CN115854999B - H-ADCP section average flow velocity self-correction method based on scene self-adaption - Google Patents
H-ADCP section average flow velocity self-correction method based on scene self-adaption Download PDFInfo
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Abstract
The invention relates to the technical field of water resource online detection, in particular to a scene self-adaption-based H-ADCP section average flow velocity self-correction method. The method comprises the following steps: obtaining a section average flow rate and H-ADCP index flow rate data sample through flow rate comparison equipment and an H-ADCP on-line monitor, judging the water level fluctuation characteristic of a flow measurement section according to historical water level monitoring data of a river section, constructing a self-correction model according to the river section with large water level fluctuation amplitude, dividing the monitoring section with small water level fluctuation into a high flow rate sample set and a low flow rate sample set, constructing a corresponding self-correction model according to the high flow rate sample set and the low flow rate sample set, and carrying out precision evaluation and inspection on the self-correction model. According to the invention, the accuracy of the measured value of the H-ADCP is improved by performing self-correction calculation on the measured data and the historical data.
Description
Technical Field
The invention relates to the technical field of hydrologic water resource automatic online detection, in particular to a scene self-adaption-based H-ADCP section average flow velocity self-correction method.
Background
The river flow test automation is one of the necessary requirements of the current society development on hydrologic work, and the real-time and accurate hydrologic test mode and method are the premise and guarantee of the development of intelligent hydrologic. H-ADCP (Horizontal Acoustic Doppler Current Profilers), a horizontal acoustic Doppler flow profiler, is a new generation of high-quality river and open channel flow online monitoring instrument, uses smaller units, can obtain equipment actual measurement index flow velocity and flow data in a shorter time step, and is widely deployed and used in an automatic hydrologic testing system nowadays. However, the H-ADCP equipment monitors the obtained index flow rate and the actual average flow rate of the river section in real time when facing different application scenes.
Disclosure of Invention
The invention provides a self-correction method for the average flow velocity of an H-ADCP section based on scene self-adaption, which aims to solve at least one technical problem.
A scene self-adaption-based H-ADCP section average flow velocity self-correction method comprises the following steps:
step S1: synchronously acquiring river reach hydrologic elements through flow ratio testing equipment and H-ADCO equipment, so as to respectively acquire section average flow velocity data and H-ADCP index flow velocity data, judging and calculating the section average flow velocity data and the H-ADCP index flow velocity data according to river section historical water level data, and generating flow measurement section water level fluctuation characteristic data;
Step S2: when the measured flow section water level fluctuation characteristic data is determined to be greater than or equal to preset measured flow section water level fluctuation data, constructing a water level fluctuation self-correction model according to section average flow velocity data and H-ADCP index flow velocity data, otherwise, executing step S4;
step S3: performing precision evaluation and inspection on the water level fluctuation self-correction model to generate a water level fluctuation inspection result, performing parameter calibration according to the water level fluctuation self-correction model when the water level fluctuation inspection result is determined to be false, generating a water level fluctuation self-correction determination model, determining the water level fluctuation self-correction model to be an H-ADCP index flow velocity section water level self-correction model when the water level fluctuation inspection result is determined to be true, executing step S6, and returning to step S2 when the water level fluctuation inspection result is determined to be false;
step S4: when the measured flow section water level fluctuation characteristic data is smaller than the preset measured flow section water level fluctuation data, respectively determining section average flow velocity data and H-ADCP index flow velocity data into a high flow velocity sample set and a low flow velocity sample set in a data sample dividing mode;
step S5: according to the high-flow-rate sample set, constructing an H-ADCP index high-flow-rate self-correction model, and according to the low-flow-rate sample set, respectively constructing an H-ADCP index low-flow-rate self-correction model;
Step S6: respectively carrying out precision evaluation and inspection on the H-ADCP index flow speed section water level self-correction model, the H-ADCP index high flow speed self-correction model and the H-ADCP index low flow speed self-correction model to generate an H-ADCP index flow speed section water level inspection result, an H-ADCP index high flow speed inspection result and an H-ADCP index low flow speed inspection result, and returning to the step S4 if the H-ADCP index flow speed section water level inspection result, the H-ADCP index high flow speed inspection result or the H-ADCP index low flow speed inspection result is determined to be false;
step S7: respectively carrying out error calculation on the H-ADCP index high flow rate test result and the H-ADCP index low flow rate self-correction model to generate a low flow measurement section water level fluctuation self-correction model;
step S8: and (3) performing verification on the water level fluctuation self-correction model and the low-flow-measurement section water level fluctuation self-correction model through an actual measurement flow process line method, generating a verification result, returning to the step (S2) when the verification result is determined to be false, and executing the H-ADCP section average flow rate self-correction operation when the verification result is determined to be true.
According to the embodiment, the numerical calculation and analysis of measured data are carried out, so that models with different inputs and corresponding outputs are constructed, the reliable self-correction model is provided through the error numerical calculation of different layers, the self-correction model in each scene is verified and checked, the accuracy of H-ADCP section mapping is improved, and automatic accurate testing of the flow speed and the flow of the multi-stage hydrological site is realized.
In one embodiment of the present specification, step S1 includes the steps of:
step S11: acquiring section data acquisition position information, and generating a position section data acquisition mode according to the section data acquisition position information;
step S12: performing data acquisition operation by corresponding flow ratio testing equipment in a section data acquisition mode and a data acquisition mode to obtain section average flow velocity data and H-ADCP index flow velocity data;
step S13: acquiring historical water level data of a river cross section, and calculating according to the historical water level data of the river cross section, the average flow velocity data of the cross section and the H-ADCP index flow velocity data by a water level fluctuation discrete coefficient calculation formula to generate flow measurement cross section water level fluctuation characteristic data;
the water level fluctuation discrete coefficient calculation formula specifically comprises the following steps:
According to the embodiment, the water level fluctuation characteristic numerical calculation is carried out on the measured data through the historical water level data of the river section, so that the water level fluctuation characteristic data of the current measurement section is generated, and the preparation work is carried out for the next step.
The embodiment provides a water level fluctuation discrete coefficient calculation formula which fully considers the average value of historical water level data
The first +.f of the section average flow velocity data and the H-ADCP index flow velocity data>
Water level data->
Number of detected water level data +.>
Historical water level data variance->
And interaction relationships with each other to form a functional relationship:
and
Thereby providing an accurate and reliable numberIs supported.
