CN114623281A - Pipeline prediction analysis alarm system and use method thereof - Google Patents
Pipeline prediction analysis alarm system and use method thereof Download PDFInfo
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- CN114623281A CN114623281A CN202111457169.6A CN202111457169A CN114623281A CN 114623281 A CN114623281 A CN 114623281A CN 202111457169 A CN202111457169 A CN 202111457169A CN 114623281 A CN114623281 A CN 114623281A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16K—VALVES; TAPS; COCKS; ACTUATING-FLOATS; DEVICES FOR VENTING OR AERATING
- F16K37/00—Special means in or on valves or other cut-off apparatus for indicating or recording operation thereof, or for enabling an alarm to be given
- F16K37/0075—For recording or indicating the functioning of a valve in combination with test equipment
- F16K37/0091—For recording or indicating the functioning of a valve in combination with test equipment by measuring fluid parameters
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
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Abstract
A pipeline prediction analysis alarm system comprises a storage unit in which historical data is stored; the processing unit calls a plurality of historical data according to the time point to be predicted and the prediction calculation time interval to calculate the average value of the historical data; calculating an alarm threshold value of the time point to be predicted according to the average value; the data acquisition unit is used for acquiring real-time data of a time point to be predicted; and the execution unit compares the real-time data with an alarm threshold value and sends an alarm instruction when the real-time data exceeds the alarm threshold value. The system can accurately predict the normal range of each parameter in continuous time, dynamically and accurately set the alarm threshold value, obviously reduce false alarm and missed alarm, can more scientifically and safely manage the urban pipe network, and has important social significance.
Description
Technical Field
The invention relates to a pipeline prediction analysis alarm system and a using method thereof.
Background
The urban comprehensive pipe network is an important component of modern city construction, and the enhancement of the intelligent management level of the urban overground and underground pipe network is an important mark of urban modernization. In modern cities, a comprehensive pipe network comprises various pipelines such as water supply and drainage, heating power, gas, power supply, communication and the like, and how to monitor and control the pipelines in a standard, efficient and safe manner is an important foundation for ensuring the safe and smooth development of the whole city. With the rapid development of urbanization, the design, construction and maintenance of the comprehensive pipeline are remarkably lagged behind, accidents such as water leakage, heat power leakage, gas leakage, explosion and the like related to the pipeline are frequently caused in recent years, and the improvement of the management level of the urban comprehensive pipe network is urgently required from an urban management level to common people.
There are many kinds of equipment in cities and special pipelines that need remote monitoring, and a valve is a common one. The valve is an indispensable control part in a fluid conveying system in cities and special pipelines, has various functions of stopping, stabilizing pressure, stopping, regulating, shunting, relieving pressure and the like, and plays a vital role in the whole system. In the daily maintenance of a pipe network, particularly when dangerous situations occur, how to accurately, effectively and quickly monitor and control a valve is an important embodiment of the management level of the urban pipe network. In the remote monitoring of the valve, when a certain node is required to be leaked, the system can automatically alarm. However, in modern city pipeline construction, in order to meet various special requirements of different levels, pipeline design is often complex, configuration of corresponding remote equipment is also very complex, and a common alarm threshold setting method is extensive at present, so that actual parameter fluctuation conditions in the use of pipelines cannot be accurately reflected. How to set the alarm threshold more scientifically and accurately according to the requirement characteristics of the user is always a difficult problem.
In order to meet the increasingly complex and urgent needs of remote equipment, such as valves and control in cities and special pipe networks, a system solution capable of specifying actual conditions, accurately judging dangerous situations and giving an alarm is urgently needed.
Disclosure of Invention
According to an aspect of the present invention, there is provided a pipeline alarm system, which includes a storage unit storing history data; the processing unit is directly or indirectly connected with the storage unit, and can call a plurality of historical data near the time point to be predicted, such as front and back, according to the time point to be predicted and the prediction calculation time interval, calculate an average value and calculate an alarm threshold value of the time point to be predicted according to the average value; the data acquisition unit can acquire real-time data of a time point to be predicted; and the execution unit is directly or indirectly connected with the processing unit and the data acquisition unit, can compare the real-time data with the alarm threshold value, and sends out an alarm instruction when the real-time data exceeds the alarm threshold value.
