CN114623950A - Self-adaptive alarm constant value setting method for optical fiber temperature measurement system - Google Patents
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Abstract
A self-adaptive alarm constant value setting method for an optical fiber temperature measurement system comprises the steps of automatically dividing temperature zones, extracting historical data, calculating a predicted temperature value by a self-adaptive algorithm, and calculating self-adaptive temperature alarm constant values to be set to each temperature zone. According to temperature values acquired by the optical fiber temperature measurement system, areas with different temperatures are divided into different temperature areas through logic calculation, the temperature of each temperature area in the next hour is predicted by using the historical data of each temperature area in the last 7 hours, and the alarm temperature of each area is set in a partitioning mode according to the predicted temperature values. By the self-adaptive alarm constant value setting method, the alarm constant values in different areas can be set in a partitioning mode and the alarm constant values in different time periods can be set in real time, and the problems of false alarm and late alarm caused by different temperatures due to different areas and different time periods acquired by the optical fiber temperature measurement system are solved.
Description
Technical Field
The invention relates to the technical field of hydropower station temperature measurement and control, in particular to a self-adaptive alarm constant value setting method for an optical fiber temperature measurement system.
Background
The cable corridor is a key fire-proof part of a power station, once a fire disaster occurs, economic loss and social influence caused by expansion of the fire disaster cannot be measured timely, and the existing optical fiber temperature measurement system is an important means for monitoring the fire disaster of the cable corridor.
However, the environment of the cable corridor is relatively complex, and has the problems that the temperature of different areas is different, the temperature of equipment in different running states is different, the temperature of the related areas is greatly changed along with the change of day and night, seasons and environment, and the like. At present, an optical fiber temperature measurement system generally adopts a fixed value, and the fixed value cannot be adjusted in a self-adaptive manner along with the change of temperature, so that the problems of some false alarms and late alarms, low detection sensitivity and low reliability exist, the monitoring of cable corridor fire is not facilitated, and the occurrence of the fire can be possibly caused in serious cases. In order to improve the sensitivity and reliability of alarming and improve the real-time dynamic monitoring of the cable, a self-adaptive alarm fixed value setting method of an optical fiber temperature measurement system is needed to be designed.
Disclosure of Invention
The invention aims to solve the technical problem of providing a self-adaptive alarm fixed value setting method for an optical fiber temperature measurement system, so that the self-adaptive setting of the optical fiber temperature measurement alarm fixed value is realized, the sensitivity and the reliability of alarm are improved, and the real-time dynamic monitoring on the operating temperature of a cable is improved.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a self-adaptive alarm constant value setting method for an optical fiber temperature measurement system comprises the following steps:
step one, dividing a target area into temperature areas and extracting temperature historical data of each divided temperature area;
step two, selecting the historical average temperature value of the latest n hours of each temperature zone by using the temperature historical data of each temperature zone, selecting the historical temperature data of the previous p hours to calculate the predicted value of the next hour according to the moving weighted average method, then adjusting the weight according to the error between the predicted value and the actual value, predicting the temperature value of the next hour by using the new weight after adjusting the weight, adjusting the weight after predicting, repeating the steps until the error is smaller than the set expected error e, and forming the self-adaptive temperature prediction method in the whole step two process;
step three, according to the temperature predicted value T of each temperature zone obtained by the self-adaptive temperature prediction method in the step two, increasing the alarm margin dT2 set by each temperature zone to obtain the set alarm temperature Tset，Tset＝T+dT2。
The temperature zone division mode in the step one is divided into two types: one is a method of dividing the detection region into sections according to the temperature zone within the detection region in order to maintain the continuity of the detection region, and the other is a method of dividing the temperature zone into sections according to the actual detection temperature using the temperature as the division target.
The method for segmenting the temperature zone in the detection area comprises the following specific processes:
setting the temperature of the first point of each channel of the detection target region as the reference temperature T0Subsequent temperature value TiIn turn with the reference temperature T0Making a comparison if | Ti-T0If the condition that the temperature values are greater than dT1 and the continuous 3 temperature values meet the requirement dT1 is the partition temperature difference limit value set according to the requirement, the temperature values are greater than the threshold valuei-T0Dividing the area corresponding to the temperature value less than or equal to dT1 into a temperature area, and simultaneously dividing the first | Ti-T0Temperature value T of | > dT1iIs assigned to T0And subsequently, sequentially carrying out temperature division on the temperature points until all temperature areas in the detection area are divided.
