step 12 and subsequent steps are executed again, that is, the photovoltaic power generation interaction data representation is debugged by the degree of association of the interaction events, and the power generation prediction processing steps are performed by the debugged photovoltaic power generation interaction data representation, until the predicted value of the power generation prediction processing does not meet the previously configured threshold. Through the above content, on the basis that the predicted value of the power generation prediction processing meets the previously configured threshold, the degree of association of the interaction events representing the photovoltaic power generation interaction data tuple can be debugged through the first power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event and the second power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event, thereby improving the stability of the degree of association of the interaction events, and continuing to debug the photovoltaic power generation interaction data representation through the debugged degree of association of the interaction events, thereby further improving the stability of the photovoltaic power generation interaction data representation, and then the degree of association of the interaction events and the photovoltaic power generation interaction data representation can complement each other, and further, can further improve the accuracy of the evaluation of the interaction events of the photovoltaic power generation interaction data.

[0053] In a possible embodiment, based on the fact that the predicted value of the power generation prediction processing does not meet the previously configured threshold, the photovoltaic power generation interaction event evaluation status of the target photovoltaic power generation interaction data can be obtained according to the first power generation description result.

[0054] In a possible implementation embodiment, a photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data and a degree of association of interaction events of at least one group of photovoltaic power generation interaction data binary groups, and the multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data, and every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary groups, and the degree of association of interaction events represents the possibility that the photovoltaic power generation interaction data binary groups belong to consistent photovoltaic power generation interaction data interaction events, and through the degree of association of interaction events, the photovoltaic power generation interaction data representation is debugged, so that the photovoltaic power generation interaction event evaluation of the target photovoltaic power generation interaction data is obtained through the debugged photovoltaic power generation interaction data representation. Therefore, by debugging the photovoltaic power generation interaction data representation according to the degree of correlation of the interaction events, the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of the consistent photovoltaic power generation interaction data interaction events can be made to be nearly similar, and the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of the photovoltaic power generation interaction data interaction events with differences can be deleted, thereby improving the stability of the photovoltaic power generation interaction data representation, and facilitating the collection of photovoltaic power generation interaction event evaluation conditions of the photovoltaic power generation interaction data representation, thereby improving the accuracy of the photovoltaic power generation interaction data event evaluation.

[0055] The description of the photovoltaic power generation interactive data evaluation method disclosed in the present invention may specifically include the following steps.

[0056] step21: Obtain the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data and the degree of association of interaction events of at least one group of photovoltaic power generation interaction data binary groups.

[0057] In this embodiment, multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data. Every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary. The degree of correlation of interaction events indicates the possibility that the photovoltaic power generation interaction data binary belongs to a consistent photovoltaic power generation interaction data interaction event.

[0058] Step 22: Debug the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data according to the correlation degree of interaction events.

[0059] Step 23: Perform power generation prediction processing through the interactive data representation of photovoltaic power generation after debugging to obtain the power generation prediction result.

[0060] In this embodiment, the power generation prediction result includes a first power generation description result in which the target photovoltaic power generation interaction data belongs to at least one target interaction event and a second power generation description result in which the target photovoltaic power generation interaction data belongs to at least one target interaction event. The target interaction event is a photovoltaic power generation interaction data interaction event to which the target photovoltaic power generation interaction data belongs.

[0061] Furthermore, the photovoltaic power generation interaction data after debugging can be used to represent the target photovoltaic power generation interaction data of power generation prediction and the power generation prediction interaction event to which the target photovoltaic power generation interaction data belongs, and the power generation prediction interaction event belongs to at least one target interaction event. After obtaining the power generation prediction interaction event, for each group of photovoltaic power generation interaction data tuples, the interaction event comparison and description matching degree of the photovoltaic power generation interaction data tuples can be obtained, and the first correlation degree between the interaction event comparison and description matching degree of the photovoltaic power generation interaction data tuples can be obtained, and the interaction event comparison indicates whether the power generation prediction interaction events to which the photovoltaic power generation interaction data tuples belong are consistent, and the description matching degree indicates the correlation degree between the photovoltaic power generation interaction data representations of the photovoltaic power generation interaction data tuples, and according to the power generation prediction interaction events and the target interaction events to which the target photovoltaic power generation interaction data belongs, the second correlation degree of the target photovoltaic power generation interaction data about the power generation prediction interaction event and the target interaction event is obtained, so that the power generation prediction result can be obtained through the first correlation degree and the second correlation degree. Through the above content, by obtaining the first correlation degree of the comparison of interaction events and the correlation degree of the photovoltaic power generation interaction data binary group, it is possible to characterize the reliability of the evaluation of the interaction event of photovoltaic power generation interaction data from the dimension of any photovoltaic power generation interaction data binary group based on the comparison of interaction events of power generation prediction interaction events and the correlation between the description matching degree, and by obtaining the second correlation degree of the target photovoltaic power generation interaction data about the power generation prediction interaction event and the target interaction event, it is possible to characterize the reliability of the evaluation of the interaction event of photovoltaic power generation interaction data from the dimension of one photovoltaic power generation interaction data based on the correlation between the power generation prediction interaction event and the target interaction event, and combine the two dimensions of any two photovoltaic power generation interaction data and one photovoltaic power generation interaction data to obtain the power generation prediction result, which can improve the accuracy of the power generation prediction result.

