CN110956340A - Engineering test detection data management early warning decision method - Google Patents
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Abstract
The invention relates to a management early warning decision method for engineering test detection data. The management early warning decision method comprises the following steps: data acquisition, data preprocessing, chart visualization, report generation and real-time early warning. The invention solves the problems of large data quantity, various data types, different standards of various test detection projects and high discrimination difficulty of the existing engineering test detection data processing, can automatically analyze and process the engineering test detection data, and achieves the technical effects of higher data analysis and processing efficiency and higher data accuracy.
Description
Technical Field
The invention relates to the field of engineering data processing, in particular to a management and early warning decision method for engineering test detection data.
Background
In the prior art, an operator usually needs to register data in a test detection report in a corresponding excel ledger, and usually the test detection data of one engineering project is very complicated, so that when the operator performs test detection data analysis processing, the operator usually needs to search the test detection data required by the operator in a huge test detection data ledger, find out the required data by means of checking, calculating and the like, and analyze the required data; the difficulty of screening, searching and extracting data is high, and the efficiency and accuracy of analyzing and processing test detection data are low.
Disclosure of Invention
In order to solve the problems, the invention provides a management and early warning decision method for engineering test detection data, which can automatically analyze and process the engineering test detection data and achieve the technical effects of higher data analysis and processing efficiency and higher data accuracy.
The technical scheme adopted by the invention is as follows: a management and early warning decision method for engineering test detection data comprises the following steps:
a. data acquisition: collecting and recording engineering test detection data;
b. data preprocessing: identifying and eliminating abnormal data which are detected by an unconventional test in the collected data;
c. the chart can be seen: visualizing the engineering test detection data to realize the automatic generation of relevant statistical analysis indexes and reports;
d. generating a report: the report generation item is used for the correlation calculation of the engineering test detection data, so that data report information is generated;
e. real-time early warning: the method is used for early warning prompt of data abnormity when engineering test detection data are collected, a multi-factor linkage early warning model and a multi-stage early warning strategy are formulated according to a risk control threshold value aiming at abnormal fluctuation parameters possibly existing in a platform, a trend envelope curve and a risk grading method are established by combining a knowledge base, a case base and an expert base, rapid automatic early warning of test data abnormity is achieved, and warning notification is carried out on related personnel when the data are not in a specified range.
Preferably, in the step a, the collected and recorded engineering test detection data includes common engineering performance test data of engineering raw materials, concrete mixing, concrete forming and filling materials.
Preferably, in the step b, a data preprocessing library is built in the platform: and identifying and rejecting abnormal detection data of unconventional tests according to abnormal test piece judgment rules containing different detection items and abnormal detection data judgment rules of test piece tests in the same batch.
Preferably, in step c, the engineering test detection data is visualized using a line graph or pie graph tool.
Preferably, in step d, the generated data report information includes the number of detection groups and the qualification rate data report information.
Furthermore, in step d, after the user selects the query time interval and the data object, the system exports the test data stream to a fixed report template, and automatically calculates the analysis statistical indexes of the detection group number, the qualification rate, the maximum planting, the minimum planting, the average planting, the standard deviation and the variation coefficient.
Furthermore, the calculation principle of the detection group number is as follows: and judging whether the numerical value of one item is null according to the input test data, wherein if the numerical value of one item is null, the number of the test group is 0, and otherwise, the number of the test group is 1.
Furthermore, the yield calculation principle is as follows: and judging whether the number of detection groups is 1 or not, if not, judging that the qualified group number is 0, if so, continuously judging whether the item number is in a specified range, if so, judging that the qualified group number is 1, otherwise, judging that the qualified group number is 0.
Preferably, the low-frequency high-amplitude abnormal fluctuation data under the qualified condition of the test is identified by adopting a boxplot theory for reminding, and the algorithm is as follows:
wherein: q1 is the first quartile, equal to the 25 th% number after all values in the sample are arranged from small to large; q3 is the third quartile, which is equal to the 75 th% of the numbers in the sample after all the numbers are arranged from small to large; QR represents a quartile distance which is the distance between the third quartile and the first quartile.
Preferably, the moving average value is calculated by sequentially increasing or decreasing new and old data by a moving average method, the detection values of one week, one month and one quarter in the future are continuously predicted, and the grading early warning prompt is performed when the predicted values exceed the allowable deviation.
