CN111551216B - Plain channel flow measurement equipment and method - Google Patents

Abstract

The embodiment of the present invention discloses a plain channel flow measurement equipment and method, installed on the channel, including: monitoring equipment and information intelligent platform; the monitoring equipment includes: radar flow measurement main station; upstream water level station, the upstream The water level station is arranged upstream of the radar current measuring main station; the downstream water level station is arranged downstream of the radar current measuring main station; the information intelligent platform is connected with the radar current measuring main station respectively , the upstream water level station, and the downstream water level station are connected by communication, and the information-based intelligent platform is used to collect the data monitored by the radar current measurement master 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 of calculating the flow rate according to the water level difference when the radar flow measurement enters the blind area, so as to make up for the defect of the radar flow measurement.

Description

A device and method for measuring flow in plain channels

technical field

The 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 plain channels.

Background technique

Flow measurement in plain ditches is to install a flow measurement device 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-measuring structures, and the flow rate and water level monitoring can only be used to calculate the flow rate. Limited to investment factors, in relatively small branch canals, the remote automatic monitoring station of the flow usually uses radar probes to measure the flow velocity and water level, which is a better solution. Non-contact measurement can avoid the interference of debris and silt in the water, and has investment Advantages of being less and unattended. However, the starting flow velocity of the radar flow rate probe is relatively large (0.22m/s). When the flow velocity is lower than the starting flow velocity, the monitored flow rate is "zero", resulting in a blind area in flow calculation when the flow velocity is slow. This is the defect of this radar flow measurement method. Since the plain channels, especially the smaller branch channels, have both the functions of water storage and water delivery, the slow flow will also occur when the water is deep. If the flow cannot be calculated, the error will be even greater.

Contents of the invention

SPSS (Statistical Product and Service Solutions) mentioned in the present invention is "statistical product and service solution" software.

For this reason, the embodiments of the present invention provide a device and method for measuring flow in plain channels to solve the problem of flow measurement blind spots in the prior art due to radar flow measurement technology when the flow velocity is slow.

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

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

Radar current measurement master station;

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

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

The information intelligent platform is respectively connected with the radar current measuring main station, the upstream water level station, and the downstream water level station, and the information intelligent platform is used to collect the radar flow measuring main station, the upstream water level station and the downstream water level station Monitored data and calculate flow.

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

Optionally, the radar flow measurement master station is provided with a radar probe, a second cable pipe, and a first collection and transmission device connected in sequence.

Optionally, the information-based 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 to the automatic collection and upload module and the intelligent algorithm module by communication. and the uploading module are connected in communication with the first collection and transmission device and the second collection and transmission device respectively.

Optionally, the automatic collection and upload 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 velocity probe and a radar water level probe, and the radar probe includes a Doppler velocity probe and a radar water level probe.

According to the second aspect of the embodiments of the present invention, there is provided a flow measurement method using the above-mentioned flow measurement equipment in plain channels, including the following steps:

a. When the water flow in the channel is at a normal flow rate, the radar Doppler flow rate probe and the radar water level probe are used to monitor the flow rate and water level respectively; the second acquisition and transmission device collects the monitored flow rate and water level and transmits them to the automatic acquisition and upload module , the automatic collection and upload module collects the monitored flow velocity and water level and uploads them to the Internet of Things database, and the intelligent algorithm module calculates the flow 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 introduces the SPSS fitting module to obtain a and b;

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

At the same time, the upstream and downstream water levels are monitored through the first pressure-type water level probe and the second pressure-type water level probe; the first collection and transmission device collects the monitored flow rate and water level and transmits them to the automatic collection and upload module, and the automatic collection and upload module collects 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 gradually decreases to the starting flow rate, and multiple sets of "flow-water level difference" data are obtained; the intelligent algorithm module tracks the data changes and performs regression analysis in real time, and updates the "flow-water level difference" in real time ” The parameters of the functional 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 calculates the flow Q2 using the "flow-water level difference" function relationship through the data of the Internet of Things database;

Q2＝ ^aX2 +bX+c

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

Embodiments of the present invention have the following advantages:

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

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings that are required in the description of the embodiments or the prior art. Apparently, the drawings in the following description are only exemplary, and those skilled in the art can also obtain other implementation drawings according to the provided drawings without creative work.

The structures, proportions, sizes, etc. shown in this manual are only used to cooperate with the content disclosed in the manual, so that people familiar with this technology can understand and read, and are not used to limit the conditions for the implementation of the present invention, so there is no technical In the substantive meaning above, any modification of structure, change of proportional relationship or adjustment of size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and goals that can be achieved by the present invention. within the scope covered.

