Real-time monitoring method and system for port channel water depth

Abstract

The present invention relates to the field of waterway monitoring technology, and specifically, to a real-time monitoring method and system for water depth of a port waterway. It includes: S1, selecting a monitoring point in the waterway, installing an intelligent buoy for monitoring waterway data at the monitoring point, the intelligent buoy is used to monitor waterway data, and deploying an acoustic Doppler current profiler to monitor the bottom flow velocity; S2, collecting waterway data collected by a sonar system and an acoustic Doppler current profiler; S3, constructing a waterway sedimentation model, and combining the real-time waterway data after preprocessing, introducing a time-dependent function, and correcting the measurement error of the real-time water depth data caused by silt silting; S4, based on the above steps S1-S3, constructing a network including multiple monitoring points to form a three-dimensional monitoring system for the waterway. By establishing a preliminary waterway sedimentation model of sedimentation rate and average bottom flow velocity, and introducing a time-dependent function and terrain factors, the prediction accuracy of future water depth changes in the waterway is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of waterway monitoring, and in particular to a method and system for real-time monitoring of water depth of a port waterway. Background Art

[0002] The real-time monitoring method of port and waterway water depth refers to the use of modern acoustics, remote sensing, communications and information technology to continuously and dynamically monitor the water depth of ports and waterways to obtain real-time and accurate water depth data, ensure navigation safety and improve waterway management efficiency.

[0003] Traditional methods, such as using only sonar depth or simple water level meters, often cannot accurately distinguish between the actual water depth and the difference in apparent water depth caused by silt deposition, especially in waterway areas with frequent siltation, which can easily lead to inaccurate water depth data. Therefore, a real-time monitoring method and system for port waterway water depth is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for real-time monitoring of water depth of a port channel, so as to solve the problem of insufficient existing resources proposed in the above background technology.

[0005] To achieve the above object, the present invention provides a method for real-time monitoring of water depth of a port channel, comprising the following steps:

[0006] S1. Select monitoring points in the waterway and install smart buoys at the monitoring points to monitor the waterway data. The smart buoys are used to monitor the waterway data and acoustic Doppler current profilers are deployed to monitor the bottom flow velocity. The smart buoys include sonar transmitting components and receiving components. GPS and underwater topographic mapping technology are used to determine the location of the monitoring points to ensure coverage of key areas of the waterway and potential siltation hotspots.

[0007] The sonar system periodically transmits sound waves and receives echoes to calculate the water depth; the acoustic Doppler current profiler monitors the water velocity of the bottom flow through the Doppler effect of sound waves;

[0008] The speed and direction of the water flow will affect the sonar echo signal. Fast-flowing water may cause the echo signal to be distorted, affecting the water depth reading. In addition, the bottom flow rate will also drive the silt to move, causing the water depth at the same location to change over time.

[0009] S2, transmit the waterway data collected by the sonar system and the acoustic Doppler current profiler to the shore-based control center in real time through wireless transmission technology, and pre-process the waterway data;

[0010] S3. Based on the historical waterway data, a waterway sedimentation model is constructed. Combined with the pre-processed real-time waterway data, the relationship between the silt layer thickness and the bottom flow velocity is evaluated based on the waterway sedimentation model. A time-dependent function is introduced to correct the measurement error of the real-time water depth data caused by silt siltation, and improve the prediction accuracy of the future water depth changes of the waterway.

[0011] S4. Based on the above steps S1-S3, a network including multiple monitoring points is constructed to continuously collect data to form a three-dimensional monitoring system for the waterway.

[0012] As a further improvement of the present technical solution, in S1, the intelligent buoy includes a sonar transmitting module and a sonar receiving module. The sonar transmitting module is responsible for regularly transmitting sonar waves underwater, and the sonar receiving module receives the sound wave echo signals reflected from the bottom of the water. The sonar transmitting module and the sonar receiving module accurately measure the total time from the transmission to the reception of the sound wave, and combine the propagation speed of the sound wave in the water to calculate the vertical distance from the buoy to the bottom of the water, that is, the water depth.

