A method for detecting sediment content in water conservancy and hydrology

Abstract

The present invention discloses a method for detecting sediment content in water conservancy and hydrology, and relates to the technical field of sediment content measurement, and comprises the following steps: the intensity of incident light after processing, the turbidity of water samples, the current water flow velocity, the current water temperature and the current conductivity are input into a calculation module, and the calculation module outputs a preliminary analysis value of sediment content, the sediment content after fluid calibration and the sediment content after comprehensive calibration. The present invention has the ability to comprehensively consider the influence of various factors on the sediment content and to perform dynamic correction and prediction, thereby improving the accuracy and reliability of detection. The method introduces optical scattering and turbidity measurement technology, combines fluid dynamics parameters for dynamic correction, and comprehensively considers environmental parameters for combined evaluation and analysis. The present invention can significantly improve the detection accuracy of sediment content, meet the needs of high-precision detection, and then can realize real-time monitoring and rapid response to sediment content, greatly shorten the detection time, and improve the detection efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of sediment content measurement, in particular to a water conservancy and hydrology sediment content detection method. Background Art

[0002] Silt is widely present in natural water bodies. When the sediment content reaches a certain value, the sediment in the water body not only affects the water environment and appearance, but also causes a series of problems such as siltation of the water diversion system, reduction of reservoir capacity, and erosion of hydropower station turbines. Therefore, it is necessary to accurately and efficiently detect the sediment content in the water body.

[0003] However, the existing methods for detecting hydrological sediment content may be affected by operational complexity and human errors, and may be difficult to achieve high-precision detection. Especially in complex and changeable hydrological environments, changes in sediment content are often accompanied by synchronous changes in parameters such as water flow velocity, water depth, water temperature and conductivity. Traditional methods are difficult to fully consider the impact of these factors, resulting in limited detection accuracy. In addition, existing methods may require a long time for sample collection, processing and detection, and cannot achieve real-time monitoring of sediment content. In addition, if the existing methods directly use sensors, although they can quickly detect sediment content, their accuracy and reliability may be limited by various factors, such as sensor accuracy, interference from environmental conditions, etc., resulting in poor accuracy. Summary of the invention

[0004] The purpose of the present invention is to provide a method for detecting sediment content in water conservancy and hydrology, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for detecting sediment content in water conservancy and hydrology, comprising the following steps:

[0006] Step I: The intensity of scattered light, the intensity of incident light, the turbidity of water samples, the current water flow velocity, the current water temperature and the current conductivity of the water body are collected in real time at the sampling point of the river to be tested through the data collection module, and the collected data are saved in the database;

[0007] Step II: Input the intensity of scattered light, the intensity of incident light, the turbidity of the water sample, the current water flow rate, the current water temperature and the current conductivity into the data processing module, and the data processing module calibrates and cleans the data;

[0008] Step III: Input the intensity of scattered light processed by the processing module, the intensity of incident light, the turbidity of the water sample, the current water flow velocity, the current water temperature and the current conductivity into the calculation module, and the calculation module outputs the preliminary analysis value SER of the sediment content, the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration;

[0009] Step IV: Input the preliminary analysis value of sediment content SER, the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration into the analysis module. The analysis module verifies and compares the data and displays the data on the administrator terminal.

[0010] Optionally, the calculation module includes: an optical preliminary analysis submodule, a fluid calibration submodule and a comprehensive analysis submodule.

[0011] Optionally, the calculation formula of the optical preliminary analysis submodule is as follows:

[0012] ;

[0013] in:

[0014] SER refers to the preliminary analysis value of sediment content, SERA refers to the intensity of scattered light, SERB refers to the intensity of incident light, SERC refers to the scattering coefficient, SERD refers to the turbidity of water sample, SERF refers to the reference turbidity of water sample, SERG refers to the density of sand, AQ refers to the turbidity correction index, SERA / SERB refers to the relative size of the scattered light intensity and the incident light intensity;

[0015] The processing process of the optical preliminary analysis submodule is as follows: the intensity of scattered light SERA, the intensity of incident light SERB and the turbidity SERD of the water sample are input into the optical preliminary analysis submodule, and the preliminary analysis value SER of the sand content is output based on the scattering coefficient SERC and the reference turbidity SERF of the water sample.

