Ladder-shaped river channel section dynamic measurement method and system based on multi-level reference network

Abstract

The application belongs to the technical field of hydraulic engineering monitoring, and provides a trapezoidal river channel section dynamic measurement method and system based on a multi-level reference network, which has the technical scheme of obtaining basic information and measurement data of each fixed reference monitoring point and artificial monitoring point; calculating section measurement results of each section based on the basic information and measurement data of each fixed reference monitoring point and artificial monitoring point; calculating data quality scores, historical stability scores and environmental interference scores of each fixed reference monitoring point and artificial monitoring point; combining the data quality scores, historical stability scores and environmental interference scores of each fixed reference monitoring point and artificial monitoring point, and adjusting combined section measurement results based on a dynamic weight distribution algorithm of the multi-level reference network to obtain final section measurement results. The accuracy and stability of river channel section measurement are improved.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy engineering monitoring technology, and in particular relates to a method and system for dynamic measurement of trapezoidal river cross sections based on a multi-level reference network. Background Technology

[0002] The statements in this section are merely 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 crucial step in assessing important parameters such as river flow, water level changes, and sediment deposition. Traditional methods of river cross-section measurement mainly rely on manual measurement, which suffers from low efficiency, limited accuracy, and significant susceptibility to environmental influences. In recent years, with technological advancements, some automated measurement systems have been introduced. However, these systems often rely on a single measurement benchmark and are easily affected by factors such as terrain changes and equipment drift, leading to a decrease in measurement accuracy.

[0004] The inventors discovered that existing technologies lack real-time monitoring and correction mechanisms for dynamic changes in river cross-sections, making it difficult to meet the high-precision measurement requirements in complex environments. Traditional trapezoidal cross-section measurements rely on fixed benchmarks, but river topography is affected by dynamic factors such as scouring and siltation, leading to benchmark shifts and a long-term decline in data reliability. Artificially deployed temporary monitoring points (such as sounding rods and buoys) are difficult to coordinate with fixed benchmark networks, making data fusion challenging and susceptible to environmental interference. Furthermore, when coupling benchmarks and artificial monitoring points, coupling errors exist due to fixed-point drift and insufficient accuracy of artificial points, resulting in deviations in the cross-section data. Summary of the Invention

[0005] To address at least one of the technical problems mentioned above, this invention provides a method and system for dynamic measurement of trapezoidal river cross-sections based on a multi-level reference network. This method achieves high-precision, dynamic measurement of trapezoidal river cross-sections through adaptive fixed point and manual monitoring point collaborative correction.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for dynamic measurement of trapezoidal river cross-sections based on a multi-level reference network, comprising the following steps: Acquire basic information and measurement data from each fixed benchmark monitoring point and manual monitoring point; The cross-sectional measurement results of each planned section are calculated 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 disturbance score for each fixed benchmark monitoring point and manual monitoring point; By combining the data quality scores, historical stability scores, and environmental interference scores of each fixed benchmark monitoring point and manual monitoring point, a dynamic weight allocation algorithm based on a multi-level benchmark network is used to adjust the cross-sectional measurement results of each section to obtain the final cross-sectional measurement results.

[0007] Furthermore, the planning for each section includes the layout of artificial monitoring points between fixed benchmark monitoring points. Specifically, artificial point 1 is set between the starting point and benchmark 1; artificial points 2 and 3 are set between benchmark 1 and benchmark 2; artificial points 4 and 5 are set between benchmark 2 and benchmark 3; and artificial point 6 is set between benchmark 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 1 is section 2; the distance between benchmark 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 2 is section 5; the distance between benchmark 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 3 is section 8; the distance between benchmark 3 and artificial point 6 is section 9; and the distance between artificial point 6 and the end point is section 10.

[0008] Furthermore, the calculation principles for cross-sectional measurements in each section are as follows: The data for the fixed benchmark monitoring points are obtained by using measurement data collected from the nearest benchmark monitoring point, combined with the parameters of the artificial monitoring point layout scheme. The average water depth, partial flow velocity, cross-sectional area, and cross-sectional discharge are calculated for the following distances: from the left bank to artificial monitoring point 1, from artificial monitoring point 1 to fixed benchmark monitoring point 1, from fixed benchmark monitoring point 1 to artificial monitoring point 2, from artificial monitoring point 2 to artificial monitoring point 3, from artificial monitoring point 3 to fixed benchmark monitoring point 2, from fixed benchmark monitoring point 2 to artificial monitoring point 4, from artificial monitoring point 4 to artificial monitoring point 5, from artificial monitoring point 5 to fixed benchmark monitoring point 3, from fixed benchmark monitoring point 3 to artificial monitoring point 6, and from artificial monitoring point 6 to the right bank.

