A method for storing industrial real-time monitoring software data

Abstract

The present invention provides an industrial real-time monitoring software data storage method, including setting a data storage strategy, creating a data set and a percentage value, judging the type of real-time monitoring data and putting or stopping putting it into the data set according to the value size, judging whether the timing time reaches the data storage interval, and then judging whether to store according to the percentage value and the storage compliance ratio in the current storage interval. The present invention uses four abnormal levels of normal, early warning, warning, and dangerous to distinguish the severity of the alarm, and sets different storage time intervals and abnormal ratios for each abnormal level, so as to achieve the purpose of accurate storage, and the data stored in each time interval can be traced back to the maximum and minimum values ​​of the monitoring, so as to better restore the data change trend at that time.

Description

Technical Field

[0001] The invention relates to the technical field of measurement and testing, and in particular to an industrial real-time monitoring software data storage method. Background Art

[0002] At present, the data for equipment monitoring in various industrial sites is often collected in real time, and each piece of vibration data contains a vibration waveform. The amount of data collected is large, and normal and alarm data exist at the same time. In order to facilitate the later query of historical monitoring data, the monitored data needs to be persistent. However, if all the collected vibration data is stored without distinction, there will be a large waste of disk space; if it is stored at fixed time intervals, the data is highly random, and key alarm data is usually missed and impact alarm data is mistakenly stored, which cannot achieve the purpose of accurate historical data tracing and alarm analysis. Summary of the invention

[0003] The present invention aims to solve the storage problem of monitoring data and provides an industrial real-time monitoring software data storage method. The method uses four abnormal levels of normal, early warning, warning and dangerous to distinguish the severity of the alarm, and sets different storage time intervals and abnormal proportions for each abnormal level, thereby achieving the purpose of accurate storage. The data stored in each time interval can be traced back to the maximum and minimum values ​​of the monitoring, better restoring the data change trend at that moment.

[0004] The present invention provides an industrial real-time monitoring software data storage method, comprising the following steps:

[0005] S1. Set a data storage strategy, which includes monitoring data storage benchmark values, including normal data storage interval N, normal data storage compliance ratio R1, warning data storage interval P, warning data storage compliance ratio R2, warning data storage interval W, warning data storage compliance ratio R3, dangerous data storage interval D, and dangerous data storage compliance ratio R4;

[0006] S2, create a normal data set T1, a normal proportion value C1, an early warning data set T2, an early warning proportion value C2, a warning data set T3, a warning proportion value C3, a dangerous data set T4 and a dangerous proportion value C4;

[0007] S3, the industrial real-time monitoring software receives the real-time monitoring data collected by the hardware and performs timing, and enters step S4 when judging the category according to the value of the real-time monitoring data, and enters step S5 when judging whether the timing reaches the storage interval;

[0008] S4, when the real-time monitoring data is normal data, put the current data into the normal data set T1; when putting it into the normal data set T1, determine whether the value of the current data is greater than the Max value in the normal data set T1 or less than the Min value in the normal data set T1, if yes, replace the original Max value or Min value in the normal data set T1, if no, stop putting it into the normal data set T1; return to step S3;

[0009] When the real-time monitoring data is the warning data, the current data is placed in the warning data set T2; when placed in the warning data set T2, it is determined whether the value of the current data is greater than the Max value in the warning data set T2 or less than the Min value in the warning data set T2. If so, the original Max value or Min value in the warning data set T2 is replaced; if not, the current data is stopped from being placed in the warning data set T2; and the process returns to step S3;

[0010] When the real-time monitoring data is warning data, the current data is placed in the warning data set T3; when placed in the warning data set T3, it is determined whether the value of the current data is greater than the Max value in the warning data set T3 or less than the Min value in the warning data set T3. If yes, the original Max value or Min value in the warning data set T3 is replaced; if not, the current data is stopped from being placed in the warning data set T3; and the process returns to step S3;

[0011] When the real-time monitoring data is dangerous data, the current data is placed in the dangerous data set T4; when placed in the dangerous data set T4, it is determined whether the value of the current data is greater than the Max value in the dangerous data set T4 or less than the Min value in the dangerous data set T4. If so, the original Max value or Min value in the dangerous data set T4 is replaced. If not, stop placing the data in the dangerous data set T4 and return to step S3.

