CN111400655A - Correction optimization method and system for warehousing traffic - Google Patents

Correction optimization method and system for warehousing traffic Download PDF

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CN111400655A
CN111400655A CN202010509783.1A CN202010509783A CN111400655A CN 111400655 A CN111400655 A CN 111400655A CN 202010509783 A CN202010509783 A CN 202010509783A CN 111400655 A CN111400655 A CN 111400655A
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warehousing
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李匡
柳俊
郜银梁
张子平
马安国
刘舒
刘业森
刘可新
柳文兵
郑敬伟
柴福鑫
臧文斌
徐美
李敏
刘媛媛
胡昌伟
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China Institute of Water Resources and Hydropower Research
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Abstract

The invention provides a correction optimization method and a correction optimization system for warehousing traffic, wherein the method comprises the step of setting the number of calculated data asnThe method also comprises the following steps: divide the warehousing traffic intomEach stage is provided with different weight coefficients; correcting the warehousing flow; calculated according to the final corrected flowtTime period unbalanced flow
Figure 100004_DEST_PATH_IMAGE001
. According to the correction optimization method and the correction optimization system for the warehousing flow, different calculation weights are set according to different flood magnitude levels, and primary calculation correction is carried out; calculating the difference between the original flow and the corrected flow in the current time period, and distributing and processing the difference to the next time period for secondary correction calculation; and finally, correcting for three times according to the flow amplitude.

Description

Correction optimization method and system for warehousing traffic
Technical Field
The invention relates to the technical field of reservoir flow calculation, in particular to a correction optimization method and a correction optimization system for warehousing flow.
Background
The reservoir warehousing flow is obtained by calculating the reservoir capacity difference and the ex-warehouse flow in a reverse-deducing manner according to a water quantity balance equation. The library tolerance is the difference between the library capacities at the beginning and the end of the time period, and reflects the change condition of the library capacity of the time period. The time interval for calculating the warehousing flow is generally divided into 15 minutes, 30 minutes, 60 minutes, 180 minutes, 240 minutes and the like according to the size of the storage capacity of the reservoir. The reservoir capacity is generally obtained by water level interpolation calculation according to a water level reservoir capacity relation curve, and fluctuation errors (such as wave influence, start and stop, gate opening and closing) during water level measurement cause errors in the water levels at the beginning and the end of a measurement period, so that the reservoir capacity has errors, further cause fluctuation phenomena of calculated reservoir flow, commonly called as 'saw teeth', and even cause that the reservoir flow is a negative value in serious conditions, which is obviously unreasonable. Therefore, correcting the warehousing flow to conform to the natural law is a research subject in the field of warehousing flow calculation. The existing correction methods include a moving average method, a five-point triple method and the like.
The moving average method has the following disadvantages: the influence of different flow grades on the correction value is not considered, and the weight value of the same flood process
Figure 315457DEST_PATH_IMAGE002
The data calculation weights of the data participating in correction are the same or similar in the stationary phase with small flow, while the weight of the correction data closer to the flood peak time period is set to be larger in the flood peak time period with large flow, namely, the flow is classified, and the weight values under different magnitudes are set
Figure 999641DEST_PATH_IMAGE002
Otherwise, the correction process is too smooth, and the correction result of the flood peak is affected, including excessive reduction of the flood peak flow and lag of the peak time.
The five-point triple method has the following disadvantages: for thetTime-phased warehousing trafficQ t Correction of (2), if necessarytAt the +1 time period,tFlow rate of entering warehouse in +2 time period
Figure 904012DEST_PATH_IMAGE003
、
Figure 158276DEST_PATH_IMAGE004
To atIn the case of a period of time,tat the +1 time period,tFlow rate of entering warehouse in +2 time period
Figure 736762DEST_PATH_IMAGE003
、
Figure 90383DEST_PATH_IMAGE004
Is unknown. Therefore, the method can be used only for post correction, but not for real-time correction. Meanwhile, the method also causes the time lag of the corrected flood peak.
Meanwhile, the water balance of the correction value is not considered in the two methods, which may cause a large error of the water amount calculated by the correction value and the original value.
The invention patent application with application publication number CN103116877A discloses a smoothing method for water level process of a book library, which comprises the following steps: processing the original actual reservoir water level measurement process; calculating and processing the time sequence process of the water level of the peer-to-peer period; and selecting different process outputs through parameters. The disadvantage of this method is that the water balance of the correction value is not taken into account, which may result in a large error in the water amount calculated for the correction value and the original value.