According to yet another aspect of the invention, the average in the system is an arithmetic average.
According to another aspect of the invention, the processing unit in the system filters and removes invalid data in the historical data.
According to yet another aspect of the invention, the historical data of the processing unit calls in the system is at least 5.
According to yet another aspect of the invention, the historical data called by the processing unit in the system includes both the historical data before the point in time to be predicted and the historical data after the point in time to be predicted.
According to still another aspect of the present invention, there is provided a pipeline alarm system setting method, including the steps of:
storing the history data in a storage unit;
the processing unit calls a plurality of historical data according to the time point to be predicted and the prediction calculation time interval;
the processing unit calculates the average value of the historical data;
the processing unit calculates an alarm threshold value of the time point to be predicted according to the average value;
the data acquisition unit acquires real-time data of a time point to be predicted; and
and the execution unit compares the real-time data with an alarm threshold value and sends an alarm instruction when the real-time data exceeds the alarm threshold value.
According to still another aspect of the present invention, there is provided a pipeline alarm system setting method, including the steps of:
calling a plurality of historical data near the time point to be predicted, such as before and after, according to the time point to be predicted and the prediction calculation time interval;
calculating an average value of the historical data;
calculating an alarm threshold value of the time point to be predicted according to the average value;
collecting real-time data of a time point to be predicted; and
and comparing the real-time data with an alarm threshold value, and sending an alarm instruction when the real-time data exceeds the alarm threshold value.
According to yet another aspect of the invention, the method further comprises the processing unit removing invalid data in the historical data by screening.
According to yet another aspect of the invention, wherein said average of said method is an arithmetic average.
According to yet another aspect of the invention, wherein the history of processing unit calls of the method is at least 5.
According to yet another aspect of the invention, the historical data called by the processing unit of the method includes both the historical data before the point in time to be predicted and the historical data after the point in time to be predicted.
According to a further aspect of the invention, the alarm threshold of the method is set according to an upper alarm limit and a lower alarm limit, the upper alarm limit is 101% of the average value, and the lower alarm limit is 99% of the average value.
According to still another aspect of the present invention, there is provided a pipeline alarm system, which includes a storage unit storing history data; and the processing unit is directly or indirectly connected with the storage unit, can call a plurality of historical data according to the time point to be predicted and the prediction calculation time interval, calculates an average value, and calculates the alarm threshold value of the time point to be predicted according to the average value.
According to still another aspect of the present invention, the system further comprises a data acquisition unit which can acquire real-time data of a time point to be predicted; and the execution unit is directly or indirectly connected with the processing unit and the data acquisition unit, can compare the real-time data with the alarm threshold value, and sends out an alarm instruction when the real-time data exceeds the alarm threshold value.
According to still another aspect of the present invention, the alarm threshold is set according to an alarm upper limit value and an alarm lower limit value, and the setting accuracy may be up to 1% of the average value.
According to still another aspect of the present invention, there is provided a pipeline alarm system setting method, including the steps of:
storing the history data in a storage unit;
the processing unit calls a plurality of historical data according to the time point to be predicted and the prediction calculation time interval;
the processing unit calculates the average value of the historical data; and
and the processing unit calculates the alarm threshold of the time point to be predicted according to the average value.
According to another aspect of the invention, the provided method further comprises the steps that the data acquisition unit acquires real-time data of the time point to be predicted; and
and the execution unit compares the real-time data with an alarm threshold value and sends an alarm instruction when the real-time data exceeds the alarm threshold value.
These and other aspects of the invention will be further elucidated with reference to the description and examples hereinafter.
Drawings
Embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:
fig. 1 is a flow chart of a system according to the present invention.
Fig. 2 is a diagram of embodiment 1 of the present invention, which shows a comparison of the conventional and the present invention alarm threshold setting methods.
