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

Abstract

Embodiments of the present disclosure disclose a method and device for information query applied to photovoltaic power generation and an electronic device. A specific embodiment of the method includes: in response to receiving power generation data query information corresponding to a target photovoltaic power generation field, generating power generation data query enhancement prompt information corresponding to data query guide characters corresponding to the power generation data query information; inputting the power generation data query enhancement prompt information into a power generation data query supplement model to obtain a power generation data query supplement information table; displaying the power generation data query supplement information table on a query interface, and in response to detecting a click operation on any power generation data query supplement information, querying a photovoltaic power generation data information set corresponding to the clicked power generation data query supplement information in a photovoltaic power generation data information library. The embodiment improves the low query efficiency, avoids repeated queries, and reduces the occupation of computing resources.

Description

TECHNICAL FIELD

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

[0002] With the continuous development of photovoltaic power generation, effective management and 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 the photovoltaic power generation equipment, the commonly used way is to query the related data information of photovoltaic power generation by traversing the data systems of each photovoltaic power generation equipment.

[0003] However, when the above-mentioned way is used to query the photovoltaic power generation data information, the following technical problems often exist:

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

[0005] The above information disclosed in the background section is only for the purpose of enhancing the understanding of the background of the present inventive concept, and therefore, it can contain information which does not form the prior art known to those of ordinary skill in the art in the country. SUMMARY

[0006] The summary section of the present disclosure is used to introduce the concepts in a brief manner, which will be described in detail in the specific embodiments section. The summary section of the present disclosure is not intended to identify key or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose an information query method and device applied to photovoltaic power generation, an electronic device and a computer readable medium, to solve one or more of the technical problems mentioned in the background 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 generation field, generating power generation data query enhancement prompt information corresponding 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 pre-trained power generation data query supplement model to obtain a power generation data query supplement information table; displaying the power generation data query supplement information table on a query interface, and in response to detecting a click operation acting on any power generation data query supplement information in the power generation data query supplement information table, querying a photovoltaic power generation data information set corresponding to the clicked power generation data query supplement information from a pre-constructed photovoltaic power generation data information library; 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 generation field, generate power generation data query enhancement prompt information corresponding to a data query guide character corresponding to the power generation data query information; an input unit configured to 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; a query unit configured to display the power generation data query supplement information table on a query interface, and in response to detecting a click operation acting on any power generation data query supplement information in the power generation data query supplement information table, query a photovoltaic power generation data information set corresponding to the clicked power generation data query supplement information from a pre-constructed photovoltaic power generation data information library; 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 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 described in any of the implementations of the 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 the program is executed by a processor to implement the method described in any of the implementations of the first aspect.

[0012] The above various embodiments of the present disclosure have the following beneficial effects: by applying the information query method for photovoltaic power generation of some embodiments of the present disclosure, the power generation data query supplement model can be used, and the information can be accurately supplemented for the data query guide character of the power generation data query information. Thus, the query efficiency is improved, and the occupation of computing resources is reduced. Specifically, a large amount of computing resources is wasted because the input power generation data query information is not supplemented, resulting in inaccurate information for the input query guide word and low query efficiency. Based on this, the information query method for photovoltaic power generation of some embodiments of the present disclosure first generates power generation data query enhancement prompt information corresponding to the data query guide character of the power generation data query information corresponding to the target photovoltaic power generation field in response to receiving the power generation data query information. Then, the power generation data query enhancement prompt information is input into the pre-trained power generation data query supplement model to obtain a power generation data query supplement information table. Thus, by 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. Then, the power generation data query supplement information table is displayed 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, the photovoltaic power generation data information set corresponding to the power generation data query supplement information is queried in the pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplement information. Thus, the corresponding photovoltaic power generation data information set can be quickly queried. Finally, the photovoltaic power generation data information set is displayed on the query interface. Thus, by using the power generation data query supplement model, the information can be accurately supplemented for the data query guide character of the power generation data query information. Thus, the query efficiency is improved, repeated queries are avoided, and the occupation of computing resources is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0013] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the attached drawings. The same or similar elements are denoted by the same or similar reference numerals throughout the drawings. It is to be understood that the drawings are schematic, and elements and elements are not necessarily drawn to scale.

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

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

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

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

[0018] Embodiments of the present disclosure will be described in more detail with reference to the drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. 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 present application are shown in the drawings. The embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0020] It should be noted that the terms "first", "second", etc. mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the functions performed by these devices, modules or units or the mutual dependency therebetween.