In one embodiment of the present specification, step S2 includes the steps of:
step S21: judging whether the water level fluctuation characteristic data of the flow measurement section is larger than or equal to preset water level fluctuation data of the flow measurement section;
step S22: when the measured flow section water level fluctuation characteristic data is determined to be greater than or equal to preset measured flow section water level fluctuation data, calculating according to section average flow velocity data and H-ADCP index flow velocity data through a fluctuation water level self-correction model calculation formula, so as to construct a water level fluctuation self-correction model;
step S23: when the measured flow section water level fluctuation characteristic data is smaller than the preset measured flow section water level fluctuation data, executing the step S4;
the self-correction model construction calculation formula specifically comprises:
In the embodiment, the flow measurement section water level fluctuation characteristic data is compared with the preset flow measurement section water level fluctuation data, when the flow measurement section water level fluctuation characteristic data is determined to be greater than or equal to the preset data, a calculation mode is built according to the self-correction model to build the model, and when the flow measurement section water level fluctuation characteristic data is determined to be less than the preset data, the step S4 is executed, so that the adaptability to a scene is improved.
The embodiment provides a self-correction model construction calculation formula which fully considers the measured index flow velocity of H-ADCP in the H-ADCP index flow velocity data
Depth information of water level actual measurement->
First constant information->
Second constant information->
Third constant information->
Fourth constant information->
Fifth frequent information->
Sixth constant information->
And interaction relationships with each other, thereby forming a functional relationship:
providing reliable data support.
In one embodiment of the present specification, step S3 includes the steps of:
Step S31: checking and calculating the water level fluctuation self-correction model through a deterministic coefficient calculation formula to generate a first water level fluctuation checking result;
step S32: when the first water level fluctuation test result is determined to be true, carrying out test calculation on the water level fluctuation self-correction model through a standard deviation and random uncertainty test calculation formula to generate a second water level fluctuation test result, otherwise, returning to the step S2;
step S33: when the second water level fluctuation test result is determined to be true, the water level fluctuation self-correction model is tested through a three-line test method, a third water level fluctuation test result is generated, and if the third water level fluctuation test result is determined to be true, the water level fluctuation self-correction model is determined to be an H-ADCP index flow velocity section water level self-correction model;
step S34: and when the third water level fluctuation test result is determined to be false, returning to the step S2.
In the embodiment, the water fluctuation self-correction model is checked and calculated by determining a coefficient calculation formula, a standard deviation and random uncertainty check calculation formula and a three-line check method, and when an unqualified intermediate result appears, the step S2 is returned to carry out a model construction step again so as to reduce interference of unqualified data and influence the accuracy of a final result.
In one embodiment of the present specification, step S5 includes the steps of:
calculating according to the high-flow-rate sample set through a flow-rate sample self-correction calculation formula, so as to construct an H-ADCP index high-flow-rate self-correction model;
and calculating according to the low-flow-rate sample set through a flow-rate sample self-correction calculation formula, so as to construct the H-ADCP index low-flow-rate self-correction model.
According to the embodiment, the H-ADCP index high-flow-rate self-correction model is built by calculating according to the high-flow-rate sample set through a flow-rate sample self-correction calculation formula, and the H-ADCP index low-flow-rate self-correction model is built by calculating according to the low-flow-rate sample set through the flow-rate sample self-correction calculation formula, so that the corresponding self-correction model is built according to different conditions of the inside of acquired data, and the adaptability of adjustment of subdivision values of a flow measurement section is improved.
In one embodiment of the present specification, the flow rate sample self-correction calculation formula is specifically:
The embodiment provides a flow velocity sample self-correction calculation formula which fully considers the measured index flow velocity of H-ADCP in the H-ADCP index flow velocity data
First constant information->
Second constant information->
Third constant information->
And interaction relationships with each other, thereby forming a functional relationship:
to provide accurate data support.
In one embodiment of the present specification, step S6 includes the steps of:
step S61: respectively checking and calculating an H-ADCP index flow speed section water level self-correction model, an H-ADCP index high flow speed self-correction model and an H-ADCP index low flow speed self-correction model through a deterministic coefficient calculation formula to generate a first H-ADCP index flow speed section water level check result, a first H-ADCP index high flow speed check result and a first H-ADCP index low flow speed check result, and returning to the step S4 if the first H-ADCP index flow speed section water level check result, the first H-ADCP index high flow speed check result or the first H-ADCP index low flow speed check result is determined to be false;
step S62: when the first H-ADCP index flow speed section water level check result or the first H-ADCP index high flow speed check result is determined to be true, checking and calculating an H-ADCP index flow speed section water level self-correction model or an H-ADCP index high flow speed self-correction model through a standard deviation and random uncertainty check calculation formula to generate a second H-ADCP index flow speed section water level check result or a second H-ADCP index high flow speed check result, and if the second H-ADCP index flow speed section water level check result or the second H-ADCP index high flow speed check result is determined to be false, returning to the step S4;
Step S63: when the second H-ADCP index flow speed section water level check result or the second H-ADCP index high flow speed check result is determined to be true, checking the H-ADCP index flow speed section water level self-correction model or the H-ADCP index high flow speed self-correction model by a three-line check method to generate a third H-ADCP index flow speed section water level check result or a third H-ADCP index high flow speed check result;
step S64: and when the third H-ADCP index flow speed section water level test result or the third H-ADCP index high flow speed test result is determined to be false, returning to the step S4.
In the embodiment, the generated self-correction model is subjected to verification calculation through a deterministic coefficient calculation formula, a standard deviation and random uncertainty verification calculation formula and a three-wire verification method, wherein when a verification result comprising verification failure appears, the step S2 is returned to carry out modeling operation again, so that the self-adaption flexibility is ensured, and the possibility of inaccurate data caused by overlarge errors is reduced.
In one embodiment of the present specification, the deterministic coefficient calculation formula is specifically:
the standard deviation and random uncertainty test calculation formula specifically comprises:
the three-wire test method is a coincidence test method, a wire adapting test method or a deviation value test method.
The present embodiment provides a deterministic coefficient calculation formula that fully considers the first
Secondary measured flow->
First->
The value flow of the sub-measured flow corresponding to the equation curve +.>
Real worldFlow measurement mean->
And interaction relationships with each other, thereby forming a functional relationship:
the ratio of the measured flow to the measured flow corresponding to the equation curve and the ratio of the measured flow to the measured flow average value are compared, so that reliable data support is provided.
The present embodiment provides a standard deviation and random uncertainty check calculation formula that fully considers the first
Secondary measured flow->
First->
The value flow of the sub-measured flow corresponding to the equation curve +.>
Numerical information of actual measurement flow rate >
Random uncertainty->
And interaction relationships with each other to form a functional relationship:
and
To provide accurate data support.