[0016] Optionally, the calculation formula of the fluid calibration submodule is as follows:

[0017] ;

[0018] in:

[0019] SCR refers to the calibrated sediment content of the fluid, SCV refers to the current water velocity, SCVF refers to the reference water velocity, BQ refers to the water velocity correction factor, SCH refers to the current water depth, SCHF refers to the reference water depth, and BW refers to the water depth correction factor;

[0020] Refers to the multiplication of the water velocity correction term;

[0021] The processing process of the fluid calibration submodule is as follows: the preliminary analysis value of sediment content SER and the current water velocity SCV are input into the fluid calibration submodule, and the calibrated sediment content SCR of the fluid is output based on the current water depth SCH.

[0022] Optionally, the calculation formula of the comprehensive analysis submodule is as follows:

[0023] ;

[0024] in:

[0025] SPE refers to the sediment content after comprehensive calibration, CA refers to the weight coefficient of SER, CB refers to the weight coefficient of SCR, CC refers to the weight coefficient of water temperature, TTA refers to the current water temperature, TTE refers to the reference water temperature, CD refers to the weight coefficient of conductivity, SPQ refers to the current conductivity, SPAQ refers to the reference conductivity, CA×SER refers to the importance of the preliminary analysis value of sediment content, CB×SCR refers to the importance of the sediment content after fluid calibration;

[0026] The processing process of the comprehensive analysis submodule is as follows: the preliminary analysis value of sediment content SER, the sediment content SCR after fluid calibration, the current water temperature TTA and the current conductivity SPQ are output to the comprehensive analysis submodule, and the sediment content SPE after comprehensive calibration is output based on the water temperature weight coefficient CC and the reference water temperature TTE.

[0027] Optionally, the data acquisition module measures the intensity of scattered light and the intensity of incident light through an optical scattering sensor, measures the turbidity of the water sample through a turbidity meter, and measures the current water flow velocity through a flow meter.

[0028] Optionally, the data acquisition module measures the current water temperature by a thermometer and measures the current conductivity by a conductivity meter.

[0029] Optionally, the data acquisition module sets multiple sampling points in the target area of ​​the river to be tested, and the sampling points are evenly distributed in the water body to reflect the sediment content of the entire water body, and the devices used by the data acquisition module are installed at the sampling points to perform real-time measurement and record the data.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention outputs a preliminary analysis value of the sand content through an optical preliminary analysis submodule. This submodule makes a preliminary estimate of the sand content by measuring the ratio of the intensity of scattered light to the intensity of incident light and combining parameters such as scattering coefficient, turbidity and sand density. This method has the advantages of being fast and non-contact and can reflect the changes of suspended particles in the water body in real time, and can provide basic data for subsequent correction and prediction.

[0032] 2. The present invention outputs the sediment content of the fluid after calibration through a fluid calibration submodule. This submodule introduces fluid dynamics factors such as water flow velocity and water depth on the basis of preliminary estimation, and dynamically corrects the preliminary estimation results. This correction can take into account the influence of water flow state on sediment content distribution, and can improve the accuracy and reliability of detection. Dynamic correction is performed on the basis of preliminary estimation, taking into account the influence of water flow state on sediment content distribution, thereby improving the accuracy and reliability of detection.

[0033] 3. The present invention outputs the calibrated sediment content through a comprehensive analysis submodule. This submodule combines the preliminary estimation results, dynamic correction results, temperature and conductivity, and obtains the final sediment content prediction value through weighted linear combination. This method can comprehensively consider the influence of various factors on the sediment content, improve the accuracy and applicability of the prediction. This submodule comprehensively considers the influence of various factors on the sediment content and obtains the final prediction value through weighted linear combination. This method can more comprehensively reflect the actual situation of suspended particulate matter in the water body and provide more accurate data support for water conservancy and hydrological monitoring and decision-making.