Fig. 3 is a diagram of embodiment 2 of the present invention, which shows a comparison of the conventional and the present alarm threshold setting methods.
Detailed Description
A pipeline alarm system setup system and method is provided, which includes storing historical data in a storage unit; the processing unit calls a plurality of historical data according to the time point to be predicted and the prediction calculation time interval; the processing unit calculates the average value of the historical data; the processing unit calculates an alarm threshold value of the time point to be predicted according to the average value; the data acquisition unit acquires real-time data of a time point to be predicted; and the execution unit compares the real-time data with an alarm threshold value and sends an alarm instruction when the real-time data exceeds the alarm threshold value. The system can accurately predict the normal range of each parameter in continuous time according to the specific condition of a user, and dynamically set an alarm threshold value, thereby obviously reducing false alarm and false alarm.
The monitoring system of the pipeline can be generally divided into a central alarm subsystem and a subsystem of the remote device. Each unit in the system generally comprises software and hardware, and the functions of each unit are also realized by depending on the hardware through the software.
Historical data of parameters associated with the remote device may be stored on the memory unit. The storage unit may be located in the central alarm subsystem or external to the central alarm subsystem, such as the cloud. For the valve, the equipment-related parameters may include flow rate, upstream pressure, downstream pressure, temperature, gas content, etc., depending on the different lines. The historical data of the parameters can be stored according to a certain rule and order, so that the parameters can be conveniently inquired, stored and read. For example, it can be stored in chronological order, by year, month, week, day, hour, minute, second, etc. The time of review of the historical data may be years, months, weeks, days, hours, etc., preferably years, months or weeks, e.g., data of a previous year or month.
The processing unit may be located in the central alarm subsystem, for example on a server, which may be connected indirectly or directly to the storage unit, so that the history data may be recalled and alarm thresholds calculated. The processing unit may filter the historical data, first which historical data is called. The processing unit automatically predicts the parameter values at each time point according to a set prediction calculation time interval or an existing time interval of historical data. For example, parameter value prediction is performed every second, i.e., the prediction calculation time interval is 1 second, and the time interval of the history data to be retrieved is generally 1 second or less. In the prediction, the processing unit retrieves historical data at or near the time point to be predicted, preferably those closest to the time point to be predicted, according to the prediction calculation time interval. The data in the vicinity of the prediction time point refers to historical data before and/or after the time point to be predicted, and includes data before the prediction time point, data after the prediction time point, and data both before and after, preferably data both before and after. Both before and after, the data is preferably the data that is closest in time to the predicted time point. The called historical data is multiple pieces. "plurality or plurality" means at least two or two, preferably at least 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 100, 200 historical data. For example, when the retrospective time is 1 year, the predicted calculation time interval is 5 seconds, and the time point to be predicted is 10 points (i.e., 10:00:00) at 9 month, 25 day, 2021 year, 10 pieces of history data are retrieved, 10 pieces of history data, 9:59:35, 9:59:40, 9:59:45, 9:59:50, 9:59:55, 10:00:00, 10:00:05, 10:00:10, 10:00:15, 10:00:20 at 9 month, 25 day, 2020 year, may be called. When the historical data does not have the data at the time points, for example, the latest data all have 2 seconds difference, the data at the time points nearest to the historical data can be called. The prediction calculation time interval, the amount of the called historical data, the amount of the historical data before and after the time point to be predicted and the like can be selected by a user according to specific situations. Preferably, the number of the called historical data before and after the time point to be predicted is as equal as possible.
Second, the processing unit may clear exception data in the retrieved history data. For example, data that is too high or too low may be considered invalid data. The data considered invalid is found to be too low or too high, mainly according to the specific conditions of the pipeline and the user, and is generally abnormal values at a single time point, which is significantly different from the data at the successive time points, such as the value is zero. These invalid data are typically outliers generated at the time of acquisition, or at the time of storage. The user can set that after the processing unit removes the invalid data, the predicted value is the average value of the remaining valid data; alternatively, the supplementary data may be selected before or after the prediction time point, and then the prediction value may be calculated.