The beneficial effects obtained by the invention are as follows: the invention solves the problems of large data quantity, various data types, different standards of various test detection projects and high discrimination difficulty of the existing engineering test detection data processing, can automatically analyze and process the engineering test detection data, and achieves the technical effects of higher data analysis and processing efficiency and higher data accuracy.
Drawings
Fig. 1-2 are schematic diagrams of the present invention.
Detailed Description
The invention will be further described with reference to the following drawings and specific embodiments.
As shown in fig. 1-2, the method for managing, early warning and deciding engineering test detection data of the present invention comprises the following steps:
data acquisition: the acquisition items are used for inputting and collecting engineering test detection data. The method comprises common engineering performance tests of engineering raw materials, concrete mixing, concrete forming and the like, and specifically comprises the following steps:
pretreatment: the preprocessing item is used for automatically identifying and eliminating abnormal data of the acquired abnormal detection of the unconventional test; the platform is internally provided with a data preprocessing library which comprises abnormal judgment rules of groups of different detection items and abnormal judgment rules of detection data of the same batch of tests.
The chart can be seen: the visual item of the chart visualizes the engineering test detection data by adopting tools such as a line chart, a pie chart and the like, and realizes the automatic generation of relevant statistical analysis indexes and reports;
1. indices suitable for use with the polyline representation include, but are not limited to, the following:
2. indices suitable for pie chart representation include, but are not limited to: the qualified number of the test detection items accounts for the percentage of the total detection number of the test detection items.
The user can see the percentage of qualified number from the pie chart, and can also view the relevant data such as qualified number, detected number, unqualified number and the like by clicking the corresponding plate.
Generating a report: and the report generation is used for automatically extracting and calculating test detection data and generating a related report, the system exports the test data stream to a fixed report template after a user selects a query time period and a data object, and analysis statistical indexes such as detection group number, qualification rate, maximum planting, minimum planting, average planting, standard deviation, variation coefficient and the like are automatically calculated. The calculation principle of the detection group number is to judge whether a numerical value of a certain item is empty according to the input test detection data, if the numerical value is empty, the detection group number is 0, otherwise, the detection group number is 1. The yield calculation principle is as follows: first, it is judged whether or not the number of test sets is 1, if not, the number of qualified sets is 0, and if the number of test sets is 1, it is continuously judged whether or not the value of the item is within a predetermined range, if so, the number of qualified sets is 1, otherwise, it is 0.
Real-time early warning: the real-time early warning is used for automatically identifying abnormal values such as unqualified values, large fluctuation values, bad trends and the like and providing real-time early warning when test detection data are acquired.
The system presets standard values and allowable deviation ranges of all test detection items, and after the detection data exceed the standard, the system reminds the data exceeding the standard. When the input test data are not in the allowable deviation range, the system can give an alarm notice to the related personnel through tools such as short message, WeChat, mailbox and the like.
The standard ranges for the partial detection items are as follows:
note: the standard values of the detection items are partial reference values, and the standard values of the specific test detection items are specified according to design requirements and related quality standard files; moreover, the above detection items are part of detection items, and the patent includes all engineering test detection items.
In addition, the system adopts the boxplot theory to identify the low-frequency high-amplitude abnormal fluctuation data under the qualified condition of the test for reminding, and the algorithm is as follows:
wherein: q1 is the first quartile, which is equal to the 25 th% of all values in the sample, arranged from small to large. Q3 is the third quartile, which is equal to the 75 th% of all values in the sample, arranged from small to large. QR (quartilerange) represents the quartile range, which is the range between the third quartile and the first quartile.
Meanwhile, the system adopts a sliding average method to sequentially increase and decrease new and old data one by one to calculate a moving average value, continuously predicts the detection values of one week, one month and one quarter in the future, and carries out grading early warning prompt on the condition that the predicted value exceeds the allowable deviation.
In addition to the above functions, the platform further includes a screening function item, where the screening function item is used to screen and check test detection data included in the platform, and specifically includes screening and checking a test detection data report, a line graph, a pie graph, and the like.
The platform is used for preprocessing input engineering test detection data and specifically comprises the following steps:
firstly, test detection data acquired by a data acquisition item are read, the read data are screened, and data which do not meet the standard are removed.
The platform diagram visual item can generate a diagram, and the method specifically comprises the following steps:
firstly, reading data acquired by a data acquisition item and screened by a preprocessing item, analyzing and calculating the read data, and generating corresponding charts such as a line graph, a bar graph, a pie graph and the like according to analysis and calculation results and user requirements.