[0062] In a possible implementation, in order to improve the power generation prediction effect, the power generation prediction interaction event to which the photovoltaic power generation interaction data belongs can be predicted based on the score evaluation thread through the debugged photovoltaic power generation interaction data representation.

[0063] In a possible implementation, on the basis that the interactive event comparison is consistent with the interactive event of power generation prediction, there is a relationship between the description matching degree and the first correlation degree, that is, the greater the description matching degree, the greater the first correlation degree, the more correlation there is between the interactive event comparison and the description matching degree, the smaller the description matching degree, the smaller the first correlation degree, the less correlation there is between the interactive event comparison and the description matching degree; and on the basis that the interactive event comparison is different from the interactive event of power generation prediction, there is no relationship between the description matching degree and the first correlation degree, that is, the greater the description matching degree, the smaller the first correlation degree, the less correlation there is between the interactive event comparison and the description matching degree, and vice versa, the smaller the description matching degree, the greater the first correlation degree, the more correlation there is between the interactive event comparison and the description matching degree. Through the above content, the possibility of collecting the consistency of the photovoltaic power generation interactive data interaction events between the photovoltaic power generation interactive data tuples in the power generation prediction process of the subsequent power generation prediction results can be improved, thereby improving the accuracy of the power generation prediction results of the power generation prediction.

[0064] In an alternative embodiment, when the power generation prediction interaction event is consistent with the target interaction event, the second correlation degree between the target photovoltaic power generation interaction data exceeds the second correlation degree between the target photovoltaic power generation interaction data when there is a difference between the power generation prediction interaction event and the target interaction event. Through the above content, it is beneficial to collect the accuracy of the photovoltaic power generation interaction data representation of a photovoltaic power generation interaction data in the power generation prediction process of the subsequent power generation prediction result, thereby being able to improve the accuracy of the power generation prediction of the power generation prediction result.

[0065] Step 24: Determine whether the predicted value of the power generation prediction processing meets the previously configured threshold. If so, execute step 25, otherwise execute step 27.

[0066] Further, the previously configured threshold may include: a predicted value of executing the power generation amount prediction process does not satisfy a previously configured determination value.

[0067] Step 25: Debug the correlation degree of interactive events through the power generation prediction results.

[0068] In this embodiment, according to the above content, the degree of association of the interaction event may specifically include: each group of photovoltaic power generation interaction data tuples belongs to the target power generation description result of the consistent photovoltaic power generation interaction data interaction event. In order to further limit it, the degree of association of the interaction event obtained by debugging after the first power generation prediction process can be recorded as the first threshold. According to the above content, before the first power generation prediction process, the degree of association of the interaction event obtained by initialization can be recorded as the second threshold. In addition, further, the x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data contained in the first threshold of the interaction event association degree belong to the target power generation description result of the consistent photovoltaic power generation interaction data interaction event can be recorded as In particular, the x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data contained in the second threshold of the interaction event association degree belong to the target power generation description result of the consistent photovoltaic power generation interaction data interaction event can be recorded as On this basis, each group of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data can be determined as the current photovoltaic power generation interaction data, and the photovoltaic power generation interaction data tuple containing the current photovoltaic power generation interaction data can be determined as the current photovoltaic power generation interaction data tuple. In the first power generation prediction processing process, the target power generation description results of each group of current photovoltaic power generation interaction data tuples belonging to the consistent photovoltaic power generation interaction data interaction event can be obtained respectively through the first power generation description result and the second power generation description result.

[0069] Furthermore, the concatenation of the target power generation description results of all current photovoltaic power generation interaction data tuples of the current photovoltaic power generation interaction data can be obtained and determined as the function processing result of the current photovoltaic power generation interaction data. Further, for the first power generation prediction processing, the degree of correlation of the interaction events after debugging can be understood as the first threshold, and the degree of correlation of the interaction events before debugging can be understood as that the x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data contained in the degree of correlation of the interaction events before debugging are consistent. The target power generation description result of the photovoltaic power generation interaction data interaction event can be recorded as Therefore, for the current photovoltaic power generation interaction data being the x-th photovoltaic power generation interaction data, on the basis that the other photovoltaic power generation interaction data in the photovoltaic power generation interaction data tuple containing the x-th photovoltaic power generation interaction data is recorded as k, the splicing of the target power generation description results of all the current photovoltaic power generation interaction data tuples of the current photovoltaic power generation interaction data can be understood as after obtaining the target power generation description result and the function processing result, for each group of current photovoltaic power generation interaction data tuples, the target power generation description result of each group of photovoltaic power generation interaction data tuples can be corrected by the function processing result and the target power generation description result respectively. The x-th photovoltaic power generation interaction data represents the current photovoltaic power generation interaction data. The x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data constitute a set of current photovoltaic power generation interaction data tuples, representing the target power generation description result of the photovoltaic power generation interaction data tuple containing the x-th photovoltaic power generation interaction data obtained by the first power generation prediction processing, representing the target power generation description result of the photovoltaic power generation interaction data interaction event that the x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data obtained by the first power generation prediction processing belong to the same, representing the target power generation description result before the debugging of the photovoltaic power generation interaction data interaction event that the x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data belong to the same in the first power generation prediction processing, representing the target power generation description result after the debugging of the photovoltaic power generation interaction data interaction event that the x-th photovoltaic power generation interaction data and the y-th photovoltaic power generation interaction data belong to the same in the l-th power generation prediction processing, representing the splicing of the target power generation description results of all the current photovoltaic power generation interaction data tuples of the current photovoltaic power generation interaction data (i.e., the x-th photovoltaic power generation interaction data).

