A method and system for statistical analysis of water conservancy and hydropower engineering test data

Abstract

The application discloses a water conservancy and hydropower engineering test data statistical analysis method and system, and relates to the technical field of water conservancy engineering sample detection. The method comprises the following steps: obtaining test data of all test samples and device information of a test device for generating the test data, wherein the test data comprises detection data of various detection indexes; screening out unqualified data in the detection data; obtaining sample information of unqualified samples corresponding to the unqualified data; combining the device information and the sample information to determine whether the unqualified data is abnormal; if the data is abnormal, marking the unqualified samples as samples to be rechecked, and replacing the test device to recheck the samples to be rechecked. The application has the effect that a large amount of manpower and material resources are not consumed when rechecking test detection results.

Description

Technical Field

[0001] This application relates to the field of sample testing technology, and in particular to a statistical analysis method and system for test data of water conservancy and hydropower projects. Background Technology

[0002] Quality is the lifeblood of an engineering project, and testing and inspection are an important component of engineering quality and a crucial means of scientifically managing engineering quality. In addition to testing and inspecting the engineering infrastructure, it is also necessary to test and inspect the various raw materials used in the project. Based on the results of these tests, the quality of the physical project can be analyzed, or the procurement channels for raw materials can be adjusted.

[0003] Existing tests and inspections are all conducted using specific testing and inspection equipment. The testing personnel record detailed information such as the batch and category of the test samples, and then use the testing and inspection equipment to complete the testing and inspection process. The testing and inspection equipment then outputs the test and inspection results. When the test results are unqualified, in order to reduce the impact of inspection errors caused by the aging of parts of the testing and inspection equipment, it is usually necessary for the inspectors to randomly select a portion of the test and inspection results for manual re-inspection.

[0004] Regarding the aforementioned related technologies, the inventors believe that the following defects exist: In the process of testing and inspecting samples, a large number of samples may be tested simultaneously. After the testing is completed, a large number of unqualified test results may also appear. At this time, it is necessary to re-inspect all unqualified test results to reduce the impact of objective factors such as equipment on the test results. However, manual re-inspection requires a lot of manpower, material resources and time. Summary of the Invention

[0005] To address the shortcomings of requiring significant manpower, resources, and time for manual re-inspection of numerous test results, this application provides a statistical analysis method and system for test data in water conservancy and hydropower engineering.

[0006] Firstly, this application provides a method for statistical analysis of experimental data from water conservancy and hydropower projects, comprising the following steps: Acquire test data for all test samples and equipment information for the test equipment that generates the test data, wherein the test data includes test data for each detection index; Filter out the non-compliant data from the test data; Obtain the sample information of the non-compliant samples corresponding to the non-compliant data; Based on the equipment information and the sample information, determine whether the non-compliant data shows any abnormality; If the data is abnormal, the non-conforming sample will be marked as a sample to be retested, and the testing equipment will be replaced to retest the sample to be retested.

[0007] By adopting the above technical solution, after obtaining the non-compliant data in the test data, the sample information of the corresponding non-compliant sample can be obtained. Combined with the equipment information of the test equipment that generated the test data and the sample information, it can be analyzed and judged whether there is data anomaly in the non-compliant data. If data anomaly occurs, it means that the non-compliant data may be affected by other objective factors during the test process. Therefore, the corresponding non-compliant sample needs to be marked as a sample to be retested, and the test equipment should be replaced for retesting to reduce the impact of objective factors such as equipment anomaly on the test results.

[0008] Optionally, the test data also includes the number of tests, and the sample information includes the sampling weight and sample category. The step of determining whether the non-compliant data shows any abnormalities by combining the equipment information and the sample information includes the following steps: Determine whether the sampled weight exceeds a preset weight threshold; If the sampled weight does not exceed the weight threshold, then a data anomaly is determined to have occurred. If the sampled weight exceeds the weight threshold, then determine whether the number of detections exceeds the preset number threshold. If the number of detections does not exceed the threshold, then the data is determined to be abnormal. If the number of tests exceeds the threshold, then the non-compliant data is determined to be abnormal based on the equipment information and the sample category.

