CN115854999A - H-ADCP section average flow velocity self-correction method based on scene self-adaptation - Google Patents
H-ADCP section average flow velocity self-correction method based on scene self-adaptation Download PDFInfo
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Abstract
The invention relates to the technical field of water resource online detection, in particular to an H-ADCP section average flow velocity self-correction method based on scene self-adaptation. The method comprises the following steps: the method comprises the steps of obtaining a section average flow velocity and an H-ADCP index flow velocity data sample through a flow velocity ratio measuring device and an H-ADCP on-line monitor, judging the water level fluctuation characteristic of a flow measuring section according to historical water level monitoring data of the river section, constructing a self-correcting 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 velocity sample set and a low flow velocity sample set, constructing a corresponding self-correcting model according to the high flow velocity sample set and the low flow velocity sample set, and carrying out precision evaluation and inspection on the self-correcting model. The invention improves the accuracy of the measured value of the H-ADCP by self-correcting calculation of the measured data and the historical data.
Description
Technical Field
The invention relates to the technical field of automatic online detection of hydrology and water resources, in particular to a scene-adaptive H-ADCP section average flow velocity self-correction method.
Background
The automation of river flow test is one of the inevitable requirements of the current social development on hydrology work, and the real-time and precise hydrology test mode and method are the premise and guarantee for developing the intelligent hydrology. H-ADCP (Horizontal Acoustic Doppler Current profiler), horizontal Acoustic Doppler Current profiler, is a new generation of high quality river flow, open channel flow on-line monitoring instrument, it uses smaller unit, can obtain the actual measurement index flow rate and flow data of the equipment in shorter time step, is widely deployed and used in the present automatic hydrological testing system. However, when the H-ADCP device faces different application scenarios, a certain error still exists between the index flow rate obtained by real-time monitoring and the actual average flow rate of the river cross section.
Disclosure of Invention
The invention provides a scene-adaptive-based H-ADCP section average flow velocity self-correction method for solving the technical problems, and aims to solve at least one technical problem.
A scene self-adaptive H-ADCP section average flow velocity self-correction method comprises the following steps:
step S1: synchronously acquiring river reach hydrological factors through a flow ratio measuring device and an H-ADCO device so as to respectively obtain section average flow velocity data and H-ADCP index flow velocity data, and judging and calculating the section average flow velocity data and the H-ADCP index flow velocity data according to historical water level data of the river section to generate current measuring section water level fluctuation characteristic data;
step S2: when the water level fluctuation characteristic data of the flow measuring section is determined to be larger than or equal to the preset water level fluctuation data of the flow measuring section, constructing a water level fluctuation self-correction model according to the average flow rate data of the section and the H-ADCP index flow rate data, and otherwise, executing the step S4;
and step S3: performing precision evaluation and inspection on the water level fluctuation self-correction model to generate a water level fluctuation inspection result, when the water level fluctuation inspection result is determined to be false, performing parameter calibration according to the water level fluctuation self-correction model to generate a water level fluctuation self-correction determination model, when the water level fluctuation inspection result is determined to be true, determining the water level fluctuation self-correction model as an H-ADCP index flow rate section water level self-correction model, executing the step S6, and when the water level fluctuation inspection result is determined to be false, returning to the step S2;
and step S4: when the fluctuation characteristic data of the water level of the flow measuring section is determined to be smaller than the preset fluctuation data of the water level of the flow measuring section, respectively determining the average flow velocity data of the section and the H-ADCP index flow velocity data as a high flow velocity sample set and a low flow velocity sample set in a data sample dividing mode;
step S5: constructing an H-ADCP index high-flow-rate self-correction model according to the high-flow-rate sample set, and respectively constructing an H-ADCP index low-flow-rate self-correction model according to the low-flow-rate sample set;
step S6: respectively carrying out precision evaluation and inspection on the H-ADCP index flow rate section water level self-correction model, the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model to generate an H-ADCP index flow rate section water level inspection result, an H-ADCP index high flow rate inspection result and an H-ADCP index low flow rate inspection result, and returning to the step S4 if the H-ADCP index flow rate section water level inspection result, the H-ADCP index high flow rate inspection result or the H-ADCP index low flow rate inspection result are determined to be false;
step S7: respectively carrying out error calculation on the H-ADCP index high flow rate inspection 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 empirical verification on the water level fluctuation self-correction model and the low flow measurement section water level fluctuation self-correction model by an actual measurement flow process line method to generate an empirical verification result, returning to the step S2 when the empirical verification result is determined to be false, and executing H-ADCP section average flow rate self-correction operation when the empirical verification result is determined to be true.
The embodiment constructs models with different inputs and corresponding outputs through numerical calculation and analysis of measured data, and calculates error numerical values of different layers, so that a reliable self-correcting model is provided, the self-correcting model under each scene is verified, the accuracy of H-ADCP section surveying and mapping is improved, and the automatic accurate test of the flow speed and the flow of the multistage hydrological station 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: carrying out data acquisition operation through corresponding flow ratio equipment in a section data acquisition mode and the 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 channel section, and calculating according to the historical water level data of the river channel section, the average section flow rate data and H-ADCP index flow rate data through a water level fluctuation discrete coefficient calculation formula to generate current measuring section water level fluctuation characteristic data;
the water level fluctuation dispersion coefficient calculation formula is specifically as follows:
In the embodiment, the water level fluctuation characteristic numerical value calculation is carried out on the measured data through the historical water level data of the river channel section, so that the water level fluctuation characteristic data of the current measuring section is generated, and the preparation work is carried out on the premise 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 mean flow velocity data of the section and the H-ADCP index flow velocity data>
Individual water level data->
And the number of the detected water level data is greater or less>
And the variance of the historical water level data->
And the interaction relationship between the two, so as to form a functional relationship:
and
thereby providing accurate and reliable data support.
In one embodiment of the present description, step S2 includes the steps of:
step S21: judging whether the water level fluctuation characteristic data of the flow measuring section is larger than or equal to the preset water level fluctuation data of the flow measuring section;
step S22: when the water level fluctuation characteristic data of the flow measuring section is determined to be larger than or equal to the preset water level fluctuation data of the flow measuring section, calculating through a fluctuation water level self-correction model calculation formula according to the average flow rate data of the section and the H-ADCP index flow rate data, and accordingly constructing a water level fluctuation self-correction model;
step S23: when the fluctuation characteristic data of the water level of the flow measuring section is determined to be smaller than the preset fluctuation data of the water level of the flow measuring section, executing the step S4;
the self-correcting model construction calculation formula specifically comprises the following steps:
In the embodiment, the characteristic data of the fluctuation of the water level of the current measuring section is compared with the preset fluctuation data of the water level of the current measuring section, when the characteristic data is determined to be greater than or equal to the preset data, a calculation mode is established according to a self-correcting model to establish a model, and when the characteristic data is determined to be smaller than the preset data, the step S4 is executed, so that the adaptability to a scene is improved.