[0021] It should be noted that the adjectives "one", "multiple" mentioned in the present disclosure are illustrative rather than limiting, and those skilled in the art should understand that, unless otherwise explicitly stated in the context, it should be understood as "one or more".

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

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

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

[0025] Step 101, in response to receiving the power generation data query information corresponding to the target photovoltaic power generation field, generating the power generation data query enhancement prompt information corresponding to the data query guide character according to the data query guide character corresponding to the above-mentioned power generation data query information.

[0026] In some embodiments, the execution subject (e.g., a computing device) of the information query method applied to photovoltaic power generation can generate power generation data query enhancement prompt information corresponding to the data query guide character according to the data query guide character corresponding to the received power generation data query information of the target photovoltaic power generation field. The power generation data query information can refer to the information for querying the data of each photovoltaic power generation device in the target photovoltaic power generation field. The target photovoltaic power generation field can be a power generation field including various photovoltaic power generation devices. The data query guide character can refer to the real-time input query guide character corresponding to the power generation data query information. For example, the data query guide character can refer to a query search prefix. The power generation data query enhancement prompt information can be a prompt word for query supplement capability enhancement of the data query guide character. For example, the power generation data query enhancement prompt information includes: a plurality of query supplement feature information corresponding to the power generation data. The query supplement feature information can be feature content corresponding to the query supplement feature. The query supplement feature can be a feature related to the query supplement operation. For example, the search supplement feature can 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 power generation data query enhancement prompt information into the 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 can input the above power generation data query enhancement prompt information into the 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 supplement for the data query guide character. The power generation data query supplement model can be a pre-trained large language model (LLM, Large Language Model) for query supplement.

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

[0030] Optionally, the data query guide character and the 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 data query guide character supplement table, the corresponding guide character related information of the data query guide character is supplemented to the initial power generation query enhancement prompt information sample template to generate an initial power generation query enhancement prompt information sample. The initial power generation query enhancement prompt information sample template includes a plurality of data query supplement features to be supplemented. The initial power generation query enhancement prompt information sample template can be a query enhancement prompt sample template to be supplemented with feature information. The feature information supplement can be to supplement the feature content under the search supplement feature. The initial power generation query enhancement prompt information sample template can be a prompt information template to be filled with feature information. As an example, for each data query guide character, the query supplement information corresponding to the data query guide character and the feature content under the multi-data query supplement feature are determined. Then, the data query guide character, the query supplement information and the feature content under the multi-data query supplement feature are filled into the initial power generation query enhancement prompt information sample template to generate an initial power generation query enhancement prompt information sample.

[0032] Optionally, the sample content of each initial power generation query enhancement prompt information sample is proofread to obtain a power generation query enhancement prompt information sample set. The power generation query enhancement prompt information sample can be a sample after each query supplement feature information is filled. For example, the execution subject can send the initial power generation query enhancement prompt information sample to a sample content proofreading end to perform manual proofreading to generate a corresponding power generation query enhancement prompt information sample.

[0033] It should be noted that the plurality of query supplement feature information includes a data query guide character, query click information, a collaborative filtering query information table for the data query guide character, and the corresponding power generation query enhancement prompt information sample can be the following sample: "<data query guide character>; <query click information>; <collaborative filtering query information list>".

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

[0035] In the first step, the first initial power generation data query supplement model is trained according to the obtained corresponding photovoltaic power generation data query domain photovoltaic power generation query behavior data set to obtain a second initial power generation data query supplement model. The photovoltaic power generation query behavior data can be data after performing a query behavior under photovoltaic power generation. For example, the photovoltaic power generation query behavior data can be data of a user inputting "the Chinese meaning of input". The corresponding query behavior can be the behavior of inputting a guide character or inputting query information. The first initial power generation data query supplement model can be a large language model (LLM, Large Language Model) that has not yet completed training. The second initial power generation data query supplement model can be a large language model trained on the photovoltaic power generation query behavior data set. The input query information can be a query question input. For example, the first initial power generation data query supplement model can be trained to obtain the second initial power generation data query supplement model by using a conventional model training method (for example, a deep model training method).

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

[0037] In the first sub-step, 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. The photovoltaic power generation query information can be query information input by a user in a query box. The photovoltaic power generation type information can be type information of the query photovoltaic power generation data input by the user.

