A method for quickly generating sacrificial anode potential detection report based on Matlab

Abstract

A method for quickly generating a sacrificial anode potential detection report based on Matlab, the steps are: start reading data, check whether there is 360° detection data, if there is, directly generate 3D graphics; if not, automatically convert 0° data into 360° data, and after checking, automatically generate 3D graphics; carry out data screening, automatically classify and check the number of wind turbines; identify the interval where the data is located, and automatically classify and screen according to the size of the data, and automatically generate the corresponding conclusion type; automatically create a word document, and automatically generate a wind turbine detection report in the order of "detection table-detection 3D graphics-detection conclusion" for a single wind turbine. The present invention can effectively solve the difficulty of processing offshore wind power sacrificial anode detection data report, and "one-click" realizes the generation of graphics and conclusions for large quantities of data.

Description

Technical Field

[0001] The invention belongs to the technical field of detection report generation, and in particular relates to a method for quickly generating a sacrificial anode potential detection report based on Matlab. Background Art

[0002] According to relevant standards, offshore wind farms need to conduct sacrificial anode potential detection on all wind turbines every year. The mode of data collection, data processing and test data report writing is relatively simple, and its biggest feature is mechanical repetitiveness and low efficiency, that is, "effort" and "return" are not proportional, which seriously consumes manpower and material resources. Therefore, a new method is urgently needed to optimize the working mode and realize the maximum utilization of human and material resources. So far, there has been no relevant report to conduct research and analysis in this regard, and most of them are based on repetitive work. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for quickly generating a sacrificial anode potential detection report based on Matlab, which can automatically identify data, and based on this, generate corresponding 3D graphics and detection conclusions, and automatically create a word document, and generate a detection report with the generated data table, 3D graphics and detection conclusions, saving a lot of manpower and material resources, and at the same time, it can also improve the accuracy of data detection.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] The method for quickly generating a sacrificial anode potential detection report based on Matlab is as follows:

[0006] Start reading data and check whether there is 360° detection data. If there is, directly generate 3D graphics; if not, automatically convert 0° data into 360° data. After checking, automatically generate 3D graphics. The significance of this step is that when creating 3D graphics, 360° data is required as graphics production needs;

[0007] Carry out data screening and automatically classify and check the number of fans. The purpose of this step is to verify whether the number of fans is consistent with the input number of fans and to determine the number of cycles.

[0008] Identify the interval where the data is located, automatically classify and filter the data according to its size, and automatically generate the corresponding conclusion type. The significance of this step is that it can automatically identify the data type, draw test conclusions, avoid manual screening, and improve the accuracy of test data.

[0009] Automatically create a word document and automatically generate a fan inspection report in the order of "inspection table - inspection 3D graphics - inspection conclusion" for a single fan. The significance of this step is that the fan sacrificial anode potential inspection report can be generated with one click, saving a lot of manpower and material resources.

[0010] Preferably, the steps of performing data screening and automatically classifying and checking the number of wind turbines are as follows:

[0011] To judge the potential value condition:

[0012] Judgment condition 1: Fan potential value>1.1V;

[0013] Judgment condition 2: fan potential value>0.8V;

[0014] If judgment condition 1 is met, output conclusion 1; proceed to the next fan judgment;

[0015] If the judgment condition 1 is not met, the judgment condition 2 is performed; if the judgment condition 2 is met, the conclusion 2 is output and the next fan judgment is performed; if the judgment condition 2 is not met, the conclusion 3 is output and the next fan judgment is performed.

[0016] Preferably, the process of automatically converting 0° data into 360° data is:

[0017] 1) Data preprocessing stage:

[0018] Data integrity check and padding algorithm:

[0019] Input: original data set D={di|di=(ai,pi)}, where ai is the measurement angle and pi is the corresponding potential value;

[0020] 2) Process: Traverse D and check whether there is a data point with a=360°;

[0021] If missing, select the data point (a0, p0) with a=0°, construct a new data point (360°, p0) and add it to D;

[0022] After ensuring the integrity of the data, the z-score standardization method was used to standardize the data and convert the potential value pi to the same scale to facilitate subsequent analysis;

[0023] 3) Output: Completed and standardized dataset D′.

