CN110044423A - A kind of water flow quantity monitoring method - Google Patents
A kind of water flow quantity monitoring method Download PDFInfo
- Publication number
- CN110044423A CN110044423A CN201910265826.3A CN201910265826A CN110044423A CN 110044423 A CN110044423 A CN 110044423A CN 201910265826 A CN201910265826 A CN 201910265826A CN 110044423 A CN110044423 A CN 110044423A
- Authority
- CN
- China
- Prior art keywords
- data
- flow
- flows
- history
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 28
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 6
- 238000009499 grossing Methods 0.000 claims abstract description 4
- 230000004907 flux Effects 0.000 claims description 20
- 238000006424 Flood reaction Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/002—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow wherein the flow is in an open channel
Landscapes
- Physics & Mathematics (AREA)
- Fluid Mechanics (AREA)
- General Physics & Mathematics (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
- Measuring Volume Flow (AREA)
Abstract
The present invention relates to hydrologic monitoring technical fields, and in particular to a kind of water flow quantity monitoring method, comprising the following steps: A) the collected data on flows of server real-time reception flow sensor；B salient point identification) is carried out to data on flows and floating is handled；C) report is generated using the data on flows after smoothing out.Substantial effect of the invention is: by identification because of data salient point caused by short-term changes in flow rate and after carrying out floating processing, flow monitoring result can be reduced to be influenced by flow short term variations, so that flow monitoring result is able to reflect water flow overall variation trend, provides foundation for the utilization of water resource and the disposition of flood.
Description
Technical field
The present invention relates to hydrologic monitoring technical fields, and in particular to a kind of water flow quantity monitoring method.
Background technique
China Southeastern Coastal Cities belong to East Asian monsoon area prevailing, flood season often vulnerable to tropical cyclone, heavy rain, climax and
The floods such as flood attack, and very big pressure is produced to flood control.On-line monitoring system is integrated by construction stage-discharge, it can
To realize the collection of monitoring survey station basic hydrology data, to ensure that channel safe provides solid data basis, for effective use
Water resource carries out work of flood prevention and provides support.But current water flow quantity monitoring method lacks effective data floating processing, leads
It causes data monitoring result to be affected by short-term changes in flow rate, cannot reflect the overall permanence of water flow.
Chinese patent CN104132710A, publication date on November 5th, 2014, a kind of method of stage-discharge monitoring, including
Following steps: the water-level detecting bar of the stage-discharge sensor is laid in channel or river to be detected according to a conventional method
Corresponding position；Voltage between different contacts and and reference voltage are acquired by the plurality of voltages comparison circuit of stage-discharge sensor
It is compared, later exports result into multiplexer circuit；Multiplexer circuit encodes the voltage signal of input
And it is input in the governor circuit of stage-discharge sensor；Governor circuit handles the encoded signal being input in it for depth of water letter
Number R, and water signal R is substituted into the flow Q that channel or river to be detected corresponding position is calculated in following formula；A is device for channel discharge cross section area in formula；N is channel roughness；I is canal grade.But it does not solve to make an uproar
The examination and floating processing method of point data.
Summary of the invention
The technical problem to be solved by the present invention is water flow quantity monitoring method is influenced by short-term changes in flow rate at present, gained
Monitoring result cannot reflect the technical issues of whole water flow variation tendency.Propose it is a kind of by data smooth out processing can
Make the water flow quantity monitoring method of monitoring result more stably reaction water flow overall variation trend.
In order to solve the above technical problems, the technical solution used in the present invention are as follows: a kind of water flow quantity monitoring method is suitable for
Flow monitoring system including flow sensor and server, comprising the following steps: A) server real-time reception flow sensor
Collected data on flows；B salient point identification) is carried out to data on flows and floating is handled；C) raw using the data on flows after smoothing out
At report.
Preferably, step B bumps know method for distinguishing the following steps are included: B11) by the data on flows received and its
History contemporaneous data value compares, and enters step B12 if difference is greater than threshold epsilon, conversely, determining that the data on flows is non-convex
Point, wherein replaced if flow sensor failure does not collect data using negative constant value；B12) server is to flow sensor
Acquisition is issued, if the difference of collected data on flows and history contemporaneous data value is still greater than threshold epsilon, determines the flow
Data are salient point.