[0009] Furthermore, the calculation formulas for the data quality score, historical stability score, and environmental disturbance score for each monitoring point are as follows: , , , in, The data quality of each monitoring point was scored. The data integrity score indicates the percentage of valid data for that monitoring point within a set period. The data consistency score is obtained through correlation analysis with data from adjacent time periods or nearby monitoring points. The data error rate reflects the degree of deviation between the measured value and the reference value; , These are the weighting coefficients for integrity, consistency, and error, respectively. The historical stability score for each monitoring point is given. For monitoring points within the selected time window i The standard deviation of the data; The maximum standard deviation among all monitoring points. Score environmental disturbances. The vegetation index reflects the impact of vegetation on the line of sight or signal at the monitoring point; It is the water turbulence index, derived from historical flow velocity and sediment concentration data; The stability index of the subsoil is determined based on geological data and historical sedimentary changes; , , These are the weighting coefficients for the effects of vegetation, turbulence, and substrate, respectively.

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

[0011] A second aspect of the present invention provides a dynamic measurement system for trapezoidal river cross-sections based on a multi-level reference network, comprising: The monitoring point information acquisition module is used to acquire basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; The 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. The monitoring point data evaluation module is used to calculate the data quality score, historical stability score, and environmental interference score for each fixed benchmark monitoring point and manual monitoring point. The cross-section dynamic measurement module combines data quality scores, historical stability scores, and environmental interference scores from various fixed benchmark monitoring points and manual monitoring points. Based on a dynamic weight allocation algorithm of a multi-level benchmark network, it adjusts the cross-section measurement results of each section 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 having a computer program stored thereon, which, when executed by a processor, implements the steps in the dynamic measurement method for trapezoidal river cross-sections based on a multi-level reference network as described above.

[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, wherein the processor executes the program to implement the steps in the dynamic measurement method for trapezoidal river cross-sections based on a multi-level reference network as described above.

[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 that, when executed by a processor, implements the steps in the trapezoidal river cross-section dynamic measurement method based on a multi-level reference network as described above.

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention combines data from fixed benchmark points and manual monitoring points for collaborative correction. Through a multi-level benchmark network and adaptive correction mechanism, it significantly improves the accuracy and stability of river cross-section measurement, realizes real-time monitoring of dynamic changes in river cross-section, and 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 management personnel. 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 invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

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

[0023] Figure 3 This is a schematic diagram of the data visualization interface provided in an embodiment of the present invention. Detailed Implementation

[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 description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

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

[0027] The lack of real-time monitoring and correction mechanisms for dynamic changes in river cross-sections in existing technologies makes it difficult to meet the high-precision measurement requirements in complex environments. Traditional trapezoidal cross-section measurements rely on fixed benchmarks, but river topography is affected by dynamic factors such as erosion and siltation, leading to benchmark shifts and a long-term decline in data reliability. Artificially deployed temporary monitoring points (such as sounding rods and buoys) are difficult to coordinate with fixed benchmark networks, making data fusion challenging and susceptible to environmental interference. Furthermore, coupling benchmarks and artificial monitoring points results in coupling errors due to fixed point drift and insufficient accuracy of artificial points, leading to deviations in the cross-section data.

[0028] This invention significantly improves the accuracy and stability of river cross-section measurements through a multi-level benchmark network and adaptive correction mechanism, enabling real-time monitoring of dynamic changes in river cross-sections and timely detection and handling of anomalies. Furthermore, this invention combines data from fixed benchmark points and manual monitoring points for collaborative correction, ensuring the long-term stability and reliability of the measurement system. Simultaneously, it provides an intuitive data visualization interface, greatly facilitating real-time monitoring and decision support for water conservancy management personnel. This comprehensive solution not only improves work efficiency but also enhances the scientific rigor and accuracy of management decisions.

