Reservoir early warning monitoring method and system based on digital twinning

Abstract

The invention is suitable for the technical field of digital twinning reservoir monitoring, and provides a reservoir early warning monitoring method and system based on digital twinning, and the method comprises the steps: determining an original sediment accumulation rate generated for a specific region of a target reservoir based on a digital twinning technology, and meanwhile, current water quality data and historical water quality evolution records of the specific area are collected. According to the reservoir early warning monitoring method based on the digital twinning technology, the sediment accumulation rate is dynamically corrected in combination with the water quality factor, and the accuracy and adaptability of sediment prediction are effectively improved. By constructing the preset reference correction model and comprehensively analyzing the influence of the dynamic change of the content of the dissolved organic matter and the nutritive salt on the scaling of the sensor, the correction of the long-term monitoring data of the laser distance measuring sensor is realized, and the measurement error caused by the scaling of the sensor is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital twin reservoir monitoring, and in particular relates to a reservoir early warning monitoring method and system based on digital twin. Background Art

[0002] In the field of digital reservoir monitoring, the accumulation of sediments during the long-term operation of the reservoir has an important impact on the reservoir capacity, water quality and dam safety. Therefore, the accurate measurement and prediction of the sediment accumulation rate is of great significance for the operation and maintenance management of the reservoir and disaster prevention and mitigation. In the existing technology, laser ranging sensors are usually used as the main method, and underwater acoustic depth sounders, multi-beam depth sounders and other equipment are used as auxiliary methods to monitor the changes in sediments at the bottom of the reservoir, and the evolution trend of sediments is analyzed based on hydrodynamic models. However, since the sedimentation process is affected by many factors, including hydrodynamic conditions, water quality parameters, seasonal changes, etc., the existing methods still have certain limitations in the measurement and prediction of sedimentation rates.

[0003] Current sediment monitoring methods mainly rely on physical measurement methods and hydrodynamic models to estimate sedimentation rates, and rarely consider the impact of water quality parameters, especially the indirect effects of factors such as dissolved organic matter and nutrient concentration on the sedimentation process. In addition, during long-term monitoring, the measurement accuracy of laser ranging sensors is easily affected by scaling and biofilm deposition, resulting in large deviations in the measurement results of sedimentation rates. Some studies have attempted to introduce water quality monitoring data, but they are usually only used for water environment assessments and are not deeply integrated with sediment monitoring. It is impossible to optimize the sedimentation prediction based on the dynamic changes in water quality, which affects the long-term accuracy and reliability of the monitoring data. Summary of the invention

[0004] The purpose of the present invention is to provide a reservoir early warning monitoring method and system based on digital twins, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a reservoir early warning monitoring method based on digital twins, the method comprising:

[0006] Determine the original sediment accumulation rate generated for a specific area of ​​the target reservoir based on digital twin technology, and simultaneously collect current water quality data and historical water quality evolution records for that specific area;

[0007] Calling a preset benchmark correction model and inputting current water quality data into the preset benchmark correction model to determine an estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state;

[0008] Analyze the historical water quality evolution records and extract the dynamic change indicators of dissolved organic matter content and nutrient salt content, and calibrate the estimated scaling degree evolution curve based on them to obtain the calibrated scaling degree evolution curve;

[0009] The original sediment accumulation rate was adjusted based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected for water quality factors.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the preset benchmark correction model refers to a pre-set mathematical model. The preset benchmark correction model can simulate the scaling degree evolution curve of the laser ranging sensor in water under different water quality conditions over time by analyzing a large amount of historical water quality and laser ranging sensor monitoring data.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the steps of analyzing the historical water quality evolution records and extracting the dynamic change indicators of the dissolved organic matter content and the nutrient salt content, and correcting the estimated scaling degree evolution curve accordingly to obtain the corrected scaling degree evolution curve include:

[0012] Preprocess the historical water quality evolution records and select the dissolved organic matter content data and nutrient salt content data within a preset time length;

[0013] The dissolved organic matter content data and the nutrient salt content data are used to construct the dissolved organic matter content change curve and the nutrient salt content change curve respectively, and the estimated scaling degree evolution curve, the dissolved organic matter content change curve and the nutrient salt content change curve are divided into several corresponding sub-segments according to the same time period;

[0014] The average slopes of the estimated scaling degree change curve, the dissolved organic matter content change curve and the nutrient salt content change curve are calculated in each sub-segment, and the local correction factor is determined by weighted combination of the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment through preset weights;

[0015] Each local correction factor is applied to the average slope of the estimated scaling degree change curve of the corresponding sub-segment in turn, and the overall corrected scaling degree evolution curve is obtained by integrating the correction results of each sub-segment.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the step of adjusting the original sediment accumulation rate based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected by water quality factors includes:

[0017] Calculate the average slope of the corrected scaling degree evolution curve and use it as a correction factor;

[0018] Retrieve the sediment accumulation rate adjustment formula, and adjust the original sediment accumulation rate in combination with the correction factor to obtain the sediment accumulation rate corrected for water quality factors;

[0019] The obtained sediment accumulation rate corrected for water quality factors is applied to the sediment accumulation prediction module in the digital twin model.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, the sediment accumulation rate adjustment formula is: ,in Refers to the sediment accumulation rate corrected for water quality factors. is the original sediment accumulation rate, Refers to the correction factor, that is, the average slope of the scaling degree evolution curve after correction, Refers to the adjustment coefficient of the correction factor.