[0013] As a further improvement of the technical solution, the smart buoy calculates the water depth based on the sonar wave parameters, which include the propagation time, velocity, frequency and amplitude of the sonar wave. The specific steps for calculating the water depth are as follows:

[0014] S3.1. Collect the sonar echo signal strength and signal morphology changes through the sonar system in the smart buoy. The signal morphology changes include time broadening, shape distortion (such as waveform distortion) and phase change of the echo signal;

[0015] S3.2. Establish an attenuation model between the thickness of the silt layer and the signal attenuation, and correct the originally calculated water depth by using the known silt characteristics (such as density and acoustic properties) obtained at the detection point;

[0016] S3.3, substituting the real-time sonar data into the attenuation model, and obtaining the thickness of the silt layer below the current monitoring point according to the attenuation of the echo signal;

[0017] S3.4. Considering the propagation speed of sound waves in silt, the water depth is calculated based on the thickness of the silt layer, the propagation speed of sound waves in water and the propagation speed in silt.

[0018] As a further improvement of the technical solution, in S3.2, the attenuation model between the silt layer thickness and the signal attenuation is specifically:

[0019] Sound wave attenuation model:

[0020] ;

[0021] Signal strength attenuation model:

[0022] ;

[0023] In the formula, Represents the attenuation factor of the signal; Indicates the signal strength when there is no silt attenuation; Indicates the signal strength after passing through the silt layer; It represents the attenuation coefficient of sound waves in silt; represents the base of natural logarithms; Indicates the thickness of the silt layer;

[0024] Based on the signal strength attenuation model, we can get:

[0025] ;

[0026] Taking the natural logarithm (ln) of both sides gives:

[0027] ;

[0028] The thickness of the silt layer is thus obtained :

[0029] ;

[0030] The water depth is:

[0031] ;

[0032] In the formula, It represents the speed of sound waves in water; It indicates the speed of sound waves in silt; represents the total round trip time of the sound wave; Indicates the actual water depth.

[0033] In step S1 of this embodiment, the real-time data provided by the smart buoy and the acoustic Doppler current profiler are used to provide a data basis for subsequent data analysis, model building and prediction;

[0034] Based on sonar echo technology, the thickness of the silt layer is estimated by analyzing the reflection characteristics of sound waves at the water-silt interface (such as signal attenuation, time broadening, etc.); however, the sonar measurement of the smart buoy is easily affected by factors such as water flow velocity, water turbidity, bubbles, and the buoy's own movement, resulting in limited measurement accuracy. Direct measurement provides instantaneous data and lacks understanding of the internal mechanisms of long-term siltation trends and seasonal changes.

[0035] As a further improvement of the technical solution, the smart buoy also includes a positioning module and a communication module;

[0036] Wherein, the positioning module determines the longitude and latitude coordinates of the smart buoy based on the global positioning system;

[0037] The communication module is responsible for sending the water depth and flow rate data collected by the smart buoy to the shore-based control center through wireless transmission technology.

[0038] As a further improvement of the technical solution, in said S2, the waterway water area data includes water depth data, silt layer thickness data and bottom flow velocity data;

[0039] Among them, the preprocessing methods of waterway water area data specifically include data cleaning and multi-source data integration.

[0040] Invalid values ​​and outliers are removed through data cleaning; water depth data, silt layer thickness data and bottom flow velocity data are effectively integrated, and consistency checks and cross-validations are performed to ensure logical coordination and data quality between different data sets.

