CN115409334A - Statistical analysis method and system for test data of hydraulic and hydroelectric engineering - Google Patents
Statistical analysis method and system for test data of hydraulic and hydroelectric engineering Download PDFInfo
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Abstract
The application discloses a statistical analysis method and a statistical analysis system for test data of hydraulic and hydroelectric engineering, which relate to the technical field of hydraulic engineering sample detection, and the method comprises the following steps: acquiring test data of all test samples and equipment information of test equipment for generating the test data, wherein the test data comprises detection data of each detection index; screening out unqualified data in the detection data; acquiring sample information of unqualified samples corresponding to the unqualified data; judging whether the unqualified data is abnormal or not by combining the equipment information and the sample information; if the data are abnormal, the unqualified sample is marked as a sample to be rechecked, and the test equipment is replaced to recheck the sample to be rechecked. The method and the device have the effect that a large amount of manpower and material resources do not need to be consumed when the detection result of the retest test is obtained.
Description
Technical Field
The application relates to the technical field of sample detection, in particular to a statistical analysis method and system for test data of hydraulic and hydroelectric engineering.
Background
The quality is the life of the project, and the test detection is an important component of the project quality and is an important means for scientific management of the project quality. Besides the need of testing and detecting the engineering infrastructure, the method also needs to test and detect various raw materials used in the engineering, and the quality of the physical engineering can be analyzed or the purchasing channel of the raw materials can be adjusted according to the test and detection result.
The existing test detection is carried out through specific test detection equipment, detection personnel record information of batch, category and the like of test samples in detail, the test detection equipment is utilized to complete a test detection process, the test detection equipment immediately outputs a detection test detection result, and when the detection result is unqualified, in order to reduce the influence of the test detection equipment caused by detection errors caused by part aging and other reasons, part of the test detection result is required to be detected manually and randomly extracted for manual retest.
With respect to the related art among the above, the inventors consider that the following drawbacks exist: in the process of testing test samples, a large number of samples are sometimes tested at the same time, a large number of unqualified test results may appear after the test is completed, all unqualified test results need to be rechecked to reduce the influence of objective reasons such as equipment on the test results, and a large amount of manpower, material resources and time need to be consumed in a manual rechecking mode.
Disclosure of Invention
In order to overcome the defect that a large amount of manpower, material resources and time are consumed when manual rechecking is carried out on a large number of test detection results, the application provides a statistical analysis method and a statistical analysis system for test data of water conservancy and hydropower engineering.
In a first aspect, the application provides a statistical analysis method for test data of hydraulic and hydroelectric engineering, comprising the following steps:
acquiring test data of all test samples and equipment information of test equipment for generating the test data, wherein the test data comprises detection data of each detection index;
screening out unqualified data in the detection data;
acquiring sample information of unqualified samples corresponding to the unqualified data;
judging whether the unqualified data has data abnormality or not by combining the equipment information and the sample information;
if the data are abnormal, the unqualified sample is marked as a sample to be rechecked, and the test equipment is replaced to recheck the sample to be rechecked.
By adopting the technical scheme, after unqualified data in the detection data is obtained, the sample information corresponding to unqualified samples is obtained, whether the unqualified data is abnormal or not is judged by combining the equipment information of the test equipment for generating the test detection data and the sample information, if the data is abnormal, the unqualified data is possibly influenced by other objective factors in the detection process, so that the corresponding unqualified samples need to be marked as samples to be rechecked, the test equipment needs to be replaced for rechecking, and the influence of the objective factors such as equipment abnormality on the detection result is reduced.
Optionally, the test data further includes a number of detection times, the sample information includes a sampling weight and a sample type, and the determining whether the data abnormality occurs in the faulty data by combining the device information and the sample information includes the following steps:
judging whether the sampling weight exceeds a preset weight threshold value or not;
if the sampling weight does not exceed the weight threshold, judging that data abnormality occurs;
if the sampling weight exceeds the weight threshold, judging whether the detection times exceed a preset time threshold;
if the detection times do not exceed the times threshold, judging that the data are abnormal;
and if the detection times exceed the time threshold, judging whether the unqualified data is abnormal or not by combining the equipment information and the sample type.
