CN112327007B - Fault detection method and system of high-speed railway gale disaster prevention monitoring system - Google Patents
Fault detection method and system of high-speed railway gale disaster prevention monitoring system Download PDFInfo
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- CN112327007B CN112327007B CN202011238307.7A CN202011238307A CN112327007B CN 112327007 B CN112327007 B CN 112327007B CN 202011238307 A CN202011238307 A CN 202011238307A CN 112327007 B CN112327007 B CN 112327007B
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- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
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- G01P21/02—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers
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Abstract
The invention discloses a fault detection method and a system of a high-speed railway strong wind disaster prevention monitoring system, wherein the fault detection method comprises the following steps: acquiring instantaneous wind speed data measured by an anemometer in a large wind disaster prevention monitoring system at a plurality of time nodes in a detection period; comparing the measured instantaneous wind speed data of the adjacent time nodes of the anemometer, and when the instantaneous wind speed value difference value of the adjacent time nodes exceeds a set value, sending out an instantaneous wind speed value difference value overrun alarm and prompting the anemometer to break down and the occurrence time of the break down. The fault detection method and the system can rapidly, accurately and reliably detect the fault of the anemometer in the high-speed railway high-wind disaster prevention monitoring system, and realize the accurate positioning of the fault of the anemometer.
Description
Technical Field
The invention relates to the technical field of high-speed railway high wind disaster prevention monitoring systems, in particular to a fault detection method and system of a high-speed railway high wind disaster prevention monitoring system.
Background
Along with the further extension of the high-speed railways in China, the high-speed running trains face various threats of severe wind environments, such as strong typhoons in southeast coastal areas, tangential winds of the canyons in western mountain areas and the like. The high-speed rail strong wind disaster prevention monitoring system is an important technical means for guaranteeing the running safety of the high-speed train, and the real-time, continuous, stable and reliable running of the high-speed rail strong wind disaster prevention monitoring system has important significance on the running safety of the train.
Since the wind disaster prevention system is built to operate, train operation is affected despite a very low failure rate. Network faults, wind monitoring equipment faults are the primary faults of wind monitoring systems. The reasons for network faults are mainly intermittent faults and network failure; the reasons for the failure of wind monitoring equipment are mainly as follows: wind speed anemometer failure, data lightning protection module failure, and transmission unit failure.
At present, the effectiveness and reliability of the data of the windy disaster prevention monitoring system lack of effective control means, how to select and choose, how to judge the true effectiveness of the data, and the current means are quite single.
Disclosure of Invention
The invention mainly aims to provide a fault detection method and system for a high-speed railway high wind disaster prevention monitoring system, which can quickly, accurately and reliably detect the fault of an anemometer in the high-speed railway high wind disaster prevention monitoring system and realize the accurate positioning of the anemometer fault.
In order to achieve the above object, the present invention provides a fault detection method for a high-speed railway gale disaster prevention monitoring system, the fault detection method comprising:
acquiring instantaneous wind speed data measured by an anemometer in a large wind disaster prevention monitoring system at a plurality of time nodes in a detection period;
comparing the measured instantaneous wind speed data of the adjacent time nodes of the anemometer, and when the instantaneous wind speed value difference value of the adjacent time nodes exceeds a set value, sending out an instantaneous wind speed value difference value overrun alarm and prompting the anemometer to break down and the occurrence time of the break down.
Further, the method further comprises the following steps:
comparing the instantaneous wind speed data measured by two anemometers in the big wind disaster prevention monitoring system at the same time node, when the difference value of the instantaneous wind speed values measured by the two anemometers at the same time node exceeds a set value, at least one of the two anemometers fails, sending out an overrun alarm for the difference value of the instantaneous wind speed values of the two anemometers, and prompting the occurrence time of the failure;
and respectively comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers to determine the specific failed anemometer.
Further, the method further comprises the following steps: when the difference value of the instantaneous wind speed values of the adjacent time nodes does not exceed a set value, dividing a detection period into a plurality of time periods, wherein each time period comprises a plurality of time nodes, calculating the average wind speed value measured by an anemometer in a big wind disaster prevention monitoring system in each time period, comparing the average wind speed values in the adjacent time periods, and when the difference value of the average wind speed values in the adjacent time periods exceeds the set value, sending out a wind speed average value difference value overrun alarm to prompt the anemometer to have faults and the occurrence time of the faults.
