Information query method and device applied to photovoltaic power generation and electronic equipment

Abstract

The embodiment of the invention discloses an information query method and device applied to photovoltaic power generation and electronic equipment. A specific embodiment of the method comprises the following steps: in response to received power generation data query information corresponding to a target photovoltaic power generation field, generating corresponding power generation data query enhancement prompt information according to a data query guide character corresponding to the power generation data query information; inputting the power generation data query enhancement prompt information into a power generation data query supplementary model to obtain a power generation data query supplementary information table; displaying the power generation data query supplementary information table on a query interface, and in response to detection of a click operation acting on any power generation data query supplementary information, according to the clicked power generation data query supplementary information, in the photovoltaic power generation data information base, displaying the power generation data query supplementary information table in the photovoltaic power generation data information base according to the clicked power generation data query supplementary information; and querying a photovoltaic power generation data information set corresponding to the power generation data query supplementary information. According to the embodiment, the query efficiency is improved, repeated query is avoided, and occupation of computing resources is reduced.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the computer field, and in particular to an information query method, device and electronic device applied to photovoltaic power generation. Background Art

[0002] With the continuous development of photovoltaic power generation, the effective control of photovoltaic power generation equipment has become one of the main development directions. For the query of photovoltaic power generation data information corresponding to photovoltaic power generation equipment, the method usually adopted is: by traversing the data system of each photovoltaic power generation equipment, to query the relevant data information of photovoltaic power generation.

[0003] However, when using the above method to query photovoltaic power generation data information, the following technical problems often occur:

[0004] The input power generation data query information is not supplemented, resulting in inaccurate information for the input query guide words, low query efficiency, and a large amount of computing resources wasted.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose information query methods, devices, electronic devices, and computer-readable media applied to photovoltaic power generation to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide an information query method applied to photovoltaic power generation, the method comprising: in response to receiving power generation data query information corresponding to a target photovoltaic power plant, generating power generation data query enhanced prompt information corresponding to the data query guide character according to the data query guide character corresponding to the power generation data query information; inputting the power generation data query enhanced prompt information into a pre-trained power generation data query supplementary model to obtain a power generation data query supplementary information table; displaying the power generation data query supplementary information table on a query interface, and in response to detecting a click operation on any power generation data query supplementary information in the power generation data query supplementary information table, querying a photovoltaic power generation data information set corresponding to the power generation data query supplementary information in a pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplementary information; and displaying the photovoltaic power generation data information set on the query interface.

[0009] In a second aspect, some embodiments of the present disclosure provide an information query device applied to photovoltaic power generation, the device comprising: a generation unit, configured to, in response to receiving power generation data query information corresponding to a target photovoltaic power plant, generate power generation data query enhanced prompt information corresponding to the data query guide character according to the data query guide character corresponding to the power generation data query information; an input unit, configured to input the power generation data query enhanced prompt information into a pre-trained power generation data query supplementary model to obtain a power generation data query supplementary information table; a query unit, configured to display the power generation data query supplementary information table on a query interface, and in response to detecting a click operation on any power generation data query supplementary information in the power generation data query supplementary information table, query the photovoltaic power generation data information set corresponding to the power generation data query supplementary information in a pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplementary information; and a display unit, configured to display the photovoltaic power generation data information set on the query interface.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0012] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the information query method applied to photovoltaic power generation of some embodiments of the present disclosure, the power generation data query supplement model can be used to accurately supplement the data query guide characters of the power generation data query information. Thereby, the query efficiency is improved and the occupation of computing resources is reduced. Specifically, the reason for wasting a lot of computing resources is that the input power generation data query information is not supplemented, resulting in inaccurate information for the input query guide words and low query efficiency. Based on this, the information query method applied to photovoltaic power generation of some embodiments of the present disclosure, first, in response to receiving the power generation data query information corresponding to the target photovoltaic power plant, according to the data query guide characters corresponding to the above power generation data query information, the power generation data query enhancement prompt information corresponding to the above data query guide characters is generated. Then, the above power generation data query enhancement prompt information is input into the pre-trained power generation data query supplement model to obtain the power generation data query supplement information table. Therefore, using the pre-trained power generation data query supplement model, each power generation data query supplement information that the user may select can be accurately generated for easy click query. Next, the above-mentioned power generation data query supplementary information table is displayed on the query interface, and in response to detecting a click operation acting on any power generation data query supplementary information in the above-mentioned power generation data query supplementary information table, according to the clicked power generation data query supplementary information, the photovoltaic power generation data information set corresponding to the above-mentioned power generation data query supplementary information is queried in the pre-constructed photovoltaic power generation data information library. In this way, the corresponding photovoltaic power generation data information set can be quickly queried. Finally, the above-mentioned photovoltaic power generation data information set is displayed on the query interface. In this way, the power generation data query supplementary model can be used to accurately supplement the data query guide characters of the power generation data query information. Thereby, the low query efficiency is improved, repeated queries are avoided, and the occupation of computing resources is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flow chart of some embodiments of the information query method applied to photovoltaic power generation according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of an information query device applied to photovoltaic power generation according to the present disclosure;