[0070] step26: Execute step22 again.

[0071] After obtaining the debugged interaction event correlation degree, the above step 22 and subsequent steps can be performed again, that is, the photovoltaic power generation interaction data representation of multiple sets of photovoltaic power generation interaction data is debugged by the debugged interaction event correlation degree. Further, the debugged interaction event correlation degree is recorded as the first threshold.

[0072] step27: Based on the first power generation description result, the evaluation of the photovoltaic power generation interaction event is obtained.

[0073] In an implementable embodiment, based on the photovoltaic power generation interaction event evaluation situation including the photovoltaic power generation interaction data interaction event of the target photovoltaic power generation interaction data, the target interaction event bound to the optimal first power generation description result can be determined as the photovoltaic power generation interaction data interaction event of the target photovoltaic power generation interaction data.

[0074] It is further explained that by configuring the power generation prediction result to also include a second power generation description result in which the target photovoltaic power generation interaction data belongs to at least one target interaction event, and before obtaining the photovoltaic power generation interaction event evaluation situation based on the first power generation description result, further on the basis that the predicted value of the power generation prediction processing meets the previously configured threshold, the degree of association of the interaction event is debugged through the power generation prediction result, and the step of debugging the photovoltaic power generation interaction data representation through the degree of association of the interaction event is executed again, and on the basis that the predicted value of the power generation prediction processing does not meet the previously configured threshold, the photovoltaic power generation interaction event evaluation situation is obtained based on the first power generation description result. Therefore, on the basis that the predicted value of the power generation prediction processing meets the previously configured threshold, the degree of association of the interaction events can be debugged through the first power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event and the second power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event, thereby improving the stability of the degree of association of the interaction events, and continue to debug the photovoltaic power generation interaction data representation through the debugged degree of association of the interaction events, thereby further improving the stability of the photovoltaic power generation interaction data representation, and then the degree of association of the interaction events and the photovoltaic power generation interaction data representation can complement each other, and further, and on the basis that the predicted value of the power generation prediction processing does not meet the previously configured threshold, according to the first power generation description result, the evaluation situation of the photovoltaic power generation interaction event is obtained, thereby being able to further improve the accuracy of the evaluation of the photovoltaic power generation interaction data interaction event.

[0075] The present invention discloses another embodiment of a photovoltaic power generation interactive data evaluation method. In this embodiment, the photovoltaic power generation interactive data evaluation is specifically performed by a photovoltaic power generation interactive data evaluation thread, and the operation steps of the photovoltaic power generation interactive data evaluation thread specifically include the following contents.

[0076] step31: Obtain the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data and the degree of association of interaction events of at least one group of photovoltaic power generation interaction data binary groups.

[0077] In this embodiment, multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data. Every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary. The degree of correlation of interaction events indicates the possibility that the photovoltaic power generation interaction data binary belongs to a consistent photovoltaic power generation interaction data interaction event.

[0078] step32: according to the first unit of the lth extraction unit, adjusting the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data through the degree of association of the interaction events.

[0079] step33: According to the second unit of the lth extraction unit, the power generation prediction processing is performed through the debugged photovoltaic power generation interactive data representation to obtain the power generation prediction result.

[0080] In this embodiment, the power generation prediction result includes a first power generation description result in which the target photovoltaic power generation interaction data belongs to at least one target interaction event and a second power generation description result in which the target photovoltaic power generation interaction data belongs to at least one target interaction event.

[0081] Step 34: Determine whether the unit that performs power generation prediction processing is a target extraction unit of the photovoltaic power generation interactive data evaluation thread. If not, execute step 35; if so, execute step 37.

[0082] Further, when the photovoltaic power generation interaction data evaluation thread includes L extraction units, it can be determined whether l is less than L. If so, it indicates that there are still extraction units that have not executed the above-mentioned photovoltaic power generation interaction data representation debugging and power generation prediction results power generation prediction steps, then the following step 35 can be continued to debug the photovoltaic power generation interaction data representation and power generation prediction results through subsequent extraction units. If not, it indicates that all extraction units of the photovoltaic power generation interaction data evaluation thread have all executed the above-mentioned photovoltaic power generation interaction data representation debugging and power generation prediction results power generation prediction steps, then the following step 37 can be executed, that is, according to the first power generation description result in the power generation prediction result, the photovoltaic power generation interaction event evaluation situation is obtained.