Further, the method further comprises the following steps: comparing average wind speed values of two anemometers in the big wind disaster prevention monitoring system in the same time period, when the difference value of the average wind speed values of the two anemometers in the same time period exceeds a set value, at least one anemometer fails, sending out an overrun alarm of the difference value of the average wind speed values of the two anemometers, and prompting the occurrence time of the failure;
and respectively comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers to determine the specific failed anemometer.
Further, the method further comprises the following steps: when the average wind speed value difference value of two anemometers in the same time period does not exceed a set value, judging whether the instantaneous wind speed value of a plurality of continuous time nodes measured by one anemometer in the large wind disaster prevention monitoring system is 0, and when the instantaneous wind speed value of a plurality of continuous time nodes measured by the anemometer is 0, sending out data missing acquisition alarm to prompt the anemometer to fail and the failure occurrence time.
Further, the method further comprises the following steps: when the instantaneous wind speed value of a plurality of continuous time nodes measured by an anemometer in the big wind disaster prevention monitoring system is not 0, calculating the standard deviation of the instantaneous wind speed values of all the time nodes of the anemometer in a detection period, judging the discrete characteristic of the instantaneous wind speed value, and when the standard deviation exceeds a set value, sending out standard deviation overrun alarm to prompt the anemometer to malfunction and the occurrence time of the malfunction.
Further, the method further comprises the following steps: when the standard deviation of the instantaneous wind speed values of all time nodes of one anemometer in one detection period does not exceed a set value; according to the instantaneous wind speed values of all time nodes of the anemometer in a detection period, calculating the turbulence degree of wind in the detection period, and when the turbulence degree exceeds a set value, giving out a turbulence degree overrun alarm to prompt the anemometer to break down and the occurrence time of the break down.
In another aspect of the present invention, there is provided a fault detection system of a high-speed railway gale disaster prevention monitoring system, the fault detection system comprising:
the data acquisition module is used for acquiring instantaneous wind speed data measured by an anemometer in the high wind disaster prevention monitoring system at a plurality of time nodes in one detection period;
the data processing and alarming module is used for comparing the instantaneous wind speed data of the adjacent time nodes measured by the anemometer, sending out an instantaneous wind speed value difference value overrun alarm when the instantaneous wind speed value difference value of the adjacent time nodes exceeds a set value, and prompting the anemometer to break down and the occurrence time of the break down.
Further, the data processing and alarming module is further used for:
comparing the instantaneous wind speed data measured by two anemometers in the big wind disaster prevention monitoring system at the same time node, when the difference value of the instantaneous wind speed values measured by the two anemometers at the same time node exceeds a set value, at least one of the two anemometers fails, sending out an overrun alarm for the difference value of the instantaneous wind speed values of the two anemometers, and prompting the occurrence time of the failure;
and respectively comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers to determine the specific failed anemometer.
Further, the data processing and alarming module is further used for: when the difference value of the instantaneous wind speed values of the adjacent time nodes does not exceed a set value, dividing a detection period into a plurality of time periods, wherein each time period comprises a plurality of time nodes, calculating the average wind speed value measured by an anemometer in a big wind disaster prevention monitoring system in each time period, comparing the average wind speed values in the adjacent time periods, and when the difference value of the average wind speed values in the adjacent time periods exceeds the set value, sending out a wind speed average value difference value overrun alarm to prompt the anemometer to have faults and the occurrence time of the faults.
Further, the data processing and alarming module is further used for: comparing average wind speed values of two anemometers in the big wind disaster prevention monitoring system in the same time period, when the difference value of the average wind speed values of the two anemometers in the same time period exceeds a set value, at least one anemometer fails, sending out an overrun alarm of the difference value of the average wind speed values of the two anemometers, and prompting the occurrence time of the failure; comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer;
further, the data processing and alarming module is further used for: when the average wind speed value difference value of two anemometers in the same time period does not exceed a set value, judging whether the instantaneous wind speed value of a plurality of continuous time nodes measured by one anemometer in the large wind disaster prevention monitoring system is 0, and when the instantaneous wind speed value of a plurality of continuous time nodes measured by the anemometer is 0, sending out data missing acquisition alarm to prompt the anemometer to fail and the failure occurrence time;
further, the data processing and alarming module is further used for: when the instantaneous wind speed value of a plurality of continuous time nodes measured by an anemometer in the big wind disaster prevention monitoring system is not 0, calculating the standard deviation of the instantaneous wind speed values of all the time nodes of the anemometer in a detection period, judging the discrete characteristic of the instantaneous wind speed value, and when the standard deviation exceeds a set value, sending out standard deviation overrun alarm to prompt the anemometer to malfunction and the occurrence time of the malfunction.