[0034] 4. The present invention cyclically iterates the water velocity correction coefficient in the fluid calibration submodule through the comprehensive calibrated sediment content. Through iteration, the water velocity correction coefficient can be dynamically adjusted according to the actual sediment content prediction result, so as to more accurately reflect the influence of water velocity on sediment content measurement, reduce the measurement error caused by the change of water velocity, and improve the accuracy of overall sediment content detection. By iteratively adapting to the sediment content measurement under different water flow velocity conditions, the generalization ability of the overall method is improved, so that a high prediction accuracy can be maintained under different hydrological conditions, thereby enhancing its practicality. The iterative process optimizes the water velocity correction coefficient so that the corrected sediment content is closer to the true value, which helps to reduce the uncertainty in the dynamic correction process and improve the reliability of the correction result. Through iteration, the value of the water velocity correction coefficient can be gradually optimized, so that the error between the corrected sediment content and the true sediment content is gradually reduced, which helps to improve the accuracy and reliability of overall sediment content detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the steps of this water conservancy and hydrology sediment content detection method;

[0036] Figure 2 This is the overall structural diagram of the water conservancy and hydrology sediment content detection method;

[0037] Figure 3 This is a schematic diagram of the structure of the calculation module in this water conservancy and hydrological sediment content detection method. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The present water conservancy and hydrological sediment content detection method is different from the existing water conservancy and hydrological sediment content detection method. The existing water conservancy and hydrological sediment content detection method may be difficult to achieve high-precision detection due to operational complexity and human errors. Especially in complex and changeable hydrological environments, changes in sediment content are often accompanied by synchronous changes in parameters such as water flow velocity, water depth, water temperature and conductivity. Traditional methods find it difficult to fully consider the influence of these factors, resulting in limited detection accuracy. Moreover, traditional methods usually require a long time for sample collection, processing and testing, and cannot achieve real-time monitoring of sediment content. This is particularly insufficient in the fields of water resources management and environmental protection, especially when responding to sudden water environment events.

[0040] The module of this water conservancy and hydrological sediment content detection method is based on the preliminary sediment content estimation of optical scattering and turbidity. By measuring the ratio of scattered light to incident light and the ratio of turbidity to reference turbidity, a preliminary estimation of sediment content is achieved. Through dynamic correction of fluid dynamics, fluid dynamic parameters such as water flow velocity and water depth are introduced. By calculating the nonlinear relationship between these parameters and sediment content, the preliminary estimated value is dynamically corrected, thereby improving the accuracy of detection.

[0041] Example 1: Please refer to Figures 1 to 3 This embodiment provides a method for detecting water conservancy and hydrological sediment content, comprising the following steps:

[0042] Step I: The intensity of scattered light, the intensity of incident light, the turbidity of water samples, the current water flow velocity, the current water temperature and the current conductivity of the water body are collected in real time at the sampling point of the river to be tested through the data collection module, and the collected data are saved in the database;

[0043] Step II: Input the intensity of scattered light, the intensity of incident light, the turbidity of the water sample, the current water flow rate, the current water temperature and the current conductivity into the data processing module, and the data processing module calibrates and cleans the data;

[0044] Step III: Input the intensity of scattered light processed by the processing module, the intensity of incident light, the turbidity of the water sample, the current water flow velocity, the current water temperature and the current conductivity into the calculation module, and the calculation module outputs the preliminary analysis value SER of the sediment content, the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration;

[0045] Step IV: Input the preliminary analysis value of sediment content SER, the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration into the analysis module, which verifies and compares the data and displays the data on the administrator terminal;

[0046] The calculation module includes: an optical preliminary analysis submodule, a fluid calibration submodule and a comprehensive analysis submodule.

[0047] In this embodiment: the present invention is based on the three groups of submodules in the calculation module to cooperate with each other to form an intelligent water conservancy and hydrology sediment content detection method. Compared with the form of direct sensor detection, although it has the advantages of fast and real-time, its accuracy and reliability are often limited by various factors, such as the accuracy of the sensor, the interference of environmental conditions, etc., and this method can comprehensively consider the influence of various factors on the sediment content through the combination of three groups of formulas, and perform dynamic correction and prediction, thereby improving the accuracy and reliability of detection. In addition, this method can also adapt to the detection needs under different environmental conditions and has a wider application prospect. Combining the three groups of submodules together can comprehensively consider the influence of various factors on the sediment content, and perform dynamic correction and prediction. This combined method can significantly improve the accuracy and reliability of detection, adapt to the detection needs under different environmental conditions, and at the same time, it can also quantify the influence of various factors, and provide more comprehensive and accurate data support for water conservancy and hydrology monitoring and decision-making. In summary, this method can comprehensively consider the influence of various factors on the sediment content, and perform dynamic correction and prediction. This method has the characteristics of high accuracy, high reliability and strong adaptability. Compared with direct sensor detection, this method has significantly improved accuracy and reliability. At the same time, it can also quantify the influence of multiple factors and provide more comprehensive and accurate data support for water conservancy and hydrological monitoring and decision-making. The emergence of this method provides new ideas and means for water conservancy and hydrological sediment content detection.