[0021] A reservoir early warning monitoring system based on digital twins, the system comprising: a data acquisition module, a model application module, a curve correction module and a rate correction module, wherein:

[0022] The data acquisition module is used to determine the original sediment accumulation rate generated for a specific area of ​​the target reservoir based on the digital twin technology, and simultaneously collect the current water quality data and historical water quality evolution records of the specific area;

[0023] A model application module is used to call a preset benchmark correction model and input current water quality data into the preset benchmark correction model to determine an estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state;

[0024] The curve correction module is used to analyze the historical water quality evolution records and extract the dynamic change indicators of dissolved organic matter content and nutrient salt content, and correct the estimated scaling degree evolution curve accordingly to obtain the corrected scaling degree evolution curve;

[0025] The rate correction module is used to adjust the original sediment accumulation rate based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected by water quality factors.

[0026] As a further limitation of the technical solution of the embodiment of the present invention, the preset benchmark correction model refers to a pre-set mathematical model. The preset benchmark correction model can simulate the scaling degree evolution curve of the laser ranging sensor in water under different water quality conditions over time by analyzing a large amount of historical water quality and laser ranging sensor monitoring data.

[0027] As a further limitation of the technical solution of the embodiment of the present invention, the curve correction module specifically includes:

[0028] A data selection unit is used to pre-process the historical water quality evolution records and select the dissolved organic matter content data and nutrient salt content data within a preset time length;

[0029] A curve construction unit is used to construct a dissolved organic matter content change curve and a nutrient salt content change curve respectively using the dissolved organic matter content data and the nutrient salt content data, and at the same time, the estimated scaling degree evolution curve and the dissolved organic matter content change curve and the nutrient salt content change curve are divided into a number of corresponding sub-segments according to the same time period;

[0030] A local correction factor determination unit is used to calculate the average slopes of the estimated scaling degree change curve, the dissolved organic matter content change curve and the nutrient salt content change curve in each sub-segment, and determine the local correction factor by weighted combination based on the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment through preset weights;

[0031] The curve correction unit is used to apply each local correction factor to the average slope of the estimated scaling degree change curve of the corresponding sub-segment in turn, and obtain the overall corrected scaling degree evolution curve after integrating the correction results of each sub-segment.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, the rate correction module specifically includes:

[0033] A correction factor determination unit is used to calculate the average slope of the corrected scaling degree evolution curve and use it as a correction factor;

[0034] The original rate correction unit is used to retrieve the sediment accumulation rate adjustment formula and adjust the original sediment accumulation rate in combination with the correction factor to obtain the sediment accumulation rate corrected by water quality factors;

[0035] The corrected rate application unit is used to apply the obtained sediment accumulation rate corrected by water quality factors to the sediment accumulation prediction module in the digital twin model.

[0036] As a further limitation of the technical solution of the embodiment of the present invention, the sediment accumulation rate adjustment formula is: ,in Refers to the sediment accumulation rate corrected for water quality factors. is the original sediment accumulation rate, Refers to the correction factor, that is, the average slope of the scaling degree evolution curve after correction, Refers to the adjustment coefficient of the correction factor.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention uses a reservoir early warning monitoring method based on digital twin technology and combines water quality factors to dynamically correct the sediment accumulation rate, effectively improving the accuracy and adaptability of sedimentation prediction. By building a preset benchmark correction model and comprehensively analyzing the impact of dynamic changes in dissolved organic matter and nutrient content on sensor scaling, the correction of long-term monitoring data of laser ranging sensors is achieved, reducing measurement errors caused by sensor scaling.

[0039] The present invention can accurately quantify the impact of water quality changes on the degree of scaling, and optimize reservoir sediment management by adjusting the sediment accumulation rate. Compared with the prior art, the present invention not only takes into account the effect of hydrodynamic conditions on the sedimentation process, but also introduces water quality change factors for dynamic adjustment, so that the sedimentation rate prediction is more in line with the actual working conditions, improves the stability and accuracy of the reservoir early warning monitoring system, and provides scientific and reliable data support for reservoir operation and maintenance management, thereby improving the reservoir safety management capabilities and reducing the potential risks caused by uneven sedimentation or measurement deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0041] Figure 2 A flow chart of generating a corrected scaling degree evolution curve in the method provided in an embodiment of the present invention;

[0042] Figure 3 A flow chart of correcting the original sediment accumulation rate in the method provided in an embodiment of the present invention;

[0043] Figure 4 An application architecture diagram of a system provided by an embodiment of the present invention;

[0044] Figure 5 A structural block diagram of a curve correction module in a system provided by an embodiment of the present invention;

[0045] Figure 6 This is a structural block diagram of a rate correction module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0048] Specifically, a reservoir early warning monitoring method based on digital twins comprises the following steps:

[0049] Step S100, determine the original sediment accumulation rate generated for a specific area of ​​the target reservoir based on the digital twin technology, and simultaneously collect the current water quality data and historical water quality evolution records of the specific area.

[0050] In an embodiment of the present invention, the specific area of ​​the target reservoir refers to the area in the reservoir where sediment changes need to be monitored. The area can be divided according to the hydrodynamic characteristics of the reservoir, sediment settling laws, historical siltation conditions and water flow patterns, and usually includes the reservoir inlet, outlet, key dam front area or other waters prone to sediment accumulation.