The invention patent application with application publication number CN108724415A discloses a warehousing flow correction system of a cascade reservoir, which comprises a historical data module, an interval control value analysis module, a data server, a water affair real-time module and a correction module, wherein the historical data module is used for storing water affair information of each power station interval in the calendar year of the cascade reservoir through the data server; the interval control value analysis module is used for calling information in the data server and analyzing and calculating a control value; the water affair real-time module is used for calculating information according to the water affairs recorded into each power station interval in the cascade reservoir on the day and storing the information through the data server; the correction module is used for correcting the warehousing flow. The method has the defects that the maximum value and the minimum value of the warehousing flow can be controlled only, the sawtooth phenomenon can not be eliminated well, and the method can only correct daily average warehousing flow and can not correct time-interval warehousing flow.
Disclosure of Invention
In order to solve the technical problem, the method and the system for correcting and optimizing the warehousing flow, which are provided by the invention, set different calculation weights according to different flood magnitudes and perform primary calculation correction; calculating the difference between the original flow and the corrected flow in the current time period, and distributing and processing the difference to the next time period for secondary correction calculation; and finally, correcting for three times according to the flow amplitude.
The invention aims to provide a correction optimization method of warehousing traffic, which comprises the steps of setting the number of calculated data asnThe method also comprises the following steps:
step 1: divide the warehousing traffic intomStages, each stage setting a different weight coefficient, thenmUnder gradenThe weight matrix of the data is
Figure 216471DEST_PATH_IMAGE005
The sum of the weight coefficients at each level is equal to 1, i.e.
Figure 946530DEST_PATH_IMAGE006
Wherein the content of the first and second substances,m、n、jis a positive integer greater than or equal to 1,
Figure 677725DEST_PATH_IMAGE007
，
Figure 438133DEST_PATH_IMAGE008
is a weighted value;
step 2: correcting the warehousing flow;
and step 3: calculated according to the final corrected flowtTime period unbalanced flow
Figure 51517DEST_PATH_IMAGE009
The calculation formula is
Figure 54108DEST_PATH_IMAGE010
Wherein, in the step (A),
Figure 639811DEST_PATH_IMAGE011
to representtThe flow rate of the warehouse-in a time period,
Figure 444823DEST_PATH_IMAGE012
indicating the final corrected flow.
Preferably, the weighted traffic at each stage near the correction period is also sequentially increased.
In any of the above aspects, it is preferable that the correction includes performing the primary correction, the secondary correction, and the tertiary correction in this order.
In any of the above schemes, preferably, the initial correction is performed by using a moving average calculation formula according totTime-phased warehousing traffic
Figure 483186DEST_PATH_IMAGE011
Determine its traffic class as
Figure 289468DEST_PATH_IMAGE013
Then primarily correct the flow
Figure 260835DEST_PATH_IMAGE014
The calculation formula of (2) is as follows:
Figure 861580DEST_PATH_IMAGE015
when in uset<nWhen the temperature of the water is higher than the set temperature,
Figure 419863DEST_PATH_IMAGE014
before takingtThe average value of the time periods is,iindicating a time period.
In any of the above aspects, preferably, the second correction means correcting the second correction by using a correction factortUnbalanced flow for a period of-1
Figure 29836DEST_PATH_IMAGE016
Is distributed totTime interval and subsequent time interval, secondary correction flow
Figure 58972DEST_PATH_IMAGE017
The calculation formula of (2) is as follows:
Figure 96198DEST_PATH_IMAGE018
wherein the content of the first and second substances,bthe coefficient of the apportionment is represented as,
Figure 374733DEST_PATH_IMAGE019
。
in any of the above aspects, preferably, the division coefficientbThe larger the value of (A), the more the expressiontThe more time slots are shared whentWhen the ratio is not less than 1,
Figure 522817DEST_PATH_IMAGE020
。
in any of the above embodiments, preferably, the third correction is to correct the flow rate twice
Figure 904995DEST_PATH_IMAGE017
Carrying out constraint limitation to obtain final corrected flow
Figure 175439DEST_PATH_IMAGE012
。
In any of the above embodiments, it is preferable that
Figure 144532DEST_PATH_IMAGE021
Then, the final corrected flow
Figure 96308DEST_PATH_IMAGE012
The calculation formula of (2) is as follows:
Figure 100036DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure 275802DEST_PATH_IMAGE023
the flow rate is a small variable amplitude coefficient,
Figure 233656DEST_PATH_IMAGE024
the flow rate is a large amplitude coefficient,
Figure 723543DEST_PATH_IMAGE025
in order to minimize the flow of the warehouse entry,
Figure 316199DEST_PATH_IMAGE026
is composed oft-a corrected flow rate for a period of 1.
In any of the above embodiments, it is preferable that
Figure 131708DEST_PATH_IMAGE027
Then, the final corrected flow
Figure 75393DEST_PATH_IMAGE012
The calculation formula of (2) is as follows:
Figure 431288DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 878450DEST_PATH_IMAGE026
is composed oft-a corrected flow rate for a period of 1.