[0048] See also Figures 1 to 3 , the processing process of the optical initial analysis submodule is as follows:

[0049] ;

[0050] in:

[0051] SER refers to the preliminary analysis value of sediment content, SERA refers to the intensity of scattered light, SERB refers to the intensity of incident light, SERC refers to the scattering coefficient, SERD refers to the turbidity of water sample, SERF refers to the reference turbidity of water sample, SERG refers to the density of sand, AQ refers to the turbidity correction index, SERA / SERB refers to the relative size of the scattered light intensity and the incident light intensity;

[0052] The processing process of the optical preliminary analysis submodule is as follows: the intensity of scattered light SERA, the intensity of incident light SERB and the turbidity SERD of the water sample are input into the optical preliminary analysis submodule, and the preliminary analysis value SER of the sand content is output based on the scattering coefficient SERC and the reference turbidity SERF of the water sample.

[0053] In this embodiment: This submodule reflects the number and size distribution of suspended particles in the water body through turbidity, which is an important parameter for preliminary estimation of sand content. Compared with the prior art, this method can more accurately reflect the actual situation of suspended particles in the water body by introducing the turbidity parameter. This submodule reflects the mass distribution and physical properties of sand particles through density, which has an important influence on the calculation of sand content. By introducing the density parameter, this method can more accurately estimate the value of sand content;

[0054] This submodule makes a preliminary estimate of the sand content by measuring the ratio of the intensity of scattered light to the intensity of incident light, and combining parameters such as scattering coefficient, turbidity, and sand density. This method has the advantages of being fast and non-contact, and can reflect the changes of suspended particles in the water in real time, providing basic data for subsequent corrections and predictions.

[0055] See also Figures 1 to 3 , the processing of the fluid calibration submodule is as follows:

[0056] ;

[0057] in:

[0058] SCR refers to the calibrated sediment content of the fluid, SCV refers to the current water velocity, SCVF refers to the reference water velocity, BQ refers to the water velocity correction factor, SCH refers to the current water depth, SCHF refers to the reference water depth, and BW refers to the water depth correction factor;

[0059] Refers to the multiplication of the water velocity correction term;

[0060] The processing process of the fluid calibration submodule is as follows: the preliminary analysis value of sediment content SER and the current water velocity SCV are input into the fluid calibration submodule, and the sediment content SCR after fluid calibration is output based on the current water depth SCH.

[0061] In this embodiment: this submodule reflects the flow state of the water body, which has an important influence on the distribution and transportation of the sediment content. By introducing the water flow velocity parameter, this method can take into account the influence of the water flow state on the distribution of the sediment content, thereby improving the accuracy of the detection. This submodule reflects the vertical structural characteristics of the water body, which has an important influence on the distribution and vertical change of the sediment content. By introducing the water depth parameter, this method can more accurately reflect the vertical distribution of suspended particles in the water body;

[0062] Based on the preliminary estimate, this submodule introduces fluid dynamics factors such as water velocity and water depth into the formula to dynamically correct the preliminary estimate results. This correction can take into account the impact of water flow status on the distribution of sediment content, thereby improving the accuracy and reliability of detection. Based on the preliminary estimate, dynamic correction is performed, taking into account the impact of water flow status on the distribution of sediment content, thereby improving the accuracy and reliability of detection.

[0063] See also Figures 1 to 3 ,The processing process of the comprehensive analysis submodule is as follows:

[0064] ;

[0065] in:

[0066] SPE refers to the sediment content after comprehensive calibration, CA refers to the weight coefficient of SER, CB refers to the weight coefficient of SCR, CC refers to the weight coefficient of water temperature, TTA refers to the current water temperature, TTE refers to the reference water temperature, CD refers to the weight coefficient of conductivity, SPQ refers to the current conductivity, SPAQ refers to the reference conductivity, CA×SER refers to the importance of the preliminary analysis value of sediment content, CB×SCR refers to the importance of the sediment content after fluid calibration;

[0067] The processing process of the comprehensive analysis submodule is as follows: the preliminary analysis value of sediment content SER, the sediment content SCR after fluid calibration, the current water temperature TTA and the current conductivity SPQ are output to the comprehensive analysis submodule, and the comprehensive calibrated sediment content SPE is output based on the water temperature weight coefficient CC and the reference water temperature TTE.

