Trapezoidal river section dynamic measurement method and system based on multistage reference network

Abstract

The invention belongs to the technical field of hydraulic engineering monitoring, and provides a trapezoidal river section dynamic measurement method and system based on a multistage reference network, and the technical scheme is as follows: obtaining basic information and measurement data of each fixed reference monitoring point and manual monitoring point; calculating a section measurement result of each planned section based on the basic information and the measurement data of each fixed reference monitoring point and each manual monitoring point; calculating a data quality score, a historical stability score and an environmental interference score of each fixed reference monitoring point and each manual monitoring point; and in combination with the data quality score, the historical stability score and the environmental interference score of each fixed reference monitoring point and each manual monitoring point, adjusting the section measurement result of each section based on a dynamic weight distribution algorithm of a multi-stage reference network to obtain a final section measurement result. And the precision and the stability of river section measurement are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy project monitoring, and in particular relates to a dynamic measurement method and system for a trapezoidal river section based on a multi-level benchmark network. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In water conservancy projects, river cross-section measurement is a critical step in assessing important parameters such as flow, water level fluctuations, and sediment deposition. Traditional river cross-section measurement methods rely primarily on manual measurement, which suffers from low efficiency, limited accuracy, and significant environmental impact. In recent years, technological advances have led to the introduction of automated measurement systems. However, these systems often rely on a single measurement benchmark and are susceptible to factors such as terrain changes and equipment drift, resulting in reduced measurement accuracy.

[0004] The inventors found that the existing technology lacks a real-time monitoring and correction mechanism for the dynamic changes of river sections, making it difficult to meet the needs of high-precision measurements in complex environments. Traditional trapezoidal cross-section measurements rely on fixed reference points, but the river topography is affected by dynamic factors such as scouring and siltation, which causes the reference points to shift and the long-term reliability of the data to decrease. Artificially laid temporary monitoring points (such as sounding rods and buoys) are difficult to coordinate with the fixed reference network, making data fusion difficult and susceptible to environmental interference. However, when coupling the reference points and artificial monitoring points, there is a coupling error between the drift of the fixed points and the insufficient accuracy of the artificial points, which leads to deviations in the cross-section data. Summary of the Invention In order to solve at least one technical problem existing in the above-mentioned background technology, the present invention provides a dynamic measurement method and system for trapezoidal river channel sections based on a multi-level benchmark network, which realizes high-precision and dynamic measurement of trapezoidal river channel sections through collaborative correction of adaptive fixed points and manual monitoring points.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A first aspect of the present invention provides a dynamic measurement method for a trapezoidal river section based on a multi-level reference network, comprising the following steps: Obtain basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; Calculate the cross-section measurement results of each planned section based on the basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; Calculate the data quality score, historical stability score and environmental interference score for each fixed benchmark monitoring point and manual monitoring point; Combining the data quality scores, historical stability scores, and environmental interference scores of each fixed benchmark monitoring point and manual monitoring point, the dynamic weight allocation algorithm based on the multi-level benchmark network is used to adjust the cross-section measurement results of each section to obtain the final cross-section measurement results.

[0006] Furthermore, the planning of each section includes the layout of artificial monitoring points between fixed benchmark monitoring points, including setting artificial point 1 between the starting point and benchmark point 1, setting artificial point 2 and artificial point 3 between benchmark point 1 and benchmark point 2, setting artificial point 4 and artificial point 5 between benchmark point 2 and benchmark point 3, and setting artificial point 6 between benchmark point 3 and the end point; the distance between the starting point and artificial point 1 is section 1, the distance between artificial point 1 and benchmark point 1 is section 2, the distance between benchmark point 1 and artificial point 2 is section 3, the distance between artificial point 2 and artificial point 3 is section 4, the distance between artificial point 3 and benchmark point 2 is section 5, the distance between benchmark point 2 and artificial point 4 is section 6, the distance between artificial point 4 and artificial point 5 is section 7, the distance between artificial point 5 and benchmark point 3 is section 8, the distance between benchmark point 3 and artificial point 6 is section 9, and the distance between artificial point 6 and the end point is section 10.