The processing unit may process the retrieved history data to calculate a prediction value of the time point to be predicted. It is most common to calculate an average, e.g. an arithmetic average, of these historical data. On the basis of the predicted value, an alarm threshold value can be obtained through calculation, for example, when the alarm threshold value is set to be +/-10% of the arithmetic mean value, namely the alarm upper limit value is 110% of the arithmetic mean value, aiming at the flow of a water supply pipeline of the urban pipe network; the lower alarm limit is 90% of the arithmetic mean. Since the threshold setting of the method of the invention is closer to the actual condition of the pipeline, the alarm threshold can be set with higher precision, for example, with 1% precision, while the amplitude between the upper/lower limit value and the average value of the alarm threshold of the conventional method can be set only roughly by increasing or decreasing by 10%, for example, by ± 10%, ± 20%, ± 30% from the average value. The present invention may set the upper limit/lower limit to increase or decrease 1% of the average value, for example, to ± 10%, ± 11%, ± 9%, ± 12%, ± 8%, etc. For example, when the value is ± 1%, the alarm upper limit value is 101% of the average value, and the alarm lower limit value is 99% of the average value.
The data acquisition unit can acquire parameters of equipment, particularly remote equipment, such as a flow value, a pressure value, a temperature and the like of a certain node in real time. The data acquisition unit is preferably arranged in a subsystem of the remote equipment, acquired real-time data can be transmitted back to the central alarm subsystem, and the central alarm subsystem can be in wireless communication with a wireless communication receiving unit under the condition of the remote equipment. Before the alarm is performed, continuous multiple sampling can be performed to confirm that the real-time data actually exceeds the alarm threshold value, so as to avoid the possibility of abnormal values of the single-sampling data.
The execution unit, which may be located in a central alarm subsystem, such as a server, may compare the received real-time data to an alarm threshold and issue an alarm signal when the real-time data exceeds the alarm threshold.
FIG. 1 illustrates a predictive alarm system of the present invention. Firstly, alarm setting is carried out: the central alarm subsystem in the system selects a monitoring node and turns on an alarm switch, selects a prediction analysis alarm mode, sets the sampling times and the analysis time range, sets an alarm percentage value in a corresponding alarm level, and finishes the setting after storing and setting to a storage unit (such as a database). For example, a primary alarm, the alarm percentage can be set to 110% of the predicted value; and (5) secondary alarm, wherein the alarm percentage is set to be 120% of the predicted value. In actual operation, the processing unit reads data acquired by the data acquisition unit on site in real time, and reads the data for multiple times to accumulate sampling times, and then enters an inspection program, wherein the inspection program comprises whether an alarm switch is turned on or not and whether a prediction alarm mode is set or not, after the two types of data are confirmed, the processing unit can call a plurality of data before and after the current time point in the history and the same period, such as 5, 35 and 150 pieces of data, remove abnormal values, such as 0 value, and then calculate an average value. The execution unit compares whether the real-time data deviates from the average value to achieve alarm, whether the set sampling times, such as continuous 3 times of sampling, achieve alarm so as to prevent abnormal values generated accidentally from triggering false alarm), and triggers alarm after confirming yes.
Example 1
As shown in fig. 2, the real-time change of the flow rate of a certain remote valve in a certain time is represented by a curve H. The traditional method is that the highest value and the lowest value are found out according to historical data within a certain time range (for example, 7 days), and then the upper line of the alarm threshold value of the traditional method, namely a straight line L1, is set to be 110% of the highest value according to the proportion, for example, the alarm threshold value is +/-10%; the lower alarm threshold limit is 90% of this lowest value, i.e. the line L2. Thus, the flow value represented by point A, B, C is already outside the alarm threshold, i.e., an alarm, i.e., a false alarm, may already be triggered. According to the method, a prediction curve (not shown, generally very close to the real-time flow curve H) in the time range can be obtained through historical data calculation, and then according to a 10% alarm threshold value, the upper limit and the lower limit of the alarm threshold value are obtained to be curves M1 and M2. It can be seen that the alarm threshold, which is also 10%, the M1 and M2 curves according to the invention better fit the actual flow variation, the flow value represented by point A, B, C is still within the range of M1 and M2 and no alarm is triggered, thus avoiding false alarms.