[0016] Figure 3is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure;

[0017] Figure 4 It is a schematic scenario diagram of a photovoltaic power generation data information set in the information query method applied to photovoltaic power generation according to the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0020] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] Figure 1 1 is a flowchart of some embodiments of the information query method applied to photovoltaic power generation according to the present disclosure. 100 of some embodiments of the information query method applied to photovoltaic power generation according to the present disclosure is shown. The information query method applied to photovoltaic power generation includes the following steps:

[0025] Step 101, in response to receiving power generation data query information corresponding to a target photovoltaic power plant, generating power generation data query enhanced prompt information corresponding to the data query guide character according to the data query guide character corresponding to the power generation data query information.

[0026] In some embodiments, the execution subject (e.g., computing device) of the information query method applied to photovoltaic power generation may generate enhanced prompt information of power generation data query corresponding to the data query guide character in response to receiving the power generation data query information corresponding to the target photovoltaic power plant, according to the data query guide character corresponding to the above power generation data query information. The power generation data query information may refer to information for data query of each photovoltaic power generation device in the target photovoltaic power plant. The target photovoltaic power plant may be a power plant including each photovoltaic power generation device. The data query guide character may refer to the query guide character input in real time corresponding to the power generation data query information. For example, the data query guide character may refer to a query search prefix. The enhanced prompt information of power generation data query may be a prompt word for enhancing the query supplement capability for the data query guide character. For example, the enhanced prompt information of power generation data query includes: multiple query supplement feature information corresponding to the power generation data. The query supplement feature information may be feature content corresponding to the query supplement feature. The query supplement feature may be a feature related to the query supplement operation. For example, the search supplement feature may be: query time, query power generation data information, data query guide character, guide character supplement list corresponding to the data query guide character, and query supplement association list corresponding to the data query guide character.

[0027] Step 102: input the above-mentioned power generation data query enhanced prompt information into a pre-trained power generation data query supplement model to obtain a power generation data query supplement information table.

[0028] In some embodiments, the execution subject may input the power generation data query enhancement prompt information into a pre-trained power generation data query supplement model to obtain a power generation data query supplement information table. The query supplement information in the power generation data query supplement information table is the complete query information after query supplementation for the data query guide character. The power generation data query supplement model may be a pre-trained large language model (LLM) for query supplementation.

[0029] Optionally, a photovoltaic power generation data query supplementary log set is obtained. The photovoltaic power generation data query supplementary log may be obtained by mining the click log of the clicked query supplementary information. That is, the photovoltaic power generation data query supplementary log may be the query supplementary information clicked by the user selected from the list of all query supplementary information under all previous data query guide characters.

[0030] Optionally, data query guide characters and corresponding character supplement click information are selected from the above photovoltaic power generation data query supplement log set to obtain a data query guide character supplement table.

[0031] Optionally, for each data query guide character in the above-mentioned data query guide character supplement table, the guide character related information corresponding to the above-mentioned data query guide character is supplemented to the initial power generation query enhanced prompt information sample template to generate an initial power generation query enhanced prompt information sample. Wherein, the above-mentioned initial power generation query enhanced prompt information sample template includes: multiple data query supplementary features to be supplemented with data. Wherein, the initial power generation query enhanced prompt information sample template can be a query enhanced prompt sample template to be supplemented with feature information. Feature information supplementation can be feature content under the supplementary search supplementary feature. The initial power generation query enhanced prompt information sample template can be a prompt information template filled with feature information to be filled. As an example, for each data query guide character, the query supplementary information corresponding to the above-mentioned data query guide character and the feature content under the multiple data query supplementary features are determined. Then, the data query guide character, the query supplementary information and the feature content under the multiple data query supplementary features are filled into the initial power generation query enhanced prompt information sample template to generate an initial power generation query enhanced prompt information sample.

