A data analysis method, a data analysis recorder and a storage medium

Abstract

The application relates to a data analysis method, a data analysis recorder and a storage medium, and belongs to the field of data processing.The method comprises the following steps: acquiring analog quantity collected by a data collector; filtering the analog quantity through a preset filter to obtain filtered data; performing data correction on the filtered data to obtain corrected data; and determining an alarm level and displaying the alarm level according to the corrected data.The analog quantity collected by the data collector is filtered and corrected, so that the alarm level can be determined according to the corrected data, thereby achieving the effect of effectively shortening the time span for finding abnormal data, without the need of manually analyzing the abnormal data after data recording is completed, that is, the abnormal data can be determined and the alarm level can be determined during data recording.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a data analysis method, a data analysis recorder and a storage medium. BACKGROUND

[0002] A data recorder is an electronic instrument that acquires measurement results from sensors and stores the measurement results for future use. It is widely used in various industries, including power and electrical, biopharmaceutical, electronic testing, new energy, transportation, meteorology, environmental protection, agriculture, various industries, etc., and is commonly used to measure physical quantities such as temperature, pressure, current, speed, tension, displacement, etc.

[0003] The data recorder is used to monitor the operation of equipment or to conduct periodic monitoring of data, for example, by using the data recorder to monitor important parameters such as pressure, temperature and flow rate, etc. of industrial production processes, storage facilities, energy plants, tank systems, production lines, etc. to determine whether the equipment is operating normally; by using the data recorder to conduct periodic monitoring of many parameters such as water quality, temperature, etc. of weather patterns, climate change, river water level / cleanliness, and natural habitats and ecosystems.

[0004] In the prior art, the data recorder is only used to record and store data, and if the operation of the equipment needs to be monitored by the data stored by the data recorder, the data needs to be manually analyzed after the data is collected by the data recorder. Therefore, the applicant believes that if there is abnormal data, the abnormal data needs to be recorded after the data is collected and analyzed manually, which results in a large time span for discovering abnormal data. SUMMARY

[0005] In order to effectively shorten the time span for discovering abnormal data, the present application provides a data analysis method, a data analysis recorder and a storage medium.

[0006] In a first aspect, the present application provides a data analysis method using the following technical solution:

[0007] A data analysis method, comprising:

[0008] acquiring analog quantities collected by a data collector;

[0009] filtering the analog quantities through a pre-set filter to obtain filtered data;

[0010] correcting the filtered data to obtain corrected data;

[0011] determining an alarm level and displaying it according to the corrected data.

[0012] By adopting the technical scheme, the analog quantity collected by the data collector is filtered and data correction is performed, so as to determine the alarm level according to the corrected data, thereby when the data is abnormal, the abnormal data can be determined and the alarm level can be determined during data recording without manual analysis after data recording is completed, and the time span for finding abnormal data is effectively shortened.

[0013] Optionally, before the data correction is performed on the filtered data to obtain corrected data, the method comprises:

[0014] obtaining data ranges of all the filtered data;

[0015] converting all the data ranges into a same standard range.

[0016] By adopting the technical scheme, the data ranges of all the filtered data are converted into the same standard range, so that subsequent processing of the filtered data is facilitated.

[0017] Optionally, the alarm level comprises a first alarm level and a second alarm level, and the first alarm level is greater than the second alarm level.

[0018] The determining of the alarm level according to the corrected data comprises:

[0019] if the corrected data is less than a preset lower limit value or greater than a preset upper limit value, the alarm level is determined as the first alarm level.

[0020] if the corrected data is less than a preset lower limit value or greater than a preset upper limit value, the alarm level is determined as the second alarm level.

[0021] By adopting the technical scheme, the alarm level is determined by the corrected data, and when the data is abnormal, the abnormal degree of the data can be determined by the staff according to the alarm level.

[0022] Optionally, the data correction performed on the filtered data to obtain corrected data comprises:

[0023] obtaining data sources of each of the filtered data according to a preset source database;

[0024] determining a basic value of the filtered data according to the data sources;

[0025] determining a correction index of the filtered data based on the basic value and the corresponding filtered data in index data;

[0026] correcting the filtered data based on the correction index.

[0027] By adopting the technical scheme, the filtered data is corrected, so as to improve the accuracy of subsequent data processing on the filtered data.