Example 2
In fig. 3, the real-time change of the flow of a certain remote valve over a certain time is represented by a curve H. According to the method of the invention, a prediction curve (not shown, located between M1 and M2) in the time range can be obtained through historical data calculation, and the upper limit and the lower limit of the alarm threshold are curves M1 and M2 according to the alarm threshold of 10%. To avoid the situation that the flow fluctuation is large and false alarm is easy to occur, the conventional method may increase the alarm threshold setting range, for example, the upper and lower limits of the alarm threshold are represented by straight lines L1 and L2, and increase to ± 20%, at which point a and point C no longer trigger the alarm. However, some real-time data, such as point B, although already significantly deviating from the expected value, will not trigger an alarm because it is still within the upper and lower limits of the alarm threshold, thereby generating a false alarm. However, according to the invention, at the alarm threshold, which is still only 10%, at this point in time point B has fallen below the lower limit M2 of the alarm threshold, an alarm is triggered. Therefore, the invention greatly reduces the false alarm.
The remote equipment in city pipe network and special pipe network, often the pipeline is complicated, and the valve is numerous, and there are many remote equipments in addition because the distance is far away or the cost is higher, and it is difficult to insert high voltage (not less than 220V) power. By adopting the method and the system, because the predicted value is better fitted with the actual flow change condition, the upper limit and the lower limit of the alarm threshold value are closer to the actual flow change curve at each predicted time point (measured in seconds).
Although a few embodiments have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles of the invention, and that modifications (additions and/or deletions) may be made to the components, structures, motions, assemblies, and steps described herein without departing from the full scope and spirit of the invention, which includes such modifications and any and all equivalents thereof.
Claims (10)
1. A pipeline alarm system comprises
A storage unit that stores history data;
the processing unit is directly or indirectly connected with the storage unit, can call a plurality of historical data near the time point to be predicted according to the prediction calculation time interval, calculates an average value, and calculates the alarm threshold value of the time point to be predicted according to the average value;
the data acquisition unit can acquire real-time data of a time point to be predicted; and
and the execution unit is directly or indirectly connected with the processing unit and the data acquisition unit and can send out an alarm instruction when the real-time data exceeds an alarm threshold value.
2. The system of claim 1, wherein the average is an arithmetic average.
3. The system of claim 1, wherein the processing unit is to filter out invalid data in the historical data.
4. The system of claim 1, wherein the processing unit calls have a history of at least 5.
5. A method for setting a pipeline alarm system comprises the following steps:
storing the history data in a storage unit;
the processing unit calls a plurality of historical data near the time point to be predicted according to the time point to be predicted and the prediction calculation time interval;
the processing unit calculates the average value of the historical data;
the processing unit calculates an alarm threshold value of the time point to be predicted according to the average value;
the data acquisition unit acquires real-time data of a time point to be predicted; and
the execution unit may issue an alarm instruction when the real-time data exceeds an alarm threshold.
6. The method of claim 5, further comprising the processing unit filtering out invalid data in the historical data.
7. The method of claim 5, wherein the average is an arithmetic average.
8. The method of claim 5, wherein the processing unit calls at least 5 historical data.
9. The method of claim 5, wherein the historical data called by the processing unit includes both historical data prior to the point in time to be predicted and historical data subsequent to the point in time to be predicted.
10. The method of claim 5, wherein the alarm threshold is set according to an alarm upper limit value and an alarm lower limit value, and the setting accuracy can reach 1% of the average value.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115451347A (en) * | 2022-08-05 | 2022-12-09 | 国家管网集团北方管道有限责任公司 | Intelligent monitoring system and method for petroleum pipeline safety |
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