[0032] Optionally, each initial power generation query enhanced prompt information sample is proofread to obtain a power generation query enhanced prompt information sample set. The power generation query enhanced prompt information sample may be a sample after each query supplementary feature information is filled. For example, the execution subject may send the initial power generation query enhanced prompt information sample to a sample content proofreading terminal for manual proofreading to generate a corresponding power generation query enhanced prompt information sample.

[0033] It should be noted that the multiple query supplementary feature information includes: data query guide characters, query click information, and a collaborative filtering query information table for the data query guide characters. The corresponding power generation query enhanced prompt information sample can be the following sample: "<data query guide characters>; <query click information>; <collaborative filtering query information list>".

[0034] The power generation data query supplement model can be trained through the following steps:

[0035] In the first step, according to the photovoltaic power generation query behavior data set corresponding to the photovoltaic power generation data query field, the first initial power generation data query supplement model is trained to obtain the second initial power generation data query supplement model. Photovoltaic power generation query behavior data may be data after executing the query behavior under photovoltaic power generation. For example, photovoltaic power generation query behavior data may be data of "the Chinese meaning of input" input by the user. The corresponding query behavior may be the behavior of inputting guide characters or inputting query information. The first initial power generation data query supplement model may be a large language model (LLM) that has not yet been trained. The second initial power generation data query supplement model may be a large language model trained for the photovoltaic power generation query behavior data set. The input query information may be the query question to be input. For example, the first initial power generation data query supplement model may be trained using a conventional model training method (e.g., a deep model training method) to obtain a second initial power generation data query supplement model.

[0036] The first step may include the following sub-steps:

[0037] The first sub-step is to extract photovoltaic power generation query information and photovoltaic power generation type information from each photovoltaic power generation query behavior data in the photovoltaic power generation query behavior data set. The photovoltaic power generation query information may be the query information entered by the user in the query box. The photovoltaic power generation type information may be the type information of the photovoltaic power generation data entered by the user.

[0038] The second sub-step is to pre-process the obtained photovoltaic power generation query information set and photovoltaic power generation type information set to generate a pre-processed query information set and a pre-processed power generation type information set. For example, the pre-processing may include: supplementing missing values ​​and removing null values.

[0039] The third sub-step is to obtain a photovoltaic power generation general data set corresponding to the photovoltaic power generation data query field, wherein the photovoltaic power generation general data set may be a photovoltaic power generation data set commonly used in the photovoltaic power generation data query field.

[0040] The fourth sub-step is to adjust the data proportion of the preprocessed query information set, the preprocessed power generation type information set and the photovoltaic power generation general data set to obtain a proportion data set. For example, the proportion data set may include: the preprocessed query information set, the preprocessed item information set and the general data subset of the field. The data ratio between the preprocessed search information set, the preprocessed power generation type information set and the photovoltaic power generation general data set is 2:2:1.

[0041] The fifth sub-step is to perform model training on the first initial power generation data query supplement model according to the above-mentioned percentage data set to obtain a second initial power generation data query supplement model. Here, the model training method may not be limited.

[0042] In the second step, the second initial power generation data query supplement model is trained according to a preset power generation query enhancement prompt information sample set to obtain a third initial power generation data query supplement model. The power generation query enhancement prompt information sample includes: a plurality of data query supplement feature information. The data query supplement feature information may be feature content corresponding to the query supplement feature. The query supplement feature may be a feature related to the search supplement operation. Here, there is no limitation on the way of training the second initial power generation data query supplement model.

[0043] In the third step, in response to determining that the model training effect corresponding to the third initial power generation data query supplement model reaches the expected effect, the third initial power generation data query supplement model is determined as the trained power generation data query supplement model. The expected effect may be a preset expectation of the accuracy of automatic supplementation of the power generation data query supplement model. For example, the expected effect may be that the accuracy of supplementation is greater than 90%. The model training effect may be the actual result (e.g., 91%) of the model prediction effect of the third initial power generation data query supplement model.