By adopting the technical scheme, the sampling weight of the test sample needs to meet the requirements of one-time detection and two-time reinspection, and if the sampling weight does not reach the preset weight threshold, the test sample is possibly not automatically reinspected through the detection equipment, so that the detection data of the test sample can be directly judged to be abnormal, and the test sample is marked as the sample to be reinspected. If the sampling weight reaches the weight threshold value but the detection frequency of the test sample does not reach the frequency threshold value, the automatic rechecking of the test sample is not carried out through the detection equipment, so that the detection data of the test sample is directly judged to be abnormal, and the test sample is marked as the sample to be rechecked.
Optionally, the device information includes a device overhaul date, and the determining whether the unqualified data has data abnormality by combining the device information and the sample category includes the following steps:
calling historical detection data corresponding to the sample type from a preset sample database based on the sample type;
screening out historical target detection data from the historical detection data, wherein the historical target detection data is the historical detection data with any detection index in the historical detection data being unqualified;
calculating the number ratio of unqualified data under each detection index in the historical target detection data and the data intervals of all the unqualified data;
constructing a data score model by combining the number ratio, the data interval and the equipment overhaul date;
and analyzing whether the unqualified data has data abnormality or not through the data score model.
By adopting the technical scheme, because the test samples have different categories and the test detection standards and indexes of the samples of each category are different, corresponding historical detection data can be called according to the categories of the samples, a data score model is constructed based on the historical detection data, and whether the unqualified data is abnormal or not is analyzed through the data score model.
Optionally, the constructing a data score model by combining the number fraction, the data interval and the equipment overhaul date includes the following steps:
combining the number ratio, a preset ratio threshold value and a preset first weight value to construct a first score calculation formula;
combining the data interval, the data to be input and a preset second weight value to construct a second score calculation formula;
constructing a basic model according to the first score calculation formula and the second score calculation formula;
acquiring a current date;
constructing a model correction formula based on the current date, the equipment overhaul date and a preset third weight value;
and combining the model correction formula and the basic model to construct a data score model of the quantity ratio and the detection indexes corresponding to the data intervals.
By adopting the technical scheme, based on the preset threshold value and the weight value and in combination with the number proportion and the data interval of unqualified data analyzed and calculated in the historical detection data, a first score calculation formula and a second score calculation formula can be respectively constructed, unqualified data to be detected is taken as data to be input to be substituted and calculated to obtain the basic data score of the unqualified data, and then the score of the basic data is corrected according to the constructed model correction formula, so that a complete data score model can be constructed in combination with the model correction formula and the score calculation formula.
Optionally, the first score calculation formula is constructed as follows:
wherein P1 is the first score, S is the number ratio, S' is the ratio threshold, and W1 is the first weight value.
By adopting the technical scheme, the score value of the unqualified data classified by the 'quantity factor' under the corresponding detection index can be calculated by the first score calculation formula.
Optionally, the second score calculation formula is constructed as follows:
in the formula, P2 is a second score, x is the data to be input, x1 is a minimum value in the data interval, x2 is a maximum value in the data interval, and W2 is the second weight value.
By adopting the technical scheme, the score value of the classification of the outlier factor of the unqualified data under the corresponding detection index can be calculated by the second score calculation formula.
Optionally, the analyzing whether the unqualified data has data abnormality through the data score model includes the following steps:
inputting the unqualified data serving as the data to be input into a corresponding data score model for calculation to obtain a data score;
judging whether the data score exceeds a preset score threshold value;
if the data score exceeds the score threshold value, judging that data abnormality occurs in the unqualified data;
and if the data score does not exceed the score threshold, judging that the unqualified data has no data abnormality.
By adopting the technical scheme, the unqualified data is comprehensively evaluated and scored through the data scoring model, and when the data score exceeds a scoring threshold, the corresponding unqualified data can be judged to be in accordance with the historical data rule, and data abnormality does not occur; otherwise, the corresponding unqualified data can be judged to be not in accordance with the historical data rule, and data abnormity occurs.
