Wind power blade test data query method, system and equipment and storage medium

Abstract

The invention provides a wind power blade test data query method, system and device and a storage medium, and the method comprises the steps: obtaining test data of multi-source fusion, the test data comprising blade test experimental data, laboratory state record data, test blade design parameters, test blade manufacturing parameters, and test blade damage and maintenance data; cleaning and uniformizing the test data to obtain imported data; classifying the imported data according to a preset category, and integrating the imported data into a wind power blade test database according to a classification result; obtaining a query request and performing legality verification, wherein the query request carries a blade model and a test condition; and according to the blade model and the test working condition, performing traversal query in the wind power blade test database, and performing fatigue query judgment to generate query data. According to the method, the test data can be efficiently and reliably queried, the test data can be fully and efficiently utilized, and the quality of subsequent iteration products can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method and system for querying wind turbine blade test data, an electronic device and a computer-readable storage medium. Background Art

[0002] In the prior art, blade test data and damage are manually collated, summarized and queried, but blade test and damage data are not systematically managed. The test data has poor traceability, which can easily lead to data omissions and errors, resulting in defects in early designs not being identified and corrected, causing these defects to recur in subsequent iterative designs, creating new product design risks.

[0003] In the prior art, the manual method of collecting blade test data and damage has the following disadvantages:

[0004] First, the data processing efficiency is low. Due to the large number of blades tested, a large amount of test data is generated, and the test data is complex and intertwined. Therefore, manual processing requires a lot of time to organize, classify and summarize the blade test data, and it is impossible to query the required test data intuitively and efficiently, which affects the progress and efficiency of the project.

[0005] Second, the degree of automation is low. The complexity of wind turbine blade design, the multilateral nature of the test process, the instability of the sensor working state, and the interference of the data acquisition environment lead to the frequent appearance of duplicate values, blank values, and abnormal values ​​in the test data, which seriously affects the effectiveness and reliability of data analysis. Existing technical solutions usually rely on manual analysis to deal with the removal of duplicate values, abnormal values, and the filling of empty values, which cannot be automated and intelligent. When faced with a large amount of data, not only is the workload large, but the coverage of data analysis is not comprehensive, and due to human factors, it may also lead to problems such as missed detection and false detection. In addition, in the research and development process of wind turbine blades, the analysis and comparison of test data of different models and different test conditions, as well as the accurate query of blade damage and maintenance data during the test cycle, still mainly rely on manual operation. This single method not only increases labor costs, but also increases the risk of errors in data analysis and processing.

[0006] Third, it is prone to omissions and errors. When manually processing a large amount of test data, omissions and errors occur in the container, including omissions and errors in data import, classification errors, calculation errors, etc. Such omissions and errors will lead to inaccurate test data, affecting subsequent test data analysis and decision-making, resulting in the failure to identify and correct defects in early designs, causing these defects to recur in subsequent iterative designs, forming new product design risks.

[0007] Fourth, data inconsistency. Different engineers may have different understandings and processing methods of test data, resulting in inconsistent test data classification and calculation standards, affecting the overall consistency and comparability of test data, and limiting the effectiveness and efficiency of test data processing and utilization.

[0008] Fifth, poor traceability. When manually processing data, there is usually a lack of detailed operation records and logs, making it difficult to trace every step of data processing, which makes it difficult to find and correct errors.

[0009] Sixth, low data utilization. Manually processed data is often difficult to deeply mine and analyze, resulting in low data utilization and failure to fully utilize the value of test data, limiting the potential of test data to drive the design of new wind turbine blade products.

[0010] Therefore, there is an urgent need for a wind turbine blade test data query method that can quickly and reliably query blade test data and fully utilize the test data to improve product quality. Summary of the invention

[0011] Based on this, it is necessary to provide a wind turbine blade test data query method, system, electronic device and computer-readable storage medium to address the above technical issues.

