Wind power plant level long time scale report generation method and system

Abstract

The invention provides a wind power plant level long-time scale report generation method and system, and the method comprises the steps: constructing a wind power plant level long-time scale parameter table, and carrying out the system parameter configuration of the wind power plant level long-time scale parameter table; after a wind power plant level long-time scale report generation instruction is received, a wind power plant level multi-element information source storage position and a report preset storage position are obtained based on the wind power plant level long-time scale report generation instruction; and reading the configured wind power plant level long-time scale parameter table, performing collection and analysis of the multi-element information source based on the multi-element information source storage position to generate a wind power plant level long-time scale report, and storing the report in a report preset storage position. According to the method, the wind power plant level operation evaluation efficiency can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a method and system for generating long-time scale reports at a wind farm level. Background Art

[0003] When the data collected by different OEMs and models to wind power operators are applied to long-term operation evaluation reports of specific wind farms, such as multi-month, annual or multi-year, they mainly need to be based on conventional processes based on data flows. For example, starting from the original data flow, wind turbines are selected and raw data are exported, data analysis, information extraction, report compilation and other links. The data volume and workload are large, the efficiency is low, and the cycle is long. It requires high personal ability and professional experience of wind power analysts. It usually takes days or weeks to complete the entire process to calculate the time to complete the report. At the same time, manual report preparation generally has problems such as the format and chart style being affected by personal style, the data in the preparation process may be wrong, and the quality of the report is uneven. If a wind farm or model is changed, these processes need to be repeated again, which is not efficient. The existing patents related to long-term operation evaluation at the wind farm level do not involve methods and systems for processing multi-factor information flows at the wind farm level and automatically generating reports. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present invention provides a method and system for generating long-time scale reports at the wind farm level, the main purpose of which is to improve the efficiency of wind farm level operation evaluation.

[0005] According to a first aspect of the present invention, a method for generating a long-time report at a wind farm level is provided, comprising:

[0006] Constructing a long-time scale parameter table at the wind farm level, and performing system parameter configuration on the long-time scale parameter table at the wind farm level;

[0007] After receiving the wind farm-level long-time scale report generation instruction, obtaining the wind farm-level multi-factor information source storage location and the report preset storage location based on the wind farm-level long-time scale report generation instruction;

[0008] The configured wind farm-level long time scale parameter table is read, and based on the multi-factor information source storage location, multi-factor information sources are aggregated and analyzed to generate a wind farm-level long time scale report, and the report is saved in the preset storage location.

[0009] In the wind farm-level long-time scale report generation method provided in the first aspect of the present invention, the system parameter configuration of the wind farm-level long-time scale parameter table includes information element selection, long-time scale report content customization, and wind farm operation evaluation configuration file reading, and the wind farm operation evaluation configuration file includes wind turbine parameters, reference wind turbine power curve, and wind farm wind turbine information.

[0010] In the wind farm-level long-time scale report generation method provided in the first aspect of the present invention, the wind farm-level multi-factor information source includes multiple information element sets, each information element set includes multiple categories of information elements, and the multiple categories of information elements include wind turbine data integrity statistics, daily statistics, wind resource statistics, operation curve analysis, fitting Kopt, and cabin acceleration analysis.

[0011] In the wind farm-level long-time scale report generation method provided in the first aspect of the present invention, the configured wind farm-level long-time scale parameter table is read, and the multi-factor information source is aggregated and analyzed based on the multi-factor information source storage location to generate a wind farm-level long-time scale report, including: reading the system parameters in the configured wind farm-level long-time scale parameter table; based on the multi-factor information source, generating a wind turbine set list of multiple periods under a medium time scale; for each wind turbine set list, the corresponding multi-factor information source is read from the multi-factor information source storage location for aggregation, and the multi-factor information source is analyzed, and the process of automatically generating a report by calling the document API generates a wind farm-level long-time scale report.

[0012] In the wind farm-level long-time scale report generation method provided in the first aspect of the present invention, the corresponding multi-element information source is read from the storage location of the multi-element information source for aggregation, including: generating a list of information elements to be aggregated according to the information elements in the configured wind farm-level long-time scale parameter table; judging whether the aggregation of all specified information elements has been completed, if not, retrieving and extracting the current type of information elements from the multi-element information source to generate a list of wind turbines to be analyzed; then judging whether all wind turbine analyses have been completed, if not, determining the current wind turbine from the aggregated current type of information elements, and obtaining the current wind turbine analysis data under the current type of information elements from the multi-element information source, analyzing the current wind turbine analysis data once to obtain the current wind turbine analysis result, until all wind turbine analyses are completed, and then re-judging whether all specified information elements have been aggregated, until all specified information elements are aggregated, and outputting the aggregation result based on the analysis results of all wind turbines.

[0013] In the wind farm-level long-time scale report generation method provided in the first aspect of the present invention, the analysis of the multi-factor information source includes: generating a list of information elements to be analyzed at the wind farm level according to the information elements in the configured wind farm-level long-time scale parameter table; judging whether the analysis of all specified information elements has been completed, if not, retrieving and extracting the current type of information elements from the aggregated results, performing a secondary analysis on the wind turbine analysis results of all wind turbines of the current type of information elements to obtain a wind farm-level analysis result, and then re-judging whether the analysis of all specified information elements has been completed, until the analysis of all specified information elements is completed, and outputting the multi-factor information flow analysis result based on the wind farm-level analysis results of all types of information elements.

