Port silting amount prediction system based on multi-source data fusion

Abstract

The invention relates to the technical field of data processing, in particular to a port silting amount prediction system based on multi-source data fusion, and the system is characterized in that an information analysis module confirms silting channel information and determines whether to adjust a monitoring point and determine the position of the monitoring point according to the change of the silting channel information in a target period; calculating a channel fluctuation parameter based on the channel information in the target period so as to adjust the data acquisition frequency of each monitoring point; and the model regulation and control module confirms the accuracy of the back-silting prediction model based on the measured data of each monitoring point and the three-dimensional water depth model so as to judge whether to regulate and control the back-silting prediction model and then calculate the port back-silting amount. According to the method, the influence of seaport channel change and different sailing numbers on the seaport siltation amount is considered, so that the accuracy of seaport siltation amount prediction is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a port siltation amount prediction system based on multi-source data fusion. BACKGROUND

[0002] Port siltation amount prediction mainly relies on physical models, numerical simulations, and empirical formulas. Modern technology more often uses advanced numerical simulation tools such as MIKE 21, Delft3D, etc. for modeling, which can comprehensively consider the interaction of factors such as waves, tidal currents, wind, and sediment characteristics, and provide more accurate prediction results. In addition, with the development of multi-source data fusion technology and machine learning algorithms, data-driven models established using remote sensing, sensor networks, and historical observation data are gradually applied to siltation amount prediction, aiming to improve prediction accuracy and adapt to specific conditions of different ports.

[0003] Chinese Patent Publication No. CN118500362B discloses a port channel water depth real-time monitoring method and system. It includes: S1, selecting a monitoring point in the channel, installing an intelligent buoy for monitoring channel water data at the monitoring point, and the intelligent buoy is used for monitoring channel water data, and an acoustic Doppler current profiler is deployed to monitor the water bottom flow rate; S2, collecting channel water data collected by the sonar system and the acoustic Doppler current profiler; S3, constructing a channel deposition model, combining the preprocessed real-time channel water data, introducing a time-dependent function, and correcting the measurement error of the real-time water depth data caused by siltation; S4, based on the above steps S1-S3, a network containing multiple monitoring points is constructed to form a three-dimensional monitoring system of the channel. By establishing a preliminary deposition rate and water bottom average flow rate channel deposition model, introducing a time-dependent function and topographic factor, and improving the prediction accuracy of future water depth changes in the channel. As can be seen, the prior art has the following problems:

[0004] The influence of changes in the sea port channel and the number of voyages on the siltation amount of the sea port is not considered, which affects the accuracy of the prediction of the siltation amount of the sea port. SUMMARY

[0005] Therefore, the present application provides a port siltation amount prediction system based on multi-source data fusion to overcome the problem in the prior art that the influence of changes in the sea port channel and the number of voyages on the siltation amount of the sea port is not considered, which affects the accuracy of the prediction of the siltation amount of the sea port.

[0006] To achieve the above-mentioned purpose, the present application provides a port siltation amount prediction system based on multi-source data fusion, comprising:

[0007] A data acquisition module for acquiring the siltation range and channel information of the port, collecting measured data and storing historical siltation data;

[0008] An information analysis module connected with the data acquisition module, for determining the monitoring points and their positions based on the siltation range;

[0009] A model regulation module connected with the data acquisition module, for regulating the siltation prediction model;

[0010] The model regulation module is provided with a siltation prediction model for predicting the three-dimensional water depth model of the siltation range based on the historical siltation data;

[0011] The information analysis module confirms the siltation channel information and determines whether to adjust the monitoring points and their positions according to the changes of the siltation channel information in the target period;

[0012] And, the channel fluctuation parameters are calculated based on the channel information in the target period to adjust the data acquisition frequency of each monitoring point;

[0013] The model regulation module determines whether to regulate the siltation prediction model based on the measured data of each monitoring point and the three-dimensional water depth model to calculate the port siltation amount.