[0012] A method for querying wind turbine blade test data comprises the following steps: obtaining multi-source fused test data, the test data comprising blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data; cleaning and unifying the test data to obtain imported data; classifying the imported data according to preset categories, and integrating the imported data into a wind turbine blade test database according to the classification results; obtaining a query request, and performing a validity verification on the query request, the query request carrying a blade model and a test condition; performing a traversal query in the wind turbine blade test database according to the blade model and the test condition, and performing a fatigue query judgment, and generating query data according to the judgment result and the query result.

[0013] In one of the embodiments, the test data is cleaned and unified to obtain imported data, including: traversing to determine whether the test data has duplication or inconsistent data types, and if so, deleting the corresponding test data; traversing to determine whether the test data has blank values, and if so, filling the missing values ​​by filling default values ​​or interpolation methods; traversing to determine whether the test data has abnormal test data, and if so, deleting the corresponding abnormal test data; unifying the date format of the processed test data to complete data cleaning; converting the data type, standardizing the format and arraying the cleaned test data to obtain imported data.

[0014] In one embodiment, the traversal to determine whether there is abnormal test data in the test data includes: calculating the average value of the data set according to all the test data, and sequentially calculating the distance between the test data point and the average value of the data set, the formula is:

[0015]

[0016] Wherein, Z is the standard score, x is the collected data point, μ is the mean of the data set, and ρ is the standard deviation of the data set; a set standard score threshold is obtained, the set standard score threshold is compared with the standard scores of all data points, and abnormal test data is obtained according to the comparison result.

[0017] In one embodiment, the preset categories include test types, test conditions, damage types and basic parameter categories; wherein the test types include resistance test, front static test, fatigue test and post static test; the test conditions include My-max, My-min, Mx-max, Mx-min, swing fatigue and swing fatigue.

[0018] In one of the embodiments, the obtaining of the query request and the verification of the legitimacy of the query request include: obtaining the query request, the query request carrying a data query password; comparing the data query password with a preset query password, and verifying the legitimacy of the query request.

[0019] In one embodiment, the fatigue query judgment includes: obtaining fatigue test data according to the test type, and calculating equivalent fatigue test data according to the fatigue test data, and the formula is:

[0020]

[0021] In the formula, F dl represents the equivalent amplitude load, N is the number of cycles, is the amplitude load of the i-th cycle, m is the slope parameter of the SN curve of the test material, m=10, K dl is the load stress transfer coefficient.

[0022] In one of the embodiments, it also includes: drawing a static chart based on the query data, the static chart includes a resistance test data table, a blade section data table, a blade disturbance table, a blade section strain data table and a fatigue test load table.

[0023] A wind turbine blade test data query system is used to implement a wind turbine blade test data query method as described above, comprising: a test data acquisition module, used to acquire multi-source fused test data, the test data including blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data; a test data processing module, used to clean and unify the test data to obtain imported data; a test data classification module, used to classify the imported data according to preset categories, and integrate the imported data into a wind turbine blade test database according to the classification results; a query request acquisition module, used to acquire a query request and verify the legitimacy of the query request, the query request carrying a blade model and a test condition; a query data generation module, used to perform a traversal query in the wind turbine blade test database according to the blade model and the test condition, and perform a fatigue query judgment, and generate query data according to the judgment result and the query result.

[0024] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a method for querying wind turbine blade test data described in the above-mentioned embodiments are implemented.

[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for querying wind turbine blade test data as described in the above-mentioned embodiments.