[0041] As a further improvement of the technical solution, in S3, the specific steps of constructing a channel sedimentation model based on historical bottom flow velocity and silt layer thickness data are as follows:

[0042] Among them, the relationship model between the initial sedimentation rate and the average bottom flow velocity is established as follows:

[0043] ;

[0044] In the formula, represents the net deposition rate of sediment (positive values ​​represent deposition, negative values ​​represent scouring); It represents the proportionality constant, which depends on factors such as sediment properties and bed roughness and needs to be determined through experiments or historical data; Indicates the average flow rate of water; represents the critical flow velocity, below which deposition begins to occur, and above which scour occurs; It represents the empirical index, reflecting the nonlinear degree of the effect of flow velocity on sedimentation rate, and is usually between 2 and 3;

[0045] Considering seasonal factors, a time-dependent function is introduced to adjust the critical flow rate and the proportionality constant , the above sedimentation rate expression is optimized, and the terrain factor Introduced into the calculation of deposition rate, the optimized deposition rate expression is:

[0046] ;

[0047] In the formula, represents the net deposition rate of the optimized sediment; It indicates the change in sedimentation efficiency, that is, the strength or efficiency of the relationship between sedimentation rate and flow velocity over time; it involves seasonal or periodic changes in the dynamics of the sedimentation process itself, such as the amount of sediment supplied, the stability of sediment particles, the intensity of water disturbance, etc. , indicating that the deposition efficiency increases in a specific period of time; if , it means that the deposition efficiency decreases, which may be due to the dominance of unfavorable conditions for deposition during this period; The variation of critical velocity with time in a river or channel sedimentation model. Define the flow velocity threshold at which deposition transitions to flushing; By adjusting (critical flow velocity) to reflect the impact of seasonality (such as rainy season and dry season) or long-term changes on this threshold. For example, in the rainy season, the water flow may be strengthened and a higher flow velocity may be required to start the flushing process. May cause Increase; on the contrary, in the dry season when the water flow is weak, in order to reflect the conditions that are easy to sediment, May prompt decline; represents the basic proportionality constant, which is used to quantify the strength of the basic relationship between flow velocity and sedimentation rate in the absence of seasonal adjustment; represents the benchmark critical flow velocity, that is, the benchmark flow velocity threshold at which deposition turns into flushing without seasonal adjustment; It indicates the seasonal variation of critical flow velocity, reflecting the range of flow velocity required to start scouring or deposition under different seasonal conditions;

[0048] In the actual operating environment of the waterway, water flow conditions and sediment characteristics change with time and environment. The directly measured silt layer thickness serves as feedback information, prompting the dynamic adjustment of the waterway sedimentation model. By optimizing the parameters of the time-dependent function, it can better adapt to changes in the actual environment and improve the accuracy of the model in water depth monitoring.

[0049] As a further improvement of the technical solution, based on the net deposition rate of the above optimized sediment , then in a continuous time period, from time arrive Changes in the thickness of the silt layer It is expressed as:

[0050] ;

[0051] Changes in silt layer thickness The expression is:

[0052] ;

[0053] In time The thickness of the silt layer The expression is:

[0054] ;

[0055] In the formula, Indicates the initial time Thickness of the silt layer; Indicates the integration interval Any time point within Indicates the initial time point, the starting time of the integration; Indicates the current time point; It represents the sedimentation rate, that is, the amount of sediment thickening per unit area per unit time.

[0056] The channel sedimentation model can integrate historical and real-time data to reveal the long-term relationship and trend between silt deposition and hydrodynamics. Through the channel sedimentation model, it can predict and identify the acceleration of silt accumulation or the increase in siltation risk in specific areas, provide early warning for channel maintenance, help plan dredging operations, avoid channel blockage due to sudden siltation, and ensure navigation safety and efficiency.

[0057] Smart buoys use sonar technology to directly measure the thickness of the silt layer and combine it with the prediction of the channel sedimentation model to correct and complement each other; the directly measured silt layer thickness data can be used to verify the accuracy of the channel sedimentation model prediction, help adjust the model parameters, and improve the prediction accuracy; and the silt layer thickness change trend predicted by the channel sedimentation model can guide the smart buoy to strengthen monitoring at which key time points or areas, or explain why the directly measured water depth has abnormal fluctuations. For example, during the seasonal transition period, even if the direct measurement shows that the water depth suddenly becomes shallower, the model prediction can reveal that this may be due to increased seasonal siltation rather than a sudden event.