[0007] Furthermore, the calculation principles for cross-section measurement of each section are as follows: The data of the fixed benchmark monitoring points adopt the measurement data collected from the nearest benchmark monitoring points, combined with the parameters of the artificial monitoring point layout plan, to calculate the average water depth, partial flow velocity, cross-sectional area and cross-sectional flow from the left bank to artificial monitoring point 1, artificial monitoring point 1 to fixed benchmark monitoring point 1, fixed benchmark monitoring point 1 to artificial monitoring point 2, artificial monitoring point 2 to artificial monitoring point 3, artificial monitoring point 3 to fixed benchmark monitoring point 2, fixed benchmark monitoring point 2 to artificial monitoring point 4, artificial monitoring point 4 to artificial monitoring point 5, artificial monitoring point 5 to fixed benchmark monitoring point 3, fixed benchmark monitoring point 3 to artificial monitoring point 6 and artificial monitoring point 6 to the right bank.

[0008] Furthermore, the calculation formula for the data quality score, historical stability score and environmental interference score of each monitoring point is as follows: , , , in, Score the data quality of each monitoring point. The data integrity score indicates the percentage of valid data at the monitoring point within the set period; is the data consistency score, obtained through correlation analysis with data from adjacent time periods or nearby monitoring points; is the data error rate, which reflects the degree of deviation between the measured value and the reference value; , are the integrity, consistency and error weight coefficients respectively, Score the historical stability of each monitoring point. For monitoring points within the selected time window i The standard deviation of the data; is the maximum standard deviation among all monitoring points, Score the environmental disturbance, is the vegetation index, which reflects the impact of vegetation on the line of sight or signal of the monitoring point; is the water turbulence index, which is derived from historical flow velocity and sediment content data; It is an index of bottom stability, determined based on geological data and historical sedimentation changes; , , are the weighted coefficients of vegetation, turbulence and bottom bed effects respectively.

[0009] Furthermore, the cross-sectional measurement results include average water depth, average flow velocity and cross-sectional flow, and the calculation formula is: The calculation formula for weighted average water depth is: , The formula for calculating the weighted average flow rate is: , The calculation formula for weighted cross-sectional flow is: , in, For the i Water depth at each measuring point; For the i The flow velocity at each measuring point, is the corresponding interval width, n is the number of measurement points, For the i The contribution weight of each monitoring point to the cross-section measurement results.

[0010] Furthermore, after completing the dynamic measurement of the trapezoidal river section, the river section mapping data, real-time flow process line and real-time flow velocity distribution are displayed based on the visualization interface.

[0011] A second aspect of the present invention provides a dynamic measurement system for a trapezoidal river section based on a multi-level reference network, comprising: Monitoring point information acquisition module, which is used to obtain basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; A cross-section measurement data calculation module is used to calculate the cross-section measurement results of each planned section based on the basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; Monitoring point data evaluation module, which is used to calculate the data quality score, historical stability score and environmental interference score of each fixed benchmark monitoring point and manual monitoring point; The cross-section dynamic measurement module is used to combine the data quality scores, historical stability scores and environmental interference scores of each fixed benchmark monitoring point and manual monitoring point, and adjust the cross-section measurement results of each section based on the dynamic weight allocation algorithm of the multi-level benchmark network to obtain the final cross-section measurement results.

[0012] A third aspect of the present invention provides a computer-readable storage medium.

[0013] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned dynamic measurement method for a trapezoidal river section based on a multi-level reference network.

[0014] A fourth aspect of the present invention provides a computer device.

[0015] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the dynamic measurement method for a trapezoidal river section based on a multi-level reference network as described above are implemented.

[0016] A fifth aspect of the present invention provides a program product.

[0017] A program product, which is a computer program product, includes a computer program. When the computer program is executed by a processor, it implements the steps in the dynamic measurement method of trapezoidal river section based on multi-level benchmark network as described above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention combines the data of fixed benchmark points and manual monitoring points for collaborative correction. Through a multi-level benchmark network and an adaptive correction mechanism, the accuracy and stability of river section measurement are significantly improved, and real-time monitoring of dynamic changes in river sections is achieved, which can promptly detect and handle abnormal situations.