[0083] Step 35: Debug the correlation degree of interactive events through the power generation prediction results.

[0084] step36: Execute step32 and subsequent steps again.

[0085] step37: Based on the first power generation description result, the evaluation of the photovoltaic power generation interaction event is obtained.

[0086] To further illustrate the above content, based on a non-target extraction unit that performs power generation prediction processing, the degree of correlation of interactive events is debugged through the power generation prediction result, and the next extraction unit is again executed to debug the photovoltaic power generation interactive data representation of multiple sets of photovoltaic power generation interactive data through the degree of correlation of interactive events. Therefore, the stability of the degree of correlation of interactive events can be improved, and the photovoltaic power generation interactive data representation can be debugged through the debugged degree of correlation of interactive events, thereby improving the stability of the photovoltaic power generation interactive data representation, and then the degree of correlation of interactive events and the photovoltaic power generation interactive data representation can complement each other, and further, the accuracy of the evaluation of photovoltaic power generation interactive data interactive events can be further improved.

[0087] The configuration steps of the photovoltaic power generation interactive data evaluation thread disclosed in the present invention may specifically include the following steps.

[0088] step51: Obtain sample photovoltaic power generation interaction data representations of multiple groups of sample photovoltaic power generation interaction data and the degree of correlation of sample interaction events of at least one group of sample photovoltaic power generation interaction data binary groups.

[0089] In this embodiment, the multiple groups of sample photovoltaic power generation interaction data include sample target photovoltaic power generation interaction data and sample target photovoltaic power generation interaction data. Every two groups of sample photovoltaic power generation interaction data in the multiple groups of sample photovoltaic power generation interaction data form a group of sample photovoltaic power generation interaction data binary. The sample interaction event correlation degree indicates the possibility that the sample photovoltaic power generation interaction data binary belongs to a consistent photovoltaic power generation interaction data interaction event. The process of obtaining the sample photovoltaic power generation interaction data representation and the sample interaction event correlation degree can refer to the process of obtaining the photovoltaic power generation interaction data representation and the interaction event correlation degree in this embodiment.

[0090] In addition, the sample target photovoltaic power generation interaction data, the sample target photovoltaic power generation interaction data and the photovoltaic power generation interaction data interaction events can also refer to the relevant explanations of the target photovoltaic power generation interaction data, the target photovoltaic power generation interaction data and the photovoltaic power generation interaction data interaction events in this embodiment.

[0091] step52: According to the first unit of the photovoltaic power generation interaction data evaluation thread, debug the sample photovoltaic power generation interaction data representation of multiple groups of sample photovoltaic power generation interaction data through the correlation degree of sample interaction events.

[0092] step53: According to the second unit of the photovoltaic power generation interaction data evaluation thread, the photovoltaic power generation interaction event evaluation situation of the sample target photovoltaic power generation interaction data is obtained through the debugged sample photovoltaic power generation interaction data representation.

[0093] step54: Correct the sample coefficient of the photovoltaic power generation interaction data evaluation thread through the photovoltaic power generation interaction event evaluation situation of the sample target photovoltaic power generation interaction data and the photovoltaic power generation interaction data interaction events recorded in the sample target photovoltaic power generation interaction data.

[0094] Furthermore, the difference between the photovoltaic power generation interaction event evaluation situation of the sample target photovoltaic power generation interaction data and the photovoltaic power generation interaction data interaction events recorded in the sample target photovoltaic power generation interaction data can be obtained through the quantitative evaluation thread, thereby obtaining the error result of the photovoltaic power generation interaction data evaluation thread.

[0095] In a possible implementation, a configuration end threshold may be configured, and when the configuration end threshold is met, the configuration may be ended. Further, the configuration end threshold may include any of the following: the error result is less than a previous configuration loss determination value, the current configuration prediction value meets the previous configuration prediction value determination value, and again no one-to-one limitation is performed.

[0096] In a replaceable embodiment, specifically according to the second unit, power generation prediction processing can be performed through the debugged sample photovoltaic power generation interaction data representation to obtain a sample power generation prediction result, and the sample power generation prediction result includes a first sample power generation description result that the sample target photovoltaic power generation interaction data belongs to at least one target interaction event and a second sample power generation description result that the sample target photovoltaic power generation interaction data belongs to at least one target interaction event, thereby obtaining a photovoltaic power generation interaction event evaluation situation of the sample target photovoltaic power generation interaction data based on the first sample power generation description result, and obtaining a photovoltaic power generation interaction event evaluation situation of the sample target photovoltaic power generation interaction data and a photovoltaic power generation interaction data interaction recorded by the sample target photovoltaic power generation interaction data. Before correcting the sample coefficient of the photovoltaic power generation interaction data evaluation thread, the sample interaction event correlation degree is debugged through the first sample power generation description result and the second sample power generation description result, so as to obtain the first error result of the photovoltaic power generation interaction data evaluation thread through the photovoltaic power generation interaction data interaction event recorded by the first sample power generation description result and the sample target photovoltaic power generation interaction data, and obtain the second error result of the photovoltaic power generation interaction data evaluation thread through the current interaction event correlation degree between the sample target photovoltaic power generation interaction data and the sample target photovoltaic power generation interaction data and the debugged sample interaction event correlation degree, and then correct the sample coefficient of the photovoltaic power generation interaction data evaluation thread according to the first error result and the second error result. Through the above content, the sample coefficient of the photovoltaic power generation interaction data evaluation thread can be corrected from the dimension of the interaction event correlation degree between two photovoltaic power generation interaction data and the dimension of the photovoltaic power generation interaction data interaction event of one photovoltaic power generation interaction data, so as to improve the accuracy of the photovoltaic power generation interaction data evaluation thread.