[0028] Optionally, the determining of the basic value of the filtered data according to the data source comprises:

[0029] determining a data weight of the filtered data corresponding to each data source according to a preset weight database;

[0030] obtaining a standard value of the filtered data;

[0031] multiplying the standard value and the data weight to obtain the basic value of the filtered data corresponding to the data source.

[0032] By adopting the technical scheme, the basic value of the filtered data is calculated according to the data weight and the standard value, so as to facilitate the subsequent determination of the correction index of the filtered data according to the basic value and the filtered data.

[0033] Optionally, the determining of the correction index of the filtered data in the index data based on the basic value and the corresponding filtered data comprises:

[0034] calculating a data difference value of the basic value and the corresponding filtered data;

[0035] comparing the data difference value with a preset standard difference value to obtain a difference level;

[0036] determining the correction index of the filtered data according to the difference level.

[0037] By adopting the technical scheme, the data difference value is compared with the standard difference value first, and then the correction index is determined according to the difference level, so as to facilitate the subsequent correction of the filtered data.

[0038] Optionally, the standard difference value comprises a first standard difference value, a second standard difference value and a third standard difference value, and the difference level comprises an original level, a final level, a first difference level and a second difference level.

[0039] The comparing of the data difference value with the preset standard difference value to obtain the difference level comprises:

[0040] if the data difference value is less than the first standard difference value, determining that the difference level is the original level;

[0041] if the data difference value is greater than or equal to the first standard difference value and less than the second standard difference value, determining that the difference level is the first difference level;

[0042] if the data difference value is greater than or equal to the second standard difference value and less than the third standard difference value, determining that the difference level is the second difference level;

[0043] if the data difference value is greater than or equal to the third standard difference value, determining that the difference level is the last level.

[0044] By adopting the technical solution, the difference level is obtained by comparing the data difference value with the standard difference value, so that the correction index of the filtered data can be determined according to the difference level.

[0045] Optionally, the correction index of the filtered data is determined according to the difference level, including:

[0046] if the difference level is the original level, determining that the correction index of the filtered data is 1;

[0047] if the difference level is the first difference level, determining that the correction index of the filtered data is a first index;

[0048] if the difference level is the second difference level, determining that the correction index of the filtered data is a second index; the second index is greater than the first index;

[0049] if the difference level is the last level, determining that the correction index of the filtered data is 0.

[0050] By adopting the technical solution, after the correction index of the filtered data is determined, the filtered data can be corrected.

[0051] In a second aspect, the data analysis recorder provided by the present application adopts the following technical solution:

[0052] A data analysis recorder includes a collection module, a data processing module, a display module, and an alarm module.

[0053] The collection module is configured to obtain an analog quantity collected by a data collector.

[0054] The data processing module is configured to filter the analog quantity through a pre-set filter to obtain filtered data, and correct the filtered data to obtain corrected data.

[0055] The alarm module is configured to determine an alarm level according to the corrected data.

[0056] The display module is configured to display the alarm level.

[0057] By adopting the above technical solution, the data processing module filters and corrects the analog quantities collected by the data acquisition device, so that the alarm module can determine the alarm level based on the corrected data. Thus, when there is abnormal data, it is not necessary to manually analyze the data after it is recorded. The abnormal data can be identified and the alarm level can be determined at the time of data recording, which effectively shortens the time span for discovering abnormal data.

[0058] Thirdly, the computer-readable storage medium provided in this application adopts the following technical solution:

[0059] A computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, employs the aforementioned data analysis method.

[0060] By adopting the above technical solution, the data analysis method is used to generate a computer program, which is then stored in a computer-readable storage medium for loading and execution by a processor. The computer-readable storage medium facilitates the reading and storage of the computer program.

[0061] In summary, this application has at least one of the following beneficial technical effects:

[0062] 1. The analog quantities collected by the data acquisition device are filtered and corrected to facilitate the determination of alarm levels based on the corrected data. Thus, when there are abnormal data, it is not necessary to manually analyze the data after it is recorded. The abnormal data and alarm levels can be determined during the data recording process, which effectively shortens the time span for discovering abnormal data.

[0063] 2. Convert the data range of all filtered data to the same standard range to facilitate subsequent processing of the filtered data.

[0064] 3. The alarm level is determined by correcting the data, which means that when data anomalies occur, it is easy for staff to determine the degree of data anomaly based on the alarm level. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application.

[0066] Figure 2 This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application.

[0067] Figure 3 This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application.

[0068] Figure 4This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application.

[0069] Figure 5 This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application.

