CN116817983A - Data analysis method, data analysis recorder and storage medium - Google Patents
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Abstract
The application relates to a data analysis method, a data analysis recorder and a storage medium, belonging to the field of data processing, wherein the method comprises the following steps: acquiring an analog quantity acquired by a data acquisition unit; filtering the analog quantity through a preset filter to obtain filtering data; carrying out data correction on the filtered data to obtain correction data; and determining and displaying the alarm level according to the correction data. The application carries out filtering and data correction on the analog quantity acquired by the data acquisition device so as to determine the alarm grade according to the corrected data, thereby obtaining the abnormal data without manual analysis after the data recording is completed when the data is abnormal, namely determining the abnormal data and determining the alarm grade when the data is recorded, and playing the role of effectively shortening the time span for finding the abnormal data.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a data analysis method, a data analysis recorder, and a storage medium.
Background
The data recorder is an electronic instrument which acquires measurement results from a sensor and stores the measurement results for standby, is widely applied to various industries including the fields of electric power and electricity, bio-pharmaceuticals, electronic testing, new energy, traffic, weather, environmental protection, agriculture, various industries and the like, and is commonly used for measuring physical quantities such as temperature, pressure, current, speed, tension, displacement and the like.
The data recorder is used for monitoring the operation of the equipment or periodically monitoring the data through the collected data, for example, the data recorder is used for monitoring important parameters such as pressure, temperature and flow of industrial production processes, storage facilities, energy factories, storage tank systems, production lines and the like in real time so as to determine whether the equipment is in normal operation; data such as weather patterns, climate changes, river water levels/cleanliness, and many parameters of natural habitats and ecosystems, e.g., water quality, temperature, etc., are monitored periodically by data loggers.
In the prior art, the data recorder is only used for recording and storing data, if the running condition of the equipment is required to be monitored through the data stored by the data recorder, the data is required to be manually analyzed after the data is collected by the data recorder, so that the applicant believes that if the data is abnormal, the abnormal data can be obtained and recorded through manual analysis after the data recording is completed if the abnormal data is required to be recorded, and the time span for finding the abnormal data is larger.
Disclosure of Invention
In order to effectively shorten the time span for finding abnormal data, the application provides a data analysis method, a data analysis recorder and a storage medium.
In a first aspect, the present application provides a data analysis method, which adopts the following technical scheme:
a method of data analysis, comprising:
acquiring an analog quantity acquired by a data acquisition unit;
filtering the analog quantity through a preset filter to obtain filtering data;
carrying out data correction on the filtered data to obtain correction data;
and determining and displaying the alarm level according to the correction data.
By adopting the technical scheme, the analog quantity acquired by the data acquisition device is filtered and data corrected so as to determine the alarm level according to the corrected data, so that when the data is abnormal, the abnormal data can be obtained without manual analysis after the data recording is completed, namely, the abnormal data can be determined when the data is recorded, the alarm level is determined, and the time span for finding the abnormal data is effectively shortened.
Optionally, before the performing data correction on the filtered data to obtain corrected data, the method includes:
acquiring the data range of all the filtering data;
and converting all the data ranges into the same standard range.
By adopting the technical scheme, the data range of all the filtering data is converted into the same standard range, so that the filtering data can be conveniently processed subsequently.
Optionally, the alarm level includes a first alarm level and a second alarm level, and the first alarm level is greater than the second alarm level;
the step of determining the alarm level according to the correction data comprises the following steps:
if the correction data is smaller than a preset lower limit value or larger than a preset upper limit value, determining the alarm level as the first alarm level;
and if the correction data is smaller than a preset lower limit value or larger than a preset upper limit value, determining the alarm level as the second alarm level.
By adopting the technical scheme, the alarm level is determined by correcting the data, namely, when the data is abnormal, the staff can conveniently determine the abnormal degree of the data according to the alarm level.