[0009] By adopting the above technical solution, the sampling weight of the test sample needs to meet the requirements of one test and two retests. If the sampling weight does not reach the preset weight threshold, it indicates that the test sample may not have undergone automatic retesting by the testing equipment. Therefore, the test data of the test sample can be directly judged as abnormal, and the test sample is marked as a sample to be retested. If the sampling weight reaches the weight threshold but the number of tests for the test sample does not reach the number of tests threshold, it also indicates that the test sample has not undergone automatic retesting by the testing equipment. Therefore, the test data of the test sample is also directly judged as abnormal, and the test sample is marked as a sample to be retested.

[0010] Optionally, the equipment information includes the equipment maintenance date, and the step of determining whether the non-conforming data shows abnormalities by combining the equipment information and the sample category includes the following steps: Based on the sample category, retrieve the historical test data corresponding to the sample category from the preset sample database; Historical target detection data is selected from the historical detection data, wherein the historical target detection data is the historical detection data in which any detection indicator fails to meet the standard. Calculate the percentage of non-compliant data under each of the aforementioned detection indicators and the data range of all non-compliant data in the historical target detection data; A data scoring model is constructed by combining the quantity percentage, the data range, and the equipment maintenance date; The data scoring model is used to analyze whether the non-compliant data shows any data anomalies.

[0011] By adopting the above technical solution, since the test samples are of different categories and the test standards and indicators of each category are different, the corresponding historical test data can be retrieved according to the sample category, and a data scoring model can be constructed based on the historical test data. The data scoring model can then be used to analyze whether the unqualified data shows data anomalies.

[0012] Optionally, constructing a data scoring model by combining the quantity proportion, the data range, and the equipment maintenance date includes the following steps: A first score calculation formula is constructed by combining the quantity ratio, the preset ratio threshold, and the preset first weight value; A second score calculation formula is constructed by combining the data range, the data to be input, and the preset second weight value; A basic model is constructed based on the first score calculation formula and the second score calculation formula; Get the current date; A model correction formula is constructed based on the current date, the equipment maintenance date, and a preset third weight value; A data scoring model is constructed by combining the model correction formula and the basic model to determine the quantity proportion and the corresponding detection indicators for the data interval.

[0013] By adopting the above technical solution, based on the preset threshold and weight values ​​and combined with the proportion and data range of non-compliant data analyzed from historical test data, a first score calculation formula and a second score calculation formula can be constructed respectively. Substituting the non-compliant data to be tested as the input data into the calculation, the basic data score of the non-compliant data can be obtained. Then, the basic data score is corrected according to the constructed model correction formula. Therefore, by combining the model correction formula and the score calculation formula, a complete data scoring model can be constructed.

[0014] Optionally, the formula for calculating the first score is as follows: In the formula, P1 is the first score, S is the quantity ratio, S' is the ratio threshold, and W1 is the first weight value.

[0015] By adopting the above technical solution, the score value of the non-compliant data in the "quantity factor" category under the corresponding detection index can be calculated using the first score calculation formula.

[0016] Optionally, the second score calculation formula is constructed as follows: In the formula, P2 is the second score, x is the input data, x1 is the minimum value in the data interval, x2 is the maximum value in the data interval, and W2 is the second weight value.

[0017] By adopting the above technical solution, the score value of the "outlier factor" classification of the non-compliant data under the corresponding detection index can be calculated using the second scoring formula.

[0018] Optionally, the step of analyzing whether the unqualified data shows data anomalies through the data scoring model includes the following steps: The non-compliant data is input as the input data to the corresponding data scoring model for calculation to obtain the data score; Determine whether the data score exceeds a preset score threshold; If the data score exceeds the score threshold, the unqualified data is determined to be abnormal. If the data score does not exceed the score threshold, then the unqualified data is determined not to be abnormal.

[0019] By adopting the above technical solution, the non-compliant data is comprehensively evaluated and scored through the data scoring model. When the data score exceeds the scoring threshold, it can be determined that the corresponding non-compliant data conforms to the historical data pattern and no data anomaly has occurred; conversely, it can be determined that the corresponding non-compliant data does not conform to the historical data pattern and data anomaly has occurred.