[0026] Compared with the prior art, the advantages and beneficial effects of the present invention are: obtaining multi-source fused test data, including blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data, so as to obtain more comprehensive test data; cleaning and consistency processing of the test data to obtain imported data, realizing unified processing of multi-source data, reducing the risk brought by manual intervention, and improving the accuracy of data, so as to facilitate subsequent data management; classifying the imported data according to preset categories, and integrating the imported data into the wind turbine blade test database according to the classification results, realizing the integration of multi-source test data of wind turbine blades, facilitating systematic management of data, and improving the traceability of data; obtaining a query request and performing a legality verification, and the query request carries the blade model and the test condition, which is used for traversal query in the wind turbine blade test database, and fatigue query judgment, generating query data according to the judgment result and the query result, realizing efficient and reliable data statistical query, significantly improving the efficiency and accuracy of wind turbine blades in the research and development, production, testing and maintenance process, and being able to realize full utilization of test data, which helps to avoid subsequent iterative design risks and improve the quality of subsequent iterative products. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a flow chart of a method for querying wind turbine blade test data in an embodiment;

[0028] Figure 2 It is a structural schematic diagram of a wind turbine blade test data query system in one embodiment;

[0029] Figure 3 A flowchart of a wind turbine blade test data query system in one embodiment;

[0030] Figure 4 A schematic diagram of a query page of a wind turbine blade test data query system in one embodiment;

[0031] Figure 5 FIG. 1 is a schematic diagram of the internal structure of an electronic device in an embodiment. DETAILED DESCRIPTION

[0032] Before describing the specific embodiments of the present invention, the overall concept of the present invention is described as follows:

[0033] The present invention is mainly developed based on the research and development, production, testing and maintenance processes of wind turbine blades. Currently, due to the large number of wind turbine blades tested, a large amount of test data is generated in multiple stages. The test data are complex and need to be processed manually, which has low efficiency and reliability, and the test data cannot be fully utilized.

[0034] Therefore, the present invention proposes a wind turbine blade test data query method, which obtains multi-source fused test data, including blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data, so as to obtain more comprehensive test data; the test data is cleaned and uniformly processed to obtain imported data, so as to realize unified processing of multi-source data, reduce the risk brought by manual intervention, and improve the accuracy of data, so as to facilitate subsequent data management; the imported data is correspondingly classified according to preset categories, and the imported data is integrated into the wind turbine blade test database according to the classification results, so as to realize the integration of multi-source test data of wind turbine blades, facilitate the systematic management of data, and improve the traceability of data; a query request is obtained and a legality verification is performed, and the query request carries the blade model and the test condition, which is used for traversal query in the wind turbine blade test database, and fatigue query judgment is performed, and query data is generated according to the judgment result and the query result, so as to realize efficient and reliable data statistical query, significantly improve the efficiency and accuracy of wind turbine blades in the research and development, production, testing and maintenance processes, and can realize full use of test data, which helps to avoid subsequent iterative design risks and improve the quality of subsequent iterative products.

[0035] After introducing the overall concept of the present invention, in order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific implementation methods combined with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] In one embodiment, Figure 1 As shown, a method for querying wind turbine blade test data is provided, comprising the following steps:

[0037] Step S101, acquiring multi-source fused test data, the test data including blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data.

[0038] Specifically, based on data such as wind turbine blade test experiment data acquisition equipment, laboratory status record data, test blade basic design parameters, test blade production parameters, test blade loss and maintenance records, multi-source fused test data of wind turbine blades is obtained. By acquiring multi-source data, full coverage of data for the entire cycle of blade research and development, production, testing and maintenance is achieved, so as to make full use of test data.

[0039] Step S102, cleaning and unifying the test data to obtain imported data.

[0040] Specifically, after obtaining the multivariate fusion test data, the test data is cleaned, and duplicate values, null values ​​and outliers are detected and processed. At the same time, the cleaned test data is consistent to obtain imported data, ensuring the accuracy and consistency of the data and improving data processing efficiency.

[0041] Among them, step S102 includes: traversing to determine whether there are duplications or inconsistent data types in the test data, if so, deleting the corresponding test data; traversing to determine whether there are blank values ​​in the test data, if so, filling the missing values ​​by filling default values ​​or interpolation methods; traversing to determine whether there are abnormal test data in the test data, if so, deleting the corresponding abnormal test data; unifying the date format of the processed test data to complete data cleaning; converting the data type, standardizing the format and arraying the cleaned test data to obtain imported data.

