River surge reverse slope dynamic elevation measuring method based on elevation water level gauge of Internet of Things

Abstract

The invention discloses a river surge reverse slope dynamic elevation measuring method based on elevation water level gauges of the internet of things. The method comprises the steps that S1, an upstream position elevation water level gauge, a downstream position elevation water level gauge and a middle position elevation water level gauge are arranged on the upstream water surface, the downstream water surface and the middle water surface of a river surge; s2, acquiring elevation data output by the three elevation water level gauges in real time; s3, before the river surge water level falls below the vertex of the target reverse slope, the gate is closed; s4, judging whether a river surge water level drop critical condition is met or not, and if yes, judging that a vertex water depression with the river surge middle water surface flush with the vertex of the target reverse slope is formed between the target reverse slope and the gate; and S5, calculating the vertex elevation of the target inverse slope according to the elevation data output by the elevation water level gauge at the middle position. The method can automatically and timely measure the vertex elevation of the target inverse slope in the river network, has the advantages of high measurement precision, good dynamic real-time performance, high measurement efficiency and low cost, and is especially suitable for hydrological environments with complex terrains and obvious dynamic changes.

Description

Technical Field

[0001] This invention relates to the field of hydrological measurement technology, specifically a method for determining the dynamic elevation of river channel reverse slope based on an Internet of Things (IoT) elevation gauge. Background Technology

[0002] River inversion slopes are protruding earthen slopes in river channels. They are topographical anomalies caused by geological movements or human engineering, and their elevation changes directly affect navigation efficiency, flood control safety, and ecological balance.

[0003] The difficulty in determining the dynamic elevation of river inversion slopes lies in the fact that the apex of the river inversion slope changes due to geological movements or human engineering, making it difficult to accurately determine the apex position.

[0004] Existing methods for detecting the elevation of river channel reverse slopes have significant drawbacks:

[0005] Firstly, manual detection methods: rely on manual reading, are inefficient and cannot be monitored in real time;

[0006] Secondly, sonar detection methods are greatly affected by water flow disturbances and lack three-dimensional accuracy.

[0007] Third, RTK detection method: high equipment cost and difficulty in achieving continuous dynamic measurement.

[0008] Fourth, the elevation monitoring method based on satellite remote sensing has the problems of long data update cycle and insufficient resolution.

[0009] Therefore, there is an urgent need for a low-cost, high-precision, real-time dynamic reverse slope elevation measurement scheme. Summary of the Invention

[0010] The technical problem to be solved by the present invention is to provide a method for dynamic elevation measurement of river channel reverse slope based on Internet of Things (IoT) elevation water level gauge.

[0011] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0012] like Figure 1 and Figure 2 As shown, a method for dynamic elevation measurement of river invert slope based on IoT-based elevation level gauge is used to determine the vertex elevation of a target invert slope in a river network.

[0013] Its features include:

[0014] Step S1: Install intermediate elevation level gauges on the water surface in the middle of the channel between the target reverse slope and the nearest gate; install upstream elevation level gauges on the upstream water surface of the channel shared by the target reverse slope and the gate; and install downstream elevation level gauges on the downstream water surface of the channel shared by the target reverse slope and the gate. The gate can be located upstream or downstream of the target reverse slope. All elevation level gauges output the elevation of their respective water surfaces based on the same elevation datum.

[0015] Step S2: Real-time acquisition of elevation data output from the upstream, downstream, and intermediate elevation level gauges;

[0016] Step S3: Close the gate before the river water level drops below the apex of the target reverse slope;

[0017] Step S4: After closing the gate in step S3, determine whether the critical condition for the drop in river water level is met. If the determination result is yes, it is determined that a apex water depression has been formed between the target reverse slope and the gate, with the water surface in the middle of the river level being the same as the apex of the target reverse slope.