In one embodiment of the present specification, step S7 includes the steps of:
respectively carrying out error calculation on the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model according to the section average flow rate data and the H-ADCP index flow rate data to generate an H-ADCP index high flow rate error set and an H-ADCP index low flow rate error set;
and determining a low-flow section water level fluctuation self-correction model according to the high-flow speed error set of the H-ADCP index and the low-flow speed error set of the H-ADCP index.
According to the embodiment, error calculation is respectively carried out on the H-ADCP index flow velocity section water level self-correction model and the H-ADCP index low flow velocity self-correction model, so that the self-correction model with small error and high qualification rate in the scene is ensured, and accurate data support is provided.
In one embodiment of the present specification, step S8 includes the steps of:
step S81: performing verification and verification on the H-ADCP index flow velocity section water level self-correction model and the low flow measurement section water level fluctuation self-correction model by an actual measurement flow process line method, so as to obtain water quantity error numerical information;
Step S82: judging whether the water quantity error numerical information is smaller than or equal to a preset water quantity relative error numerical value or not;
step S83: when the water quantity error value information is determined to be smaller than or equal to a preset water quantity relative error value, generating a true verification result, and executing H-ADCP section average flow rate self-correction operation;
step S84: and when the water quantity error numerical value information is determined to be larger than the preset water quantity relative error numerical value, generating a false verification result, and returning to the step S2.
According to the embodiment, a plurality of time periods are selected based on the final self-correction model obtained by each scene, water quantity statistics in the station time periods is carried out by adopting a continuous actual measurement flow process line method, water quantity error calculation and rationality analysis are carried out by referring to statistical water quantity obtained by the comparison device or water quantity discharged from a water conservancy junction near the upstream and downstream, the relative error of the water quantity is within 5%, the artificial self-correction model is reasonable, otherwise, an equation is required to be fitted again, and therefore the data reliability based on scene self-adaption adjustment is improved.
The invention provides a section average flow rate self-correction method based on H-ADCP by taking an H-ADCP on-line monitor as a core component and combining the technical means of flow rate comparison measurement, multi-scene self-adaptive modeling, multiple inspection, water quantity analysis and the like, solves the problem of larger error between the index flow rate and the section actual flow rate in different application scenes, ensures the high accuracy of continuous actual measurement flow rate, and realizes automatic accurate test of the flow rate and the flow rate of a multi-stage hydrological site. The method is suitable for real-time automatic monitoring of section flow under various river channels, river networks and various flow velocity scenes. The method provides powerful technical support for hydrologic distributed, cross-regional automation and accurate hydrologic detection in the complex geographic environment at present, and has wide practical significance.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting implementations made with reference to the following drawings in which:
FIGS. 1 and 2 are flow charts showing steps of a scene-adaptive H-ADCP section average flow rate self-correction method according to an embodiment;
FIG. 3 is a flow chart showing the steps of a method for generating profile water level fluctuation characteristic data according to an embodiment;
FIG. 4 is a flow chart showing the steps of a method for generating a self-correction model of water level fluctuation according to an embodiment;
FIG. 5 is a flow chart showing the steps of a method for verification calculation of a water level fluctuation self-correction model according to an embodiment;
FIG. 6 is a flow chart illustrating the steps of a self-correcting model verification calculation method of an embodiment;
FIG. 7 is a flow chart illustrating the steps of a self-correcting model verification method according to one embodiment.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In one embodiment, step one: and (obtaining samples), selecting proper flow rate comparison equipment according to a river reach of a specific engineering construction, synchronously collecting the comparison equipment and the H-ADCP equipment, and obtaining data samples of actual flow rate and index flow rate of the section. And judging the fluctuation characteristic of the water level of the flow measurement section according to the historical water level monitoring data of the river section.
At present, the BL station flow test mainly adopts a sailing ADCP (600 kHz) to perform measurement on a 50m section at the bottom. In the conventional flow measurement process of ADCP, the requirement of acoustic Doppler flow test Specification (SL 337-2006) is strictly executed, and the dual influences of BL station tidal and upstream water accumulation and drainage by the water-friendly hub are considered, each flow test is carried out to measure the section flow test of two times in total, then the arithmetic average value of the two times of flow measurement is calculated, when the deviation between the flow value of each time of measurement and the average value is within +/-5%, the average value is taken as the actual flow value, and if the deviation exceeds +/-5%, one measurement is added to judge whether the flow is greatly changed in a short time.
The collection and arrangement work of the sailing ADCP (600 kHz) and the H-ADCP synchronous comparison data is completed in 8 months 2021 to 6 months 2022. In the synchronous data collection process, the water passing area of the H-ADCP on-line section is calculated by adopting the measured water level (H) and measured section data (H-ADCP on-line section, base 80 m) corresponding to the corresponding moment of flow measurement. Dividing the measured flow of the sailing ADCP by the water passing area of the H-ADCP on-line section to obtain the average flow velocity of the H-ADCP on-line section 
). The average value of the flow velocity data corresponding to the H-ADCP index is adopted as the flow velocity of the secondary index>
。
Because the BL station is a tidal river section and is influenced by the water accumulation and drainage of the upstream water-friendly junction, the BL station is suitable for application scenes with large water level fluctuation range or tidal, and therefore, the acquired data samples are taken as a whole to construct the H-ADCP index flow velocity
And the section water level H is used as an input self-correction model.
Step two: and (3) constructing a self-correction model by taking the H-ADCP index flow velocity Vindex and the section water level H as inputs aiming at the characteristics of large water level fluctuation and tidal wave of the BL station. And taking all the data samples as a whole, and adopting a gradient descent algorithm to perform binary quadratic equation fitting.
All sample data points collected in 2021, 8-2022 and 6 are combined, and the total number of samples is 102 and shown in tables 1-3. The actual measurement flow amplitude is: 194-5210 m3/s, the uniform flow velocity amplitude of the H-ADCP section is: 1.18-4.76 m/s, the index flow velocity amplitude is as follows: 1.61-4.15 m/s, the measured water level amplitude is: 0.8 to 6.16m.
Table 1:
table 2:
table 3:
the 102 samples are subjected to comprehensive line calibration, and the result is shown in formula (1):
step four: and (3) performing precision evaluation and inspection on the self-correction model established in the step two, determining that the self-correction model is a self-correction model under a corresponding scene if the self-correction model passes the inspection, and otherwise, performing parameter calibration of the model again. Based on the final self-correction equation, performing verification by adopting a water quantity statistical method in a period.
Three-line inspection is performed with reference to a class of precision hydrologic stations, and all inspection passes. The test results are shown in Table 4 below.