[0012] S5, determine whether the timing time reaches the normal data storage interval N, if yes, go to step S6, if no, return to step S3;

[0013] Determine whether the timing time reaches the warning data storage interval P, if yes, go to step S7, if no, return to step S3;

[0014] Determine whether the timing time reaches the warning data storage interval W, if yes, go to step S8, if no, return to step S3;

[0015] Determine whether the timing time reaches the dangerous data storage interval D, if yes, go to step S9, if no, return to step S3;

[0016] S6, determine whether the normal proportion value C1 in the current storage interval is greater than or equal to the normal data storage standard proportion R1, if yes, store the data in the normal data set T1, if not, give up the storage this time;

[0017] Start retiming the normal data storage interval N, clear the normal data set T1, reset C1 to 0, and return to step S3;

[0018] S7, determine whether the warning proportion value C2 in the current storage interval is greater than or equal to the warning data storage standard proportion R2, if yes, store the data in the warning data set T2, if not, give up the storage this time;

[0019] Start to re-time the warning data storage interval P, clear the warning data set T2, reset C2 = 0, and return to step S3;

[0020] S8, determine whether the warning proportion value C3 in the current storage interval is greater than or equal to the warning data storage standard proportion R3, if yes, store the data in the warning data set T3, if not, give up the storage this time;

[0021] Start to re-time the warning data storage interval W, clear the warning data set T3, reset C3 = 0, and return to step S3;

[0022] S9, determine whether the risk ratio value C4 in the current storage interval is greater than or equal to the risk data storage standard ratio R4, if yes, store the data in the risk data set T4, if not, give up the storage this time;

[0023] Start re-timing the dangerous data storage interval D, clear the dangerous data set T4, reset C4=0, and return to step S3.

[0024] In the industrial real-time monitoring software data storage method described in the present invention, as a preferred embodiment, the normal data storage interval N, the early warning data storage interval P, the warning data storage interval W and the dangerous data storage interval D are all in seconds.

[0025] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred embodiment, in step S1, the normal data storage interval N> the early warning data storage interval P> the warning data storage interval W> the dangerous data storage interval D.

[0026] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred mode, that the normal data storage interval N is greater than or equal to five times the early warning data storage interval P, the early warning data storage interval P is greater than or equal to twice the warning storage interval W, and the warning storage interval W is greater than or equal to five times the danger storage interval D.

[0027] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred embodiment, a normal storage interval N is 3600 seconds, an early warning storage interval P is 600 seconds, a warning storage interval W is 300 seconds, and a danger storage interval D is 60 seconds.

[0028] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred embodiment, in step S1, the normal storage standard ratio value R1> the early warning storage standard ratio value R2> the warning storage standard ratio value R3> the dangerous storage standard ratio value R4.

[0029] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred embodiment, that the normal storage compliance ratio value R1 is greater than or equal to twice the early warning storage compliance ratio value R2, the early warning storage compliance ratio value R2 is greater than or equal to twice the warning storage compliance ratio value R3, and the warning storage compliance ratio value R3 is greater than or equal to twice the dangerous storage compliance ratio value R4.

[0030] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred embodiment, that the normal storage standard ratio value R1 is greater than the sum of the early warning storage standard ratio value R2, the warning storage standard ratio value R3, and the dangerous storage standard ratio value R4.

[0031] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred mode, a normal storage standard ratio value R1 is 50%, an early warning storage standard ratio value R2 is 25%, a warning storage standard ratio value R3 is 12%, and a dangerous storage standard ratio value R4 is 6%.

[0032] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred mode, in step S2, the lengths of the normal data set T1, the early warning data set T2, the warning data set T3 and the danger data set T4 are all 2, and two pieces of data are fixedly stored, which are the maximum value Max and the minimum value Min of each type of data.

[0033] The industrial real-time monitoring software data storage method described in the present invention is, as a preferred embodiment, in step S2, C1=normal data number / (normal data number+warning data number+warning data number+dangerous data number);

[0034] C2 = number of warning data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items);

[0035] C3 = number of warning data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items);

[0036] C4 = number of dangerous data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items).