[0018] The critical condition for the decline of the river water level is: |X| must be continuously satisfied within a preset time period. C -X A |>ΔX and |X C -X B |>ΔX, where X A X B and X C The elevation values ​​output by the upstream, downstream, and intermediate elevation level gauges are represented sequentially, with ΔX being a preset elevation difference threshold.

[0019] Step S5: When it is determined in step S4 that a apex puddle has been formed, the apex elevation of the target reverse slope is calculated based on the elevation data output by the middle position elevation level gauge; wherein, after obtaining the data required to calculate the apex elevation of the target reverse slope, the gate can be reopened at any time.

[0020] Therefore, the working principle and technical effects of the present invention are as follows:

[0021] Utilizing the natural rise and fall of the river network's water level with the tides, when the river water level rises above the peak of the target reverse slope with the tides, the elevations output by the upstream, downstream, and intermediate elevation gauges are basically the same, not satisfying the critical condition for the river water level drop described in step S4. However, when the river water level falls below the peak of the target reverse slope with the tides, because the gate was closed in step S3, the water level in the middle of the river is restricted by the target reverse slope and the gate, causing a peak depression to form between the target reverse slope and the gate, where the water level in the middle of the river is level with the peak of the target reverse slope. Meanwhile, the water levels upstream and downstream of the river continue to fall. Step S4 then determines that the critical condition for the river water level drop is met, thus automatically executing step S5 to calculate the peak elevation of the target reverse slope. Since the peak depression is located below the peak elevation of the target reverse slope, the water level in the middle of the river is lower than the peak elevation of the target reverse slope. Due to the constraints of the target slope and the gate, the river can only be in a near-completely closed state. The water level in the middle of the river will continue to drop over time. Therefore, step S4, which determines the critical condition for the river's water level drop, ensures the timely execution of step S5 and guarantees the accuracy of the measured peak elevation. Thus, this invention can automatically and promptly determine the peak elevation of the target slope in the river network, offering advantages such as high measurement accuracy, good dynamic real-time performance, high measurement efficiency, and low cost. It is particularly suitable for hydrological environments with complex terrain and significant dynamic changes. Actual measurements show that the measurement accuracy of this invention reaches ±1cm, significantly higher than the ±5cm of the sonar detection method and the ±3cm of the RTK detection method. The measurement standard deviation of this invention is 0.8cm, an 83% improvement over the sonar detection method. The cost of this invention is more than 60% lower than that of manual detection methods and RTK detection methods.

[0022] Preferably, step S3 is implemented automatically using any of the following methods:

[0023] Method 1: By setting a low elevation threshold below the peak of the target reverse slope, the gate will be automatically closed when the river water level rises and the elevation output by the middle position elevation gauge is higher than the low elevation threshold.

[0024] Method 2: By setting a high elevation threshold higher than the peak of the target reverse slope, the gate will be automatically closed when the river water level falls and the elevation output by the middle position elevation gauge is lower than the high elevation threshold.

[0025] Method 3: Using image recognition methods, automatically close the gate when the river surface completely overflows the target slope;

[0026] Method 4: When it is determined by manual observation that the water surface of the river channel has completely flooded the target slope, the gate is closed remotely by manual control.

[0027] Method 5: By analyzing the tidal patterns of the river network, the gate is automatically closed when the river surface completely overflows the target slope.

[0028] Preferably, in step S5, the elevation H of the peak of the target reverse slope is calculated according to the following formula. (C) :

[0029]

[0030] In the formula, X i The i-th valid elevation value output by the intermediate position elevation level gauge is defined within a preset water level stabilization time starting from the formation of the apex puddle in step S4, where 1 ≤ i ≤ n, and n is the total number of valid elevation values ​​output by the intermediate position elevation level gauge within the preset water level stabilization time. The value of n depends on the sampling frequency of the intermediate position elevation level gauge and the number of invalid elevation data points removed.

[0031] Preferably, all the elevation level gauges use pressure sensors or ultrasonic sensors, and the elevation reference surface is unified through the Beidou positioning system or GNSS system.