[0024] Preferably, the process of data screening and classification:

[0025] 1) Setting potential value condition classification algorithm:

[0026] Input: Completed and standardized dataset D′;

[0027] 2) Process: For each data point di′ = (ai′, pi′), classify it according to the potential value pi′.

[0028] The classification logic is based on the preset thresholds θ1 = 1.1 and θ2 = 0.8. If pi′ > θ1, it is classified into category C1. If θ2 < pi′ ≤ θ1, it is classified into category C2. Otherwise, it is classified into category C3.

[0029] 3) Output: The classification result set C = {Cj∣Cj⊂D′, j∈{1,2,3}}.

[0030] Preferably, the 3D graphics generation process:

[0031] 1) Establish a 3D visualization model:

[0032] Input: The data subset Cj after screening and classification, which contains angles and normalized potential values.

[0033] 2) Process: Select an appropriate data subset Ck, construct a 3D coordinate system, where the angle and potential value are used as two axes, and the third axis can be an additional dimension such as a fan identifier or a time series.

[0034] Apply graphical representation methods such as scatter plots or surface plots to map the data points Ck into 3D space, and use color coding or markers to distinguish different categories or features.

[0035] 3) Output: A 3D graph visually showing the sacrificial anode potential distribution characteristics.

[0036] Preferably, the automated report generation process is as follows:

[0037] Input: The outputs of all processing stages (including preprocessed data, classification results, 3D graphs, etc.).

[0038] Process: Design a template, define the report structure (such as the detection table template, the insertion position of the 3D graph, the conclusion template).

[0039] Through an automated script or interface (such as using the interaction function between MATLAB and Word), traverse the processing results of each fan and automatically fill the template.

[0040] For each fan, insert the potential detection table, the corresponding 3D graph, and the conclusion obtained from the classification into the report in sequence.

[0041] Output: A complete sacrificial anode potential detection report document, formatted to display all analysis results and conclusions.

[0042] A method for quickly generating a sacrificial anode potential detection report comprises an analysis and processing module, wherein the program instructions executed by the analysis and processing module are the method for quickly generating a sacrificial anode potential detection report based on Matlab.

[0043] The present invention can achieve the following beneficial effects:

[0044] This solution can effectively solve the difficulties in processing offshore wind power sacrificial anode inspection data reports, realize the generation of graphics and conclusions for large quantities of data with one click, and automatically sort and generate regular inspection report styles according to wind turbine numbers, that is, "wind turbine inspection data-inspection 3D graphics-inspection conclusions" as a fixed mode, saving a lot of manpower and material resources, and at the same time, it can also improve the accuracy of inspection data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0046] Figure 1 This is a flow chart of the method for quickly generating sacrificial anode potential detection report based on Matlab. DETAILED DESCRIPTION

[0047] The present invention develops a solution for quickly generating sacrificial anode potential detection reports based on Matlab, wherein MATLAB is a programming platform developed by MathWorks and is widely used in engineering calculations, data analysis, scientific research, algorithm development and other fields. The specific solution is as follows:

[0048] The preferred solution is Figure 1 As shown in the figure, the method for quickly generating a sacrificial anode potential detection report based on Matlab is as follows:

[0049] S1. Start reading data and check whether there is 360° detection data. If yes, directly generate 3D graphics; if not, automatically convert 0° data into 360° data. After checking, automatically generate 3D graphics. The significance of this step is that when creating 3D graphics, 360° data is required as graphics production needs;

[0050] Specifically, the process of automatically converting 0° data into 360° data is as follows:

[0051] 1) Data preprocessing stage:

[0052] Data integrity check and padding algorithm:

[0053] Input: original data set D={di|di=(ai,pi)}, where ai is the measurement angle and pi is the corresponding potential value;

[0054] 2) Process: Traverse D to check if there is a data point with a = 360°;

[0055] If missing, select the data point (a0, p0) with a = 0°, and construct a new data point (360°, p0) to be added to D;

[0056] After ensuring data integrity, use the z-score normalization method to normalize the data, converting the potential value pi to the same scale for subsequent analysis;

[0057] 3) Output: The completed and normalized dataset D'.

