CN116910324A - Visual report configuration method and system for experimental big data - Google Patents
Visual report configuration method and system for experimental big data Download PDFInfo
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Abstract
The application discloses a visual report configuration method and a visual report configuration system for experimental big data, wherein the method comprises the following steps: dividing experimental big data into a plurality of item data according to item types, and preprocessing each item data; configuring data processing and calculating logic aiming at each item data, and calculating each item data by utilizing the configured data processing and calculating logic to obtain a calculation result; performing qualification judgment through judgment configuration logic based on the calculation result of each item data to obtain a judgment result; and outputting the target calculation result which is qualified in the judgment result to a visual report for display based on the result item configuration file. The method does not need intervention of a tester and can be performed asynchronously, so that time of laboratory personnel is saved, and accuracy of data can be greatly guaranteed. And the system can be designed independently aiming at different services or data processing logic so as to be basically decoupled from the system function, thereby being beneficial to the system expansibility.
Description
Technical Field
The application relates to the technical field of laboratory big data report processing, in particular to a visual report configuration method and system for laboratory big data.
Background
At present, millions of raw data may be generated after laboratory equipment is tested, laboratory testers often need to perform steps such as filtering, screening and calculating on a large amount of data to obtain final result data for reporting or archiving, and due to inconsistent requirements of various detection projects or detection standards and data processing logic, laboratory related personnel may need a large amount of time to refine the result data from the raw data and are prone to errors.
Disclosure of Invention
Aiming at the problems shown above, the application provides a visual report configuration method and a visual report configuration system for experimental big data, which are used for solving the problems that the requirements of various detection items or detection standards and data processing logics mentioned in the background art are inconsistent, so that a great deal of time is needed for extracting result data from original data by laboratory related personnel, and errors are easy to occur.
A visual report configuration method of experimental big data comprises the following steps:
dividing experimental big data into a plurality of item data according to item types, and preprocessing each item data;
configuring data processing and calculating logic aiming at each item data, and calculating each item data by utilizing the configured data processing and calculating logic to obtain a calculation result;
performing qualification judgment through judgment configuration logic based on the calculation result of each item data to obtain a judgment result;
and outputting the target calculation result which is qualified in the judgment result to a visual report for display based on the result item configuration file.
Preferably, the dividing the experimental big data into a plurality of item data according to item types, and preprocessing each item data includes:
retrieving statistical experiment big data of a single experiment from a database and determining a plurality of experiment projects according to experiment subjects;
determining the item type of each experimental item, and determining the data characteristic of each experimental item according to the item type;
dividing the statistical experiment big data into a plurality of item data based on the data characteristics of each experiment item, and determining the data format of each item data;
and selecting a data deduplication and abnormal data filtering mode based on the data format of each item data, and preprocessing each item data according to the data deduplication and abnormal data filtering mode.
Preferably, the configuring data processing and calculating logic for each item data, calculating each item data by using the configured data processing and calculating logic, and obtaining a calculation result includes:
acquiring a correlation condition among all sub-data in each item of data, and configuring an operation engine according to the correlation condition;
configuring data processing and computing logic of each item data based on a state data change condition of an operation engine of each item data;
acquiring staged calculation demand parameters of data processing and calculation logic of each item of data;
and carrying out staged calculation and comprehensive calculation on each item data according to the staged calculation demand parameters of the data processing and calculation logic of each item data to obtain a calculation result.
Preferably, the step of performing qualification judgment by the judgment configuration logic based on the calculation result of each item data to obtain a judgment result includes:
obtaining a final calculation result value of each item data according to the calculation result of the item data;
judging the final calculation result value of each item data based on the judgment configuration logic of the calculation result of the item data, and acquiring a first judgment result;
determining whether a final calculation result value of each item data is in a qualified judgment range value interval of the item data according to the first judgment result, and obtaining a determination result;
and performing qualification judgment on the final calculation result value of each item of data according to the determination result, and obtaining a second judgment result.