Fig. 1 is a schematic layout diagram 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 informationized 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 flow measurement master station of a plain channel flow 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 measuring device provided by an embodiment of the present invention;

Fig. 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;

Fig. 6 is a schematic diagram of the regression curve of the relationship between the upstream and downstream water level elevation difference and flow rate of a kind of plain channel flow measuring equipment provided by the embodiment of the present invention;

Fig. 7 is a schematic diagram of the process of water depth and upstream and downstream water head changes over time of 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;

Fig. 9 is a schematic diagram of the water level-flow data processing process in the case of a small water level difference (the radar flow measurement is in the blind area and there is no radar measured data) of a plain channel flow measurement device provided by the 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 flow measurement master station; 21 -radar probe; 211-radar Doppler velocity probe; 212-radar water level probe; 22-first collection and transmission equipment; 3-downstream water level station; 4-water surface; 5-channel; 6-information intelligent 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 implementation mode of the present invention is illustrated by specific specific examples below, and those who are familiar with this technology can easily understand other advantages and effects of the present invention from the contents disclosed in this description. Obviously, the described embodiments are a part of the present invention. , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

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

Radar flow measurement master station 2;

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

Downstream water level station 3, the downstream water level station 3 is set downstream of the radar current measurement master station 2;

The informatization intelligent platform 6 is respectively connected to the main radar flow measurement station 2, the upstream water level station 1, and the downstream water level station 3. The informationization intelligent platform 6 is used to collect the radar flow measurement main station 2, the upstream water level station 1 and the downstream water level station 3 Monitored data and calculate flow.

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

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

As an optional embodiment of the present invention, as shown in Figure 3, the radar flow measurement master station 2 is provided with a radar probe 21, and the radar probe 21 is as close as possible to the water surface 4, and is 2m higher than the highest water level, which is conducive to obtaining better Radar Doppler signal; in cross-section, the probe should be located 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. Detecting the flow velocity and water level of the water surface 4 is used to calculate the flow rate; the 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 Figure 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 collection and transmission device 12 connected in sequence; Station 2 is provided with a radar probe 21, a second cable pipe, and a first acquisition and transmission device 22 connected in sequence; Complete the automatic collection and reporting, and report to the automatic collection and upload module 61 of the information intelligent platform 6 synchronously. 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 erosion and debris blockage; 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 side slope of the canal 5 from the first pressure type water level probe 14 and the second pressure type water level probe upwards there is a sandstone filter tank 13 of not less than 100cm, and the cross section of the sandstone filter tank 13 It is 40*50cm, sand and gravel are filled in the tank of the sand filter tank 13, which has good water permeability and filtering effect, so as to ensure the induction to the water level; the upper and lower water level stations use leveling to determine the relative elevation.

As an optional embodiment of the present invention, as shown in Figure 2, the intelligent information platform 6 includes an automatic collection and upload module 61, an Internet of Things database 62 and an intelligent algorithm module 63, and the Internet of Things database 62 is connected with the automatic collection and upload module respectively. 61 communicates with the intelligent algorithm module 63, and the automatic collection and upload module 61 communicates 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 Internet of Things database 62 is used for storage and access services of monitoring data, and can be called by the intelligent information platform 6 .

As an optional embodiment of the present invention, as shown in Figure 4 and Figure 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 core is to track the radar current 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 based on the measured data. The role 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 the parameters of the "flow-water level difference" function relationship by tracking the monitoring data. The data processing unit 633 selects different calculation methods to calculate the flow rate according to the current state of the radar flow measurement and the current water depth.

As an optional embodiment of the present invention, as shown in Figure 1, there can be no pumping station water intake facilities between the upstream water level station 1 and the downstream water level station 3, there can be a channel 5 bifurcation, the upstream water level station 1 and the downstream water level station 3 The distance between them is greater than 300m.

As shown in Figures 1 to 5, an embodiment of the present invention also provides a flow measurement method using the above-mentioned flow measurement equipment in plain channels, including the following steps:

a. When the water flow in the channel is at a normal flow rate, the radar Doppler flow rate probe 211 and the radar water level probe 212 are used to monitor the flow rate and water level respectively; the second collection and transmission device 12 collects the monitored flow rate and water level and transmits them to the automatic collection With the upload module 61, the automatic collection and upload module 61 collects and monitors the flow velocity and water level and uploads to the Internet of Things database 62, and the intelligent algorithm module 63 calculates the flow Q1 by the data of the Internet of Things database 62; specifically: the tracking learning unit 631 real-time Track the radar current 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 based on 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 the parameters of the "flow-water level difference" function relationship by tracking the monitoring data. The data processing unit 633 performs state identification according to the state of the current radar flow measurement and the current water depth, and selects different calculation methods to calculate the flow rate according to different states such as whether it is greater than the starting flow rate, deep water, shallow water, or MR mark;

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 condition is constantly updated with the radar current measurement data, and the data "0,0" means that the flow rate is also zero when the water level difference is zero. The regression parameters a and b track and change with the update of radar data.