This embodiment provides a self-calibration model building calculation formula, which fully considers the measured flow rate of H-ADCP in the H-ADCP flow rate data
The actually measured depth information of the water level->
The first constant information->
The second constant information->
And the third constant information->
And the fourth constant information->
And the fifth constant information->
And the sixth constant information->
And the functional relationship between each other, thereby forming the functional relationship:
providing reliable data support.
In one embodiment of the present description, step S3 includes the steps of:
step S31: carrying out inspection calculation on the water level fluctuation self-correction model through a deterministic coefficient calculation formula to generate a first water level fluctuation inspection result;
step S32: when the first water level fluctuation inspection result is determined to be true, the water level fluctuation self-correction model is inspected and calculated through a standard deviation and random uncertainty inspection calculation formula to generate a second water level fluctuation inspection result, and if not, the step S2 is returned to;
step S33: when the second water level fluctuation inspection result is determined to be true, inspecting the water level fluctuation self-correction model by a three-line inspection method to generate a third water level fluctuation inspection result, and if the third water level fluctuation inspection result is determined to be true, determining the water level fluctuation self-correction model as an H-ADCP index flow rate section water level self-correction model;
step S34: and returning to the step S2 when the third water level fluctuation checking result is determined to be false.
In the embodiment, the water fluctuation self-correction model is checked and calculated by determining the coefficient calculation formula, the standard deviation and random uncertainty check calculation formula and the three-line check method, and when an unqualified intermediate result occurs, the step S2 is returned to perform the model construction step again, so that the interference of unqualified data is reduced, and the accuracy of a final result is influenced.
In one embodiment of the present specification, step S5 includes the steps of:
calculating by a flow rate sample self-correction calculation formula according to the high flow rate sample set, thereby constructing an H-ADCP index high flow rate self-correction model;
and calculating by using a flow rate sample self-correction calculation formula according to the low flow rate sample set, thereby constructing an H-ADCP index low flow rate self-correction model.
In the embodiment, the high-flow-rate self-correction model with the H-ADCP index is constructed by calculating through the flow-rate sample self-correction calculation formula according to the high-flow-rate sample set, and the low-flow-rate self-correction model with the H-ADCP index is constructed by calculating through the flow-rate sample self-correction calculation formula according to the low-flow-rate sample set, so that the corresponding self-correction model is constructed according to different conditions inside the collected data, and the adaptability of the adjustment of the subdivision number of the flow measurement section is improved.
In an embodiment of the present specification, the flow rate sample self-calibration 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 H-ADCP index flow velocity data
The first constant information->
The second constant information->
And the third constant information->
And the functional relationship between each other, thereby forming the functional relationship:
to provide accurate data support.
In one embodiment of the present specification, step S6 includes the steps of:
step S61: respectively carrying out inspection calculation on the H-ADCP index flow rate section water level self-correction model, the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model through a certainty coefficient calculation formula to generate a first H-ADCP index flow rate section water level inspection result, a first H-ADCP index high flow rate inspection result and a first H-ADCP index low flow rate inspection result, and returning to the step S4 if the first H-ADCP index flow rate section water level inspection result, the first H-ADCP index high flow rate inspection result or the first H-ADCP index low flow rate inspection result is determined to be false;
step S62: when the first H-ADCP index flow rate section water level inspection result or the first H-ADCP index high flow rate inspection result is determined to be true, the H-ADCP index flow rate section water level self-correction model or the H-ADCP index high flow rate self-correction model is inspected and calculated through a standard deviation and random uncertainty inspection calculation formula to generate a second H-ADCP index flow rate section water level inspection result or a second H-ADCP index high flow rate inspection result, and if the second H-ADCP index flow rate section water level inspection result or the second H-ADCP index high flow rate inspection result is determined to be false, the step S4 is returned;
step S63: when the second H-ADCP index flow rate section water level inspection result or the second H-ADCP index high flow rate inspection result is determined to be true, inspecting the H-ADCP index flow rate section water level self-correction model or the H-ADCP index high flow rate self-correction model by a three-wire inspection method to generate a third H-ADCP index flow rate section water level inspection result or a third H-ADCP index high flow rate inspection result;
step S64: and returning to the step S4 when the third H-ADCP index flow rate section water level inspection result or the third H-ADCP index high flow rate inspection result is determined to be false.
In the embodiment, the generated self-correcting model is checked and calculated through a deterministic coefficient calculation formula, a standard deviation and random uncertainty check calculation formula and a three-line check method, and when a check result containing a check failure occurs, the step S2 is returned to perform modeling operation again, so that the adaptive flexibility is ensured, and the possibility of inaccurate data caused by excessive errors is reduced.
In an embodiment of the present specification, the deterministic coefficient calculation formula is specifically:
the standard deviation and random uncertainty test calculation formula is specifically as follows:
wherein the three-line inspection method is a coincidence inspection method, a fitting line inspection method or a deviation value inspection method.
The present embodiment provides a deterministic coefficient calculation formula that takes into account
Measured flow rate>
And/or a second->
Value flow on the equation curve corresponding to the sub-actual measured flow>
The mean value of the measured flow rate>
And the functional relationship between each other, thereby forming the functional relationship:
the ratio of the value flow on the corresponding equation curve of the actual measurement flow and the actual measurement flow at each time is compared with the ratio of the average value of the actual measurement flow at each time, so that reliable data support is provided.
The present embodiment provides a standard deviation and random uncertainty test calculation formula that takes into account
Sub-measured flow rate>
And/or a second->
The value flow on the equation curve corresponding to the sub-measured flow is greater or less>
The number value of actually measured flowInformation and/or device>
And a random uncertainty>
And the functional relationship between each other to form the 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 average cross-section 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 measurement section water level fluctuation self-correction model according to the H-ADCP index high flow rate error set and the H-ADCP index low flow rate error set.
In the embodiment, the H-ADCP index flow rate section water level self-correction model and the H-ADCP index low flow rate self-correction model are subjected to error calculation respectively, so that the self-correction model with small error and high qualification rate is ensured under the scene, and accurate data support is provided.