[0020] Secondly, this application also provides a statistical analysis system for test data of water conservancy and hydropower projects, including a memory, a processor, and a program stored in the memory and executable on the processor. When the program is loaded and executed by the processor, it can implement a statistical analysis method for test data of water conservancy and hydropower projects as described in the first aspect.

[0021] By adopting the above technical solution, after obtaining the non-compliant data in the test data through program retrieval, the sample information of the corresponding non-compliant sample can be obtained. Combined with the equipment information of the test equipment that generated the test data and the sample information, it can be analyzed and judged whether there is data anomaly in the non-compliant data. If data anomaly occurs, it indicates that the non-compliant data may be affected by other objective factors during the test process. Therefore, the corresponding non-compliant sample needs to be marked as a sample to be retested, and the test equipment should be replaced for retesting to reduce the impact of objective factors such as equipment anomaly on the test results.

[0022] In summary, this application includes the following beneficial technical effects: After obtaining the non-compliant data in the test data, the sample information of the corresponding non-compliant sample can be obtained. Combined with the equipment information of the test equipment that generated the test data and the sample information, it can be analyzed to determine whether there is any data abnormality in the non-compliant data. If data abnormality occurs, it means that the non-compliant data may be affected by other objective factors during the test. Therefore, the corresponding non-compliant sample needs to be marked as a sample to be retested, and the test equipment should be replaced to retest it, so as to reduce the impact of objective factors such as equipment abnormality on the test results. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating one implementation of the statistical analysis method for test data of water conservancy and hydropower engineering according to an embodiment of this application.

[0024] Figure 2 This is a flowchart illustrating one implementation of the statistical analysis method for test data of water conservancy and hydropower engineering according to an embodiment of this application.

[0025] Figure 3 This is a flowchart illustrating one implementation of the statistical analysis method for test data of water conservancy and hydropower engineering according to an embodiment of this application.

[0026] Figure 4 This is a flowchart illustrating one implementation of the statistical analysis method for test data of water conservancy and hydropower engineering according to an embodiment of this application.

[0027] Figure 5 This is a flowchart illustrating one implementation of the statistical analysis method for test data of water conservancy and hydropower engineering according to an embodiment of this application. Detailed Implementation

[0028] The following is in conjunction with the appendix Figures 1 to 5 This application will be described in further detail.

[0029] This application discloses a method for statistical analysis of test data in water conservancy and hydropower projects.

[0030] Reference Figure 1The statistical analysis method for civil engineering test data of water conservancy and hydropower projects includes the following steps: S101. Obtain test data for all test samples and equipment information for the test equipment that generates the test data.

[0031] The test samples can be various raw materials used in water conservancy and hydropower projects, such as cement, steel, stone, and wood. Different test samples will be tested using different equipment. Test data includes the results of various indicators for the test samples. For example, for cement, the indicators include, but are not limited to, magnesium oxide, alkali content, setting time, strength, and insoluble matter. Equipment information includes the equipment maintenance date and the equipment production date.

[0032] S102. Filter out non-compliant data from the test data.

[0033] According to the testing standards, each testing indicator has a preset threshold or range. If the test data exceeds the threshold of the corresponding testing indicator or does not belong to the range of the corresponding testing indicator, the test data is determined to be unqualified.

[0034] S103. Obtain sample information for non-compliant samples corresponding to non-compliant data.

[0035] Before the test samples are tested, the QR code attached to the test samples can be scanned using a QR code scanner. The QR code contains the sample information of the test samples, and the scanned sample information will be saved to a preset information database. The sample information includes the sampling weight and sample category of the test samples.

[0036] S104. Combine the equipment information and sample information to determine whether there is any data abnormality in the non-conforming data. If there is data abnormality, proceed to step S105.

[0037] If no data anomalies are found, a test report will be generated based on the test data.

[0038] S105. Mark the non-conforming sample as a sample to be retested, and replace the testing equipment to retest the sample to be retested.

[0039] The implementation principle of this embodiment is as follows: After obtaining the non-compliant data in the test data, the sample information of the corresponding non-compliant sample can be obtained. Combined with the equipment information of the test equipment that generated the test data and the sample information, it can be analyzed to determine whether there is any data abnormality in the non-compliant data. If data abnormality occurs, it means that the non-compliant data may be affected by other objective factors during the test. Therefore, the corresponding non-compliant sample needs to be marked as a sample to be retested, and the test equipment should be replaced to retest it, so as to reduce the impact of objective factors such as equipment abnormality on the test results.