In a second aspect, the application further provides a statistical analysis system for hydraulic and hydroelectric engineering test data, which includes a memory, a processor, and a program stored in the memory and executable on the processor, where the program can be loaded and executed by the processor to implement the statistical analysis method for hydraulic and hydroelectric engineering test data as described in the first aspect.
By adopting the technical scheme, through calling of the program, the sample information corresponding to the unqualified sample can be obtained after the unqualified data in the detection data is obtained, whether the unqualified data is abnormal or not is judged by combining the equipment information of the test equipment for generating the test detection data and the sample information, if the data is abnormal, the unqualified data is possibly influenced by other objective factors in the detection process, so that the corresponding unqualified sample needs to be marked as a sample to be rechecked, the test equipment needs to be replaced for rechecking the sample, and the influence of the objective factors such as equipment abnormality on the detection result is reduced.
To sum up, the application comprises the following beneficial technical effects:
the method comprises the steps of obtaining sample information corresponding to unqualified samples after unqualified data in detection data are obtained, analyzing and judging whether the unqualified data are abnormal or not by combining equipment information of a test device generating test detection data and the sample information, and if the data are abnormal, indicating that the unqualified data are possibly influenced by other objective factors in the detection process, so that the corresponding unqualified samples need to be marked as samples to be rechecked, and the test device needs to be replaced to recheck the samples, so that the influence of the objective factors such as equipment abnormality on the detection result is reduced.
Drawings
FIG. 1 is a schematic flow chart of one implementation mode of the statistical analysis method for the test data of the hydraulic and hydroelectric engineering in the embodiment of the application.
FIG. 2 is a schematic flow chart of one implementation manner of the statistical analysis method for the test data of the hydraulic and hydroelectric engineering in the embodiment of the application.
FIG. 3 is a schematic flow chart of one embodiment of the statistical analysis method for the test data of the hydraulic and hydroelectric engineering in the embodiment of the present application.
FIG. 4 is a schematic flow chart of one embodiment of the statistical analysis method for the test data of the hydraulic and hydroelectric engineering in the embodiment of the present application.
FIG. 5 is a schematic flow chart of one embodiment of the statistical analysis method for the test data of the hydraulic and hydroelectric engineering in the embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1 to 5.
The embodiment of the application discloses a statistical analysis method for test data of water conservancy and hydropower engineering.
Referring to fig. 1, the statistical analysis method for civil engineering test data of the hydraulic and hydroelectric engineering comprises the following steps:
s101, obtaining test data of all test samples and equipment information of test equipment for generating the test data.
The test samples can be various raw materials in the hydraulic and hydroelectric engineering, such as cement, steel, stone, wood and the like, and different test samples can be tested and detected by different test equipment. The test data includes data for each test indicator of the test sample, such as each test indicator of the cement including, but not limited to, magnesium oxide, alkali content, setting time, strength, insolubles, and the like. The equipment information includes equipment overhaul date, equipment production date, and the like.
And S102, screening out unqualified data in the detection data.
And if the detection data exceeds the index threshold value of the corresponding detection index or does not belong to the index interval of the corresponding detection index, judging that the detection data is unqualified data.
S103, obtaining sample information of unqualified samples corresponding to the unqualified data.
Before a test sample is tested and detected, a two-dimensional code attached to the test sample can be scanned through a two-dimensional code scanner, the two-dimensional code comprises sample information of the test sample, the scanned sample information can be stored in a preset information database, and the sample information comprises sampling weight and sample category of the test sample.
And S104, judging whether the unqualified data has data abnormality or not by combining the equipment information and the sample information, and if the data is abnormal, executing the step S105.
And if the data abnormality does not occur, generating a test detection report based on the test data.
And S105, marking the unqualified sample as a sample to be rechecked, and replacing the test equipment to recheck the sample to be rechecked.
The implementation principle of the embodiment is as follows:
the method comprises the steps of obtaining sample information corresponding to unqualified samples after unqualified data in detection data are obtained, analyzing and judging whether the unqualified data are abnormal or not by combining equipment information of a test device generating test detection data and the sample information, and if the data are abnormal, indicating that the unqualified data are possibly influenced by other objective factors in the detection process, so that the corresponding unqualified samples need to be marked as samples to be rechecked, and the test device needs to be replaced to recheck the samples, so that the influence of the objective factors such as equipment abnormality on the detection result is reduced.