Optionally, the performing data correction on the filtered data to obtain corrected data includes:
acquiring a data source of each piece of filtering data according to a preset source database;
determining a base value of filtered data based on the data source;
determining a correction index of the filter data in index data based on the basic value and the corresponding filter data;
and correcting the filtering data based on the correction index.
By adopting the technical scheme, the data correction is carried out on the filtered data, so that the accuracy of the subsequent data processing on the filtered data is improved.
Optionally, the determining the basic value of the filtered data according to the data source includes:
determining the data weight of the filtering data corresponding to each data source according to a preset weight database;
obtaining a standard value of the filtering data;
and multiplying the standard value by the data weight to obtain a basic value of the filtered data corresponding to the data source.
By adopting the technical scheme, the basic value of the filtering data is calculated according to the data weight and the standard value, so that the correction index of the filtering data can be conveniently determined for the subsequent filtering data according to the basic value and the filtering data.
Optionally, the determining, in the index data, a correction index of the filter data based on the base value and the corresponding filter data includes:
calculating a data difference value between the basic value and the corresponding filtering data;
comparing the data difference value with a preset standard difference value to obtain a difference value grade;
and determining a correction index of the filtering data according to the difference grade.
By adopting the technical scheme, firstly, the data difference value is compared with the standard difference value, and secondly, the correction index is determined according to the difference value grade, so that the subsequent correction of the filtered data is facilitated.
Optionally, the standard deviation value includes a first standard deviation value, a second standard deviation value and a third standard deviation value; the difference grade comprises a primary grade, a final grade, a first difference grade and the second difference grade;
comparing the data difference value with a preset standard difference value to obtain a difference value grade, wherein the method comprises the following steps:
if the data difference value is smaller than the first standard difference value, judging that the difference value grade is the original grade;
if the data difference value is greater than or equal to the first standard difference value and smaller than the second standard difference value, judging that the difference value grade is the first difference value grade;
if the data difference value is greater than or equal to the second standard difference value and smaller than the third standard difference value, judging that the difference value grade is the second difference value grade;
and if the data difference value is greater than or equal to the third standard difference value, judging that the difference grade is the final grade.
By adopting the technical scheme, the difference value level is obtained by comparing the data difference value with the standard difference value, so that the correction index of the filtering data can be conveniently determined according to the difference value level.
Optionally, the determining the correction index of the filtering data according to the difference level includes:
if the difference level is the original level, determining that the correction index of the filtering data is 1;
if the difference level is the first difference level, determining a correction index of the filtering data as a first index;
if the difference level is the second difference level, determining a correction index of the filtering data as a second index; the second index is larger than the first index;
and if the difference grade is the last grade, determining that the correction index of the filtering data is 0.
By adopting the technical scheme, the filter data can be corrected after the correction index of the filter data is determined.
In a second aspect, the present application provides a data analysis recorder adopting the following technical scheme:
the data analysis recorder comprises an acquisition module, a data processing module, a display module and an alarm module;
the acquisition module is used for acquiring the analog quantity acquired by the data acquisition unit;
the data processing module is used for filtering the analog quantity through a preset filter to obtain filtered data, and carrying out data correction on the filtered data to obtain corrected data;
the alarm module is used for determining alarm grade according to the correction data;
the display module is used for displaying the alarm grade.
By adopting the technical scheme, the data processing module carries out filtering and data correction on the analog quantity acquired by the data acquisition device, so that the alarm module can determine the alarm level according to the correction data, when the data is abnormal, the abnormal data can be obtained without manual analysis after the data recording is completed, namely, the abnormal data can be determined during the data recording, the alarm level is determined, and the time span for finding the abnormal data is effectively shortened.
In a third aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium having a computer program stored therein, which when loaded and executed by a processor, employs the data analysis method described above.
By adopting the technical scheme, the data analysis method generates a computer program, and the computer program is stored in a computer readable storage medium to be loaded and executed by a processor, and the computer program is convenient to read and store by the computer readable storage medium.