The second purpose of the invention is to provide a correction optimization system for the warehousing traffic, which comprises a correction optimization module for setting the number of the calculated data asnThe setting module of (2), further comprising the following modules:
a grading module: for dividing the flow of warehousing intomStages, each stage setting a different weight coefficient, thenmUnder gradenThe weight matrix of the data is
Figure 103676DEST_PATH_IMAGE005
The sum of the weight coefficients at each level is equal to 1, i.e.
Figure 331395DEST_PATH_IMAGE006
Wherein the content of the first and second substances,m、n、jis a positive integer greater than or equal to 1,
Figure 163085DEST_PATH_IMAGE007
，
Figure 792649DEST_PATH_IMAGE008
is a weighted value;
a correction module: the system is used for correcting the warehousing flow;
a calculation module: for calculating according to the final corrected flowtTime period unbalanced flow
Figure 418802DEST_PATH_IMAGE009
The calculation formula is
Figure 635282DEST_PATH_IMAGE010
Wherein, in the step (A),
Figure 536242DEST_PATH_IMAGE011
to representtThe flow rate of the warehouse-in a time period,
Figure 957996DEST_PATH_IMAGE012
indicating the final corrected flow.
Preferably, the weighted traffic at each stage near the correction period is also sequentially increased.
In any of the above aspects, it is preferable that the correction includes performing the primary correction, the secondary correction, and the tertiary correction in this order.
In any of the above schemes, preferably, the initial correction is performed by using a moving average calculation formula according totTime-phased warehousing traffic
Figure 286209DEST_PATH_IMAGE011
Determine its traffic class as
Figure 426204DEST_PATH_IMAGE013
Then primarily correct the flow
Figure 865275DEST_PATH_IMAGE014
The calculation formula of (2) is as follows:
Figure 203853DEST_PATH_IMAGE015
when in uset<nWhen the temperature of the water is higher than the set temperature,
Figure 935923DEST_PATH_IMAGE014
before takingtThe average value of the time periods is,iindicating a time period.
In any of the above aspects, preferably, the second correction means correcting the second correction by using a correction factortUnbalanced flow for a period of-1
Figure 563214DEST_PATH_IMAGE016
Is distributed totTime interval and subsequent time interval, secondary correction flow
Figure 805976DEST_PATH_IMAGE017
The calculation formula of (2) is as follows:
Figure 202322DEST_PATH_IMAGE018
wherein the content of the first and second substances,bthe coefficient of the apportionment is represented as,
Figure 669076DEST_PATH_IMAGE019
。
in any of the above aspects, preferably, the division coefficientbThe larger the value of (A), the more the expressiontThe more time slots are shared whentWhen the ratio is not less than 1,
Figure 314821DEST_PATH_IMAGE020
。
in any of the above embodiments, preferably, the third correction is to correct the flow rate twice
Figure 597160DEST_PATH_IMAGE017
Carrying out constraint limitation to obtain final corrected flow
Figure 113592DEST_PATH_IMAGE012
。
In any of the above embodiments, it is preferable that
Figure 954509DEST_PATH_IMAGE021
Then, the final corrected flow
Figure 290812DEST_PATH_IMAGE012
The calculation formula of (2) is as follows:
Figure 875377DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure 43054DEST_PATH_IMAGE023
the flow rate is a small variable amplitude coefficient,
Figure 54872DEST_PATH_IMAGE024
the flow rate is a large amplitude coefficient,
Figure 105568DEST_PATH_IMAGE025
in order to minimize the flow of the warehouse entry,
Figure 493824DEST_PATH_IMAGE026
is composed oft-a corrected flow rate for a period of 1.
In any of the above embodiments, it is preferable that
Figure 719269DEST_PATH_IMAGE027
Then, the final corrected flow
Figure 901989DEST_PATH_IMAGE012
The calculation formula of (2) is as follows:
Figure 275201DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 201569DEST_PATH_IMAGE026
is composed oft-a corrected flow rate for a period of 1.
The invention provides a correction optimization method and a correction optimization system for warehousing flow, which can ensure that the correction of the peak flow is not delayed, can ensure the water balance and can be used for real-time correction.
Drawings
Fig. 1 is a flowchart of a method for correcting and optimizing warehousing traffic according to a preferred embodiment of the present invention.
Fig. 2 is a block diagram of a preferred embodiment of the system for corrective optimization of warehousing traffic according to the present invention.
Fig. 3 is a calculation flowchart of another preferred embodiment of the correction optimization method for warehousing traffic according to the present invention.
Fig. 4 is a comparison graph of the results of another preferred embodiment of the whole-process correction of the correction optimization method of the warehousing traffic according to the invention.