[0014] In the wind farm-level long-time scale report generation method provided in the first aspect of the present invention, the process of calling the document API to automatically generate a report generates a wind farm-level long-time scale report, including: generating a report title and creating a directory; generating a wind farm overview based on the multi-factor information flow analysis results and based on the configured wind farm operation evaluation configuration file; generating a wind farm-level chapter, judging whether all wind farm-level designated sub-contents have been completed, if not, obtaining the wind farm-level designated sub-contents to be completed based on the configured long-time scale report content customization; generating a wind turbine-level chapter, judging whether all wind turbine-level designated sub-contents have been completed, if not, obtaining the wind turbine-level designated sub-contents to be completed based on the configured long-time scale report content customization; and automatically updating the index directory to obtain a wind farm-level long-time scale report.

[0015] In the wind farm level long time scale report generation method provided in the first aspect of the present invention, before saving the wind farm level long time scale report, it is also necessary to automatically convert the report format of the wind farm level long time scale report.

[0016] According to a second aspect of the present invention, there is also provided a wind farm-level long-time scale report generation system, comprising:

[0017] A parameter configuration module, used to construct a long-time scale parameter table at the wind farm level, and perform system parameter configuration on the long-time scale parameter table at the wind farm level;

[0018] An instruction acquisition module, after receiving a wind farm-level long-time scale report generation instruction, obtains a wind farm-level multi-factor information source storage location and a report preset storage location based on the wind farm-level long-time scale report generation instruction;

[0019] The report generation module is used to read the configured wind farm-level long-time scale parameter table, aggregate and analyze the multi-factor information source based on the multi-factor information source storage location to generate a wind farm-level long-time scale report, and save it in the report preset storage location.

[0020] According to the third aspect of the present invention, a wind farm level long time scale report generation device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the wind farm level long time scale report generation method proposed in the first aspect of the present invention.

[0021] In one or more aspects of the present invention, a long-time scale parameter table at the wind farm level is constructed, and system parameters are configured for the long-time scale parameter table at the wind farm level; after receiving a long-time scale report generation instruction at the wind farm level, a multi-factor information source storage location and a preset report storage location at the wind farm level are obtained based on the long-time scale report generation instruction at the wind farm level; the configured long-time scale parameter table at the wind farm level is read, and the multi-factor information source is collected and analyzed based on the multi-factor information source storage location to generate a long-time scale report at the wind farm level, and the report is saved in the preset report storage location. In this case, by configuring the system parameters of the long-time scale parameter table at the wind farm level, the multi-factor information source is collected and analyzed based on the configured wind farm-level operation data table and the multi-factor information source storage location to generate a long-time scale report at the wind farm level, thereby avoiding the use of the traditional lengthy original data stream mode to generate a report. Compared with the lengthy and messy original data stream, the multi-factor information source classifies the data into elements, and the use of the multi-factor information source can simplify the data processing process, thereby improving the efficiency of wind farm-level operation evaluation.

[0022] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 A schematic flow chart of a method for generating a long-time wind farm report provided by an embodiment of the present invention is shown;

[0025] Figure 2 A schematic diagram showing a flow chart of system parameter configuration provided by an embodiment of the present invention;

[0026] Figure 3 A schematic diagram showing a flow chart of a report generation method provided by an embodiment of the present invention;

[0027] Figure 4 A schematic diagram showing a flow chart of a process of aggregating multiple factor information sources provided by an embodiment of the present invention;

[0028] Figure 5 A schematic diagram showing a flow chart of an analysis process of a multi-factor information source provided by an embodiment of the present invention;

[0029] Figure 6 A flowchart showing the automatic generation of a report by a document API provided by an embodiment of the present invention;

[0030] Figure 7 A block diagram of a wind farm-level long-time scale report generation system provided by an embodiment of the present invention is shown;

[0031] Figure 8 The invention is a block diagram of a wind farm level long time scale report generation device for implementing the wind farm level long time scale report generation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the appended claims.

[0033] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0034] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. It should also be understood that the term "and / or" used in the present invention refers to and includes any or all possible combinations of one or more associated listed items.

[0035] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0036] At present, when generating multi-month, annual or multi-year long-term operation evaluation reports for wind farms, conventional processes based on data streams are still mainly required. For example, starting from the original data stream, selecting wind turbines and exporting original data, data analysis, information extraction, report compilation and other links, the data volume and workload are large, the efficiency is low, and the cycle is long. It requires high personal ability and professional experience of wind power analysts. It usually takes days or weeks to complete the entire process to calculate the report completion time. At the same time, manual report preparation generally has problems such as the format and chart style being affected by personal style, the data in the preparation process may be wrong, and the report quality is uneven. If you change a wind farm or model, these processes need to be repeated again, which is not efficient. In addition, the existing patents related to long-term operation evaluation at the wind farm level do not involve the processing of multi-element information flows at the wind farm level and the method and system for automatic report generation. In view of the above-mentioned problems, the present invention provides a method and system for generating long-term report at the wind farm level, the main purpose of which is to improve the efficiency of wind farm-level operation evaluation.

[0037] The wind farm-level long-time scale report generation method and system of the present invention are an automatic report generation method and system for wind farm-level long-time scale operation evaluation based on multi-element information flow generated by wind power data analysis.

[0038] In the present invention, the multi-element information flow (also called multi-element information source) at the wind farm level is not the original data of each wind turbine in the wind farm, but a set of information elements named in accordance with the specification obtained by analyzing each wind turbine based on a fixed medium time scale period. For example, when a fixed medium time scale period of a wind turbine is measured on a monthly basis, a set of information elements is a summary of the results obtained by analyzing the data of this wind turbine for a whole month. Each set of information elements is composed of multiple types of information elements, including but not limited to: wind turbine data integrity statistics, daily statistics, wind resource statistics, operation curve analysis, fitting Kopt (a proportional constant for controlling the speed of the generator for optimal wind energy absorption by wind turbine blades, also known as optimal gain), cabin acceleration analysis, etc. The long time scale includes multiple fixed medium time scale periods, and the number of information element sets is equal to the number of long time scales divided by the fixed medium time scale period. Therefore, when the medium time scale period is a month, the number of information element sets is equal to the number of months.