[0097] In a possible embodiment, a first error result between the first sample power generation description result and the photovoltaic power generation interaction data interaction event of the sample target photovoltaic power generation interaction data record may be calculated through a quantitative evaluation thread.

[0098] In another independently implemented embodiment, a second error result between the current interaction event correlation degree between the sample target photovoltaic power generation interaction data and the sample target photovoltaic power generation interaction data and the debugged sample interaction event correlation degree can be calculated through a binary classification quantitative evaluation thread. Further, on the basis that the photovoltaic power generation interaction data interaction events of the photovoltaic power generation interaction data tuple are consistent, the current interaction event correlation degree of the corresponding photovoltaic power generation interaction data tuple can be configured as a first previously configured limit value, and on the basis that the photovoltaic power generation interaction data interaction events of the photovoltaic power generation interaction data tuple are different, the current interaction event correlation degree of the corresponding photovoltaic power generation interaction data tuple can be configured as an abnormal situation.

[0099] According to the above content, sample photovoltaic power generation interaction data representations of multiple groups of sample photovoltaic power generation interaction data and the degree of sample interaction event correlation of at least one group of sample photovoltaic power generation interaction data binary groups are obtained, and the multiple groups of sample photovoltaic power generation interaction data include sample target photovoltaic power generation interaction data and sample target photovoltaic power generation interaction data, and every two groups of sample photovoltaic power generation interaction data in the multiple groups of sample photovoltaic power generation interaction data form a group of sample photovoltaic power generation interaction data binary groups, and the degree of sample interaction event correlation indicates the possibility that the sample photovoltaic power generation interaction data binary groups belong to a consistent photovoltaic power generation interaction data interaction event, and according to the first unit of the photovoltaic power generation interaction data evaluation thread, the sample photovoltaic power generation interaction data representations of the multiple groups of sample photovoltaic power generation interaction data are debugged through the degree of sample interaction event correlation, so as to obtain the photovoltaic power generation interaction event evaluation situation of the sample target photovoltaic power generation interaction data through the debugged sample photovoltaic power generation interaction data representation according to the second unit of the photovoltaic power generation interaction data evaluation thread, and then correct the sample coefficient of the photovoltaic power generation interaction data evaluation thread through the photovoltaic power generation interaction event evaluation situation and the photovoltaic power generation interaction data interaction event recorded by the sample target photovoltaic power generation interaction data. Therefore, by debugging the sample photovoltaic power generation interaction data representation according to the correlation degree of the sample interaction events, the sample photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of the consistent photovoltaic power generation interaction data interaction events can be made close to similar, and the sample photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of the photovoltaic power generation interaction data interaction events with differences can be deleted, thereby improving the stability of the sample photovoltaic power generation interaction data representation, and facilitating the collection of photovoltaic power generation interaction event evaluation conditions of the sample photovoltaic power generation interaction data representation, thereby improving the accuracy of the photovoltaic power generation interaction data evaluation thread.

[0100] In this embodiment, the photovoltaic power generation interactive data evaluation thread includes at least one extraction unit arranged and distributed, and each extraction unit includes a first unit and a second unit. Specifically, the following steps may be included.

[0101] Step 601: Obtain sample photovoltaic power generation interaction data representations of multiple groups of sample photovoltaic power generation interaction data and the degree of correlation of sample interaction events of at least one group of sample photovoltaic power generation interaction data binary groups.

[0102] In this embodiment, multiple groups of sample photovoltaic power generation interaction data include sample target photovoltaic power generation interaction data and sample target photovoltaic power generation interaction data. Every two groups of sample photovoltaic power generation interaction data in the multiple groups of sample photovoltaic power generation interaction data form a group of sample photovoltaic power generation interaction data tuples. The degree of correlation of sample interaction events indicates the possibility that the sample photovoltaic power generation interaction data tuples belong to consistent photovoltaic power generation interaction data interaction events.

[0103] Step 602: According to the first unit of the lth extraction unit, the sample photovoltaic power generation interaction data representation of the plurality of groups of sample photovoltaic power generation interaction data is debugged through the correlation degree of the sample interaction events.

[0104] Step 603: According to the second unit of the lth extraction unit, the sample photovoltaic power generation interactive data after debugging is used to perform power generation prediction processing to obtain a sample power generation prediction result.