In one embodiment of the present specification, step S8 includes the steps of:
step S81: performing empirical 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 flow measurement process line method, thereby obtaining water quantity error numerical value information;
step S82: judging whether the water quantity error numerical value information is less 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 the preset water quantity relative error value, generating a true proof verification result, and executing H-ADCP section average flow rate self-correction operation;
step S84: and when the water amount error value information is determined to be larger than the preset water amount relative error value, generating a false proof verification result, and returning to the step S2.
In the embodiment, a plurality of time intervals are selected through a final self-correction model obtained based on each scene, water quantity statistics in the time intervals of a station is carried out by adopting a continuous actual measurement flow process line method, the water quantity statistics obtained by comparison equipment or the water quantity discharged from a hydro junction nearby the upstream and the downstream are referred, water quantity error calculation and rationality analysis are carried out, the artificial self-correction model is reasonable when the relative error of the water quantity is within 5%, otherwise, an equation needs to be fitted again, and therefore the data reliability based on the scene self-adaptive adjustment is improved.
The H-ADCP-based section average flow velocity self-correction method is provided by taking an H-ADCP online monitor as a core component and combining technical means such as flow ratio measurement, multi-scene self-adaptive modeling, multiple inspection, water quantity analysis and the like, the problem that the error between the index flow velocity and the section actual flow velocity is large in different application scenes is solved, the high accuracy of continuously measured flow is ensured, and the automatic accurate test of the flow velocity and the flow quantity of a multi-stage hydrological station is realized. The device is suitable for real-time automatic monitoring of the section flow under various river channels, river networks and various flow speed scenes. The method provides powerful technical support for hydrologic distributed, cross-regional automatic and accurate hydrologic detection in the current complex geographic environment, and has wide practical significance.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting implementations with reference to the accompanying drawings in which:
fig. 1 and fig. 2 are flowcharts illustrating steps of a scene-adaptive-based H-ADCP cross-section average flow velocity self-correction method according to an embodiment;
FIG. 3 is a flow chart illustrating steps of a method for generating data of fluctuation characteristics of a water level of a current measuring section according to an embodiment;
FIG. 4 is a flow chart illustrating steps of a method for generating a model for self-correction of water level fluctuation according to an embodiment;
FIG. 5 is a flow chart illustrating steps of a water level fluctuation self-correction model verification calculation method according to an embodiment;
FIG. 6 is a flow diagram illustrating the steps of a self-correcting model verification calculation method of an embodiment;
FIG. 7 is a flow diagram that illustrates the steps of a self-correcting model validation method, according to one embodiment.
Detailed Description
The technical method of the invention is described in detail with reference to the accompanying drawings, and it is obvious that the described embodiments are a part of the embodiments of the invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and 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 the form of software, or in one or more hardware modules or integrated circuits, or in different network 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. For example, a first element may be termed a second element, and, similarly, a second element may be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In one embodiment, step one: and (obtaining samples), selecting appropriate flow rate ratio equipment according to the specific engineering construction river reach, and synchronously acquiring the ratio equipment and H-ADCP equipment to obtain the data samples of the actual flow rate and the index flow rate of the section. And judging the fluctuation characteristic of the water level of the flow measuring section according to the historical water level monitoring data of the river section.
At present, the BL station flow test is mainly carried out on a 50m section under the base by adopting an aerial ADCP (600 kHz). In the ADCP conventional flow measurement process, the requirements of acoustic Doppler flow measurement specifications (SL 337-2006) are strictly executed, considering that a BL station is affected by moisture and water storage and discharge of an upstream hydraulic junction, section flow measurement of two measurement times is carried out for one return measurement in each flow measurement, then the arithmetic mean value of the two measurement flow values is calculated, when the deviation of the flow value of each measurement time and the mean value is within +/-5%, the mean value is taken as an actually measured flow value, and if the deviation exceeds +/-5%, one return measurement is added to judge whether the flow changes greatly in a short time.
The collection and arrangement of the navigation type ADCP (600 kHz type) and H-ADCP synchronous ratio measurement data are completed in 8 months to 2022 months in 2021. In the synchronous data collection process, the water passing area of the H-ADCP online section is calculated by adopting the recalled water level (H) corresponding to the corresponding current measuring moment and the actually measured section data (H-ADCP online section, 80m on the basis). 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
). Taking the average value of the corresponding H-ADCP index flow speed data as the flow speed->
。
The BL station is a tidal river section and is influenced by the water storage and drainage of an upstream hydro-junction, so the BL station is suitable for a large water level fluctuation range or tidal application scene, and the acquired data samples are regarded as a whole to construct a system with H-ADCP index flow velocity
With section waterBit H is the input self-correcting model.
Step two: and (constructing a self-correcting model) aiming at the characteristics of large water level fluctuation and moisture sensitivity of the BL station, constructing the self-correcting model taking the H-ADCP index flow velocity Vindex and the section water level H as inputs. All data samples are regarded as a whole, and a binary quadratic equation is fitted by adopting a gradient descent algorithm.
All collected sample data points from 8 months to 6 months in 2022 are combined, and the total number of samples is 102, which is shown in tables 1 to 3. The actually measured flow variation is as follows: 194 to 5210m3/s, wherein the average flow velocity amplitude of the H-ADCP section is as follows: 1.18 to 4.76m/s, and the index flow velocity variation is as follows: 1.61 to 4.15m/s, and the actually measured water level amplitude is as follows: 0.8 to 6.16m.
Table 1:
table 2:
table 3:
the overall line rating result of 102 samples is shown in formula (1):
step four: and D, performing precision evaluation and inspection on the self-correcting model established in the step two, determining the self-correcting model to be the self-correcting model in the corresponding scene if the self-correcting model passes the inspection, and otherwise, re-performing parameter calibration on the model. And (4) performing empirical verification by adopting a water quantity statistical method in a time period based on a final self-correction equation.
And (4) performing three-line inspection by referring to a type of precision hydrological station, wherein all the inspections are passed. The results of the test are shown in Table 4 below.
Table 4 comprehensive line calculation flow error statistical table
The H-ADCP on-line monitoring flow is calculated according to a calibration relation, the H-ADCP on-line monitoring flow is compared with the actual measurement flow of the sailing ADCP, the relative error accounts for 85.0 percent within +/-5 percent, only the absolute value of the relative error of individual points exceeds 10 percent, the relative error accounts for 98.3 percent within +/-10 percent, and the statistical result is shown in a table 5.