Fig. 5 is a comparison graph of the results of another preferred embodiment of negative value correction of the correction optimization method of warehousing traffic according to the invention.
Fig. 6 is a comparison graph of the results of another preferred embodiment of the grace period correction of the correction optimization method of warehousing traffic according to the present invention.
Detailed Description
The invention is further illustrated with reference to the figures and the specific examples.
Example one
As shown in fig. 1 and 2, a method for correcting and optimizing warehousing traffic executes step 100, and the setting module 200 sets the number of calculated data to ben。
Step 110 is executed, and the classification module 210 classifies the warehousing traffic intomStages, each stage setting a different weight coefficient, thenmUnder gradenThe weight matrix of the data is
Figure 314144DEST_PATH_IMAGE005
The sum of the weight coefficients at each level is equal to 1, i.e.
Figure 933344DEST_PATH_IMAGE006
Wherein the content of the first and second substances,m、n、jis a positive integer greater than or equal to 1,
Figure 465956DEST_PATH_IMAGE007
，
Figure 196015DEST_PATH_IMAGE008
for the weight value, the weight flow rate close to the correction period at each stage is also increased in sequence.
And executing the step 120, wherein the correction module 220 corrects the warehousing traffic, and the correction includes sequentially performing primary correction, secondary correction and tertiary correction.
1. The initial correction is performed by adopting a moving average calculation formula according totTime-phased warehousing traffic
Figure 130473DEST_PATH_IMAGE011
Determine its traffic class as
Figure 717312DEST_PATH_IMAGE013
Then primarily correct the flow
Figure 2800DEST_PATH_IMAGE014
The calculation formula of (2) is as follows:
Figure 503926DEST_PATH_IMAGE015
when in uset<nWhen the temperature of the water is higher than the set temperature,
Figure 355207DEST_PATH_IMAGE014
before takingtThe average value of the time periods is,iindicating a time period.
2. The second correction is totUnbalanced flow for a period of-1
Figure 316210DEST_PATH_IMAGE016
Is distributed totTime interval and subsequent time interval, secondary correction flow
Figure 823415DEST_PATH_IMAGE017
The calculation formula of (2) is as follows:
Figure 629697DEST_PATH_IMAGE018
wherein the content of the first and second substances,bthe coefficient of the apportionment is represented as,
Figure 804326DEST_PATH_IMAGE019
. The division coefficientbThe larger the value of (A), the more the expressiontThe more time slots are shared whentWhen the ratio is not less than 1,
Figure 670651DEST_PATH_IMAGE020
。
3. the third correction refers to correcting the flow rate twice
Figure 228934DEST_PATH_IMAGE017
Carrying out constraint limitation to obtain final corrected flow
Figure 838907DEST_PATH_IMAGE012
。
When in use
Figure 868042DEST_PATH_IMAGE021
Then, the final corrected flow
Figure 905269DEST_PATH_IMAGE012
The calculation formula of (2) is as follows:
Figure 387065DEST_PATH_IMAGE022
when in use
Figure 800729DEST_PATH_IMAGE027
Then, the final corrected flow
Figure 985504DEST_PATH_IMAGE012
The calculation formula of (2) is as follows:
Figure 193631DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 162724DEST_PATH_IMAGE023
the flow rate is a small variable amplitude coefficient,
Figure 114500DEST_PATH_IMAGE024
the flow rate is a large amplitude coefficient,
Figure 118228DEST_PATH_IMAGE025
in order to minimize the flow of the warehouse entry,
Figure 293994DEST_PATH_IMAGE026
is composed oft-a corrected flow rate for a period of 1.
Executing step 130, the calculating module 230 calculates according to the final corrected flowtTime period unbalanced flow
Figure 484804DEST_PATH_IMAGE009
The calculation formula is
Figure 741735DEST_PATH_IMAGE010
Wherein, in the step (A),
Figure 599970DEST_PATH_IMAGE011
to representtThe flow rate of the warehouse-in a time period,
Figure 212217DEST_PATH_IMAGE012
indicating the final corrected flow.
Example two
The method comprises the steps of providing a cubic correction algorithm based on a moving average idea, setting different calculation weights according to different flood magnitude, and performing primary calculation correction; calculating the difference between the original flow and the corrected flow in the current time period, and distributing and processing the difference to the next time period for secondary correction calculation; and finally, correcting for three times according to the flow amplitude. As shown in fig. 3, the calculation steps are as follows:
(1) setting the number of calculation data
The number of data used for the correction calculation of the warehousing flow is set asn，nIs composed of>A positive integer of = 1.