In step S104 of the embodiment shown in fig. 1, the test data further includes the number of detections, and the failure data is preliminarily determined by combining the number of detections and the sampling weight of the test sample. This is explained in detail with reference to the embodiment shown in fig. 2.
Referring to fig. 2, the step of determining whether the data abnormality occurs in the unqualified data by combining the device information and the sample information includes the following steps: s201, judging whether the sampling weight exceeds a preset weight threshold value, and if the sampling weight does not exceed the weight threshold value, executing a step S202; if the sample weight exceeds the weight threshold, step S203 is performed.
The sampling detection of the sample needs to carry out primary detection and repeated detection for many times, and a certain amount of sample needs to be consumed in each detection, so that a sufficient amount of sample needs to be collected in the sampling process, a weight threshold value is preset according to the weight standard of the repeated detection, and the weight threshold value is the minimum total weight required by the test sample for satisfying the repeated detection.
S202, judging that data abnormity occurs.
S203, judging whether the detection times exceed a preset time threshold, and if the detection times do not exceed the time threshold, executing a step S204; if the number of detections exceeds the threshold number, step S204 is executed.
And S204, judging that data abnormity exists.
And S205, judging whether the unqualified data has data abnormality or not by combining the equipment information and the sample category.
The implementation principle of the embodiment is as follows:
the sampling weight of the test sample needs to meet the requirements of one-time detection and two-time reinspection, and if the sampling weight does not reach a preset weight threshold, the test sample may not pass through the detection equipment for automatic reinspection, so that the detection data of the test sample can be directly judged as data abnormality, and the test sample is marked as a sample to be reinspected. If the sampling weight reaches the weight threshold value but the detection frequency of the test sample does not reach the frequency threshold value, the test sample is not automatically rechecked through the detection equipment, so that the detection data of the test sample is directly judged to be abnormal, and the test sample is marked as a sample to be rechecked.
In step S205 of the embodiment shown in fig. 2, after the detection times and the sampling weight of the test sample both meet the preset threshold requirement, historical detection data may be retrieved to construct a data score model, and the data score model is used to analyze whether data abnormality occurs in the unqualified data. This is explained in detail with reference to the embodiment shown in fig. 3.
Referring to fig. 3, the step of determining whether the data abnormality occurs in the unqualified data by combining the equipment information and the sample category includes the following steps: s301, calling historical detection data corresponding to the sample types from a preset sample database based on the sample types.
The preset sample database contains historical detection data of all sample types, such as various types of cement, wood and the like described in the detailed description of step S101, and the historical detection data may be historical detection results in a testing device or detection results downloaded and stored through internet retrieval.
S302, screening out historical target detection data from the historical detection data.
The historical target detection data is historical detection data with unqualified detection indexes in the historical detection data.
And S303, calculating the number proportion of unqualified data under each detection index in the historical target detection data and the data interval of all unqualified data.
The method comprises the steps of obtaining historical target detection data under the same sample category, wherein the detection indexes contained in the historical target detection data under the same sample category are the same, classifying all data according to the different detection indexes, screening out unqualified data under each detection index one by one, calculating the proportion of the total data quantity of the unqualified data under the detection indexes under the same detection index, namely the quantity proportion, screening out all unqualified data under the detection index, sequencing the unqualified data from small to large, and obtaining a data interval from the smallest unqualified data and the largest unqualified data.
S304, a data score model is constructed by combining the number ratio, the data interval and the equipment overhaul date.
S305, analyzing whether the unqualified data is abnormal or not through a data score model.
The implementation principle of the embodiment is as follows:
because the test samples have different categories and the test detection standards and indexes of the samples of each category are different, corresponding historical detection data can be called according to the categories of the samples, a data score model is constructed based on the historical detection data, and whether unqualified data are abnormal or not is analyzed through the data score model.