In a preferred embodiment, the temperature zone dividing method using the temperature as the dividing object according to the actual detected temperature includes the following steps:
setting a reference temperature T0Detecting the temperature T of all points in the target areaiWith a reference temperature T0Comparing the temperature with a reference temperature T0Temperature difference of (dT), (i) ═ Ti-T0The temperature difference dT (i) is divided according to a division principle of dT1(n) to dT (i) to dT1(n +1), the temperature of corresponding points in the same range is divided into a temperature zone, dT (i), dT1(n) is dT1(n-1) + dT1(n) is 1, 2, 3 … n, and dT1(0) is 0), dT1 is a zone temperature difference limit value set according to requirements, and the specific temperature difference interval of the zones is controlled by the size of dT 1.
The weight adjustment formula in the second step is:
wherein n is the number of the most recent temperature data, p is the number of the history data used for the prediction calculation,
for the prediction value in the weight adjustment process, TiIs the actual temperature value of the temperature,
in order to adjust the value of the weight,
for the initial weight, k is 1/(T)1 2+T2 2+T3 2+…+Tp 2) To learn constants, ei+1The weight is adjusted to be constant for the error value of the actual temperature and the predicted temperature in the (i +1) th hour, and the weight is adjusted to obtain ei+1When the current weight is less than or equal to e, sequentially obtaining the prediction error e of each time period in the latest n-p hours by using the current weighti+1If the maximum prediction error emax＝max(ei+1) If the error is larger than e, the weight iteration adjustment is carried out again, and the maximum error e ismaxAnd (3) calculating the temperature value of the next hour by using the optimal weight at the time at the side less than or equal to e to obtain a predicted temperature value T of the next hour:
in the second step, n is 7 and p is 3, and the predicted temperature value T is:
according to the self-adaptive alarm fixed value setting method for the optical fiber temperature measurement system, the temperature region is automatically divided, the temperature in the next hour is predicted by using historical data, the division and time-sharing self-adaptive setting of the alarm fixed value of the optical fiber temperature measurement is realized, and the sensitivity and the reliability of the alarm of the optical fiber temperature measurement system are improved.
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The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a setting flow chart of the adaptive alarm constant value setting method of the present invention;
FIG. 2 is a sectional division flow chart of the temperature zone according to the present invention;
FIG. 3 is a flow chart of the temperature zone predicted by the adaptive filtering method according to the present invention;
FIG. 4 is a schematic diagram of the adaptive alarm setting principle of the present invention;
fig. 5 is an exemplary diagram of segmentation of the temperature zone according to the present invention.
Detailed Description
The technical scheme of the invention is explained in detail by combining the drawings and the embodiment.
An adaptive alarm constant value setting method for an optical fiber temperature measurement system is implemented by the steps as shown in figure 1, and comprises the following steps:
assigning a partition temperature difference limit value dT1, an expected error e and an alarm margin dT2, and dividing a temperature zone after assignment is completed;
secondly, calculating and predicting the temperature T by using a self-adaptive algorithm based on historical data of each temperature area divided by the temperature area after the temperature area is divided;
and step three, after the predicted temperature T is obtained, setting the predicted temperature T plus an alarm margin dT2 as the setting value of the self-adaptive alarm constant value to each temperature subarea, and finishing the setting of the self-adaptive alarm constant value.
The temperature zone segmentation method and the segmentation process in the first temperature zone division are shown in fig. 2:
step one, judging whether temperature zone division is needed or not, if a temperature zone division mark exists, dividing the temperature zone, and keeping the temperature zone divided in the early stage without re-dividing the side without the mark;
secondly, a temperature zone division mark exists, average temperature data of each point in the last 1 hour is read from a database, meanwhile, the temperature value of the first temperature point is used as a reference temperature, and the temperature value is assigned to T0；
Thirdly, sequentially measuring the temperature T of each pointiAnd T0Making a difference and solving a temperature difference value | Ti-T0|；
Fourthly, the temperature difference value | Ti-T0Comparing | T with the partition temperature difference limit dT1, if | Ti-T0|>dT1, and 3 continuous temperature points meet the requirement, and simultaneously, whether the temperature point is the last temperature point is judged, if not, the first temperature value larger than dT1 is assigned to the reference temperature value T0Will | Ti-T0Dividing an area corresponding to a temperature value less than or equal to dT1 into an area of temperature, calibrating the area of temperature, storing the area of temperature into a database, and simultaneously performing subsequent temperature area division by using a new reference temperature; if the temperature point is the last temperature point, dividing all the last points into a temperature area, calibrating the temperature area, storing the temperature area into a database, and finishing the temperature area division;
according to the condition of the calibration temperature zone, the system calculates the average value of each temperature zone per hour and stores the average value for subsequent use, and meanwhile, when the system collects the temperature of each point, the system calculates the average temperature of each point per hour and stores the average temperature for subsequent use.