[0039] In the present invention, the multi-factor information source of the wind farm level at a long time scale can be represented by a set L, where L = {L1, L2, ..., Ln}, n represents the number of divisions according to a fixed medium time scale period. For example, when the medium time scale period is a month and the long time scale spans from January to June of 23 years, the set L can be represented as a set of the following six subsets: {L 23年1月 , L 23年2月 , …, L 23年6月 Subset L j represents the set of wind turbines in the jth medium time scale period, j = 1, 2, ..., n, i.e., L j ={WT1, WT2, ..., WT m}, m represents the number of wind turbines to be evaluated in the wind farm. For example, the multi-factor information set of the five wind turbines in the wind farm in January 23, from wind turbine No. 1 to wind turbine No. 5, then L 23年1月 ={WT 1号风机 , WT 2号风机 , …, WT 5号风机}, subset WT k Represents the information element set of the kth wind turbine, k = 1, 2, ..., m, that is, WT k ={S1, S2, ..., S p}, p represents the number of information elements of the kth wind turbine. For example, the No. 1 wind turbine in the wind farm in January 23 has 5 types of elements, then WT 1号风机 ={S1, S2, ..., S5}. Subset S g Represents a type of information element of a wind turbine, g = 1, 2, ..., p. For example, the data completeness statistics of wind turbine No. 1 in the wind farm in January 23 can be expressed as S 数据完整度 ={wind farm number, wind turbine number, start and end time, data volume, power generation time ratio, ..., data interval}. Subset WT k The subsets corresponding to the remaining information elements are similar to data completeness.

[0040] Based on the above-mentioned multi-factor information sources, the wind farm-level long-time scale report generation method of the present invention is described in detail below with reference to the accompanying drawings.

[0041] In a first embodiment, Figure 1 FIG. 2 is a flow chart showing a method for generating a long-time wind farm report according to an embodiment of the present invention. Figure 1 As shown, the wind farm-level long-term report generation method includes:

[0042] Step S101: construct a long-time scale parameter table at the wind farm level, and perform system parameter configuration on the long-time scale parameter table at the wind farm level.

[0043] In step S101, the constructed wind farm-level long-time scale parameter table includes multiple sub-tables, which are information element sub-table, long-time scale report content customization sub-table and wind farm operation evaluation configuration file sub-table. System parameter configuration for the wind farm-level long-time scale parameter table can be regarded as system parameter configuration for each sub-table respectively.

[0044] Figure 2 A schematic diagram showing a flow chart of system parameter configuration provided by an embodiment of the present invention.

[0045] like Figure 2 As shown, the system parameter configuration of the wind farm-level long-time scale parameter table in step S101 includes information element selection (step S201), long-time scale report content customization (step S202), and wind farm operation evaluation configuration file reading (step S203). The order of the system parameter configuration process is only for illustration, and each sub-process is relatively independent and the order can be swapped arbitrarily.

[0046] In step S201, information elements that need to be collected and processed are selected, and operating parameters corresponding to the information elements are configured.

[0047] In step S202, the information flow at the wind farm level long time scale that needs to be integrated into the report is customized, including the selection and enablement of information elements, information element descriptions, table contents, table header name mapping, pictures and picture name mapping, etc. The process of customizing the information flow at the wind turbine level long time scale in the wind farm is similar to the information flow at the wind farm level long time scale.

[0048] In step S203, the set of wind turbines (i.e., subset L) in the jth medium time scale period is read. j ,j=1,2,…,n) matching wind farm operation evaluation configuration file. The wind farm operation evaluation configuration file includes wind turbine parameters, reference wind turbine power curve, wind farm wind turbine information and other information.

[0049] Step S102, after receiving the wind farm level long time scale report generation instruction, obtaining the wind farm level multi-factor information source storage location and the report preset storage location based on the wind farm level long time scale report generation instruction.

[0050] In step S102, the wind farm-level long-time scale report generation instruction includes information such as the multi-factor information source storage location and the report preset storage location, so the wind farm-level multi-factor information source storage location and the report preset storage location can be obtained based on the wind farm-level long-time scale report generation instruction. The corresponding multi-factor information source can be obtained based on the multi-factor information source storage location. The report preset storage location is used to store the final wind farm-level long-time scale report.

[0051] In step S102, based on the above-mentioned multi-factor information source, it can be known that the multi-factor information source at the wind farm level includes multiple information element sets, each information element set includes multiple categories of information elements, and the multiple categories of information elements include wind turbine data integrity statistics, daily statistics, wind resource statistics, operation curve analysis, fitting Kopt, and nacelle acceleration analysis.

[0052] Step S103, reading the configured wind farm-level long time scale parameter table, aggregating and analyzing the multi-factor information sources based on the multi-factor information source storage location to generate a wind farm-level long time scale report, and saving it in the report preset storage location.

[0053] Specifically, in step S103, the configured long-time scale parameter table of the wind farm level is read, and the multi-factor information source is aggregated and analyzed based on the storage location of the multi-factor information source to generate a long-time scale report of the wind farm level, including: reading the system parameters in the configured long-time scale parameter table of the wind farm level; based on the multi-factor information source, generating a list of wind turbine sets for multiple periods under the medium time scale; for each wind turbine set list, reading the corresponding multi-factor information source from the storage location of the multi-factor information source for aggregation, analyzing the multi-factor information source, and calling the document API to automatically generate a report process to generate a long-time scale report of the wind farm level.