[0014] As a preferred technical solution of the port siltation amount prediction system based on multi-source data fusion, the data acquisition module includes an information acquisition unit, a data monitoring unit and a data storage unit;

[0015] The information acquisition unit is used to acquire the siltation range and channel information of the port;

[0016] The data monitoring unit is used to collect measured data at each monitoring point;

[0017] The data storage unit is used to store the historical siltation data of the port;

[0018] The channel information includes the channel and the number of ships in each channel, and the historical siltation data includes the historical siltation amount and the corresponding hydrological data.

[0019] As a preferred technical solution of the port siltation amount prediction system based on multi-source data fusion, the information analysis module confirms the channel information in the siltation range as the siltation channel information, and determines whether to adjust the monitoring points according to the changes of each siltation channel in the target period;

[0020] The monitoring point adjustment mode is determined based on the number of different siltation channels in the target period, including:

[0021] In response to the number of different siltation channels being less than a preset number, the information analysis module adds a second monitoring point based on the different siltation channels;

[0022] In response to the number of different siltation channels being greater than or equal to a preset number, the information analysis module redetermines the monitoring points, including a first monitoring point and a second monitoring point;

[0023] The monitoring points include a first monitoring point and a second monitoring point.

[0024] As a preferred technical solution for a port siltation prediction system based on multi-source data fusion, the information analysis module determines the corresponding siltation impact area according to each siltation channel and divides the siltation impact range into several monitoring areas according to the overlap between the siltation impact areas.

[0025] The overlap between adjacent monitoring areas varies, and the siltation impact range includes the location range of all the siltation impact areas.

[0026] As a preferred technical solution for a port siltation prediction system based on multi-source data fusion, the information analysis module determines the number of first monitoring points within each monitoring area based on the overlap of the monitoring areas to determine the location of the first monitoring points.

[0027] The number of the first monitoring points is positively correlated with the degree of overlap of the corresponding monitoring areas.

[0028] As a preferred technical solution for a port siltation prediction system based on multi-source data fusion, the information analysis module determines the turning point of each siltation channel according to the information of each siltation channel, and determines the location of the second monitoring point of the corresponding siltation channel according to the location and turning direction of the turning point.

[0029] As a preferred technical solution for a port siltation prediction system based on multi-source data fusion, the information analysis module determines the total number of voyages based on channel information within the target period, and determines channel fluctuation parameters based on the total number of voyages and the preset number of voyages to adjust the data acquisition frequency of each monitoring point, including:

[0030] Based on the determination result that the channel fluctuation parameter is greater than the preset fluctuation range, the data acquisition frequency of each monitoring point is adjusted so that the adjusted data acquisition frequency is greater than the data acquisition frequency of the previous target cycle.

[0031] Based on the determination result that the channel fluctuation parameter is less than the preset fluctuation range, the data acquisition frequency of each monitoring point is adjusted so that the adjusted data acquisition frequency is less than the data acquisition frequency of the previous target cycle.

[0032] As a preferred technical solution for a port siltation prediction system based on multi-source data fusion, the information analysis module determines the total number of voyages based on channel information within the target period, and determines channel fluctuation parameters based on the total number of voyages and the preset number of voyages to adjust the data acquisition frequency of each monitoring point. It also includes:

[0033] According to the determination result of whether the channel fluctuation parameter is in the preset fluctuation range, it is determined not to adjust the data acquisition frequency of each monitoring point to be the data acquisition frequency of the previous target period.

[0034] As a preferred technical solution of the port siltation amount prediction system based on multi-source data fusion, the model regulation module determines the predicted water depth data of each monitoring point according to the three-dimensional water depth model to combine the measured water depth data to determine the water depth data deviation of each monitoring point, and calculates the average value, standard deviation and average deviation of the water depth data deviation of each monitoring point to determine the location attribute of the corresponding monitoring point.

[0035] The location attribute includes an abnormal monitoring point and a normal monitoring point.