TABLE 5 flow error statistics table
In the embodiment, self-correction modeling is performed on the H-ADCP index flow velocity and the section average flow velocity, a binary regression self-correction model considering the moisture sensitivity is established, the regression precision of index flow velocity and section average flow velocity curves is researched by introducing the hydrologic data compilation specification (SL 247-2012) water level-flow relation curve test, the regression flow and water quantity errors are respectively calculated, a calibration model is optimized, and the single measurement flow monitoring data precision is improved.
And the monitoring distortion flow is corrected by adopting an amplitude limiting filtering method, and the flow is monitored in real time by utilizing H-ADCP (H-ADCP) for editing, so that the problem that the standard precision requirement cannot be met by using traditional plug flow methods such as a water level flow relation and the like on the cross section of the observation station is solved. The defects of single related research application environment and single research method in the prior art are overcome, and the research of the embodiment provides ideas and experiences for popularization and application of H-ADCP under the condition of complex hydrological environment.
The 3.3km upstream of the BL hydrological station is built into an HZDJ hydro-junction in 2006, incoming water is influenced by the regulation and storage of the junction, the tide type in the downstream of 2007 is basically consistent with that of the SL (FW) station in the flood season, the flood season Hong Chao is mixed, and the hydrological situation is complex.
After the BL (II) station is affected by water reservoir regulation and moisture, the traditional whole-editing flow pushing methods such as a water level-flow relation method and the like cannot meet the standard precision requirement, and the water amount calculation error is large.
After the H-ADCP is operated, the H-ADCP is used for monitoring the flow in real time, a continuous actual flow measurement process line method is used for pushing flow, wherein flow fluctuation is abnormal due to floaters, ship passing or other noise influences in individual time periods, a limiting filtering method is used for correcting distorted flow, and the under-section drainage quantity in the time periods is obtained.
According to the method, three time intervals are randomly selected, the BL station and the HZDJ hydro junction are adopted to perform the integral point flow every two hours, the continuous actual flow measurement process line method is adopted to calculate the water quantity in the time intervals of the BL station, the ex-warehouse flow of the hydro junction is referred to perform water quantity rationality analysis, and the conclusion is valid.
(1) H-ADCP, voyage ADCP and a traditional current meter are synchronously measured in late 8 months in 2013, the 1 st time period is selected from 21/8 months, 10 hours and 10-8 months, 31/24 hours, and the measured flow variation in the time period is 80.2 to 747m3/s. In the calculation time period, the radial flow of the BL (II) station is 3.03 hundred million m < 3 >, the generated water amount of the Dongjiang hydro-junction is 3.04 hundred million m < 3 >, and the difference between the two is 0.07 percent, so that the radial flow in the time period is considered to be reasonable in comparative analysis.
(2) The water quantity of the open water season of the Ministry of winter and spring in 2013 is formally scheduled to begin in 10 months, the time interval from 0 day 1 to 10 days 24 days in 10 months in 2013 is selected as the 2 nd time interval, and the measured flow variation in the time interval is 87.4 to 578m3/s. In the calculation period, the runoff of the BL (second) station is 2.88 hundred million m < 3 >, the generated water quantity of the Dongjiang hydro-junction is 2.84 hundred million m < 3 >, and the difference between the runoff and the generated water quantity is 1.41%, so that the comparison and analysis of the runoff in the period are reasonable.
(3) The initial stage of the dragon boat water is selected to be the 3 rd time period from 0 day 1 month 6 to 10 days 24 days 2014, and the measured flow variation range in the time period is 99.7 to 1730m3/s. In the calculation period, the runoff of the BL (II) station is monitored on line to be 8.24 hundred million m < 3 >, the generated water quantity of the Dongjiang hydro-junction is 7.89 hundred million m < 3 >, and the generated water quantity is sharply reduced due to the influence of opening and closing of the gate within 6 months, 2 days and 10 hours to 18 hours. The two differ by 4.41% over the time period. The comparison analysis of the radial flow in the time segment is considered reasonable overall.
Referring to fig. 1 to 7, a method for self-correcting an average flow velocity of an H-ADCP cross section based on scene adaptation includes the following steps:
step S1: synchronously acquiring river reach hydrological factors through a flow ratio measuring device and an H-ADCO device so as to respectively acquire section average flow velocity data and H-ADCP index flow velocity data, and judging and calculating the section average flow velocity data and the H-ADCP index flow velocity data according to historical water level data of the river section to generate flow measuring section water level fluctuation characteristic data;
step S2: when the water level fluctuation characteristic data of the flow measuring section is determined to be larger than or equal to the preset water level fluctuation data of the flow measuring section, constructing a water level fluctuation self-correction model according to the average flow rate data of the section and the H-ADCP index flow rate data, and otherwise, executing the step S4;
and step S3: performing precision evaluation and inspection on the water level fluctuation self-correction model to generate a water level fluctuation inspection result, when the water level fluctuation inspection result is determined to be false, performing parameter calibration according to the water level fluctuation self-correction model to generate a water level fluctuation self-correction determination model, when the water level fluctuation inspection result is determined to be true, determining the water level fluctuation self-correction model as an H-ADCP index flow rate section water level self-correction model, executing the step S6, and when the water level fluctuation inspection result is determined to be false, returning to the step S2;
and step S4: when the fluctuation characteristic data of the water level of the flow measuring section is determined to be smaller than the preset fluctuation data of the water level of the flow measuring section, respectively determining the average flow velocity data of the section and the H-ADCP index flow velocity data as 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 acquired index flow rate data samples, the high flow rate sample set and the low flow rate sample set are divided by taking the median as the junction.
Step S5: constructing an H-ADCP index high-flow-rate self-correction model according to the high-flow-rate sample set, and respectively constructing an H-ADCP index low-flow-rate self-correction model according to the low-flow-rate sample set;
step S6: respectively carrying out precision evaluation and inspection on the H-ADCP index flow rate section water level self-correction model, the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model to generate an H-ADCP index flow rate section water level inspection result, an H-ADCP index high flow rate inspection result and an H-ADCP index low flow rate inspection result, and returning to the step S4 if the H-ADCP index flow rate section water level inspection result, the H-ADCP index high flow rate inspection result or the H-ADCP index low flow rate inspection result are determined to be false;
step S7: respectively carrying out error calculation on the H-ADCP index high flow rate inspection 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 performing empirical verification on the water level fluctuation self-correction model and the low flow measurement section water level fluctuation self-correction model by an actual measurement flow process line method to generate an empirical verification result, returning to the step S2 when the empirical verification result is determined to be false, and executing H-ADCP section average flow rate self-correction operation when the empirical verification result is determined to be true.