[0058] On the other hand, the present invention provides a real-time monitoring system for water depth of a port and waterway, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned methods for real-time monitoring water depth of a port and waterway.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. In the real-time monitoring method and system for water depth in the port channel, the intelligent buoy calculates the water depth based on the sonar wave parameters, and corrects the originally calculated water depth by establishing an attenuation model between the thickness of the silt layer and the signal attenuation;

[0061] 2. Direct measurement of silt layer thickness based on smart buoys is easily affected by external factors such as water flow velocity, resulting in measurement errors. Therefore, by establishing a preliminary channel sedimentation model of sedimentation rate and average bottom flow velocity, combined with real-time channel water area data, the relationship between silt layer thickness and bottom flow velocity is finally evaluated, and time-dependent functions and terrain factors are introduced to correct the measurement errors caused by silt siltation in the real-time water depth data calculated by the smart buoy, and to improve the prediction accuracy of future water depth changes in the channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0063] 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.

[0064] Embodiment 1:

[0065] See also Figure 1 As shown, this embodiment provides a method for real-time monitoring of port channel water depth, comprising the following steps:

[0066] S1. Select monitoring points in the waterway and install smart buoys at the monitoring points to monitor the waterway data. The smart buoys are used to monitor the waterway data and acoustic Doppler current profilers are deployed to monitor the bottom flow velocity. The smart buoys include sonar transmitting components and receiving components. GPS and underwater topographic mapping technology are used to determine the location of the monitoring points to ensure coverage of key areas of the waterway and potential siltation hotspots.

[0067] The sonar system periodically transmits sound waves and receives echoes to calculate the water depth; the acoustic Doppler current profiler monitors the water velocity of the bottom flow through the Doppler effect of sound waves;

[0068] The speed and direction of the water flow will affect the sonar echo signal. Fast-moving water may cause the echo signal to be distorted, affecting the water depth reading. In addition, the water flow may also cause silt to move, causing the water depth at the same location to change over time.

[0069] In S1, the smart buoy includes a sonar transmitting module and a sonar receiving module. The sonar transmitting module is responsible for regularly transmitting sonar waves underwater, and the sonar receiving module receives the sound wave echo signal reflected from the bottom of the water. The sonar transmitting module and the sonar receiving module accurately measure the total time from the transmission to the reception of the sound wave, and combine the propagation speed of the sound wave in the water to calculate the vertical distance from the buoy to the bottom of the water, that is, the water depth;

[0070] The smart buoy also includes a positioning module and a communication module;

[0071] Wherein, the positioning module determines the longitude and latitude coordinates of the smart buoy based on the global positioning system;

[0072] The communication module is responsible for sending the water depth and flow rate data collected by the smart buoy to the shore-based control center through wireless transmission technology.

[0073] In this embodiment, the smart buoy calculates the water depth based on sonar wave parameters, which include sonar wave propagation time, sonar wave velocity, sonar wave frequency and sonar wave amplitude. The specific steps for calculating the water depth are as follows:

[0074] S3.1. Collect the sonar echo signal strength and signal morphology changes through the sonar system in the smart buoy. The signal morphology changes include time broadening, shape distortion (such as waveform distortion) and phase change of the echo signal;

[0075] S3.2. Establish an attenuation model between the thickness of the silt layer and the signal attenuation, and correct the originally calculated water depth by using the known silt characteristics (such as density and acoustic properties) obtained at the detection point;

[0076] Among them, the attenuation model between the silt layer thickness and signal attenuation is specifically:

[0077] Sound wave attenuation model:

[0078] ;

[0079] Signal strength attenuation model:

[0080] ;

[0081] In the formula, Represents the attenuation factor of the signal; Indicates the signal strength when there is no silt attenuation; Indicates the signal strength after passing through the silt layer; It represents the attenuation coefficient of sound waves in silt; represents the base of natural logarithms (approximately equal to 2.718); Indicates the thickness of the silt layer;

[0082] Based on the signal strength attenuation model, we can get:

[0083] ;

[0084] Taking the natural logarithm (ln) of both sides gives:

[0085] ;

[0086] The thickness of the silt layer is thus obtained :

[0087] ;

[0088] The water depth is:

[0089] ;

[0090] In the formula, It represents the speed of sound waves in water; represents the propagation speed of sound waves in silt, Based on historical data or on-site sampling analysis results; represents the total round trip time of the sound wave; Indicates the actual water depth.