[0044] In the fourth step, in response to determining that the model training effect does not achieve the expected effect, the third initial power generation data query supplement model is trained according to the power generation query enhancement prompt information test sample set corresponding to the above model training effect to obtain the trained third initial power generation data query supplement model. For example, the third initial power generation data query supplement model can be trained by a deep neural network model training method to obtain the trained third initial power generation data query supplement model.

[0045] The fourth step may include the following sub-steps:

[0046] In the first sub-step, a positive sample set of power generation query enhancement prompt information representing a positive supplementary effect and a negative sample set of power generation query enhancement prompt information representing a negative supplementary effect are selected from the above-mentioned power generation query enhancement prompt information test sample set. Among them, the positive supplementary effect can represent that the query supplementary information obtained by inputting the corresponding test sample into the third initial power generation data query supplementary model meets the effect of user needs. The negative supplementary effect represents that the query supplementary information obtained by inputting the corresponding test sample into the third initial power generation data query supplementary model does not meet the effect of user needs. That is, the test sample is input into the third initial power generation data query supplementary model, and the query supplementary information list output is not clicked by the user.

[0047] In the second sub-step, a target power generation query enhancement prompt information sample set under the photovoltaic power generation query operation is generated according to the power generation query enhancement prompt information positive sample set and the power generation query enhancement prompt information negative sample set.

[0048] The third sub-step is to train the third initial power generation data query supplement model according to the target power generation query enhanced prompt information sample set to obtain the trained third initial power generation data query supplement model.

[0049] In the fifth step, the trained third initial power generation data query supplement model is used as the third initial power generation data query supplement model, and the third initial power generation data query supplement model is trained again.

[0050] Therefore, through multiple rounds of model training, the power generation data query supplement model can learn more knowledge in the field of photovoltaic power generation, and can also combine more query supplement features to predict more accurate query supplement information. Through multiple rounds of model testing and adaptive training, a power generation data query supplement model that outputs more accurate query supplement information can be obtained.

[0051] Step 103: display the above-mentioned power generation data query supplementary information table on the query interface, and in response to detecting a click operation on any power generation data query supplementary information in the above-mentioned power generation data query supplementary information table, query the photovoltaic power generation data information set corresponding to the above-mentioned power generation data query supplementary information in a pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplementary information.

[0052] In some embodiments, the execution subject may display the above-mentioned power generation data query supplementary information table on the query interface, and in response to detecting a click operation on any power generation data query supplementary information in the above-mentioned power generation data query supplementary information table, query the photovoltaic power generation data information set corresponding to the above-mentioned power generation data query supplementary information in the pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplementary information. The query interface may refer to a query page for receiving the power generation data query information input by the user. The photovoltaic power generation data information library may refer to a database storing various types of photovoltaic power generation data corresponding to the target photovoltaic power plant. For example, the photovoltaic power generation data information set corresponding to the power generation data query supplementary information may be retrieved in the above-mentioned photovoltaic power generation data information library. Photovoltaic power generation data information may include but is not limited to: photovoltaic power generation equipment information, geographic location data, conversion efficiency of solar cells of photovoltaic power generation equipment, open circuit voltage, short circuit current, maximum output power, operating temperature range, etc.

[0053] The photovoltaic power generation data information database can be constructed by the following steps:

[0054] The first step is to obtain the photovoltaic power generation data set of each photovoltaic power generation equipment in the target photovoltaic power plant. Photovoltaic power generation equipment may include but is not limited to: photovoltaic modules, inverters, junction boxes, control cabinets, energy storage batteries, transformers. Photovoltaic power generation data may include: device connection information and device basic information corresponding to photovoltaic power generation equipment. Photovoltaic power generation equipment in each photovoltaic power generation equipment corresponds to photovoltaic power generation data in the photovoltaic power generation data set.