[0042] Specifically, the program code traverses the data to determine whether there are duplications or whether a certain piece of data is inconsistent with the overall type. If the program determines that it is true, the corresponding test data is deleted, thereby achieving automatic identification and statistics of the data and intelligent elimination of duplicate data, thereby realizing data deduplication processing.

[0043] The program code traverses the data to determine whether there are blank values ​​in the test data. If the program judges that there are blank values, the missing values ​​are filled by filling the default value or interpolation method to achieve the missing value filling operation. The interpolation method is calculated as follows: Function: S(x)∈C 2 [a,b], indicating that the function S(x) has a continuous second-order derivative on the closed interval [a,b], and in each small interval [x i ,x i+1 ] is a cubic polynomial S i (x), a set of n+1 test data points (x0,y0),(x1,y1),...,(x n ,y n ), where a = x0 <x1...<x n = b is the data point read by the program, then S(x) is the data point x0, x1, ..., x n The interpolation function on is composed of n cubic polynomials S i (x) yields:

[0044] S i (x i )=y i and S i (x i+1 )=y i+1 , where i = 0, 1, ..., n-1;

[0045] S' i (x i+1 )=S' i+1 (x i+1 ) and S” i (x i+1 )=S” i+1 (x i+1 ), where i = 0, 1, ..., n-2.

[0046] The program code traverses the data to determine whether there is abnormal test data in the test data. If the program determines that it is true, the corresponding abnormal test data is deleted to implement the removal operation of the abnormal test data.

[0047] Among them, traversing and judging whether there is abnormal test data in the test data includes: calculating the average value of the data set according to all test data, and calculating the distance between the test data point and the average value of the data set in turn. The formula is:

[0048]

[0049] Where Z is the standard score, x is the collected data point, μ is the mean of the data set, and ρ is the standard deviation of the data set. The set standard score threshold is obtained, and the set standard score threshold is compared with the standard scores of all data points, and the abnormal test data is obtained according to the comparison result.

[0050] Specifically, a test data set is constructed based on all the test data, the average value of the data set is calculated, and the distance between the test data in the test data set and the average value of the data set is calculated in turn using the Z-score (standard score); the set standard score threshold is obtained, the standard score of the data point is compared with the standard score threshold, and the abnormal test data is obtained according to the comparison result. For example, if the standard score threshold is set to ±3, then the data point with a Z value greater than 3 or less than -3 is the abnormal test data, thereby realizing the identification of abnormal test data far from the average value, so as to realize the elimination of abnormal data.

[0051] The date format of the test data processed through the above three steps is unified, for example, converted into the standard ISO 8601 format, to complete all data cleaning operations.

[0052] The data type of the cleaned test data is converted. For example, the data is converted into floating point format and string format according to the settings through program code; the data format of the test data is standardized, and the data collected by the acquisition device is uniformly dimensionalized; the data structure is adjusted, and the test data is arrayed to finally obtain the imported data.

[0053] The above steps can be used to achieve unified processing of test data, improve the overall consistency and comparability of test data, so as to improve the processing efficiency and utilization effect of subsequent test data, and improve the efficiency of data processing.

[0054] Step S103, classifying the imported data according to preset categories, and integrating the imported data into a wind turbine blade test database according to the classification results.

[0055] Specifically, after the test data processing is completed and the imported data is obtained, the imported data is classified according to the preset categories, and the data can be classified according to the test type, test condition, damage type and basic parameter type of the imported data to obtain the classification results corresponding to each category. According to the classification results, the imported data is integrated into the wind turbine blade test database, and the test data is stored in the database to obtain a wind turbine blade test database with a clear data structure, which is convenient for data query and acquisition, and can ensure the reliability and validity of the data, and facilitate the reuse of data. In addition, it is also possible to analyze and compare test data of multiple categories to achieve accurate query of blade damage and maintenance data within the test cycle.

