CN111551216A - Device and method for measuring flow in plain channel - Google Patents

Abstract

The embodiment of the invention discloses a plain channel flow measurement device and method, which is installed on the channel and includes: monitoring equipment and an information-based intelligent platform; the monitoring device includes: a radar current measurement main station; an upstream water level station, the upstream The water level station is arranged upstream of the main radar current measurement station; the downstream water level station is arranged downstream of the main radar current measurement station; the information intelligence platform is respectively connected to the main radar current measurement station , The upstream water level station and the downstream water level station are communicatively connected, and the information intelligent platform is used to collect the data monitored by the radar current measuring main station, the upstream water level station and the downstream water level station and calculate the flow. The embodiment of the present invention has the characteristics that when the radar current measurement enters the blind area, the flow is calculated according to the water level difference, so as to make up for the defect of the radar current measurement.

Description

Device and method for measuring flow in plain channel

technical field

Embodiments of the present invention relate to the technical field of water resources engineering, and in particular, to a device and method for measuring flow in a plain channel.

Background technique

In plain ditch flow measurement, a flow measurement device is installed at the head of the ditch to monitor the flow of water. The slope of the ditches in the plain area is very slow, which generally cannot meet the conditions for building water-volume buildings. Limited by the investment factor, in relatively small branch canals, the remote automatic monitoring station of flow mostly uses radar probes to measure the flow rate and water level, which is a better solution. Advantages of less and unattended. However, the start-up flow rate of the radar flow rate probe is relatively large (0.22m/s). When the flow rate is less than the start-up flow rate, the monitored flow rate is "zero", resulting in a blind area in the flow rate calculation when the flow rate is slow, which is a defect of this radar flow measurement method. Since plain canals, especially smaller branch canals, have both the functions of water storage and water delivery, the slow flow rate will also occur when the water is deep, and the error will be greater if the flow rate cannot be calculated.

SUMMARY OF THE INVENTION

The SPSS (Statistical Product and Service Solutions) mentioned in the present invention is the "Statistical Product and Service Solutions" software.

To this end, the embodiments of the present invention provide a device and method for measuring flow in a plain channel, so as to solve the problem of the blind area of flow measurement that occurs when the flow velocity is slow due to the radar flow measurement technology in the prior art.

In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:

According to a first aspect of the embodiments of the present invention, a plain channel flow measurement device is provided, which is installed on the channel and includes: a monitoring device and an information-based intelligent platform; the monitoring device includes:

Radar current measurement master station;

an upstream water level station, the upstream water level station is arranged upstream of the main radar current measuring station;

a downstream water level station, the downstream water level station is arranged downstream of the main radar current measuring station;

The information-based intelligent platform is respectively connected to the main radar current measurement station, the upstream water level station and the downstream water level station, and the information-based intelligent platform is used to collect the radar current measurement main station, the upstream water level station and the downstream water level station. Monitor the data and calculate the flow.

Optionally, the upstream water level station is provided with a first pressure-type water level probe, a first cable pipe, and a second collection and transmission device connected in sequence.

Optionally, the radar current measurement main station is provided with a radar probe, a second cable tube, and a first acquisition and transmission device connected in sequence.

Optionally, the informatization intelligent platform includes an automatic collection and upload module, an Internet of Things database and an intelligent algorithm module, and the Internet of Things database is respectively connected in communication with the automatic collection and upload module and the intelligent algorithm module. and the uploading module is respectively connected to the first collection and transmission device and the second collection and transmission device in communication.

Optionally, the automatic collection and uploading module includes a collection device, an Internet of Things protocol and a platform database, and the collection device transmits the collected monitoring data to the platform database through the Internet of Things protocol.

Optionally, the intelligent algorithm module includes a tracking learning unit, a historical data analysis unit and a data processing unit.

Optionally, the distance between the upstream water level station and the downstream water level station is greater than 300m.

Optionally, the distance between the radar probe and the water surface is greater than or equal to 2m.

Optionally, the radar probe includes a radar Doppler flow velocity probe and a radar water level probe, and the radar probe includes a Doppler flow velocity probe and a radar water level probe.