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

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

Simultaneously by the first pressure type water level probe 14 and the second pressure type water level probe monitoring upstream and downstream water levels; the first collection and transmission device 22 collects the monitored flow velocity and water level and transmits to the automatic collection and upload module 61, automatic collection and The upload module 61 collects the monitored flow velocity and water level and uploads to the Internet of Things database 62;

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

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

Q2＝ ^aX2 +bX+c

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

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

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

T, unit h, is the time from the monitoring record to the start of the monitoring, and the monitoring frequency is once every half an hour. Some records are selected here to illustrate the calculation process;

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

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

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

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

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

The water level difference increases and 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 the regression parameter a is calculated one by one starting from the radar flow measurement data of more than 3 times , b; then use the newly obtained regression parameters to calculate the flow Q for the previous A-U segment, which is called "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 write it directly into Q, here "real-time calibration and 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 rate calculated by the regression parameters of the latest same water depth in the database is selected, and the MR is marked as "10" at the same time; as shown in Figure 9(2) As shown, when the water level difference is reset again, the flow measurement data is processed to make Q=Q3, and the MR mark is "00".

Although the present invention has been described in detail with general descriptions and specific examples above, it is obvious to those skilled in the art that some modifications or improvements can be made on the basis of the present invention. Therefore, the modifications or improvements made on the basis of not departing from the spirit of the present invention all belong to the protection scope of the present invention.

Claims (5)

1. Plain channel flow measurement equipment installs on the channel, and a serial communication port, include: monitoring equipment and an informationized intelligent platform; the monitoring device includes:

a radar current measurement master station;

an upstream water level station, which is arranged upstream of the radar flow measurement master station;

a downstream water level station disposed downstream of the radar flow measurement master station;

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

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;

the radar flow measurement main station is provided with a radar probe, a second cable pipe and first acquisition and transmission equipment which are connected in sequence, wherein the radar probe comprises a radar Doppler flow velocity probe and a radar water level probe;

the information intelligent 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 respectively in communication connection with the automatic acquisition and uploading module and the intelligent algorithm module, and the automatic acquisition and uploading module is respectively in communication connection with the first acquisition and transmission equipment and the second acquisition and transmission equipment;

the intelligent algorithm module comprises a tracking learning unit, a historical data analysis unit and a data processing unit; the tracking learning unit tracks radar flow measurement and upstream and downstream water level data in real time, and calculates correlation parameters of flow-water level difference in real time according to measured data; the historical data analysis unit analyzes the historical monitoring data and determines qualitative relations of flow-water head under different water levels; the tracking learning unit generates parameters of a 'flow-water head' function relation through tracking the monitoring data; and the data processing unit performs state identification tracking according to the current radar flow measurement state and the current water depth, and selects different calculation methods to calculate the flow according to different states of whether the current radar flow measurement state is larger than the starting flow rate.

2. The plain channel flow measurement apparatus according to claim 1, 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.

3. The plain channel flow measurement apparatus according to claim 1, wherein: the distance between the upstream water level station and the downstream water level station is greater than 300m.

4. The plain channel flow measurement apparatus according to claim 1, wherein: the distance between the radar probe and the water surface is more than or equal to 2m.

5. A flow measuring method using the plain channel flow measuring device according to any one of claims 1 to 4, characterized in that,

the method comprises the following steps:

a. when the water flow in the channel is normal flow velocity, a radar Doppler flow velocity probe and a radar water level probe are adopted to monitor respectively

Flow rate and water level; the second collecting and transmitting device collects the monitored flow speed and water level and transmits the flow speed and water level to the automatic collecting and uploading die

The intelligent algorithm module is used for automatically acquiring and uploading the monitored flow rate and water level to the database of the Internet of things

Calculating flow Q1 through data of database of Internet of things

According to the conditions

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

wherein Q1i is the flow calculated by the flow rate and the water level of the radar station; x is X _i Is the upstream-downstream water level difference;

a. b is a regression parameter; c is a constant;

simultaneously monitoring the water levels at the upstream and downstream through a first pressure type water level probe and a second pressure type water level probe; the first collecting and transmitting device collects the monitored flow velocity and water level and transmits the flow velocity and water level to the automatic collecting and uploading module, and the automatic collecting and uploading module collects the monitored flow velocity and water level and uploads the flow velocity and water level to the database of the Internet of things;

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

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

Q2＝aX ² +bX+c

and Q2 is the flow calculated according to the water level difference in the radar speed measurement blind area.

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