[0038] In the second sub-step, the obtained photovoltaic power generation query information set and photovoltaic power generation type information set are preprocessed to generate a preprocessed query information set and a preprocessed power generation type information set. For example, preprocessing can include supplementing missing values and removing null values.

[0039] In the third sub-step, a photovoltaic power generation general data set corresponding to the photovoltaic power generation data query domain is obtained. The photovoltaic power generation general data set can be a photovoltaic power generation data set commonly used in the photovoltaic power generation data query domain.

[0040] In the fourth sub-step, the preprocessed query information set, the preprocessed power generation type information set, and the photovoltaic power generation general data set are adjusted in data proportion to obtain a proportion data set. For example, the proportion data set can include the preprocessed query information set, the preprocessed item information set, and the domain general data subset. The data proportion 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] A fifth sub-step, according to the above proportion data set, the first initial power generation data query supplement model is trained to obtain a second initial power generation data query supplement model. Here, the model training method can not be limited.

[0042] Second, according to the pre-set power query enhancement prompt information sample set, the second initial power generation data query supplement model is trained to obtain a third initial power generation data query supplement model. Among them, the power query enhancement prompt information sample includes: a plurality of data query supplement feature information. Data query supplement feature information can be a feature content corresponding to query supplement feature. Query supplement feature can be a feature related to search supplement operation. Here, the way of training the second initial power generation data query supplement model is not limited.

[0043] Third, in response to determining that the model training effect of 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. Wherein, the expected effect can be the expected pre-set to represent the accuracy of the power generation data query supplement model automatic supplement. For example, the expected effect can be that the supplement accuracy is greater than 90%. The model training effect can be the actual result (for example, 91%) of the model prediction effect of the third initial power generation data query supplement model.

[0044] Fourth, in response to determining that the model training effect does not reach the expected effect, according to the power query enhancement prompt information test sample set corresponding to the model training effect, the third initial power generation data query supplement model is trained to obtain a 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 to obtain a trained third initial power generation data query supplement model.

[0045] Among them, the above fourth step can include the following sub-steps:

[0046] The first sub-step, from the power query enhancement prompt information test sample set, the power query enhancement prompt information positive sample set representing the positive supplement effect and the power query enhancement prompt information negative sample set representing the negative supplement effect are selected. Among them, the positive supplement effect can represent the effect that the query supplement information obtained by inputting the corresponding test sample into the third initial power generation data query supplement model meets the user's demand. The negative supplement effect represents the effect that the query supplement information obtained by inputting the corresponding test sample into the third initial power generation data query supplement model does not meet the user's demand. That is, the user does not click to select the query supplement information list output by inputting the test sample into the third initial power generation data query supplement model.

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

[0048] A third sub-step, training the third initial power generation data query supplement model according to the above target power generation query enhancement prompt information sample set to obtain a trained third initial power generation data query supplement model.

[0049] A fifth step, taking the trained third initial power generation data query supplement model as a third initial power generation data query supplement model, and training the third initial power generation data query supplement model again.

[0050] Thus, through multiple rounds of model training, the power generation data query supplement model can learn more knowledge in the photovoltaic power generation field, can combine more query supplement features to predict more accurate query supplement information, and can obtain a power generation data query supplement model with more accurate output query supplement information through multiple rounds of model testing and adaptive training.

[0051] Step 103, displaying the above 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 above power generation data query supplement information table, querying a photovoltaic power generation data information set corresponding to the power generation data query supplement information from a pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplement information.

[0052] In some embodiments, the above execution subject can display the above 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 above power generation data query supplement information table, query a photovoltaic power generation data information set corresponding to the power generation data query supplement information from a pre-constructed photovoltaic power generation data information library according to the clicked power generation data query supplement information. The query interface can be a query page that receives user input power generation data query information. The photovoltaic power generation data information library can be a database that stores various types of photovoltaic power generation data corresponding to a target photovoltaic power plant. For example, the photovoltaic power generation data information set corresponding to the power generation data query supplement information can be retrieved from the above photovoltaic power generation data information library. The photovoltaic power generation data information can 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 library can be constructed by the following steps:

[0054] A first step, obtaining a photovoltaic power generation dataset of each photovoltaic power generation device in a target photovoltaic power plant. The photovoltaic power generation device can include but is not limited to: photovoltaic module, inverter, combiner box, control cabinet, energy storage battery, transformer. The photovoltaic power generation data can include: device connection information and device basic information corresponding to the photovoltaic power generation device. The photovoltaic power generation data in the photovoltaic power generation dataset corresponding to the photovoltaic power generation device in each photovoltaic power generation device.