[0070] Figure 6 This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application.

[0071] Figure 7 This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application.

[0072] Figure 8 This is a flowchart illustrating one implementation of a data analysis method according to an embodiment of this application. Detailed Implementation

[0073] The following is in conjunction with the appendix Figures 1 to 8 This application will be described in further detail.

[0074] This application discloses a data analysis method.

[0075] Reference Figure 1 A data analysis method includes the following steps:

[0076] S101. Obtain the analog quantity collected by the data acquisition device.

[0077] In this embodiment, the data acquisition device refers to a sensor, which collects analog quantities and then transmits them to the current execution entity. Analog quantities refer to the output signals of the data acquisition device, i.e., the sensor, and are physical quantities that are continuous in time or in value.

[0078] S102. The analog signal is filtered through a preset filter to obtain filtered data.

[0079] Filters include high-pass filters and low-pass filters. Passing an analog signal through a high-pass filter primarily eliminates low-frequency noise; passing it through a low-pass filter primarily eliminates high-frequency noise. The filtered analog signal yields the filtered data.

[0080] S103. Correct the filtered data to obtain corrected data.

[0081] If the collected filtered data has quality issues, it needs to be corrected to obtain normal data. Specifically, if the difference between the filtered data and a pre-set standard value exceeds a preset threshold, the filtered data is considered to have quality problems and requires correction.

[0082] S104. Determine and display the alarm level based on the corrected data.

[0083] Corrected data refers to the data obtained after correcting the filtered data. After obtaining the corrected data, it can be compared with the manually preset alarm threshold to determine the alarm level and display it on the display module. The display module is a display screen.

[0084] The implementation principle of this embodiment is as follows: the analog quantities collected by the data acquisition device are filtered and corrected so that the alarm level can be determined based on the corrected data. Thus, when there is abnormal data, it is not necessary to manually analyze the abnormal data after the data is recorded. That is, the abnormal data can be identified and the alarm level can be determined at the time of data recording, which effectively shortens the time span for discovering abnormal data.

[0085] exist Figure 1 Before step S103 in the illustrated embodiment, since the range of the filtered data may be inconsistent, it is necessary to unify the range of all filtered data. Specifically, through... Figure 2 The embodiments shown will be described in detail.

[0086] Reference Figure 2 Before correcting the filtered data to obtain the corrected data, the following steps are included:

[0087] S201. Obtain the data range of all filtered data.

[0088] In this embodiment, the data range refers to the unit of measurement of the data, such as cm, m, km, etc. When the filtered data is obtained, the unit of measurement is included in the filtered data, so the data range of the filtered data can be obtained through the filtered data.

[0089] S202. Convert all data ranges to the same standard range.

[0090] Converting all filtered data ranges to the same standard range helps ensure the uniformity of the filtered data ranges. For example, if the filtered data ranges before conversion are cm, m, km, etc., after converting all filtered data to the same standard range, such as m, the standard range of all filtered data will be m.

[0091] The data analysis method provided in this embodiment converts the data range of all filtered data into the same standard range, which facilitates subsequent processing of the filtered data.

[0092] exist Figure 1 In step S104 of the illustrated embodiment, the alarm level is determined by comparing preset data with corrected data. Specifically, through... Figure 3 The embodiments shown will be described in detail.

[0093] Reference Figure 3 Alarm levels include a first alarm level and a second alarm level, with the first alarm level being higher than the second alarm level.

[0094] Based on the corrected data, the alarm level is determined, including the following steps:

[0095] S301. If the corrected data is less than the preset lower limit or greater than the preset upper limit, the alarm level is determined to be the first alarm level.

[0096] For example, if the corrected data is 130, the lower limit is set to 20, and the upper limit is set to 120, since the corrected data 130 is greater than the upper limit 120, the alarm level is determined to be the first alarm level.

[0097] S302. If the corrected data is less than the preset lower limit or greater than the preset upper limit, the alarm level is determined to be the second alarm level.

[0098] Taking step S301 as an example, if the lower limit value is set to 40 and the upper limit value is set to 90, and the corrected data is 30, since the corrected data 30 is less than the lower limit value 40, the alarm level is determined to be the second alarm level. When the corrected data is less than the lower limit value or greater than the upper limit value, there exists a range where the corrected data simultaneously satisfies the values ​​of the first alarm level and the second alarm level. In this case, the alarm level is determined to be the first alarm level. If the corrected data does not satisfy the first alarm level but satisfies the second alarm level, then the alarm level is determined to be the second alarm level.