Further, the data processing and alarming module is further used for: when the standard deviation of the instantaneous wind speed values of all time nodes of one anemometer in one detection period does not exceed a set value; according to the instantaneous wind speed values of all time nodes of the anemometer in a detection period, calculating the turbulence degree of wind in the detection period, and when the turbulence degree exceeds a set value, giving out a turbulence degree overrun alarm to prompt the anemometer to break down and the occurrence time of the break down.
Compared with the prior art, the invention has the following beneficial effects: the fault detection method and the system of the high-speed railway high-wind disaster prevention monitoring system can identify faults including abnormal data, data missing and the like in the running process of the high-speed railway high-wind disaster prevention monitoring system, are stable and reliable, have no pathological problems, have high running efficiency, can give an alarm in real time and can realize accurate positioning of the faults.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a fault detection method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a high-speed railway gale disaster prevention monitoring system.
Detailed Description
The present invention will be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments are shown, for the purpose of illustrating the invention, but the scope of the invention is not limited to the specific embodiments shown. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. Unless defined otherwise, all technical and scientific terms used hereinafter have the same meaning as commonly understood by one of ordinary skill in the art.
Referring to fig. 1, in the fault detection method of the high-speed railway gale disaster prevention monitoring system according to the embodiment of the invention, the structure of the high-speed railway gale disaster prevention monitoring system is shown in fig. 2, and the high-speed railway gale disaster prevention monitoring system comprises two anemometers. The fault detection method comprises the following steps: taking the moment 0 as a starting point, and taking every 10 minutes as a detection period; acquiring instantaneous wind speed data measured by an anemometer in a large wind disaster prevention monitoring system at a plurality of time nodes in a detection period; comparing the measured instantaneous wind speed data of the adjacent time nodes of the anemometer, and when the instantaneous wind speed value difference value of the adjacent time nodes exceeds a set value, sending out an instantaneous wind speed value difference value overrun alarm and prompting the anemometer to break down and the occurrence time of the break down. The fault detection method of the high-speed railway high-wind disaster prevention monitoring system can rapidly, accurately and reliably detect the fault of the anemometer in the high-speed railway high-wind disaster prevention monitoring system, and realizes the accurate positioning of the fault of the anemometer.
Further, in this embodiment, the fault detection method further includes: comparing the instantaneous wind speed data measured by two anemometers in the big wind disaster prevention monitoring system at the same time node, when the difference value of the instantaneous wind speed values measured by the two anemometers at the same time node exceeds a set value, at least one of the two anemometers fails, and at the moment, giving out an overrun alarm for the difference value of the instantaneous wind speed values of the two anemometers and prompting the occurrence time of the failure; and then comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer.
Under normal conditions, the instantaneous wind speed values measured by two anemometers at the same time node of the same measuring point are very close, and when one anemometer fails, but the data self-detection of the anemometer does not detect the failure, the failure can be detected by comparing the instantaneous wind speed difference values of the two anemometers at the same time node through the method, so that the mutual supplementing effect is achieved.
Further, the fault detection method further includes: when the difference value of the instantaneous wind speed values of the adjacent time nodes does not exceed a set value, dividing a detection period into a plurality of time periods (such as 30 seconds), wherein each time period comprises a plurality of time nodes, calculating the average wind speed value measured by an anemometer in a big wind disaster prevention monitoring system in each time period, comparing the average wind speed values in the adjacent time periods, and when the difference value of the average wind speed values in the adjacent time periods exceeds the set value, sending out an overrun alarm of the average wind speed value difference value to prompt the anemometer to have faults and fault occurrence time.
The average wind speed value is one of scales representing wind characteristics in a period of time, reflects the average value of the wind speed values in the period of time, and can be well reflected in the difference value of the average wind speed values if the anemometer breaks down. The method further compares the average wind speed value difference values in adjacent time periods, and can more accurately and reliably detect the faults of the anemometer in the high-speed railway high-wind disaster prevention monitoring system.
In this embodiment, the fault detection method further includes: comparing average wind speed values of two anemometers in the big wind disaster prevention monitoring system in the same time period, when the difference value of the average wind speed values of the two anemometers in the same time period exceeds a set value, at least one anemometer fails, sending out an overrun alarm of the difference value of the average wind speed values of the two anemometers, and prompting the occurrence time of the failure; and then comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer.