[0029] Example 1 like Figure 1 As shown, this embodiment provides a dynamic measurement method for trapezoidal river cross-sections based on a multi-level reference network, including the following steps: Step 1: Arrange artificial monitoring points between fixed benchmark monitoring points; like Figure 2 As shown, in this embodiment, the fixed benchmark monitoring points include benchmark point 1, benchmark point 2, and benchmark point 3; where H is the cross-sectional height; h1 is the water depth at benchmark point 1, h2 is the water depth at benchmark point 2, h3 is the water depth at benchmark point 3, and h is the elevation of the riverbed at the cross-section. To monitor flow velocity 1, 2 is for monitoring flow velocity 2, 3 is for monitoring flow velocity 3; The method for setting up manual monitoring points is as follows: set up manual point 1 between the starting point and reference point 1, set up manual point 2 and manual point 3 between reference point 1 and reference point 2, set up manual point 4 and manual point 5 between reference point 2 and reference point 1, and set up manual point 6 between reference point 3 and the end point. Figure 2 In the diagram, 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 endpoint.

[0030] Step 2: Obtain basic information and measurement data from fixed benchmark monitoring points and manual monitoring points respectively, for data parsing, storage and subsequent visualization; The fields in the fixed point basic information table include: fixed point number, fixed point name, installation location, reporting time, number of fixed point measurements, cross-sectional length, cross-sectional height, number of measuring points, distance from the starting point, distance from the ending point, velocity coefficient at the starting point bank, and velocity coefficient at the ending point bank.

[0031] The monitoring point information table for the fixed point includes the following fields: 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.

[0032] The fields in the measuring point interval setting information table include: fixed point number, measuring point number, starting water level, ending water level, and flow velocity coefficient.

[0033] The automatic measurement table fields include: fixed point number, measurement time, measurement point location, number of measurements, partial flow rate, water level, partial average water depth, partial average flow velocity, partial area, measurement point spacing, and creation time.

[0034] The fields in the flow measurement history table include: fixed point number, measurement point number, measurement location, number of measurements, reporting IP address, reporting time, creation time, reporting type, current flow velocity, water level, riverbed elevation, water depth, measurement point spacing, voltage, and measurement method.

[0035] The fields in the measurement summary table include: fixed point number, reporting time, creation time, number of measurements, water level, cross-sectional flow rate, cross-sectional area, average flow velocity, average water depth, water surface width, and measurement type.

[0036] Step 3: Based on the basic data and measurement data obtained from the 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 as the second bank, and the adjacent downward 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 as the second bank, and the adjacent downward direction can be defined as the direction from the first bank to the second bank.

[0037] The specific calculation principles include: the data for the fixed benchmark monitoring points are the measurement data collected from the nearest benchmark monitoring point, combined with the parameters of the artificial monitoring point layout scheme, to calculate the average water depth, partial flow velocity, cross-sectional area, and cross-sectional discharge from the left bank to artificial monitoring point 1, from artificial monitoring point 1 to fixed benchmark monitoring point 1, from fixed benchmark monitoring point 1 to artificial monitoring point 2, from artificial monitoring point 2 to artificial monitoring point 3, from artificial monitoring point 3 to fixed benchmark monitoring point 2, from fixed benchmark monitoring point 2 to artificial monitoring point 4, from artificial monitoring point 4 to artificial monitoring point 5, from artificial monitoring point 5 to fixed benchmark monitoring point 3, from fixed benchmark monitoring point 3 to artificial monitoring point 6, and from artificial monitoring point 6 to the right bank.

[0038] 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 are calculated using the following formulas: H1 , V1 , S1 , Q1 , Where h1 is the water depth monitored at reference point 1, and H1 is the average water depth from the first bank to artificial point 1. Flow velocity is monitored at reference point 1. k V1 is the velocity coefficient at the measuring point, W1 is the velocity from the first bank 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 rate from the first bank to artificial point 1.

[0039] The formulas for calculating the average water depth, partial flow velocity, cross-sectional area, and cross-sectional flow rate from artificial point 1 to monitoring point 1 are as follows: H2 , V2 , S2 , Q2 2, Among them, h1 Reference point 1 is used to monitor water depth, and H2 is the average water depth from artificial point 1 to monitoring point 1. Let k be the velocity at reference point 1, k be the velocity coefficient at the measuring point, V2 be the velocity at the point from artificial point 1 to monitoring point 1, W2 be the distance between artificial point 1 and reference point 1, S2 be the cross-sectional area from artificial point 1 to monitoring point 1, and Q2 be the cross-sectional flow rate from artificial point 1 to monitoring point 1.