According to a second aspect of the embodiments of the present invention, there is provided a flow measurement method using the above-mentioned plain channel flow measurement device, comprising the following steps:

a. When the water flow in the channel is normal flow velocity, use radar Doppler flow velocity probe and radar water level probe to monitor the flow velocity and water level respectively; the second collection and transmission equipment collects the monitored flow velocity and water level and transmits them to the automatic collection and upload module , the automatic collection and upload module collects the monitored flow rate and water level and uploads them to the Internet of Things database, and the intelligent algorithm module calculates the flow rate Q1 through the data of the Internet of Things database;

C＝0引入SPSS拟合模块得出a、b；By condition

C=0 is introduced into the SPSS fitting module to obtain a and b;

Among them, Q1 _i is the flow rate calculated from the flow velocity and water level of the radar station; X _i is the difference between the upstream and downstream water levels; a and b are regression parameters; c is a constant;

At the same time, the upstream and downstream water levels are monitored by the first pressure type water level probe and the second pressure type water level probe; the first acquisition and transmission device collects the monitored flow rate and water level and transmits them to the automatic acquisition and upload module, which automatically collects and uploads. The monitored flow rate and water level are uploaded to the IoT database;

b. Repeat step a every 30 minutes, the water flow in the channel is gradually reduced to the starting flow rate, and multiple sets of "flow-water level difference" data are obtained; the intelligent algorithm module tracks data changes and performs regression analysis in real time, and updates the "flow-water level difference" in real time. ” parameter of the function relationship;

c. In the blind area of radar speed measurement, according to the water level difference monitored by the upstream and downstream water level stations and the current water depth, the intelligent algorithm module uses the data of the Internet of Things database to calculate the flow rate Q2 by using the "flow-water level difference" function relationship;

Q2=aX ² +bX+c

Among them, Q2 is the flow calculated according to the water level difference in the blind area of radar flow measurement.

The embodiments of the present invention have the following advantages:

The embodiment of the invention has the advantages of simple structure, low cost and novel design idea, which can make up for the blind area of radar flow measurement, thereby improving the flow measurement accuracy, and is beneficial to popularization and application in remote automatic monitoring of flow in plain channels. There is a blind spot in radar current measurement, which leads to a large error in current measurement at low flow rates. The embodiment of the present invention proposes a method of adding water level stations on the upstream and downstream of the water flow section to measure the upstream and downstream water levels to calculate the overflow at low flow rates, which makes up for the defects of radar flow measurement and improves the flow measurement accuracy under the premise of low cost. The intelligent algorithm module of the embodiment of the present invention relies on the main station of radar current measurement to "real-time automatically" fit regression parameters, and is characterized in that each time the radar current measurement enters the blind area, it uses the latest calibrated "water level difference-flow" relationship for calculation, The result is accurate.

Description of drawings

In order to illustrate the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that are required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, other implementation drawings can also be obtained according to the extension of the drawings provided without creative efforts.

The structures, proportions, sizes, etc. shown in this specification are only used to cooperate with the contents disclosed in the specification, so as to be understood and read by those who are familiar with the technology, and are not used to limit the conditions for the implementation of the present invention, so there is no technical The substantive meaning, any modification of the structure, the change of the proportional relationship or the adjustment of the size, without affecting the effect that the present invention can produce and the purpose that can be achieved, should still fall within the technical content disclosed in the present invention. within the scope of coverage.

1 is a schematic diagram of the layout of a plain channel flow measurement device provided by an embodiment of the present invention;

FIG. 2 is a schematic diagram of the composition of an information-based intelligent platform of a plain channel flow measurement device provided by an embodiment of the present invention;

3 is a schematic diagram of a radar current measurement master station of a plain channel current measurement device provided by an embodiment of the present invention;

4 is a schematic diagram of a water level station of a plain channel flow measurement device provided by an embodiment of the present invention;

5 is a schematic diagram of each functional unit and mutual relationship of an intelligent algorithm module of a plain channel flow measurement device provided by an embodiment of the present invention;

6 is a schematic diagram of a regression curve of the relationship between upstream and downstream water level elevation difference and flow rate of a plain channel flow measurement device provided by an embodiment of the present invention;

7 is a schematic diagram of the process of changing the water depth and the difference between the upstream and downstream water levels with time at a radar station of a plain channel flow measurement device provided by an embodiment of the present invention;