[0036] As a preferred technical solution of the port siltation amount prediction system based on multi-source data fusion, the model regulation module determines whether to use the measured data of the monitoring point to retrain the siltation prediction model to predict the three-dimensional water depth model of the siltation range again according to the location attribute of each monitoring point, and calculates the port siltation amount according to the three-dimensional water depth model predicted again.

[0037] Compared with the prior art, the port siltation amount prediction system based on multi-source data fusion provided by the present application effectively improves the problem of insufficient accuracy of traditional prediction from the aspects of dynamic monitoring, intelligent regulation, and accurate analysis. The present application confirms the siltation channel information through the information analysis module, and adjusts the monitoring point position according to the change of the channel information in the target period, thereby breaking through the limitation of traditional prediction which does not consider the dynamic change of the channel. For the situation of channel bending and widening, the system can timely adjust the monitoring layout to ensure that the data acquisition covers the key area, accurately captures the siltation amount fluctuation caused by the change of the channel, and significantly improves the fitting degree of the prediction and the actual situation.

[0038] Especially, the present application calculates the channel fluctuation parameter based on the channel information in the target period, and then adjusts the data acquisition frequency of each monitoring point, fully considering the influence of the difference in the number of ship navigation on the siltation amount. In the period of dense ship navigation, the system automatically increases the monitoring frequency to track the changes of sediment movement caused by ship wave, propeller stirring and other factors in real time. In the period of low navigation amount, the frequency is reasonably reduced to improve the monitoring efficiency while ensuring the effectiveness of the data, so that the prediction result is more consistent with the actual siltation condition.

[0039] Especially, the model regulation module combines the measured data of each monitoring point with the three-dimensional water depth model to determine and regulate the accuracy of the siltation prediction model. When the prediction deviation is caused by the change of the channel or navigation, the system can retrain the model using the measured data to optimize the parameters, so that the model always adapts to the dynamic change of the port environment, effectively avoids the prediction error caused by the change of external factors, and realizes the high-precision and intelligent prediction of the port siltation amount.

[0040] Especially, the information analysis module quantifies the complexity of the siltation environment by the degree of overlap of the siltation affected area, and dynamically adjusts the number of first monitoring points according to the degree of overlap (the degree of overlap is positively correlated with the number of points); this strategy can accurately match the siltation risk level of different areas: areas with high degree of overlap are often affected by multiple channel flows, ship disturbances and other factors, and the siltation mechanism is more complex, so dense monitoring points can capture the details of sediment movement under the coupling action of multiple sources; while areas with low degree of overlap reduce redundant monitoring to avoid waste of resources; this method breaks through the limitations of traditional fixed density monitoring, making the monitoring point layout deeply consistent with the actual impact strength of siltation, improving resource utilization efficiency while ensuring the integrity of data collection in high-risk areas;

[0041] Especially, the information analysis module delimits the fan-shaped second monitoring area according to the change of water flow speed caused by ship turning and the sediment deposition characteristics, which covers the high-risk area (turning side) where the flow speed slows down and the sediment deposits due to water flow extrusion when the ship turns, while also considering the erosion changes on the non-turning side; at least three monitoring points (one point on the circle and two points in the area) are scientifically arranged in the area, through multi-point collaborative monitoring, not only can the siltation differences at different positions around the turning point (such as thickness changes near the center of the circle, erosion effects at the boundary of the circle) be captured, but also the data accuracy can be cross-verified to avoid accidental errors of single-point monitoring;

[0042] Especially, the processing mechanism of the model regulation module avoids the problems of resource waste or insufficient optimization caused by traditional "one-size-fits-all" model optimization, which can quickly repair the prediction defects of the model in high-complexity areas, while maintaining the stability of the model in stable areas, significantly improving the accuracy and adaptability of the siltation prediction model, and providing more reliable data support for port dredging decisions. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The connection diagram of the port siltation amount prediction system based on multi-source data fusion of the embodiment of the present application;

[0044] Figure 2 The workflow diagram of the information analysis module of the embodiment of the present application;