[0055] A second step, according to the above photovoltaic power generation dataset, generating each photovoltaic power generation device correlation graph corresponding to the above target photovoltaic power plant. Each photovoltaic power generation device correlation graph has a corresponding photovoltaic power generation data query element. The photovoltaic power generation device has a corresponding full amount of multiple photovoltaic power generation data query elements. The photovoltaic power generation data query element can be the smallest element information for the user to query the photovoltaic power generation data. For example, the multiple photovoltaic power generation data query elements can include: photovoltaic power generation device identification element, photovoltaic power generation device type element, photovoltaic power generation device running state element, photovoltaic power generation device specification element, photovoltaic power generation location range element. Each photovoltaic power generation device correlation graph can include: a photovoltaic power generation device correlation graph corresponding to the photovoltaic power generation device identification element, a photovoltaic power generation device correlation graph corresponding to the photovoltaic power generation device specification element, a photovoltaic power generation device correlation graph corresponding to the photovoltaic power generation location range element, a photovoltaic power generation device correlation graph corresponding to the photovoltaic power generation device running state element, and a photovoltaic power generation device correlation graph corresponding to the photovoltaic power generation device type element. For example, for each photovoltaic power generation data query element, a set of elements in the above photovoltaic power generation dataset corresponding to the above photovoltaic power generation data query element can be determined. Then according to the element set, generate a photovoltaic power generation device correlation graph corresponding to the photovoltaic power generation data query element in the form of spatial index.

[0056] A third step, graph verification is performed on each photovoltaic power generation device correlation graph to generate a graph verification result. The graph verification result can represent whether the node position and node connection in each photovoltaic power generation device correlation graph are correct. For example, the node position and node connection corresponding to each graph node in each photovoltaic power generation device correlation graph can be verified to obtain the graph verification result.

[0057] A fourth step, in response to determining that the graph verification result represents no error, storing each photovoltaic power generation device correlation graph and each photovoltaic power generation data query element corresponding thereto in a predetermined format in a pre-set database as a photovoltaic power generation data information library.

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

[0059] A first sub-step, determining storage resource call information. The storage resource call information can be the call resource size corresponding to the storage operation in a unit of time.

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

[0061] A third sub-step, for each photovoltaic power generation device association graph in the above-mentioned each photovoltaic power generation device association graph, the following processing steps are performed:

[0062] 1. Determine the graph call number sequence corresponding to the photovoltaic power generation device association graph. The graph call number can be the number of times the photovoltaic power generation device association graph is called in a predetermined time.

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

[0064] 3. In response to determining that the graph storage mode is the target storage mode, determine the call number sequence corresponding to the photovoltaic power generation device association graph in the predetermined time period according to the preprocessed graph call number sequence. For example, a time series neural network model (e.g., a recurrent neural network model) can be used to determine the call number sequence corresponding to the photovoltaic power generation device association graph in the predetermined time period.

[0065] 4. Determine the total call number and the average call number corresponding to the call number sequence. The total call number can be the sum of each call number in the call number sequence. The average call number can be the mean of each call number in the call number sequence.

[0066] A fourth sub-step, according to each total call number, each average call number and the above target hierarchical quantity, the above each photovoltaic power generation equipment association graph is processed hierarchically to obtain storage configuration information representing the association relationship between the hierarchical photovoltaic power generation equipment association graph set sequence and the graph level group. Wherein, each hierarchical photovoltaic power generation equipment association graph set corresponds to different graph levels. For example, first, according to each total call number, each average call number, each photovoltaic power generation equipment association graph is sorted to obtain a photovoltaic power generation equipment association graph sequence. Then, according to the arrangement order corresponding to the photovoltaic power generation equipment association graph sequence, the above photovoltaic power generation equipment association graph sequence is divided into target hierarchical quantity to generate hierarchical photovoltaic power generation equipment association graph set sequence. According to the arrangement order corresponding to the photovoltaic power generation equipment association graph sequence, each hierarchical photovoltaic power generation equipment association graph set is assigned a corresponding graph level to obtain graph level distribution information. Finally, according to the graph level distribution information, the storage configuration information is generated.