[0040] The formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional flow rate from reference point 1 to artificial point 2 are as follows: H3 , V3 , S3 W3, Q3 , Where h1 is the water depth monitored at reference point 1, h2 is the water depth monitored at reference point 2, and H3 is the average water depth from reference point 1 to artificial point 2. Flow velocity is monitored at reference point 1. 2 represents the flow velocity monitored at reference point 2, k represents the flow velocity coefficient at the measuring point, V3 represents the partial flow velocity from reference point 1 to artificial point 2, W3 represents the distance between reference point 1 and artificial point 2, S3 represents the cross-sectional area from reference point 1 to artificial point 2, and Q3 represents the cross-sectional flow rate from reference point 1 to artificial point 2.

[0041] The formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional flow rate from artificial point 2 to artificial point 3 are as follows: H4 , V4 , S4 W4, Q4 , Where h1 is the water depth monitored at reference point 1, h2 is the water depth monitored at reference point 2, and H4 is the average water depth from artificial point 2 to artificial point 3. Flow velocity is monitored at reference point 1. 2 represents the flow velocity at reference point 2, k represents the flow velocity coefficient at the measuring point, V4 represents the partial flow velocity, W4 represents the distance between artificial point 2 and artificial point 3, S4 represents the cross-sectional area between artificial point 2 and artificial point 3, and Q4 represents the cross-sectional flow rate between artificial point 2 and artificial point 3.

[0042] The formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional flow rate from artificial point 3 to reference point 2 are as follows: H5 , V5 , S5 W5 Q5 , Where h1 is the water depth monitored at reference point 1, h2 is the water depth monitored at reference point 2, and H5 is the average water depth from artificial point 3 to reference point 2. Flow velocity is monitored at reference point 1. 2 represents the flow velocity monitored at reference point 2, k represents the flow velocity coefficient at the measuring point, V5 represents the flow velocity from artificial point 3 to reference point 2, W5 represents the distance between artificial point 3 and reference point 2, S5 represents the cross-sectional area from artificial point 3 to reference point 2, and Q5 represents the cross-sectional flow rate from artificial point 3 to reference point 2.

[0043] The formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional flow rate from reference point 2 to artificial point 4 are as follows: H6 , V6 , S6 W6, Q6 , Where h2 is the water depth monitored at reference point 2, h3 is the water depth monitored at reference point 3, and H6 is the average water depth from reference point 2 to artificial point 4. For monitoring flow velocity at reference point 2, 3 represents the flow velocity monitored at reference point 3, k represents the flow velocity coefficient at the measuring point, V6 represents the partial flow velocity, W6 represents the distance between reference point 2 and artificial point 4, S6 represents the cross-sectional area between reference point 2 and artificial point 4, and Q6 represents the cross-sectional flow rate between reference point 2 and artificial point 4.

[0044] The formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional flow rate from artificial point 4 to artificial point 5 are as follows: H7 , V7 , S7 W7, Q7 , Where h2 is the water depth monitored at reference point 2, h3 is the water depth monitored at reference point 3, and H7 is the average water depth from artificial point 4 to artificial point 5. For monitoring flow velocity at reference point 2, 3 is the reference point 3 for monitoring flow velocity, k is the flow velocity coefficient of the measuring point, V7 is the flow velocity of the section from artificial point 4 to artificial point 5, W7 is the distance between artificial point 4 and artificial point 5, S7 is the cross-sectional area from artificial point 4 to artificial point 5, and Q7 is the cross-sectional flow rate from artificial point 4 to artificial point 5.

[0045] The formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional flow rate from artificial point 5 to reference point 3 are as follows: H8 , V8 , S8 W8, Q8 , Where h2 is the water depth monitored at reference point 2, h3 is the water depth monitored at reference point 3, and H8 is the average water depth from artificial point 5 to reference point 3. For monitoring flow velocity at reference point 2, 3 represents the flow velocity monitored at reference point 3, k represents the flow velocity coefficient at the measuring point, V8 represents the partial flow velocity from artificial point 5 to reference point 3, W8 represents the distance between artificial point 5 and reference point 3, S8 represents the cross-sectional area from artificial point 5 to reference point 3, and Q8 represents the cross-sectional flow rate from artificial point 5 to reference point 3.

[0046] The formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional flow rate from reference point 3 to artificial point 6 are as follows: H9 , V9 , S9 W9, Q9 , Where h3 is the water depth monitored at reference point 3, and H9 is the average water depth from reference point 3 to artificial point 6. The flow velocity is monitored at reference point 3, k is the flow velocity coefficient at the measuring point, V9 is the flow velocity between reference point 3 and artificial point 6, W9 is the distance between reference point 3 and artificial point 6, S9 is the cross-sectional area between reference point 3 and artificial point 6, and Q9 is the cross-sectional flow rate between reference point 3 and artificial point 6.