[0068] In this embodiment: this submodule reflects the thermal characteristics of the water body, which has an important influence on the dissolution and precipitation process of the sand content. By introducing the water temperature parameter, this method can take into account the influence of water temperature on the change of sand content, thereby improving the accuracy of the prediction, and reflects the electrolyte content and ion concentration of the water body, which has an important influence on the dissolution and ion exchange process of the sand content. By introducing the conductivity parameter, this method can more comprehensively reflect the chemical characteristics of suspended particles in the water body. Compared with the prior art, this method can more accurately predict the change trend and value of the sand content by introducing multiple parameters for weighted linear combination;

[0069] This submodule combines the preliminary estimation results, dynamic correction results and other environmental factors, such as temperature and conductivity, and obtains the final predicted value of sediment content through weighted linear combination. This method can comprehensively consider the impact of multiple factors on sediment content and improve the accuracy and applicability of the prediction. This submodule comprehensively considers the impact of multiple factors on sediment content and obtains the final predicted value through weighted linear combination. This method can more comprehensively reflect the actual situation of suspended particulate matter in water bodies and provide more accurate data support for water conservancy and hydrological monitoring and decision-making.

[0070] It is worth noting that based on the mutual calculation between the sediment content SPE after comprehensive calibration and the target sediment content value SSL, the water velocity correction coefficient BQ in the fluid calibration submodule can be iterated cyclically, thereby continuously optimizing and improving the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration. The specific processing process is as follows:

[0071] First: BQ new =BQ old +α×(SPE-SSL);

[0072] Second: Set the iteration termination condition:

[0073] Termination condition 1: The number of iterations is 50;

[0074] Termination condition 2: |BQ new -BQ old |<0.003;

[0075] in:

[0076] BQ new Refers to the updated water velocity correction factor, BQ old It refers to the water velocity correction coefficient before updating, α refers to the learning rate parameter, which is used to control the step size of the update, and SSL refers to the target sediment content value, which is an expected value set based on historical data.

[0077] In this embodiment: through the iterative process, the water flow velocity correction coefficient BQ can be dynamically adjusted according to the actual sediment content prediction result SPE, so as to more accurately reflect the influence of water flow velocity on sediment content measurement, help to reduce the measurement error caused by the change of water flow velocity, and improve the accuracy of overall sediment content detection. Through iteration, it can adapt to the sediment content measurement under different water flow velocity conditions, and improve the generalization ability of the overall method. This makes it possible to maintain a high prediction accuracy under different hydrological conditions and enhance its practicality. The iterative process is essentially an optimization process for the dynamic correction coefficient BQ, so that the corrected sediment content SCR is closer to the true value, which helps to reduce the uncertainty in the dynamic correction process and improve the reliability of the correction result. The value of BQ will be dynamically adjusted according to the SPE in each iteration, so that the corrected sediment content SCR is closer to the actual sediment content. This dynamic adjustment helps to reduce the error caused by The measurement error caused by the change of water flow velocity is used to improve the accuracy of the correction result. The value of the correction coefficient BQ can be gradually optimized through iteration, so that the error between the corrected sediment content SCR and the actual sediment content is gradually reduced, which is helpful to improve the accuracy and reliability of the overall sediment content detection. The calculation of SPE depends on the correction coefficient BQ, and the iterative update of BQ depends on the feedback of SPE. This interdependence constitutes the basis of the iterative process, so that the overall method can optimize the prediction results of SPE by continuously adjusting BQ. SPE, as a comprehensive sediment content prediction value, reflects the comprehensive influence of multiple factors, such as optical scattering, turbidity and water flow velocity on sediment content, and BQ, as a water flow velocity correction coefficient, is the key parameter connecting water flow velocity and sediment content measurement. Through the iterative process, SPE can continuously approach the actual sediment content, thereby indirectly optimizing the value of BQ, making the corrected sediment content more accurate.