[0058] This step is to ensure data integrity and build a complete 360° perspective graphic basis. Traverse the original dataset D. If a 360° data point is missing, copy the 0° data point and adjust its angle to 360° to form the dataset D'. Use the z-score method to normalize D' to eliminate the influence of dimension for cross-data comparison. Based on the improved dataset D', generate a 3D graphic based on the angle and normalized potential value to visually display the anode potential distribution.

[0059] S2. Conduct data screening to automatically classify and check the number of fans. The significance of this step is to verify whether the number of fans is consistent with the input number of fan units and determine the number of cycles.

[0060] Specifically, the process of data screening and classification:

[0061] 1) Set the potential value condition classification algorithm:

[0062] Input: The completed and normalized dataset D';

[0063] 2) Process: For each data point di' = (ai', pi'), classify it according to the potential value pi';

[0064] The classification logic is based on the preset thresholds θ1 = 1.1 and θ2 = 0.8. If pi' > θ1, it is classified into category C1. If θ2 < pi' ≤ θ1, it is classified into category C2. Otherwise, it is classified into category C3;

[0065] 3) Output: The classification result set C = {Cj∣Cj⊂D', j∈{1,2,3}}.

[0066] This step is to classify and check the fan status to ensure the accuracy and pertinence of the analysis. By setting the thresholds θ1 = 1.1V and θ2 = 0.8V, the data points in D' are grouped according to the potential value into three categories: C1 (strong protection), C2 (medium protection), and C3 (weak protection), providing a basis for subsequent analysis and report writing.

[0067] S3. Identify the interval where the data is located, and automatically classify and filter the data according to its size, and automatically generate the corresponding conclusion type. The significance of this step is that it can automatically identify the data type, draw the test conclusion, avoid manual screening, and improve the accuracy of the test data.

[0068] 3D graphics generation process:

[0069] 1) Establish a 3D visualization model:

[0070] Input: The filtered and classified data subset Cj, which contains angles and standardized potential values;

[0071] 2) Process: Select a suitable data subset Ck and construct a 3D coordinate system, where the angle and potential value are two axes, and the third axis can be an additional dimension such as wind turbine identification or time series;

[0072] Apply graphical representation methods such as scatter plots or surface plots to map the data points Ck into 3D space, using color coding or markings to distinguish different categories or features.

[0073] 3) Output: Three-dimensional graphics that intuitively display the potential distribution characteristics of the sacrificial anode.

[0074] Automatically identify and report key data features, provide intuitive visual feedback, select representative subsets Ck based on the classification results Cj, and create 3D scatter plots or surface plots with color coding or labeling to enhance the visual interpretation of potential distribution.

[0075] S4. Automatically create a word document and automatically generate a fan inspection report in the order of "inspection table - inspection 3D graphics - inspection conclusion" for a single fan. The significance of this step is that the fan sacrificial anode potential inspection report can be generated with one click, saving a lot of manpower and material resources.

[0076] Specifically, the steps for conducting data screening and automatically classifying and checking the number of wind turbines are as follows:

[0077] To judge the potential value condition:

[0078] Judgment condition 1: Fan potential value>1.1V;

[0079] Judgment condition 2: Fan potential value>0.8V;

[0080] If judgment condition 1 is met, output conclusion 1; proceed to the next fan judgment;

[0081] If the judgment condition 1 is not met, the judgment condition 2 is performed; if the judgment condition 2 is met, the conclusion 2 is output and the next fan judgment is performed; if the judgment condition 2 is not met, the conclusion 3 is output and the next fan judgment is performed.

[0082] Preferably, the report automatic generation process is as follows:

[0083] Input: Output of all processing stages (including preprocessed data, classification results, 3D graphics, etc.).

[0084] Process: Design templates and define report structure (e.g. test table template, 3D graphics insertion location, conclusion template).

[0085] Through automated scripts or interfaces (such as using the interactive function of MATLAB and Word), the processing results of each wind turbine are traversed and the template is automatically filled.

[0086] For each wind turbine, the potential detection table, the corresponding 3D graph and the conclusions drawn based on the classification are inserted into the report in sequence.

[0087] Output: Complete sacrificial anode potential test report document, formatted to display all analysis results and conclusions.