[0019] 2. This invention provides an intuitive data visualization interface, greatly facilitating real-time monitoring and decision support for water conservancy managers. This comprehensive solution not only improves work efficiency but also enhances the scientific nature and accuracy of management decisions.

[0020] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 This is a flow chart of a dynamic measurement method for a trapezoidal river section based on a multi-level benchmark network provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the layout of various fixed reference monitoring points and manual monitoring points provided by an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the data visualization interface provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0026] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0027] Existing technologies lack a real-time monitoring and correction mechanism for the dynamic changes in river sections, making it difficult to meet the needs of high-precision measurements in complex environments. Traditional trapezoidal cross-section measurements rely on fixed benchmarks, but the river topography is affected by dynamic factors such as scouring and siltation, which causes benchmark offsets and reduces the long-term reliability of the data. Artificially deployed temporary monitoring points (such as sounding rods and buoys) are difficult to coordinate with the fixed benchmark network, making data fusion difficult and susceptible to environmental interference. However, when coupling benchmark points and artificial monitoring points, there is a coupling error between fixed point drift and insufficient accuracy of artificial points, resulting in deviations in cross-section data. This invention significantly improves the accuracy and stability of river cross-section measurements through a multi-level benchmark network and an adaptive correction mechanism. This enables real-time monitoring of dynamic changes in river cross-sections, enabling the timely detection and resolution of anomalies. Furthermore, the invention combines data from fixed benchmarks and manual monitoring points for collaborative correction, ensuring the long-term stability and reliability of the measurement system. Furthermore, it provides an intuitive data visualization interface, greatly facilitating real-time monitoring and decision-making support for water conservancy managers. This comprehensive solution not only improves work efficiency but also enhances the scientific nature and accuracy of management decisions.

[0028] Example 1 like Figure 1 As shown, this embodiment provides a dynamic measurement method for a trapezoidal river section based on a multi-level benchmark network, comprising the following steps: Step 1: Arrange artificial monitoring points between fixed benchmark monitoring points; like Figure 2 As shown, in this embodiment, the fixed reference monitoring points include reference point 1, reference point 2 and reference point 3; where H is the cross-section height; h1 is the water depth of reference point 1, h2 is the water depth of reference point 2, and h3 is the water depth of reference point 3; h is the cross-section riverbed elevation; To monitor flow rate 1, 2 is the monitoring flow rate 2, 3 is the monitoring flow rate 3; The layout method of artificial monitoring points is as follows: artificial point 1 is set between the starting point and benchmark point 1, artificial points 2 and 3 are set between benchmark point 1 and benchmark point 2, artificial points 4 and 5 are set between benchmark point 2 and benchmark point 3, and artificial point 6 is set between benchmark point 3 and the end point. Figure 2 Where, W1 is the distance between the starting point and artificial point 1, W2 is the distance between artificial point 1 and reference point 1, W3 is the distance between reference point 1 and artificial point 2, W4 is the distance between artificial point 2 and artificial point 3, W5 is the distance between artificial point 3 and reference point 2, W6 is the distance between reference point 2 and artificial point 4, W7 is the distance between artificial point 4 and artificial point 5, W8 is the distance between artificial point 5 and reference point 3, W9 is the distance between reference point 3 and artificial point 6, and W10 is the distance between artificial point 6 and the end point.

[0029] Step 2: Obtain basic information and measurement data of fixed benchmark monitoring points and manual monitoring points respectively for data analysis, storage, and subsequent visualization; The fields of the fixed point basic information table include: fixed point number, fixed point name, installation location, reporting time, number of fixed point measurements, section length, section height, number of measuring points, starting point distance, end point distance, starting point shore velocity coefficient, end point shore velocity coefficient, etc.

[0030] The monitoring point information table fields corresponding to the fixed point include: fixed point number, measuring point number, measuring point name, manufacturer, measuring surface length, current water level, current mud level, current water depth, current flow velocity, flow rate, equipment status, voltage, default flow velocity coefficient, whether it is an actual probe, distance from the left bank, etc.

[0031] The fields of the measuring point interval setting information table include: fixed point number, measuring point number, starting water level, ending water level, and flow rate coefficient.