8 is a schematic diagram of a water level difference-flow data processing process with radar actual measurement data of a plain channel flow measurement device provided by an embodiment of the present invention;

9 is a schematic diagram of a water level difference-flow data processing process in the case of a small water level difference (the radar current measurement is in a blind area, and there is no radar actual measurement data) of a plain channel flow measurement device provided by an embodiment of the present invention;

In the figure: 1-upstream water level station; 11-first cable pipe; 12-second collection and transmission equipment; 13-sand filter tank; 14-first pressure water level probe; 2-radar current measurement main station; 21 - Radar probe; 211 - Radar Doppler velocity probe; 212 - Radar water level probe; 22 - First acquisition and transmission equipment; 3 - Downstream water level station; 4 - Water surface; 5 - Channel; 6 - Information intelligence platform; 61 -Automatic collection and upload module; 62-Internet of things database; 63-Intelligent algorithm module; 631-Tracking learning unit; 632-Historical data analysis unit; 633-Data processing unit; 7-Water flow section.

Detailed ways

The embodiments of the present invention are described below by specific specific embodiments. Those who are familiar with the technology can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the present invention. , not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

As shown in FIG. 1 to FIG. 9 , the embodiment of the present invention provides a plain channel flow measurement device, which is installed on the side wall of the channel 5 and includes: a monitoring device and an intelligent information platform; the monitoring device includes:

Radar current measurement master station 2;

Upstream water level station 1, the upstream water level station 1 is set upstream of the main radar current measurement station 2;

The downstream water level station 3 is arranged downstream of the main radar current measuring station 2;

The information-based intelligent platform 6 is respectively connected to the radar current measurement master station 2, the upstream water level station 1, and the downstream water level station 3, and the information-based intelligent platform 6 is used to collect the radar current measurement master station 2, the upstream water level station 1 and the downstream water level station 3. Monitor the data and calculate the flow.

The invention has the advantages of simple structure, low cost and novel design idea, can improve the measurement accuracy of radar flow measurement, and is favorable for popularization and application in remote automatic monitoring of flow in plain channels. There is a blind area in the radar current measurement of the plain channel, which leads to a large flow measurement error under the condition of low flow velocity. For example, when the flow rate is 0.15m/s and the water flow section 7 is 1m ² , the daily water flow is about 7000m ³ , which cannot be measured by radar flow measurement alone. In response to this problem, the present invention proposes a method of adding water level stations to measure the upstream and downstream water levels on the upstream and downstream of the water flow section 7 to calculate the overflow at low flow rates, which makes up for the defects of radar flow measurement and improves flow measurement under the premise of low cost. accuracy.

However, although the relationship between the upstream and downstream water levels alone is used to calculate the flow, although there is a relationship in theory, it has great limitations. The relationship between "water level difference and flow" is unstable, and the data is very discrete. The intelligent algorithm module 63 of the present invention relies on the radar current measurement master station 2 to "real-time and automatically" fit the regression parameters, and is characterized in that each time the radar current measurement enters the blind area, it uses the latest calibrated "water level difference-flow" relationship for calculation, The result is accurate.

As an optional embodiment of the present invention, as shown in FIG. 3 , the radar current measuring main station 2 is provided with a radar probe 21, and the radar probe 21 is as close to the water surface 4 as possible, and is only 2m higher than the highest water level, which is conducive to obtaining better Radar Doppler signal; in cross-section, the probe should be positioned as close to the center as possible to ensure maximum surface velocity.

As an optional embodiment of the present invention, as shown in FIG. 3 , the radar probe 21 includes a radar Doppler flow velocity probe 211 and a radar water level probe 212 , and the radar probe 21 includes a Doppler flow velocity probe 211 and a radar water level probe 212 . The flow velocity and water level of the detected water surface 4 are used for the calculation of the flow; automatic collection and reporting are completed through the first collection and transmission device 22 .