The method and the flow for calculating the predicted temperature value by the adaptive algorithm in the second step are as follows:
by selecting the historical temperature data of the last 7 hours and using 3 historical data to predict the temperature of the next hour, the adaptive algorithm formula is as follows:
in the formula (I), the compound is shown in the specification,
for the prediction value in the weight adjustment process, TiIs the actual temperature value of the temperature,
is a weight value, the initial weight is
k＝1/(T1 2+T2 2+T3 2) K is a learning constant, ei+1Is the error value between the actual temperature and the predicted temperature in the (i +1) th hour.
In the weight adjusting process, e is obtained when the weight is adjustedi+1When the current weight is less than or equal to e, the prediction error e of each time period of the latest 4 hours is obtained by using the current weighti+1If the maximum prediction error emax＝max(e4,…,e7) If more than e, the weight iteration adjustment is carried out again, and the maximum error emaxThe side less than or equal to e calculates the temperature value of the next hour by using the optimal weight at the moment to obtain the predicted temperature value of the next hour
The specific implementation flow of the adaptive algorithm is shown in fig. 3:
step one, judging whether temperature prediction calculation is needed, if the temperature prediction calculation is needed, calculating the predicted temperature on the sign side of the calculated predicted temperature, ending the flow on the no sign side, and keeping the predicted temperature in the early stage;
secondly, a sign for calculating the temperature to be measured exists, and the average temperature data of each hour in the corresponding temperature area within 7 hours is read from a lateral database;
thirdly, calculating a predicted value of the i +1 stage by using average temperature data of each hour within 7 hours and adopting a self-adaptive filtering method, and calculating a difference value e between the predicted value and an actual valuei+1And continuously performing iterative adjustment through the weight to reduce the difference value | e between the predicted value and the actual valuei+1I, the difference e between the predicted value and the actual value in the adjustment processi+1Comparing | with the expected error e when | ei+1|<e, calculating the fourth step, otherwise, repeatedly adjusting the weight iteration until | e is satisfiedi+1|<e；
The fourth step, when | ei+1|<At time e, the difference | e between the predicted temperature and the actual temperature in the last 4 hours in the historical data is calculated by using the latest weighti+1L, then find the maximum e of the 4 differencesmaxE.g. emaxIf the weight is more than e, the side returns to the third step to continue the weight iterative adjustment, emaxIf not more than e, taking the weight as the optimal weight, and entering the fifth step;
and fifthly, calculating the predicted temperature value T within the next hour to be predicted by using the optimal weight, storing the obtained predicted temperature into a database, and then sequentially calculating the predicted temperature values T of other temperature areas.
As shown in FIG. 1, the corresponding predicted temperature T is called in the main flow, and the adaptive alarm temperature constant value T is obtained after dT2 is addedsetAnd (4) T + dT2, and finally, the adaptive alarm temperature constant value setting system is carried out, and the process is ended.
Compared with the traditional alarm constant value method, the self-adaptive alarm constant value setting method for the optical fiber temperature measurement system has the characteristics of sensitive alarm, strong adaptability, high reliability and the like.
As shown in fig. 4, in the adaptive alarm constant value setting method for the optical fiber temperature measurement system, the temperature in the next hour is predicted by using historical temperature data through an adaptive algorithm, and an alarm margin dT2 is added to the predicted temperature T to obtain an alarm temperature constant value; the alarm constant value method is different from the conventional alarm constant value, the self-adaptive alarm constant value has different alarm constant values in different temperature areas and different alarm constant values in different time periods, the alarm constant value can be automatically adjusted along with the change of environment and time, and the self-adaptive alarm constant value method has stronger self-adaptability.
Secondly, the difference between the actual value and the alarm value of the conventional alarm constant value at low temperature is too large, the alarm sensitivity is too low,
at high temperature, the difference between the actual temperature value and the alarm value is too small, so that the problem of false alarm exists; the self-adaptive alarm fixed value changes along with the changes of temperature measurement areas, environments and time, and the alarm fixed value is dynamically adjusted, so that the self-adaptive alarm fixed value has stronger adaptability, and the sensitivity and the reliability of alarm are improved.