[0091] In step S1 of this embodiment, the real-time data provided by the smart buoy and the acoustic Doppler current profiler are used to provide a data basis for subsequent data analysis, model building and prediction;

[0092] Based on sonar echo technology, the thickness of the silt layer is estimated by analyzing the reflection characteristics of sound waves at the water-silt interface (such as signal attenuation, time broadening, etc.); however, the sonar measurement of the smart buoy is easily affected by factors such as water flow velocity, water turbidity, bubbles, and the buoy's own movement, resulting in limited measurement accuracy. Direct measurement provides instantaneous data and lacks understanding of long-term siltation trends.

[0093] S3.3, substituting the real-time sonar data into the attenuation model, and obtaining the thickness of the silt layer below the current monitoring point according to the attenuation of the echo signal;

[0094] S3.4. Considering the propagation speed of sound waves in silt, the water depth is calculated based on the thickness of the silt layer, the propagation speed of sound waves in water and the propagation speed in silt.

[0095] S2, transmitting the waterway data collected by the sonar system and the acoustic Doppler current profiler to the shore-based control center in real time through wireless transmission technology, and pre-processing the waterway data before constructing the waterway sedimentation model;

[0096] In this embodiment, the waterway water area data includes water depth data, silt layer thickness data and bottom flow velocity data;

[0097] Among them, the pre-processing methods of waterway waters data specifically include data cleaning and multi-source data integration;

[0098] Specifically, invalid values ​​and outliers are removed through data cleaning; water depth data, silt layer thickness data and bottom flow velocity data are effectively integrated, and consistency checks and cross-validations are performed to ensure the logical coordination and data quality between different data sets.

[0099] S3. Direct measurement of silt layer thickness based on smart buoys is easily disturbed by external factors such as water flow velocity, resulting in measurement errors. Therefore, a channel sedimentation model is constructed based on historical waterway water data. The historical waterway water data includes water depth data, silt layer thickness data, and bottom flow velocity data. Combined with the pre-processed real-time waterway water data, the relationship between silt layer thickness and bottom flow velocity is evaluated based on the channel sedimentation model, and a time-dependent function is introduced to correct the measurement error of real-time water depth data caused by silt siltation, and improve the prediction accuracy of future water depth changes in the channel;

[0100] In this embodiment, based on the historical bottom flow velocity and silt layer thickness data, the specific steps of constructing the channel sedimentation model are:

[0101] Among them, the relationship model between the initial sedimentation rate and the average bottom flow velocity is established as follows:

[0102] ;

[0103] In the formula, represents the net deposition rate of sediment (positive values ​​represent deposition, negative values ​​represent scouring); It represents the proportionality constant, which depends on factors such as sediment properties and bed roughness and needs to be determined through experiments or historical data; Indicates the average flow rate of water; represents the critical flow velocity, below which deposition begins to occur, and above which scour occurs; It represents the empirical index, reflecting the nonlinear degree of the effect of flow velocity on sedimentation rate, and is usually between 2 and 3;

[0104] Considering seasonal factors, a time-dependent function is introduced to adjust the critical flow rate and the proportionality constant , the above sedimentation rate expression is optimized, and at the same time, considering the influence of terrain on sedimentation rate, the terrain factor Introduced into the calculation of deposition rate, where:

[0105] ;

[0106] In the formula, represents an empirical parameter indicating the degree to which depth affects the sedimentation rate; deeper waters may have less sedimentation due to higher current energy; Indicates water depth;