[0045] Figure 3 The workflow diagram of the model regulation module of the embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose and advantages of the present application clearer and more apparent, the present application will be further described below with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0047] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that the embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0048] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0049] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0050] Please refer to Figure 1 The figure is a connection diagram of the port siltation amount prediction system based on multi-source data fusion according to the embodiment of the present application. The embodiment of the present application provides a port siltation amount prediction system based on multi-source data fusion, which comprises:

[0051] A data acquisition module for acquiring the siltation range and channel information of the port, collecting measured data and storing historical siltation data;

[0052] An information analysis module connected with the data acquisition module, for determining the monitoring points and their positions based on the siltation range;

[0053] A model control module connected with the data acquisition module, for controlling the siltation prediction model;

[0054] The model control module is provided with a siltation prediction model for predicting the three-dimensional water depth model of the siltation range based on the historical siltation data. It can be understood that the siltation prediction model has been used in the prior art, and is usually used for predicting and simulating according to the hydrological data and the corresponding siltation amount. The present application uses any siltation prediction model in the prior art and determines its accuracy according to the measured data, and when the accuracy is lower than the standard, the siltation prediction model is retrained through the measured data and a three-dimensional water depth model is generated according to the trained model to predict the port siltation amount again;

[0055] The information analysis module confirms the siltation channel information and determines whether to adjust the monitoring points and the monitoring point positions according to the changes of the siltation channel information in a target period; it can be understood that the target period is usually 1 month to 3 months, that is, the monitoring point positions and the monitoring frequencies of the next target period are determined according to the channel information of the current target period and the channel information of the previous target period after each target period; in the implementation, the channel information usually changes little, so the target period is usually 3 months in the implementation;

[0056] and the channel fluctuation parameters are calculated based on the channel information in the target period to adjust the data acquisition frequencies of the monitoring points.

[0057] The model regulation module determines the accuracy of the siltation prediction model based on the measured data of the monitoring points and the three-dimensional water depth model to determine whether to regulate the siltation prediction model to calculate the port siltation amount.

[0058] It can be understood that the steps of the application in application include: S1, predicting a three-dimensional water depth model of a siltation range according to historical siltation data; S2, determining siltation channel information in the siltation range according to the siltation range and the channel information in a target period, and determining the monitoring point positions according to the siltation channel information; S3, determining channel fluctuation parameters according to the channel information in the target period to adjust the data acquisition frequencies of the monitoring points, and enabling the data monitoring unit to work according to the data acquisition frequencies; S4, regulating the siltation prediction model according to the measured data of the monitoring points and the three-dimensional water depth model to calculate the port siltation amount according to the regulated siltation prediction model.

[0059] It can be understood that the prior art usually accurately defines the siltation range by combining topography, power, and sediment data to provide quantitative basis for dredging engineering, and the determination method is not the technical problem to be solved by the present application, so it will not be described again.

[0060] Specifically, the data acquisition module includes an information acquisition unit, a data monitoring unit, and a data storage unit.

[0061] The information acquisition unit is used to acquire the siltation range and the channel information of the port.

[0062] The data monitoring unit is used to acquire measured data at the monitoring points.

[0063] The data storage unit is used to store historical siltation data of the port.

[0064] The channel information includes channels and the number of voyages of each channel, and the historical siltation data includes historical siltation amount and corresponding hydrological data.

[0065] Please refer to Figure 2As shown, it is a workflow diagram of the information analysis module of the embodiment of the present application. Specifically, the information analysis module confirms the channel information within the siltation range as siltation channel information, and determines whether to adjust the monitoring point according to the changes of each siltation channel within the target period;

[0066] The information analysis module determines the monitoring point adjustment mode based on the number of different siltation channels within the target period, including:

[0067] In response to the number of different siltation channels being less than the preset number, the information analysis module adds a second monitoring point based on the different siltation channels;

[0068] In response to the number of different siltation channels being greater than or equal to the preset number, the information analysis module re-determines the monitoring points, including the first monitoring point and the second monitoring point;

[0069] The monitoring points include the first monitoring point and the second monitoring point.