Preferably, the method smoothed out in step B is the following steps are included: B21) salient point data are rejected and the place that backups
Reason；B22 flux prediction model) is established；B23) predicted value of flux prediction model and flow histories contemporaneous data are weighted
Interpolation calculates, and replaces flow salient point data using calculated result.
Preferably, the weighted factor that calculates of the weighting interpolation according to flux prediction model and history contemporaneous data can
Reliability is allocated.
Preferably, the confidence level of flux prediction modelWherein, xT, modelFor flow
The prediction result of prediction model, xtFor measured result, n is the data bulk for participating in calculating；The confidence level of history contemporaneous dataWherein,xtFor history measured result, n is the data bulk for participating in calculating,
For the mean value of n measured result.
Preferably, flux prediction model weighted factor a and history contemporaneous data weighted factor b according to:It calculates and obtains.
Preferably, establishing the method for flux prediction model in step B22 the following steps are included: B221) obtain corresponding stream
The acquisition data on the quantity sensor same day carry out discrete Fourier transform to same day data, obtain each frequency of same day data on flows
Rate forms fi, i ∈ Md, wherein MdSet is formed for the frequency of same day data on flows；B222) if obtaining corresponding flow sensor
The flow collection data of dry history day carry out discrete Fourier transform to history daily flow data, obtain history daily flow and adopt
The frequency for collecting data forms fj, j ∈ MH, wherein H is history day set, for the frequency set of the flow collection data of whole history days
Close MH；B223) from the frequency sets M of history daily flow acquisition dataHIn, each composition with same day data on flows is selected respectively
Frequency fiFrequency and the similar frequency of amplitude form fj；B224 the similar frequencies composition superposition for) obtaining step B223As volume forecasting result.
Substantial effect of the invention is: data salient point caused by by identifying because of short-term changes in flow rate simultaneously carries out at floating
After reason, can reduce flow monitoring result is influenced by flow short term variations, and flow monitoring result is made to be able to reflect water flow entirety
Variation tendency provides foundation for the utilization of water resource and the disposition of flood.
Detailed description of the invention
Fig. 1 is one water flow quantity monitoring method flow diagram of embodiment.
Specific embodiment
Below by specific embodiment, and in conjunction with attached drawing, a specific embodiment of the invention is further described in detail.
Embodiment one:
A kind of water flow quantity monitoring method, suitable for the flow monitoring system including flow sensor and server, including following step
It is rapid: A) the collected data on flows of server real-time reception flow sensor；B salient point identification and floating) are carried out to data on flows
Processing；C) report is generated using the data on flows after smoothing out.
Salient point is known method for distinguishing and is followed the steps below: B11) by the data on flows received and its history contemporaneous data
Value compares, and enters step B12 if difference is greater than threshold epsilon, conversely, determining that the data on flows is non-salient point, wherein if flow
Sensor fault is not collected data and is then replaced using negative constant value；B12) server issues acquisition to flow sensor,
If the difference of collected data on flows and history contemporaneous data value is still greater than threshold epsilon, determine the data on flows for salient point.
The method smoothed out in step B is the following steps are included: B21) salient point data are rejected and the processing that backups；B22 it) builds
Vertical flux prediction model；B23 the predicted value of flux prediction model and flow histories contemporaneous data) are weighted interpolation to calculate,
Flow salient point data are replaced using calculated result.The method of flux prediction model is established in step B22 the following steps are included:
B221 the acquisition data for) obtaining the corresponding flow sensor same day carry out discrete Fourier transform to same day data, obtain same day stream
The each frequency for measuring data forms fi, i ∈ Md, wherein MdSet is formed for the frequency of same day data on flows；B222 it) obtains and corresponds to
The flow collection data of several history days of flow sensor carry out discrete Fourier transform to history daily flow data, obtain
The frequency for obtaining history daily flow acquisition data forms fi, j ∈ MH, wherein H is history day set, and the flow for whole history days is adopted
Collect the frequency sets M of dataH；B223) from the frequency sets M of history daily flow acquisition dataHIn, it selects respectively and works as daily flow
Each component frequency f of dataiFrequency and the similar frequency of amplitude form fj；B224) the similar frequency for obtaining step B223
Rate composition superpositionAs volume forecasting result.
Weighting the weighted factor that interpolation calculates is divided according to the confidence level of flux prediction model and history contemporaneous data
Match.