[0067] A fifth sub-step, according to the above storage configuration information, each hierarchical photovoltaic power generation equipment association graph set in the above hierarchical photovoltaic power generation equipment association graph set sequence and the photovoltaic power generation data query element subset are stored in the corresponding storage node in the above database.

[0068] Therefore, through the photovoltaic power generation data information library constructed, the corresponding photovoltaic power generation data can be quickly and efficiently queried for the user's power generation data query information.

[0069] Step 104, the above photovoltaic power generation data information set is displayed on the query interface.

[0070] In some embodiments, the above execution subject can display the above photovoltaic power generation data information set on the query interface. As Figure 4 shown, the photovoltaic power generation data information set can be displayed in the form of a table, which can include: component land area, fixed double-sided power generation; component land area, fixed double-sided power generation corresponding category, numerical value, unit, note / recommended value. The category corresponding to the component land area includes: project capacity, geographic latitude, photovoltaic array inclination angle, component model, technical route, peak power, north-south spacing, component land area. The category corresponding to the fixed double-sided power generation includes: annual inclined surface cumulative irradiance, component first-year attenuation, system efficiency, component aging attenuation rate, first-year utilization hours, 25 average utilization hours.

[0071] It should be noted that, Figure 4 the text, numerical value displayed in the above are only exemplary descriptions, not representing the fixed limitation of the photovoltaic power generation data involved in the present application.

[0072] Further reference Figure 2As an implementation of the method shown in the above figures, the present disclosure provides some embodiments of an information query device applied to photovoltaic power generation, which correspond to the method embodiments shown in Figure 1 The information query device applied to photovoltaic power generation can be applied to various electronic devices.

[0073] As shown in Figure 2 The information query device applied to photovoltaic power generation 200 of some embodiments includes a generating unit 201, an input unit 202, a querying unit 203, and a display unit 204. The generating unit 201 is configured to, in response to receiving power generation data query information corresponding to a target photovoltaic power generation field, generate power generation data query enhanced prompt information corresponding to a 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 a pre-trained power generation data query supplement model to obtain a power generation data query supplement information table. The querying unit 203 is configured to display the power generation data query supplement information table on a query interface, and in response to detecting a click operation acting on any power generation data query supplement information in the power generation data query supplement information table, query a photovoltaic power generation data information set corresponding to the clicked power generation data query supplement information from a pre-constructed photovoltaic power generation data information library. 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 described in the information query device applied to photovoltaic power generation 200 correspond to the steps in the method described with reference to Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the information query device applied to photovoltaic power generation 200 and the units contained therein, which will not be described here.

[0075] Reference is made below to Figure 3 which shows a structural schematic diagram of an electronic device 300 (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. The electronic device in some embodiments of the present disclosure can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), and the like, as well as fixed terminals such as digital TVs, desktop computers, and the like. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functions and use range of embodiments of the present disclosure.

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

[0077] Generally, the following devices can 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 can allow the electronic device 300 to communicate wirelessly or wired with other devices to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that all of the illustrated devices are not required, and more or fewer devices can alternatively be implemented. Figure 3 Each block shown in the flowcharts can represent a device, or multiple devices, as necessary.

[0078] In particular, processes described above with reference to the flowcharts can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product including a computer program carried on a computer readable medium, the computer program containing program code for performing the methods illustrated by the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a network through the communication devices 309, or installed from the storage devices 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-described functions defined in the methods of some embodiments of the present disclosure are performed.

[0079] Note that the computer readable medium in some embodiments of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device. In some embodiments of the present disclosure, the computer readable signal medium can include a data signal propagated in baseband or propagated as a carrier wave in a propagated data signal, in which the computer readable program code is contained. Such propagated data signal can take a variety of forms, including but not limited to electro-magnetic, optical or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport program for use by or in connection with an instruction execution system, apparatus or device. Program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to wire, cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

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

[0081] The computer readable medium can be included in the electronic device, or can exist separately from the electronic device. The computer readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: in response to receiving power generation data query information corresponding to a target photovoltaic power plant, generate power generation data query enhancement prompt information corresponding to a data query guide character corresponding to the power generation data query information, according to the data query guide character; 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; display the power generation data query supplement information table on a 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 a photovoltaic power generation data information set corresponding to the clicked power generation data query supplement information from a pre-constructed photovoltaic power generation data information library, according to the clicked power generation data query supplement information; and display the photovoltaic power generation data information set on the query interface.