[0107] Terrain Factor The ups and downs of the riverbed (such as the slope and curvature of the riverbed) will cause changes in the speed and direction of the water flow. The terrain factor can help the model adjust the sedimentation rate in the local area; considering the complex influence of the terrain on the sedimentation rate, the accuracy and practicality of the model in predicting the changes in the thickness of the silt layer can be improved;

[0108] The optimized deposition rate expression is:

[0109] ;

[0110] In the formula, represents the net deposition rate of the optimized sediment; It indicates the change in sedimentation efficiency, that is, the strength or efficiency of the relationship between sedimentation rate and flow velocity over time; it involves seasonal or periodic changes in the dynamics of the sedimentation process itself, such as the amount of sediment supplied, the stability of sediment particles, the intensity of water disturbance, etc. , indicating that the deposition efficiency increases in a specific period of time; if , it means that the deposition efficiency decreases, which may be due to the dominance of unfavorable conditions for deposition during this period; The variation of critical velocity with time in a river or channel sedimentation model. Define the flow velocity threshold at which deposition transitions to flushing; By adjusting (critical flow velocity) to reflect the impact of seasonality (such as rainy season and dry season) or long-term changes on this threshold. For example, in the rainy season, the water flow may be strengthened and a higher flow velocity may be required to start the flushing process. May cause Increase; on the contrary, in the dry season when the water flow is weak, in order to reflect the conditions that are easy to sediment, May prompt decline; represents the basic proportionality constant, which is used to quantify the strength of the basic relationship between flow velocity and sedimentation rate in the absence of seasonal adjustment; represents the benchmark critical flow velocity, that is, the benchmark flow velocity threshold at which deposition turns into flushing without seasonal adjustment; It indicates the seasonal variation of critical flow velocity, reflecting the range of flow velocity required to start scouring or deposition under different seasonal conditions;

[0111] In the actual operation environment of the waterway, the flow conditions and sediment characteristics change with time and environment. The directly measured silt layer thickness is used as feedback information to promote the dynamic adjustment of the waterway sedimentation model. By comparing the model prediction with the actual measurement data, the model parameters can be continuously adjusted. By optimizing the parameters of the time-dependent function, it can better adapt to the changes in the actual environment and improve the accuracy of the model in water depth monitoring and prediction of future sedimentation trends.

[0112] Specifically, in this embodiment:

[0113] ;

[0114] ;

[0115] In the formula, represents the amplitude, which determines the strength of seasonal changes; represents the cycle, for annual changes; represents the phase shift, which is used to adjust the peak time of the function to match the actual rainy season start time; represents the vertical offset, representing the base level above which all seasonal variations are present; is the amplitude, which indicates the maximum amplitude of the change of deposition efficiency over time; is the phase shift, which is used to adjust the position of the function graph relative to the starting point of time; is the vertical offset, representing the base level of sedimentation efficiency without seasonal variation. , indicating that without seasonal adjustment, the deposition efficiency itself has a positive baseline; if , then the baseline is negative, which means that in some cases, even without considering the seasonal effect, the deposition efficiency may be reduced; Indicates time variables;

[0116] Among them, the net deposition rate based on the above optimized sediment is , then in a continuous time period, from time arrive Changes in the thickness of the silt layer It is expressed as:

[0117] ;

[0118] Changes in silt layer thickness The expression is:

[0119] ;

[0120] In time The thickness of the silt layer The expression is:

[0121] ;

[0122] In the formula, Indicates the initial time Thickness of the silt layer; Indicates the integration interval Any time point within Indicates the initial time point, the starting time of the integration; Indicates the current time point; It represents the sedimentation rate, that is, the amount of sediment thickening per unit area per unit time; It refers to the changes in hydrodynamic characteristics caused by topographic changes at different locations and times, and the impact on sediment deposition or scouring rates;

[0123] Specifically, the channel sedimentation model can integrate historical and real-time data to reveal the long-term relationship and trend between silt deposition and hydrodynamics. Through the channel sedimentation model, it can predict and identify situations where the silt accumulation rate is accelerating or the risk of siltation in a specific area is increasing, providing early warning for channel maintenance, helping to plan dredging operations, avoid channel blockages due to sudden siltation, and ensure navigation safety and efficiency.