[0032] The fields of the automatic measurement table include: fixed point number, measurement time, measurement point position, number of measurements, partial flow, water level, partial average water depth, partial average flow velocity, partial area, measurement point spacing, and creation time.

[0033] The fields of the flow measurement history table include: fixed point number, measurement point number, measurement point position, number of measurements, reporting IP address, reporting time, creation time, reporting type, current flow rate, water level, river bottom elevation, water depth, measurement point spacing, voltage, and measurement method.

[0034] The fields of the measurement summary table include: fixed point number, reporting time, creation time, number of measurements, water level, cross-sectional flow, cross-sectional area, average flow velocity, average water depth, water surface width, and measurement type.

[0035] Step 3: Based on the basic data and measurement data obtained from fixed benchmark monitoring points and manual monitoring points, calculate the cross-sectional parameters of each section; In this embodiment, the left bank is taken as the first bank, the right bank is taken as the second bank, and the adjacent lower direction is defined as the direction from the first bank to the second bank; in other embodiments, the right bank can also be taken as the first bank, the left bank can be taken as the second bank, and the adjacent lower direction is defined as the direction from the first bank to the second bank.

[0036] The specific calculation principles include: the data of the fixed benchmark monitoring points adopt the measurement data collected from the nearest benchmark monitoring points, combined with the parameters of the artificial monitoring point layout plan, and calculate the average water depth, partial flow velocity, cross-sectional area and cross-sectional flow from the left bank to artificial monitoring point 1, artificial monitoring point 1 to fixed benchmark monitoring point 1, fixed benchmark monitoring point 1 to artificial monitoring point 2, artificial monitoring point 2 to artificial monitoring point 3, artificial monitoring point 3 to fixed benchmark monitoring point 2, fixed benchmark monitoring point 2 to artificial monitoring point 4, artificial monitoring point 4 to artificial monitoring point 5, artificial monitoring point 5 to fixed benchmark monitoring point 3, fixed benchmark monitoring point 3 to artificial monitoring point 6 and artificial monitoring point 6 to the right bank respectively.

[0037] The specific calculation process includes: the average water depth from the first bank to artificial point 1, partial flow velocity, cross-sectional area, and cross-sectional flow rate calculation formula is: H1 , V1 , S1 , Q1 , Among them, h1 is the monitoring water depth of benchmark point 1, H1 is the average water depth from the first bank to artificial point 1, Monitor the flow rate for reference point 1, k is the flow velocity coefficient of the measuring point, V1 is the flow velocity from the first bank to artificial point 1, W1 is the starting distance (the distance from the starting point to artificial point 1), S1 is the cross-sectional area from the first bank to artificial point 1, and Q1 is the cross-sectional flow from the first bank to artificial point 1.

[0038] The calculation formulas for the average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from artificial point 1 to monitoring point 1 are as follows: H2 , V2 , S2 , Q2 2, Among them, h1 The water depth of benchmark point 1 is monitored, and H2 is the average water depth from artificial point 1 to monitoring point 1. is the monitoring flow velocity of reference point 1, k is the flow velocity coefficient of the measuring point, V2 is the partial flow velocity from artificial point 1 to monitoring point 1, W2 is the distance from artificial point 1 to reference point 1, S2 is the cross-sectional area from artificial point 1 to monitoring point 1, and Q2 is the cross-sectional flow from artificial point 1 to monitoring point 1.

[0039] The calculation formulas for average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from benchmark point 1 to artificial point 2 are: H3 , V3 , S3 W3, Q3 , Among them, h1 is the monitoring water depth of benchmark point 1, h2 is the monitoring water depth of benchmark point 2, and H3 is the average water depth from benchmark point 1 to artificial point 2. Monitor the flow rate for reference point 1, 2 is the monitored flow velocity of reference point 2, k is the flow velocity coefficient of the measuring point, V3 is the partial flow velocity from reference point 1 to artificial point 2, W3 is the distance from reference point 1 to artificial point 2, S3 is the cross-sectional area from reference point 1 to artificial point 2, and Q3 is the cross-sectional flow from reference point 1 to artificial point 2.