[0047] Formulas for calculating the average water depth, partial velocity, cross-sectional area, and cross-sectional discharge from point 6 to the second bank: H10 , V10 , S10 W10, Q10 , Where h3 is the water depth monitored at reference point 3, and 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 at 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 rate from artificial point 6 to the second bank.

[0048] 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 cross-sectional measurement results of each section are adjusted based on the dynamic weight allocation algorithm of the multi-level benchmark network to obtain the final cross-sectional measurement results.

[0049] This invention improves the stability and reliability of overall river cross-section measurement data by dynamically adjusting the weight ratio between fixed points and manual monitoring points in real time to evaluate the data quality, stability and environmental interference of each monitoring point.

[0050] The input parameters of the dynamic weight allocation algorithm include: real-time operational data such as flow velocity, water depth, voltage, and equipment status at each measuring point; historical measurement data (such as the standard deviation and bias of the past N measurement results); the changing trends of current water level, sediment level, and riverbed elevation; whether the fixed point has shifted (judged through periodic calibration); external environmental factors (such as wind speed, rainfall, and water flow disturbance intensity); and the location of manual monitoring points and feedback information from operators, used to comprehensively evaluate the data quality and stability of each monitoring point.

[0051] Specifically, the steps include the following: Step 401: Evaluate the data quality of each fixed benchmark monitoring point and manual monitoring point to obtain the data quality score, historical stability score and environmental interference score for each monitoring point; In this embodiment, the calculation process for the data quality score of each monitoring point is as follows: For each monitoring point Its data quality score A comprehensive evaluation is conducted based on the completeness, consistency, and deviation of the currently collected data, using the following formula: , in: The data integrity score indicates the percentage of valid data for that monitoring point within a set period. The data consistency score is obtained through correlation analysis with data from adjacent time periods or nearby monitoring points. The data error rate reflects the degree of deviation between the measured value and the reference value; , These are the weighting coefficients for integrity, consistency, and error, respectively, satisfying... =1.

[0052] The calculation process for the historical stability score of each monitoring point is as follows: Historical stability score This reflects the data fluctuations during the long-term operation of the monitoring points, and is calculated using the sliding window standard deviation method: , in: For monitoring points within the selected time window (e.g., the past year) i The standard deviation of the data; The maximum standard deviation among all monitoring points is used for normalization. A higher score indicates that the monitoring point has a more stable historical performance.

[0053] The calculation process for the environmental disturbance score at each monitoring point is as follows: Environmental disturbance score This score is used to quantify the impact of external environmental factors on the measurement accuracy of the monitoring point, including but not limited to factors such as vegetation cover changes, water flow disturbance, and siltation. The score is derived from the fusion analysis of multi-source remote sensing data and on-site inspection records, using the following formula:

[0054] in, The vegetation index reflects the impact of vegetation on the line of sight or signal at the monitoring point; It is the water turbulence index, derived from historical flow velocity and sediment concentration data; The stability index of the subsoil is determined based on geological data and historical sedimentary changes; , , : These are the weighting coefficients for the effects of vegetation, turbulence, and bedrock, respectively, which can be determined based on actual engineering experience.

[0055] Step 402: Combine 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-sectional measurement results; In this embodiment, the weighting factor is defined as follows: , indicating the first i The contribution weight of each monitoring point to the cross-sectional measurement results is calculated using the following formula: , in, Score the data quality. Score the stability of historical data. Score environmental disturbances; , and These are adjustment coefficients for data quality, historical stability, and environmental interference, which can be dynamically adjusted according to actual needs.

[0056] The weights of all measurement points are normalized to satisfy: .

[0057] Step 403: Combine the contribution weight of each monitoring point to the cross-sectional measurement results with the corresponding measurement results to obtain the final cross-sectional measurement results by weighted average.

[0058] like Figure 3 The diagram shown is a data visualization interface provided in an embodiment of the present invention. It is used to display the river cross-section mapping data, real-time flow process line and real-time flow velocity distribution after the dynamic measurement of the trapezoidal river cross-section is completed, so as to realize the visualization and dynamic monitoring of the measurement results.