Preferably, the presenting the target calculation result qualified in the determination result to the visual report based on the result item configuration file for display includes:
screening out qualified target calculation results according to the judgment result of each item of data, and carrying out specific specification name configuration on the target calculation results based on the result item configuration file;
acquiring equipment experimental data corresponding to each item of data, and acquiring a plurality of data items according to the equipment experimental data;
correlating the target calculation result with a target data item of target item data corresponding to the target calculation result;
and outputting the associated target item data to a visual report for display.
A visual report configuration system for experimental big data, the system comprising:
the division module is used for dividing the experimental big data into a plurality of item data according to item types and preprocessing each item data;
the computing module is used for configuring data processing and computing logic for each item data, and computing each item data by utilizing the configured data processing and computing logic to obtain a computing result;
the judging module is used for carrying out qualification judgment through judging configuration logic based on the calculation result of each item data, and obtaining a judgment result;
and the display module is used for displaying the target calculation result which is qualified in the judgment result in the visual report based on the result item configuration file.
Preferably, the dividing module includes:
the first determining submodule is used for retrieving statistical experiment big data of a single experiment from the database and determining a plurality of experiment items according to experiment subjects;
the second determining submodule is used for determining the item type of each experimental item and determining the data characteristic of each experimental item according to the item type;
dividing the sub-module, which is used for dividing the statistical experiment big data into a plurality of item data based on the data characteristics of each experiment item, and determining the data format of each item data;
and the preprocessing sub-module is used for selecting a data deduplication and abnormal data filtering mode based on the data format of each item data and preprocessing each item data according to the data deduplication and abnormal data filtering mode.
Preferably, the computing module includes:
the first configuration sub-module is used for acquiring the correlation condition among all sub-data in each item data and configuring an operation engine according to the correlation condition;
a second configuration sub-module for configuring data processing and calculation logic of each item data based on a state data change condition of an operation engine of each item data;
the first acquisition submodule is used for acquiring the staged calculation demand parameters of the data processing and calculating logic of each item data;
and the calculation sub-module is used for carrying out staged calculation and comprehensive calculation on each item data according to the staged calculation demand parameters of the data processing and calculation logic of each item data to obtain a calculation result.
Preferably, the determining module includes:
the second acquisition sub-module is used for acquiring a final calculation result value of each item data according to the calculation result of the item data;
the first judging submodule is used for judging the final calculation result value of the item data based on the judging configuration logic of the calculation result of the item data, and obtaining a first judging result;
a third determining sub-module, configured to determine, according to the first determination result, whether a final calculation result value of each item data is within a qualified determination range value interval of the item data, and obtain a determination result;
and the second judging sub-module is used for carrying out qualification judgment on the final calculation result value of each item of data according to the determination result to obtain a second judging result.
Preferably, the display module includes:
the third configuration sub-module is used for screening out qualified target calculation results according to the judgment result of each item data, and carrying out specific specification name configuration on the target calculation results based on the result item configuration file;
the third acquisition sub-module is used for acquiring equipment experiment data corresponding to each item of data and acquiring a plurality of data items according to the equipment experiment data;
the association sub-module is used for associating the target calculation result with a target data item of target item data corresponding to the target calculation result;
and the display sub-module is used for displaying the associated target item data in the visual report.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the application is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application.
FIG. 1 is a workflow diagram of a visual report configuration method for experimental big data provided by the application;
FIG. 2 is another workflow diagram of a visual report configuration method for experimental big data provided by the present application;
FIG. 3 is a schematic diagram of a visual report configuration system for experimental big data according to the present application;
fig. 4 is a schematic structural diagram of a partitioning module in the visual report configuration system of big experimental data provided by the application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
At present, millions of raw data may be generated after laboratory equipment is tested, laboratory testers often need to perform steps such as filtering, screening and calculating on a large amount of data to obtain final result data for reporting or archiving, and due to inconsistent requirements of various detection projects or detection standards and data processing logic, laboratory related personnel may need a large amount of time to refine the result data from the raw data and are prone to errors. In order to solve the above problems, the present embodiment discloses a visual report configuration method for experimental big data.
A visual report configuration method of experimental big data, as shown in figure 1, comprises the following steps:
step S101, dividing experimental big data into a plurality of item data according to item types, and preprocessing each item data;
step S102, configuring data processing and calculating logic for each item data, and calculating each item data by using the configured data processing and calculating logic to obtain a calculation result;
step S103, performing qualification judgment through judgment configuration logic based on the calculation result of each item data, and obtaining a judgment result;
and step S104, outputting the target calculation result which is qualified in the judgment result to a visual report based on the result item configuration file for display.