[0070] It should be understood that the channel information includes the complete channel path, which will cross the siltation range and the non-siltation range, so the part within the siltation range is determined as the siltation channel information, and the dredging is only carried out within the siltation range, so the present application only considers the siltation channel information;

[0071] It can be understood that the change of the siltation channel refers to the newly added siltation channel or the changed siltation channel within the target period; in implementation, the changed siltation channel satisfies that the distance between the original siltation channel is greater than or equal to 0.05 nautical miles; in implementation, the step of determining the distance between the siltation channel and the original siltation channel is: 1) respectively determine the start and end points and the siltation channel length of the siltation channel and the original siltation channel; 2) taking the start points of the two siltation channels as the origin, the same proportion of points on the two channels are recorded as the mirror points (for example, the tenth of the siltation channel and the original siltation channel, respectively: the position from the siltation channel origin and the length of the tenth of the siltation channel, the position from the original siltation channel origin and the length of the tenth of the original siltation channel); 3) determine the straight line distance between each mirror point; 4) determine whether the siltation channel is changed according to whether the average value of the straight line distance between each mirror point is greater than or equal to 0.05 nautical miles.

[0072] It can be understood that the information analysis module flexibly adjusts the monitoring points according to the changes (newly added or significantly changed) of the siltation channel in the target period, breaking through the limitations of the traditional fixed monitoring mode; when a new siltation channel appears or the original channel significantly changes, the application can capture and deploy monitoring points in a targeted manner in time to ensure that the monitoring range fully covers the dynamic siltation area; different adjustment strategies are implemented based on the number of different siltation channels to achieve efficient configuration of monitoring resources; when the number of different siltation channels is small, the local monitoring density is optimized by adding a second monitoring point; when the number of different siltation channels is large, the channel situation will become complex, and the first and second monitoring points need to be re-planned comprehensively, taking into account the whole and the details; this hierarchical processing method can not only improve the monitoring efficiency in simple scenarios, but also ensure the monitoring accuracy in complex scenarios, avoid resource waste or insufficient monitoring, and accurately match the monitoring layout with the actual siltation complexity; in addition, a scientific and quantitative method is used to determine whether the siltation channel has changed to provide an objective basis for monitoring point adjustment, with 0.05 nautical miles as the threshold to effectively distinguish between normal fluctuations and substantial changes in the channel, avoid resource waste caused by frequent adjustment of monitoring points due to small fluctuations, and ensure that significantly changed channels can be included in monitoring in time, achieving a balance between stability and sensitivity and improving the reliability and practicality of the siltation monitoring system.

[0073] Specifically, the information analysis module determines the corresponding siltation influence area according to each siltation channel and divides the siltation influence range into a plurality of monitoring areas according to the overlap degree between each siltation influence area.

[0074] Among them, the overlap degree between adjacent monitoring areas is different, and the siltation influence range includes the position range where all the siltation influence areas are located.

[0075] It can be understood that the area within a predetermined distance (0.05 nautical miles to 0.08 nautical miles, i.e. 93 meters to 148 meters) on both sides of the siltation channel is determined as the siltation influence area of the siltation channel; preferably, the predetermined distance is set to 0.6 nautical miles, and in implementation, the size of the predetermined distance is corrected according to the angle between the channel and the tidal current, and if the angle between the channel and the tidal current is greater than 30°, the predetermined distance is preferably 0.7 nautical miles.

[0076] Specifically, the information analysis module determines the number of first monitoring points in each monitoring area according to the overlap degree of each monitoring area to determine the position of the first monitoring point.

[0077] The number of first monitoring points and the overlap degree of the corresponding monitoring area are in a positive correlation.

[0078] It can be understood that for a monitoring area with an overlap degree of 1 (i.e., the monitoring area has no overlap and belongs to only one siltation influence range), at least one first monitoring point is owned in each 1000 square meters of the monitoring area, for a monitoring area with an overlap degree of 2 (i.e., two siltation influence ranges overlap), at least two first monitoring points are owned in each 1000 square meters of the monitoring area, for a monitoring area with an overlap degree of 3 (i.e., three siltation influence ranges overlap), at least three first monitoring points are owned in each 1000 square meters of the monitoring area, and so on; therefore, in implementation, the number of first monitoring points of each monitoring area can be determined according to the overlap degree and area of the monitoring area, and the first monitoring points in a single monitoring area are uniformly distributed to more completely and carefully determine the accuracy of the three-dimensional water depth model of the monitoring area through measured data.