[0105] In this embodiment, the sample power generation prediction result includes a first sample power generation description result in which the sample target photovoltaic power generation interaction data belongs to at least one target interaction event and a second sample power generation description result in which the sample target photovoltaic power generation interaction data belongs to at least one target interaction event. The at least one target interaction event is a photovoltaic power generation interaction data interaction event to which the sample target photovoltaic power generation interaction data belongs.

[0106] Step 604: Based on the first sample power generation description result, the evaluation of the photovoltaic power generation interaction event of the sample target photovoltaic power generation interaction data tuple corresponding to the lth extraction unit is obtained.

[0107] For further limitation, the x-th photovoltaic power generation interaction data tuple corresponding to the photovoltaic power generation interaction event evaluation situation of the l-th extraction unit may be recorded as , which represents a set of at least one photovoltaic power generation interaction data interaction event.

[0108] Step 605: Debug the correlation degree of sample interaction events through the first sample power generation description result and the second sample power generation description result.

[0109] step606: Obtain the first error result bound to the lth extraction unit through the photovoltaic power generation interaction data interaction event recorded by the first sample power generation description result and the sample target photovoltaic power generation interaction data, and obtain the second error result of the lth extraction unit through the current interaction event correlation degree between the sample target photovoltaic power generation interaction data and the sample target photovoltaic power generation interaction data and the sample interaction event correlation degree after debugging.

[0110] Therefore, the second error result bound to the lth extraction unit can be obtained through the binary classification quantitative evaluation thread by the current interaction event correlation degree between the sample target photovoltaic power generation interaction data and the sample target photovoltaic power generation interaction data and the debugged sample interaction event correlation degree. In order to make further limitations, the first error result is calculated only for the sample target photovoltaic power generation interaction data.

[0111] Step 607: Determine whether the current extraction unit is the target layer extraction unit of the photovoltaic power generation interactive data evaluation thread, if not, execute step 608, otherwise execute step 609.

[0112] step608: Execute step602 and subsequent steps again.

[0113] On the basis that the current extraction unit is not the target first-layer extraction unit of the photovoltaic power generation interactive data evaluation thread, l can be added by 1, so that the next extraction unit of the current extraction unit is used to execute again the steps of debugging the sample photovoltaic power generation interactive data representation of multiple groups of sample photovoltaic power generation interactive data according to the first unit of the photovoltaic power generation interactive data evaluation thread and the subsequent steps through the sample interaction event correlation degree, until the current extraction unit is the target first-layer extraction unit of the photovoltaic power generation interactive data evaluation thread. In this process, the first error result and the second error result bound to each extraction unit of the photovoltaic power generation interactive data evaluation thread can be obtained.

[0114] step609: weight processing is performed on the first error result bound to each extraction unit using the first weight coefficient bound to each extraction unit to obtain a first error weight result.

[0115] Step 610: weight processing is performed on the second error result bound to each extraction unit by using the second weight coefficient bound to each extraction unit to obtain a second error weight result.

[0116] step611: Correct the sample coefficient of the photovoltaic power generation interactive data evaluation thread based on the first error weight result and the second error weight result.

[0117] Furthermore, the first error weight result and the second error weight result can be weighted by weight coefficients respectively bound to the first error weight result and the second error weight result to obtain error weight results, and the sample coefficient can be corrected by the error weight results. Furthermore, the weight coefficient bound to the first error weight result can be configured as 2, and the weight coefficient bound to the second error weight result can also be configured as 2, to indicate that the first error weight result and the second error weight result have the same importance when correcting the sample coefficient. The photovoltaic power generation interaction data evaluation thread is configured to include at least one extraction unit with an arrangement distribution, and each extraction unit includes a first unit and a second unit, and on the basis that the current extraction unit is not the target layer extraction unit of the photovoltaic power generation interaction data evaluation thread, the next extraction unit of the current extraction unit is used to execute again the steps of debugging the sample photovoltaic power generation interaction data representation according to the first unit of the photovoltaic power generation interaction data evaluation thread through the degree of association of the sample interaction event and the subsequent steps, until the current extraction unit is the accurate information of the photovoltaic power generation interaction data evaluation thread extraction unit, so that the first error result bound to each extraction unit is weighted by the first weight coefficient bound to each extraction unit to obtain the first error weight result, and the sample coefficient can be corrected by the error weight result. The second weight coefficient bound to an extraction unit will weight the second error result bound to each extraction unit respectively to obtain the second error weight result, and then correct the sample coefficient of the photovoltaic power generation interaction data evaluation thread based on the first error weight result and the second error weight result. The later the extraction unit is in the photovoltaic power generation interaction data evaluation thread, the larger the first weight coefficient and the second weight coefficient bound to the extraction unit are. The error result bound to the extraction unit of each unit in the photovoltaic power generation interaction data evaluation thread can be obtained, and the weight coefficient bound to the more lagged extraction unit is configured to be larger, so that the data processed by each unit extraction unit can be fully used to correct the sample coefficient of the photovoltaic power generation interaction data evaluation, which is beneficial to improving the accuracy of the photovoltaic power generation interaction data evaluation thread.