[0099] It should be noted that the lower limit value is less than the lower limit value, and the upper limit value is greater than the upper limit value. If the alarm level corresponding to the corrected data is neither the first alarm level nor the second alarm level, it is determined that no alarm has occurred due to the corrected data.

[0100] In this embodiment, if no alarm is triggered by the corrected data, the corresponding icon on the display screen will be green; if the alarm level corresponding to the corrected data is the first alarm level, the corresponding icon on the display screen will be red; if the alarm level corresponding to the corrected data is the second alarm level, the corresponding icon on the display screen will be orange.

[0101] The data analysis method provided in this embodiment determines the alarm level by correcting the data. That is, when data is abnormal, it is convenient for staff to determine the degree of abnormality of the data based on the alarm level.

[0102] exist Figure 1 In step S103 of the illustrated embodiment, the basic value of the filtered data can be determined through the data source of the filtered data, and the correction index of the filtered data can be determined through the basic value and the filtered data. After determining the correction index, the filtered data can be corrected. Specifically, through... Figure 4 The embodiments shown will be described in detail.

[0103] Reference Figure 4 The filtered data is corrected to obtain corrected data, including the following steps:

[0104] S401. Obtain the data source for each filtered data according to the preset source database.

[0105] The current execution entity is a device with eight analog input I / O interfaces, meaning that the filtered data can be input from different channels. Different channels indicate different data sources. The source of each input filtered data is stored in the source database, so the data source of each filtered data can be obtained based on the source database.

[0106] S402. Determine the basic values ​​of the filtered data based on the data source.

[0107] The base value refers to the value within a preset range. Specifically, the base value of the filtered data will be different depending on the data source. For example, in data source A, the base value ranges from 50 to 70; in data source B, the base value ranges from 70 to 90. In this embodiment, the base value is the middle value of the range. For example, if the range is 70 to 90, then the base value is 80.

[0108] S403. Determine the correction index for the filter data based on the basic values ​​and the corresponding filter data in the index data.

[0109] The correction index is used to correct the filtered data to make it more accurate. In this embodiment, if the base value and the filtered data are known, the correction index can be determined based on the difference between the base value and the filtered data.

[0110] S404. Correct the filtered data based on the correction index.

[0111] Once the correction index is known, the filtered data is multiplied by the correction index to correct the filtered data.

[0112] The data analysis method provided in this embodiment corrects the filtered data to improve the accuracy of subsequent data processing.

[0113] exist Figure 4 In step S402 of the illustrated implementation, the basic value of the filtered data can be obtained through the data weight of the data source and the standard value of the filtered data. Specifically, through... Figure 5 The embodiments shown will be described in detail.

[0114] Reference Figure 5 Determining the basic values ​​of the filtered data based on the data source involves the following steps:

[0115] S501. Determine the data weight of the filtered data corresponding to each data source according to the preset weight database.

[0116] The weight database stores the data weights of the filtered data corresponding to all data sources. The data weight refers to the different weights of the filtered data in different data sources, that is, the weight of the filtered data is related to the data source.

[0117] S502, Obtain the standard values ​​of the filtered data.

[0118] Standard values ​​can be entered manually in the background or obtained from a database that stores standard values.

[0119] S503. Multiply the standard value by the data weight to obtain the basic value of the filtered data corresponding to the data source.

[0120] The base value of the filtered data corresponding to the data source = standard value * data weight. The base value refers to the value that the filtered data should display in the corresponding data source.

[0121] The data analysis method provided in this embodiment calculates the basic values ​​of the filtered data based on the data weights and standard values, which facilitates the subsequent determination of the correction index of the filtered data based on the basic values ​​and the filtered data.

[0122] exist Figure 4 In step S403 of the illustrated implementation, the correction index for the filtered data can be obtained through the data difference and standard deviation between the base data and the filtered data. Specifically, through... Figure 6 The embodiments shown will be described in detail.

[0123] Reference Figure 6 The correction index for the filtered data is determined based on the base values ​​and the corresponding filtered data in the index data, including the following steps:

[0124] S601. Calculate the data difference between the basic value and the corresponding filtered data.

[0125] Data difference = |base value - filtered data|.

[0126] S602. Compare the data difference with the preset standard deviation to obtain the difference level.