[0078] In the specific implementation process, a plurality of submodules in the method are used to form a water conservancy and hydrology sediment content detection method system, by inputting the intensity of scattered light SERA, the intensity of incident light SERB and the turbidity SERD of the water sample into the optical preliminary analysis submodule, and outputting the preliminary analysis value SER of the sediment content based on the scattering coefficient SERC and the reference turbidity SERF of the water sample. This submodule makes a preliminary estimate of the sediment content by measuring the ratio of the intensity of scattered light to the intensity of incident light and combining parameters such as the scattering coefficient, turbidity and density of sand. This method has the advantages of being fast and non-contact, and can reflect the changes of suspended particles in the water body in real time, and provide basic data for subsequent correction and prediction;

[0079] By inputting the preliminary analysis value of sediment content SER and the current water velocity SCV into the fluid calibration submodule, and outputting the fluid calibrated sediment content SCR based on the current water depth SCH, this submodule introduces fluid dynamics factors such as water velocity and water depth on the basis of preliminary estimation, and dynamically corrects the preliminary estimation results. This correction can take into account the influence of water flow state on sediment content distribution, thereby improving the accuracy and reliability of detection. Dynamic correction is performed on the basis of preliminary estimation, taking into account the influence of water flow state on sediment content distribution, thereby improving the accuracy and reliability of detection;

[0080] By outputting the preliminary analysis value SER of sediment content, the sediment content SCR after fluid calibration, the current water temperature TTA and the current conductivity SPQ to the comprehensive analysis submodule, and outputting the comprehensive calibrated sediment content SPE based on the water temperature weight coefficient CC and the reference water temperature TTE, this submodule combines the preliminary estimation results, dynamic correction results, temperature and conductivity, and obtains the final sediment content prediction value through weighted linear combination. This method can comprehensively consider the influence of various factors on sediment content and improve the accuracy and applicability of the prediction. This submodule comprehensively considers the influence of various factors on sediment content and obtains the final prediction value through weighted linear combination. This method can more comprehensively reflect the actual situation of suspended particles in the water body and provide more accurate data support for water conservancy and hydrological monitoring and decision-making;

[0081] Through the mutual operation of the sediment content SPE after comprehensive calibration and the target sediment content value SSL, the water flow velocity correction coefficient BQ in the fluid calibration submodule can be iterated cyclically, so that the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration can be continuously optimized and improved. Through iteration, the water flow velocity correction coefficient BQ can be dynamically adjusted according to the actual sediment content prediction result SPE, so as to more accurately reflect the influence of water flow velocity on sediment content measurement, which helps to reduce the measurement error caused by the change of water flow velocity and improve the accuracy of overall sediment content detection. Through iteration, it can adapt to the sediment content measurement under different water flow velocity conditions, improve the generalization ability of the overall method, which makes it possible to maintain a high prediction accuracy under different hydrological conditions and enhance its practicality. The iteration process is an optimization process of the dynamic correction coefficient BQ, which makes the corrected sediment content SCR closer to the true value, helps to reduce the uncertainty in the dynamic correction process, and improves the reliability of the correction result. Through iteration, the value of the correction coefficient BQ can be gradually optimized, so that the error between the corrected sediment content SCR and the true sediment content is gradually reduced, which helps to improve the accuracy and reliability of overall sediment content detection.

[0082] This allows the various sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0083] Example 2: Please refer to Figure 1 , Figure 2 and Figure 3 The data acquisition module measures the intensity of scattered light and the intensity of incident light through an optical scattering sensor, measures the turbidity of the water sample through a turbidity meter, and measures the current water flow rate through a flow meter. The data acquisition module measures the current water temperature through a thermometer and measures the current conductivity through a conductivity meter. The data acquisition module sets multiple sampling points in the target area of ​​the river to be tested. The sampling points are evenly distributed in the water body to reflect the sediment content of the entire water body, and the devices used by the data acquisition module are installed at the sampling points to measure and record the data in real time.