In summary, the application has at least one of the following beneficial technical effects:
1. the analog quantity collected by the data collector is filtered and data corrected so as to determine the alarm level according to the corrected data, so that when the data is abnormal, abnormal data can be obtained without manual analysis after the data recording is completed, namely, the abnormal data can be determined when the data is recorded, the alarm level is determined, and the time span for finding the abnormal data is effectively shortened.
2. And converting the data range of all the filtering data into the same standard range, so that the filtering data can be conveniently processed subsequently.
3. The alarm level is determined by correcting the data, namely, when the data is abnormal, the staff can conveniently determine the abnormal degree of the data according to the alarm level.
Drawings
Fig. 1 is a schematic flow chart of one of the data analysis methods according to the embodiment of the present application.
Fig. 2 is a schematic flow chart of one of the data analysis methods according to the embodiment of the present application.
FIG. 3 is a flow chart of one of the data analysis methods according to the embodiment of the present application.
Fig. 4 is a schematic flow chart of one of the data analysis methods according to the embodiment of the present application.
Fig. 5 is a schematic flow chart of one of the data analysis methods according to the embodiment of the present application.
FIG. 6 is a flow chart of one of the data analysis methods according to the embodiment of the present application.
FIG. 7 is a flow chart of one of the data analysis methods according to the embodiment of the present application.
FIG. 8 is a flow chart of one of the data analysis methods according to the embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to fig. 1 to 8.
The embodiment of the application discloses a data analysis method.
Referring to fig. 1, a data analysis method includes the steps of:
s101, acquiring analog quantity acquired by a data acquisition unit.
In this embodiment, the data collector refers to a sensor, that is, the sensor collects the analog quantity and transmits the analog quantity to the current execution body. Analog refers to the output signal of the data collector, i.e. the sensor, analog refers to a physical quantity which is continuous in time or value.
S102, filtering the analog quantity through a preset filter to obtain filtering data.
The filter comprises a high-pass filter and a low-pass filter, and is mainly used for eliminating low-frequency noise of the analog quantity if the analog quantity is filtered by the high-pass filter; if the analog quantity is filtered by a low-pass filter, the analog quantity is mainly used for eliminating high-frequency noise. And filtering the analog quantity to obtain filtering data.
And S103, performing data correction on the filtered data to obtain correction data.
If the acquired filtered data has quality problems, the filtered data is subjected to data correction so as to correct the data with the quality problems, and normal data are obtained. Specifically, if the difference between the filtered data and the artificially preset standard value is greater than a preset threshold, it can be determined that the quality problem exists in the filtered data, and at this time, data correction is required for the filtered data.
S104, determining and displaying the alarm level according to the correction data.
The correction data refers to data obtained by correcting the filtering data, and after the correction data is obtained, the correction data can be compared with an alarm threshold manually preset according to the correction data so as to determine the alarm level and display the alarm level in the display module. The display module is a display screen.
The implementation principle of the embodiment is as follows: the analog quantity collected by the data collector is filtered and data corrected so as to determine the alarm level according to the corrected data, so that when the data is abnormal, abnormal data can be obtained without manual analysis after the data recording is completed, namely, the abnormal data can be determined when the data is recorded, the alarm level is determined, and the time span for finding the abnormal data is effectively shortened.
Before step S103 in the embodiment shown in fig. 1, since the range of the filtered data may not be uniform, the data ranges of all the filtered data need to be uniform. The embodiment shown in fig. 2 is specifically described in detail.
Referring to fig. 2, before performing data correction on the filtered data to obtain corrected data, the method includes the steps of:
s201, acquiring the data range of all the filtering data.
Data measurement ranges in this embodiment refers to the unit of measure of data, e.g., cm, m, km, etc. When the filtered data is obtained, the filtered data contains a measurement unit, so that the data range of the filtered data can be obtained through the filtered data.