In the present embodiment, the item type is represented as a type of experimental item;
in the present embodiment, the item data is represented as division data for each experimental item;
in this embodiment, preprocessing refers to preprocessing such as deduplication and abnormal data filtering on project data;
in this embodiment, the data processing and calculation logic is represented as system calculation logic between configuration data for each item data;
in this embodiment, the determination configuration logic is represented as configuration logic for performing qualification determination on the calculation result;
in this embodiment, the result item profile is a related profile defined by the name of the result item that normalizes the calculation result.
The working principle of the technical scheme is as follows: dividing experimental big data into a plurality of item data according to item types, and preprocessing each item data; configuring data processing and calculating logic aiming at each item data, and calculating each item data by utilizing the configured data processing and calculating logic to obtain a calculation result; performing qualification judgment through judgment configuration logic based on the calculation result of each item data to obtain a judgment result; and outputting the target calculation result which is qualified in the judgment result to a visual report for display based on the result item configuration file.
The beneficial effects of the technical scheme are as follows: by performing self-adaptive calculation and qualification judgment logic configuration on each experimental project, the intervention of a tester is not needed, and the configuration can be performed asynchronously, so that the time of laboratory personnel is saved, and the accuracy of data can be greatly ensured. The system can be designed independently aiming at different services or data processing logic so as to be basically decoupled from system functions, system expansibility is facilitated, and the problems that in the prior art, due to the fact that requirements of various detection items or detection standards are inconsistent with the data processing logic, a great deal of time is needed for extracting result data from original data by relevant laboratory personnel, and errors are prone to occur are solved.
In one embodiment, as shown in fig. 2, the dividing the experimental big data into a plurality of item data according to item types, and preprocessing each item data includes:
step S201, retrieving statistical experiment big data of a single experiment from a database and determining a plurality of experiment items according to experiment subjects;
step S202, determining the item type of each experimental item, and determining the data characteristic of each experimental item according to the item type;
step S203, dividing the statistical experiment big data into a plurality of item data based on the data characteristics of each experiment item, and determining the data format of each item data;
and step S204, selecting a data deduplication and abnormal data filtering mode based on the data format of each item data, and preprocessing each item data according to the data deduplication and abnormal data filtering mode.
In this embodiment, the experimental subjects are represented as subject items of a single experiment;
in the present embodiment, the data characteristics are expressed as data yield characteristics and unit characteristics of the relevant experimental data for each experimental item;
in the present embodiment, the data format is represented as a data presentation format of each item data.
The beneficial effects of the technical scheme are as follows: the project data can be rapidly and accurately divided into the project attributions by dividing the project data according to the data characteristics, so that the working efficiency and the stability are improved, and further, the project data of different types can be optimized in all directions to the greatest extent by intelligently selecting a preprocessing mode, so that the practicability is improved.
In this embodiment, determining the item type of each experimental item, determining the data characteristic of each experimental item according to the item type, includes:
determining the operation type of each experimental project according to the project type of the experimental project, wherein the operation type comprises the following steps: operating from beginning to end, operating only the beginning step, operating the beginning step and the intermediate step, operating only the intermediate step, operating the intermediate step and the ending step, and operating only the ending step;
determining an acquisition mode of experimental data of each experimental project based on the operation type of each experimental project and experimental project demand equipment, wherein the acquisition mode comprises the following steps: human eye collection and equipment collection;
determining an activity action characteristic value change curve of an experimenter according to the acquisition mode of experimental data of each experimental project;
acquiring a historical original data sequence of each experimental project;
analyzing the historical original data sequence of each experimental project, and extracting experimental parameters in a time period vector corresponding to the holding state of the same experimental element;
superposing and averaging experimental parameters in a plurality of time period vectors to obtain parameter instantaneous data of the experimental element in a holding state;
acquiring a parameter change rule of each experimental item when the experimental element jumps according to the parameter instantaneous data in the experimental element holding state;
ranking the data output frequency of each experimental project based on the characteristic value change curve of the activity action of the experimenter of each experimental project and the parameter change rule of the experimental project when the experimental elements jump, and obtaining a ranking result;
and determining the data characteristics of each experimental project according to the rating result of the experimental project and the data presentation mode of the experimental project or the digital signal presentation mode of the data acquisition equipment.