In step S304 of the embodiment shown in fig. 3, a basic model is constructed based on the number ratio and the data interval, and then a model modification formula is constructed in combination with the current date and the equipment overhaul date, so as to finally construct a data score model. This is illustrated in detail by the embodiment shown in fig. 4.
Referring to fig. 4, the step of constructing the data score model by combining the number fraction, the data interval and the equipment overhaul date comprises the following steps: s401, a first score calculation formula is constructed by combining the number ratio, a preset ratio threshold value and a preset first weight value.
The constructed first score calculation formula is as follows:
in the formula, P1 is a first score, S is a number ratio, S' is a ratio threshold, and W1 is a first weight value.
S402, combining the data interval, the data to be input and a preset second weight value to construct a second score calculation formula.
The second score calculation formula is constructed as follows:
in the formula, P2 is the second score, x is the data to be input, 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. Typically the second weight value is greater than the first weight value.
And S403, constructing a basic model according to the first score calculation formula and the second score calculation formula.
And substituting the unqualified data as x into the basic model for calculation to obtain the basic score.
S404, acquiring the current date.
The current world time is acquired through the Internet, and the current date is identified according to the current world time.
S405, establishing a model correction formula based on the current date, the equipment overhaul date and a preset third weight value.
And the constructed model correction formula is obtained by subtracting the difference value of the equipment overhaul date from the current date and multiplying the difference value by a third weight value, and the calculated score correction value is added to the basic score to finish the correction of the basic score. The value range of the third weight value is (0,1).
And S406, constructing a data score model of the quantity ratio and the data interval corresponding to the detection indexes by combining the model correction formula and the basic model.
And combining the basic model and the model correction formula to obtain the complete data score model.
The implementation principle of the embodiment is as follows:
based on a preset threshold value and a preset weight value and in combination with the number proportion and the data interval of unqualified data analyzed and calculated in historical detection data, a first score calculation formula and a second score calculation formula can be respectively constructed, unqualified data to be detected is used as data to be input and substituted into calculation to obtain the basic data score of the unqualified data, and then the basic data score is subjected to score correction according to a constructed model correction formula, so that a complete data score model can be constructed in combination with the model correction formula and the score calculation formula.
In step S305 of the embodiment shown in fig. 3, the unqualified data is substituted into the data score model and analyzed and calculated to obtain a data score, and whether the unqualified data has data abnormality is determined according to a preset score threshold. This is illustrated in detail by the embodiment shown in fig. 5.
Referring to fig. 5, analyzing whether data abnormality occurs in the unqualified data through the data score model includes the following steps:
and S501, inputting the unqualified data serving as data to be input into a corresponding data score model for calculation to obtain a data score.
S502, judging whether the data score exceeds a preset score threshold value, and if the data score exceeds the score threshold value, executing a step S503; if the data score does not exceed the score threshold, step S504 is executed.
S503, judging that data abnormality occurs in the unqualified data.
And S504, judging that the unqualified data has no data abnormality.
The implementation principle of the embodiment is as follows:
comprehensively evaluating and scoring the unqualified data through the data scoring model, and judging that the corresponding unqualified data conforms to the historical data rule and data abnormality does not occur when the data score exceeds a score threshold; otherwise, the corresponding unqualified data can be judged to be not in accordance with the historical data rule, and data abnormity occurs.
The embodiment of the application also discloses a statistical analysis system for the test data of the hydraulic and hydroelectric engineering, which comprises a memory, a processor and a program which is stored on the memory and can run on the processor, wherein the program can be loaded and executed by the processor to realize the statistical analysis method for the test data of the hydraulic and hydroelectric engineering shown in fig. 1 to 5.
The implementation principle of the embodiment is as follows:
through calling of the program, after unqualified data in the detection data is obtained, sample information corresponding to unqualified samples is obtained, whether the unqualified data is abnormal or not is judged by combining equipment information of the test equipment for generating the test detection data and the sample information, if the data is abnormal, the unqualified data is possibly influenced by other objective factors in the detection process, and therefore the corresponding unqualified samples need to be marked as samples to be rechecked, the test equipment is replaced to recheck the samples, and the influence of the objective factors such as equipment abnormality on the detection result is reduced.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (8)
1. A statistical analysis method for test data of hydraulic and hydroelectric engineering is characterized by comprising the following steps:
acquiring test data of all test samples and equipment information of test equipment for generating the test data, wherein the test data comprises detection data of each detection index;
screening out unqualified data in the detection data;
acquiring sample information of unqualified samples corresponding to the unqualified data;
judging whether the unqualified data has data abnormality or not by combining the equipment information and the sample information;
if the data are abnormal, the unqualified sample is marked as a sample to be rechecked, and the test equipment is replaced to recheck the sample to be rechecked.