S202, converting all the data ranges into the same standard range.
The data range of all the filtering data is converted into the same standard range, so that the uniformity of the data range of the filtering data is guaranteed, for example, the data range of the filtering data before conversion is cm, m, km and the like, and the standard range of all the filtering data is m after all the filtering data are converted into the same standard range, for example, m.
According to the data analysis method provided by the embodiment, the data range of all the filtering data is converted into the same standard range, so that the filtering data can be conveniently processed subsequently.
In step S104 of the embodiment shown in fig. 1, the alarm level may be determined by comparing the preset data with the correction data. The embodiment shown in fig. 3 is specifically described in detail.
Referring to fig. 3, the alarm levels include a first alarm level and a second alarm level, the first alarm level being greater than the second alarm level;
according to the correction data, determining the alarm level comprises the following steps:
s301, if the correction data is smaller than a preset lower limit value or larger than a preset upper limit value, determining that the alarm level is a first alarm level.
For example, if the correction data is 130, the lower limit value is 20, the upper limit value is 120, and the alarm level is determined to be the first alarm level at this time because the correction data 130 is greater than the upper limit value 120.
S302, if the correction data is smaller than a preset lower limit value or larger than a preset upper limit value, determining the alarm level as a second alarm level.
Based on the illustration in step S301, if the lower limit value is set to 40, the upper limit value is set to 90, and if the correction data is set to 30, since the correction data 30 is smaller than the lower limit value 40, the alarm level is determined to be the second alarm level. When the correction data is smaller than the lower limit value or larger than the upper limit value, the correction data simultaneously meets the numerical range of the first alarm level and the second alarm level, the alarm level is judged to be the first alarm level at the moment, and when the correction data does not meet the first alarm level and meets the second alarm level, the alarm level is the second alarm level.
It should be noted that, the lower limit value is smaller than the lower limit value, the upper limit value is larger than the upper limit value, if the alarm level corresponding to the correction data is not the first alarm level and is not the second alarm level, then it is determined that the correction data has no alarm.
In this embodiment, if no alarm occurs in the correction data, the color of the corresponding icon on the display screen is displayed as green; if the alarm level corresponding to the correction data is the first alarm level, displaying the color of the corresponding icon on the display screen as red; and if the alarm level corresponding to the correction data is the second alarm level, displaying the color of the corresponding icon on the display screen as orange.
According to the data analysis method provided by the embodiment, the alarm level is determined by correcting the data, namely, when the data is abnormal, the worker can conveniently determine the abnormal degree of the data according to the alarm level.
In step S103 of the embodiment shown in fig. 1, the basic value of the filtering data may be determined according to the data source of the filtering data, and the correction index of the filtering data may be determined according to the basic value and the filtering data, so that the filtering data may be corrected after the correction index is determined. The embodiment shown in fig. 4 is specifically described in detail.
Referring to fig. 4, the data correction is performed on the filtered data to obtain corrected data, including the steps of:
s401, acquiring the data source of each piece of filtering data according to a preset source database.
The current execution body is a device integrated with eight paths of analog input/output (IO) interfaces, namely, filtering data can be input from different paths, the different paths represent different sources of the data, and the source of each input filtering data is stored in a source database, so that the data source of each filtering data can be acquired based on the source database.
S402, determining a basic value of the filtered data according to the data source.
The basic value refers to a value within a preset value range, specifically, the data sources are different, and the basic value of the filtered data is also different, for example, in the data source A, the value range of the basic value is 50 to 70; in the B data source, the basic value is in the range of 70 to 90, and in this embodiment, the basic value is 80 when the basic value is in the middle of the range, for example, in the range of 70 to 90.
S403, determining a correction index of the filter data based on the basic value and the corresponding filter data in the index data.
The correction index is used for correcting the filtered data so as to make the filtered data more accurate. In this embodiment, if the base value and the filter data are known, the correction index may be determined based on the difference between the base value and the filter data.