In this embodiment, the characteristic value change curve of the activity action of the experimenter is represented as a graph formed by periodic recording points of action change when the experimenter needs to check and record data in the experimental process of each experimental project;
in this embodiment, the same experimental element holding state is expressed as a state value of an environmental element in which a relatively critical experimental state is maintained during an experiment;
in the present embodiment, the period vector is expressed as a period vector in which the experimental element remains in a stable state;
in this embodiment, the data output frequency indicates the output frequency and efficiency of the experimental data to be recorded for each experimental project.
The beneficial effects of the technical scheme are as follows: the data characteristics of each experimental project can be accurately and objectively determined by evaluating the data characteristics of each experimental project according to the historical experience and the operation parameters of each experimental project, the accuracy and objectivity of an evaluation result are ensured, conditions are laid for the subsequent experiment big data classification, and the practicability and the stability are further improved.
In one embodiment, the configuring the data processing and calculating logic for each item data, calculating each item data by using the configured data processing and calculating logic, and obtaining a calculation result includes:
acquiring a correlation condition among all sub-data in each item of data, and configuring an operation engine according to the correlation condition;
configuring data processing and computing logic of each item data based on a state data change condition of an operation engine of each item data;
acquiring staged calculation demand parameters of data processing and calculation logic of each item of data;
and carrying out staged calculation and comprehensive calculation on each item data according to the staged calculation demand parameters of the data processing and calculation logic of each item data to obtain a calculation result.
In the present embodiment, the correlation condition is expressed as a correlation calculation condition between the respective sub data;
in this embodiment, the operation engine is represented as a network calculation engine capable of performing automatic intelligent calculation;
in this embodiment, the state data change condition is represented as a change condition of the data state of the operation engine after the initial calculation data is filled;
in this embodiment, the staged computing demand parameter is expressed as a data parameter required at each computing stage.
The beneficial effects of the technical scheme are as follows: the logical smoothness and the accuracy and stability of the calculation data can be ensured by configuring the operation engine and further configuring the data processing and calculating logic, further, larger errors caused by calculation results can be avoided by carrying out staged calculation and comprehensive calculation, the step calculation from the leading level is realized, and the accuracy and reliability of the calculation results are ensured.
In this embodiment, acquiring a correlation condition between respective sub-data in each item data includes:
acquiring a data source corresponding to each item of data, and determining experimental equipment according to the data source;
acquiring a plurality of experimental components of experimental equipment and flow parameters of each experimental component, and constructing a correlation model between two adjacent experimental components according to the flow parameters of each experimental component;
according to the operation time sequence parameter of each experimental component, time sequence arrangement is carried out on each piece of sub data in each item of data, and an arrangement result is obtained;
determining division sub-data of each experimental component based on the arrangement result;
according to a correlation model between two adjacent experimental components; correlation conditions between the partitioned sub-data of each experimental component are determined.
In this embodiment, the data source is represented as a data collection source of project data, i.e., data collected from what experimental facilities;
in this embodiment, the experimental component is represented as an experimental functional component that performs various experiments on the experimental apparatus;
in this embodiment, the flow parameter is expressed as an experimental flow parameter of each experimental component;
in this embodiment, the correlation model is represented as a correlation model of usage restrictions and transitions between two adjacent experimental components.
The beneficial effects of the technical scheme are as follows: by determining the correlation conditions among the sub-data according to the correlation model of the experimental component, the correlation conditions of the data can be accurately determined based on the experimental process, and the accuracy and reliability of the determined result are ensured.
In one embodiment, the calculating result based on each item data performs qualification determination through determination configuration logic, and obtains a determination result, including:
obtaining a final calculation result value of each item data according to the calculation result of the item data;
judging the final calculation result value of each item data based on the judgment configuration logic of the calculation result of the item data, and acquiring a first judgment result;
determining whether a final calculation result value of each item data is in a qualified judgment range value interval of the item data according to the first judgment result, and obtaining a determination result;
and performing qualification judgment on the final calculation result value of each item of data according to the determination result, and obtaining a second judgment result.