[0051] The original sediment accumulation rate generated for a specific area of ​​the target reservoir based on digital twin technology is achievable by existing technical means and has been maturely applied in water conservancy project monitoring. Existing hydrodynamic modeling, sediment monitoring sensor technology and data analysis methods can be used to establish a dynamic model of sediment accumulation, thereby calculating the sedimentation rate in the target area.

[0052] The original sediment accumulation rate is used to provide an initial baseline value for the dynamic evolution of sediments in digital twin-based reservoir early warning monitoring, and serves as the basic data for subsequent correction and trend prediction in combination with water quality factors. It mainly relies on laser ranging sensors to monitor the temporal variation trend of sediment thickness, and combines the sediment layer distribution information collected by underwater acoustic depth sounders, multi-beam depth sounders and other equipment to build a preliminary model of sediment accumulation in specific areas of the reservoir. This data enters the digital twin model as an input variable, enabling the system to simulate the sedimentation process, and after combining the correction of water quality parameters, it can achieve dynamic optimization and prediction of the sediment accumulation rate, thereby improving the accuracy of early warning monitoring.

[0053] The original sediment accumulation rate is mainly analyzed and constructed based on the sediment thickness change data collected by the laser ranging sensor. At the same time, it can be combined with underwater acoustic depth sounders, multi-beam depth sounders and other equipment for auxiliary monitoring to obtain more comprehensive sediment dynamic information.

[0054] Current water quality data should include multiple key water quality parameters that directly or indirectly affect the accumulation rate of sediment, including but not limited to dissolved oxygen content, pH value, temperature, turbidity, total suspended solids (TSS), dissolved organic matter (DOC) content, nitrogen and phosphorus nutrient concentrations, etc.

[0055] Historical water quality evolution records are mainly derived from long-term water quality monitoring data, which can be obtained through historical sensor data in the reservoir water quality monitoring system, manual sampling experimental data, remote sensing monitoring data, etc. Historical water quality evolution records should include the changing trends of various water quality parameters in different time periods, such as seasonal fluctuations in dissolved oxygen content, periodic changes in nutrient concentrations, and long-term evolution of suspended particulate matter, so as to analyze the long-term impact of water quality on sediment accumulation.

[0056] Furthermore, the reservoir early warning monitoring method based on digital twins also includes the following steps:

[0057] Step S200, calling a preset benchmark calibration model, and inputting current water quality data into the preset benchmark calibration model to determine an estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state.

[0058] The preset benchmark correction model refers to a pre-set mathematical model. The preset benchmark correction model can simulate the scaling degree evolution curve of the laser ranging sensor in water under different water quality conditions over time by analyzing a large amount of historical water quality and laser ranging sensor monitoring data.

[0059] In the embodiment of the present invention, the basis for setting the preset benchmark correction model is mainly derived from the long-term monitoring data and historical statistical analysis between water quality parameters and scaling evolution of laser ranging sensors. Since the measurement accuracy of laser ranging sensors is affected by scaling or biofilm deposition when they are operated underwater for a long time, and the degree of influence depends on key factors such as dissolved organic matter, nutrient concentration, pH value, dissolved oxygen, etc. in the water quality environment. Therefore, the model establishes a mathematical relationship between the evolution of sensor surface scaling degree over time under different water quality conditions by performing regression analysis, feature extraction and trend modeling on a large amount of historical data, so as to predict the scaling trend of the sensor under the current water quality conditions.

[0060] The specific establishment process of the preset benchmark correction model includes the following steps: first, collect a large amount of long-term monitoring data of laser ranging sensors under different water quality environments, focusing on recording the time changes of water quality parameters and sensor signal attenuation or ranging deviation; second, use machine learning methods or statistical regression models to analyze the correlation between water quality parameters and sensor scaling rate, and establish a mathematical model so that it can fit the scaling degree evolution curve under different water quality conditions in historical data; then, calibrate the model through laboratory simulation tests or field verification data to ensure that it has high applicability and accuracy in different reservoir environments. This type of mathematical modeling method has been studied and applied to a certain extent in the field of water quality monitoring and sensor calibration, and is a relatively mature method in the prior art, but its application in digital twin reservoir early warning monitoring is still relatively small. The present invention dynamically corrects the sediment accumulation rate by combining water quality factors to achieve more accurate early warning capabilities.

[0061] The preset benchmark correction model is called, and the current water quality data is input into the model to determine the estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state. The specific implementation process is as follows: First, the water quality data of the current target area of ​​the reservoir is obtained, including dissolved organic matter content, nutrient concentration, pH value, dissolved oxygen, etc., and these data are input into the preset benchmark correction model; secondly, the model matches the corresponding mathematical function or machine learning prediction curve according to the input data, and calculates the scaling rate under the water quality conditions; then, using the preset time length as the calculation window, the differential equation or data regression method is used to derive the trend of the sensor scaling degree over time, and the corresponding scaling degree evolution curve is output.

[0062] Furthermore, the reservoir early warning monitoring method based on digital twins also includes the following steps:

[0063] Step S300, analyzing the historical water quality evolution records and extracting the dynamic change indicators of dissolved organic matter content and nutrient salt content, and correcting the estimated scaling degree evolution curve accordingly to obtain a corrected scaling degree evolution curve.

[0064] Specifically, Figure 2 A flow chart for generating a corrected fouling degree evolution curve is shown.