Table 4 comprehensive line calculation flow error statistical table
And (3) calculating the H-ADCP online monitoring flow according to the calibration relation, comparing the H-ADCP online monitoring flow with the actual measurement flow of the sailing type ADCP, wherein the relative error is 85.0% within +/-5%, the absolute value of the relative error of only individual points exceeds 10%, and the relative error is 98.3% within +/-10, and the statistical result is shown in Table 5.
TABLE 5 flow error statistics
In the embodiment, the H-ADCP index flow rate and the section average flow rate are subjected to self-correction modeling, a binary regression self-correction model considering tidal is established, the regression accuracy of the index flow rate and the section average flow rate curve is researched by introducing the water level-flow relation curve test of hydrologic data integral code (SL 247-2012), regression flow rate and water quantity errors are calculated respectively, a calibration model is optimized, and the single-measurement flow monitoring data accuracy is improved.
And the monitoring distortion flow is corrected by adopting a limiting filtering method, the H-ADCP real-time monitoring flow is utilized to carry out reorganization, and the problem that the conventional water level flow relation and other plug flow methods for measuring the section of the station cannot meet the standard precision requirement is solved. Overcomes the defect of single research application environment and research method in the prior related research, and provides ideas and experiences for the popularization and application of H-ADCP under the condition of complex hydrologic environment.
The upstream 3.3km of the BL hydrologic station is in 2006 to build an HZDJ water junction, the incoming water is influenced by junction regulation, the downstream of 2007 is tidal, the non-flood period tidal is basically consistent with the downstream tidal level station SL (FW) station, the flood period Hong Chao is mixed, and the hydrologic situation is complex.
Because BL (second) station is influenced by reservoir regulation and moisture sensing, traditional integral flow-pushing methods such as water level-flow relation method can not meet the standard precision requirement, and the water quantity calculation error is larger.
After the H-ADCP operates, the H-ADCP is utilized to monitor the flow in real time, a flow process line method which is connected with actual measurement is adopted to carry out flow pushing, wherein flow fluctuation abnormality is caused by floating objects, ship passing or other acoustic noise influences in individual time periods, and a limiting filtering method is adopted to correct the distorted flow, so as to obtain the drainage quantity under the section in the time period.
According to the method, three time periods are randomly selected, the BL station and the HZDJ water conservancy junction are adopted for two-hour whole-point flow, the water quantity calculation in the time period of the BL station is carried out by adopting a continuous actual measurement flow process line method, the water quantity rationality analysis is carried out by referring to the water conservancy junction delivery flow, and the conclusion is effective.
(1) H-ADCP, sailing ADCP and traditional flow velocity meter synchronous comparison measurement are carried out in the late 8 th 2013, the 1 st time period is selected from 10 days of 8 months to 21 days of 8 months to 31 days of 24 days of 8 months, and the actual measurement flow amplitude is 80.2-747 m < 3 >/s in the time period. In the calculation period, BL (second) station runoff is 3.03 hundred million m < 3 >, east Jiang Shuili hub power generation water quantity is 3.04 hundred million m < 3 >, the difference between the BL (second) station runoff and the east Jiang Shuili hub power generation water quantity is 0.07%, and comparative analysis of the BL (second) station runoff in the period is considered to be reasonable.
(2) The water quantity scheduling in the winter open spring dead water period in 2013 is formally started in 10 months, the time period from 0 time to 24 time of 10 days in 2013 is selected as the 2 nd time period, and the actual measurement flow amplitude in the time period is 87.4-578 m < 3 >/s. In the calculation period, BL (second) station runoff is 2.88 hundred million m < 3 >, east Jiang Shuili hub power generation water quantity is 2.84 hundred million m < 3 >, and the difference between the BL (second) station runoff and the east Jiang Shuili hub power generation water quantity is 1.41%, and the comparative analysis of the inner runoff in the period is considered to be reasonable.
(3) Selecting the initial period of 'dragon boat water' of 2014, 6, 1, 0, 10 and 24 days as the 3 rd period, and the actual measurement flow amplitude in the period is 99.7-1730 m3/s. In the calculation period, BL (second) station diameter flow is monitored to be 8.24 hundred million m < 3 >, the electricity generation water quantity of the east Jiang Shuili hub is 7.89 hundred million m < 3 >, and the electricity generation flow is suddenly reduced under the influence of opening and closing of a gate in 6 months and 2 days and 10 hours to 18 hours. The two differ by 4.41% in time period. The comparative analysis of the internal diameter flow in this period is generally considered reasonable.
Referring to fig. 1 to 7, the method for self-correcting the average flow velocity of the H-ADCP section based on scene adaptation comprises the following steps:
step S1: synchronously acquiring river reach hydrologic elements through flow ratio testing equipment and H-ADCO equipment, so as to respectively acquire section average flow velocity data and H-ADCP index flow velocity data, judging and calculating the section average flow velocity data and the H-ADCP index flow velocity data according to river section historical water level data, and generating flow measurement section water level fluctuation characteristic data;
Step S2: when the measured flow section water level fluctuation characteristic data is determined to be greater than or equal to preset measured flow section water level fluctuation data, constructing a water level fluctuation self-correction model according to section average flow velocity data and H-ADCP index flow velocity data, otherwise, executing step S4;
step S3: performing precision evaluation and inspection on the water level fluctuation self-correction model to generate a water level fluctuation inspection result, performing parameter calibration according to the water level fluctuation self-correction model when the water level fluctuation inspection result is determined to be false, generating a water level fluctuation self-correction determination model, determining the water level fluctuation self-correction model to be an H-ADCP index flow velocity section water level self-correction model when the water level fluctuation inspection result is determined to be true, executing step S6, and returning to step S2 when the water level fluctuation inspection result is determined to be false;
step S4: when the measured flow section water level fluctuation characteristic data is smaller than the preset measured flow section water level fluctuation data, respectively determining section average flow velocity data and H-ADCP index flow velocity data into a high flow velocity sample set and a low flow velocity sample set in a data sample dividing mode;
specifically, for example, according to the actual situation of the collected index flow velocity data sample, the high flow velocity sample set and the low flow velocity sample set are divided by taking the median as the engagement point.