[0055] In the second step, based on the photovoltaic power generation data set, generate the association graphs of each photovoltaic power generation device corresponding to the target photovoltaic power generation field. Among them, each photovoltaic power generation device association graph has a corresponding photovoltaic power generation data query element. There are multiple corresponding and full photovoltaic power generation data query elements for photovoltaic power generation equipment. Photovoltaic power generation data query elements can be the minimum element information for users to query photovoltaic power generation data. For example, multiple photovoltaic power generation data query elements may include: photovoltaic power generation equipment identification element, photovoltaic power generation equipment type element, photovoltaic power generation equipment operation status element, photovoltaic power generation equipment specification element, photovoltaic power generation location range element. Each photovoltaic power generation equipment association graph may include: photovoltaic power generation equipment association graph corresponding to photovoltaic power generation equipment identification element, photovoltaic power generation equipment association graph corresponding to photovoltaic power generation equipment specification element, photovoltaic power generation equipment association graph corresponding to photovoltaic power generation location range element, photovoltaic power generation equipment association graph corresponding to photovoltaic power generation equipment operation status element, photovoltaic power generation equipment association graph corresponding to photovoltaic power generation equipment type element. For example, for each photovoltaic power generation data query element, the element set corresponding to the photovoltaic power generation data query element in the photovoltaic power generation data set can be determined. Then, based on the element set, the photovoltaic power generation equipment association graph corresponding to the photovoltaic power generation data query element is generated in the form of spatial index.

[0056] The third step is to perform graph verification on the above-mentioned photovoltaic power generation equipment association graphs to generate graph verification results. The graph verification results can indicate whether there are errors in the node positions and node connections in the association graphs of each photovoltaic power generation equipment. For example, the node positions and node connections corresponding to each graph node in each photovoltaic power generation equipment association graph can be verified to obtain graph verification results.

[0057] In the fourth step, in response to determining that the above-mentioned diagram verification result is correct, the above-mentioned photovoltaic power generation equipment association diagrams and the corresponding photovoltaic power generation data query elements are stored in a predetermined format in a pre-set database as a photovoltaic power generation data information library.

[0058] The fourth step may include the following sub-steps:

[0059] The first sub-step is to determine storage resource call information, wherein the storage resource call information may be the size of the call resource corresponding to the storage operation executed in a unit of time.

[0060] In the second sub-step, in response to determining that the storage resource call information does not meet the preset storage resource condition, the graph storage mode corresponding to each photovoltaic power generation device association graph is determined to be a target storage mode and a target number of levels. The preset storage resource condition indicates that the storage resource call information is less than the predetermined call resource. The target storage mode may be a storage mode that indicates that the photovoltaic power generation device association graph is stored according to the number of calls within a preset time period. The target number of levels may be the number of graph levels into which each photovoltaic power generation device association graph is divided. For example, if the target number of levels is 2, each photovoltaic power generation device association graph is divided into 2 graph levels.

[0061] The third sub-step is to perform the following processing steps for each photovoltaic power generation equipment association diagram in the above photovoltaic power generation equipment association diagrams:

[0062] 1. Determine the diagram call times sequence corresponding to the photovoltaic power generation equipment association diagram, wherein the diagram call times may be the times the photovoltaic power generation equipment association diagram is called within a preset time.

[0063] 2. Preprocess each graph call number in the graph call number sequence to generate a preprocessed graph call number sequence. The preprocessing may include: removing the maximum value and removing the minimum value.

[0064] 3. In response to determining that the above-mentioned graph storage mode is the target storage mode, the call number sequence corresponding to the above-mentioned photovoltaic power generation equipment association graph within a preset time period is determined according to the above-mentioned pre-processed graph call number sequence. For example, a time series neural network model (for example, a recurrent neural network model) can be used to determine the call number sequence corresponding to the above-mentioned photovoltaic power generation equipment association graph within a preset time period.

[0065] 4. Determine the total number of calls and the average number of calls corresponding to the above call number sequence. The total number of calls may be the sum of the numbers of calls in the call number sequence. The average number of calls may be the mean of the numbers of calls in the call number sequence.

[0066] The fourth sub-step is to perform classification processing on the above-mentioned photovoltaic power generation equipment association diagrams according to the total number of calls, the average number of calls and the target number of classifications, and obtain storage configuration information that characterizes the association relationship between the hierarchical photovoltaic power generation equipment association atlas sequence and the diagram level group. Among them, the diagram levels corresponding to the hierarchical photovoltaic power generation equipment association atlas are different. For example, first, the photovoltaic power generation equipment association diagrams can be sorted according to the total number of calls and the average number of calls to obtain the photovoltaic power generation equipment association diagram sequence. Then, according to the arrangement order corresponding to the photovoltaic power generation equipment association diagram sequence, the photovoltaic power generation equipment association diagram sequence is evenly divided into the target number of classifications to generate a hierarchical photovoltaic power generation equipment association diagram sequence. According to the arrangement order corresponding to the photovoltaic power generation equipment association diagram sequence, the corresponding diagram level is assigned to each hierarchical photovoltaic power generation equipment association atlas to obtain the diagram level distribution information. Finally, according to the diagram level distribution information, the storage configuration information is generated.