[0040] exist Figure 1 In step S104 of the illustrated embodiment, the test data also includes the number of tests, and a preliminary judgment is made on the non-compliant data by combining the number of tests and the sampling weight of the test sample. Specifically, through... Figure 2 The illustrated embodiments will be described in detail.

[0041] Reference Figure 2 The process of determining whether non-compliant data is abnormal by combining equipment information and sample information includes the following steps: S201. Determine whether the sampling weight exceeds the preset weight threshold. If the sampling weight does not exceed the weight threshold, proceed to step S202; if the sampling weight exceeds the weight threshold, proceed to step S203.

[0042] The sampling and testing of samples requires initial testing and multiple retests. Each test consumes a certain amount of sample. Therefore, sufficient samples need to be collected during sampling. A weight threshold is preset based on the weight standard for multiple tests. The weight threshold is the minimum total weight required for the test sample to meet the requirements of multiple tests.

[0043] S202. Data anomaly detected.

[0044] S203. Determine whether the number of detections exceeds the preset threshold. If the number of detections does not exceed the threshold, proceed to step S204. If the number of detections exceeds the threshold, proceed to step S204.

[0045] S204. Data anomaly detected.

[0046] S205. Determine whether non-compliant data shows abnormalities by combining equipment information and sample category.

[0047] The implementation principle of this embodiment is as follows: The sampling weight of the test sample must meet the requirements for one test and two retests. If the sampling weight does not reach the preset weight threshold, it indicates that the test sample may not have passed the automatic retest by the testing equipment. Therefore, the test data of this test sample can be directly judged as abnormal, and the test sample is marked as a sample to be retested. If the sampling weight reaches the weight threshold but the number of tests for the test sample does not reach the number of tests threshold, it also indicates that the test sample has not passed the automatic retest by the testing equipment. Therefore, the test data of this test sample is also directly judged as abnormal, and the test sample is marked as a sample to be retested.

[0048] exist Figure 2In step S205 of the illustrated embodiment, after the number of tests and the sampling weight of the test sample both meet the preset threshold requirements, historical test data can be retrieved to construct a data scoring model, and the data scoring model can be used to analyze whether the unqualified data shows data anomalies. Specifically, through... Figure 3 The illustrated embodiments will be described in detail.

[0049] Reference Figure 3 The process of determining whether non-compliant data is abnormal by combining equipment information and sample category includes the following steps: S301. Retrieve historical test data corresponding to the sample category from the preset sample database based on the sample category.

[0050] The preset sample database contains historical test data for all sample categories, such as cement and wood as described in the detailed explanation of step S101. The historical test data can be historical test results from the testing equipment or test results downloaded and saved via the Internet.

[0051] S302. Filter out historical target detection data from historical detection data.

[0052] Among them, historical target detection data refers to historical detection data in which any detection indicator fails to meet the standard.

[0053] S303. Calculate the percentage of non-compliant data under each detection indicator and the data range of all non-compliant data in the historical target detection data.

[0054] In the historical target detection data under the same sample category, the detection indicators are the same. Therefore, all data can be classified according to the different detection indicators, and the non-compliant data under each detection indicator can be screened out one by one. The proportion of the non-compliant data under the same detection indicator to the total data under the same detection indicator is the quantity ratio. All non-compliant data under the detection indicator are screened out and sorted from smallest to largest. The interval between the smallest and largest non-compliant data is the data interval.

[0055] S304. Construct a data scoring model by combining quantity percentage, data range, and equipment maintenance date.

[0056] S305. Analyze whether non-compliant data exhibits data anomalies using a data scoring model.

[0057] The implementation principle of this embodiment is as follows: Since the test samples are of different categories, and the test standards and indicators for each category are different, the corresponding historical test data can be retrieved according to the sample category. Then, a data scoring model can be constructed based on the historical test data, and the non-compliant data can be analyzed to determine whether there are data anomalies.