[0056] Among them, the preset categories include test types, test conditions, damage types and basic parameter categories; among them, the test types include resistance test, front static test, fatigue test and rear static test; the test conditions include My-max, My-min, Mx-max, Mx-min, swing fatigue and swing fatigue.

[0057] Specifically, resistance testing is usually used to verify the effectiveness of the lightning protection system. The lightning protection conductor is responsible for directing lightning current to the ground, reducing the risk of blade damage caused by lightning strikes. By testing the resistance value, it can be determined whether the resistance value of the conductor meets the design requirements, ensuring that it can work normally when a lightning strike occurs, and preventing the blades from being unable to effectively conduct current due to high resistance, which may lead to blade damage or wind turbine shutdown.

[0058] Pre-static test and post-static test are used to evaluate the mechanical properties of materials under static load. Among them, the pre-static test is to verify the structural integrity and strength of the blade under the design load. By applying a certain static load, the deformation, stress distribution and other key parameters of the blade in the initial state can be detected to ensure that it can withstand the static stress requirements in actual working conditions. This step is a necessary process to ensure that the blade structure meets the design requirements before entering the fatigue test. The purpose of the post-static test is to evaluate whether the blade maintains its design strength after long-term cyclic load. Wind turbine blades will be subjected to repeated wind loads during long-term operation, which may cause fatigue damage to the material. Through the post-fatigue static test, it can be tested whether the blade can still withstand the static design load after experiencing fatigue load, and verify its residual strength and safety. By comparing the test results of the two, it can reflect the changes in the material and structural properties of the blade after the fatigue cycle, evaluate whether there is damage, strength reduction or stiffness degradation, verify its durability, stability and safety within the design life, and detect whether the blade still meets the design requirements within the service life to avoid failure due to fatigue damage in actual operation.

[0059] The purpose of fatigue testing is to simulate long-term operating environments, evaluate the structural integrity and durability of the blades under repeated loads, and ensure their safe and reliable operation within their design life.

[0060] The safety and reliability of wind turbine blades can be ensured through performance evaluation of the aforementioned various test types.

[0061] The My-max and My-min test conditions correspond to the maximum and minimum displacements or forces in a certain direction, respectively, and are used to evaluate the maximum and minimum load-bearing capacity of wind turbine blades in that direction; the Mx-max and Mx-min test conditions are tests of the maximum and minimum displacements or forces in another direction, and are also used to evaluate the load-bearing capacity of wind turbine blades. Swing fatigue and flapping fatigue tests are used to evaluate the durability and stability of wind turbine blades under long-term repeated swinging or flapping motions, ensuring the long-term reliability and safety of wind turbine blades. Through the aforementioned multiple test conditions, the performance of wind turbine blades can be comprehensively evaluated to ensure their stability and safety under various actual working conditions.

[0062] Step S104, obtaining a query request and verifying the legitimacy of the query request, wherein the query request carries the blade model and the test condition.

[0063] Specifically, when it is necessary to query the wind turbine blade test data, the query request is sent to the corresponding system by running the program. The system obtains the query request. In order to ensure data security, the query request needs to be verified for legitimacy before data query is performed based on the query request. The query request carries the blade model and test conditions to facilitate corresponding test data query based on the blade model and test conditions.

[0064] Wherein, step S104 includes: obtaining a query request, the query request carrying a data query password; comparing the data query password with a preset query password, and verifying the legitimacy of the query request.

[0065] Specifically, in order to ensure the security of blade test data, when making a query, the user needs to simultaneously type in the data query password. After the system obtains the user's query request, it parses the data query password carried in the query request, compares the data query password with the preset query password, and after determining the legitimacy of the query request, performs data query based on the query request, thereby ensuring the security of wind turbine blade data.