[0067] The fifth sub-step is to store each hierarchical photovoltaic power generation equipment association atlas and photovoltaic power generation data query element subset in the hierarchical photovoltaic power generation equipment association atlas sequence into a corresponding storage node in the database according to the storage configuration information.

[0068] Therefore, the constructed photovoltaic power generation data information database can be used to quickly and efficiently query information on the user's power generation data and query the corresponding photovoltaic power generation data.

[0069] Step 104: display the photovoltaic power generation data information set on a query interface.

[0070] In some embodiments, the execution entity may display the photovoltaic power generation data information set on a query interface. Figure 4 In the example, the photovoltaic power generation data information set can be displayed in the form of a table, which may include: component footprint, fixed bifacial power generation; component footprint, fixed bifacial power generation have corresponding categories, values, units, notes / recommended values. The categories corresponding to the component footprint include: project capacity, geographical latitude, photovoltaic array tilt angle, component model, technical route, peak power, north-south spacing, and component footprint. The categories corresponding to fixed bifacial power generation include: annual tilt surface cumulative irradiation, component first-year attenuation, system efficiency, component aging attenuation rate, first-year utilization hours, and 25 average utilization hours.

[0071] It should be noted that Figure 4 The text and numerical values ​​shown are merely illustrative descriptions and do not represent fixed limitations on the photovoltaic power generation data involved in this application.

[0072] Further references Figure 2As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an information query device applied to photovoltaic power generation. These embodiments of the information query device applied to photovoltaic power generation are similar to Figure 1 Corresponding to the method embodiments shown, the information query device applied to photovoltaic power generation can be specifically applied to various electronic devices.

[0073] like Figure 2 As shown, some embodiments of the information query device 200 applied to photovoltaic power generation include: a generating unit 201, an input unit 202, a query unit 203 and a display unit 204. The generating unit 201 is configured to generate, in response to receiving the power generation data query information corresponding to the target photovoltaic power plant, the power generation data query enhanced prompt information corresponding to the data query guide character according to the data query guide character corresponding to the power generation data query information; the input unit 202 is configured to input the power generation data query enhanced prompt information into the pre-trained power generation data query supplement model to obtain the power generation data query supplement information table; the query unit 203 is configured to display the power generation data query supplement information table on the query interface, and in response to detecting a click operation on any power generation data query supplement information in the power generation data query supplement information table, query the photovoltaic power generation data information set corresponding to the power generation data query supplement information in the pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplement information; the display unit 204 is configured to display the photovoltaic power generation data information set on the query interface.

[0074] It can be understood that the units recorded in the information query device 200 for photovoltaic power generation are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the information query device 200 for photovoltaic power generation and the units contained therein, and will not be described in detail here.

[0075] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0076] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0077] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0078] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0079] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0080] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0081] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to receiving the power generation data query information corresponding to the target photovoltaic power plant, according to the data query guide character corresponding to the power generation data query information, generates the power generation data query enhancement prompt information corresponding to the data query guide character; inputs the power generation data query enhancement prompt information into the pre-trained power generation data query supplement model to obtain the power generation data query supplement information table; displays the power generation data query supplement information table on the query interface; and in response to detecting a click operation on any power generation data query supplement information in the power generation data query supplement information table, according to the clicked power generation data query supplement information, in the pre-constructed photovoltaic power generation data information library, queries the photovoltaic power generation data information set corresponding to the power generation data query supplement information; and displays the photovoltaic power generation data information set on the query interface.

[0082] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0083] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor comprising: a generation unit, an input unit, a query unit and a display unit. Among them, the names of these units do not constitute a limitation on the unit itself in some cases, for example, the generation unit may also be described as "a unit that generates, in response to receiving the power generation data query information corresponding to the target photovoltaic power plant, the power generation data query enhanced prompt information corresponding to the data query guide character corresponding to the above power generation data query information according to the data query guide character corresponding to the above power generation data query information."