Under normal conditions, the average wind speed values measured by two anemometers at the same measuring point at the same time node are very close; when a certain anemometer fails, but the data self-inspection of the anemometer does not detect the failure, the failure can also be detected by comparing the average wind speed value difference value of the two anemometers through the method, and further comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively, so that the anemometer with the specific failure can be determined. Through the method, the complementary effects can be achieved, and fault detection is more accurate and reliable.
In this embodiment, the fault detection method further includes: when the average wind speed value difference value of two anemometers in the same time period does not exceed a set value, judging whether the instantaneous wind speed value of a plurality of continuous time nodes measured by one anemometer in the large wind disaster prevention monitoring system is 0, and when the instantaneous wind speed value of a plurality of continuous time nodes measured by the anemometer is 0, sending out data missing acquisition alarm to prompt the anemometer to fail and the failure occurrence time.
When the anemometer fails, the condition of data missing is caused, the value of the instantaneous wind speed value acquired by the anemometer is 0, the anemometer failure can be judged to be the data missing by judging whether the instantaneous wind speed values of a plurality of continuous time nodes are 0, and targeted measures can be taken according to the failure type.
In this embodiment, the fault detection method further includes: when the instantaneous wind speed value of a plurality of continuous time nodes measured by an anemometer in the big wind disaster prevention monitoring system is not 0, calculating the standard deviation of the instantaneous wind speed values of all the time nodes of the anemometer in a detection period, judging the discrete characteristic of the instantaneous wind speed value, and when the standard deviation exceeds a set value, sending out standard deviation overrun alarm to prompt the anemometer to malfunction and the occurrence time of the malfunction.
The standard deviation of the instantaneous wind speed value reflects the data discrete degree of the instantaneous wind speed value in a period of time, and if the anemometer fails, the instantaneous wind speed standard deviation can be accurately identified by analyzing the instantaneous wind speed standard deviation. By adopting the method, the accuracy and the reliability of the fault detection of the anemometer in the high-speed railway high-wind disaster prevention monitoring system are further improved.
Further, in this embodiment, the fault detection method further includes: when the standard deviation of the instantaneous wind speed values of all time nodes of one anemometer in one detection period does not exceed a set value; according to the instantaneous wind speed values of all time nodes of the anemometer in a detection period, calculating the turbulence degree of wind in the detection period, and when the turbulence degree exceeds a set value, giving out a turbulence degree overrun alarm to prompt the anemometer to break down and the occurrence time of the break down.
By adopting the method for detecting the turbulence degree of wind in the period, the method can play a role in mutually supplementing with other detection indexes, and the accuracy and the reliability of fault detection of the anemometer are further improved. The instantaneous wind speed value, the average wind speed value in a period of time, the continuous 0 of the instantaneous wind speed value, the standard deviation of the instantaneous wind speed value, the turbulence level and the like of the anemometer are all scales representing the wind characteristics in a period of time. According to the invention, through multi-scale comparison, the self-checking scales are mutually complemented, so that the fault of the anemometer can be accurately identified to the greatest extent, and the scales are mutually complemented.
The invention also provides a fault detection system corresponding to the fault detection method of the high-speed railway gale disaster prevention monitoring system, which comprises a data acquisition module and a data processing and alarming module, wherein the data acquisition module is connected with the data processing and alarming module. The data acquisition module is used for acquiring instantaneous wind speed data measured by an anemometer in the high wind disaster prevention monitoring system in a plurality of time nodes in one detection period; the data processing and alarming module is used for comparing the instantaneous wind speed data of the adjacent time nodes measured by the anemometer, sending out an instantaneous wind speed value difference value overrun alarm when the instantaneous wind speed value difference value of the adjacent time nodes exceeds a set value, and prompting the anemometer to break down and the occurrence time of the break down.
Further, the data processing and alarming module is also used for: comparing the instantaneous wind speed data measured by two anemometers in the big wind disaster prevention monitoring system at the same time node, when the difference value of the instantaneous wind speed values measured by the two anemometers at the same time node exceeds a set value, at least one of the two anemometers fails, sending out an overrun alarm for the difference value of the instantaneous wind speed values of the two anemometers, and prompting the occurrence time of the failure; and then comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer.