[0066] Step S105, performing a traversal query in the wind turbine blade test database according to the blade model and the test condition, and performing a fatigue query judgment, and generating query data according to the judgment result and the query result.

[0067] Specifically, the blade model and test condition are obtained according to the query request analysis and used as query parameters to perform a traversal query in the wind turbine blade test database to obtain the query result. At the same time, it is determined whether there is a fatigue test data query demand. If so, the fatigue data processing calculation is run to obtain the judgment result. The query data is generated based on the query result and the judgment result, and stored in the data transfer point to facilitate subsequent data acquisition, thereby achieving full utilization of the test data, helping to avoid early design defects, reduce new product design risks, and improve the quality of subsequent iterative products.

[0068] The fatigue query and judgment step includes: obtaining fatigue test data according to the test type, and calculating equivalent fatigue test data according to the fatigue test data, and the formula is:

[0069]

[0070] In the formula, F dl represents the equivalent amplitude load, N is the number of cycles, is the amplitude load of the i-th cycle, m is the slope parameter of the SN curve of the test material, m=10, K dlis the load stress transfer coefficient.

[0071] Specifically, when a fatigue test data query demand is detected in a query request, fatigue test data is obtained in a wind turbine blade test database according to the test type, and statistical analysis is performed on the fatigue test data. Based on the equivalent damage theory, equivalent fatigue test data is calculated to obtain equivalent fatigue test data, thereby realizing a query on the fatigue test data for subsequent data processing and viewing.

[0072] In this embodiment, multi-source fused test data is obtained, including blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data, so as to obtain more comprehensive test data; the test data is cleaned and consistent to obtain imported data, so as to realize unified processing of multi-source data, reduce the risk brought by manual intervention, and improve the accuracy of data, so as to facilitate subsequent data management; the imported data is classified according to preset categories, and the imported data is integrated into the wind turbine blade test database according to the classification results, so as to realize the integration of multi-source test data of wind turbine blades, facilitate the systematic management of data, and improve the traceability of data; a query request is obtained and the legality is verified, and the query request carries the blade model and the test condition, which is used for traversal query in the wind turbine blade test database, and fatigue query judgment is performed, and query data is generated according to the judgment result and the query result, so as to realize efficient and reliable data statistical query, significantly improve the efficiency and accuracy of wind turbine blades in the research and development, production, testing and maintenance process, and can realize full utilization of test data, which helps to avoid subsequent iterative design risks and improve the quality of subsequent iterative products.

[0073] In one embodiment, the method further comprises: drawing a static chart according to the query data, the static chart comprising a resistance test data table, a blade section data table, a blade disturbance table, a blade section strain data table and a fatigue test load table.

[0074] Specifically, static charts are drawn based on the query data obtained, including but not limited to the resistance test data table of a certain model of blade, the strain data table of each section of the blade and the blade disturbance table under various working conditions of the front static test and the post-static test, such as My-max, My-min, Mx-max, Mx-min, etc., and fatigue tests, such as the strain data table of each section of the blade for swing fatigue and flapping fatigue, and fatigue test load table. The blade test data can be efficiently and intuitively displayed through a variety of icons.

[0075] Static charts can select multiple chart types to facilitate clear data display and easy viewing of test data changes and trends. In addition, you can also add specific blade models, test condition labels, titles, legends, etc. through chart layout to show the readability of specific blade models in the chart. You can also add interactive functions such as zooming, filtering, click events, etc. to reuse test data.

[0076] like Figure 2 and Figure 3 As shown, a wind turbine blade test data query system 20 is provided, which is used to implement a wind turbine blade test data query method as described above, including: a test data acquisition module 21, a test data processing module 22, a test data classification module 23, a query request acquisition module 24 and a query data generation module 25, wherein:

[0077] A test data acquisition module 21 is used to acquire multi-source fused test data, the test data including blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data;

[0078] The test data processing module 22 is used to clean and unify the test data to obtain the imported data;

[0079] A test data classification module 23, used to classify the imported data according to preset categories, and integrate the imported data into the wind turbine blade test database according to the classification results;

[0080] A query request acquisition module 24 is used to acquire a query request and verify the legitimacy of the query request, wherein the query request carries a blade model and a test condition;

[0081] The query data generation module 25 is used to perform traversal query in the wind turbine blade test database according to the blade model and test conditions, and perform fatigue query judgment, and generate query data according to the judgment result and the query result.