[0058] exist Figure 3 In step S304 of the illustrated embodiment, a basic model is first constructed based on the quantity ratio and data range. Then, a model correction formula is constructed by combining the current date and equipment maintenance date, and finally, a data scoring model is constructed. Specifically, through... Figure 4 The illustrated embodiments will be described in detail.

[0059] Reference Figure 4 The data scoring model is constructed by combining quantity proportion, data range and equipment maintenance date, including the following steps: S401. Construct the first score calculation formula by combining quantity proportion, preset proportion threshold and preset first weight value.

[0060] The formula for calculating the first score is as follows: In the formula, P1 is the first score, S is the quantity percentage, S' is the percentage threshold, and W1 is the first weight value.

[0061] S402. Construct a second score calculation formula by combining the data range, the data to be input, and the preset second weight value.

[0062] The formula for calculating the second score is as follows: In the formula, P2 is the second score, x is the input data, x1 is the minimum value in the data interval, x2 is the maximum value in the data interval, and W2 is the second weight value. Usually, the second weight value is greater than the first weight value.

[0063] S403. Construct a basic model based on the first score calculation formula and the second score calculation formula.

[0064] The basic model is formed by combining the first and second score calculation formulas. The basic score can be calculated by substituting the unqualified data as x into the basic model.

[0065] S404. Get the current date.

[0066] This involves obtaining the current world time via the internet and then identifying the current date based on that time.

[0067] S405. Construct a model correction formula based on the current date, equipment maintenance date, and preset third weight value.

[0068] The constructed model correction formula is the difference between the current date and the equipment maintenance date, multiplied by the third weight value. The calculated score correction value is then added to the base score to complete the correction of the base score. The value range of the third weight value is (0,1).

[0069] S406. Combine the model correction formula and the basic model to construct a data scoring model for the quantity proportion and the corresponding detection indicators for the data interval.

[0070] The basic model and the model correction formula are then combined to form a complete data scoring model.

[0071] The implementation principle of this embodiment is as follows: Based on preset thresholds and weights, and combined with the proportion and range of non-compliant data analyzed from historical testing data, a first score calculation formula and a second score calculation formula can be constructed respectively. Substituting the non-compliant data to be tested as input data into the calculation yields the basic data score for the non-compliant data. Then, the basic data score is corrected according to the constructed model correction formula. Therefore, a complete data scoring model can be constructed by combining the model correction formula and the score calculation formula.

[0072] exist Figure 3 In step S305 of the illustrated embodiment, the non-compliant data is first substituted into the data scoring model and analyzed to obtain a data score. Then, a preset scoring threshold is used to determine whether the non-compliant data exhibits data anomalies. Specifically, through... Figure 5 The illustrated embodiments will be described in detail.

[0073] Reference Figure 5 Analyzing whether non-compliant data exhibits anomalies using a data scoring model includes the following steps: S501. Input the non-compliant data as input data into the corresponding data scoring model for calculation to obtain the data score.

[0074] S502. Determine whether the data score exceeds the preset score threshold. If the data score exceeds the score threshold, proceed to step S503; if the data score does not exceed the score threshold, proceed to step S504.

[0075] S503. Data anomalies are detected and deemed unqualified.

[0076] S504. No data anomalies were found in the non-compliant data.

[0077] The implementation principle of this embodiment is as follows: The data scoring model is used to comprehensively evaluate and score non-compliant data. When the data score exceeds the scoring threshold, it can be determined that the corresponding non-compliant data conforms to the historical data pattern and no data anomaly has occurred; conversely, it can be determined that the corresponding non-compliant data does not conform to the historical data pattern and data anomaly has occurred.

[0078] This application also discloses a statistical analysis system for experimental data of water conservancy and hydropower projects, including a memory, a processor, and a program stored in the memory and executable on the processor. This program, when loaded and executed by the processor, can achieve the following: Figures 1 to 5 The above illustrates a statistical analysis method for experimental data of water conservancy and hydropower projects.

[0079] The implementation principle of this embodiment is as follows: By retrieving the program, after obtaining the non-compliant data in the test data, the sample information of the corresponding non-compliant sample can be obtained. Combined with the equipment information of the test equipment that generated the test data and the sample information, it can be analyzed to determine whether there is any data anomaly in the non-compliant data. If data anomaly is found, it means that the non-compliant data may have been affected by other objective factors during the test. Therefore, the corresponding non-compliant sample needs to be marked as a sample to be retested, and the test equipment should be replaced for retesting to reduce the impact of objective factors such as equipment anomalies on the test results.