[0037] The present invention relates to industrial site real-time data monitoring, which is applied to the persistent storage process of industrial Internet site real-time monitoring alarm data. The present invention relates to an industrial real-time monitoring software data storage method, which belongs to data analysis. By combining the industrial site scene, the accurate storage of data is completed, which is convenient for users to trace the monitoring data, locate equipment failures, and predict the service life of equipment.

[0038] The present invention provides an industrial real-time monitoring software data storage method, which has the following advantages:

[0039] (1) The present invention saves disk space resources and extends the use cycle of software applications by dividing different exception levels and different storage time intervals.

[0040] (2) The present invention can better restore the data trend collected at that time by storing the maximum and minimum values ​​of the abnormal level within a period of time, thereby making fault analysis and equipment service life prediction more accurate.

[0041] (3) The present invention can flexibly set the storage interval and storage ratio value according to different equipment measurement point characteristics, thereby meeting a variety of different scenarios.

[0042] (4) The present invention can fully utilize disk storage space, accurately store valuable monitoring data, flexibly adjust and set storage strategies, and better adapt to different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a data storage method for industrial real-time monitoring software;

[0044] Figure 2 A schematic diagram of a normal data state of an industrial real-time monitoring software data storage method;

[0045] Figure 3 A schematic diagram of a normal data state of an industrial real-time monitoring software data storage method;

[0046] Figure 4 A schematic diagram of an early warning state of an industrial real-time monitoring software data storage method;

[0047] Figure 5 A warning status diagram of an industrial real-time monitoring software data storage method;

[0048] Figure 6 A schematic diagram of a dangerous state of an industrial real-time monitoring software data storage method;

[0049] Figure 7 A warning data schematic diagram of an industrial real-time monitoring software data storage method. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0051] Example 1

[0052] like Figure 1 As shown, a method for storing industrial real-time monitoring software data, for data of a certain monitoring point, specifically includes the following steps:

[0053] S1. Set the data storage strategy for the monitoring point in the host computer software as the reference value for storage:

[0054] Normal data storage interval N seconds, normal data storage compliance ratio R1;

[0055] Warning data storage interval P seconds, warning data storage compliance ratio R2;

[0056] Warning data storage interval W seconds, warning data storage compliance ratio R3;

[0057] The hazardous data storage interval is D seconds and the hazardous data storage compliance ratio is R4.

[0058] S2. Create 4 sets and 4 ratio values ​​in the upper computer software system program:

[0059] Normal data set T1, normal proportion value C1;

[0060] Warning data set T2, warning proportion value C2;

[0061] Warning data set T3, warning percentage value C3;

[0062] Dangerous data set T4, dangerous proportion value C4;

[0063] The length of each set is 2, and two pieces of data are fixedly stored, which are the maximum value Max and the minimum value Min under each abnormal level.

[0064] C1 = normal data number / (normal data number + warning data number + warning data number + dangerous data number)

[0065] C2 = number of warning data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items)

[0066] C3 = number of warning data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items)

[0067] C4 = number of dangerous data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items)

[0068] S3. After the software receives a piece of real-time data collected by the hardware, the abnormality level of the data is G, and the following is executed:

[0069] S4. If G is normal, it is placed in the normal set. When placed in the set: if the current data value is greater than the normal Max value in the set, then the original normal Max value in the normal set is replaced; if the current data value is less than the normal Min value, then the original normal Min value in the normal set is replaced.

[0070] If G is a warning, it is placed in the warning set. When placed in the set: if the current data value is greater than the warning Max in the set, then the original warning Max value in the normal set is replaced; if the current data value is less than the warning Min, then the original warning Min value in the warning set is replaced.

[0071] If G is a warning, it is placed in the warning set. When placed in the set: if the current data value is greater than the warning Max value in the set, then the original warning Max value in the warning set is replaced; if the current data value is less than the warning Min value, then the original warning Min value in the warning set is replaced.

[0072] If G is dangerous, it is placed in the dangerous set. When placed in the set: if the current data value is greater than the dangerous Max value in the set, then the original dangerous Max value in the dangerous set is replaced; if the current data value is less than the dangerous Min value, then the original dangerous Min value in the dangerous set is replaced.