[0032] As a preferred embodiment of the present invention: Steps S4 and S5 are executed by the cloud service center; each elevation level gauge and the cloud service center form an Internet of Things system, and each elevation level gauge collects elevation data at a preset frequency in step S2 and transmits it to the cloud service center in real time through LoRa communication or NB-IoT communication; the cloud service center closes the gate through the gate control unit in step S3.

[0033] Preferably, the cloud service center uses a sliding window method or a Kalman filter algorithm to filter the elevation data transmitted in real time by the elevation level gauge to remove outliers caused by environmental noise or equipment failure, and obtain the effective elevation values ​​for calculation in steps S4 and S5.

[0034] Preferably, the cloud service center uses the least squares method to perform curve fitting for the effective elevation values ​​corresponding to the upstream, downstream, and intermediate elevation level gauges, generating the upstream elevation-time curve, downstream elevation-time curve, and intermediate elevation-time curve, which are then output as a dynamic elevation change map of the river channel.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] First, this invention utilizes the natural rise and fall of the river network's water level with the tides. When the river water level rises above the apex of the target reverse slope 1 with the tides, the elevations output by the upstream elevation gauge A, the downstream elevation gauge B, and the intermediate elevation gauge C are basically the same, not satisfying the critical condition for the river water level to drop described in step S4. However, when the river water level falls below the apex of the target reverse slope 1 with the tides, since the gate 2 was closed before step S3, the water level P in the middle of the river... C The descent is halted due to the constraints of the target slope 1 and the gate 2, creating a water surface P in the middle of the river channel between the target slope 1 and the gate 2. C The apex of the crater 3 is level with the apex of the target slope 1, while the upstream water surface P of the river channel... A and downstream water surface P B The water level continues to fall. Step S4 then determines that the critical condition for the river channel's water level drop is met, thus automatically executing step S5 to calculate the elevation of the peak of the target reverse slope 1. Since the peak pool 3 is only in a nearly completely closed state due to the constraints of the target reverse slope 1 and the gate 2, the water level P in the middle of the river channel... C As the water level continues to drop over time, step S4, which determines the critical condition for the decline of the river channel water level, ensures the timely execution of step S5 and guarantees the accuracy of the measured peak elevation. Therefore, this invention can automatically and promptly determine the peak elevation of the target reverse slope 1 in the river channel network. It has the advantages of high measurement accuracy, good dynamic real-time performance, high measurement efficiency, and low cost, and is especially suitable for hydrological environments with complex terrain and significant dynamic changes. According to actual measurements, the measurement accuracy of this invention reaches ±1cm, which is significantly higher than ±5cm of the sonar detection method and ±3cm of the RTK detection method. The measurement standard deviation of this invention is 0.8cm, which is 83% higher than that of the sonar detection method. The cost of this invention is more than 60% lower than that of the manual detection method and the RTK detection method.

[0037] Second, this invention supports the synchronous monitoring of multiple reverse slope nodes in a river network and can generate dynamic elevation change maps of the river. Attached Figure Description

[0038] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments:

[0039] Figure 1 This is a schematic diagram of the deployment of the elevation water level gauge in this invention, showing the state when a apex puddle is formed;

[0040] Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0041] The present invention will now be described in detail with reference to the embodiments and accompanying drawings to help those skilled in the art better understand the inventive concept of the present invention. However, the scope of protection of the claims of the present invention is not limited to the following embodiments. For those skilled in the art, all other embodiments obtained without creative effort without departing from the inventive concept of the present invention are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1 and Figure 2 As shown, the present invention discloses a method for dynamic elevation measurement of river invert slope based on an Internet of Things (IoT) elevation level gauge, which is used to measure the vertex elevation of target invert slope 1 in a river network.