Step S5: according to the high-flow-rate sample set, constructing an H-ADCP index high-flow-rate self-correction model, and according to the low-flow-rate sample set, respectively constructing an H-ADCP index low-flow-rate self-correction model;
step S6: respectively carrying out precision evaluation and inspection on the H-ADCP index flow speed section water level self-correction model, the H-ADCP index high flow speed self-correction model and the H-ADCP index low flow speed self-correction model to generate an H-ADCP index flow speed section water level inspection result, an H-ADCP index high flow speed inspection result and an H-ADCP index low flow speed inspection result, and returning to the step S4 if the H-ADCP index flow speed section water level inspection result, the H-ADCP index high flow speed inspection result or the H-ADCP index low flow speed inspection result is determined to be false;
step S7: respectively carrying out error calculation on the H-ADCP index high flow rate test result and the H-ADCP index low flow rate self-correction model to generate a low flow measurement section water level fluctuation self-correction model;
step S8: and (3) performing verification on the water level fluctuation self-correction model and the low-flow-measurement section water level fluctuation self-correction model through an actual measurement flow process line method, generating a verification result, returning to the step (S2) when the verification result is determined to be false, and executing the H-ADCP section average flow rate self-correction operation when the verification result is determined to be true.
Specifically, for example, step one: selecting a flow ratio measuring device, synchronously collecting hydrologic elements of a river reach with an H-ADCP device, and obtaining the average flow velocity of a section
) Flow rate (+_ADCP) with the H-ADCP index>
) And (5) data samples. And judging the fluctuation characteristic of the water level of the flow measurement section according to the historical water level monitoring data of the river section.
Step two: for river sections with large water level fluctuation range or tidal river channel, taking the acquired data samples as a whole to construct the flow velocity of the H-ADCP index
And the section water level H is used as an input self-correction model. And dividing the data sample into a high-flow-rate sample set and a low-flow-rate sample set aiming at the monitoring section with small water level fluctuation amplitude.
Step three: for a high flow sample set, constructing a flow rate index by H-ADCP
Is an input self-correcting model. For the low flow sample set, constructing the flow rate index of H-ADCP respectively>
Self-correction model inputted with section water level H, and using H-ADCP index flow rate +.>
Is an input self-correcting model.
Step four: and (3) performing precision evaluation and inspection on the self-correction model established in the second step and the third step, determining that the self-correction model is a self-correction model under a corresponding scene if the self-correction model passes the inspection, and otherwise, performing parameter calibration on the model again. And (3) respectively carrying out error and qualification rate analysis on the two self-correction models in the low-flow-rate scene in the third step, and selecting the self-correction model with small error and high qualification rate as the self-correction model in the scene. Based on the final self-correction equation obtained in each scene, performing verification by adopting a water quantity statistical method in a period.
According to the embodiment, the numerical calculation and analysis of measured data are carried out, so that models with different inputs and corresponding outputs are constructed, the reliable self-correction model is provided through the error numerical calculation of different layers, the self-correction model in each scene is verified and checked, the accuracy of H-ADCP section mapping is improved, and automatic accurate testing of the flow speed and the flow of the multi-stage hydrological site is realized.
In one embodiment of the present specification, step S1 includes the steps of:
step S11: acquiring section data acquisition position information, and generating a position section data acquisition mode according to the section data acquisition position information;
step S12: performing data acquisition operation by corresponding flow ratio testing equipment in a section data acquisition mode and a data acquisition mode to obtain section average flow velocity data and H-ADCP index flow velocity data;
step S13: acquiring historical water level data of a river cross section, and calculating according to the historical water level data of the river cross section, the average flow velocity data of the cross section and the H-ADCP index flow velocity data by a water level fluctuation discrete coefficient calculation formula to generate flow measurement cross section water level fluctuation characteristic data;
specifically, for example, in step 11, while it has been determined to collect data with H-ADCP, collecting section average flow rate data may select different flow rate ratio measurement devices according to different situations. And selecting proper flow ratio measuring equipment according to the river reach of specific engineering construction.
The navigation ADCP can be selected at the position of wider general river channel and deeper water depth; the handheld ADV can be selected at the position with narrower river channel, shallower water depth and smaller flow velocity; and when the river channel is narrower, the water depth is shallower and the flow velocity is larger, the traditional flow velocity meter can be selected to measure the flow and the section area, so that the section average flow velocity data is obtained.
When data are collected, time synchronization of the H-ADCP and various comparison equipment is ensured, and data recording is started; and under the conditions of different water levels and different flow levels, the comparison measurement is preferably carried out for a plurality of times so as to ensure the accuracy of comparing the average flow speed data, minimize the data error and have better representativeness.
And 12, collecting historical water level monitoring data of the river cross section, measuring the water level fluctuation characteristic by adopting a discrete coefficient, and when the discrete coefficient is greater than or equal to 50%, determining that the water level fluctuation amplitude of the cross section is large, otherwise, determining that the water level fluctuation amplitude is small.
The water level fluctuation discrete coefficient calculation formula specifically comprises the following steps:
According to the embodiment, the water level fluctuation characteristic numerical calculation is carried out on the measured data through the historical water level data of the river section, so that the water level fluctuation characteristic data of the current measurement section is generated, and the preparation work is carried out for the next step.
The embodiment provides a water level fluctuation discrete coefficient calculation formula which fully considers the average value of historical water level data
The first +.f of the section average flow velocity data and the H-ADCP index flow velocity data>
Water level data->
Number of detected water level data +.>
Historical water level data variance->
And the interaction relationship with each other to form a functional relationship +.>
and 
Thereby providing accurate and reliable data support.
In one embodiment of the present specification, step S2 includes the steps of:
step S21: judging whether the water level fluctuation characteristic data of the flow measurement section is larger than or equal to preset water level fluctuation data of the flow measurement section;
step S22: when the measured flow section water level fluctuation characteristic data is determined to be greater than or equal to preset measured flow section water level fluctuation data, calculating according to section average flow velocity data and H-ADCP index flow velocity data through a fluctuation water level self-correction model calculation formula, so as to construct a water level fluctuation self-correction model;
Step S23: when the measured flow section water level fluctuation characteristic data is smaller than the preset measured flow section water level fluctuation data, executing the step S4;
specifically, for example, for an application scene with large water level fluctuation amplitude or tidal, a self-correction model with H-ADCP index flow velocity V_index and section water level H as inputs is constructed by utilizing all samples. And dividing the data sample into a high-flow-rate sample set and a low-flow-rate sample set aiming at the monitoring section with small water level fluctuation amplitude.
The self-correction model construction calculation formula specifically comprises:
In the embodiment, the flow measurement section water level fluctuation characteristic data is compared with the preset flow measurement section water level fluctuation data, when the flow measurement section water level fluctuation characteristic data is determined to be greater than or equal to the preset data, a calculation mode is built according to the self-correction model to build the model, and when the flow measurement section water level fluctuation characteristic data is determined to be less than the preset data, the step S4 is executed, so that the adaptability to a scene is improved.