[0084] In this embodiment: through the multiple sensors and detection devices in the data acquisition module, multiple data in the target water body can be collected in real time, and the multiple data are input into the calculation module. The calculation module analyzes, integrates and summarizes the multiple data to make the calculation of the sand content of the water body output finally more accurate and effective.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting sediment content in water conservancy and hydrology, characterized in that: The following steps are involved: Step I: The intensity of scattered light, the intensity of incident light, the turbidity of water samples, the current water flow velocity, the current water temperature and the current conductivity of the water body are collected in real time at the sampling point of the river to be tested through the data collection module, and the collected data are saved in the database; Step II: Input the intensity of scattered light, the intensity of incident light, the turbidity of the water sample, the current water flow rate, the current water temperature and the current conductivity into the data processing module, and the data processing module calibrates and cleans the data; Step III: Input the intensity of scattered light processed by the processing module, the intensity of incident light, the turbidity of the water sample, the current water flow velocity, the current water temperature and the current conductivity into the calculation module, and the calculation module outputs the preliminary analysis value SER of the sediment content, the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration; Step IV: Input the preliminary analysis value of sediment content SER, the sediment content SCR after fluid calibration and the sediment content SPE after comprehensive calibration into the analysis module, which verifies and compares the data and displays the data on the administrator terminal; The calculation module includes: an optical preliminary analysis submodule, a fluid calibration submodule and a comprehensive analysis submodule; The calculation formula of the optical preliminary analysis submodule is as follows: ； in: SER refers to the preliminary analysis value of sediment content, SERA refers to the intensity of scattered light, SERB refers to the intensity of incident light, SERC refers to the scattering coefficient, SERD refers to the turbidity of water sample, SERF refers to the reference turbidity of water sample, SERG refers to the density of sand, AQ refers to the turbidity correction index, SERA / SERB refers to the relative size of the scattered light intensity and the incident light intensity; The processing process of the optical preliminary analysis submodule is as follows: the intensity of scattered light SERA, the intensity of incident light SERB and the turbidity SERD of the water sample are input into the optical preliminary analysis submodule, and the preliminary analysis value SER of the sand content is output based on the scattering coefficient SERC and the reference turbidity SERF of the water sample; The calculation formula of the fluid calibration submodule is as follows: ； in: SCR refers to the calibrated sediment content of the fluid, SCV refers to the current water velocity, SCVF refers to the reference water velocity, BQ refers to the water velocity correction factor, SCH refers to the current water depth, SCHF refers to the reference water depth, and BW refers to the water depth correction factor; Refers to the multiplication of the water velocity correction term; The processing process of the fluid calibration submodule is as follows: the preliminary analysis value of the sediment content SER and the current water velocity SCV are input into the fluid calibration submodule, and the sediment content SCR after fluid calibration is output based on the current water depth SCH; The calculation formula of the comprehensive analysis submodule is as follows: ； in: SPE refers to the sediment content after comprehensive calibration, CA refers to the weight coefficient of SER, CB refers to the weight coefficient of SCR, CC refers to the weight coefficient of water temperature, TTA refers to the current water temperature, TTE refers to the reference water temperature, CD refers to the weight coefficient of conductivity, SPQ refers to the current conductivity, SPAQ refers to the reference conductivity, CA×SER refers to the importance of the preliminary analysis value of sediment content, CB×SCR refers to the importance of the sediment content after fluid calibration; The processing process of the comprehensive analysis submodule is as follows: the preliminary analysis value of sediment content SER, the sediment content SCR after fluid calibration, the current water temperature TTA and the current conductivity SPQ are output to the comprehensive analysis submodule, and the sediment content SPE after comprehensive calibration is output based on the water temperature weight coefficient CC and the reference water temperature TTE.

2. The method for detecting water conservancy and hydrological sediment content according to claim 1, characterized in that: The data acquisition module measures the intensity of scattered light and the intensity of incident light through an optical scattering sensor, measures the turbidity of a water sample through a turbidity meter, and measures the current water flow velocity through a flow meter.

3. The method for detecting water conservancy and hydrological sediment content according to claim 2, characterized in that: The data acquisition module measures the current water temperature through a thermometer and measures the current conductivity through a conductivity meter.

4. The method for detecting water conservancy and hydrological sediment content according to claim 1, characterized in that: The data acquisition module sets multiple sampling points in the target area of ​​the river to be tested, and the sampling points are evenly distributed in the water body to reflect the sediment content of the entire water body. The devices used by the data acquisition module are installed at the sampling points to perform real-time measurement and record the data.

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