[0065] The process of analyzing the historical water quality evolution records and extracting the dynamic change indicators of dissolved organic matter content and nutrient salt content, and correcting the estimated scaling degree evolution curve based on the dynamic change indicators to obtain the corrected scaling degree evolution curve specifically includes the following steps:

[0066] Step S301, preprocessing the historical water quality evolution records, and selecting dissolved organic matter content data and nutrient salt content data within a preset time length;

[0067] Step S302, using the dissolved organic matter content data and the nutrient salt content data to construct a dissolved organic matter content change curve and a nutrient salt content change curve respectively, and at the same time, dividing the estimated scaling degree evolution curve, the dissolved organic matter content change curve and the nutrient salt content change curve into a number of corresponding sub-segments according to the same time period;

[0068] Step S303, respectively calculating the average slopes of the estimated scaling degree change curve, the dissolved organic matter content change curve and the nutrient salt content change curve in each sub-segment, and determining the local correction factor according to the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment by weighted combination with preset weights;

[0069] Step S304 , sequentially applying each local correction factor to the average slope of the estimated scaling degree change curve of the corresponding sub-segment, and integrating the correction results of each sub-segment to obtain the overall corrected scaling degree evolution curve.

[0070] In the embodiment of the present invention, the reason for mainly selecting and referring to "dissolved organic matter content data and nutrient salt content data" is that these two water quality parameters have a particularly significant impact on the scaling process of the sensor in the water environment. Dissolved organic matter (DOC), as the main carbon source for microbial growth, promotes the formation of biofilms, thereby accelerating scaling on the sensor surface, while changes in the concentration of nutrients (such as nitrogen, phosphorus, etc.) affect the reproduction of algae and microbial communities in the water body, thereby affecting the measurement stability of the sensor. Therefore, the dynamic changes in the dissolved organic matter content and the nutrient salt content can more directly reflect the trend of the impact of water quality on sensor scaling, and provide a reliable reference for subsequent scaling corrections.

[0071] The specific implementation process of step S302 includes the following steps: first, obtain and organize the dissolved organic matter content data and the nutrient salt content data, and align them with the estimated scaling degree evolution curve in the time dimension; then, based on the time series relationship between the water quality data and the scaling degree data, draw the dissolved organic matter content change curve and the nutrient salt content change curve respectively, and ensure that the time span and data sampling interval of these three curves are consistent; then, according to the set time segmentation strategy, divide the estimated scaling degree evolution curve, the dissolved organic matter content change curve and the nutrient salt content change curve into the same number of sub-segments, so as to subsequently calculate the local change trend and correlation within each time sub-segment.

[0072] The significance of drawing the curves of dissolved organic matter content and nutrient salt content is that they can intuitively present the changing trends of water quality parameters in different time periods, and further compare and analyze with the scaling evolution trend to determine how water quality fluctuations affect the scaling rate of the sensor. At the same time, the changing trend of the curve can be used to quantify the dynamic change degree of water quality in different time periods, so as to more accurately construct the dynamic correlation between water quality parameters and sensor scaling degree.

[0073] In step S303, the basis and significance of determining the local correction factor based on the average slope of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment by weighted combination with preset weights is that different water quality parameters have different degrees of influence on the sensor scaling process, so it is necessary to reflect their relative contributions through a weight distribution mechanism. Specifically, the concentration change of dissolved organic matter usually has a more direct impact on biofilm formation, while the nutrient salt concentration affects the overall trend of microbial growth. The weight of the influence of the two on sensor scaling can be determined through historical data analysis and regression modeling.

[0074] The specific generation process of the local correction factor includes the following steps: first, in each time sub-segment, the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve are calculated respectively to quantify the change rate of the water quality parameters in this time period; second, according to the preset influence weights, the two average slopes are weighted and combined to calculate the local correction factor of the corresponding sub-segment; finally, the local correction factors of each sub-segment are summarized to form a time-series correction parameter set for subsequent scaling correction calculations.

[0075] Each local correction factor is applied to the average slope of the estimated scaling degree change curve of the corresponding sub-segment in turn, and the overall corrected scaling degree evolution curve is obtained after integrating the correction results of each sub-segment. The advantage of this method is that it can combine the dynamic impact of water quality changes to make a more accurate correction to the sensor scaling trend. Compared with the original prediction curve, the overall corrected scaling degree evolution curve can more accurately reflect the actual sensor scaling process, thereby reducing the prediction error caused by water quality fluctuations, making the subsequent sediment accumulation rate adjustment more accurate, and improving the reliability and practicality of the reservoir early warning monitoring system.

[0076] Furthermore, the reservoir early warning monitoring method based on digital twins also includes the following steps:

[0077] Step S400: adjusting the original sediment accumulation rate based on the corrected scaling degree evolution curve to obtain a sediment accumulation rate corrected for water quality factors.

[0078] Specifically, Figure 3 A flow chart showing the correction of the original sediment accumulation rate.

[0079] The process of adjusting the original sediment accumulation rate based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected by water quality factors specifically includes the following steps:

[0080] Step S401, calculating the average slope of the corrected scaling degree evolution curve and using it as a correction factor;

[0081] Step S402, retrieve the sediment accumulation rate adjustment formula, and adjust the original sediment accumulation rate in combination with the correction factor to obtain the sediment accumulation rate corrected by water quality factors;

[0082] Step S403: Apply the obtained sediment accumulation rate corrected by water quality factors to the sediment accumulation prediction module in the digital twin model.