The beneficial effects of the technical scheme are as follows: the deviation degree of the calculation result can be determined and whether the calculation result accords with the expected result can be determined through double determination, so that the accurate determination of the calculation result is ensured, the determination error is reduced, and the stability and the practicability are improved.
In one embodiment, the presenting the target calculation result which is qualified in the judging result in the visual report based on the result item configuration file includes:
screening out qualified target calculation results according to the judgment result of each item of data, and carrying out specific specification name configuration on the target calculation results based on the result item configuration file;
acquiring equipment experimental data corresponding to each item of data, and acquiring a plurality of data items according to the equipment experimental data;
correlating the target calculation result with a target data item of target item data corresponding to the target calculation result;
and outputting the associated target item data to a visual report for display.
The beneficial effects of the technical scheme are as follows: the special standard name configuration of the calculation result is carried out through the price, so that the effect of intuitionism and easy understanding in the visual report is achieved, the experience of a user is improved, furthermore, the user can intuitively determine which equipment is abnormal in data through associating the calculation result of the project data with the data item, further, follow-up processing measures are carried out, and the practicability and the experience of the user are further improved.
In one embodiment, the embodiment also discloses a visual report configuration system of the experimental big data, as shown in fig. 3, the system comprises:
the dividing module 301 is configured to divide the experimental big data into a plurality of item data according to item types, and preprocess each item data;
the computing module 302 is configured to configure data processing and computing logic for each item data, and compute each item data by using the configured data processing and computing logic to obtain a computing result;
a determining module 303, configured to perform qualification determination through a determination configuration logic based on the calculation result of each item data, and obtain a determination result;
and the display module 304 is configured to display the target calculation result that is qualified in the determination result in the visual report based on the result item configuration file.
The working principle of the technical scheme is as follows: firstly, dividing experimental big data into a plurality of item data according to item types through a dividing module, and preprocessing each item data; secondly, configuring data processing and calculating logic for each item data by using a calculating module, and calculating each item data by using the configured data processing and calculating logic to obtain a calculating result; then, the qualification judgment is carried out through judgment configuration logic based on the calculation result of each item of data through a judgment module, and a judgment result is obtained; and finally, utilizing a display module to display the target calculation result which is qualified in the judgment result in the visual report based on the result item configuration file.
The beneficial effects of the technical scheme are as follows: by performing self-adaptive calculation and qualification judgment logic configuration on each experimental project, the intervention of a tester is not needed, and the configuration can be performed asynchronously, so that the time of laboratory personnel is saved, and the accuracy of data can be greatly ensured. And the system can be designed independently aiming at different services or data processing logic so as to be basically decoupled from the system function, thereby being beneficial to the system expansibility.
In one embodiment, as shown in fig. 2, the dividing module 301 includes:
a first determining submodule 3011, configured to retrieve statistical experiment big data of a single experiment from a database and determine a plurality of experiment items according to an experiment subject;
a second determining submodule 3012, configured to determine a project type of each experimental project, and determine a data characteristic of each experimental project according to the project type;
a dividing sub-module 3013, configured to divide the statistical experiment big data into a plurality of item data based on the data characteristics of each experiment item, and determine a data format of each item data;
the preprocessing sub-module 3014 is configured to select a data deduplication and abnormal data filtering mode based on a data format of each item data, and perform preprocessing on each item data according to the data deduplication and abnormal data filtering mode.
The beneficial effects of the technical scheme are as follows: the project data can be rapidly and accurately divided into the project attributions by dividing the project data according to the data characteristics, so that the working efficiency and the stability are improved, and further, the project data of different types can be optimized in all directions to the greatest extent by intelligently selecting a preprocessing mode, so that the practicability is improved.