The platform generates a report, and specifically includes:
firstly, reading data which is acquired by a data acquisition item and then screened by a preprocessing item, analyzing and calculating the read data, generating a report according to analysis and calculation results, and displaying data items such as qualification rate, maximum value, minimum value, average value and the like on the report.
Wherein the platform further comprises a record item for recording the operation of the platform data by the user.
The foregoing shows and describes the general principles and principal structural features of the present invention. The present invention is not limited to the above examples, and various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the claimed invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A management early warning decision method for engineering test detection data is characterized by comprising the following steps: comprises the following steps;
a. data acquisition: collecting and recording engineering test detection data;
b. data preprocessing: identifying and eliminating abnormal data which are detected by an unconventional test in the collected data;
c. the chart can be seen: visualizing the engineering test detection data to realize the automatic generation of relevant statistical analysis indexes and reports;
d. generating a report: the report generation item is used for the correlation calculation of the engineering test detection data, so that data report information is generated;
e. real-time early warning: the method is used for early warning prompt of data abnormity when engineering test detection data are collected, a multi-factor linkage early warning model and a multi-stage early warning strategy are formulated according to a risk control threshold value aiming at abnormal fluctuation parameters possibly existing in a platform, a trend envelope curve and a risk grading method are established by combining a knowledge base, a case base and an expert base, rapid automatic early warning of test data abnormity is achieved, and warning notification is carried out on related personnel when the data are not in a specified range.
2. The engineering test detection data management early warning decision method as claimed in claim 1, characterized in that: in the step a, collected and input engineering test detection data comprise common engineering performance test data of engineering raw materials, concrete mixing, concrete forming and filling materials.
3. The engineering test detection data management early warning decision method as claimed in claim 1, characterized in that: in the step b, a data preprocessing library is built in the platform: and identifying and rejecting abnormal detection data of unconventional tests according to abnormal test piece judgment rules containing different detection items and abnormal detection data judgment rules of test piece tests in the same batch.
4. The engineering test detection data management early warning decision method as claimed in claim 1, characterized in that: and c, visualizing the engineering test detection data by adopting a line drawing or pie chart tool.
5. The engineering test detection data management early warning decision method as claimed in claim 1, characterized in that: in step d, the generated data report information comprises the detection group number and the qualified rate data report information.
6. The engineering test detection data management early warning decision method as claimed in claim 5, characterized in that: in step d, after the user selects the query time interval and the data object, the system exports the test data stream to a fixed report template, and automatically calculates the analysis statistical indexes of the detection group number, the qualification rate, the maximum planting, the minimum planting, the average planting, the standard deviation and the variation coefficient.
7. The engineering test detection data management early warning decision method as claimed in claim 6, characterized in that: the detection group number calculation principle is as follows: and judging whether the numerical value of one item is null according to the input test data, wherein if the numerical value of one item is null, the number of the test group is 0, and otherwise, the number of the test group is 1.
8. The engineering test detection data management early warning decision method as claimed in claim 7, characterized in that: the calculation principle of the qualified rate is as follows: and judging whether the number of detection groups is 1 or not, if not, judging that the qualified group number is 0, if so, continuously judging whether the item number is in a specified range, if so, judging that the qualified group number is 1, otherwise, judging that the qualified group number is 0.
9. The engineering test detection data management early warning decision method as claimed in claim 1, characterized in that: the low-frequency high-amplitude abnormal fluctuation data under the qualified test condition is identified by adopting a boxplot theory for reminding, and the algorithm is as follows:
wherein: q1 is the first quartile, equal to the 25 th% number after all values in the sample are arranged from small to large; q3 is the third quartile, which is equal to the 75 th% of the numbers in the sample after all the numbers are arranged from small to large; QR represents a quartile distance which is the distance between the third quartile and the first quartile.
10. The engineering test detection data management early warning decision method as claimed in claim 1, characterized in that: and (3) increasing and decreasing new and old data periodically by adopting a moving average method to calculate a moving average value, continuously predicting detection values of one week, one month and one quarter in the future, and carrying out grading early warning prompt on the condition that the predicted values exceed allowable deviation.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112595909A (en) * | 2020-11-27 | 2021-04-02 | 江苏中浩电力工程有限公司 | Method for intelligent substation detection test |
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Application publication date: 20200403 |