Specifically, for example, step one: selecting a flow ratio device, synchronously acquiring river reach hydrological factors with H-ADCP equipment to obtain average flow velocity of cross section
) And H-ADCP marker flow Rate (` H `)>
) A data sample. And judging the fluctuation characteristic of the water level of the flow measuring section according to the historical water level monitoring data of the river section.
Step two: regarding the river channel section with large water level fluctuation amplitude or tidal channel, the acquired data samples are regarded as a whole, and H-ADCP index flow velocity is constructed
And a self-correcting model taking the section water level H as input. And aiming at the monitoring section with small water level fluctuation amplitude, dividing the data sample into a high-flow-rate sample set and a low-flow-rate sample set.
Step three: for high flow sample set, constructing H-ADCP index flow rate
Is an input self-correcting model. For low flow sample sets, a marker flow rate @ was constructed separately from H-ADCP>
A self-correcting model taking the section water level H as input and taking the H-ADCP index flow speed->
Is an input self-correcting model.
Step four: and D, performing precision evaluation and inspection on the self-correcting model established in the second step and the third step, determining the self-correcting model to be the self-correcting model under the corresponding scene if the self-correcting model passes the inspection, and otherwise, re-performing parameter calibration on the model. And (4) respectively analyzing errors and qualification rate of the two self-correcting models in the low-flow-rate scene in the third step, and selecting the self-correcting model with small error and high qualification rate as the self-correcting model in the scene. And (4) performing empirical verification by adopting a time-interval water quantity statistical method based on the final self-correction equation obtained in each scene.
The embodiment constructs models with different inputs and corresponding outputs through the numerical calculation and analysis of measured data, and calculates error numerical values of different layers, so as to provide a reliable self-correcting model, perform empirical verification on the self-correcting model in each scene, improve the accuracy of H-ADCP section surveying and mapping, and realize the automatic accurate test of the flow speed and the flow of the multilevel hydrological station.
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: carrying out data acquisition operation through corresponding flow ratio equipment in a section data acquisition mode and the 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 channel section, and calculating according to the historical water level data of the river channel section, the average section flow rate data and H-ADCP index flow rate data through a water level fluctuation discrete coefficient calculation formula to generate current measuring section water level fluctuation characteristic data;
specifically, for example, in step 11, while it is determined that the H-ADCP is used for data acquisition, different flow ratio devices can be selected according to different situations for acquiring the cross-sectional average flow velocity data. Namely, selecting proper flow ratio equipment according to a specific engineering construction river reach.
At the wider part and deeper part of the water depth of a general river channel, an sailing type ADCP can be selected; the handheld ADV can be selected at the position where the river channel is narrow, the water depth is shallow and the flow speed is low; and when the river channel is narrow, the water depth is shallow, and the flow velocity is great, then can select traditional current meter, measure flow and section area to obtain section average velocity of flow data.
When data are collected, the time synchronization of the H-ADCP and various comparison equipment is ensured, and data recording is started; under the conditions of different water levels and different flow magnitudes, multiple comparison measurements are preferably carried out to ensure the data accuracy of average flow velocity of the comparison measurements, minimize the data error and have better representativeness.
And step 12, collecting historical water level monitoring data of the river channel section, measuring the water level fluctuation characteristic by using a dispersion coefficient, and determining that the section water level fluctuation amplitude is large when the dispersion coefficient is larger than or equal to 50%, or determining that the water level fluctuation amplitude is small.
The water level fluctuation discrete coefficient calculation formula is specifically as follows:
In the embodiment, the measured data is subjected to water level fluctuation characteristic numerical value calculation through the historical water level data of the river channel section, so that the water level fluctuation characteristic data of the current measuring section is generated, and the preparation work is prepared 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 mean flow velocity data of the section and the H-ADCP index flow velocity data>
Individual water level data->
And the number of the detected water level data is greater or less>
And the variance of the historical water level data->
And in functional relationship with one another to form a functional relationship>
And
thereby providing accurate and reliable data support. />
In one embodiment of the present description, step S2 includes the steps of:
step S21: judging whether the water level fluctuation characteristic data of the flow measuring section is larger than or equal to the preset water level fluctuation data of the flow measuring section;
step S22: when the water level fluctuation characteristic data of the flow measuring section is determined to be larger than or equal to the preset water level fluctuation data of the flow measuring section, calculating through a fluctuation water level self-correction model calculation formula according to the average flow rate data of the section and the H-ADCP index flow rate data, and accordingly constructing a water level fluctuation self-correction model;
step S23: when the fluctuation characteristic data of the water level of the flow measuring section is determined to be smaller than the preset fluctuation data of the water level of the flow measuring section, executing the step S4;
specifically, for example, for an application scenario where the water level fluctuation amplitude is large or a tidal situation is felt, a self-correction model with H-ADCP index flow velocity V _ index and section water level H as inputs is constructed by using all samples. And aiming at the monitoring section with small water level fluctuation amplitude, dividing the data sample into a high-flow-rate sample set and a low-flow-rate sample set.
The self-correcting model construction calculation formula specifically comprises the following steps:
In the embodiment, the fluctuation characteristic data of the water level of the current measuring section is compared with the preset fluctuation data of the water level of the current measuring section, when the fluctuation characteristic data is greater than or equal to the preset data, a calculation mode is established according to a self-correcting model to establish a model, and when the fluctuation characteristic data is smaller than the preset data, the step S4 is executed, so that the adaptability to the scene is improved.
This embodiment provides a self-calibration model building calculation formula, which fully considers the measured H-ADCP index flow rate in the H-ADCP index flow rate data
The actually measured depth information of the water level->
The first constant information->
The second constant information->
And the third constant information->
And the fourth constant information->
And the fifth constant information->
And the sixth constant information->
And the functional relationship between each other, thereby forming the functional relationship:
providing reliable data support.
In one embodiment of the present description, step S3 includes the steps of:
step S31: carrying out inspection calculation on the water level fluctuation self-correction model through a deterministic coefficient calculation formula to generate a first water level fluctuation inspection result;
step S32: when the first water level fluctuation inspection result is determined to be true, the water level fluctuation self-correction model is inspected and calculated through a standard deviation and random uncertainty inspection calculation formula to generate a second water level fluctuation inspection result, and if not, the step S2 is returned to;
step S33: when the second water level fluctuation inspection result is determined to be true, inspecting the water level fluctuation self-correction model by a three-line inspection method to generate a third water level fluctuation inspection result, and if the third water level fluctuation inspection result is determined to be true, determining the water level fluctuation self-correction model as an H-ADCP index flow rate section water level self-correction model;
step S34: and returning to the step S2 when the third water level fluctuation checking result is determined to be false.