In one embodiment, the computing module includes:
the first configuration sub-module is used for acquiring the correlation condition among all sub-data in each item data and configuring an operation engine according to the correlation condition;
a second configuration sub-module for configuring data processing and calculation logic of each item data based on a state data change condition of an operation engine of each item data;
the first acquisition submodule is used for acquiring the staged calculation demand parameters of the data processing and calculating logic of each item data;
and the calculation sub-module is used for carrying out staged calculation and comprehensive calculation on each item data according to the staged calculation demand parameters of the data processing and calculation logic of each item data to obtain a calculation result.
The beneficial effects of the technical scheme are as follows: the logical smoothness and the accuracy and stability of the calculation data can be ensured by configuring the operation engine and further configuring the data processing and calculating logic, further, larger errors caused by calculation results can be avoided by carrying out staged calculation and comprehensive calculation, the step calculation from the leading level is realized, and the accuracy and reliability of the calculation results are ensured.
In one embodiment, the determining module includes:
the second acquisition sub-module is used for acquiring a final calculation result value of each item data according to the calculation result of the item data;
the first judging submodule is used for judging the final calculation result value of the item data based on the judging configuration logic of the calculation result of the item data, and obtaining a first judging result;
a third determining sub-module, configured to determine, according to the first determination result, whether a final calculation result value of each item data is within a qualified determination range value interval of the item data, and obtain a determination result;
and the second judging sub-module is used for carrying out qualification judgment on the final calculation result value of each item of data according to the determination result to obtain a second judging result.
The beneficial effects of the technical scheme are as follows: the deviation degree of the calculation result can be determined and whether the calculation result accords with the expected result can be determined through double determination, so that the accurate determination of the calculation result is ensured, the determination error is reduced, and the stability and the practicability are improved.
In one embodiment, the display module comprises:
the third configuration sub-module is used for screening out qualified target calculation results according to the judgment result of each item data, and carrying out specific specification name configuration on the target calculation results based on the result item configuration file;
the third acquisition sub-module is used for acquiring equipment experiment data corresponding to each item of data and acquiring a plurality of data items according to the equipment experiment data;
the association sub-module is used for associating the target calculation result with a target data item of target item data corresponding to the target calculation result;
and the display sub-module is used for displaying the associated target item data in the visual report.
The beneficial effects of the technical scheme are as follows: the special standard name configuration of the calculation result is carried out through the price, so that the effect of intuitionism and easy understanding in the visual report is achieved, the experience of a user is improved, furthermore, the user can intuitively determine which equipment is abnormal in data through associating the calculation result of the project data with the data item, further, follow-up processing measures are carried out, and the practicability and the experience of the user are further improved.
It will be appreciated by those skilled in the art that the first and second aspects of the present application refer to different phases of application.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. The visual report configuration method of the experimental big data is characterized by comprising the following steps of:
dividing experimental big data into a plurality of item data according to item types, and preprocessing each item data;
configuring data processing and calculating logic aiming at each item data, and calculating each item data by utilizing the configured data processing and calculating logic to obtain a calculation result;
performing qualification judgment through judgment configuration logic based on the calculation result of each item data to obtain a judgment result;
and outputting the target calculation result which is qualified in the judgment result to a visual report for display based on the result item configuration file.
2. The visual report configuration method of experimental big data according to claim 1, wherein the dividing the experimental big data into a plurality of item data according to item types, preprocessing each item data, comprises:
retrieving statistical experiment big data of a single experiment from a database and determining a plurality of experiment projects according to experiment subjects;
determining the item type of each experimental item, and determining the data characteristic of each experimental item according to the item type;
dividing the statistical experiment big data into a plurality of item data based on the data characteristics of each experiment item, and determining the data format of each item data;
and selecting a data deduplication and abnormal data filtering mode based on the data format of each item data, and preprocessing each item data according to the data deduplication and abnormal data filtering mode.
3. The method for configuring visual report of experimental big data according to claim 1, wherein the configuring data processing and calculating logic for each item data, calculating each item data by using the configured data processing and calculating logic, and obtaining a calculation result comprises:
acquiring a correlation condition among all sub-data in each item of data, and configuring an operation engine according to the correlation condition;
configuring data processing and computing logic of each item data based on a state data change condition of an operation engine of each item data;
acquiring staged calculation demand parameters of data processing and calculation logic of each item of data;
and carrying out staged calculation and comprehensive calculation on each item data according to the staged calculation demand parameters of the data processing and calculation logic of each item data to obtain a calculation result.