S404, correcting the filter data based on the correction index.
After the correction index is known, the filtered data is multiplied by the correction index, i.e., corrected.
According to the data analysis method provided by the embodiment, the data correction is performed on the filtered data, so that the accuracy of the subsequent data processing on the filtered data is improved.
In step S402 of the embodiment shown in fig. 4, the basic value of the filtered data may be obtained by the data weight of the data source and the standard value of the filtered data. The embodiment shown in fig. 5 is specifically described in detail.
Referring to fig. 5, determining the basic values of the filtered data according to the data source includes the steps of:
s501, determining the data weight of the filtering data corresponding to each data source according to a preset weight database.
The weight database stores the data weights of the filtered data corresponding to all the data sources, wherein the data weights refer to different weights of the filtered data in different data sources, namely the weights of the filtered data are related to the data sources.
S502, obtaining a standard value of the filtering data.
The standard values may be entered by a human in the background or may be obtained from a database in which the standard values are stored.
S503, multiplying the standard value by the data weight to obtain a basic value of the filtered data corresponding to the data source.
Basic value=standard value of the filtered data corresponding to the data source. The base value refers to the value that the filtered data should exhibit in the corresponding data source.
According to the data analysis method provided by the embodiment, the basic value of the filtering data is calculated according to the data weight and the standard value, so that the correction index of the filtering data can be conveniently determined for the follow-up according to the basic value and the filtering data.
In step S403 of the embodiment shown in fig. 4, the correction index of the filtered data may be obtained by the data difference and the standard difference of the base data and the filtered data. The embodiment shown in fig. 6 is specifically described in detail.
Referring to fig. 6, determining a correction index of filter data based on a base value and corresponding filter data in index data includes the steps of:
s601, calculating a data difference value between the basic value and the corresponding filtering data.
Data difference = |base value-filtered data.
S602, comparing the data difference value with a preset standard difference value to obtain a difference value grade.
In this embodiment, the standard deviation is manually preset, and the larger the data difference is, the greater the rejection possibility of the data is, so that the standard deviation can be divided into a plurality of levels, and after each data difference is compared with the standard deviation, a plurality of difference levels are obtained, so that the correction index of the filtered data can be determined according to the difference levels.
S603, determining a correction index of the filtering data according to the difference grade.
The difference value grades are different, and the correction indexes corresponding to the difference value grades are different.
According to the data analysis method provided by the embodiment, firstly, the data difference value is compared with the standard difference value, and secondly, the correction index is determined according to the difference value level, so that the subsequent correction of the filtered data is facilitated.
In step S602 in the embodiment shown in fig. 6, the standard deviation may be divided into a plurality of standard deviations, and compared with the data differences one by one to obtain a difference level. The embodiment shown in fig. 7 is specifically described.
Referring to fig. 7, the standard deviation values include a first standard deviation value, a second standard deviation value, and a third standard deviation value; the difference grade comprises a primary grade, a final grade, a first difference grade and a second difference grade;
comparing the data difference value with a preset standard difference value to obtain a difference value grade, and the method comprises the following steps:
s701, if the data difference value is smaller than the first standard difference value, judging the difference value grade as the original grade.
If the data difference is smaller than the first standard difference, the data difference is smaller, and the difference grade is judged to be the original grade, namely the filtered data is not required to be subjected to data correction.
S702, if the data difference is greater than or equal to the first standard difference and less than the second standard difference, determining the difference level as the first difference level.
If the data difference is greater than or equal to the first standard deviation and less than the second standard deviation, the difference level is determined to be the first difference level, for example, the data difference is 20, the first standard deviation is 15, and the second standard deviation is 40, and since the data difference is greater than the first standard deviation and less than the second standard deviation, the difference level corresponding to the data difference is the first difference level.
S703, if the data difference is greater than or equal to the second standard deviation and less than the third standard deviation, determining the difference level as the second difference level.