[0040] The calculation formulas for average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from artificial point 2 to artificial point 3 are as follows: H4 , V4 , S4 W4, Q4 , Among them, h1 is the monitoring water depth of benchmark point 1, h2 is the monitoring water depth of benchmark point 2, and H4 is the average water depth from artificial point 2 to artificial point 3. Monitor the flow rate for reference point 1, 2 is the monitoring flow velocity of reference point 2, k is the flow velocity coefficient of the measuring point, V4 is the partial flow velocity, W4 is the distance between artificial point 2 and artificial point 3, S4 is the cross-sectional area between artificial point 2 and artificial point 3, and Q4 is the cross-sectional flow rate between artificial point 2 and artificial point 3.

[0041] The calculation formulas for the average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from artificial point 3 to benchmark point 2 are as follows: H5 , V5 , S5 W5, Q5 , Among them, h1 is the monitoring water depth of benchmark 1, h2 is the monitoring water depth of benchmark 2, and H5 is the average water depth from artificial point 3 to benchmark 2. Monitor the flow rate for reference point 1, 2 is the monitored flow velocity of benchmark point 2, k is the flow coefficient of the measuring point, V5 is the flow velocity of the part from artificial point 3 to benchmark point 2, W5 is the distance from artificial point 3 to benchmark point 2, S5 is the cross-sectional area from artificial point 3 to benchmark point 2, and Q5 is the cross-sectional flow from artificial point 3 to benchmark point 2.

[0042] The calculation formulas for average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from benchmark point 2 to artificial point 4 are as follows: H6 , V6 , S6 W6, Q6 , Among them, h2 is the monitoring water depth of benchmark point 2, h3 is the monitoring water depth of benchmark point 3, and H6 is the average water depth from benchmark point 2 to artificial point 4. Monitor the flow rate for reference point 2, 3 is the monitoring flow velocity of reference point 3, k is the flow velocity coefficient of the measuring point, V6 is the partial flow velocity, W6 is the distance from reference point 2 to artificial point 4, S6 is the cross-sectional area from reference point 2 to artificial point 4, and Q6 is the cross-sectional flow rate from reference point 2 to artificial point 4.

[0043] The calculation formulas for average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from artificial point 4 to artificial point 5 are as follows: H7 , V7 , S7 W7, Q7 , Among them, h2 is the monitoring water depth of benchmark point 2, h3 is the monitoring water depth of benchmark point 3, and H7 is the average water depth from artificial point 4 to artificial point 5. Monitor the flow rate for reference point 2, 3 is the monitoring flow velocity of reference point 3, k is the flow velocity coefficient of the measuring point, V7 is the partial flow velocity from artificial point 4 to artificial point 5, W7 is the distance from artificial point 4 to artificial point 5, S7 is the cross-sectional area from artificial point 4 to artificial point 5, and Q7 is the cross-sectional flow from artificial point 4 to artificial point 5.

[0044] The calculation formulas for the average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from artificial point 5 to benchmark point 3 are as follows: H8 , V8 , S8 W8, Q8 , Among them, h2 is the monitoring water depth of benchmark point 2, h3 is the monitoring water depth of benchmark point 3, and H8 is the average water depth from artificial point 5 to benchmark point 3. Monitor the flow rate for reference point 2, 3 is the monitored flow velocity of reference point 3, k is the flow velocity coefficient of the measuring point, V8 is the partial flow velocity from artificial point 5 to reference point 3, W8 is the distance from artificial point 5 to reference point 3, S8 is the cross-sectional area from artificial point 5 to reference point 3, and Q8 is the cross-sectional flow rate from artificial point 5 to reference point 3.

[0045] The calculation formulas for average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from benchmark point 3 to artificial point 6 are: H9 , V9 , S9 W9, Q9 , Among them, h3 is the monitoring water depth of benchmark point 3, H9 is the average water depth from benchmark point 3 to artificial point 6, is the flow velocity monitored at benchmark point 3, k is the flow velocity coefficient of the measuring point, V9 is the partial flow velocity from benchmark point 3 to artificial point 6, W9 is the distance from benchmark point 3 to artificial point 6, S9 is the cross-sectional area from benchmark point 3 to artificial point 6, and Q9 is the cross-sectional flow rate from benchmark point 3 to artificial point 6.