[0082] Computer program code for carrying out operations of some embodiments of the disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0083] The flow and block diagrams in the drawings represent possible architectural, functional, and operational architectures of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block can represent a module, a segment, or a portion of code that comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or combinations of hardware and software.

[0084] The units described in some embodiments of the present disclosure can be implemented by means of software, or can be implemented by hardware. The described units can also be arranged in a processor, for example, can be described as: a processor comprising a generating unit, an input unit, a querying unit and a displaying unit. Among them, the names of these units do not constitute a limitation to the units themselves in some cases, for example, the generating unit can also be described as: "a unit for generating power generation data query enhancement prompt information corresponding to the data query guide character in response to receiving power generation data query information corresponding to the target photovoltaic power plant, and the data query guide character corresponding to the above-mentioned power generation data query information".

[0085] The functions described above in the specification can be implemented at least in part by one or more hardware logic components. For example, and without limitation, non-limiting examples of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), etc.

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

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 generation field, generating power generation data query enhancement prompt information corresponding to a data query guide character corresponding to the power generation data query information according to the data query guide character, the power generation data query enhancement prompt information being a prompt word for query supplement capability enhancement of the data query guide character, the power generation data query enhancement prompt information including: a plurality of query supplement feature information corresponding to power generation data, the query supplement feature information being feature content corresponding to a query supplement feature, the query supplement feature being a feature related to a query supplement operation, the search supplement feature being: query time, query power generation data information, data query guide character, guide character supplement list corresponding to the data query guide character, query supplement association list corresponding to the data query guide character; 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; obtaining a photovoltaic power generation data set of each photovoltaic power generation device in the target photovoltaic power generation field; generating each photovoltaic power generation device association graph corresponding to the target photovoltaic power generation field according to the photovoltaic power generation data set, wherein each photovoltaic power generation device association graph has a corresponding photovoltaic power generation data query element; performing graph verification on the each photovoltaic power generation device association graph to generate a graph verification result; in response to determining that the graph verification result is error-free, storing the each photovoltaic power generation device association graph and the corresponding each photovoltaic power generation data query element in a pre-set database in a predetermined format as a photovoltaic power generation data information library, comprising: determining storage resource call information, wherein the storage resource call information is the call resource size corresponding to the storage operation performed in a unit of time; in response to determining that the storage resource call information does not satisfy a pre-set storage resource condition, determining that the graph storage mode corresponding to the each photovoltaic power generation device association graph is a target storage mode and a target hierarchical number, wherein the pre-set storage resource condition represents that the storage resource call information is less than a predetermined call resource, and the target storage mode is a storage mode representing storing the photovoltaic power generation device association graph according to the number of calls in a pre-set time period; for each photovoltaic power generation device association graph in the each photovoltaic power generation device association graph, the following processing steps are performed: determining a graph call number sequence corresponding to the photovoltaic power generation device association graph, wherein the graph call number is the number of times the photovoltaic power generation device association graph is called in a pre-set time; preprocessing each graph call number in the graph call number sequence to generate a preprocessed graph call number sequence, the preprocessing including: removing the maximum value, removing the minimum value; in response to determining that the graph storage mode is the target storage mode, determining a call number sequence corresponding to the photovoltaic power generation device association graph in a pre-set time period according to the preprocessed graph call number sequence; determining the total call number and the average call number corresponding to the call number sequence; According to the total number of calls, the average number of calls and the target number of levels, the hierarchical photovoltaic power generation equipment association graph is processed to obtain storage configuration information representing the association relationship between the hierarchical photovoltaic power generation equipment association graph set sequence and the graph level group; According to the storage configuration information, each hierarchical photovoltaic power generation equipment association graph set in the hierarchical photovoltaic power generation equipment association graph set sequence and a photovoltaic power generation data query element subset are stored in the corresponding storage node in the database; 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, the clicked power generation data query supplementary information is queried in the pre-constructed photovoltaic power generation data information library to obtain a photovoltaic power generation data information set corresponding to the power generation data query supplementary information; The photovoltaic power generation data information set is displayed on the query interface.