If the data difference is greater than or equal to the second standard deviation and less than the third standard deviation, the difference grade is judged to be the second difference grade. It should be noted that, the second difference level is greater than the first difference level, and the step S702 is based on the illustration, if the data difference is 45, the third standard difference is 60, the second standard difference is 40, and the data difference is greater than the second standard difference and less than the third standard difference, where the difference level corresponding to the data difference is the second difference level.
S704, if the data difference value is greater than or equal to the third standard difference value, determining that the difference value level is the last level.
If the data difference value is greater than or equal to the third standard difference value, the data difference value is larger, and the data discard is judged at the moment, namely the difference value grade is judged to be the last grade.
According to the data analysis method provided by the embodiment, the difference value level is obtained by comparing the data difference value with the standard difference value, so that the correction index of the filtering data can be conveniently determined according to the difference value level.
In step S603 in the embodiment shown in fig. 6, after the difference level is known, a correction index of the filtered data may be determined according to the difference level. The embodiment shown in fig. 8 is specifically described.
Referring to fig. 8, determining a correction index of the filtered data according to the difference level includes the steps of:
s801, if the difference level is the original level, determining that the correction index of the filtering data is 1.
For example, the filtered data is 70, the difference level is the original level, and at this time, since the correction index of the filtered data is 1, the corrected data obtained after the correction of the filtered data is 70×1=70.
S802, if the difference level is the first difference level, determining a correction index of the filtering data as the first index.
Taking step S801 as an example, for example, the filtered data is 70, the difference level is the first difference level, the first index is 87%, and the corrected data obtained after the correction of the filtered data is 70×87% =60.9.
S803, if the difference level is the second difference level, determining that the correction index of the filter data is the second index; the second index is greater than the first index.
Taking step S801 as an example, for example, the filtered data is 70, the difference level is a second difference level, the second index is 93%, and the corrected data obtained by correcting the filtered data is 70×93% =65.1.
S804, if the difference level is the last level, determining that the correction index of the filter data is 0.
Taking step S801 as an example, for example, the filtered data is 70, the difference level is the last level, and at this time, since the correction index of the filtered data is 0, the corrected data obtained after the correction of the filtered data is 70×0=0.
In the data analysis method according to the present embodiment, the filter data can be corrected after the correction index of the filter data is determined.
The embodiment of the application also discloses a data analysis recorder.
The data analysis recorder comprises an acquisition module, a data processing module, a display module and an alarm module;
the acquisition module is used for acquiring the analog quantity acquired by the data acquisition unit;
the data processing module is used for filtering the analog quantity through a preset filter to obtain filtered data, and carrying out data correction on the filtered data to obtain corrected data;
the alarm module is used for determining alarm grades according to the correction data;
the display module is used for displaying the alarm grade. In addition, the display module is also used for displaying a bar graph, a real-time curve, data export, a history curve, event viewing, parameter setting, system setting and code scanning connection of nine pages. The system setting page provides functions of wireless module selection, corresponding configuration parameters, network port and serial port configuration parameters, local area network radio frequency module configuration, parameters of a connection server, time setting, passwords, equipment addresses, system languages, emptying records and the like.
Besides the modules, the data analysis recorder also comprises a storage module and a remote transmission module, wherein the storage module is used for storing the filter data acquisition time, and the remote transmission module can be a 4G module, a WIFI module or a network port module.
The implementation principle of the data analysis recorder of the embodiment of the application is as follows: the data processing module carries out filtering and data correction on the analog quantity acquired by the data acquisition device so as to facilitate the alarm module to determine the alarm grade according to the corrected data, thereby obtaining the abnormal data without manual analysis after the data recording is completed when the data is abnormal, namely determining the abnormal data when the data is recorded, determining the alarm grade and effectively shortening the time span for finding the abnormal data.
The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores a computer program, wherein the data analysis method in the embodiment is adopted when the computer program is executed by a processor.