The embodiment provides a self-correction model construction calculation formula which fully considers the measured index flow velocity of H-ADCP in the H-ADCP index flow velocity data
Depth information of water level actual measurement->
First constant information->
Second constant information->
Third constant information->
Fourth constant information->
Fifth frequent information->
Sixth constant information->
And mutually with each otherThe action relation between the two components is formed as a functional relation:
providing reliable data support.
In one embodiment of the present specification, step S3 includes the steps of:
step S31: checking and calculating the water level fluctuation self-correction model through a deterministic coefficient calculation formula to generate a first water level fluctuation checking result;
step S32: when the first water level fluctuation test result is determined to be true, carrying out test calculation on the water level fluctuation self-correction model through a standard deviation and random uncertainty test calculation formula to generate a second water level fluctuation test result, otherwise, returning to the step S2;
step S33: when the second water level fluctuation test result is determined to be true, the water level fluctuation self-correction model is tested through a three-line test method, a third water level fluctuation test result is generated, and if the third water level fluctuation test result is determined to be true, the water level fluctuation self-correction model is determined to be an H-ADCP index flow velocity section water level self-correction model;
Step S34: and when the third water level fluctuation test result is determined to be false, returning to the step S2.
Specifically, for example, a unitary quadratic equation is to be adopted to construct a self-correction model under the application scene of small fluctuation amplitude of water level and high flow velocity;
a unitary quadratic equation and a binary quadratic equation are adopted to respectively construct a self-correction model under the application scene of small water level fluctuation amplitude and low flow velocity;
in the embodiment, the water fluctuation self-correction model is checked and calculated by determining a coefficient calculation formula, a standard deviation and random uncertainty check calculation formula and a three-line check method, and when an unqualified intermediate result appears, the step S2 is returned to carry out a model construction step again so as to reduce interference of unqualified data and influence the accuracy of a final result.
In one embodiment of the present specification, step S5 includes the steps of:
calculating according to the high-flow-rate sample set through a flow-rate sample self-correction calculation formula, so as to construct an H-ADCP index high-flow-rate self-correction model;
and calculating according to the low-flow-rate sample set through a flow-rate sample self-correction calculation formula, so as to construct the H-ADCP index low-flow-rate self-correction model.
Specifically, for example, a unitary quadratic equation is to be adopted to construct a self-correction model under the application scene of small fluctuation amplitude of water level and high flow velocity;
And a self-correction model with small water level fluctuation amplitude and low flow velocity under an application scene is respectively constructed by adopting a unitary quadratic equation and a binary quadratic equation.
According to the embodiment, the H-ADCP index high-flow-rate self-correction model is built by calculating according to the high-flow-rate sample set through a flow-rate sample self-correction calculation formula, and the H-ADCP index low-flow-rate self-correction model is built by calculating according to the low-flow-rate sample set through the flow-rate sample self-correction calculation formula, so that the corresponding self-correction model is built according to different conditions of the inside of acquired data, and the adaptability of adjustment of subdivision values of a flow measurement section is improved.
In one embodiment of the present specification, the flow rate sample self-correction calculation formula is specifically:
The embodiment provides a flow velocity sample self-correction calculation formula which fully considers the measured index flow velocity of H-ADCP in the H-ADCP index flow velocity data
First constant information->
Second constant information->
Third constant information- >
And the interaction relationship with each other, thereby forming a functional relationship +.>
To provide accurate data support.
In one embodiment of the present specification, step S6 includes the steps of:
step S61: respectively checking and calculating an H-ADCP index flow speed section water level self-correction model, an H-ADCP index high flow speed self-correction model and an H-ADCP index low flow speed self-correction model through a deterministic coefficient calculation formula to generate a first H-ADCP index flow speed section water level check result, a first H-ADCP index high flow speed check result and a first H-ADCP index low flow speed check result, and returning to the step S4 if the first H-ADCP index flow speed section water level check result, the first H-ADCP index high flow speed check result or the first H-ADCP index low flow speed check result is determined to be false;
step S62: when the first H-ADCP index flow speed section water level check result or the first H-ADCP index high flow speed check result is determined to be true, checking and calculating an H-ADCP index flow speed section water level self-correction model or an H-ADCP index high flow speed self-correction model through a standard deviation and random uncertainty check calculation formula to generate a second H-ADCP index flow speed section water level check result or a second H-ADCP index high flow speed check result, and if the second H-ADCP index flow speed section water level check result or the second H-ADCP index high flow speed check result is determined to be false, returning to the step S4;
Step S63: when the second H-ADCP index flow speed section water level check result or the second H-ADCP index high flow speed check result is determined to be true, checking the H-ADCP index flow speed section water level self-correction model or the H-ADCP index high flow speed self-correction model by a three-line check method to generate a third H-ADCP index flow speed section water level check result or a third H-ADCP index high flow speed check result;
step S64: and when the third H-ADCP index flow speed section water level test result or the third H-ADCP index high flow speed test result is determined to be false, returning to the step S4.
In the embodiment, the generated self-correction model is subjected to verification calculation through a deterministic coefficient calculation formula, a standard deviation and random uncertainty verification calculation formula and a three-wire verification method, wherein when a verification result comprising verification failure appears, the step S2 is returned to carry out modeling operation again, so that the self-adaption flexibility is ensured, and the possibility of inaccurate data caused by overlarge errors is reduced.
In one embodiment of the present specification, the deterministic coefficient calculation formula is specifically:
the standard deviation and random uncertainty test calculation formula specifically comprises:
specifically, for example, the system standard deviation and uncertainty threshold are referred to the following table 6:
table 6 relationship alignment precision index table
And if the model is in accordance with the model, checking, otherwise, returning to the corresponding modeling step.
The three-wire test method is a coincidence test method, a wire adapting test method or a deviation value test method.
Specifically, for example, the significance level a value is selected and the critical value is determined in accordance with the following rule:
symbol inspection, wherein the value a is 0.25, and the critical value is determined according to table 7;
checking the fit line, wherein the value a is 0.05-0.10, and the critical value is determined according to the table 7;
deviation value test: the value a is 0.10-0.20, and the critical value is determined according to table 8.
Symbol inspection: the number of positive and negative signs of the measuring point deviation curve is counted, and when the deviation value is zero, the statistics u value is calculated according to the following formula and compared with the u1-a/2 value obtained by looking up the table 7 with the given significance level a as each half allocation of the positive and negative signs of the measuring points. When calculated u < u1-a/2. Then it is considered reasonable, i.e. accept assumptions; otherwise the original hypothesis should be rejected.