2. The statistical analysis method for the test data of the hydraulic and hydroelectric engineering, as claimed in claim 1, wherein the test data further comprises the number of detections, the sample information comprises the sampling weight and the sample category, and the step of determining whether the data abnormality occurs in the non-qualified data by combining the equipment information and the sample information comprises the following steps:
judging whether the sampling weight exceeds a preset weight threshold value;
if the sampling weight does not exceed the weight threshold, judging that data abnormality occurs;
if the sampling weight exceeds the weight threshold, judging whether the detection times exceed a preset time threshold;
if the detection times do not exceed the times threshold, judging that the data are abnormal;
and if the detection times exceed the time threshold, judging whether the unqualified data is abnormal or not by combining the equipment information and the sample type.
3. The statistical analysis method for the test data of the hydraulic and hydroelectric engineering, as claimed in claim 2, wherein the equipment information includes the equipment overhaul date, and the step of determining whether the unqualified data has data abnormality or not by combining the equipment information and the sample category comprises the following steps:
calling historical detection data corresponding to the sample type from a preset sample database based on the sample type;
screening out historical target detection data from the historical detection data, wherein the historical target detection data is the historical detection data with any detection index in the historical detection data being unqualified;
calculating the number proportion of unqualified data under each detection index in the historical target detection data and the data intervals of all the unqualified data;
constructing a data score model by combining the number ratio, the data interval and the equipment overhaul date;
and analyzing whether the unqualified data has data abnormality or not through the data score model.
4. The statistical analysis method for the test data of the hydraulic and hydroelectric engineering, according to claim 3, wherein the step of constructing the data score model by combining the quantity fraction, the data interval and the overhaul date of the equipment comprises the following steps:
combining the number ratio, a preset ratio threshold value and a preset first weight value to construct a first score calculation formula; combining the data interval, the data to be input and a preset second weight value to construct a second score calculation formula;
constructing a basic model according to the first score calculation formula and the second score calculation formula;
acquiring a current date;
constructing a model correction formula based on the current date, the equipment overhaul date and a preset third weight value;
and combining the model correction formula and the basic model to construct a data score model of the quantity ratio and the detection indexes corresponding to the data intervals.
5. The statistical analysis method for test data of hydraulic and hydroelectric engineering according to claim 4, characterized in that:
the first score calculation formula is constructed as follows:
wherein P1 is the first score, S is the number ratio, S' is the ratio threshold, and W1 is the first weight value.
6. The statistical analysis method for the test data of the hydraulic and hydroelectric engineering according to claim 4, which comprises the following steps:
the second score calculation formula is constructed as follows:
in the formula, P2 is a second score, x is the data to be input, x1 is a minimum value in the data interval, x2 is a maximum value in the data interval, and W2 is the second weight value.
7. The statistical analysis method for the test data of the hydraulic and hydroelectric engineering, according to claim 4, wherein the step of analyzing whether the unqualified data has data abnormality through the data scoring model comprises the following steps:
inputting the unqualified data serving as the data to be input into a corresponding data score model for calculation to obtain a data score;
judging whether the data score exceeds a preset score threshold value;
if the data score exceeds the score threshold value, judging that data abnormality occurs in the unqualified data;
and if the data score does not exceed the score threshold, judging that the unqualified data has no data abnormality.
8. A system for statistical analysis of test data for hydraulic and hydro-power engineering, comprising a memory, a processor and a program stored in the memory and executable on the processor, the program being capable of being loaded and executed by the processor to implement a method for statistical analysis of test data for hydraulic and hydro-power engineering as claimed in any one of claims 1 to 7.
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