[0083] The sediment accumulation rate adjustment formula is: ,in Refers to the sediment accumulation rate corrected for water quality factors. is the original sediment accumulation rate, Refers to the correction factor, that is, the average slope of the scaling degree evolution curve after correction, Refers to the adjustment coefficient of the correction factor.

[0084] In the embodiment of the present invention, the average slope of the scale evolution curve after correction is used as a correction factor, the sediment accumulation rate adjustment formula is retrieved, and the original sediment accumulation rate is adjusted in combination with the correction factor to obtain the sediment accumulation rate corrected by water quality factors, mainly based on the following considerations:

[0085] First, the corrected scaling evolution curve has comprehensively considered the long-term impact of water quality factors on the laser ranging sensor, and adjusted the scaling trend through the dynamic changes of dissolved organic matter content and nutrient content to make it more in line with the actual monitoring environment. Therefore, the average slope of the curve can better quantify the overall scaling trend of the sensor and reflect the impact of water quality fluctuations on measurement errors.

[0086] Secondly, using the average slope as a correction factor can effectively adjust the sediment accumulation rate in the long-term trend and avoid the impact of short-term abnormal fluctuations on the rate calculation. Compared with single-point or short-term data correction, using the average slope for correction can improve the stability and reliability of the model, making the adjusted sediment accumulation rate more representative.

[0087] In addition, by setting the sediment accumulation rate adjustment formula, a mathematical relationship can be established between the changing trend of scaling and the sediment settling process, thereby achieving rate optimization based on dynamic changes in water quality. The advantage of this method is that it can compensate for the measurement deviation caused by sensor scaling, making the calculation of sediment accumulation rate more accurate, and providing more reliable data support for reservoir management and prediction.

[0088] Furthermore, Figure 4 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0089] Among them, in another preferred embodiment provided by the present invention, a reservoir early warning monitoring system based on digital twins includes:

[0090] The data acquisition module 100 is used to determine the original sediment accumulation rate generated for a specific area of ​​the target reservoir based on the digital twin technology, and simultaneously collect the current water quality data and historical water quality evolution records of the specific area.

[0091] In an embodiment of the present invention, the specific area of ​​the target reservoir refers to the area in the reservoir where sediment changes need to be monitored. The area can be divided according to the hydrodynamic characteristics of the reservoir, sediment settling laws, historical siltation conditions and water flow patterns, and usually includes the reservoir inlet, outlet, key dam front area or other waters prone to sediment accumulation.

[0092] The original sediment accumulation rate generated for a specific area of ​​the target reservoir based on digital twin technology is achievable by existing technical means and has been maturely applied in water conservancy project monitoring. Existing hydrodynamic modeling, sediment monitoring sensor technology and data analysis methods can be used to establish a dynamic model of sediment accumulation, thereby calculating the sedimentation rate in the target area.

[0093] The original sediment accumulation rate is used to provide an initial baseline value for the dynamic evolution of sediments in digital twin-based reservoir early warning monitoring, and serves as the basic data for subsequent correction and trend prediction in combination with water quality factors. It mainly relies on laser ranging sensors to monitor the temporal variation trend of sediment thickness, and combines the sediment layer distribution information collected by underwater acoustic depth sounders, multi-beam depth sounders and other equipment to build a preliminary model of sediment accumulation in specific areas of the reservoir. This data enters the digital twin model as an input variable, enabling the system to simulate the sedimentation process, and after combining the correction of water quality parameters, it can achieve dynamic optimization and prediction of the sediment accumulation rate, thereby improving the accuracy of early warning monitoring.

[0094] The original sediment accumulation rate is mainly analyzed and constructed based on the sediment thickness change data collected by the laser ranging sensor. At the same time, it can be combined with underwater acoustic depth sounders, multi-beam depth sounders and other equipment for auxiliary monitoring to obtain more comprehensive sediment dynamic information.

[0095] Current water quality data should include multiple key water quality parameters that directly or indirectly affect the accumulation rate of sediment, including but not limited to dissolved oxygen content, pH value, temperature, turbidity, total suspended solids (TSS), dissolved organic matter (DOC) content, nitrogen and phosphorus nutrient concentrations, etc.

[0096] Historical water quality evolution records are mainly derived from long-term water quality monitoring data, which can be obtained through historical sensor data in the reservoir water quality monitoring system, manual sampling experimental data, remote sensing monitoring data, etc. Historical water quality evolution records should include the changing trends of various water quality parameters in different time periods, such as seasonal fluctuations in dissolved oxygen content, periodic changes in nutrient concentrations, and long-term evolution of suspended particulate matter, so as to analyze the long-term impact of water quality on sediment accumulation.

[0097] Furthermore, the reservoir early warning monitoring system based on digital twins also includes:

[0098] The model application module 200 is used to call a preset benchmark correction model and input current water quality data into the preset benchmark correction model to determine an estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state.

[0099] The preset benchmark correction model refers to a pre-set mathematical model. The preset benchmark correction model can simulate the scaling degree evolution curve of the laser ranging sensor in water under different water quality conditions over time by analyzing a large amount of historical water quality and laser ranging sensor monitoring data.