[0088] Based on the above steps, the present invention can generate a detailed and formatted test report with one click, thereby improving work efficiency. Design a report template, including a test table, a 3D graphics area, and a conclusion template. Use the Word interactive function of MATLAB to traverse the processing results of each fan and automatically fill them into the corresponding template paragraphs. For each fan, output the corresponding conclusion (Conclusion 1-Conclusion 3) according to the judgment condition of its potential value, and integrate the test table, 3D graphics and conclusion in sequence to form a complete test report. Generate a complete report document containing all fan test results, clearly display the test details, analysis conclusions and suggestions, and meet the needs of rapid feedback and decision support.

[0089] A method for quickly generating a sacrificial anode potential detection report comprises an analysis and processing module, wherein the program instructions executed by the analysis and processing module are the method for quickly generating a sacrificial anode potential detection report based on Matlab.

[0090] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for quickly generating a sacrificial anode potential detection report based on Matlab, characterized in that: Start reading the data, check if there is 360° detection data. If there is, directly generate a 3D graph; if not, automatically convert the 0° data into 360° data. After checking and ensuring it is correct, automatically generate a 3D graph; Carry out data screening and automatically classify and check the number of fans; Identify the data range and automatically classify and screen according to the data size, and automatically generate the corresponding conclusion type; Automatically create a word document and generate a fan detection report automatically in the order of "detection form - detection 3D graph - detection conclusion" for each fan; The steps of carrying out data screening and automatically classifying and checking the number of fans are as follows: Perform potential value condition judgment: Judgment condition 1: The fan potential value > 1.1V; Judgment condition 2: The fan potential value > 0.8V; If judgment condition 1 is satisfied, output conclusion 1; proceed to the next fan judgment; If judgment condition 1 is not satisfied, then perform the judgment of judgment condition 2; if judgment condition 2 is satisfied, output conclusion 2 and proceed to the next fan judgment; if judgment condition 2 is not satisfied, output conclusion 3 and proceed to the next fan judgment; The process of automatically converting 0° data into 360° data is as follows: 1) Data preprocessing stage: Data integrity check and filling algorithm: Input: The original data set D = {di∣di=(ai,pi)}, where ai is the measurement angle and pi is the corresponding potential value; 2) Process: Traverse D to check if there is a data point with a = 360°; If it is missing, select the data point (a0,p0) with a = 0°, and construct a new data point (360°,p0) and add it to D; After ensuring data integrity, use the z-score normalization method to normalize the data and convert the potential value pi to the same scale for convenient subsequent analysis; 3) Output: The completed and normalized data set D′; The process of data screening and classification: 1) Set the potential value condition classification algorithm: Input: The completed and normalized data set D′; 2) Process: For each data point di′=(ai′,pi′), classify it according to the potential value pi′; The classification logic is based on the preset thresholds θ1 = 1.1 and θ2 = 0.

8. If pi′>θ1, it is classified into category C1. If θ2<pi′≤θ1, it is classified into category C2, and the rest are classified into category C3; 3) Output: The classification result set C = {Cj∣Cj⊂D′,j∈{1,2,3}}; The 3D graph generation process: 1) Establish a three-dimensional visualization model: Input: The data subset Cj after screening and classification, which contains angles and normalized potential values; 2) Process: Select the data subset Ck, construct a 3D coordinate system, where the angle and potential value are used as two axes, and the third axis is the fan identifier or time series; Apply the graphical representation methods of scatter plots or surface plots to map the data points Ck into 3D space, and use color coding or markers to distinguish different categories or features; 3) Output: A three-dimensional graph that intuitively shows the distribution characteristics of the sacrificial anode potential.

2. The method for quickly generating a sacrificial anode potential detection report based on Matlab according to claim 1, characterized in that: The process of automatic report generation is as follows: Input: Output of all processing stages, including preprocessed data, classification results, and 3D graphics; Process: Design templates and define report structure, including test table templates, 3D graphics insertion locations, and conclusion templates; Through automated scripts or interfaces, the processing results of each wind turbine are traversed and the template is automatically filled; For each wind turbine, insert the potential detection table, the corresponding 3D graph, and the conclusions drawn based on the classification into the report in order; Output: Complete sacrificial anode potential test report document, formatted to display all analysis results and conclusions.

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