[0127] In this embodiment, the standard deviation is preset by human intervention. The larger the data difference, the greater the possibility of data being scrapped. Therefore, the standard deviation can be divided into multiple levels. After comparing each data difference with the standard deviation, multiple difference levels are obtained, so that the correction index of the filtered data can be determined according to the difference level.

[0128] S603. Determine the correction index for the filtered data based on the difference level.

[0129] Different difference levels correspond to different correction indicators.

[0130] The data analysis method provided in this embodiment first compares the data difference with the standard deviation, and then determines the correction index according to the difference level, which facilitates subsequent correction of the filtered data.

[0131] exist Figure 6 In step S602 of the illustrated implementation, the standard deviation can be divided into multiple values, and each value can be compared with the data difference to obtain the difference level. Specifically, through... Figure 7 The embodiments shown will be described in detail.

[0132] Reference Figure 7 The standard deviation includes the first standard deviation, the second standard deviation, and the third standard deviation; the difference grades include the original grade, the last grade, the first difference grade, and the second difference grade.

[0133] The data difference is compared with a preset standard deviation to obtain the difference level, including the following steps:

[0134] S701. If the data difference is less than the first standard deviation, the difference level is determined to be the original level.

[0135] If the data difference is less than the first standard deviation, it indicates that the data difference is small. In this case, the difference level is determined to be the original level, which means that the filtered data does not need to be corrected.

[0136] S702. 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.

[0137] 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, if the data difference is 20, the first standard deviation is 15, and the second standard deviation is 40, since the data difference is greater than the first standard deviation and less than the second standard deviation, the difference level corresponding to this data difference is the first difference level.

[0138] S703. If the data difference is greater than or equal to the second standard deviation and less than the third standard deviation, the difference level is determined to be the second difference level.

[0139] If the data difference is greater than or equal to the second standard deviation and less than the third standard deviation, the difference level is determined to be the second difference level. It should be noted that the second difference level is greater than the first difference level. Taking step S702 as an example, if the data difference is 45, the third standard deviation is 60, and the second standard deviation is 40, since the data difference is greater than the second standard deviation and less than the third standard deviation, the difference level corresponding to this data difference is the second difference level.

[0140] S704. If the data difference is greater than or equal to the third standard deviation, the difference level is determined to be the lowest level.

[0141] If the data difference is greater than or equal to the third standard deviation, it indicates that the data difference is large, and the data is deemed unusable, meaning the difference level is classified as the lowest level.

[0142] The data analysis method provided in this embodiment obtains the difference level by comparing the data difference with the standard deviation, which facilitates the subsequent determination of the correction index for the filtered data based on the difference level.

[0143] exist Figure 6 In step S603 of the illustrated implementation, after the difference level is known, the correction index for the filtered data can be determined based on the difference level. Specifically, through... Figure 8 The embodiments shown will be described in detail.

[0144] Reference Figure 8 The correction index for the filtered data is determined based on the difference level, including the following steps:

[0145] S801. If the difference level is the original level, the correction index for the filtered data is determined to be 1.

[0146] For example, if the filtered data is 70 and the difference level is the original level, then since the correction index of the filtered data is 1, the corrected data obtained after the filtering data is corrected is 70*1=70.

[0147] S802. If the difference level is the first difference level, determine the correction index of the filtered data as the first index.

[0148] Taking step S801 as an example, if the filtered data is 70, the difference level is the first difference level, and the first index is 87%, then the corrected data obtained after filtering is 70*87%=60.9.

[0149] S803. If the difference level is the second difference level, determine the correction index of the filtered data as the second index; the second index is greater than the first index.

[0150] Taking step S801 as an example, if the filtered data is 70, the difference level is the second difference level, and the second index is 93%, then the corrected data obtained after the filtered data is corrected is 70*93%=65.1.

[0151] S804. If the difference level is the lowest level, the correction index for the filtered data is set to 0.

[0152] Taking step S801 as an example, if the filtered data is 70 and the difference level is the lowest level, then since the correction index of the filtered data is 0, the corrected data obtained after the filtered data is corrected is 70*0=0.

[0153] The data analysis method provided in this embodiment can correct the filtered data after determining the correction index of the filtered data.

[0154] This application also discloses a data analysis recorder.

[0155] A data analysis recorder includes a data acquisition module, a data processing module, a display module, and an alarm module;

[0156] The acquisition module is used to obtain analog quantities collected by the data acquisition unit;

[0157] The data processing module is used to filter analog quantities through a preset filter to obtain filtered data, and to correct the filtered data to obtain corrected data.