[0100] In the embodiment of the present invention, the basis for setting the preset benchmark correction model is mainly derived from the long-term monitoring data and historical statistical analysis between water quality parameters and scaling evolution of laser ranging sensors. Since the measurement accuracy of laser ranging sensors is affected by scaling or biofilm deposition when they are operated underwater for a long time, and the degree of influence depends on key factors such as dissolved organic matter, nutrient concentration, pH value, dissolved oxygen, etc. in the water quality environment. Therefore, the model establishes a mathematical relationship between the evolution of sensor surface scaling degree over time under different water quality conditions by performing regression analysis, feature extraction and trend modeling on a large amount of historical data, so as to predict the scaling trend of the sensor under the current water quality conditions.

[0101] The specific establishment process of the preset benchmark correction model includes the following steps: first, collect a large amount of long-term monitoring data of laser ranging sensors under different water quality environments, focusing on recording the time changes of water quality parameters and sensor signal attenuation or ranging deviation; second, use machine learning methods or statistical regression models to analyze the correlation between water quality parameters and sensor scaling rate, and establish a mathematical model so that it can fit the scaling degree evolution curve under different water quality conditions in historical data; then, calibrate the model through laboratory simulation tests or field verification data to ensure that it has high applicability and accuracy in different reservoir environments. This type of mathematical modeling method has been studied and applied to a certain extent in the field of water quality monitoring and sensor calibration, and is a relatively mature method in the prior art, but its application in digital twin reservoir early warning monitoring is still relatively small. The present invention dynamically corrects the sediment accumulation rate by combining water quality factors to achieve more accurate early warning capabilities.

[0102] The preset benchmark correction model is called, and the current water quality data is input into the model to determine the estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state. The specific implementation process is as follows: First, the water quality data of the current target area of ​​the reservoir is obtained, including dissolved organic matter content, nutrient concentration, pH value, dissolved oxygen, etc., and these data are input into the preset benchmark correction model; secondly, the model matches the corresponding mathematical function or machine learning prediction curve according to the input data, and calculates the scaling rate under the water quality conditions; then, using the preset time length as the calculation window, the differential equation or data regression method is used to derive the trend of the sensor scaling degree over time, and the corresponding scaling degree evolution curve is output.

[0103] Furthermore, the reservoir early warning monitoring system based on digital twins also includes:

[0104] The curve correction module 300 is used to analyze the historical water quality evolution records and extract the dynamic change indicators of dissolved organic matter content and nutrient salt content, and correct the estimated scaling degree evolution curve accordingly to obtain a corrected scaling degree evolution curve.

[0105] Specifically, Figure 5 It shows a structural block diagram of the curve correction module 300 in the system provided by the embodiment of the present invention.

[0106] Among them, in the preferred embodiment provided by the present invention, the curve correction module 300 specifically includes:

[0107] The data selection unit 301 is used to perform data preprocessing on the historical water quality evolution records and select the dissolved organic matter content data and nutrient salt content data within a preset time length;

[0108] The curve construction unit 302 is used to construct a dissolved organic matter content change curve and a nutrient salt content change curve respectively using the dissolved organic matter content data and the nutrient salt content data, and at the same time divide the estimated scaling degree evolution curve, the dissolved organic matter content change curve and the nutrient salt content change curve into a number of corresponding sub-segments according to the same time period;

[0109] The local correction factor determination unit 303 is used to calculate the average slopes of the estimated scaling degree change curve, the dissolved organic matter content change curve and the nutrient salt content change curve in each sub-segment, and determine the local correction factor according to the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment by weighted combination with preset weights;

[0110] The curve correction unit 304 is used to apply each local correction factor to the average slope of the estimated scaling degree change curve of the corresponding sub-segment in sequence, and obtain the overall corrected scaling degree evolution curve after integrating the correction results of each sub-segment.

[0111] In the embodiment of the present invention, the reason for mainly selecting and referring to "dissolved organic matter content data and nutrient salt content data" is that these two water quality parameters have a particularly significant impact on the scaling process of the sensor in the water environment. Dissolved organic matter (DOC), as the main carbon source for microbial growth, promotes the formation of biofilms, thereby accelerating scaling on the sensor surface, while changes in the concentration of nutrients (such as nitrogen, phosphorus, etc.) affect the reproduction of algae and microbial communities in the water body, thereby affecting the measurement stability of the sensor. Therefore, the dynamic changes in the dissolved organic matter content and the nutrient salt content can more directly reflect the trend of the impact of water quality on sensor scaling, and provide a reliable reference for subsequent scaling corrections.

[0112] The specific implementation process of the curve construction unit 302 includes the following steps: first, obtain and organize the dissolved organic matter content data and the nutrient salt content data, and align them with the estimated scaling degree evolution curve in the time dimension; then, based on the time series relationship between the water quality data and the scaling degree data, draw the dissolved organic matter content change curve and the nutrient salt content change curve respectively, and ensure that the time span and data sampling interval of these three curves are consistent; then, according to the set time segmentation strategy, divide the estimated scaling degree evolution curve, the dissolved organic matter content change curve and the nutrient salt content change curve into the same number of sub-segments, so as to subsequently calculate the local change trend and correlation within each time sub-segment.

[0113] The significance of drawing the curves of dissolved organic matter content and nutrient salt content is that they can intuitively present the changing trends of water quality parameters in different time periods, and further compare and analyze with the scaling evolution trend to determine how water quality fluctuations affect the scaling rate of the sensor. At the same time, the changing trend of the curve can be used to quantify the dynamic change degree of water quality in different time periods, so as to more accurately construct the dynamic correlation between water quality parameters and sensor scaling degree.