The computer program may be stored in a computer readable medium, where the computer program includes computer program code, where the computer program code may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable medium includes, but is not limited to, the above components.
The data analysis method in the above embodiment is stored in the computer readable storage medium through the present computer readable storage medium, and is loaded and executed on a processor, so as to facilitate the storage and application of the method.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (10)
1. A method of data analysis, comprising:
acquiring an analog quantity acquired by a data acquisition unit;
filtering the analog quantity through a preset filter to obtain filtering data;
carrying out data correction on the filtered data to obtain correction data;
and determining and displaying the alarm level according to the correction data.
2. A data analysis method according to claim 1, wherein before said data correction is performed on said filtered data, the method comprises:
acquiring the data range of all the filtering data;
and converting all the data ranges into the same standard range.
3. The method of claim 1, wherein the alert level comprises a first alert level and a second alert level, the first alert level being greater than the second alert level;
the step of determining the alarm level according to the correction data comprises the following steps:
if the correction data is smaller than a preset lower limit value or larger than a preset upper limit value, determining the alarm level as the first alarm level;
and if the correction data is smaller than a preset lower limit value or larger than a preset upper limit value, determining the alarm level as the second alarm level.
4. The method of claim 1, wherein performing data correction on the filtered data to obtain corrected data comprises:
acquiring a data source of each piece of filtering data according to a preset source database;
determining a base value of filtered data based on the data source;
determining a correction index of the filter data in index data based on the basic value and the corresponding filter data;
and correcting the filtering data based on the correction index.
5. A method of analyzing data according to claim 4, wherein said determining a base value of filtered data based on said data source comprises:
determining the data weight of the filtering data corresponding to each data source according to a preset weight database;
obtaining a standard value of the filtering data;
and multiplying the standard value by the data weight to obtain a basic value of the filtered data corresponding to the data source.
6. The method according to claim 5, wherein determining a correction index of the filter data based on the base value and the corresponding filter data in the index data comprises:
calculating a data difference value between the basic value and the corresponding filtering data;
comparing the data difference value with a preset standard difference value to obtain a difference value grade;
and determining a correction index of the filtering data according to the difference grade.
7. The method of claim 6, wherein the standard deviation comprises a first standard deviation, a second standard deviation, and a third standard deviation; the difference grade comprises a primary grade, a final grade, a first difference grade and the second difference grade;
comparing the data difference value with a preset standard difference value to obtain a difference value grade, wherein the method comprises the following steps:
if the data difference value is smaller than the first standard difference value, judging that the difference value grade is the original grade;
if the data difference value is greater than or equal to the first standard difference value and smaller than the second standard difference value, judging that the difference value grade is the first difference value grade;
if the data difference value is greater than or equal to the second standard difference value and smaller than the third standard difference value, judging that the difference value grade is the second difference value grade;
and if the data difference value is greater than or equal to the third standard difference value, judging that the difference grade is the final grade.
8. The method of claim 7, wherein said determining a correction index for said filtered data based on said difference level comprises:
if the difference level is the original level, determining that the correction index of the filtering data is 1;
if the difference level is the first difference level, determining a correction index of the filtering data as a first index;
if the difference level is the second difference level, determining a correction index of the filtering data as a second index; the second index is larger than the first index;
and if the difference grade is the last grade, determining that the correction index of the filtering data is 0.
9. A data analysis recorder, characterized in that: the system comprises an acquisition module, a data processing module, a display module and an alarm module;
the acquisition module is used for acquiring the analog quantity acquired by the data acquisition unit;
the data processing module is used for filtering the analog quantity through a preset filter to obtain filtered data, and carrying out data correction on the filtered data to obtain corrected data;
the alarm module is used for determining alarm grade according to the correction data;
the display module is used for displaying the alarm grade.
10. A computer readable storage medium having a computer program stored therein, characterized in that the method according to any of claims 1 to 8 is employed when the computer program is loaded and executed by a processor.
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