Wherein: u is a statistic; n is the total number of measuring points; k is the number of positive signs or negative signs, and each is 0.5; p and q are positive and negative probabilities of 0.5 respectively;
and (3) line fitting inspection: and according to the arrangement sequence of the water levels of the measuring points from low to high, starting counting from the second point, deviating from positive and negative sign conversion, converting the sign record 1, and otherwise, recording 0. Counting the number of times of '1', calculating a u value according to the following formula, comparing the u value with a u1-a value obtained by looking up table 7 for a given significance level a, and if u is smaller than u1-a, judging that the test is reasonable, namely, accepting the test; otherwise the original hypothesis should be rejected.
Wherein u is a statistic; n is the total number of measuring points; k is the number of positive or negative numbers, and is checked when k is less than 0.5 (n-1), otherwise, is not checked.
TABLE 7 critical value u 1-a/2 And u is equal to 1-a
Deviation value test: respectively calculating t value,
The value, given the significance level a, is used to derive the t1-a/2 value by looking up table 8, comparing the t value with the t1-a/2 value, when t<t1-a/2, then it is considered reasonable, namely, accept the hypothesis; otherwise the original hypothesis should be rejected.
t=
Wherein: t is the statistic of the number of times,
is the average relative deviation value; />
Is->
Standard deviation of (2); s is the standard deviation of p; and the total number of n measuring points, pi is the relative deviation value of the measuring points and the relation curve.
TABLE 8 critical value t 1-a/2
And if the three terms are consistent, checking, otherwise, returning to the corresponding modeling step.
The present embodiment provides a deterministic coefficient calculation formula that fully considers the first
Secondary measured flow->
First->
The value flow of the sub-measured flow corresponding to the equation curve +.>
Measured flow mean->
And interaction relationships with each other, thereby forming a functional relationship:
the ratio of the measured flow to the measured flow corresponding to the equation curve and the ratio of the measured flow to the measured flow average value are compared, so that reliable data support is provided.
The present embodiment provides a standard deviation and random walkA certainty test calculation formula that fully considers the first
Secondary measured flow->
First->
The value flow of the sub-measured flow corresponding to the equation curve +.>
Numerical information of actual measurement flow rate>
Random uncertainty->
And interaction relationships with each other to form a functional relationship:
and
To provide accurate data support.
In one embodiment of the present specification, step S7 includes the steps of:
respectively performing error calculation on the H-ADCP index flow speed section water level self-correction model and the H-ADCP index low-flow speed self-correction model according to the section average flow speed data and the H-ADCP index flow speed data to generate an H-ADCP index flow speed section water level error set and an H-ADCP index low-flow speed error set;
And determining a low-flow-measurement section water level fluctuation self-correction model according to the H-ADCP index flow-speed section water level error set and the H-ADCP index low-flow-speed error set.
Specifically, for example, based on the actually measured flow velocity, the maximum error, the minimum error, the average error and the qualification rate index are calculated for the two self-correction models in the low flow velocity scene respectively, the selection error is small, and the final self-correction model in the scene with high qualification rate is obtained.
According to the embodiment, error calculation is respectively carried out on the H-ADCP index flow velocity section water level self-correction model and the H-ADCP index low flow velocity self-correction model, so that the self-correction model with small error and high qualification rate in the scene is ensured, and accurate data support is provided.
In one embodiment of the present specification, step S8 includes the steps of:
step S81: performing verification and verification on the H-ADCP index flow velocity section water level self-correction model and the low flow measurement section water level fluctuation self-correction model by an actual measurement flow process line method, so as to obtain water quantity error numerical information;
step S82: judging whether the water quantity error numerical information is smaller than or equal to a preset water quantity relative error numerical value or not;
step S83: when the water quantity error value information is determined to be smaller than or equal to a preset water quantity relative error value, generating a true verification result, and executing H-ADCP section average flow rate self-correction operation;
Step S84: and when the water quantity error numerical value information is determined to be larger than the preset water quantity relative error numerical value, generating a false verification result, and returning to the step S2.
Specifically, for example, based on the final self-correction model obtained in each scene, selecting a plurality of time periods, carrying out water quantity statistics in the time periods of the station by adopting a continuous actual measurement flow process line method, carrying out water quantity error calculation and rationality analysis by referring to the statistical water quantity obtained by the comparison device or the water quantity discharged from the water junction near the upstream and downstream, and if the relative water quantity error is within 5%, considering the self-correction model as reasonable, otherwise, re-fitting the equation (set) is needed, and returning to the step S2.
According to the embodiment, a plurality of time periods are selected based on the final self-correction model obtained by each scene, water quantity statistics in the station time periods is carried out by adopting a continuous actual measurement flow process line method, water quantity error calculation and rationality analysis are carried out by referring to statistical water quantity obtained by the comparison device or water quantity discharged from a water conservancy junction near the upstream and downstream, the relative error of the water quantity is within 5%, the artificial self-correction model is reasonable, otherwise, an equation is required to be fitted again, and therefore the data reliability based on scene self-adaption adjustment is improved.