[0054] Among them, the corresponding multi-factor information source is read from the storage location of the multi-factor information source for aggregation, including: generating a list of information elements to be aggregated according to the information elements in the configured wind farm-level long-time scale parameter table; judging whether the aggregation of all specified information elements has been completed, if not, retrieving and extracting the current type of information elements from the multi-factor information source to generate a list of wind turbines to be analyzed; then judging whether all wind turbine analyses have been completed, if not, determining the current wind turbine from the aggregated current type of information elements, and obtaining the current wind turbine analysis data under the current type of information elements from the multi-factor information source, analyzing the current wind turbine analysis data once to obtain the current wind turbine analysis result, until all wind turbine analyses are completed, and then re-judging whether all specified information elements have been aggregated, until all specified information elements are aggregated, and outputting the aggregation result based on the analysis results of all wind turbines.

[0055] Among them, the multi-factor information source is analyzed, including: generating a list of information elements to be analyzed at the wind farm level according to the information elements in the configured wind farm-level long-time scale parameter table; judging whether the analysis of all specified information elements has been completed, if not, retrieving and extracting the current type of information elements from the aggregated results, performing a secondary analysis on the wind turbine analysis results of all wind turbines of the current type of information elements to obtain the wind farm-level analysis results, and then re-judging whether the analysis of all specified information elements has been completed, until the analysis of all specified information elements is completed, outputting the multi-factor information flow analysis results based on the wind farm-level analysis results of all types of information elements.

[0056] Among them, the process of calling the document API to automatically generate a report generates a long-time scale report at the wind farm level, including: generating a report title and creating a directory; generating a wind farm overview based on the results of multi-factor information flow analysis and based on the configured wind farm operation evaluation configuration file; generating a wind farm level chapter to determine whether all wind farm level specified sub-contents have been completed, if not, based on the configured long-time scale report content customization to obtain the wind farm level specified sub-content to be completed; generating a wind turbine level chapter to determine whether all wind turbine level specified sub-contents have been completed, if not, based on the configured long-time scale report content customization to obtain the wind turbine level specified sub-content to be completed; automatically updating the index directory to obtain a wind farm level long-time scale report.

[0057] In this embodiment, the method for generating a long-time wind farm report in step S103 further includes automatically converting the report format of the long-time wind farm report before saving the report.

[0058] In some embodiments, Figure 3 FIG. 1 is a flow chart showing a report generation method according to an embodiment of the present invention. Figure 3 As shown, the method for generating a long-term wind farm-level report includes: reading and allocating system parameter configuration (step S301); generating a subset L of the set L; j List (step S302); aggregation of multi-element information flows and wind turbine-level analysis (step S303); wind farm-level analysis based on multi-element information flows (step S304); automatic report generation by calling document API (step S305); automatic conversion of report format (step S306); document output (step S307) and end of process (step S308).

[0059] Specifically, in step S301: read the system parameters in the configured wind farm-level long-time scale parameter table, and assign the system parameters to the corresponding sub-processes for subsequent processing.

[0060] In step S302: in the report preset storage location, a wind turbine set list of multiple periods (i.e., multiple medium time scale periods) under the medium time scale is generated, and the wind turbine set list is a subset L of the set L j List, thereby generating an information transfer subdirectory under the specified directory.

[0061] In step S303: according to the subset L j List, collect the required multi-element information flow from the multi-element information source storage location, and perform the collected wind turbine level analysis, where the collection process is as follows Figure 4If fan-level analysis is not required, it is also possible to aggregate the multi-factor information flows according to actual needs without performing the aggregated fan-level analysis.

[0062] In step S304: the wind farm level analysis of the collected multi-element information flow is performed, wherein the analysis process is as follows: Figure 5 If wind farm level analysis is not required, this step can be deleted according to actual needs.

[0063] In step S305: the process of calling the document API to automatically generate a report includes calling the document API (Application Program Interface), creating a document named according to the specification, and automatically writing the document. Figure 6 Among them, the application programming interface (API) is a set of definitions, programs and protocols that enable mutual communication between computer software through the API interface. One of the main functions of API is to provide a common function set. Programmers can reduce programming tasks by developing applications by calling API functions. API is also a kind of middleware that provides data sharing for various platforms.

[0064] In step S306: the report format is automatically converted according to the specified format. For example, the default format is word, and it can be automatically converted to pdf, htm, html and other formats supported by office.

[0065] In step S307: the document (ie, the generated report) is automatically output to the specified directory (ie, the preset storage location of the report). If the report does not need to be automatically generated, steps S305 to S307 can be deleted according to actual needs.

[0066] Figure 4 A flowchart diagram showing a process of aggregating multiple factor information sources provided by an embodiment of the present invention is shown.

[0067] like Figure 4 As shown, the collection process includes: generating a list of information elements to be collected (step S401); determining whether all specified information elements have been collected (step S402); if not, collecting the specified information elements according to the subset L j List retrieval and extraction (step S403); generate a list of fans to be analyzed (step S404); determine whether all fan analyses have been completed (step S405); if all fan analyses have not been completed, obtain the current fan from the information elements (step S406); perform fan analysis based on the current information elements (step S407); return to step S405 to re-judge until all fan analyses are completed, then return to step S402 to re-judge until all specified information elements are collected, output the collection results based on all fan analysis results, and end the process (step S408).

[0068] Specifically, in step S401, a list of information elements to be collected is generated according to the types of information elements selected in step S201.

[0069] In step S402: determine whether all designated information elements (ie, all types of information elements in the information element list) have been collected. If not, proceed to step S403 item by item (ie, proceed to step S403 for one type of information element each time).