As an optional embodiment of the present invention, as shown in FIG. 4 , the upstream water level station 1 is provided with a first pressure-type water level probe 14, a first cable pipe, and a second acquisition and transmission device 12 connected in sequence; The station 2 is provided with a radar probe 21, a second cable tube, and a first acquisition and transmission device 22 connected in sequence; the first pressure water level probe 14 and the second pressure water level probe are used to sense the water depth, and pass the acquisition and transmission device 12. Complete automatic collection and reporting, and synchronously report to the automatic collection and uploading module 61 of the information intelligent platform 6 . The first pressure type water level probe 14 and the second pressure type water level probe are buried below the slope ground of the channel 5 and are not disturbed by water level scouring and debris blocking; the first pressure type water level probe 14 and the second pressure type water level probe should be low 50cm at the bottom of the canal to ensure the monitoring of the lowest water level; along the slope of the channel 5, there is a sand and gravel filter tank 13 that is not less than 100cm upward from the first pressure type water level probe 14 and the second pressure type water level probe, and the section of the sand and gravel filter tank 13 is not less than 100cm. It is 40*50cm, and the sand and gravel filter tank 13 is filled with sand and gravel, which has good water permeability and has a filtering effect to ensure the induction of the water level; the upstream and downstream water level stations are measured by leveling to determine the relative elevation.

As an optional embodiment of the present invention, as shown in FIG. 2 , the information-based intelligent platform 6 includes an automatic collection and upload module 61, an Internet of Things database 62, and an intelligent algorithm module 63. The Internet of Things database 62 is respectively connected with the automatic collection and upload module. 61 is connected in communication with the intelligent algorithm module 63, and the automatic collection and upload module 61 is connected in communication with the first collection and transmission device 22 and the second collection and transmission device 12 respectively.

As an optional embodiment of the present invention, the automatic collection and upload module 61 includes a collection device, an Internet of Things protocol, and a platform database. The data is collected synchronously and transmitted to the platform database through the Internet of Things protocol.

As an optional embodiment of the present invention, the IoT database 62 is used for storage and access services of monitoring data, and can be called by the information intelligence platform 6 .

As an optional embodiment of the present invention, as shown in FIG. 4 and FIG. 5 , the intelligent algorithm module 63 includes a tracking learning unit 631, a historical data analysis unit 632 and a data processing unit 633; the tracking learning unit 631 is a part of the intelligent algorithm module 63. The core is to track radar flow measurement and upstream and downstream water level data in real time, and the automatic learning function can calculate the correlation parameters of "flow-water level difference" in real time according to the measured data. The function of the historical data analysis unit 632 is to analyze the historical monitoring data, and determine the qualitative relationship of "flow-water level difference" under different water levels. The tracking learning unit 631 generates parameters of the "flow-water level difference" functional relationship by tracking the monitoring data. The data processing unit 633 selects different calculation methods to calculate the flow according to the current state of the current radar flow measurement and the current water depth.

As an optional embodiment of the present invention, as shown in FIG. 1 , there cannot be a pumping station water intake facility between the upstream water level station 1 and the downstream water level station 3, and there may be a bifurcation of the channel 5, and the upstream water level station 1 and the downstream water level station 3 The distance between them is greater than 300m.

As shown in FIG. 1 to FIG. 5 , an embodiment of the present invention further provides a method for measuring flow using the above-mentioned flow measuring device of a plain channel, including the following steps:

a. When the water flow in the channel is a normal flow velocity, the radar Doppler flow velocity probe 211 and the radar water level probe 212 are used to monitor the flow velocity and water level respectively; the second collection and transmission device 12 collects the monitored flow velocity and water level and transmits them to the automatic collection With the uploading module 61, the automatic collection and uploading module 61 collects the monitored flow rate and water level and uploads them to the Internet of Things database 62, and the intelligent algorithm module 63 calculates the flow rate Q1 through the data of the Internet of Things database 62; Specifically: the tracking learning unit 631 real-time Track radar flow measurement and upstream and downstream water level data, and the automatic learning function calculates the correlation parameters of "flow-water level difference" in real time according to the measured data. The historical data analysis unit 632 determines the qualitative relationship of "flow-water level difference" under different water levels. The tracking learning unit 631 generates parameters of the "flow-water level difference" functional relationship by tracking the monitoring data. The data processing unit 633 performs state identification according to the current state of the current radar current measurement and the current water depth, and selects different calculation methods to calculate the flow rate according to whether it is greater than the start flow rate, deep water, shallow water, MR marks and other different states;

C＝0引入SPSS拟合模块得出a、b；拟合条件的数据阵列随雷达测流数据不断更新，数据“0，0”代表水位差为零时流量也为零。回归参数a、b随雷达数据的更新而跟踪变化。By condition

C=0 is introduced into the SPSS fitting module to obtain a and b; the data array of the fitting conditions is continuously updated with the radar flow measurement data, and the data "0, 0" means that the flow is also zero when the water level difference is zero. The regression parameters a and b track changes with the update of radar data.