[0073] S5, determine whether the timing time reaches the normal data storage interval N, if yes, go to step S6, if no, return to step S3;

[0074] Determine whether the timing time reaches the warning data storage interval P, if yes, go to step S7, if no, return to step S3;

[0075] Determine whether the timing time reaches the warning data storage interval W, if yes, go to step S8, if no, return to step S3;

[0076] Determine whether the timing time reaches the dangerous data storage interval D, if yes, go to step S9, if no, return to step S3;

[0077] S6. For normal data: perform storage once every N seconds, and judge within this time period: if C1≥R1, perform the storage of normal data this time, and store the normal Max and normal Min values. Otherwise, give up storing normal data this time, and start to count again N, clear the T1 set, reset C1=0, and return to step S3.

[0078] S7. For the warning data: perform storage once every P seconds, and judge within this time period: if C2 ≥ R2, then perform storage of this warning data, and store the warning Max and warning Min values. Otherwise, give up storing the warning data this time, and start to count P again, clear the T2 set, reset C2 = 0, and return to step S3.

[0079] S8. For warning data: perform storage once every W seconds, and judge within this time period: if C3 ≥ R3, then perform storage of this warning data, and store the warning Max and warning Min values. Otherwise, give up storing the warning data this time, and start to count W again, clear the T3 set, reset C3 = 0, and return to step S3.

[0080] S9. For dangerous data: perform storage every D seconds, and judge within this time period: if C4 ≥ R4, then perform storage of this dangerous data, store the dangerous Max and dangerous Min values. Otherwise, give up storing dangerous data this time, and start re-timing D. Clear T4 set, reset C4 = 0, and return to step S3.

[0081] In practical applications, an industrial monitoring software alarm data storage method of the present invention can be implemented by the following steps to monitor and store the horizontal vibration measurement points of the motor free end of the gas blower equipment in real time:

[0082] like Figure 2 As shown in the figure, for the horizontal vibration measurement point of the motor free end of the gas blower equipment, the storage interval and storage ratio of each abnormal level are set as follows:

[0083] The normal storage interval N is 3600 seconds, and the normal storage compliance ratio R1 is 50%.

[0084] The warning storage interval P is 600 seconds, and the warning storage compliance ratio value R2 is 25%.

[0085] The warning storage interval W is 300 seconds, and the warning storage reaching ratio value R3 is 12%.

[0086] The dangerous storage interval D is 60 seconds, and the dangerous storage compliance ratio value R4 is 6%.

[0087] 2. For normal examples:

[0088] A total of 4,200 data were uploaded at the horizontal vibration measurement point of the motor free end of the gas blower equipment within one hour from 08:00 to 09:00 on October 10, 2024. According to statistics, the hardware collected a total of: 900 normal data, 120 data warnings, 180 data warnings, and 30 data dangers.

[0089] At 09:00, the normal storage interval is reached, and the normal data accounts for C1: 900 / 4200 = 21.4%. Because: 21.4% < 50%, the normal storage ratio is not reached at this time, so the normal level data will not be stored because there is a large amount of more valuable abnormal data. Figure 3 As shown, the storage ratio is not met from 08:00 to 09:00, and no data is stored.

[0090] 3. Examples of early warning are as follows:

[0091] The horizontal vibration measurement point of the motor free end of the gas blower equipment uploaded a total of 200 data within 10 minutes from 09:00 to 09:10 on October 16. According to statistics, the hardware collected a total of: 50 normal data, 70 warning data, 30 warning data, and 50 dangerous data.

[0092] At 09:10, the warning storage interval is reached, and the warning data proportion C2 is calculated as: 70 / 200=35%, because: 35%>25%. At this time, the maximum value and minimum value of the 200 warning data are stored.

[0093] Figure 4 To store the historical data traceability diagram of warning level data, two warning level data are stored from 09:00 to 09:10, with the minimum value being 1 mm / s and the maximum value being 4 mm / s.