[0114] In the local correction factor determination unit 303, the basis and significance of determining the local correction factor by weighting and combining the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment with preset weights is that different water quality parameters have different influences on the sensor scaling process, so their relative contributions need to be reflected through a weight distribution mechanism. Specifically, the concentration change of dissolved organic matter usually has a more direct impact on biofilm formation, while the nutrient salt concentration affects the overall trend of microbial growth. The weights of the influence of the two on sensor scaling can be determined through historical data analysis and regression modeling.

[0115] The specific generation process of the local correction factor includes the following steps: first, in each time sub-segment, the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve are calculated respectively to quantify the change rate of the water quality parameters in this time period; second, according to the preset influence weights, the two average slopes are weighted and combined to calculate the local correction factor of the corresponding sub-segment; finally, the local correction factors of each sub-segment are summarized to form a time-series correction parameter set for subsequent scaling correction calculations.

[0116] Each local correction factor is applied to the average slope of the estimated scaling degree change curve of the corresponding sub-segment in turn, and the overall corrected scaling degree evolution curve is obtained after integrating the correction results of each sub-segment. The advantage of this method is that it can combine the dynamic impact of water quality changes to make a more accurate correction to the sensor scaling trend. Compared with the original prediction curve, the overall corrected scaling degree evolution curve can more accurately reflect the actual sensor scaling process, thereby reducing the prediction error caused by water quality fluctuations, making the subsequent sediment accumulation rate adjustment more accurate, and improving the reliability and practicality of the reservoir early warning monitoring system.

[0117] Furthermore, the reservoir early warning monitoring system based on digital twins also includes:

[0118] The rate correction module 400 is used to adjust the original sediment accumulation rate based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected by water quality factors.

[0119] Specifically, Figure 6It shows a structural block diagram of the rate correction module 400 in the system provided by an embodiment of the present invention.

[0120] In a preferred embodiment of the present invention, the rate modification module 400 specifically includes:

[0121] The correction factor determination unit 401 is used to calculate the average slope of the scale formation degree evolution curve after correction and use it as the correction factor;

[0122] The original rate correction unit 402 is used to retrieve the sediment accumulation rate adjustment formula and adjust the original sediment accumulation rate in combination with the correction factor to obtain the sediment accumulation rate corrected by the water quality factor;

[0123] The corrected rate application unit 403 is used to apply the obtained sediment accumulation rate corrected by water quality factors to the sediment accumulation prediction module in the digital twin model.

[0124] The sediment accumulation rate adjustment formula is: ,in Refers to the sediment accumulation rate corrected for water quality factors. is the original sediment accumulation rate, Refers to the correction factor, that is, the average slope of the scaling degree evolution curve after correction, Refers to the adjustment coefficient of the correction factor.

[0125] In the embodiment of the present invention, the average slope of the scale evolution curve after correction is used as a correction factor, the sediment accumulation rate adjustment formula is retrieved, and the original sediment accumulation rate is adjusted in combination with the correction factor to obtain the sediment accumulation rate corrected by water quality factors, mainly based on the following considerations:

[0126] First, the corrected scaling evolution curve has comprehensively considered the long-term impact of water quality factors on the laser ranging sensor, and adjusted the scaling trend through the dynamic changes of dissolved organic matter content and nutrient content to make it more in line with the actual monitoring environment. Therefore, the average slope of the curve can better quantify the overall scaling trend of the sensor and reflect the impact of water quality fluctuations on measurement errors.

[0127] Secondly, using the average slope as a correction factor can effectively adjust the sediment accumulation rate in the long-term trend and avoid the impact of short-term abnormal fluctuations on the rate calculation. Compared with single-point or short-term data correction, using the average slope for correction can improve the stability and reliability of the model, making the adjusted sediment accumulation rate more representative.

[0128] In addition, by setting the sediment accumulation rate adjustment formula, a mathematical relationship can be established between the changing trend of scaling and the sediment settling process, thereby achieving rate optimization based on dynamic changes in water quality. The advantage of this method is that it can compensate for the measurement deviation caused by sensor scaling, making the calculation of sediment accumulation rate more accurate, and providing more reliable data support for reservoir management and prediction.

[0129] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0130] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0131] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0133] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A reservoir early warning monitoring method based on digital twins, characterized in that: The method comprises: Determine the original sediment accumulation rate generated for a specific area of ​​the target reservoir based on digital twin technology, and simultaneously collect current water quality data and historical water quality evolution records for that specific area; Calling a preset benchmark correction model and inputting current water quality data into the preset benchmark correction model to determine an estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state; Analyze the historical water quality evolution records and extract the dynamic change indicators of dissolved organic matter content and nutrient salt content, and calibrate the estimated scaling degree evolution curve based on them to obtain the calibrated scaling degree evolution curve; The original sediment accumulation rate was adjusted based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected for water quality factors.

2. The reservoir early warning monitoring method based on digital twin according to claim 1 is characterized in that: The preset benchmark correction model refers to a pre-set mathematical model. The preset benchmark correction model can simulate the scaling degree evolution curve of the laser ranging sensor in water under different water quality conditions over time by analyzing a large amount of historical water quality and laser ranging sensor monitoring data.