[0044] include:

[0045] Step S1: At the water level P in the middle of the river channel between the target reverse slope 1 and the nearest gate 2. C Install the intermediate elevation level C at the upstream water level P of the river channel shared by the target reverse slope 1 and gate 2. A Deploy an upstream elevation gauge A at the downstream water level P of the river channel shared by target reverse slope 1 and gate 2. B Deploy elevation level gauge B at the downstream location; wherein, gate 2 can be located upstream or downstream of the target reverse slope 1; all elevation level gauges output the elevation of the water surface at their location based on the same elevation datum.

[0046] Step S2: Real-time acquisition of elevation data output from upstream elevation level gauge A, downstream elevation level gauge B, and intermediate elevation level gauge C;

[0047] Step S3: Close gate 2 before the river water level drops below the apex of the target reverse slope 1;

[0048] Step S4: After closing gate 2 in step S3, determine whether the critical condition for the drop in river water level is met. If the determination result is yes, it is determined that a middle water surface P has been formed between the target reverse slope 1 and gate 2. C The apex of the waterhole 3 is level with the apex of the target reverse slope 1;

[0049] The critical condition for the decline of the river water level is: |X| must be continuously satisfied within a preset time period. C -X A |>ΔX and |X C -X B |>ΔX, where X A X B and X CThe elevation values ​​output by the upstream elevation level gauge A, the downstream elevation level gauge B, and the intermediate elevation level gauge C are represented sequentially, and ΔX is the preset elevation difference threshold.

[0050] Step S5: When it is determined in step S4 that the apex puddle 3 has been formed, the apex elevation of the target reverse slope 1 is calculated based on the elevation data output by the middle position elevation level gauge C; wherein, after obtaining the data required to calculate the apex elevation of the target reverse slope 1, the gate 2 can be reopened at any time.

[0051] Therefore, the working principle and technical effects of the present invention are as follows:

[0052] Utilizing the natural rise and fall of the river network's water level with the tides, when the river water level rises above the apex of the target reverse slope 1, the elevations output by the upstream elevation gauge A, downstream elevation gauge B, and intermediate elevation gauge C are basically the same, not satisfying the critical condition for the river water level drop described in step S4; however, when the river water level falls below the apex of the target reverse slope 1 with the tides, since gate 2 was closed before this in step S3, the water level P in the middle of the river... C The descent is halted due to the constraints of the target slope 1 and the gate 2, creating a water surface P in the middle of the river channel between the target slope 1 and the gate 2. C The apex of the crater 3 is level with the apex of the target slope 1, while the upstream water surface P of the river channel... A and downstream water surface P B The water level continues to fall. Step S4 then determines that the critical condition for the river channel's water level drop is met, thus automatically executing step S5 to calculate the elevation of the peak of the target reverse slope 1. Since the peak pool 3 is only in a nearly completely closed state due to the constraints of the target reverse slope 1 and the gate 2, the water level P in the middle of the river channel... C As the water level continues to drop over time, step S4, which determines the critical condition for the decline of the river channel water level, ensures the timely execution of step S5 and guarantees the accuracy of the measured peak elevation. Therefore, this invention can automatically and promptly determine the peak elevation of the target reverse slope 1 in the river channel network. It has the advantages of high measurement accuracy, good dynamic real-time performance, high measurement efficiency, and low cost, and is especially suitable for hydrological environments with complex terrain and significant dynamic changes. According to actual measurements, the measurement accuracy of this invention reaches ±1cm, which is significantly higher than ±5cm of the sonar detection method and ±3cm of the RTK detection method. The measurement standard deviation of this invention is 0.8cm, which is 83% higher than that of the sonar detection method. The cost of this invention is more than 60% lower than that of the manual detection method and the RTK detection method.

[0053] The above is the basic implementation method of this embodiment one, and further optimizations, improvements and limitations can be made based on this basic implementation method:

[0054] Preferably, step S3 is implemented automatically using any of the following methods:

[0055] Method 1: By setting a low elevation threshold below the peak of the target reverse slope 1, when the river water level rises and the elevation output by the middle position elevation gauge C is higher than the low elevation threshold, the gate 2 is automatically closed.