The confidence level of flux prediction modelWherein, xT, modelFor flux prediction model
Prediction result, xtFor measured result, n is the data bulk for participating in calculating；The confidence level of history contemporaneous dataIts
In,xtFor history measured result, n is the data bulk for participating in calculating,For n measured result
Mean value.
The weighted factor b of the weighted factor a of flux prediction model and history contemporaneous data according to:It calculates
It obtains.
Above-mentioned embodiment is only a preferred solution of the present invention, not the present invention is made in any form
Limitation, there are also other variations and modifications on the premise of not exceeding the technical scheme recorded in the claims.
Claims (7)
1. a kind of water flow quantity monitoring method, suitable for the flow monitoring system including flow sensor and server, feature exists
In, comprising the following steps:
A) the collected data on flows of server real-time reception flow sensor；
B salient point identification) is carried out to data on flows and floating is handled；
C) report is generated using the data on flows after smoothing out.
2. a kind of water flow quantity monitoring method according to claim 1, which is characterized in that
Step B bumps know method for distinguishing the following steps are included:
B11 it) by the data on flows received compared with its history contemporaneous data value, is entered step if difference is greater than threshold epsilon
B12, conversely, then determining that the data on flows is non-salient point, wherein using negative normal if flow sensor failure does not collect data
Numerical value replaces；
B12) server issues acquisition to flow sensor, if the difference of collected data on flows and history contemporaneous data value
Value is still greater than threshold epsilon, then determines the data on flows for salient point.
3. a kind of water flow quantity monitoring method according to claim 1 or 2, which is characterized in that
The method that data on flows is smoothed out in step B be the following steps are included:
B21) salient point data are rejected and the processing that backups；
B22 flux prediction model) is established；
B23 the predicted value of flux prediction model and flow histories contemporaneous data) are weighted interpolation to calculate, use calculated result
Replace flow salient point data.
4. a kind of water flow quantity monitoring method according to claim 3, which is characterized in that
The weighted factor that the weighting interpolation calculates is allocated according to the confidence level of flux prediction model and history contemporaneous data.
5. a kind of water flow quantity monitoring method according to claim 4, which is characterized in that
The confidence level of flux prediction modelWherein, xT, modelFor the prediction of flux prediction model
As a result, xtFor measured result, n is the data bulk for participating in calculating；
The confidence level of history contemporaneous dataWherein,xtFor history measured result, n
To participate in the data bulk calculated,For the mean value of n measured result.
6. a kind of water flow quantity monitoring method according to claim 5, which is characterized in that
The weighted factor b of the weighted factor a of flux prediction model and history contemporaneous data according to:Calculating obtains
?.
7. a kind of water flow quantity monitoring method according to claim 3, which is characterized in that
The method of flux prediction model is established in step B22 the following steps are included:
B221 the acquisition data for) obtaining the corresponding flow sensor same day carry out discrete Fourier transform to same day data, are worked as
Each frequency of daily flow data forms fi, i ∈ Md, wherein MdSet is formed for the frequency of same day data on flows；
B222 the flow collection data for) obtaining several history days of corresponding flow sensor, carry out history daily flow data
Discrete Fourier transform, the frequency for obtaining history daily flow acquisition data form fj, j ∈ MH, it is complete that wherein H, which is history day set,
The frequency sets M of the flow collection data of portion's history dayH；
B223) from the frequency sets M of history daily flow acquisition dataHIn, it selects respectively and each composition of same day data on flows frequency
Rate fiFrequency and the similar frequency of amplitude form fj；
B224 the similar frequencies composition superposition ∑ f for) obtaining step B223k, k ∈ (Md∩MH), as volume forecasting result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910265826.3A CN110044423B (en) | 2019-04-03 | 2019-04-03 | Water flow monitoring method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910265826.3A CN110044423B (en) | 2019-04-03 | 2019-04-03 | Water flow monitoring method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110044423A true CN110044423A (en) | 2019-07-23 |
| CN110044423B CN110044423B (en) | 2021-02-26 |
Family
ID=67276073