[0059] Example 2 This invention provides a dynamic measurement system for trapezoidal river cross-sections based on a multi-level reference network, comprising: The monitoring point information acquisition module is used to acquire basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; The 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. The monitoring point data evaluation module is used to calculate the data quality score, historical stability score, and environmental interference score for each fixed benchmark monitoring point and manual monitoring point. The cross-section dynamic measurement module combines the data quality scores, historical stability scores, and environmental interference scores of various fixed benchmark monitoring points and manual monitoring points. Based on a dynamic weight allocation algorithm of a multi-level benchmark network, it adjusts the cross-section measurement results of each section to obtain the final cross-section measurement result.

[0060] It should be noted that the specific implementation of the trapezoidal river cross-section dynamic measurement system based on a multi-level reference network in this embodiment of the invention is similar to the specific implementation of the trapezoidal river cross-section dynamic measurement method based on a multi-level reference network in this embodiment of the invention. For details, please refer to the description in the method section. To reduce redundancy, it will not be repeated here.

[0061] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the trapezoidal river cross-section dynamic measurement method based on a multi-level reference network as described above.

[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, it implements the steps in the trapezoidal river cross-section dynamic measurement method based on a multi-level reference network as described above.

[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 trapezoidal river cross-section dynamic measurement method based on a multi-level reference network as described above.

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

[0065] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic measurement method for trapezoidal river cross-sections based on a multi-level reference network, characterized in that, Includes the following steps: Acquire basic information and measurement data from each fixed benchmark monitoring point and manual monitoring point; The cross-sectional measurement results of each planned section are calculated 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 disturbance score for each fixed benchmark monitoring point and manual monitoring point; By 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-sectional measurement results of each section to obtain the final cross-sectional measurement results. The planning for each section includes the layout of artificial monitoring points between fixed benchmark monitoring points. Specifically, artificial point 1 is set between the starting point and benchmark 1; artificial points 2 and 3 are set between benchmark 1 and benchmark 2; artificial points 4 and 5 are set between benchmark 2 and benchmark 3; and artificial point 6 is set between benchmark 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 1 is section 2; the distance between benchmark 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 2 is section 5; the distance between benchmark 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 3 is section 8; the distance between benchmark 3 and artificial point 6 is section 9; and the distance between artificial point 6 and the end point is section 10. The formulas for calculating the data quality score, historical stability score, and environmental disturbance score for each monitoring point are as follows: ， ， ， in, The data quality of each monitoring point was scored. The data integrity score indicates the percentage of valid data for that monitoring point within a set period. The data consistency score is obtained through correlation analysis with data from adjacent time periods or nearby monitoring points. The data error rate reflects the degree of deviation between the measured value and the reference value; , These are the weighting coefficients for integrity, consistency, and error, respectively. The historical stability score for each monitoring point is given. For monitoring points within the selected time window i The standard deviation of the data; The maximum standard deviation among all monitoring points. Score environmental disturbances. The vegetation index reflects the impact of vegetation on the line of sight or signal at the monitoring point; It is the water turbulence index, derived from historical flow velocity and sediment concentration data; The stability index of the subsoil is determined based on geological data and historical sedimentary changes; , , These are the weighting coefficients for the effects of vegetation, turbulence, and substrate, respectively.

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

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

4. A dynamic measurement device for trapezoidal river cross-sections based on a multi-level reference network, characterized in that, The method for dynamically measuring trapezoidal river cross-sections based on a multi-level reference network as described in any one of claims 1-3 includes: The monitoring point information acquisition module is used to acquire basic information and measurement data of each fixed benchmark monitoring point and manual monitoring point; The 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 artificial monitoring point. The monitoring point data evaluation module is used to calculate the data quality score, historical stability score, and environmental interference score for each fixed benchmark monitoring point and manual monitoring point. The cross-section dynamic measurement module combines data quality scores, historical stability scores, and environmental interference scores from various fixed benchmark monitoring points and manual monitoring points. Based on a dynamic weight allocation algorithm of a multi-level benchmark network, it adjusts the cross-section measurement results of each section to obtain the final cross-section measurement results.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the trapezoidal river cross-section dynamic measurement device based on a multi-level reference network as described in any one of claims 1-3.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the trapezoidal river cross-section dynamic measurement device based on a multi-level reference network as described in any one of claims 1-3.

7. A program product, said program product being a computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps in the trapezoidal river cross-section dynamic measurement device based on a multi-level reference network as described in any one of claims 1-3.

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