[0080] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for statistical analysis of experimental data in water conservancy and hydropower projects, characterized in that, Includes the following steps: Acquire test data for all test samples and equipment information for the test equipment that generates the test data, wherein the test data includes test data for each detection index; Filter out the non-compliant data from the test data; Obtain the sample information of the non-compliant samples corresponding to the non-compliant data; Based on the equipment information and the sample information, determine whether the non-compliant data shows any abnormality; If the data is abnormal, the non-conforming sample is marked as a sample to be retested, and the testing equipment is replaced to retest the sample to be retested. The test data also includes the number of tests, and the sample information includes the sampling weight and sample category. The step of determining whether the non-compliant data shows anomalies by combining the equipment information and the sample information includes the following steps: Determine whether the sampled weight exceeds a preset weight threshold; If the sampled weight does not exceed the weight threshold, then a data anomaly is determined to have occurred. If the sampled weight exceeds the weight threshold, then determine whether the number of detections exceeds the preset number threshold. If the number of detections does not exceed the threshold, then the data is determined to be abnormal. If the number of tests exceeds the threshold, then the non-compliant data is determined to be abnormal based on the equipment information and the sample category. The equipment information includes the equipment maintenance date. The step of determining whether the non-compliant data shows an anomaly, based on the equipment information and the sample category, includes the following steps: Based on the sample category, retrieve the historical test data corresponding to the sample category from the preset sample database; Historical target detection data is selected from the historical detection data, wherein the historical target detection data is the historical detection data in which any detection indicator fails to meet the standard. Calculate the percentage of non-compliant data under each of the aforementioned detection indicators and the data range of all non-compliant data in the historical target detection data; A data scoring model is constructed by combining the quantity percentage, the data range, and the equipment maintenance date; The data scoring model is used to analyze whether the non-compliant data shows any data anomalies.

2. The method for statistical analysis of experimental data in water conservancy and hydropower engineering according to claim 1, characterized in that, The process of constructing a data scoring model by combining the quantity percentage, the data range, and the equipment maintenance date includes the following steps: A first score calculation formula is constructed by combining the quantity ratio, the preset ratio threshold, and the preset first weight value; A second score calculation formula is constructed by combining the data range, the data to be input, and the preset second weight value; A basic model is constructed based on the first score calculation formula and the second score calculation formula; Get the current date; A model correction formula is constructed based on the current date, the equipment maintenance date, and a preset third weight value; A data scoring model is constructed by combining the model correction formula and the basic model to determine the quantity proportion and the corresponding detection indicators for the data interval.

3. The method for statistical analysis of experimental data in water conservancy and hydropower engineering according to claim 2, characterized in that: The formula for calculating the first score is as follows: In the formula, P1 is the first score, S is the quantity ratio, S' is the ratio threshold, and W1 is the first weight value.

4. The statistical analysis method for test data of water conservancy and hydropower projects according to claim 2, characterized in that: The constructed formula for calculating the second score is as follows: In the formula, P2 is the second score, x is the input data, x1 is the minimum value in the data interval, x2 is the maximum value in the data interval, and W2 is the second weight value.

5. The method for statistical analysis of experimental data in water conservancy and hydropower engineering according to claim 2, characterized in that, The step of analyzing whether the non-compliant data exhibits data anomalies using the data scoring model includes the following steps: The non-compliant data is input as the input data to the corresponding data scoring model for calculation to obtain the data score; Determine whether the data score exceeds a preset score threshold; If the data score exceeds the score threshold, the unqualified data is determined to be abnormal. If the data score does not exceed the score threshold, then the unqualified data is determined not to be abnormal.

6. A statistical analysis system for experimental data of water conservancy and hydropower projects, characterized in that, It includes a memory, a processor, and a program stored in the memory and executable on the processor, which, when loaded and executed by the processor, implements a statistical analysis method for test data of water conservancy and hydropower engineering as described in any one of claims 1 to 5.

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