[0094] 4. Examples of warnings are as follows:

[0095] The horizontal vibration measurement point of the motor free end of the gas blower equipment uploaded a total of 100 data within 5 minutes from 09:10 to 09:15 on October 16. According to statistics, the hardware collected a total of: 20 normal data, 30 data warnings, 30 data warnings, and 20 data dangers.

[0096] At 09:15, the warning storage interval is reached, and the warning data proportion C3 is calculated as: 30 / 100=30%, because: 30%>12%, and at this time, the maximum value and the minimum value of the 100 warning data are stored.

[0097] Figure 5This is a historical data traceability chart that stores warning level data, where two pieces of warning level data are stored from 09:10 to 09:15, with a minimum value of 5 mm / s and a maximum value of 8 mm / s.

[0098] 5. Examples of hazards are as follows:

[0099] The horizontal vibration measurement point of the motor free end of the gas blower equipment uploaded a total of 20 data within 1 minute from 09:15 to 09:16 on October 16. According to statistics, the hardware collected a total of: 5 normal data, 5 data warnings, 5 data warnings, and 5 data dangers.

[0100] At 09:16, the proportion of dangerous data C4 is calculated as: 5 / 20=25%, because: 25%>6%, at this time, the maximum value and the minimum value of the 20 dangerous data are stored.

[0101] Figure 6 This is a historical data tracing diagram for storing danger level data, where two danger level data are stored from 09:15 to 09:16, with the minimum value being 11 mm / s and the maximum value being 17 mm / s.

[0102] The warning data stored from October 9th 14:39 to October 11th 09:14 is as follows Figure 7 shown.

[0103] The present invention combines industrial site scenarios to achieve accurate data storage, making it convenient for users to trace monitoring data, locate equipment failures, and predict equipment service life.

[0104] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for storing industrial real-time monitoring software data, characterized in that: The following steps are involved: S1, set a data storage strategy, the data storage strategy includes monitoring data storage benchmark values, the monitoring data storage benchmark values ​​include normal data storage interval N, normal data storage compliance ratio R1, early warning data storage interval P, early warning data storage compliance ratio R2, warning data storage interval W, warning data storage compliance ratio R3, dangerous data storage interval D and dangerous data storage compliance ratio R4; S2, create a normal data set T1, a normal proportion value C1, an early warning data set T2, an early warning proportion value C2, a warning data set T3, a warning proportion value C3, a dangerous data set T4 and a dangerous proportion value C4; S3, the industrial real-time monitoring software receives the real-time monitoring data collected by the hardware and performs timing, and enters step S4 when judging the category according to the value of the real-time monitoring data, and enters step S5 when judging whether the timing reaches the storage interval; S4, when the real-time monitoring data is normal data, the current data is placed in the normal data set T1; when placed in the normal data set T1, it is determined whether the value of the current data is greater than the Max value in the normal data set T1 or less than the Min value in the normal data set T1, if yes, the original Max value or Min value in the normal data set T1 is replaced, if no, the current data is stopped from being placed in the normal data set T1; Return to step S3; When the real-time monitoring data is early warning data, the current data is put into the early warning data set T2; When placing the data into the warning data set T2, determine whether the value of the current data is greater than the Max value in the warning data set T2 or less than the Min value in the warning data set T2. If yes, replace the original Max value or Min value in the warning data set T2. If no, stop placing the data into the warning data set T2. Return to step S3; When the real-time monitoring data is warning data, the current data is placed in the warning data set T3; when placed in the warning data set T3, it is determined whether the value of the current data is greater than the Max value in the warning data set T3 or less than the Min value in the warning data set T3, if yes, then the original Max value or Min value in the warning data set T3 is replaced, if not, then stop placing the data in the warning data set T3; Return to step S3; When the real-time monitoring data is dangerous data, the current data is put into the dangerous data set T4; When placing the data into the dangerous data set T4, determine whether the value of the current data is greater than the Max value in the dangerous data set T4 or less than the Min value in the dangerous data set T4. If yes, replace the original Max value or Min value in the dangerous data set T4. If no, stop placing the data into the dangerous data set T4 and return to step S3. S5, determine whether the timing time reaches the normal data storage interval N, if yes, go to step S6, if no, return to step S3; Determine whether the timing time reaches the warning data storage interval P, if yes, go to step S7, if no, return to step S3; Determine whether the timing time reaches the warning data storage interval W, if yes, go to step S8, if no, return to step S3; Determine whether the timing time reaches the dangerous data storage interval D, if yes, go to step S9, if no, return to step S3; S6, determining whether the normal proportion value C1 in the current storage interval is greater than or equal to the normal data storage standard proportion R1, if yes, storing the data in the normal data set T1, if no, giving up the storage this time; Start retiming the normal data storage interval N, clear the normal data set T1, reset C1 to 0, and return to step S3; S7, determining whether the warning proportion value C2 in the current storage interval is greater than or equal to the warning data storage standard proportion R2, if yes, storing the data in the warning data set T2, if no, giving up the storage this time; Start to re-time the warning data storage interval P, clear the warning data set T2, reset C2=0, and return to step S3; S8, determining whether the warning proportion value C3 in the current storage interval is greater than or equal to the warning data storage standard proportion R3, if yes, storing the data in the warning data set T3, if no, giving up the storage this time; Start to re-time the warning data storage interval W, clear the warning data set T3, reset C3=0, and return to step S3; S9, determining whether the risk ratio value C4 in the current storage interval is greater than or equal to the risk data storage standard ratio R4, if yes, storing the data in the risk data set T4, if no, giving up the storage this time; Start retiming the dangerous data storage interval D, clear the dangerous data set T4, reset C4=0, and return to step S3.