(2) Flow classification
Divide the flow intomThe number of stages is such that,mis composed of>A positive integer of =1, the flow rate per stage increases in turn. Each stage sets different weight coefficients, thenmUnder gradenThe weight matrix for each datum is:
Figure 155902DEST_PATH_IMAGE029
（1）
the sum of the weight coefficients at each stage is equal to 1, i.e.
Figure 183901DEST_PATH_IMAGE006
。
In order to embody the principle that the closer to the correction time interval, the greater the influence is, the larger the large flow rate is, the weight flow rate close to the correction time interval at each stage should be increased in turn.
(3) Initial correction
Performing primary correction by using a moving average calculation formula according totTime-phased warehousing traffic
Figure 896642DEST_PATH_IMAGE011
Determine its traffic class as
Figure 178326DEST_PATH_IMAGE013
Then primarily correct the flow
Figure 609307DEST_PATH_IMAGE014
The calculation formula of (2) is as follows:
Figure 706576DEST_PATH_IMAGE015
（2）
when in uset<nWhen the temperature of the water is higher than the set temperature,
Figure 8244DEST_PATH_IMAGE014
before takingtAverage of the time periods.
(4) Second order correction
To ensure water balance, the water supply device is provided withtImbalance of period 1Flow rate
Figure 165556DEST_PATH_IMAGE016
Is distributed totThe time interval and the subsequent time interval need to be corrected and calculated for the second time, and the formula is as follows:
Figure 83834DEST_PATH_IMAGE018
（3）
bthe coefficient of the apportionment is represented as,
Figure 515952DEST_PATH_IMAGE019
the larger the value is, the more the expression istThe more time slots are shared whentWhen the ratio is not less than 1,
Figure 704750DEST_PATH_IMAGE020
。
(5) triple correction
In order to prevent the excessively large amplitude caused by excessive correction or the corrected flow rate is smaller than the minimum warehousing flow rate, the flow rate needs to be corrected for the second time
Figure 767384DEST_PATH_IMAGE017
Constraint limits are performed. Final corrected flow
Figure 704116DEST_PATH_IMAGE012
The calculation formula of (a) is as follows:
when in use
Figure 143188DEST_PATH_IMAGE021
The method comprises the following steps:
Figure 481765DEST_PATH_IMAGE022
（4）
when in use
Figure 715300DEST_PATH_IMAGE027
The method comprises the following steps:
Figure 858704DEST_PATH_IMAGE028
（5）
the 1 st term in the formulas (4) and (5) is compared with the correction value in the previous period; item 2 is a comparison with the original flow; the 4 th term in the formula (4) is the minimum warehousing traffic.
Small flow amplitude coefficient
Figure 835887DEST_PATH_IMAGE023
: the minimum warehousing flow variation amplitude under the normal condition is represented, the average warehousing flow for many years can be taken, and when the reservoir has an upstream power station, the single-machine common power generation flow or the average value of the single-machine power generation flow plus a certain interval flow estimation value can be taken.
Large flow amplitude coefficient
Figure 497813DEST_PATH_IMAGE024
: the maximum warehousing flow variation amplitude under the normal condition is shown, and the reservoir warehousing flow variation amplitude is determined according to the long-term operation rule and the water level fluctuation range of the reservoir. Generally, the reservoir capacity difference flow rate of 1-10cm change of water level is taken under the normal water storage level.
Figure 964566DEST_PATH_IMAGE025
: minimum warehousing traffic, the minimum of warehousing traffic calculated over the years.
(6) Traffic accounting
Figure 79153DEST_PATH_IMAGE010
（6）
Wherein:
Figure 594448DEST_PATH_IMAGE009
is composed oftThe time period is unbalanced flow.
EXAMPLE III
Selecting and correcting 431 warehouse-in flow data of 2016-07-1417: 00-08-0115: 00, namely, of a Qingjiang river basin water distribution puerh power station with a 1-hour time periodN=431；
Number of flow fractionsm=3, respectively 0,1000, 2500, i.e. the flow rate is level 1 at [0,1000 ], and [1000- & lt2500) is2 nd stage, 2500 or more is the 3 rd stage;
number of data for moving average calculationn=6；
Setting a weight matrix
Figure 409082DEST_PATH_IMAGE030
The division coefficient in the formula (3)b=0.382；
Large flow amplitude coefficient in formulas (4) and (5)
Figure 249999DEST_PATH_IMAGE024
Taking the reservoir capacity difference flow of 5cm amplitude below the normal water storage level
Figure 586303DEST_PATH_IMAGE024
=950m3S, large flow amplitude coefficient
Figure 170868DEST_PATH_IMAGE023
=150m3S, minimum warehousing traffic
Figure 338544DEST_PATH_IMAGE025
=89m3/s。
And respectively correcting the warehousing flow by using a text method, a five-point three-time method and a moving average method. The correction effects on the whole process, the negative value and the stationary phase flow are respectively analyzed, and the following are introduced:
(1) correction of the overall process
The results are shown in table 1:
Figure 350362DEST_PATH_IMAGE031
TABLE 1 comparison of results of three correction methods
As shown in Table 1, the error of the flood peak flow corrected by the method is minimum and is-4.1% compared with the original value, and the five-point three-time method and the moving average method are respectively-5.5% and-12.2%;
the peak time error is 0, so that the flood peak time is well ensured not to be delayed, and the five-point three-time method and the moving average method are delayed for 1 hour and 4 hours respectively;
the flood error was 0, and the quincunx cubic method and the moving average method were-0.2% and-3.7%, respectively, because the water balance mechanism of the method herein worked.