[0046] The calculation formula for average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow from artificial point 6 to the second bank is: H10 , V10 , S10 W10, Q10 , Among them, h3 is the monitoring water depth of benchmark point 3, H10 is the average water depth from artificial point 6 to the second bank, The flow velocity is monitored at reference point 3, k is the flow velocity coefficient of the measuring point, V10 is the flow velocity from artificial point 6 to the second bank, W10 is the distance from artificial point 6 to the second bank, S10 is the cross-sectional area from artificial point 6 to the second bank, and Q10 is the cross-sectional flow from artificial point 6 to the second bank.

[0047] Step 4: Combining the data quality scores, historical stability scores, and environmental interference scores of each fixed benchmark monitoring point and manual monitoring point, the dynamic weight allocation algorithm based on the multi-level benchmark network is used to adjust the cross-section measurement results of each section to obtain the final cross-section measurement results.

[0048] The present invention evaluates the data quality, stability and environmental interference of each monitoring point in real time, and dynamically adjusts the weight ratio between fixed points and manual monitoring points to minimize measurement errors caused by equipment drift, terrain changes or sudden interference, thereby improving the stability and reliability of the overall river section measurement data.

[0049] The input parameters of the dynamic weight allocation algorithm include: real-time operating data such as flow velocity, water depth, voltage and equipment status of each measuring point, historical measurement data (such as the standard deviation and deviation of the past N measurement results), the changing trends of the current water level, mud level and riverbed elevation, whether the fixed point has shifted (determined through periodic calibration), external environmental factors (such as wind speed, rainfall and water flow disturbance intensity), the layout of manual monitoring points and feedback information from operators, which are used to comprehensively evaluate the data quality and stability of each monitoring point.

[0050] The specific steps include: Step 401: Evaluate the quality of data obtained from each fixed benchmark monitoring point and manual monitoring point to obtain a data quality score, a historical stability score, and an environmental interference score for each monitoring point; In this embodiment, the calculation process of the data quality score result of each monitoring point is: For each monitoring point , its data quality score A comprehensive assessment is conducted based on the integrity, consistency, and deviation of the currently collected data. The formula is as follows: , in: The data integrity score indicates the percentage of valid data at the monitoring point within the set period; is the data consistency score, obtained through correlation analysis with data from adjacent time periods or nearby monitoring points; is the data error rate, which reflects the degree of deviation between the measured value and the reference value; , are integrity, consistency and error weight coefficients respectively, satisfying =1.

[0051] The calculation process of the historical stability score of each monitoring point is as follows: Historical stability score It reflects the data fluctuation during the long-term operation of the monitoring point and is calculated using the sliding window standard deviation method: , in: For monitoring points within a selected time window (e.g., the past year) i The standard deviation of the data; is the maximum standard deviation value among all monitoring points, used for normalization processing; The higher the score, the more stable the historical performance of the monitoring point.

[0052] The calculation process of the environmental interference score of each monitoring point is as follows: Environmental interference score It is used to quantify the impact of external environmental factors on the measurement accuracy of the monitoring point, including but not limited to vegetation cover changes, water flow disturbances, sediment deposition, etc. The score can be derived based on the fusion analysis of multi-source remote sensing data and on-site inspection records. The specific formula is as follows:

[0053] in, is the vegetation index, which reflects the impact of vegetation on the line of sight or signal of the monitoring point; is the water turbulence index, which is derived from historical flow velocity and sediment content data; It is an index of bottom stability, determined based on geological data and historical sedimentation changes; , , : are the weighted coefficients of vegetation, turbulence and bottom bed influence, which can be determined based on actual engineering experience.

[0054] Step 402: combining the data quality score, historical stability score, and environmental interference score of each monitoring point to obtain the contribution weight of each monitoring point to the cross-section measurement result; In this embodiment, the weight factor is defined as , indicating the i The contribution weight of each monitoring point to the cross-section measurement results. The calculation formula of the weight factor is as follows: , in, Score the data quality, Score the stability of historical data, score environmental disturbances; 、 and They are the adjustment coefficients for data quality, historical stability, and environmental interference, which can be dynamically adjusted according to actual needs.