2. The industrial real-time monitoring software data storage method according to claim 1, characterized in that: The units of the normal data storage interval N, the warning data storage interval P, the warning data storage interval W and the danger data storage interval D are all seconds.

3. The industrial real-time monitoring software data storage method according to claim 1, characterized in that: In step S1 , the normal data storage interval N>the pre-warning data storage interval P>the warning data storage interval W>the dangerous data storage interval D.

4. The industrial real-time monitoring software data storage method according to claim 3 is characterized in that: The normal data storage interval N is greater than or equal to five times the warning data storage interval P, the warning data storage interval P is greater than or equal to twice the warning storage interval W, and the warning storage interval W is greater than or equal to five times the danger storage interval D.

5. The industrial real-time monitoring software data storage method according to claim 4, characterized in that: The normal storage interval N is 3600 seconds, the warning storage interval P is 600 seconds, the warning storage interval W is 300 seconds, and the danger storage interval D is 60 seconds.

6. The industrial real-time monitoring software data storage method according to claim 1, characterized in that: In step S1, the normal storage standard-reaching ratio value R1>the early warning storage standard-reaching ratio value R2>the warning storage standard-reaching ratio value R3>the dangerous storage standard-reaching ratio value R4.

7. The industrial real-time monitoring software data storage method according to claim 6 is characterized by: The normal storage standard ratio value R1 is greater than or equal to twice the early warning storage standard ratio value R2, the early warning storage standard ratio value R2 is greater than or equal to twice the warning storage standard ratio value R3, and the warning storage standard ratio value R3 is greater than or equal to twice the dangerous storage standard ratio value R4.

8. The industrial real-time monitoring software data storage method according to claim 7, characterized in that: The normal storage standard ratio value R1 is 50%, the early warning storage standard ratio value R2 is 25%, the warning storage standard ratio value R3 is 12%, and the dangerous storage standard ratio value R4 is 6%.

9. The industrial real-time monitoring software data storage method according to claim 1, characterized in that: The normal storage standard-reaching ratio value R1 is greater than the sum of the early warning storage standard-reaching ratio value R2, the warning storage standard-reaching ratio value R3, and the dangerous storage standard-reaching ratio value R4.

10. The industrial real-time monitoring software data storage method according to claim 1, characterized in that: In step S2, the lengths of the normal data set T1, the early warning data set T2, the warning data set T3 and the dangerous data set T4 are all 2, and two pieces of data are fixedly stored, which are the maximum value Max and the minimum value Min of each type of data; C1 = normal data number / (normal data number + warning data number + warning data number + dangerous data number); C2 = number of warning data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items); C3 = number of warning data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items); C4 = number of dangerous data items / (number of normal data items + number of warning data items + number of warning data items + number of dangerous data items).

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