The standard deviation reflects the smoothness of the corrected flow process, the standard deviation of the original sequence is 2280.5, and the method is 2163.7, which indicates that the corrected warehousing flow sequence is smoother. As shown in fig. 4, compared with the quincunx-cubic method and the moving average method, the difference between the method and the quincunx-cubic method is not large, and the difference between the method and the moving average method is slightly large, which indicates that the smoothness after the correction of the method is not as good as that of the moving average method, but is similar to that of the quincunx-cubic method.
(2) Correction of negative values
The warehousing flow rates of the flood in the 215 th time period and the 219 th time period are negative values, and are respectively-369 m3S and-387 m3And/s, selecting the correction result of the 200 th-250 th time period for analysis, wherein the statistical result is shown in the table 2:
Figure 908382DEST_PATH_IMAGE032
TABLE 2 comparison of the three methods for negative correction results
As can be seen from table 2, the correction for negative values for the method herein is between the five point cubic method and the moving average method.
In addition, the standard deviation of the original data in the 200 th-250 th period is 354.9, the method is 189.5, the five-point three-time method is 217.5, and the moving average method is 185.6. As shown in fig. 5, it is shown that the method has a significant effect of correction compared with the original data, and the smoothing degree is between the moving average method and the five-point three-time method.
(3) Correction of stationary phase
The 300 th-400 th period of the flood in the field is a stable stage of the flood process, the flow fluctuation variation range is small, and the correction result of the period is selected for analysis.
The standard deviation of the raw data was 111.2, the method herein was 68.1, the quincunx cubic method was 73.5, and the running average method was 64.6. As shown in fig. 6, it is shown that the method has a significant effect of correction compared with the original data, and the smoothing degree is between the moving average method and the five-point three-time method.
Example four
The existing correction methods include a moving average method, a five-point triple method and the like.
(1) Moving average method
The moving average method has the following calculation formula:
Figure 795174DEST_PATH_IMAGE033
（7）
in the formula (I), the compound is shown in the specification,
Figure 20619DEST_PATH_IMAGE034
：ta correction value of the time interval warehousing flow;
Figure 76DEST_PATH_IMAGE035
：ithe time-interval warehousing flow rate is increased,i=t-n+1，...，t；
Figure 576551DEST_PATH_IMAGE036
: weighting of warehousing traffic at each time intervali=1，...，n；
n: the number of the moving average warehouse entry flow data;
when in use
Figure 502918DEST_PATH_IMAGE037
At each time interval
Figure 114028DEST_PATH_IMAGE035
Are the same in weight, i.e. arenAn
Figure 234693DEST_PATH_IMAGE035
The arithmetic mean of (1), referred to as the simple moving average; otherwise, it can be for different periods
Figure 32885DEST_PATH_IMAGE035
Setting different weights to distinguish
Figure 231785DEST_PATH_IMAGE035
Is called a weighted moving average, in general, the distancetThe closer the period of time is,
Figure 431822DEST_PATH_IMAGE036
the larger.
(2) Five-point and three-time method
Five-point three-time method requires warehousing flow
Figure 221924DEST_PATH_IMAGE035
Is not less than 5, provided that
Figure 38570DEST_PATH_IMAGE035
Has a sequence length ofn. The corrected calculation formula for the inflow flow in each time period is as follows:
Figure 572320DEST_PATH_IMAGE038
（8）
Figure 131258DEST_PATH_IMAGE039
（9）
Figure 561102DEST_PATH_IMAGE040
（10）
Figure 333886DEST_PATH_IMAGE041
（11）
Figure 405747DEST_PATH_IMAGE042
（12）
the moving average method has the following disadvantages: the influence of different flow grades on the correction value is not considered, and the weight value of the same flood process
Figure 314798DEST_PATH_IMAGE036
The same is true, which can cause the correction process to be too smooth, and affect the correction result of the flood peak, including excessive reduction of the flood peak flow and lag of the peak time.