[0070] Step S403: According to the subset L j List, from the set form L j |j=1,2,…,n->WT k |k=1,2,…,m->S g |g=1,2,…,p) to retrieve and extract the current class information elements, thereby obtaining the current class information elements of each wind turbine.

[0071] Step S404: Generate a list of wind turbines to be analyzed according to the collected current category information elements.

[0072] Step S405: Determine whether the analysis of all fans has been completed. If not, proceed to step S406 for each fan in the fan list one by one.

[0073] Step S406: Determine the current wind turbine from the collected current category information elements, and obtain the current wind turbine analysis data under the current category information elements from the multi-element information source.

[0074] Step S407: According to the wind farm operation evaluation configuration file in step S203, the current wind turbine analysis data obtained according to step 406 is used to analyze the current wind turbine analysis data to obtain the current wind turbine analysis result. Taking the current class information element as data integrity statistics and the current wind turbine as unit 1 as an example, the current wind turbine analysis data obtained in step 406 is, for example, the data integrity statistics of wind turbine No. 1 in the wind farm from January to June 23, and the change trend of the proportion of the power generation time of the wind turbine between January and June is analyzed with a monthly time interval. The change trend is the analysis result of the previous wind turbine under the current class information element. Return to step S405 for re-judgment. If it is still not completed, continue to analyze the next wind turbine under the current class information element to obtain the corresponding analysis result until all wind turbine analyses are completed, and then return to step S402 for re-judgment. If it is still not completed, enter step S403 for the next class information element until all designated information elements are collected, and output the collection result based on the analysis results of all wind turbines. The aggregated result includes data of all designated information elements obtained from the set L of multi-element information flows, and wind turbine analysis results of all wind turbines under all designated information elements.

[0075] Figure 5 A schematic flow chart showing the analysis process of multiple factor information sources provided by an embodiment of the present invention.

[0076] like Figure 5 As shown in Figure 1, the analysis process of multiple information sources includes:

[0077] Generate a list of information elements to be analyzed (step S501); determine whether the analysis of all specified information elements has been completed (step S502); if not, perform information flow matching based on the current information elements (step S503); perform wind farm analysis based on the current information elements (step S504); return to step S502 and re-judge until all specified information elements are analyzed and the multi-element information flow analysis results are output, and the process ends (step S505).

[0078] Specifically, in step S501, a list of information elements to be analyzed is generated according to the types of information elements selected in step S201.

[0079] In step S502, it is determined whether the analysis of all designated information elements has been completed. If not, the process proceeds to step S503 item by item (ie, the process proceeds to step S503 for each type of information element each time).

[0080] In step S503 , the matching process refers to: based on the current class information element, searching and extracting the current class information element from the collection result output in step 303 .

[0081] In step S504, taking the current type of information element as data completeness statistics as an example, a secondary analysis is performed on the wind turbine analysis results of all wind turbines in the data completeness statistics to obtain a wind farm-level analysis result. For example, the data completeness statistics of all wind turbines in the wind farm from January to June 2024 are obtained, and the average of the power generation time proportion of all wind turbines in the past six months is obtained from the collected multi-element information flow, and the power generation time proportions of all wind turbines in the whole field are compared. The comparison result is the wind farm-level analysis result of the data completeness statistics, and then returns to step S502 for re-judgment. If it is still not completed, enter step S503 for the next type of information element until the analysis of all specified information elements is completed, and the multi-element information flow analysis result is output based on the wind farm-level analysis results of all types of information elements.

[0082] Figure 6 A flow chart showing the automatic generation of reports by a document API provided by an embodiment of the present invention.

[0083] like Figure 6As shown, the process of automatically generating a report by the document API includes: generating a document title (step S601); creating a directory (step S602); generating a wind farm overview (step S603); generating a wind farm-level chapter (step S604); judging whether all wind farm-level designated sub-contents have been completed (step S605); if not, generating a wind farm-level sub-content chapter (step S606); writing a table that meets the search criteria, generating a number and a name (step S607); writing a picture that meets the search criteria, generating a number and a name (step S608) and returning to step S605 to continue judging until all wind farm-level designated sub-contents have been completed. content; if all wind farm-level designated sub-contents are completed, a wind turbine-level chapter is generated (step S609); it is determined whether all wind turbine-level designated sub-contents have been completed (step S610); if not, a wind turbine-level sub-content chapter is generated (step S611); the table that meets the search criteria is written, and a number and name are generated (step S612); the picture that meets the search criteria is written, and a number and name are generated (step S613) and returns to step S610 to continue judging until all wind turbine-level designated sub-contents are completed; if all wind turbine-level designated sub-contents are completed, the index directory is automatically updated (step S614); the document is written to the memory (step S615).

[0084] Specifically, in step S601: the time span and wind farm name of the multi-element information flow are automatically extracted to generate a document title.

[0085] In step S603: the aggregation result outputted from step S303 and the multi-factor information flow analysis result outputted from step S304 are obtained, and main data are extracted from the wind farm wind turbine information provided in step S203 and written into a document to form an overview of the wind farm.

[0086] In steps S606-S608: according to the long-time scale content of the wind farm level customized in step S202: a) generate wind farm-level sub-content chapter numbers and chapter titles, and convert the specified content items into document paragraphs; b) retrieve pictures that comply with the naming rules in the specified directory, generate document pictures, and generate picture numbers and names; c) retrieve tables that comply with the naming rules in the specified directory, generate document tables, and generate table numbers and names.

[0087] In step S609 to step S613: the document generation process of the long-time scale content at the wind turbine level is similar to step S606 to step S608.