When the difference value of the instantaneous wind speed values of the adjacent time nodes does not exceed a set value, dividing a detection period into a plurality of time periods, wherein each time period comprises a plurality of time nodes, calculating the average wind speed value measured by an anemometer in a big wind disaster prevention monitoring system in each time period, comparing the average wind speed values in the adjacent time periods, and when the difference value of the average wind speed values in the adjacent time periods exceeds the set value, sending out a wind speed average value difference value overrun alarm to prompt the anemometer to have faults and the occurrence time of the faults.
Comparing average wind speed values of two anemometers in the big wind disaster prevention monitoring system in the same time period, when the difference value of the average wind speed values of the two anemometers in the same time period exceeds a set value, at least one anemometer fails, sending out an overrun alarm of the difference value of the average wind speed values of the two anemometers, and prompting the occurrence time of the failure; and then comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer.
When the average wind speed value difference value of two anemometers in the same time period does not exceed a set value, judging whether the instantaneous wind speed value of a plurality of continuous time nodes measured by one anemometer in the large wind disaster prevention monitoring system is 0, and when the instantaneous wind speed value of a plurality of continuous time nodes measured by the anemometer is 0, sending out data missing acquisition alarm to prompt the anemometer to fail and the failure occurrence time.
When the instantaneous wind speed value of a plurality of continuous time nodes measured by an anemometer in the big wind disaster prevention monitoring system is not 0, calculating the standard deviation of the instantaneous wind speed values of all the time nodes of the anemometer in a detection period, judging the discrete characteristic of the instantaneous wind speed value, and when the standard deviation exceeds a set value, sending out standard deviation overrun alarm to prompt the anemometer to malfunction and the occurrence time of the malfunction.
When the standard deviation of the instantaneous wind speed values of all time nodes of one anemometer in one detection period does not exceed a set value; according to the instantaneous wind speed values of all time nodes of the anemometer in a detection period, calculating the turbulence degree of wind in the detection period, and when the turbulence degree exceeds a set value, giving out a turbulence degree overrun alarm to prompt the anemometer to break down and the occurrence time of the break down.
In general, the fault detection method and the fault detection system of the high-speed railway high-wind disaster prevention monitoring system are characterized in that the instantaneous wind speed value, the average wind speed value in a period of time, the instantaneous wind speed value continuously being 0, the standard deviation of the instantaneous wind speed value, the turbulence degree and other scales are compared, and the self-checking scales are mutually complemented, so that the fault of the anemometer can be accurately identified to the greatest extent. The fault detection method and the fault detection system can identify faults such as abnormal data, data missing and the like in the operation process of the high-speed railway strong wind disaster prevention monitoring system; the fault detection method and the system are stable and reliable, have no pathological problems, have high operation efficiency, can give an alarm in real time, and can realize accurate positioning of faults.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. The fault detection method of the high-speed railway gale disaster prevention monitoring system is characterized by comprising the following steps of:
acquiring instantaneous wind speed data measured by an anemometer in a large wind disaster prevention monitoring system at a plurality of time nodes in a detection period; comparing the measured instantaneous wind speed data of the adjacent time nodes of the anemometer, and when the instantaneous wind speed value difference value of the adjacent time nodes exceeds a set value, sending out an instantaneous wind speed value difference value overrun alarm and prompting the occurrence of faults and the occurrence time of the faults of the anemometer;
comparing the instantaneous wind speed data measured by two anemometers in the big wind disaster prevention monitoring system at the same time node, when the difference value of the instantaneous wind speed values measured by the two anemometers at the same time node exceeds a set value, at least one of the two anemometers fails, sending out an overrun alarm for the difference value of the instantaneous wind speed values of the two anemometers, and prompting the occurrence time of the failure; comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer;
when the difference value of the instantaneous wind speed values of the adjacent time nodes does not exceed a set value, dividing a detection period into a plurality of time periods, wherein each time period comprises a plurality of time nodes, calculating the average wind speed value measured by an anemometer in a big wind disaster prevention monitoring system in each time period, comparing the average wind speed values in the adjacent time periods, and when the difference value of the average wind speed values in the adjacent time periods exceeds the set value, sending out a wind speed average value difference value overrun alarm to prompt the anemometer to have faults and fault occurrence time;
comparing average wind speed values of two anemometers in the big wind disaster prevention monitoring system in the same time period, when the difference value of the average wind speed values of the two anemometers in the same time period exceeds a set value, at least one anemometer fails, sending out an overrun alarm of the difference value of the average wind speed values of the two anemometers, and prompting the occurrence time of the failure; comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer;
when the average wind speed value difference value of two anemometers in the same time period does not exceed a set value, judging whether the instantaneous wind speed value of a plurality of continuous time nodes measured by one anemometer in the large wind disaster prevention monitoring system is 0, and when the instantaneous wind speed value of a plurality of continuous time nodes measured by the anemometer is 0, sending out data missing acquisition alarm to prompt the anemometer to fail and the failure occurrence time;
when the instantaneous wind speed value of a plurality of continuous time nodes measured by an anemometer in the big wind disaster prevention monitoring system is not 0, calculating the standard deviation of the instantaneous wind speed values of all the time nodes of the anemometer in a detection period, judging the discrete characteristic of the anemometer, and when the standard deviation exceeds a set value, sending out standard deviation overrun alarm to prompt the anemometer to have faults and the occurrence time of the faults;
when the standard deviation of the instantaneous wind speed values of all time nodes of one anemometer in one detection period does not exceed a set value; according to the instantaneous wind speed values of all time nodes of the anemometer in a detection period, calculating the turbulence degree of wind in the detection period, and when the turbulence degree exceeds a set value, giving out a turbulence degree overrun alarm to prompt the anemometer to break down and the occurrence time of the break down.