[0056] Method 2: By setting a high elevation threshold higher than the peak of the target reverse slope 1, when the river water level falls and the elevation output by the middle position elevation level gauge C is determined to be lower than the high elevation threshold, the gate 2 is automatically closed.

[0057] Method 3: Using image recognition, when the river surface completely overflows the target reverse slope 1, automatically close the gate 2;

[0058] Method 4: When it is determined by manual observation that the water surface of the river channel has completely flooded the target reverse slope 1, the gate 2 is closed remotely by manual control.

[0059] Method 5: Based on the tidal patterns of the river network, when the river surface completely overflows the target reverse slope 1, automatically close the gate 2.

[0060] Preferably, in step S5, the elevation H of the peak of the target reverse slope 1 is calculated according to the following formula. (C) :

[0061]

[0062] In the formula, X i The i-th valid elevation value output by the intermediate position elevation level gauge C during the preset water level stabilization time starting from the determination of the formation of the apex puddle 3 in step S4, 1≤i≤n, where n is the total number of valid elevation values ​​output by the intermediate position elevation level gauge C during the preset water level stabilization time. The value of n depends on the sampling frequency of the intermediate position elevation level gauge C and the number of invalid elevation data points removed.

[0063] Preferably, all the elevation level gauges use pressure sensors or ultrasonic sensors, and the elevation reference surface is unified through the Beidou positioning system or GNSS system.

[0064] Example 2

[0065] Based on the above embodiment one, this embodiment two also adopts the following preferred implementation method:

[0066] Steps S4 and S5 are executed by the cloud service center; each elevation level gauge and the cloud service center form an Internet of Things system. In step S2, each elevation level gauge collects elevation data at a preset frequency and transmits it to the cloud service center in real time via LoRa or NB-IoT communication; in step S3, the cloud service center closes the gate 2 through the gate control unit.

[0067] The above is the basic implementation method of this embodiment two, and further optimizations, improvements and limitations can be made based on this basic implementation method:

[0068] Preferably, the cloud service center uses a sliding window method or a Kalman filter algorithm to filter the elevation data transmitted in real time by the elevation level gauge to remove outliers caused by environmental noise or equipment failure, and obtain the effective elevation values ​​for calculation in steps S4 and S5.

[0069] Preferably, the cloud service center uses the least squares method to perform curve fitting for the effective elevation values ​​corresponding to the upstream elevation level A, the downstream elevation level B, and the intermediate elevation level C, respectively, to generate the upstream elevation-time curve, the downstream elevation-time curve, and the intermediate elevation-time curve, which are then output as a dynamic elevation change map of the river channel.

[0070] This invention is not limited to the specific embodiments described above. Based on the above content and in accordance with common technical knowledge and conventional methods in the field, without departing from the basic technical concept of this invention, this invention can also make other equivalent modifications, substitutions or alterations, all of which fall within the protection scope of this invention.

Claims

1. A method for determining the dynamic elevation of a river channel reverse slope based on an Internet of Things (IoT) elevation gauge, used to determine the vertex elevation of a target reverse slope (1) in a river channel network; Its features are, include: Step S1: At the midpoint of the channel between the target reverse slope (1) and the nearest gate (2) (P) C ) Arrange an intermediate elevation gauge (C) at the upstream water level (P) of the river channel shared by the target reverse slope (1) and the gate (2). A ) Install an upstream elevation gauge (A) at the downstream water level (P) of the river channel shared by the target reverse slope (1) and the gate (2). B Arrange elevation level gauges at downstream locations (B); wherein all elevation level gauges output the elevation of the water surface at their location based on the same elevation datum. Step S2: Real-time acquisition of elevation data output from the upstream elevation level gauge (A), the downstream elevation level gauge (B), and the intermediate elevation level gauge (C); Step S3: Close the gate (2) before the river water level drops below the apex of the target reverse slope (1); Step S4: After closing the gate (2) in step S3, determine whether the critical condition for the drop in river water level is met. If the determination result is yes, it is determined that a middle water surface (P) of the river has been formed between the target reverse slope (1) and the gate (2). C The apex of the trough (3) is level with the apex of the target reverse slope (1); The critical condition for the decline of the river water level is: |X| must be continuously satisfied within a preset time period. C -X A |>ΔX and |X C -X B |>ΔX, where X A X B and X C The elevation values ​​output by the upstream elevation level gauge (A), the downstream elevation level gauge (B), and the intermediate elevation level gauge (C) are represented in sequence, and ΔX is the preset elevation difference threshold. Step S5: When it is determined in step S4 that a apex puddle (3) has been formed, the apex elevation of the target reverse slope (1) is calculated based on the elevation data output by the middle position elevation level gauge (C).