Among them, Q1 _i , in m ³ /s, is the flow rate calculated from the flow velocity and water level of the radar station; X _i = L1 _i -L2 _i , in cm, L1 _i and L2 _i are the upstream and downstream water level stations, respectively 3 Measured water level elevation;

is the upstream and downstream water level difference; a and b are regression parameters; c is a constant;

At the same time, the upstream and downstream water levels are monitored by the first pressure type water level probe 14 and the second pressure type water level probe; the first collection and transmission device 22 collects the monitored flow velocity and water level and transmits them to the automatic collection and upload module 61, which automatically collects and transmits The uploading module 61 collects the monitored flow rate and water level and uploads them to the IoT database 62;

b. Repeat step a every 30 minutes, the water flow in the channel is gradually reduced to the starting flow rate, and multiple sets of "flow-water level difference" data are obtained; the intelligent algorithm module 63 tracks the data changes and performs regression analysis in real time, and updates the "flow-water level difference" in real time. The parameter of the "difference" function relationship;

c. In the blind area of radar speed measurement, according to the water level difference monitored by the upstream and downstream water level stations and the current water depth, the intelligent algorithm module 63 calculates the flow rate Q2 by using the "flow-water level difference" functional relationship through the data of the Internet of Things database 62;

Q2=aX ² +bX+c

Among them, Q2, unit m ³ /s, is the flow calculated according to the water level difference in the blind spot of radar flow measurement.

Specifically, as shown in Figure 7 and Figure 8, in the figure:

H1, in m, is the water depth of the radar station;

T, in h, is the time from the monitoring record to the start of monitoring, and the monitoring frequency is once every half an hour. Here, some records are selected to illustrate the calculation and processing process;

Q3, unit m ³ /s, when there is no latest radar flow measurement data in this water diversion process, as shown in AU in Figure 7(1), select the flow calculated by the latest regression parameters of the same water depth in the database;

Q, unit m ³ /s, the flow measurement result after analysis and arrangement;

MR, recursive calculation and water level difference reset mark, 2 characters, M--0 is normal, 1 is waiting for "recursive" calculation, R--0 is normal, 1 is that the water level difference returns to zero.

As shown in Figure 7(1), it is summarized as "water level difference reset-blind area-radar actual measurement-blind area-water level difference reset" type, A-U, D-B are blind areas, U-D is the normal radar current measurement area; as shown in Figure 7(2) It can be summarized as "water level difference reset-blind zone-water level difference reset" type, the water diversion volume of channel 5 is small, the water level difference gradually increases, the flow rate does not reach the starting flow rate of the radar Doppler flow velocity probe 211, and then the water level difference gradually returns to zero, From point A to point B all run in the radar blind spot.

As shown in the A-U section of Fig. 7(1), as shown in Fig. 8(1), the water level difference begins to increase, but it is in the blind area. Select the flow calculated by the latest regression parameters of the same water depth in the database, and mark the MR as "10". .

As the water level difference increases, it begins to enter the U-D section, as shown in Figure 7(1) and Figure 8(2), the flow Q directly adopts the radar flow Q1, and from the data of more than 3 times of radar flow measurement, the regression parameter a is calculated one by one , b; then use the newly obtained regression parameters to calculate the flow Q for the preceding A-U segment, which is referred to as "recursive algorithm" for short, and mark MR as "00".

As shown in Section D-B in Figure 7(1) and Figure 8(2), use the regression parameters a and b of the last record in the data table to calculate the flow Q2, and directly write Q, here "real-time calibration real-time regression calculation flow" .

As shown in Figure 7(2) and Figure 9(1), the entire flow measurement is located in the blind area, and the flow calculated by the latest regression parameters of the same water depth in the database is selected, and the MR is marked as "10"; as shown in Figure 9(2) As shown, when the water level difference is reset again, the flow measurement data is processed so that Q=Q3, and the MR is marked as "00".