2. A fault detection system for a high-speed railway gale disaster prevention monitoring system, the fault detection system comprising:
the data acquisition module is used for acquiring instantaneous wind speed data measured by an anemometer in the high wind disaster prevention monitoring system at a plurality of time nodes in one detection period;
the data processing and alarming module is used for comparing the instantaneous wind speed data of the adjacent time nodes measured by the anemometer, sending out an instantaneous wind speed value difference value overrun alarm when the instantaneous wind speed value difference value of the adjacent time nodes exceeds a set value, and prompting the anemometer to generate faults and the occurrence time of the faults;
the data processing and alarming module is further used for: comparing the instantaneous wind speed data measured by two anemometers in the big wind disaster prevention monitoring system at the same time node, when the difference value of the instantaneous wind speed values measured by the two anemometers at the same time node exceeds a set value, at least one of the two anemometers fails, sending out an overrun alarm for the difference value of the instantaneous wind speed values of the two anemometers, and prompting the occurrence time of the failure; comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer;
the data processing and alarming module is further used for: when the difference value of the instantaneous wind speed values of the adjacent time nodes does not exceed a set value, dividing a detection period into a plurality of time periods, wherein each time period comprises a plurality of time nodes, calculating the average wind speed value measured by an anemometer in a big wind disaster prevention monitoring system in each time period, comparing the average wind speed values in the adjacent time periods, and when the difference value of the average wind speed values in the adjacent time periods exceeds the set value, sending out a wind speed average value difference value overrun alarm to prompt the anemometer to have faults and fault occurrence time;
the data processing and alarming module is further used for: comparing average wind speed values of two anemometers in the big wind disaster prevention monitoring system in the same time period, when the difference value of the average wind speed values of the two anemometers in the same time period exceeds a set value, at least one anemometer fails, sending out an overrun alarm of the difference value of the average wind speed values of the two anemometers, and prompting the occurrence time of the failure; comparing the instantaneous wind speed data measured by the two anemometers with the historical data of the corresponding anemometers respectively to determine the specific failed anemometer;
the data processing and alarming module is further used for: when the average wind speed value difference value of two anemometers in the same time period does not exceed a set value, judging whether the instantaneous wind speed value of a plurality of continuous time nodes measured by one anemometer in the large wind disaster prevention monitoring system is 0, and when the instantaneous wind speed value of a plurality of continuous time nodes measured by the anemometer is 0, sending out data missing acquisition alarm to prompt the anemometer to fail and the failure occurrence time;
the data processing and alarming module is further used for: when the instantaneous wind speed value of a plurality of continuous time nodes measured by an anemometer in the big wind disaster prevention monitoring system is not 0, calculating the standard deviation of the instantaneous wind speed values of all the time nodes of the anemometer in a detection period, judging the discrete characteristic of the anemometer, and when the standard deviation exceeds a set value, sending out standard deviation overrun alarm to prompt the anemometer to have faults and the occurrence time of the faults;
the data processing and alarming module is further used for: when the standard deviation of the instantaneous wind speed values of all time nodes of one anemometer in one detection period does not exceed a set value; according to the instantaneous wind speed values of all time nodes of the anemometer in a detection period, calculating the turbulence degree of wind in the detection period, and when the turbulence degree exceeds a set value, giving out a turbulence degree overrun alarm to prompt the anemometer to break down and the occurrence time of the break down.
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