[0088] It should be noted that Figure 6The automatic document writing process is just an illustration. For example, the internal and external order of steps S609-step S613 and steps S604-step S608 can be exchanged; for example, steps S609-step S613 can be deleted (i.e., the document chooses not to generate wind turbine-level evaluation content), and steps S604-step S608 can also be deleted (i.e., the document chooses not to generate wind farm-level evaluation content).

[0089] Step S103 of this embodiment automatically connects in series the steps of reading and allocating system parameter configuration, collecting wind farm-level information sources, processing information, automatically generating reports by calling the document interface, automatically converting the report format, and outputting documents, thereby turning the previously tedious processes into fully automatic processes, saving manpower and material resources, and achieving rapid report output.

[0090] In this embodiment, the method for generating a long-time wind farm report further includes: reviewing the long-time wind farm report and then sending it to the target user end. The review process includes, for example, checking the conclusion and deleting the content as needed.

[0091] In the wind farm-level long time scale report generation method of the embodiment of the present invention, a wind farm-level long time scale parameter table is constructed, and the system parameter configuration is performed on the wind farm-level long time scale parameter table; after receiving the wind farm-level long time scale report generation instruction, the wind farm-level multi-factor information source storage location and the report preset storage location are obtained based on the wind farm-level long time scale report generation instruction; the configured wind farm-level long time scale parameter table is read, and the multi-factor information source is collected and analyzed based on the multi-factor information source storage location to generate a wind farm-level long time scale report, and saved in the report preset storage location. In this case, by configuring the system parameters of the wind farm-level long time scale parameter table, the multi-factor information source is collected and analyzed based on the configured wind farm-level operation data table and the multi-factor information source storage location to generate a wind farm-level long time scale report, which avoids the use of the traditional lengthy original data flow mode to generate a report. Compared with the lengthy and messy original data flow, the multi-factor information source classifies the data into elements, and the use of the multi-factor information source can simplify the data processing process, thereby improving the efficiency of wind farm-level operation evaluation.

[0092] In the method of the present invention, the flow of process links is standardized and optimized, and the key elements of information flow are integrated to establish a long-term operation evaluation process for wind farms, thereby improving the efficiency of operation evaluation at the long-term wind farm level, which has the following multiple beneficial effects:

[0093] a) The method of the present invention establishes a long-term operation evaluation process at the wind farm level based on multi-element information flow, changes the traditional lengthy data flow-based model, and transforms the previous fragmented and inefficient process into a standardized automatic process, thereby improving the efficiency of long-term operation evaluation at the wind farm level.

[0094] b) By formulating process node rules and report generation rules under long time scales, the current non-standard status quo of long time scale process interfaces, file naming, data naming, and free play of document styles can be changed to lay the foundation for rapid report generation.

[0095] c) By adapting the operation evaluation of different OEMs and models under the same wind power operator, the current situation where wind farm-level analysis is difficult to integrate information from different OEMs and different models is changed, and the goal of making the same report compatible with different OEMs and different models is achieved.

[0096] d) The method of the present invention unifies the report style and content over a long time scale, changes the current situation of different report formats and styles and uneven quality, and realizes high-quality standard report output.

[0097] e) The method of the present invention reduces the probability of errors in report data and content over a long time scale by transforming traditional manual compilation into automatic reporting, and reduces the professional ability and experience requirements of wind power analysts.

[0098] f) The method of the present invention can flexibly customize the content of long-term reports in different scenarios.

[0099] g) The method of the present invention changes the status quo of traditional wind farm-level long-time scale report generation measured in days or weeks, and realizes minute-level rapid report generation based on information flow.

[0100] The following is a system embodiment of the present invention, which can be used to implement the method embodiment of the present invention. For details not disclosed in the system embodiment of the present invention, please refer to the method embodiment of the present invention.

[0101] See also Figure 7 , Figure 7 A block diagram of a wind farm-level long-time scale report generation system provided by an embodiment of the present invention is shown. The wind farm-level long-time scale report generation system can be implemented as all or part of the system through software, hardware, or a combination of both. The wind farm-level long-time scale report generation system 10 includes a parameter configuration module 11, an instruction acquisition module 12, and a report generation module 13, wherein:

[0102] The parameter configuration module 11 is used to construct a long-time scale parameter table of the wind farm level and perform system parameter configuration on the long-time scale parameter table of the wind farm level;

[0103] The instruction acquisition module 12, after receiving the wind farm-level long-time scale report generation instruction, obtains the wind farm-level multi-factor information source storage location and the report preset storage location based on the wind farm-level long-time scale report generation instruction;

[0104] The report generation module 13 is used to read the configured wind farm-level long-time scale parameter table, collect and analyze the multi-factor information sources based on the multi-factor information source storage location to generate a wind farm-level long-time scale report, and save it in the report preset storage location.

[0105] Optionally, in the parameter configuration module 11, system parameter configuration of the long-time scale parameter table at the wind farm level includes information element selection, long-time scale report content customization, and wind farm operation evaluation configuration file reading. The wind farm operation evaluation configuration file includes wind turbine parameters, reference wind turbine power curve, and wind farm wind turbine information.

[0106] Optionally, in the instruction acquisition module 12, the multi-factor information source at the wind farm level includes multiple information element sets, each information element set includes multiple categories of information elements, and the multiple categories of information elements include wind turbine data integrity statistics, daily statistics, wind resource statistics, operation curve analysis, fitting Kopt, and cabin acceleration analysis.

[0107] Optionally, the report generation module 13 is specifically used to: read the system parameters in the configured wind farm-level long-time scale parameter table; based on the multi-factor information source, generate a wind turbine set list of multiple periods under the medium time scale; for each wind turbine set list, read the corresponding multi-factor information source from the multi-factor information source storage location for aggregation, and analyze the multi-factor information source, and call the document API to automatically generate a report process to generate a wind farm-level long-time scale report.