2. The method of claim 1, wherein, Before the 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 supplementary information table, the method further comprises: According to the obtained 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; According to the pre-set power generation query enhancement prompt information sample set, the second initial power generation data query supplement model is trained to obtain a third initial power generation data query supplement model, wherein the power generation query enhancement prompt information sample comprises a plurality of data query supplement feature information; In response to determining that the model training effect of 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; In response to determining that the model training effect does not reach 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 model training effect to obtain a 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 of claim 2, wherein, The training of the first initial power generation data query supplement model according to the obtained 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 comprises: 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; The obtained photovoltaic power generation query information set and photovoltaic power generation type information set are preprocessed to generate a preprocessed query information set and a preprocessed power generation type information set; A photovoltaic power generation general data set corresponding to the photovoltaic power generation data query field is obtained; The preprocessing query information set, the preprocessing power generation type information set and the photovoltaic power generation general data set are subjected to data proportion adjustment to obtain a proportion data set; According to the proportion data set, a 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. An information query device applied to photovoltaic power generation, comprising: A generating unit is configured to, in response to receiving power generation data query information corresponding to a target photovoltaic power generation field, generate power generation data query enhancement prompt information corresponding to a data query guide character according to the data query guide character corresponding to the power generation data query information. The power generation data query enhancement prompt information is a prompt word for query supplement capability enhancement of the data query guide character. The power generation data query enhancement prompt information includes: a plurality of query supplement feature information corresponding to power generation data. The query supplement feature information is feature content corresponding to a query supplement feature. The query supplement feature is a feature related to a query supplement operation. The search supplement feature is: 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. An input unit is configured to 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. An obtaining unit is configured to obtain a photovoltaic power generation data set of each photovoltaic power generation device in a target photovoltaic power generation field. A photovoltaic power generation device association graph generating unit is configured to generate, according to the photovoltaic power generation data set, a photovoltaic power generation device association graph corresponding to each photovoltaic power generation device of the target photovoltaic power generation field. Each photovoltaic power generation device association graph has a corresponding photovoltaic power generation data query element. A graph verification unit is configured to perform graph verification on the photovoltaic power generation device association graphs to generate a graph verification result. A storage unit is configured to, in response to determining that the graph verification result represents no error, store the photovoltaic power generation device association graphs and the corresponding photovoltaic power generation data query elements in a pre-set database in a predetermined format as a photovoltaic power generation data information library, including: Determine storage resource calling information, wherein the storage resource calling information is the calling resource size corresponding to the storage operation performed in a unit of time; In response to determining that the storage resource calling information does not satisfy a pre-set storage resource condition, determine that the graph storage mode corresponding to the photovoltaic power generation device association graphs is a target storage mode and a target hierarchical number, wherein the pre-set storage resource condition represents that the storage resource calling information is less than a predetermined calling resource, and the target storage mode is a storage mode representing that the photovoltaic power generation device association graphs are stored according to the number of calls in a pre-set time period. For each photovoltaic power generation device association graph in the photovoltaic power generation device association graphs, the following processing steps are performed: Determine a graph call number sequence corresponding to the photovoltaic power generation device association graph, wherein the graph call number is the number of times the photovoltaic power generation device association graph is called in a pre-set time. The graph call times in the graph call time sequence are preprocessed to generate a preprocessed graph call time sequence, and the preprocessing includes removing maximum values and removing minimum values; In response to determining that the graph storage mode is the target storage mode, a call time sequence corresponding to the photovoltaic power generation device associated graph in a preset time period is determined according to the preprocessed graph call time sequence; A total call time and an average call time corresponding to the call time sequence are determined; According to each total call time, each average call time, and the target hierarchical quantity, the hierarchical processing is performed on the photovoltaic power generation device associated graphs to obtain storage configuration information representing an association relationship between a hierarchical photovoltaic power generation device associated graph set sequence and a graph level group; According to the storage configuration information, each hierarchical photovoltaic power generation device associated graph set in the hierarchical photovoltaic power generation device associated graph set sequence and a photovoltaic power generation data query element subset are stored into a corresponding storage node in the database; The query unit is configured to display the power generation data query supplementary information table on a query interface, and in response to detecting a click operation acting on any power generation data query supplementary information in the power generation data query supplementary information table, query out a photovoltaic power generation data information set corresponding to the clicked power generation data query supplementary information from a pre-constructed photovoltaic power generation data information library; The display unit is configured to display the photovoltaic power generation data information set on the query interface. 5.An electronic device, comprising: one or more processors; storage having stored thereon one or more programs; 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 1-3.

6. A computer readable medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the method according to any one of claims 1-3.

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