The five-point triple method has the following disadvantages: for thetTime-phased warehousing trafficQ t Correction of (2), if necessarytAt the +1 time period,tFlow rate of entering warehouse in +2 time period
Figure 977860DEST_PATH_IMAGE003
、
Figure 972361DEST_PATH_IMAGE004
To atIn the case of a period of time,tat the +1 time period,tFlow rate of entering warehouse in +2 time period
Figure 146116DEST_PATH_IMAGE003
、
Figure 175251DEST_PATH_IMAGE004
Is unknown. Therefore, the method can be used only for post correction, but not for real-time correction. Meanwhile, the method also causes the time lag of the corrected flood peak.
Meanwhile, the water balance of the correction value is not considered in the two methods, which may cause a large error of the water amount calculated by the correction value and the original value.
For a better understanding of the present invention, the foregoing detailed description has been given in conjunction with specific embodiments thereof, but not with the intention of limiting the invention thereto. Any simple modifications of the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention. In the present specification, each embodiment is described with emphasis on differences from other embodiments, and the same or similar parts between the respective embodiments may be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Claims (10)

1. A correction optimization method for warehousing traffic comprises setting the number of calculated data asnThe method is characterized by also comprising the following steps:
step 1: divide the warehousing traffic intomStages, each stage setting a different weight coefficient, thenmUnder gradenThe weight matrix of the data is
Figure DEST_PATH_IMAGE001
The sum of the weight coefficients at each level is equal to 1, i.e.
Figure DEST_PATH_IMAGE002
，
Wherein the content of the first and second substances,m、n、jis a positive integer greater than or equal to 1,
Figure DEST_PATH_IMAGE003
，
Figure DEST_PATH_IMAGE004
is a weighted value;
step 2: correcting the warehousing flow;
and step 3: calculated according to the final corrected flowtTime period unbalanced flow
Figure DEST_PATH_IMAGE005
The calculation formula is
Figure DEST_PATH_IMAGE006
Wherein, in the step (A),
Figure DEST_PATH_IMAGE007
to representtThe flow rate of the warehouse-in a time period,
Figure DEST_PATH_IMAGE008
indicating the final corrected flow.
2. The correction optimization method for warehousing traffic of claim 1, wherein the weighted traffic of each stage is increased in sequence near the correction period.
3. The correction optimization method for warehousing traffic of claim 2, characterized in that the correction includes a primary correction, a secondary correction and a tertiary correction in sequence.
4. A correction optimization method for warehousing traffic as claimed in claim 3 wherein the initial correction is based on a moving average calculation formulatTime-phased warehousing traffic
Figure 657940DEST_PATH_IMAGE007
Determine its traffic class as
Figure DEST_PATH_IMAGE009
Then primarily correct the flow
Figure DEST_PATH_IMAGE010
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE011
when in uset<nWhen the temperature of the water is higher than the set temperature,
Figure 542457DEST_PATH_IMAGE010
before takingtThe average value of the time periods is,iindicating a time period.
5. The correction optimization method for warehousing traffic of claim 4, characterized in that the secondary correction is to be performedtUnbalanced flow for a period of-1
Figure DEST_PATH_IMAGE012
Is distributed totTime interval and subsequent time interval, secondary correction flowMeasurement of
Figure DEST_PATH_IMAGE013
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE014
wherein the content of the first and second substances,bthe coefficient of the apportionment is represented as,
Figure DEST_PATH_IMAGE015
。
6. the method for correction and optimization of warehousing traffic as claimed in claim 5 wherein the partition coefficientsbThe larger the value of (A), the more the expressiontThe more time slots are shared whentWhen the ratio is not less than 1,
Figure DEST_PATH_IMAGE016
。
7. the correction optimization method for warehousing traffic of claim 5, characterized in that the third correction is to correct the traffic twice
Figure 571462DEST_PATH_IMAGE013
Carrying out constraint limitation to obtain final corrected flow
Figure 435513DEST_PATH_IMAGE008
。
8. The correction optimization method for warehousing traffic of claim 7, characterized in that when the correction optimization method is used for the warehousing traffic of a specific area
Figure DEST_PATH_IMAGE017
Then, the final corrected flow
Figure 921989DEST_PATH_IMAGE008
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE019
the flow rate is a small variable amplitude coefficient,
Figure DEST_PATH_IMAGE020
the flow rate is a large amplitude coefficient,
Figure DEST_PATH_IMAGE021
in order to minimize the flow of the warehouse entry,
Figure DEST_PATH_IMAGE022
is composed oft-a corrected flow rate for a period of 1.
9. The correction optimization method for warehousing traffic of claim 8, characterized in that when the correction optimization method is used, the correction optimization method is used
Figure DEST_PATH_IMAGE023
Then, the final corrected flow
Figure 456701DEST_PATH_IMAGE008
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure 832057DEST_PATH_IMAGE022
is composed oft-a corrected flow rate for a period of 1.