[0108] Optionally, the report generation module 13 is specifically used to: generate a list of information elements to be collected based on the information elements in the configured wind farm-level long-time scale parameter table; determine whether the collection of all specified information elements has been completed, and if not, retrieve and extract the current type of information elements from the multi-element information source to generate a list of wind turbines to be analyzed; then determine whether all wind turbine analyses have been completed, and if not, determine the current wind turbine from the collected current type of information elements, and obtain the current wind turbine analysis data under the current type of information elements from the multi-element information source, analyze the current wind turbine analysis data once to obtain the current wind turbine analysis result, until all wind turbine analyses are completed, and then re-determine whether all specified information elements have been collected, until all specified information elements are collected, and output the collection result based on the analysis results of all wind turbines.

[0109] Optionally, the report generation module 13 is specifically used to: generate a list of information elements to be analyzed at the wind farm level based on the information elements in the configured wind farm-level long-time scale parameter table; determine whether the analysis of all specified information elements has been completed, if not, retrieve and extract the current type of information elements from the aggregated results, perform a secondary analysis on the wind turbine analysis results of all wind turbines of the current type of information elements to obtain wind farm-level analysis results, and then re-determine whether the analysis of all specified information elements has been completed, until the analysis of all specified information elements is completed, and output the multi-element information flow analysis results based on the wind farm-level analysis results of all types of information elements.

[0110] Optionally, the report generation module 13 is specifically used to: generate a report title and create a directory; generate a wind farm overview based on the results of the multi-factor information flow analysis and based on the configured wind farm operation assessment configuration file; generate a wind farm-level chapter to determine whether all wind farm-level specified sub-contents have been completed, if not, obtain the wind farm-level specified sub-contents to be completed based on the configured long-time scale report content customization; generate a wind turbine-level chapter to determine whether all wind turbine-level specified sub-contents have been completed, if not, obtain the wind turbine-level specified sub-contents to be completed based on the configured long-time scale report content customization; automatically update the index directory to obtain a wind farm-level long-time scale report.

[0111] Optionally, the wind farm level long time scale report generation system further includes automatically converting the report format of the wind farm level long time scale report before saving the wind farm level long time scale report.

[0112] It should be noted that the wind farm-level long time scale report generation system provided in the above embodiment only uses the division of the above functional modules as an example when executing the wind farm-level long time scale report generation method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the wind farm-level long time scale report generation device is divided into different functional modules to complete all or part of the functions described above. In addition, the wind farm-level long time scale report generation system provided in the above embodiment and the wind farm-level long time scale report generation method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0113] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0114] In the wind farm-level long time scale report generation system of the embodiment of the present invention, the parameter configuration module is used to construct a wind farm-level long time scale parameter table, and perform system parameter configuration on the wind farm-level long time scale parameter table; after receiving the wind farm-level long time scale report generation instruction, the instruction acquisition module obtains the wind farm-level multi-factor information source storage location and the report preset storage location based on the wind farm-level long time scale report generation instruction; the report generation module is used to read the configured wind farm-level long time scale parameter table, and collect and analyze the multi-factor information source based on the multi-factor information source storage location to generate a wind farm-level long time scale report, and save it in the report preset storage location. In this case, by configuring the system parameters of the wind farm-level long time scale parameter table, collecting and analyzing the multi-factor information source based on the configured wind farm-level operation data table and the multi-factor information source storage location to generate a wind farm-level long time scale report, it is avoided to generate a report using the traditional lengthy original data stream mode. Compared with the lengthy and messy original data stream, the multi-factor information source classifies the data into elements, and the use of the multi-factor information source can simplify the data processing process, thereby improving the efficiency of wind farm-level operation evaluation.

[0115] According to an embodiment of the present invention, the present invention also provides a wind farm-level long-time scale report generation device, a non-transitory computer-readable storage medium storing computer instructions (hereinafter referred to as readable storage medium) and a computer program product.

[0116] Figure 8 1 is a block diagram of a wind farm level long time scale report generation device for implementing a wind farm level long time scale report generation method of an embodiment of the present invention. The wind farm level long time scale report generation device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The wind farm level long time scale report generation device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable electronic devices and other similar computing devices. The components, connections and relationships of the components, and functions of the components shown in the present invention are merely examples and are not intended to limit the implementation of the present invention described and / or required in the present invention.

[0117] like Figure 8As shown, the wind farm level long time scale report generating device 20 includes a computing unit 21, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 22 or a computer program loaded from a storage unit 28 to a random access memory (RAM) 23. In the RAM 23, various programs and data required for the operation of the wind farm level long time scale report generating device 20 can also be stored. The computing unit 21, the ROM 22, and the RAM 23 are connected to each other through a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0118] Multiple components in the wind farm level long time scale report generating device 20 are connected to the I / O interface 25, including: an input unit 26, such as a keyboard, a mouse, etc.; an output unit 27, such as various types of displays, speakers, etc.; a storage unit 28, such as a disk, an optical disk, etc., which is communicatively connected to the computing unit 21; and a communication unit 29, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 29 allows the wind farm level long time scale report generating device 20 to exchange information / data with other wind farm level long time scale report generating devices through a computer network such as the Internet and / or various telecommunication networks.

[0119] The computing unit 21 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 21 performs the various methods and processes described above, such as executing the wind farm level long time scale report generation method. For example, in some embodiments, the wind farm level long time scale report generation method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 28. In some embodiments, part or all of the computer program may be loaded and / or installed on the wind farm level long time scale report generation device 20 via the ROM 22 and / or the communication unit 29. When the computer program is loaded into the RAM 23 and executed by the computing unit 21, one or more steps of the wind farm level long time scale report generation method described above may be performed. Alternatively, in other embodiments, the computing unit 21 may be configured to execute the wind farm level long time scale report generation method in any other appropriate manner (for example, by means of firmware).