[0082] In one embodiment, the test data processing module 22 is specifically used to: traverse and determine whether there are duplications or inconsistent data types in the test data, and if so, delete the corresponding test data; traverse and determine whether there are blank values ​​in the test data, and if so, fill the missing values ​​by filling in default values ​​or interpolation methods; traverse and determine whether there are abnormal test data in the test data, and if so, delete the corresponding abnormal test data; unify the date format of the processed test data to complete data cleaning; convert the data type, standardize the format and array the cleaned test data to obtain imported data.

[0083] In one embodiment, the query request acquisition module 24 is specifically used to: acquire a query request carrying a data query password; compare the data query password with a preset query password to verify the legitimacy of the query request.

[0084] In one embodiment, the system can automatically analyze and display relevant data according to different stages, such as R&D cycle and non-R&D cycle, and provide R&D design early warning prompts. The system combines modular design to provide data display and R&D design early warning prompts in stages.

[0085] Among them, in the R&D cycle stage, the system focuses on displaying the design parameters, manufacturing parameters and data generated during the test process of the test blade. Through the data analysis model, the system can automatically identify key test data points and display the performance of the test blade under various test conditions. The system can also discover potential problems in iterating new products through data comparison, generate early warnings, and provide possible design or manufacturing defects.

[0086] During the non-R&D cycle, the system mainly reflects the damage and maintenance records of the test blades. The system will automatically analyze the historical damage and maintenance data, and warn of possible damage points on mass-produced blades, prompting the manufacturing department to focus on this area and reduce production quality issues.

[0087] like Figure 4 As shown in the figure, it is a schematic diagram of the system query page, which can query data according to the blade model and test conditions. In addition, in order to further display the data more comprehensively, the detailed parameters such as the PS main beam, SS main beam, check buckling coefficient, check IFF (Inter fiber fracture) coefficient, design weight, first-order / second-order swing array, web structure, measured weight, first-order / second-order flapping can be displayed according to the query request.

[0088] In summary, compared with the existing technology, the advantages of this system are:

[0089] First, all wind turbine blade test and full-cycle test and damage data are integrated into the database, and the wind turbine blade test and damage data are systematically managed, which improves the traceability of test data, realizes intelligent and automatic data processing, and reduces data omissions and erroneous analysis caused by manual processing.

[0090] Second, various visualization tools and techniques, such as charts and static parameter visualization, are used to display data, which can automatically, quickly, visually, and intuitively use data to support product design and achieve repeated and efficient use of test data.

[0091] Third, test data is used to avoid the risks of subsequent iterative designs, so that defects in early product designs can be identified and corrected in subsequent iterative designs, greatly reducing the design risks of new products and improving the quality of subsequent iterative products.

[0092] Fourth, it provides an efficient and reliable query and statistical system for wind turbine blade testing, damage, and maintenance data, which will significantly improve the efficiency and accuracy of wind turbine blade research and development, production, testing, and maintenance, and reduce the risks brought by human intervention, thereby providing strong support for the research and development of wind turbine blades.

[0093] In one embodiment, an electronic device is provided. The device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the device is used to store configuration templates and can also be used to store target web page data. The network interface of the device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for querying wind turbine blade test data is implemented.

[0094] Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the device to which the scheme of the present application is applied. The specific device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0095] In one embodiment, a computer-readable storage medium may be provided, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method described in the aforementioned embodiment. The computer may be part of the above-mentioned wind turbine blade test data query system.