2. The method for determining the dynamic elevation of a river channel's reverse slope based on an IoT-based elevation gauge according to claim 1, characterized in that: Step S3 can be automatically implemented using any of the following methods: Method 1: By setting a low elevation threshold below the peak of the target reverse slope (1), the gate (2) is automatically closed when the river water level rises and the elevation output by the middle position elevation gauge (C) is higher than the low elevation threshold. Method 2: By setting a high elevation threshold higher than the peak of the target reverse slope (1), when the river water level falls and the elevation output by the middle position elevation level gauge (C) is lower than the high elevation threshold, the gate (2) is automatically closed. Method 3: When the river surface completely overflows the target reverse slope (1) by using image recognition method, the gate (2) is automatically closed; Method 4: When it is determined by manual observation that the river surface has completely flooded the target reverse slope (1), the gate is closed remotely by manual control (2); Method 5: Based on the tidal fluctuation pattern of the river network, when it is determined that the river surface completely overflows the target reverse slope (1), the gate (2) is automatically closed.

3. The method for determining the dynamic elevation of a river channel's reverse slope based on an IoT-based elevation gauge according to claim 1, characterized in that: In step S5, the vertex elevation H of the target reverse slope (1) is calculated according to the following formula. (C) : In the formula, X i The i-th effective elevation value output by the intermediate position elevation level gauge (C) is defined as follows: 1≤i≤n, where n is the total number of effective elevation values ​​output by the intermediate position elevation level gauge (C) during the preset water level stabilization time starting from the determination of the formation of the apex puddle (3) in step S4.

4. The method for determining the dynamic elevation of a river channel's reverse slope based on an IoT-based elevation gauge according to claim 1, characterized in that: All of the elevation level gauges use pressure sensors or ultrasonic sensors, and the elevation datum is unified through the Beidou positioning system or GNSS system.

5. The method for determining the dynamic elevation of a river channel's reverse slope based on an IoT-based elevation gauge, as described in any one of claims 1 to 4, is characterized in that: Steps S4 and S5 are executed by the cloud service center; each elevation level gauge and the cloud service center form an Internet of Things system. In step S2, each elevation level gauge collects elevation data at a preset frequency and transmits it to the cloud service center in real time via LoRa or NB-IoT communication. In step S3, the cloud service center closes the gate (2) through the gate control unit.

6. The method for determining the dynamic elevation of a river channel's reverse slope based on an IoT-based elevation gauge according to claim 5, characterized in that: The cloud service center uses a sliding window method or a Kalman filter algorithm to filter the elevation data transmitted in real time by the elevation gauge to obtain the effective elevation value used for calculation in steps S4 and S5.

7. The method for determining the dynamic elevation of a river channel's reverse slope based on an IoT-based elevation gauge according to claim 6, characterized in that: The cloud service center uses the least squares method to perform curve fitting for the effective elevation values ​​corresponding to the upstream elevation level gauge (A), downstream elevation level gauge (B), and intermediate elevation level gauge (C), respectively, to generate the upstream elevation-time curve, downstream elevation-time curve, and intermediate elevation-time curve, which are then output as a dynamic elevation change map of the river channel.

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