[0120] Various embodiments of the systems and techniques described above in the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or wind farm-level long-time scale report generation device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or electronic device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage electronic devices, magnetic storage electronic devices, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0124] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by 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), the Internet, and a blockchain network.

[0125] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0126] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and the present invention is not limited here.

[0127] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating long-term wind farm-level reports, characterized in that: include: Constructing a long-time scale parameter table at the wind farm level, and performing system parameter configuration on the long-time scale parameter table at the wind farm level; After receiving the wind farm-level long-time scale report generation instruction, obtaining the wind farm-level multi-factor information source storage location and the report preset storage location based on the wind farm-level long-time scale report generation instruction; The configured wind farm-level long time scale parameter table is read, and based on the multi-factor information source storage location, multi-factor information sources are aggregated and analyzed to generate a wind farm-level long time scale report, and the report is saved in the preset storage location.

2. The method for generating a long-time wind farm report according to claim 1, characterized in that: The system parameter configuration of the wind farm-level long-time scale parameter table includes information element selection, long-time scale report content customization, and wind farm operation evaluation configuration file reading. The wind farm operation evaluation configuration file includes wind turbine parameters, reference wind turbine power curve, and wind farm wind turbine information.

3. The method for generating a long-term report at a wind farm level according to claim 2, characterized in that: The multi-factor information source at the wind farm level includes multiple information element sets, each of which includes multiple types of information elements, including wind turbine data integrity statistics, daily statistics, wind resource statistics, operation curve analysis, fitting Kopt, and cabin acceleration analysis.

4. The method for generating a long-time wind farm report according to claim 3, characterized in that: The step of reading the configured wind farm-level long time scale parameter table and aggregating and analyzing the multi-factor information source based on the storage location of the multi-factor information source to generate a wind farm-level long time scale report includes: Read the system parameters in the configured wind farm-level long-time scale parameter table; Based on the multi-factor information source, a list of wind turbine sets for multiple periods at a medium time scale is generated; For each wind turbine set list, the corresponding multi-factor information source is read from the multi-factor information source storage location for aggregation, and the multi-factor information source is analyzed, and the process of automatically generating a report by calling the document API generates a long-time scale report at the wind farm level.

5. The method for generating a long-time wind farm report according to claim 4, characterized in that: The step of reading the corresponding multi-factor information source from the multi-factor information source storage location for aggregation includes: Generate a list of information elements to be collected according to the configured information elements in the wind farm-level long time scale parameter table; Determine whether all specified information elements have been collected. If not, retrieve and extract the current type of information elements from the multi-element information source to generate a list of wind turbines to be analyzed. Then determine whether all wind turbine analyses have been completed. If not, determine the current wind turbine from the collected current type of information elements, obtain the current wind turbine analysis data under the current type of information elements from the multi-element information source, analyze the current wind turbine analysis data once to obtain the current wind turbine analysis result, until all wind turbine analyses are completed. Then re-determine whether all specified information elements have been collected. Until all specified information elements are collected, output the collection result based on all wind turbine analysis results.

6. The method for generating a long-time wind farm report according to claim 5, characterized in that: The analysis of multiple factor information sources includes: Generate a list of wind farm-level information elements to be analyzed according to the configured information elements in the wind farm-level long time scale parameter table; Determine whether the analysis of all specified information elements has been completed. If not, retrieve and extract the current type of information elements from the aggregated results, perform secondary analysis on the wind turbine analysis results of all wind turbines of the current type of information elements to obtain wind farm-level analysis results, and then re-determine whether the analysis of all specified information elements has been completed. Until the analysis of all specified information elements is completed, output the multi-element information flow analysis results based on the wind farm-level analysis results of all types of information elements.

7. The method for generating a long-time wind farm report according to claim 6, characterized in that: The process of automatically generating a report by calling the document API generates a long-term report at the wind farm level, including: Generate report title and create table of contents; Generate a wind farm overview based on the multi-factor information flow analysis results and based on a configured wind farm operation assessment profile; Generate a wind farm level chapter, determine whether all wind farm level specified sub-contents have been completed, and if not, obtain the wind farm level specified sub-contents to be completed based on the configured long time scale report content customization; Generate a wind turbine level chapter, determine whether all wind turbine level specified sub-contents have been completed, and if not, obtain the wind turbine level specified sub-contents to be completed based on the configured long time scale report content customization; The index catalog is automatically updated, resulting in long-term reports at the wind farm level.

8. The method for generating a long-time wind farm report according to claim 1, characterized in that: Before saving the wind farm-level long-time-scale report, it is also necessary to automatically convert the report format of the wind farm-level long-time-scale report.

9. A wind farm-level long-time scale report generation system, characterized in that: include: A parameter configuration module, used to construct a long-time scale parameter table at the wind farm level, and perform system parameter configuration on the long-time scale parameter table at the wind farm level; An instruction acquisition module, after receiving a wind farm-level long-time scale report generation instruction, obtains a wind farm-level multi-factor information source storage location and a report preset storage location based on the wind farm-level long-time scale report generation instruction; The report generation module is used to read the configured wind farm-level long-time scale parameter table, aggregate and analyze the multi-factor information source based on the multi-factor information source storage location to generate a wind farm-level long-time scale report, and save it in the report preset storage location.

10. A wind farm-level long-time scale report generation device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the wind farm-level long-time scale report generation method described in any one of claims 1-8.

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