As shown in fig. 5, by dividing the temperature regions with similar temperatures into one temperature region and calculating the average value of the temperatures as the historical data for calculating the predicted temperature, the calculation amount can be greatly reduced, and at the same time, because the temperature regions are divided into one region according to the actual temperature, the predicted value can be effectively guaranteed to reflect the temperature value of the region more truly, and the reliability is higher.
Claims (6)
1. A self-adaptive alarm constant value setting method of an optical fiber temperature measurement system is characterized by comprising the following steps:
step one, dividing a target area into temperature areas and extracting temperature historical data of each divided temperature area;
step two, selecting the historical average temperature value of the latest n hours of each temperature zone by using the temperature historical data of each temperature zone, selecting the historical temperature data of the previous p hours to calculate the predicted value of the next hour according to the moving weighted average method, then adjusting the weight according to the error between the predicted value and the actual value, predicting the temperature value of the next hour by using the new weight after adjusting the weight, adjusting the weight after predicting, repeating the steps until the error is smaller than the set expected error e, and forming the self-adaptive temperature prediction method in the whole step two process;
step three, according to the temperature predicted value T of each temperature zone obtained by the self-adaptive temperature prediction method in the step two, increasing the alarm margin dT2 set by each temperature zone to obtain the set alarm temperature Tset，Tset＝T+dT2。
2. The method for setting the self-adaptive alarm fixed value of the optical fiber temperature measurement system according to claim 1, wherein the temperature zone division in the first step is divided into two types: one is a method of dividing the detection region into sections according to the temperature zone within the detection region in order to maintain the continuity of the detection region, and the other is a method of dividing the temperature zone into sections according to the actual detection temperature using the temperature as the division target.
3. The method for setting the self-adaptive alarm fixed value of the optical fiber temperature measurement system according to claim 2, wherein the method for sectionalizing according to the temperature zone in the detection zone comprises the following specific processes:
setting the temperature of the first point of each channel of the detection target region as the reference temperature T0Subsequent temperature value TiIn turn with the reference temperature T0Making a comparison if | Ti-T0If the temperature is greater than dT1 and 3 continuous temperature values meet the condition, and dT1 is a partition temperature difference limit value set according to requirements, the temperature is adjusted to be greater than the threshold valuei-T0Dividing the area corresponding to the temperature value less than or equal to dT1 into a temperature area, and simultaneously dividing the first | Ti-T0Temperature value T of | > dT1iIs assigned to T0And subsequently, sequentially carrying out temperature division on the temperature points until all temperature areas in the detection area are divided.
4. The method for setting the self-adaptive alarm fixed value of the optical fiber temperature measurement system according to claim 2, wherein the temperature zone dividing method using the temperature as the dividing object according to the actual detection temperature comprises the following specific processes:
setting a reference temperature T0Detecting the temperature T of all points in the target areaiWith reference temperature T0Comparing the temperature with a reference temperatureDegree T0Temperature difference of (dT), (i) ═ Ti-T0The temperature difference value dT (i) is divided according to a division principle of dT1(n) to dT (i) to dT1(n +1), the temperature of corresponding points in the same range is divided into a temperature zone, dT (i), dT1(n) is dT1(n-1) + dT1(n) is 1, 2, 3.
5. The method for setting the alarm setting value of the optical fiber temperature measurement system according to one of claims 3 or 4, wherein the weight adjustment formula in the second step is as follows:
in the formula (I), the compound is shown in the specification,
for the prediction value in the weight adjustment process, TiIs the actual temperature value of the temperature,
is a weight value, the initial weight is
k＝1/(T1 2+T2 2+T3 2) K is a learning constant, ei+1Performing weight adjustment for the error value between the actual temperature and the predicted temperature in the (i +1) th hourConstant, when weight is adjusted, ei+1When the current weight is less than or equal to e, sequentially obtaining the prediction error e of each time period in the latest n-p hours by using the current weighti+1If the maximum prediction error emax＝max(ei+1) If the error is larger than e, the weight iteration adjustment is carried out again, and the maximum error e ismaxAnd (e) calculating the temperature value of the next hour by using the optimal weight at the moment to obtain the predicted temperature value T of the next hour:
6. the adaptive alarm fixed value setting method of claim 5, wherein in the second step, n is 7 and p is 3, and the predicted temperature value T is:
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| CN115457745A (en) * | 2022-09-20 | 2022-12-09 | 浙江振东光电科技有限公司 | Exclusion method for distributed optical fiber temperature measurement zone alarm |
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