[0118] Based on the above, please refer to Figure 2 , a photovoltaic power generation evaluation device 200 is provided, which is applied to an e-commerce website sales data evaluation system, and the device comprises: The data acquisition module 210 is used to acquire the photovoltaic power generation interaction data representation of multiple groups of photovoltaic power generation interaction data and the degree of association of interaction events of at least one group of photovoltaic power generation interaction data binary groups; wherein the multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data, and every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary groups, and the degree of association of interaction events indicates the possibility that the photovoltaic power generation interaction data binary groups belong to consistent photovoltaic power generation interaction data interaction events; The situation evaluation module 220 is used to debug the photovoltaic power generation interaction data representation of the multiple groups of photovoltaic power generation interaction data according to the correlation degree of the interaction events; and obtain the photovoltaic power generation interaction event evaluation situation of the target photovoltaic power generation interaction data through the debugged photovoltaic power generation interaction data representation.

[0119] Based on the above, please refer to Figure 3 , shows a photovoltaic power generation evaluation system 300, including a processor 310 and a memory 320 that communicate with each other, and the processor 310 is used to read and execute a computer program from the memory 320 to implement the above method.

[0120] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0121] In summary, based on the above scheme, photovoltaic power generation interaction data representations of multiple groups of photovoltaic power generation interaction data and the degree of interaction event correlation of at least one group of photovoltaic power generation interaction data binary groups are obtained, and the multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data. Every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary groups. The degree of interaction event correlation indicates the possibility that the photovoltaic power generation interaction data binary groups belong to consistent photovoltaic power generation interaction data interaction events, and through the degree of interaction event correlation, the photovoltaic power generation interaction data representation is debugged, so that the photovoltaic power generation interaction event evaluation of the target photovoltaic power generation interaction data is obtained through the debugged photovoltaic power generation interaction data representation. Therefore, by debugging the photovoltaic power generation interaction data representation through the correlation degree of interaction events, the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of consistent photovoltaic power generation interaction data interaction events can be made close to similar, and the photovoltaic power generation interaction data representations bound to the photovoltaic power generation interaction data of photovoltaic power generation interaction data interaction events with differences can be deleted, thereby improving the stability of the photovoltaic power generation interaction data representation, and facilitating the collection of photovoltaic power generation interaction event evaluation conditions of the photovoltaic power generation interaction data representation, thereby improving the accuracy of the photovoltaic power generation interaction data event evaluation.

[0122] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0123] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or may be any other beneficial effects that may be obtained.

[0124] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only determined as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and corrections to the present application. Such modifications, improvements and corrections are suggested in the present application, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present application.

[0125] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0126] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0127] A computer storage medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0128] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0129] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0130] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0131] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0132] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the description, definition, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the description, definition, and / or use of terms in this application shall prevail.

[0133] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, the alternative configurations of the embodiments of the present application, which are determined as examples rather than limitations, may be considered consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0134] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A photovoltaic power generation evaluation method, characterized in that: The method at least comprises: Obtaining photovoltaic power generation interaction data representations of multiple groups of photovoltaic power generation interaction data and the degree of association of interaction events of at least one group of photovoltaic power generation interaction data binary groups; wherein the multiple groups of photovoltaic power generation interaction data include target photovoltaic power generation interaction data and target photovoltaic power generation interaction data, and every two groups of photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data constitute a group of photovoltaic power generation interaction data binary groups, and the degree of association of interaction events indicates the possibility that the photovoltaic power generation interaction data binary groups belong to consistent photovoltaic power generation interaction data interaction events; The photovoltaic power generation interaction data representation of the multiple groups of photovoltaic power generation interaction data is debugged according to the correlation degree of the interaction events; and the photovoltaic power generation interaction event evaluation status of the target photovoltaic power generation interaction data is obtained through the debugged photovoltaic power generation interaction data representation.

2. The method according to claim 1, characterized in that: The step of debugging the photovoltaic power generation interactive data representation of the plurality of sets of photovoltaic power generation interactive data according to the degree of association of the interactive events comprises: Obtaining a first photovoltaic power generation interactive data representation and a second photovoltaic power generation interactive data representation through the interactive event association degree and the photovoltaic power generation interactive data representation; The first photovoltaic power generation interactive data representation and the second photovoltaic power generation interactive data representation are optimized to obtain a debugged photovoltaic power generation interactive data representation.

3. The method according to claim 1, characterized in that: The photovoltaic power generation interaction data after debugging represents the photovoltaic power generation interaction event evaluation of the target photovoltaic power generation interaction data, including: The photovoltaic power generation interaction data representation after debugging is used to perform power generation prediction processing to obtain a power generation prediction result, wherein the power generation prediction result includes a first power generation description result that the target photovoltaic power generation interaction data belongs to at least one target interaction event, and the target interaction event is a photovoltaic power generation interaction data interaction event to which the target photovoltaic power generation interaction data belongs; According to the first power generation description result, the photovoltaic power generation interaction event evaluation situation is obtained; wherein the photovoltaic power generation interaction event evaluation situation is used to characterize the photovoltaic power generation interaction data interaction event to which the target photovoltaic power generation interaction data belongs.