Specifically, for example, a quadratic equation with one unit is used for constructing a self-correcting model under an application scene with small water level fluctuation amplitude and high flow rate;
respectively constructing a self-correcting model under the application scene of small water level fluctuation amplitude and low flow rate by adopting a unitary quadratic equation and a binary quadratic equation;
in the embodiment, the water fluctuation self-correction model is checked and calculated by determining the coefficient calculation formula, the standard deviation and random uncertainty check calculation formula and the three-line check method, and when an unqualified intermediate result occurs, the step S2 is returned to perform the model construction step again, so that the interference of unqualified data is reduced, and the accuracy of a final result is influenced.
In one embodiment of the present specification, step S5 includes the steps of:
calculating by a flow rate sample self-correction calculation formula according to the high flow rate sample set, thereby constructing an H-ADCP index high flow rate self-correction model;
and calculating by using a flow rate sample self-correction calculation formula according to the low flow rate sample set, thereby constructing an H-ADCP index low flow rate self-correction model.
Specifically, for example, a quadratic equation with one unit is used for constructing a self-correcting model under an application scene with small water level fluctuation amplitude and high flow rate;
a self-correcting model under the application scene of small water level fluctuation amplitude and low flow rate is constructed by adopting a one-dimensional quadratic equation and a two-dimensional quadratic equation respectively.
In the embodiment, the high-flow-rate self-correction model with the H-ADCP index is constructed by calculating through the flow-rate sample self-correction calculation formula according to the high-flow-rate sample set, and the low-flow-rate self-correction model with the H-ADCP index is constructed by calculating through the flow-rate sample self-correction calculation formula according to the low-flow-rate sample set, so that the corresponding self-correction model is constructed according to different conditions inside the collected data, and the adaptability of the adjustment of the subdivision number of the flow measurement section is improved.
In an embodiment of the present specification, the flow rate sample self-calibration 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 H-ADCP index flow velocity data
The first constant information->
Second constant information +>
And the third constant information->
And in a functional relationship with one another, 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 carrying out inspection calculation on the H-ADCP index flow rate section water level self-correction model, the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model through a certainty coefficient calculation formula to generate a first H-ADCP index flow rate section water level inspection result, a first H-ADCP index high flow rate inspection result and a first H-ADCP index low flow rate inspection result, and returning to the step S4 if the first H-ADCP index flow rate section water level inspection result, the first H-ADCP index high flow rate inspection result or the first H-ADCP index low flow rate inspection result is determined to be false;
step S62: when the first H-ADCP index flow rate section water level inspection result or the first H-ADCP index high flow rate inspection result is determined to be true, the H-ADCP index flow rate section water level self-correction model or the H-ADCP index high flow rate self-correction model is inspected and calculated through a standard deviation and random uncertainty inspection calculation formula to generate a second H-ADCP index flow rate section water level inspection result or a second H-ADCP index high flow rate inspection result, and if the second H-ADCP index flow rate section water level inspection result or the second H-ADCP index high flow rate inspection result is determined to be false, the step S4 is returned;
step S63: when the second H-ADCP index flow rate section water level inspection result or the second H-ADCP index high flow rate inspection result is determined to be true, inspecting the H-ADCP index flow rate section water level self-correction model or the H-ADCP index high flow rate self-correction model by a three-wire inspection method to generate a third H-ADCP index flow rate section water level inspection result or a third H-ADCP index high flow rate inspection result;
step S64: and returning to the step S4 when the third H-ADCP index flow rate section water level inspection result or the third H-ADCP index high flow rate inspection result is determined to be false.
In the embodiment, the generated self-correcting model is checked and calculated through a deterministic coefficient calculation formula, a standard deviation and random uncertainty check calculation formula and a three-line check method, and when a check result containing a check failure occurs, the step S2 is returned to perform modeling operation again, so that the adaptive flexibility is ensured, and the possibility of inaccurate data caused by excessive errors is reduced.
In an embodiment of the present specification, the deterministic coefficient calculation formula is specifically:
the standard deviation and random uncertainty test calculation formula is specifically as follows:
specifically, for example, the system standard deviation versus the uncertainty threshold is referenced in table 6 below:
table 6 relation alignment accuracy index table
If the model is matched with the model, the model passes the test, otherwise, the corresponding modeling step is returned.
Wherein the three-line test method is a coincidence test method, an adaptive line test method or a deviation value test method.
Specifically, for example, the significance level a value is selected and the threshold value is determined according to the following rules:
checking the symbols, wherein the value a adopts 0.25, and the critical value is determined according to the table 7;
performing line fitting test, wherein the value a is 0.05-0.10, and the critical value is determined according to the table 7;
and (4) deviation value checking: the value a is 0.10 to 0.20, and the critical value is determined according to the table 8.
Symbol checking: respectively counting the number of positive signs and negative signs of the deviation curve of the measuring points, taking the deviation value as zero, distributing the deviation value as the positive sign measuring point and the negative sign measuring point respectively, calculating a statistic u value according to the following formula, and comparing the statistic u value with a u1-a/2 value obtained by looking up a table 7 by using a given significance level a. When u < u1-a/2 is calculated. Then it is considered reasonable, i.e. accepting the assumption; otherwise the original hypothesis should be rejected.
In the formula: u is a statistic; n is the total number of the measuring points; k is the number of positive signs or negative signs, and each k is 0.5; p and q are positive and negative probabilities, each 0.5;
and (4) line fitting inspection: and counting deviation positive and negative sign changes from a second point according to the arrangement sequence of the water levels of the measuring points from low to high, wherein the change sign is marked with 1, and otherwise, the change sign is marked with 0. Counting the times of recording 1, calculating a u value according to the following formula, comparing the u value with a u1-a value obtained by looking up a table 7 with a given significance level a, and if u is less than u1-a, judging that the u is reasonable, namely, accepting the test; otherwise the original hypothesis should be rejected.
Wherein u is a statistic; n is the total number of the measuring points; k is the number of times of positive sign or negative sign, and if k is less than 0.5 (n-1), the test is carried out, otherwise, the test is not carried out.