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910265826.3A Active CN110044423B (en) | 2019-04-03 | 2019-04-03 | Water flow monitoring method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110044423B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110992209A (en) * | 2019-12-17 | 2020-04-10 | 上海威派格智慧水务股份有限公司 | Flow prediction method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106203769A (en) * | 2016-06-23 | 2016-12-07 | 上海交通大学 | A kind of festivals or holidays based on the time difference coefficient sky level water requirement on-line prediction method |
| US20170177008A1 (en) * | 2015-12-21 | 2017-06-22 | International Business Machines Corporation | Topological connectivity and relative distances from temporal sensor measurements of physical delivery system |
| CN108154263A (en) * | 2017-12-21 | 2018-06-12 | 上海网波软件股份有限公司 | The monitoring and controlling forecast method of natural water resource |
| CN108319649A (en) * | 2017-12-27 | 2018-07-24 | 南瑞集团有限公司 | A kind of system and method improving the automatic Hydrological Systems quality of data |
| CN108920429A (en) * | 2018-06-12 | 2018-11-30 | 河海大学 | A kind of abnormal data analysis method of Water level trend monitoring |
-
2019
- 2019-04-03 CN CN201910265826.3A patent/CN110044423B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170177008A1 (en) * | 2015-12-21 | 2017-06-22 | International Business Machines Corporation | Topological connectivity and relative distances from temporal sensor measurements of physical delivery system |
| CN106203769A (en) * | 2016-06-23 | 2016-12-07 | 上海交通大学 | A kind of festivals or holidays based on the time difference coefficient sky level water requirement on-line prediction method |
| CN108154263A (en) * | 2017-12-21 | 2018-06-12 | 上海网波软件股份有限公司 | The monitoring and controlling forecast method of natural water resource |
| CN108319649A (en) * | 2017-12-27 | 2018-07-24 | 南瑞集团有限公司 | A kind of system and method improving the automatic Hydrological Systems quality of data |
| CN108920429A (en) * | 2018-06-12 | 2018-11-30 | 河海大学 | A kind of abnormal data analysis method of Water level trend monitoring |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110992209A (en) * | 2019-12-17 | 2020-04-10 | 上海威派格智慧水务股份有限公司 | Flow prediction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110044423B (en) | 2021-02-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hisdal et al. | Drought event definition | |
| CN105785411B (en) | A kind of abnormal track-detecting method based on region division | |
| CN106066901B (en) | A kind of datum mark method for analyzing stability of GNSS automation deformation monitoring | |
| Toukourou et al. | Rainfall-runoff modeling of flash floods in the absence of rainfall forecasts: the case of “Cévenol flash floods” | |
| CN113538861B (en) | Geological disaster information management system based on mineral geological exploration | |
| CN117057616B (en) | Water conservancy monitoring method and system based on digital twin | |
| CN115311828B (en) | Intelligent underwater engineering structure detection method and system based on digital twins | |
| CN107944156B (en) | The choosing method of wave height threshold value | |
| CN109918364B (en) | Data cleaning method based on two-dimensional probability density estimation and quartile method | |
| CN112461340B (en) | Fault correcting and detecting method and device for water level meter | |
| CN108269016A (en) | A kind of small watershed mountain flood risk analysis method based on diffusion of information | |
| CN109239553A (en) | A kind of clustering method based on local density of partial discharge pulse | |
| CN111639865A (en) | High-speed rail line meteorological disaster occurrence risk analysis method | |
| CN114116689A (en) | Big data cleaning method based on building structure safety monitoring | |
| CN108921409A (en) | A kind of voltage dip severity grade determination method | |
| CN110044423A (en) | A kind of water flow quantity monitoring method | |
| CN114547541B (en) | System applied to river surge patrol survey | |
| CN110031917A (en) | A kind of rain condition monitoring method | |
| CN112348866A (en) | Water surface floater pollution evaluation system and method based on big data | |
| CN109671248A (en) | Mountain flood method for early warning based on object of taking precautions against natural calamities | |
| CN117367348A (en) | System and method for measuring sludge height in real time based on mine sump | |
| CN107238837A (en) | A kind of drauht detection method | |
| ten Veldhuis et al. | Statistical analysis of hydrological response in urbanising catchments based on adaptive sampling using inter-amount times | |
| CN103983323A (en) | Hydropower station water level measurement method and system | |
| CN109190717A (en) | A kind of Multiple Source Sensor fault detection method based on ICA and kNN |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20211101 Address after: 200333 no.442, Lane 3671, Zhongshan North Road, Putuo District, Shanghai Patentee after: Shanghai Linlan Environmental Technology Co.,Ltd. Address before: 200333 No. 442, Lane 3671, Zhongshan North Road, Putuo District, Shanghai Patentee before: Yin Jian |
|
| TR01 | Transfer of patent right |