4. The method according to claim 3, characterized in that The power generation prediction result also includes a second power generation description result in which the target photovoltaic power generation interaction data belongs to the at least one target interaction event; Before obtaining the photovoltaic power generation interaction event evaluation situation according to the first power generation description result, the method further includes: On the basis that the predicted value of the power generation prediction processing satisfies the previously configured threshold, debugging the degree of association of the interaction events through the power generation prediction result, and again performing the step of debugging the photovoltaic power generation interaction data representation of the multiple sets of photovoltaic power generation interaction data through the degree of association of the interaction events; The obtaining of the photovoltaic power generation interaction event evaluation situation based on the first power generation description result includes: on the basis that the predicted value of the power generation prediction processing does not meet the previously configured threshold, obtaining the photovoltaic power generation interaction event evaluation situation based on the first power generation description result.

5. The method according to claim 4, characterized in that The degree of association of the interaction event includes: each group of photovoltaic power generation interaction data binary group belongs to the target power generation description result of the consistent photovoltaic power generation interaction data interaction event; the debugging of the degree of association of the interaction event through the power generation prediction result includes: Determine each group of the photovoltaic power generation interaction data in the multiple groups of photovoltaic power generation interaction data as the current photovoltaic power generation interaction data, and determine the photovoltaic power generation interaction data tuple containing the current photovoltaic power generation interaction data as the current photovoltaic power generation interaction data tuple; Acquire the splicing of the target power generation description results of all the current photovoltaic power generation interaction data tuples of the current photovoltaic power generation interaction data, and determine them as the function processing result of the current photovoltaic power generation interaction data; And, obtaining the target power generation description result of each group of the current photovoltaic power generation interaction data tuples belonging to the consistent photovoltaic power generation interaction data interaction event through the first power generation description result and the second power generation description result; The target power generation capacity description result of each group of the current photovoltaic power generation interaction data binary group is corrected by respectively using the function processing result and the target power generation capacity description result.

6. The method according to claim 5, characterized in that The photovoltaic power generation interactive data after debugging is used to perform power generation prediction processing to obtain a power generation prediction result, including: The photovoltaic power generation interaction data after debugging indicates that the power generation prediction interaction event to which the photovoltaic power generation interaction data belongs is predicted, wherein the power generation prediction interaction event belongs to the at least one target interaction event; For each group of photovoltaic power generation interaction data tuples, the interaction event comparison situation and the description matching degree of the photovoltaic power generation interaction data tuples are obtained, and the first correlation degree between the interaction event comparison situation and the description matching degree of the photovoltaic power generation interaction data tuples is obtained; wherein the interaction event comparison situation indicates whether the power generation prediction interaction events to which the photovoltaic power generation interaction data tuples belong are consistent, and the description matching degree indicates the correlation degree between the photovoltaic power generation interaction data representations of the photovoltaic power generation interaction data tuples; Furthermore, based on the power generation prediction interaction event to which the target photovoltaic power generation interaction data belongs and the target interaction event, a second degree of correlation between the target photovoltaic power generation interaction data regarding the power generation prediction interaction event and the target interaction event is obtained; and the power generation prediction result is obtained through the first degree of correlation and the second degree of correlation.

7. The method according to claim 6, characterized in that On the basis that the interaction event comparison is consistent with the power generation prediction interaction event, the description matching degree is related to the first correlation degree; on the basis that the interaction event comparison is different from the power generation prediction interaction event, the description matching degree is not related to the first correlation degree, and the second correlation degree when the power generation prediction interaction event is consistent with the target interaction event exceeds the second correlation degree when the power generation prediction interaction event is different from the target interaction event; The photovoltaic power generation interactive data after debugging indicates that the power generation prediction interactive event to which the photovoltaic power generation interactive data belongs includes: According to the score evaluation thread, the photovoltaic power generation interaction data after debugging represents the power generation prediction interaction event to which the photovoltaic power generation interaction data belongs; Wherein, obtaining the power generation prediction result through the first correlation degree and the second correlation degree includes: According to the decision function, the power generation prediction result is obtained through the first correlation degree and the second correlation degree.

8. The method according to claim 4, characterized in that The previously configured threshold value includes: a predicted value for executing the power generation amount prediction process does not satisfy a previously configured determination value.

9. The method according to claim 1, characterized in that: The method further comprises: On the basis that the photovoltaic power generation interaction data tuple belongs to a consistent photovoltaic power generation interaction data interaction event, determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as a first limit value configured in advance; On the basis that the photovoltaic power generation interaction data tuple belongs to a photovoltaic power generation interaction data interaction event with differences, determining the initial interaction event correlation degree of the photovoltaic power generation interaction data tuple as a previously configured second limit value; On the basis that at least one of the photovoltaic power generation interaction data tuples is the target photovoltaic power generation interaction data, the initial interaction event association degree of the photovoltaic power generation interaction data tuple is determined as a previously configured third limit value between the previously configured second limit value and the previously configured first limit value.

10. A photovoltaic power generation evaluation system, characterized in that: It comprises a processor and a memory communicating with each other, the processor is used to call a computer program from the memory, and implement the method according to any one of claims 1 to 9 by running the computer program.

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