TABLE 7 Critical value u 1-a/2 And u 1-a
And (4) deviation value checking: respectively calculating t value,
The value of t1-a/2 is obtained by looking up table 8 with a given significance level a, comparing the value of t with the value of t1-a/2 when t is<t1-a/2, the method is considered to be reasonable, namely, the hypothesis is accepted; otherwise the original hypothesis should be rejected.
t=
In the formula: t is the statistical quantity and is the statistical quantity,
is the average relative deviation value; />
Is->
Standard deviation of (d); s is the standard deviation of p; n, the total number of the measuring points, and pi is the relative deviation value of the measuring points and the relation curve.
TABLE 8 Critical value t 1-a/2
If all the three items are in accordance, the test is passed, otherwise, the corresponding modeling step is returned.
The present embodiment provides a deterministic coefficient calculation formula that takes into account
Measured flow rate>
And/or a second->
Value flow on the equation curve corresponding to the sub-actual measured flow>
The mean value of the measured flow rate>
And the functional relationship between each other, thereby forming the functional relationship: />
The ratio of the value flow on the corresponding equation curve of the actual measurement flow and the actual measurement flow at each time is compared with the ratio of the average value of the actual measurement flow at each time, so that reliable data support is provided.
The present embodiment provides a standard deviation and random uncertainty test calculation formula that takes into account
Measured flow rate>
And/or a second->
The value flow on the equation curve corresponding to the sub-measured flow is greater or less>
The information of the measured flow times value>
And a random uncertainty>
And the functional relationship between each other to form the 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 flow rate section water level 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 flow rate section water level error set and an H-ADCP index low flow rate error set;
and determining a low flow measurement section water level fluctuation self-correction model according to the H-ADCP index flow rate section water level error set and the H-ADCP index low flow rate error set.
Specifically, for example, based on the measured flow velocity, the maximum error, the minimum error, the average error, and the yield index are calculated for two self-calibration models in a low flow velocity scene, and the final self-calibration model in the scene is selected as the one with a small error and a high yield.
In the embodiment, error calculation is respectively carried out on the H-ADCP index flow rate section water level self-correction model and the H-ADCP index low flow rate self-correction model, so that the self-correction model under the scene with small error and high qualification rate is ensured, and accurate data support is provided.
In one embodiment of the present specification, step S8 includes the steps of:
step S81: performing empirical 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 flow measurement process line method, thereby obtaining water quantity error numerical value information;
step S82: judging whether the water quantity error numerical value information is less 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 the preset water quantity relative error value, generating a true empirical verification result, and executing H-ADCP section average flow rate self-correction operation;
step S84: and when the water quantity error value information is determined to be larger than the preset water quantity relative error value, generating a false proof verification result, and returning to the step S2.
Specifically, for example, based on the final self-correction model obtained in each scene, a plurality of time intervals are selected, water quantity statistics is performed in the time intervals of the site by adopting a continuous actually-measured flow process line method, the statistical water quantity obtained by comparison equipment or the water quantity discharged from a hydro junction nearby the upstream and downstream are referenced, water quantity error calculation and rationality analysis are performed, if the relative error of the water quantity is within 5%, the self-correction model is considered to be rational, otherwise, an equation (set) needs to be fitted again, and the step S2 is returned.
In the embodiment, a plurality of time intervals are selected through a final self-correction model obtained based on each scene, water quantity statistics in the time intervals of a station is carried out by adopting a continuous actual measurement flow process line method, the water quantity statistics obtained by comparison equipment or the water quantity discharged from a hydro junction nearby the upstream and the downstream are referred, water quantity error calculation and rationality analysis are carried out, the artificial self-correction model is reasonable when the relative error of the water quantity is within 5%, otherwise, an equation needs to be fitted again, and therefore the data reliability based on the scene self-adaptive adjustment is improved.
According to the invention, the H-ADCP online monitor is used as a core component, and the H-ADCP-based section average flow velocity self-correction method is provided by combining technical means such as flow ratio measurement, multi-scene self-adaptive modeling, multiple inspection, water quantity analysis and the like, so that the problem of large error between the index flow velocity and the section actual flow velocity in different application scenes is solved, the high precision of continuously measured flow is ensured, and the automatic precision test of the flow velocity and the flow quantity of the multistage hydrological station is realized. The device is suitable for real-time automatic monitoring of the section flow under various river channels, river networks and various flow speed scenes. The method provides powerful technical support for hydrologic distributed, cross-regional automatic and accurate hydrologic detection in the current complex geographic environment, 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 are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present 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 (10)
1. A scene self-adaptive H-ADCP section average flow velocity self-correction method is characterized by comprising the following steps:
step S1: synchronously acquiring river reach hydrological factors through a flow ratio measuring device and an H-ADCO device so as to respectively obtain section average flow velocity data and H-ADCP index flow velocity data, and judging and calculating the section average flow velocity data and the H-ADCP index flow velocity data according to historical water level data of the river section to generate current measuring section water level fluctuation characteristic data;
step S2: when the water level fluctuation characteristic data of the flow measuring section is determined to be larger than or equal to the preset water level fluctuation data of the flow measuring section, constructing a water level fluctuation self-correction model according to the average flow rate data of the section and the H-ADCP index flow rate data, and otherwise, executing the step S4;
and step S3: performing precision evaluation and inspection on the water level fluctuation self-correction model to generate a water level fluctuation inspection result, when the water level fluctuation inspection result is determined to be false, performing parameter calibration according to the water level fluctuation self-correction model to generate a water level fluctuation self-correction determination model, when the water level fluctuation inspection result is determined to be true, determining the water level fluctuation self-correction model as an H-ADCP index flow rate section water level self-correction model, executing the step S6, and when the water level fluctuation inspection result is determined to be false, returning to the step S2;
and step S4: when the fluctuation characteristic data of the water level of the flow measuring section is determined to be smaller than the preset fluctuation data of the water level of the flow measuring section, respectively determining the average flow velocity data of the section and the H-ADCP index flow velocity data as a high flow velocity sample set and a low flow velocity sample set in a data sample dividing mode;
step S5: constructing an H-ADCP index high-flow-rate self-correction model according to the high-flow-rate sample set, and respectively constructing an H-ADCP index low-flow-rate self-correction model according to the low-flow-rate sample set;
step S6: respectively carrying out precision evaluation and inspection on the H-ADCP index flow rate section water level self-correction model, the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model to generate an H-ADCP index flow rate section water level inspection result, an H-ADCP index high flow rate inspection result and an H-ADCP index low flow rate inspection result, and returning to the step S4 if the H-ADCP index flow rate section water level inspection result, the H-ADCP index high flow rate inspection result or the H-ADCP index low flow rate inspection result are determined to be false;
step S7: respectively carrying out error calculation on the H-ADCP index high flow rate inspection 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 empirical verification on the water level fluctuation self-correction model and the low flow measurement section water level fluctuation self-correction model by an actual measurement flow process line method to generate an empirical verification result, returning to the step S2 when the empirical verification result is determined to be false, and executing H-ADCP section average flow rate self-correction operation when the empirical verification result is determined to be true.