4. The visual report configuration method of experimental big data according to claim 1, wherein the calculating result based on each item data is qualified by the decision configuration logic to obtain the decision result, comprising:
obtaining a final calculation result value of each item data according to the calculation result of the item data;
judging the final calculation result value of each item data based on the judgment configuration logic of the calculation result of the item data, and acquiring a first judgment result;
determining whether a final calculation result value of each item data is in a qualified judgment range value interval of the item data according to the first judgment result, and obtaining a determination result;
and performing qualification judgment on the final calculation result value of each item of data according to the determination result, and obtaining a second judgment result.
5. The method for configuring a visual report of experimental big data according to claim 1, wherein the step of showing the target calculation result which is qualified in the determination result based on the result item configuration file in the visual report includes:
screening out qualified target calculation results according to the judgment result of each item of data, and carrying out specific specification name configuration on the target calculation results based on the result item configuration file;
acquiring equipment experimental data corresponding to each item of data, and acquiring a plurality of data items according to the equipment experimental data;
correlating the target calculation result with a target data item of target item data corresponding to the target calculation result;
and outputting the associated target item data to a visual report for display.
6. A visual report configuration system for experimental big data, the system comprising:
the division module is used for dividing the experimental big data into a plurality of item data according to item types and preprocessing each item data;
the computing module is used for configuring data processing and computing logic for each item data, and computing each item data by utilizing the configured data processing and computing logic to obtain a computing result;
the judging module is used for carrying out qualification judgment through judging configuration logic based on the calculation result of each item data, and obtaining a judgment result;
and the display module is used for displaying the target calculation result which is qualified in the judgment result in the visual report based on the result item configuration file.
7. The visual report configuration system of claim 6, wherein the partitioning module comprises:
the first determining submodule is used for retrieving statistical experiment big data of a single experiment from the database and determining a plurality of experiment items according to experiment subjects;
the second determining submodule is used for determining the item type of each experimental item and determining the data characteristic of each experimental item according to the item type;
dividing the sub-module, which is used for dividing the statistical experiment big data into a plurality of item data based on the data characteristics of each experiment item, and determining the data format of each item data;
and the preprocessing sub-module is used for selecting a data deduplication and abnormal data filtering mode based on the data format of each item data and preprocessing each item data according to the data deduplication and abnormal data filtering mode.
8. The visual report configuration system of claim 6, wherein the computing module comprises:
the first configuration sub-module is used for acquiring the correlation condition among all sub-data in each item data and configuring an operation engine according to the correlation condition;
a second configuration sub-module for configuring data processing and calculation logic of each item data based on a state data change condition of an operation engine of each item data;
the first acquisition submodule is used for acquiring the staged calculation demand parameters of the data processing and calculating logic of each item data;
and the calculation sub-module is used for carrying out staged calculation and comprehensive calculation on each item data according to the staged calculation demand parameters of the data processing and calculation logic of each item data to obtain a calculation result.
9. The visual report configuration system of claim 6, wherein the decision module comprises:
the second acquisition sub-module is used for acquiring a final calculation result value of each item data according to the calculation result of the item data;
the first judging submodule is used for judging the final calculation result value of the item data based on the judging configuration logic of the calculation result of the item data, and obtaining a first judging result;
a third determining sub-module, configured to determine, according to the first determination result, whether a final calculation result value of each item data is within a qualified determination range value interval of the item data, and obtain a determination result;
and the second judging sub-module is used for carrying out qualification judgment on the final calculation result value of each item of data according to the determination result to obtain a second judging result.
10. The visual report configuration system of experimental big data of claim 6, wherein the presentation module comprises:
the third configuration sub-module is used for screening out qualified target calculation results according to the judgment result of each item data, and carrying out specific specification name configuration on the target calculation results based on the result item configuration file;
the third acquisition sub-module is used for acquiring equipment experiment data corresponding to each item of data and acquiring a plurality of data items according to the equipment experiment data;
the association sub-module is used for associating the target calculation result with a target data item of target item data corresponding to the target calculation result;
and the display sub-module is used for displaying the associated target item data in the visual report.
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