[0055] The weights of all measurement points are normalized to meet the following requirements: .

[0056] Step 403: Combine the contribution weight of each monitoring point to the cross-section measurement result and the corresponding measurement result to obtain the final cross-section measurement result by weighted average.

[0057] In this embodiment, the cross-sectional measurement results include average water depth, average flow velocity, and cross-sectional flow rate; Specifically, the calculation formula for the weighted average water depth is: , The formula for calculating the weighted average flow rate is: , The calculation formula for weighted cross-sectional flow is: , in, For the i Water depth at each measuring point; For the i The flow velocity at each measuring point, To correspond to the interval width, in this embodiment, n is selected as 10.

[0058] like Figure 3 As shown, this is a schematic diagram of the data visualization interface provided by an embodiment of the present invention, which is used to display the river section mapping data, real-time flow process lines and real-time flow velocity distribution after completing the dynamic measurement of the trapezoidal river section, so as to realize the visualization presentation and dynamic monitoring of the measurement results.

[0059] Example 2 The embodiment of the present invention provides a dynamic measurement system for a trapezoidal river section based on a multi-level reference network, comprising: Monitoring point information acquisition module, which is used to obtain basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; A cross-section measurement data calculation module is used to calculate the cross-section measurement results of each planned section based on the basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; Monitoring point data evaluation module, which is used to calculate the data quality score, historical stability score and environmental interference score of each fixed benchmark monitoring point and manual monitoring point; The cross-section dynamic measurement module is used to combine the data quality scores, historical stability scores and environmental interference scores of each fixed benchmark monitoring point and manual monitoring point, and adjust the cross-section measurement results of each section based on the dynamic weight allocation algorithm of the multi-level benchmark network to obtain the final cross-section measurement results.

[0060] It should be noted that the specific implementation method of the dynamic measurement system of the trapezoidal river section based on the multi-level reference network in the embodiment of the present invention is similar to the specific implementation method of the dynamic measurement method of the trapezoidal river section based on the multi-level reference network in the embodiment of the present invention. Please refer to the description of the method part for details. In order to reduce redundancy, it will not be repeated here.

[0061] Example 3 This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the dynamic measurement method of a trapezoidal river section based on a multi-level reference network as described above are implemented.

[0062] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the dynamic measurement method for a trapezoidal river section based on a multi-level reference network as described above are implemented.

[0063] Example 5 This embodiment provides a program product, which is a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps in the dynamic measurement method of trapezoidal river section based on multi-level reference network as described above.

[0064] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0068] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0069] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A dynamic measurement method for trapezoidal river sections based on a multi-level benchmark network, characterized in that: The steps include: Obtain basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; Calculate the cross-section measurement results of each planned section based on the basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; Calculate the data quality score, historical stability score and environmental interference score for each fixed benchmark monitoring point and manual monitoring point; Combining the data quality scores, historical stability scores, and environmental interference scores of each fixed benchmark monitoring point and manual monitoring point, the dynamic weight allocation algorithm based on the multi-level benchmark network is used to adjust the cross-section measurement results of each section to obtain the final cross-section measurement results.

2. The dynamic measurement method for a trapezoidal river section based on a multi-level reference network according to claim 1, characterized in that: The planning of each section includes the layout of artificial monitoring points between fixed benchmark monitoring points, including setting up artificial point 1 between the starting point and benchmark point 1, setting up artificial point 2 and artificial point 3 between benchmark point 1 and benchmark point 2, setting up artificial point 4 and artificial point 5 between benchmark point 2 and the benchmark point, and setting up artificial point 6 between benchmark point 3 and the end point; the distance between the starting point and artificial point 1 is section 1, the distance between artificial point 1 and benchmark point 1 is section 2, the distance between benchmark point 1 and artificial point 2 is section 3, the distance between artificial point 2 and artificial point 3 is section 4, the distance between artificial point 3 and benchmark point 2 is section 5, the distance between benchmark point 2 and artificial point 4 is section 6, the distance between artificial point 4 and artificial point 5 is section 7, the distance between artificial point 5 and benchmark point 3 is section 8, the distance between benchmark point 3 and artificial point 6 is section 9, and the distance between artificial point 6 and the end point is section 10.