2. The method according to claim 1, characterized in that step S1 comprises the steps of:
acquiring section data acquisition position information, and generating a position section data acquisition mode according to the section data acquisition position information;
carrying out data acquisition operation through corresponding flow ratio equipment in a section data acquisition mode and the 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 channel section, and calculating according to the historical water level data of the river channel section, the average section flow rate data and H-ADCP index flow rate data through a water level fluctuation discrete coefficient calculation formula to generate current measuring section water level fluctuation characteristic data;
the water level fluctuation discrete coefficient calculation formula is specifically as follows:
3. The method according to claim 1, characterized in that step S2 comprises the steps of:
judging whether the water level fluctuation characteristic data of the flow measuring section is larger than or equal to the preset water level fluctuation data of the flow measuring section;
when the water level fluctuation characteristic data of the current measuring section is determined to be larger than or equal to the preset water level fluctuation data of the current measuring section, calculating through a fluctuation water level self-correction model calculation formula according to the average flow rate data of the section and the H-ADCP index flow rate data, and accordingly constructing a water level fluctuation self-correction model;
when the fluctuation characteristic data of the water level of the flow measuring section is determined to be smaller than the preset fluctuation data of the water level of the flow measuring section, executing the step S4;
the self-correcting model construction calculation formula specifically comprises the following steps:
4. The method according to claim 1, characterized in that step S3 comprises the steps of:
carrying out inspection calculation on the water level fluctuation self-correction model through a deterministic coefficient calculation formula to generate a first water level fluctuation inspection result;
when the first water level fluctuation inspection result is determined to be true, the water level fluctuation self-correction model is inspected and calculated through a standard deviation and random uncertainty inspection calculation formula to generate a second water level fluctuation inspection result, and if not, the step S2 is returned to;
when the second water level fluctuation inspection result is determined to be true, inspecting the water level fluctuation self-correction model by a three-line inspection method to generate a third water level fluctuation inspection result, and if the third water level fluctuation inspection result is determined to be true, determining the water level fluctuation self-correction model as an H-ADCP index flow rate section water level self-correction model;
and returning to the step S2 when the third water level fluctuation checking result is determined to be false.
5. The method according to claim 1, wherein step S5 comprises the steps of:
calculating by a flow rate sample self-correction calculation formula according to the high flow rate sample set, thereby constructing an H-ADCP index high flow rate self-correction model;
and calculating by using a flow rate sample self-correction calculation formula according to the low flow rate sample set, thereby constructing an H-ADCP index low flow rate self-correction model.
6. The method of claim 5, wherein the flow rate sample self-calibration calculation formula is specifically:
7. The method according to claim 1, wherein step S6 comprises the steps of:
respectively carrying out inspection calculation on the H-ADCP index flow rate section water level self-correction model, the H-ADCP index high flow rate self-correction model and the H-ADCP index low flow rate self-correction model through a certainty coefficient calculation formula to generate a first H-ADCP index flow rate section water level inspection result, a first H-ADCP index high flow rate inspection result and a first H-ADCP index low flow rate inspection result, and returning to the step S4 if the first H-ADCP index flow rate section water level inspection result, the first H-ADCP index high flow rate inspection result or the first H-ADCP index low flow rate inspection result is determined to be false;
when the first H-ADCP index flow rate section water level inspection result or the first H-ADCP index high flow rate inspection result is determined to be true, the H-ADCP index flow rate section water level self-correction model or the H-ADCP index high flow rate self-correction model is inspected and calculated through a standard deviation and random uncertainty inspection calculation formula to generate a second H-ADCP index flow rate section water level inspection result or a second H-ADCP index high flow rate inspection result, and if the second H-ADCP index flow rate section water level inspection result or the second H-ADCP index high flow rate inspection result is determined to be false, the step S4 is returned;
when the second H-ADCP index flow rate section water level inspection result or the second H-ADCP index high flow rate inspection result is determined to be true, inspecting the H-ADCP index flow rate section water level self-correction model or the H-ADCP index high flow rate self-correction model by a three-wire inspection method to generate a third H-ADCP index flow rate section water level inspection result or a third H-ADCP index high flow rate inspection result;
and returning to the step S4 when the third H-ADCP index flow rate section water level inspection result or the third H-ADCP index high flow rate inspection result is determined to be false.
8. The method according to claim 4 or 7, wherein the deterministic coefficient calculation formula is specifically:
the standard deviation and random uncertainty test calculation formula is specifically as follows:
wherein the three-line inspection method is a coincidence inspection method, a fitting line inspection method or a deviation value inspection method.
9. 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 average flow-rate data of the cross section 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 measurement section water level fluctuation self-correction model according to the H-ADCP index high flow rate error set and the H-ADCP index low flow rate error set.
10. The method according to claim 1, wherein step S8 comprises the steps of:
performing empirical 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 flow measurement process line method, thereby obtaining water quantity error numerical value information;
judging whether the water quantity error numerical value information is less 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 the preset water quantity relative error value, generating a true empirical verification result, and executing H-ADCP section average flow rate self-correction operation;
and when the water quantity error value information is determined to be larger than the preset water quantity relative error value, generating a false proof verification result, and returning to the step S2.
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| CN116399305A (en) * | 2023-06-08 | 2023-07-07 | 广东省水文局惠州水文分局 | ADCP (automatic dependent control protocol) current measurement result on-site warehouse entry and self-correction method based on cloud platform |
| CN117148252A (en) * | 2023-10-31 | 2023-12-01 | 湖南智焜能源科技有限公司 | Switch measurement unit and multi-CT interconnection precision self-calibration method |
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| CN116399305A (en) * | 2023-06-08 | 2023-07-07 | 广东省水文局惠州水文分局 | ADCP (automatic dependent control protocol) current measurement result on-site warehouse entry and self-correction method based on cloud platform |
| CN117148252A (en) * | 2023-10-31 | 2023-12-01 | 湖南智焜能源科技有限公司 | Switch measurement unit and multi-CT interconnection precision self-calibration method |
| CN117148252B (en) * | 2023-10-31 | 2024-01-12 | 湖南智焜能源科技有限公司 | Switch measurement unit and multi-CT interconnection precision self-calibration method |
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