3. The reservoir early warning monitoring method based on digital twin according to claim 2 is characterized in that: The steps of analyzing the historical water quality evolution records and extracting the dynamic change indicators of dissolved organic matter content and nutrient salt content, and correcting the estimated scaling degree evolution curve accordingly to obtain the corrected scaling degree evolution curve include: Preprocess the historical water quality evolution records and select the dissolved organic matter content data and nutrient salt content data within a preset time length; The dissolved organic matter content data and the nutrient salt content data are used to construct the dissolved organic matter content change curve and the nutrient salt content change curve respectively, and the estimated scaling degree evolution curve, the dissolved organic matter content change curve and the nutrient salt content change curve are divided into several corresponding sub-segments according to the same time period; The average slopes of the estimated scaling degree change curve, the dissolved organic matter content change curve and the nutrient salt content change curve are calculated in each sub-segment, and the local correction factor is determined by weighted combination of the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment through preset weights; Each local correction factor is applied to the average slope of the estimated scaling degree change curve of the corresponding sub-segment in turn, and the overall corrected scaling degree evolution curve is obtained by integrating the correction results of each sub-segment.

4. The reservoir early warning monitoring method based on digital twin according to claim 3 is characterized in that: The steps of adjusting the original sediment accumulation rate based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected for water quality factors include: Calculate the average slope of the corrected scaling degree evolution curve and use it as a correction factor; Retrieve the sediment accumulation rate adjustment formula, and adjust the original sediment accumulation rate in combination with the correction factor to obtain the sediment accumulation rate corrected for water quality factors; The obtained sediment accumulation rate corrected for water quality factors is applied to the sediment accumulation prediction module in the digital twin model.

5. The reservoir early warning monitoring method based on digital twin according to claim 4 is characterized in that: The sediment accumulation rate adjustment formula is: ,in Refers to the sediment accumulation rate corrected for water quality factors. is the original sediment accumulation rate, Refers to the correction factor, that is, the average slope of the scaling degree evolution curve after correction, Refers to the adjustment coefficient of the correction factor.

6. A reservoir early warning monitoring system based on digital twins, characterized in that: The system comprises: a data acquisition module, a model application module, a curve correction module and a rate correction module, wherein: The data acquisition module is used to determine the original sediment accumulation rate generated for a specific area of ​​the target reservoir based on the digital twin technology, and simultaneously collect the current water quality data and historical water quality evolution records of the specific area; A model application module is used to call a preset benchmark correction model and input current water quality data into the preset benchmark correction model to determine an estimated scaling degree evolution curve of the laser ranging sensor within a preset time length under the current water quality state; The curve correction module is used to analyze the historical water quality evolution records and extract the dynamic change indicators of dissolved organic matter content and nutrient salt content, and correct the estimated scaling degree evolution curve accordingly to obtain the corrected scaling degree evolution curve; The rate correction module is used to adjust the original sediment accumulation rate based on the corrected scaling degree evolution curve to obtain the sediment accumulation rate corrected by water quality factors.

7. The reservoir early warning monitoring system based on digital twin according to claim 6 is characterized in that: The preset benchmark correction model refers to a pre-set mathematical model. The preset benchmark correction model can simulate the scaling degree evolution curve of the laser ranging sensor in water under different water quality conditions over time by analyzing a large amount of historical water quality and laser ranging sensor monitoring data.

8. The reservoir early warning monitoring system based on digital twin according to claim 7 is characterized in that: The curve correction module specifically includes: A data selection unit is used to pre-process the historical water quality evolution records and select the dissolved organic matter content data and nutrient salt content data within a preset time length; A curve construction unit is used to construct a dissolved organic matter content change curve and a nutrient salt content change curve respectively using the dissolved organic matter content data and the nutrient salt content data, and at the same time, the estimated scaling degree evolution curve and the dissolved organic matter content change curve and the nutrient salt content change curve are divided into a number of corresponding sub-segments according to the same time period; A local correction factor determination unit is used to calculate the average slopes of the estimated scaling degree change curve, the dissolved organic matter content change curve and the nutrient salt content change curve in each sub-segment, and determine the local correction factor by weighted combination based on the average slopes of the dissolved organic matter content change curve and the nutrient salt content change curve in the sub-segment through preset weights; The curve correction unit is used to apply each local correction factor to the average slope of the estimated scaling degree change curve of the corresponding sub-segment in turn, and obtain the overall corrected scaling degree evolution curve after integrating the correction results of each sub-segment.

9. The reservoir early warning monitoring system based on digital twin according to claim 8 is characterized in that: The rate correction module specifically includes: A correction factor determination unit is used to calculate the average slope of the corrected scaling degree evolution curve and use it as a correction factor; The original rate correction unit is used to retrieve the sediment accumulation rate adjustment formula and adjust the original sediment accumulation rate in combination with the correction factor to obtain the sediment accumulation rate corrected by water quality factors; The corrected rate application unit is used to apply the obtained sediment accumulation rate corrected by water quality factors to the sediment accumulation prediction module in the digital twin model.

10. The reservoir early warning monitoring system based on digital twin according to claim 9 is characterized in that: The sediment accumulation rate adjustment formula is: ,in Refers to the sediment accumulation rate corrected for water quality factors. is the original sediment accumulation rate, Refers to the correction factor, that is, the average slope of the scaling degree evolution curve after correction, Refers to the adjustment coefficient of the correction factor.

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