10. A correction optimization system for the flow in storage includes a system for setting the number of calculated data tonThe setting module of (2), characterized by, still include the following module:
grading mouldBlock (2): for dividing the flow of warehousing intomStages, each stage setting a different weight coefficient, thenmUnder gradenThe weight matrix of the data is
Figure 375165DEST_PATH_IMAGE001
The sum of the weight coefficients at each level is equal to 1, i.e.
Figure 512885DEST_PATH_IMAGE002
Wherein the content of the first and second substances,m、n、jis a positive integer greater than or equal to 1,
Figure DEST_PATH_IMAGE025
，
Figure 676888DEST_PATH_IMAGE004
is a weighted value;
a correction module: the system is used for correcting the warehousing flow;
a calculation module: for calculating according to the final corrected flowtTime period unbalanced flow
Figure 493534DEST_PATH_IMAGE005
The calculation formula is
Figure 699387DEST_PATH_IMAGE006
Wherein, in the step (A),
Figure 566980DEST_PATH_IMAGE007
to representtThe flow rate of the warehouse-in a time period,
Figure 465666DEST_PATH_IMAGE008
indicating the final corrected flow.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112287528A (en) * 2020-10-16 2021-01-29 黄河勘测规划设计研究院有限公司 Flood control high water level determination method for sandy river reservoir based on high beach trough
CN113239642A (en) * 2021-04-12 2021-08-10 大唐甘肃发电有限公司碧口水力发电厂 Method for calculating reservoir warehousing flow
WO2024066902A1 (en) * 2022-09-28 2024-04-04 中国长江三峡集团有限公司 Reservoir-inflow runoff correction optimization method and apparatus

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7895023B2 (en) * 2005-11-02 2011-02-22 Frederick Chou Simulation system and method for establishing reservoir operational rule curves
CN103116877A (en) * 2013-02-25 2013-05-22 中国水利水电科学研究院 Reservoir level process smoothing method
CN103164628A (en) * 2013-03-29 2013-06-19 中国长江三峡集团公司 Method of forecasting incoming flow of reservoir
CN106803131A (en) * 2016-12-23 2017-06-06 华中科技大学 A kind of river flood forecasting procedure based on discrete generalized Nash Confluence Models
CN107679021A (en) * 2017-09-27 2018-02-09 中国科学院南京地理与湖泊研究所 A kind of river Ru Hu storehouses method of calculating flux
CN108717581A (en) * 2018-04-04 2018-10-30 河海大学 A kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation
CN109255476A (en) * 2018-08-24 2019-01-22 华中科技大学 A kind of nonlinear discharge of river prediction technique of variable element
CN111189504A (en) * 2020-02-26 2020-05-22 无锡市政设计研究院有限公司 Graded measurement type flow monitoring well and flow measurement method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7895023B2 (en) * 2005-11-02 2011-02-22 Frederick Chou Simulation system and method for establishing reservoir operational rule curves
CN103116877A (en) * 2013-02-25 2013-05-22 中国水利水电科学研究院 Reservoir level process smoothing method
CN103164628A (en) * 2013-03-29 2013-06-19 中国长江三峡集团公司 Method of forecasting incoming flow of reservoir
CN106803131A (en) * 2016-12-23 2017-06-06 华中科技大学 A kind of river flood forecasting procedure based on discrete generalized Nash Confluence Models
CN107679021A (en) * 2017-09-27 2018-02-09 中国科学院南京地理与湖泊研究所 A kind of river Ru Hu storehouses method of calculating flux
CN108717581A (en) * 2018-04-04 2018-10-30 河海大学 A kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation
CN109255476A (en) * 2018-08-24 2019-01-22 华中科技大学 A kind of nonlinear discharge of river prediction technique of variable element
CN111189504A (en) * 2020-02-26 2020-05-22 无锡市政设计研究院有限公司 Graded measurement type flow monitoring well and flow measurement method

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112287528A (en) * 2020-10-16 2021-01-29 黄河勘测规划设计研究院有限公司 Flood control high water level determination method for sandy river reservoir based on high beach trough
CN112287528B (en) * 2020-10-16 2021-07-20 黄河勘测规划设计研究院有限公司 Flood control high water level determination method for sandy river reservoir based on high beach trough
CN113239642A (en) * 2021-04-12 2021-08-10 大唐甘肃发电有限公司碧口水力发电厂 Method for calculating reservoir warehousing flow
WO2024066902A1 (en) * 2022-09-28 2024-04-04 中国长江三峡集团有限公司 Reservoir-inflow runoff correction optimization method and apparatus

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