[0158] The alarm module is used to determine the alarm level based on the corrected data;

[0159] The display module shows the alarm level. In addition, it displays nine pages: bar charts, real-time curves, data export, historical curves, event viewing, parameter settings, system settings, and QR code connection. The system settings page provides functions such as wireless module selection and corresponding configuration parameters, network port and serial port configuration parameters, LAN RF module configuration, server connection parameters, time settings, password, device address, system language, and clearing records.

[0160] In addition to the modules mentioned above, the data analysis recorder also includes a storage module and a remote transmission module. The storage module is used to store the time of filter data acquisition, and the remote transmission module can be a 4G module, a WIFI module, or a network port module.

[0161] The implementation principle of a data analysis recorder according to an embodiment of this application is as follows: the data processing module filters and corrects the analog quantities collected by the data acquisition device so that the alarm module can determine the alarm level based on the corrected data. Thus, when there is abnormal data, it is not necessary to manually analyze the abnormal data after the data recording is completed. That is, the abnormal data can be identified and the alarm level can be determined at the time of data recording, which effectively shortens the time span for discovering abnormal data.

[0162] This application also discloses a computer-readable storage medium, which stores a computer program, wherein when the computer program is executed by a processor, it employs the data analysis method described in the above embodiments.

[0163] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0164] The data analysis methods described in the above embodiments are stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the methods.

[0165] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A data analysis method, characterized in that, include: Acquire analog quantities collected by the data acquisition device; The analog quantity is filtered through a preset filter to obtain filtered data; The filtered data is corrected to obtain corrected data; Based on the corrected data, the alarm level is determined and displayed; Before performing data correction on the filtered data to obtain corrected data, the process includes: Obtain the data range of all the filtered data; Convert all the aforementioned data ranges to the same standard range; The alarm levels include a first alarm level and a second alarm level, wherein the first alarm level is greater than the second alarm level; The step of determining the alarm level based on the corrected data includes: If the corrected data is less than a preset lower limit or greater than a preset upper limit, the alarm level is determined to be the first alarm level. If the corrected data is less than a preset lower limit or greater than a preset upper limit, the alarm level is determined to be the second alarm level. The step of correcting the filtered data to obtain corrected data includes: According to the preset source database, obtain the data source for each of the filtered data; The basic values ​​of the filtered data are determined based on the data source. Based on the baseline values ​​and the corresponding filtered data, a correction index for the filtered data is determined from the index data. The filtered data is corrected based on the aforementioned correction index; The determination of the basic values ​​of the filtered data based on the data source includes: The data weight of the filtered data corresponding to each data source is determined according to a preset weight database; Obtain the standard value of the filtered data; Multiplying the standard value by the data weight yields the base value of the filtered data corresponding to the data source; The step of determining the correction index for the filtered data based on the base value and the corresponding filtered data in the index data includes: Calculate the data difference between the base value and the corresponding filtered data; The data difference is compared with a preset standard deviation to obtain the difference level; The correction index for the filtered data is determined based on the difference level; The standard deviation includes a first standard deviation, a second standard deviation, and a third standard deviation; the difference levels include the original level, the lowest level, the first difference level, and the second difference level. The step of comparing the data difference with a preset standard deviation to obtain the difference level includes: If the data difference is less than the first standard deviation, the difference level is determined to be the original 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; If the data difference is greater than or equal to the second standard deviation and less than the third standard deviation, the difference level is determined to be the second difference level; If the data difference is greater than or equal to the third standard deviation, the difference level is determined to be the lowest level. The step of determining the correction index for the filtered data based on the difference level includes: If the difference level is the original level, the correction index of the filtered data is determined to be 1; If the difference level is the first difference level, the correction index of the filtered data is determined to be the first index; If the difference level is the second difference level, the correction index of the filtered data is determined to be the second index; the second index is greater than the first index. If the difference level is the lowest level, the correction index of the filtered data is determined to be 0.

2. A data analysis recorder, employing the method of claim 1, characterized in that: It includes a data acquisition module, a data processing module, a display module, and an alarm module; The acquisition module is used to acquire analog quantities collected by the data acquisition device; The data processing module is used to filter the analog quantity through a preset filter to obtain filtered data, and to correct the filtered data to obtain corrected data. The alarm module is used to determine the alarm level based on the corrected data; The display module is used to display the alarm level.

3. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, the method described in claim 1 is employed.

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