[0124] Smart buoys use sonar technology to directly measure the thickness of the silt layer, which can be combined with the prediction of the channel sedimentation model to correct and complement each other; the directly measured silt layer thickness data can be used to verify the accuracy of the channel sedimentation model prediction, help adjust the model parameters, and improve the prediction accuracy; and the silt layer thickness change trend predicted by the channel sedimentation model can guide smart buoys to strengthen monitoring at key time points or areas, or explain why the directly measured water depth has abnormal fluctuations. For example, during the seasonal transition period, even if the direct measurement shows that the water depth suddenly becomes shallower, the model prediction can reveal that this may be due to increased seasonal siltation rather than sudden events;

[0125] The directly measured silt layer thickness and water depth are affected by many factors, such as signal distortion caused by changes in water flow velocity. The signal distortion caused by water flow may cause deviations in the water depth measurement at a certain point in time. The channel sedimentation model integrates multi-source data and considers the impact of seasonal changes on sediment movement. It can provide more accurate water depth estimates and correct short-term fluctuations caused by silt siltation. The silt layer thickness predicted and calculated by the channel sedimentation model can effectively correct the silt layer thickness directly measured by the smart buoy.

[0126] S4. Based on the above steps S1-S3, a network including multiple monitoring points is constructed to continuously collect data to form a three-dimensional monitoring system for the waterway;

[0127] Specifically, each smart buoy works independently, monitors and uploads the water depth, silt layer thickness and bottom flow velocity data at its location in real time; the shore-based control center needs to design an efficient data synchronization mechanism to ensure that the data collected from each monitoring point can be integrated in real time to form a complete waterway data network; data synchronization includes not only the instant uploading of new data, but also the time synchronization of data between monitoring points to facilitate subsequent spatiotemporal analysis;

[0128] Based on real-time data analysis of the three-dimensional waterway monitoring system, the monitoring strategy is adjusted dynamically. For areas where the siltation trend is intensifying or the water depth changes are abnormal, the monitoring frequency is increased or additional monitoring equipment is deployed. At the same time, according to seasonal changes and special weather events, the monitoring focus is adjusted in advance, such as strengthening monitoring during the rainy season to cope with possible water level rise and accelerated siltation.

[0129] Embodiment 2:

[0130] This embodiment provides a real-time monitoring system for port and waterway water depth, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned real-time monitoring method for port and waterway water depth.

[0131] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for real-time monitoring of water depth of a port channel, characterized in that: The following steps are involved: S1. Select monitoring points in the waterway and install smart buoys at the monitoring points to monitor the waterway data. The smart buoys are used to monitor the waterway data, and acoustic Doppler current profilers are deployed to monitor the bottom flow velocity. S2, transmit the waterway data collected by the sonar system and the acoustic Doppler current profiler to the shore-based control center in real time through wireless transmission technology, and pre-process the waterway data; S3. Based on the historical waterway water data, a waterway sedimentation model is constructed. In combination with the pre-processed real-time waterway water data, the relationship between the silt layer thickness and the bottom flow velocity is evaluated based on the waterway sedimentation model. A time-dependent function is introduced to correct the measurement error of the real-time water depth data caused by silt silting, and improve the prediction accuracy of the future water depth change of the waterway. In S3, the specific steps of constructing the waterway sedimentation model based on the historical bottom flow velocity and silt layer thickness data are as follows: Among them, the relationship model between the initial sedimentation rate and the average bottom flow velocity is established as follows: ； In the formula, represents the net deposition rate of sediment; represents the proportionality constant; Indicates the average flow rate of water; represents the critical flow velocity; represents the experience index; Considering seasonal factors, a time-dependent function is introduced to adjust the critical flow rate and the proportionality constant , the above sedimentation rate expression is optimized, and the terrain factor Introduced into the calculation of deposition rate, the optimized deposition rate expression is: ； In the formula, represents the net deposition rate of the optimized sediment; represents the change in deposition efficiency; Variation of critical flow velocity with time; represents the basic proportional constant; Indicates the benchmark critical flow velocity; represents the seasonal variation of the critical velocity; the net deposition rate of the sediment based on the above optimization , then in a continuous time period, from time arrive Changes in the thickness of the silt layer It is expressed as: ； In time The thickness of the silt layer The expression is: ； In the formula, Indicates the initial time Thickness of the silt layer; Indicates the integration interval Any time point within Indicates the initial time point, the starting time of the integration; Indicates the current time point; represents the deposition rate; S4. Based on the above steps S1-S3, a network including multiple monitoring points is constructed to continuously collect data to form a three-dimensional monitoring system for the waterway.