3. The dynamic measurement method for a trapezoidal river section based on a multi-level reference network according to claim 1, characterized in that: The calculation principles for cross-section measurement of each section are as follows: The data of the fixed benchmark monitoring points adopt the measurement data collected from the nearest benchmark monitoring points, combined with the parameters of the artificial monitoring point layout plan, to calculate the average water depth, partial flow velocity, cross-sectional area and cross-sectional flow from the left bank to artificial monitoring point 1, artificial monitoring point 1 to fixed benchmark monitoring point 1, fixed benchmark monitoring point 1 to artificial monitoring point 2, artificial monitoring point 2 to artificial monitoring point 3, artificial monitoring point 3 to fixed benchmark monitoring point 2, fixed benchmark monitoring point 2 to artificial monitoring point 4, artificial monitoring point 4 to artificial monitoring point 5, artificial monitoring point 5 to fixed benchmark monitoring point 3, fixed benchmark monitoring point 3 to artificial monitoring point 6 and artificial monitoring point 6 to the right bank.

4. The dynamic measurement method for a trapezoidal river section based on a multi-level reference network according to claim 1, characterized in that: The calculation formula for the data quality score, historical stability score and environmental interference score of each monitoring point is: ， ， ， in, Score the data quality of each monitoring point. The data integrity score indicates the percentage of valid data at the monitoring point within the set period; is the data consistency score, obtained through correlation analysis with data from adjacent time periods or nearby monitoring points; is the data error rate, which reflects the degree of deviation between the measured value and the reference value; , are the integrity, consistency and error weight coefficients respectively, Score the historical stability of each monitoring point. For monitoring points within the selected time window i The standard deviation of the data; is the maximum standard deviation among all monitoring points, Score the environmental disturbance, is the vegetation index, which reflects the impact of vegetation on the line of sight or signal of the monitoring point; is the water turbulence index, which is derived from historical flow velocity and sediment content data; It is an index of bottom stability, determined based on geological data and historical sedimentation changes; , , are the weighted coefficients of vegetation, turbulence and bottom bed effects respectively.

5. The dynamic measurement method for a trapezoidal river section based on a multi-level reference network according to claim 1, characterized in that: The cross-sectional measurement results include average water depth, average flow velocity and cross-sectional flow rate, and the calculation formula is: The calculation formula for weighted average water depth is: ， The formula for calculating the weighted average flow velocity is: ， The calculation formula for weighted cross-sectional flow is: ， in, For the i Water depth at each measuring point; For the i The flow velocity at each measuring point, is the corresponding interval width, n is the number of measurement points, For the i The contribution weight of each monitoring point to the cross-section measurement results.

6. The dynamic measurement method for a trapezoidal river section based on a multi-level reference network according to claim 1, characterized in that: After completing the dynamic measurement of the trapezoidal river section, the river section mapping data, real-time flow process line and real-time flow velocity distribution are displayed based on the visual interface.

7. A dynamic measurement device for a trapezoidal river section based on a multi-level reference network, characterized in that: include: Monitoring point information acquisition module, which is used to obtain basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; A cross-section measurement data calculation module is used to calculate the cross-section measurement results of each planned section based on the basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; Monitoring point data evaluation module, which is used to calculate the data quality score, historical stability score and environmental interference score of each fixed benchmark monitoring point and manual monitoring point; The cross-section dynamic measurement module is used to combine the data quality scores, historical stability scores and environmental interference scores of each fixed benchmark monitoring point and manual monitoring point, and adjust the cross-section measurement results of each section based on the dynamic weight allocation algorithm of the multi-level benchmark network to obtain the final cross-section measurement results.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the dynamic measurement device for a trapezoidal river section based on a multi-level reference network as described in any one of claims 1 to 6 are implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the dynamic measurement device for a trapezoidal river section based on a multi-level reference network as described in any one of claims 1 to 6 are implemented.

10. A program product, wherein the program product is a computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, the steps of the dynamic measurement device for a trapezoidal river section based on a multi-level reference network as described in any one of claims 1 to 6 are implemented.

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