Although the present invention has been described in detail above with general description and specific embodiments, some modifications or improvements can be made on the basis of the present invention, which will be obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention fall within the scope of the claimed protection of the present invention.

Claims (10)

1. The utility model provides a plain channel flow measurement equipment, installs on the channel which characterized in that includes: monitoring equipment and an information intelligent platform; the monitoring device includes:

a radar current measurement master station;

the upstream water level station is arranged at the upstream of the radar current measuring main station;

the downstream water level station is arranged at the downstream of the radar flow measurement main station;

the intelligent information platform is in communication connection with the radar current measuring master station, the upstream water level station and the downstream water level station respectively, and is used for collecting data monitored by the radar current measuring master station, the upstream water level station and the downstream water level station and calculating flow.

2. The plain channel flow gauging apparatus of claim 1, wherein: the upstream water level station is provided with a first pressure type water level probe, a first cable pipe and a second acquisition and transmission device which are connected in sequence.

3. The plain channel flow gauging apparatus of claim 2, wherein: the radar current surveying main station is provided with a radar probe, a second cable pipe and a first acquisition and transmission device which are connected in sequence.

4. The plain channel flow gauging apparatus of claim 3, wherein: the intelligent information platform comprises an automatic acquisition and uploading module, an Internet of things database and an intelligent algorithm module, wherein the Internet of things database is in communication connection with the automatic acquisition and uploading module and the intelligent algorithm module respectively, and the automatic acquisition and uploading module is in communication connection with the first acquisition and transmission equipment and the second acquisition and transmission equipment respectively.

5. The plain channel flow gauging apparatus of claim 4, wherein: the automatic acquisition and uploading module comprises acquisition equipment, an Internet of things protocol and a platform database, wherein the acquisition equipment transmits acquired monitoring data to the platform database through the Internet of things protocol.

6. The plain channel flow gauging apparatus of claim 5, wherein: the intelligent algorithm module comprises a tracking learning unit, a historical data analysis unit and a data processing unit.

7. The plain channel flow gauging apparatus of claim 6, wherein: the distance between the upstream water level station and the downstream water level station is more than 300 m.

8. The plain channel flow gauging apparatus of claim 7, wherein: the distance between the radar probe and the water surface is more than or equal to 2 m.

9. The plain channel flow gauging apparatus of claim 8, wherein: the radar probe comprises a radar Doppler flow velocity probe and a radar water level probe, and the radar probe comprises a Doppler flow velocity probe and a radar water level probe.

10. A flow measuring method using the flow measuring apparatus of the plain canal of any one of claims 1 to 9, comprising the steps of:

a. when the water flow in the channel is at normal flow rate, respectively monitoring the flow rate and the water level by using a radar Doppler flow rate probe and a radar water level probe; the second acquisition and transmission equipment acquires and transmits the monitored flow rate and water level to the automatic acquisition and uploading module, the automatic acquisition and uploading module acquires and uploads the monitored flow rate and water level to the Internet of things database, and the intelligent algorithm module calculates the flow Q1 according to the data in the Internet of things database;

according to the condition

C is 0, an SPSS fitting module is introduced to obtain a and b;

wherein, Q1_iThe flow rate is calculated according to the flow velocity and the water level of the radar measuring station; x_iIs the difference between the water level of the upstream and the downstream; a. b is a regression parameter; c is a constant;

simultaneously monitoring the upstream and downstream water levels by a first pressure type water level probe and a second pressure type water level probe; the first acquisition and transmission equipment acquires and transmits the monitored flow rate and water level to the automatic acquisition and uploading module, and the automatic acquisition and uploading module acquires and uploads the monitored flow rate and water level to the Internet of things database;

b. repeating the step a every 30 minutes, and gradually reducing the water flow in the channel to the starting flow velocity to obtain a plurality of groups of flow-water level difference data; the intelligent algorithm module tracks the data change to perform regression analysis in real time, and updates the parameters of the function relationship of the flow-water head in real time;

c. in a blind area of radar speed measurement, according to the water level difference monitored by the upstream and downstream water level stations and the current water depth, the intelligent algorithm module calculates the flow Q2 by adopting a flow-water level difference functional relation through data of an Internet of things database;

Q2＝aX²+bX+c

wherein, Q2 is the flow that calculates according to the water head when radar current surveying blind area.

Images