[0096] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0097] Obviously, those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a computer storage medium (ROM / RAM, magnetic disk, optical disk) and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0098] The above contents are further detailed descriptions of the present invention in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for querying wind turbine blade test data, characterized in that: The following steps are involved: Acquire multi-source fused test data, wherein the test data includes blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, and test blade damage and maintenance data; Cleaning and unifying the test data to obtain imported data; Classifying the imported data according to preset categories, and integrating the imported data into a wind turbine blade test database according to the classification results; Obtaining a query request and verifying the legitimacy of the query request, wherein the query request carries a blade model and a test condition; According to the blade model and the test condition, a traversal query is performed in the wind turbine blade test database, and a fatigue query judgment is performed, and query data is generated according to the judgment result and the query result.

2. A wind turbine blade test data query method according to claim 1, characterized in that: The step of cleaning and unifying the test data to obtain the imported data includes: Traverse and determine whether the test data is repeated or the data type is inconsistent. If so, delete the corresponding test data; Traverse and determine whether there are blank values ​​in the test data. If so, fill the missing values ​​by filling default values ​​or interpolation methods; Traverse and determine whether there is abnormal test data in the test data, and if so, delete the corresponding abnormal test data; Unify the date format of the processed test data and complete data cleaning; The cleaned test data is converted into data type, format standardized and arrayed to obtain the imported data.

3. A wind turbine blade test data query method according to claim 2, characterized in that: The traversal to determine whether the test data contains abnormal test data includes: The average value of the data set is calculated based on all test data, and the distance between the test data points and the average value of the data set is calculated in turn. The formula is: In the formula, Z is the standard score, x is the collected data point, μ is the mean of the data set, and ρ is the standard deviation of the data set; A set standard score threshold is obtained, the set standard score threshold is compared with the standard scores of all data points, and abnormal test data is obtained according to the comparison result.

4. A wind turbine blade test data query method according to claim 1, characterized in that: The preset categories include test types, test conditions, damage types and basic parameter categories; Among them, the test types include resistance test, front static test, fatigue test and rear static test; the test conditions include My-max, My-min, Mx-max, Mx-min, swing fatigue and swing fatigue.

5. A method for querying wind turbine blade test data according to claim 1, characterized in that: The obtaining of the query request and verifying the legitimacy of the query request includes: Obtaining a query request, wherein the query request carries a data query password; The data query password is compared with a preset query password to verify the legitimacy of the query request.

6. A method for querying wind turbine blade test data according to claim 4, characterized in that: The fatigue query judgment includes: Fatigue test data is obtained according to the test type, and equivalent fatigue test data is calculated according to the fatigue test data. The formula is: In the formula, F dl represents the equivalent amplitude load, N is the number of cycles, is the amplitude load of the i-th cycle, m is the slope parameter of the SN curve of the test material, m=10, K dl is the load stress transfer coefficient.

7. A wind turbine blade test data query method according to claim 1, characterized in that: Also includes: A static chart is drawn according to the query data, and the static chart includes a resistance test data table, a blade section data table, a blade disturbance table, a blade section strain data table and a fatigue test load table.

8. A wind turbine blade test data query system, characterized in that: A method for querying wind turbine blade test data according to any one of claims 1 to 7, comprising: A test data acquisition module is used to acquire multi-source fused test data, wherein the test data includes blade test experimental data, laboratory status record data, test blade design parameters, test blade manufacturing parameters, test blade damage and maintenance data; A test data processing module, used for cleaning and unifying the test data to obtain imported data; A test data classification module, used to classify the imported data according to preset categories, and integrate the imported data into a wind turbine blade test database according to the classification results; A query request acquisition module, used to acquire a query request and verify the legitimacy of the query request, wherein the query request carries a blade model and a test condition; The query data generation module is used to perform traversal query in the wind turbine blade test database according to the blade model and test conditions, and perform fatigue query judgment, and generate query data according to the judgment result and query result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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