2. The method for real-time monitoring of port channel water depth according to claim 1, characterized in that: In S1, the intelligent buoy includes a sonar transmitting module and a sonar receiving module. The sonar transmitting module is responsible for regularly transmitting sonar waves underwater, and the sonar receiving module receives the sound wave echo signal reflected from the bottom of the water. The sonar transmitting module and the sonar receiving module accurately measure the total time from the transmission to the reception of the sound wave, combined with the propagation speed of the sound wave in the water, to calculate the vertical distance from the buoy to the bottom of the water, that is, the water depth.

3. The method for real-time monitoring of port channel water depth according to claim 2 is characterized in that: The smart buoy calculates the water depth based on the sonar wave parameters, and the specific steps of calculating the water depth are as follows: S3.

1. Collect the sonar echo signal strength and signal morphology changes through the sonar system in the smart buoy; S3.2, establish an attenuation model between the thickness of the silt layer and the signal attenuation, and correct the originally calculated water depth by using the known silt characteristics of the detection point; S3.3, substituting the real-time sonar data into the attenuation model, and obtaining the thickness of the silt layer below the current monitoring point according to the attenuation of the echo signal; S3.

4. Considering the propagation speed of sound waves in silt, the water depth is calculated based on the thickness of the silt layer, the propagation speed of sound waves in water and the propagation speed in silt.

4. The method for real-time monitoring of port channel water depth according to claim 3 is characterized in that: In S3.2, the attenuation model between the thickness of the silt layer and the signal attenuation is specifically: Sound wave attenuation model: ； Signal strength attenuation model: ； In the formula, Represents the attenuation factor of the signal; Indicates the signal strength when there is no silt attenuation; Indicates the signal strength after passing through the silt layer; It represents the attenuation coefficient of sound waves in silt; represents the base of natural logarithms; Indicates the thickness of the silt layer; Based on the signal strength attenuation model, we can get: ； Taking the natural logarithm (ln) of both sides gives: ； The thickness of the silt layer is thus obtained : ； The water depth is: ； In the formula, It represents the speed of sound waves in water; It indicates the propagation speed of sound waves in silt; represents the total round trip time of the sound wave; Indicates the actual water depth.

5. The method for real-time monitoring of port channel water depth according to claim 3 is characterized in that: The smart buoy also includes a positioning module and a communication module; Wherein, the positioning module determines the longitude and latitude coordinates of the smart buoy based on the global positioning system; The communication module is responsible for sending the water depth and flow rate data collected by the smart buoy to the shore-based control center through wireless transmission technology.

6. The method for real-time monitoring of port channel water depth according to claim 1, characterized in that: In S2, the waterway water area data includes water depth data, silt layer thickness data and bottom flow velocity data; Among them, the preprocessing methods of waterway water area data specifically include data cleaning and multi-source data integration.

7. A real-time monitoring system for water depth of a port channel, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the real-time monitoring method for port channel water depth as described in any one of claims 1-6.

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