The invention provides a section average flow rate self-correction method based on H-ADCP by taking an H-ADCP on-line monitor as a core component and combining the technical means of flow rate comparison measurement, multi-scene self-adaptive modeling, multiple inspection, water quantity analysis and the like, solves the problem of larger error between the index flow rate and the section actual flow rate in different application scenes, ensures the high accuracy of continuous actual measurement flow rate, and realizes automatic accurate test of the flow rate and the flow rate of a multi-stage hydrological site. The method is suitable for real-time automatic monitoring of section flow under various river channels, river networks and various flow velocity scenes. The method provides powerful technical support for hydrologic distributed, cross-regional automation and accurate hydrologic detection in the complex geographic environment at present, and has wide practical significance.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The self-correction method for the average flow velocity of the H-ADCP section based on scene self-adaption is characterized by comprising the following steps of:
step S1: acquiring section data acquisition position information, and generating a position section data acquisition mode according to the section data acquisition position information;
Performing data acquisition operation by corresponding flow ratio testing equipment in a section data acquisition mode and a data acquisition mode to obtain section average flow velocity data and H-ADCP index flow velocity data;
acquiring historical water level data of a river cross section, and calculating according to the historical water level data of the river cross section, the average flow velocity data of the cross section and the H-ADCP index flow velocity data by a water level fluctuation discrete coefficient calculation formula to generate flow measurement cross section water level fluctuation characteristic data;
the water level fluctuation discrete coefficient calculation formula specifically comprises the following steps:
step S2: judging whether the water level fluctuation characteristic data of the flow measurement section is larger than or equal to preset water level fluctuation data of the flow measurement section;
when the measured flow section water level fluctuation characteristic data is determined to be greater than or equal to preset measured flow section water level fluctuation data, calculating according to section average flow velocity data and H-ADCP index flow velocity data through a fluctuation water level self-correction model calculation formula, so as to construct a water level fluctuation self-correction model;
When the measured flow section water level fluctuation characteristic data is smaller than the preset measured flow section water level fluctuation data, executing the step S4;
the self-correction model construction calculation formula specifically comprises:
step S3: checking and calculating the water level fluctuation self-correction model through a deterministic coefficient calculation formula to generate a first water level fluctuation checking result;
when the first water level fluctuation test result is determined to be true, carrying out test calculation on the water level fluctuation self-correction model through a standard deviation and random uncertainty test calculation formula to generate a second water level fluctuation test result, otherwise, returning to the step S2;
when the second water level fluctuation test result is determined to be true, the water level fluctuation self-correction model is tested through a three-line test method, a third water level fluctuation test result is generated, and if the third water level fluctuation test result is determined to be true, the water level fluctuation self-correction model is determined to be an H-ADCP index flow velocity section water level self-correction model;
When the third water level fluctuation detection result is determined to be false, returning to the step S2;
step S4: when the measured flow section water level fluctuation characteristic data is smaller than the preset measured flow section water level fluctuation data, respectively determining section average flow velocity data and H-ADCP index flow velocity data into a high flow velocity sample set and a low flow velocity sample set in a data sample dividing mode;
step S5: according to the high-flow-rate sample set, constructing an H-ADCP index high-flow-rate self-correction model, and according to the low-flow-rate sample set, respectively constructing an H-ADCP index low-flow-rate self-correction model;
step S6: respectively carrying out precision evaluation and inspection on the H-ADCP index flow speed section water level self-correction model, the H-ADCP index high flow speed self-correction model and the H-ADCP index low flow speed self-correction model to generate an H-ADCP index flow speed section water level inspection result, an H-ADCP index high flow speed inspection result and an H-ADCP index low flow speed inspection result, and returning to the step S4 if the H-ADCP index flow speed section water level inspection result, the H-ADCP index high flow speed inspection result or the H-ADCP index low flow speed inspection result is determined to be false;
step S7: respectively carrying out error calculation on the H-ADCP index high flow rate test result and the H-ADCP index low flow rate self-correction model to generate a low flow measurement section water level fluctuation self-correction model;
Step S8: and (3) performing verification on the water level fluctuation self-correction model and the low-flow-measurement section water level fluctuation self-correction model through an actual measurement flow process line method, generating a verification result, returning to the step (S2) when the verification result is determined to be false, and executing the H-ADCP section average flow rate self-correction operation when the verification result is determined to be true.
2. The method according to claim 1, wherein step S5 comprises the steps of:
calculating according to the high-flow-rate sample set through a flow-rate sample self-correction calculation formula, so as to construct an H-ADCP index high-flow-rate self-correction model;
and calculating according to the low-flow-rate sample set through a flow-rate sample self-correction calculation formula, so as to construct the H-ADCP index low-flow-rate self-correction model.
3. The method of claim 2, wherein the flow rate sample self-correction calculation formula is specifically:
4. The method according to claim 1, wherein step S6 comprises the steps of:
Respectively checking and calculating an H-ADCP index flow speed section water level self-correction model, an H-ADCP index high flow speed self-correction model and an H-ADCP index low flow speed self-correction model through a deterministic coefficient calculation formula to generate a first H-ADCP index flow speed section water level check result, a first H-ADCP index high flow speed check result and a first H-ADCP index low flow speed check result, and returning to the step S4 if the first H-ADCP index flow speed section water level check result, the first H-ADCP index high flow speed check result or the first H-ADCP index low flow speed check result is determined to be false;
when the first H-ADCP index flow speed section water level check result or the first H-ADCP index high flow speed check result is determined to be true, checking and calculating an H-ADCP index flow speed section water level self-correction model or an H-ADCP index high flow speed self-correction model through a standard deviation and random uncertainty check calculation formula to generate a second H-ADCP index flow speed section water level check result or a second H-ADCP index high flow speed check result, and if the second H-ADCP index flow speed section water level check result or the second H-ADCP index high flow speed check result is determined to be false, returning to the step S4;
when the second H-ADCP index flow speed section water level check result or the second H-ADCP index high flow speed check result is determined to be true, checking the H-ADCP index flow speed section water level self-correction model or the H-ADCP index high flow speed self-correction model by a three-line check method to generate a third H-ADCP index flow speed section water level check result or a third H-ADCP index high flow speed check result;
And when the third H-ADCP index flow speed section water level test result or the third H-ADCP index high flow speed test result is determined to be false, returning to the step S4.
5. The method according to claim 1 or 4, wherein the deterministic coefficient calculation formula is specifically:
the standard deviation and random uncertainty test calculation formula specifically comprises:
the three-wire test method is a coincidence test method, a wire adapting test method or a deviation value test method.
6. The method according to claim 1, wherein step S7 comprises the steps of:
respectively carrying out error calculation on the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model according to the section average flow rate data and the H-ADCP index flow rate data to generate an H-ADCP index high flow rate error set and an H-ADCP index low flow rate error set;
And determining a low-flow section water level fluctuation self-correction model according to the high-flow speed error set of the H-ADCP index and the low-flow speed error set of the H-ADCP index.
7. The method according to claim 1, wherein step S8 comprises the steps of:
performing verification and verification on the H-ADCP index flow velocity section water level self-correction model and the low flow measurement section water level fluctuation self-correction model by an actual measurement flow process line method, so as to obtain water quantity error numerical information;
judging whether the water quantity error numerical information is smaller than or equal to a preset water quantity relative error numerical value or not;
when the water quantity error value information is determined to be smaller than or equal to a preset water quantity relative error value, generating a true verification result, and executing H-ADCP section average flow rate self-correction operation;
and when the water quantity error numerical value information is determined to be larger than the preset water quantity relative error numerical value, generating a false verification result, and returning to the step S2.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110726445A (en) * | 2019-11-04 | 2020-01-24 | 国家海洋技术中心 | Tidal river reach pollutant flux online monitoring system and method |
| CN111474383A (en) * | 2020-04-23 | 2020-07-31 | 水利部南京水利水文自动化研究所 | River online flow calculation method and system based on big data |
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-
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Patent Citations (2)
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| CN111474383A (en) * | 2020-04-23 | 2020-07-31 | 水利部南京水利水文自动化研究所 | River online flow calculation method and system based on big data |
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