[0085] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0086] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. An information query method applied to photovoltaic power generation, comprising: In response to receiving power generation data query information corresponding to a target photovoltaic power plant, generating power generation data query enhanced prompt information corresponding to the data query guide character according to the data query guide character corresponding to the power generation data query information; Inputting the power generation data query enhancement prompt information into a pre-trained power generation data query supplement model to obtain a power generation data query supplement information table; The power generation data query supplementary information table is displayed on the query interface, and in response to detecting a click operation on any power generation data query supplementary information in the power generation data query supplementary information table, according to the clicked power generation data query supplementary information, a photovoltaic power generation data information set corresponding to the power generation data query supplementary information is searched in a pre-constructed photovoltaic power generation data information database; The photovoltaic power generation data information set is displayed on a query interface.

2. The method according to claim 1, wherein: Before inputting the power generation data query enhanced prompt information into a pre-trained power generation data query supplementary model to obtain a power generation data query supplementary information table, the method further includes: According to the acquired photovoltaic power generation query behavior data set corresponding to the photovoltaic power generation data query field, the first initial power generation data query supplement model is trained to obtain a second initial power generation data query supplement model; The second initial power generation data query supplement model is trained according to a preset power generation query enhancement prompt information sample set to obtain a third initial power generation data query supplement model, wherein the power generation query enhancement prompt information sample includes: a plurality of data query supplement feature information; In response to determining that the model training effect corresponding to the third initial power generation data query supplementary model reaches the expected effect, determining the third initial power generation data query supplementary model as the trained power generation data query supplementary model; In response to determining that the model training effect does not achieve the expected effect, training the third initial power generation data query supplement model according to the power generation query enhancement prompt information test sample set corresponding to the model training effect to obtain the trained third initial power generation data query supplement model; The trained third initial power generation data query supplement model is used as the third initial power generation data query supplement model, and the third initial power generation data query supplement model is trained again.

3. The method according to claim 2, wherein: The method of training the first initial power generation data query supplement model according to the acquired photovoltaic power generation query behavior data set corresponding to the photovoltaic power generation data query field to obtain the second initial power generation data query supplement model includes: For each photovoltaic power generation query behavior data in the photovoltaic power generation query behavior data set, photovoltaic power generation query information and photovoltaic power generation type information are extracted from the photovoltaic power generation query behavior data; Preprocessing the obtained photovoltaic power generation query information set and photovoltaic power generation type information set to generate a preprocessed query information set and a preprocessed power generation type information set; Acquire a photovoltaic power generation general data set corresponding to the photovoltaic power generation data query field; Adjusting the data proportions of the preprocessed query information set, the preprocessed power generation type information set, and the photovoltaic power generation general data set to obtain a proportion data set; According to the percentage data set, model training is performed on the first initial power generation data query supplement model to obtain a second initial power generation data query supplement model.

4. The method according to claim 1, wherein: Before searching for a photovoltaic power generation data information set corresponding to the power generation data query supplementary information clicked in a pre-built photovoltaic power generation data information database, the method further includes: Acquire the photovoltaic power generation data set of each photovoltaic power generation equipment in the target photovoltaic power plant; Generate, according to the photovoltaic power generation data set, a photovoltaic power generation equipment association graph corresponding to the target photovoltaic power plant, wherein each photovoltaic power generation equipment association graph has a corresponding photovoltaic power generation data query element; Performing diagram verification on the association diagrams of the various photovoltaic power generation devices to generate a diagram verification result; In response to determining that the diagram verification result represents correctness, the respective photovoltaic power generation equipment association diagrams and the corresponding respective photovoltaic power generation data query elements are stored in a predetermined format in a pre-set database as a photovoltaic power generation data information library.

5. An information query device applied to photovoltaic power generation, comprising: A generating unit is configured to, in response to receiving the power generation data query information corresponding to the target photovoltaic power plant, generate the power generation data query enhanced prompt information corresponding to the data query guide character according to the data query guide character corresponding to the power generation data query information; An input unit is configured to input the power generation data query enhanced prompt information into a pre-trained power generation data query supplement model to obtain a power generation data query supplement information table; a query unit configured to display the power generation data query supplementary information table on a query interface, and in response to detecting a click operation on any power generation data query supplementary information in the power generation data query supplementary information table, query a photovoltaic power generation data information set corresponding to the power generation data query supplementary information in a pre-constructed photovoltaic power generation data information database according to the clicked power generation data query supplementary information; The display unit is configured to display the photovoltaic power generation data information set on a query interface.

6. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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