[0079] It can be understood that by increasing the first monitoring points and uniformly distributing them in the monitoring area with a high overlap degree, more intensive measured data (such as water depth, flow velocity, and sediment concentration) can be obtained, thereby significantly improving the construction accuracy of the three-dimensional water depth model; in addition, the uniformly distributed monitoring points can cover all directions of the siltation influence area, avoid data blind areas, enable the model to accurately reflect the spatial distribution difference of the siltation thickness (such as the siltation characteristics of the channel intersection section), and thus strengthen the matching degree of the measured data and the model, thereby improving the adaptability of the siltation prediction model to complex scenes and reducing the prediction deviation caused by insufficient monitoring data, to provide a more reliable quantitative basis for dredging engineering.

[0080] Specifically, the information analysis module determines the turning points of each siltation channel according to the siltation channel information of each siltation channel, and determines the positions of the second monitoring points of the siltation channel according to the positions and turning directions of the turning points.

[0081] It can be understood that when a ship turns, the turning side of the ship body will squeeze the water flow, causing the water flow velocity on the turning side to decrease, and according to the relationship between the sediment carrying capacity of the water flow and the flow velocity, the decrease in the flow velocity will cause the sediment carrying capacity to decrease and the sediment to easily deposit on the turning side of the ship body; at the same time, the turning of the ship will form a relatively low-pressure area on the non-turning side of the ship body, causing the water flow velocity to relatively increase and the sediment carrying capacity to increase, and the non-turning side may experience erosion rather than siltation.

[0082] In implementation, a circle with a radius of 100 meters is drawn with the position of the turning point as the center, a sector formed by the circle and the straight line where the directions before and after the turning are located is determined as the second monitoring area of the turning point, and at least three second monitoring points are determined in the second monitoring area, one of which is located on the circle and the other two are located at any positions inside the second monitoring area.

[0083] It can be understood that the information analysis module determines a second monitoring area in the form of a sector with the turning point as the center and 100 meters as the radius according to the water flow velocity change caused by the ship turning and the sediment deposition characteristics, which covers the high-risk area (the turning side) of sediment deposition caused by the flow compression when the ship turns, and also takes into account the erosion changes on the non-turning side. At least three monitoring points (one point on the circle + two points in the area) are scientifically arranged in the area, through the coordinated monitoring of multiple points, the differences in siltation thickness at different positions around the turning point (such as the thickness change near the center of the circle, the erosion influence at the boundary of the circle) can be captured, and the data accuracy can be cross-verified to avoid accidental errors of single-point monitoring; compared with traditional uniform arrangement or random monitoring, this method realizes high-density and full-coverage monitoring of the key area of turning siltation with the least monitoring cost, significantly improves the pertinence and effectiveness of siltation data collection, and provides reliable support for accurately predicting the siltation amount in the turning area and optimizing the dredging scheme.

[0084] Specifically, the information analysis module determines the total number of voyages according to the channel information in the target period, and determines a channel fluctuation parameter according to the total number of voyages and a preset number of voyages to adjust the data collection frequency of each monitoring point, including:

[0085] According to the determination result that the channel fluctuation parameter is greater than the preset fluctuation range, it is determined to adjust the data collection frequency of each monitoring point so that the adjusted data collection frequency is greater than the data collection frequency of the previous target period;

[0086] According to the determination result that the channel fluctuation parameter is less than the preset fluctuation range, it is determined to adjust the data collection frequency of each monitoring point so that the adjusted data collection frequency is less than the data collection frequency of the previous target period.

[0087] In implementation, the preset number of voyages is the total number of voyages in the previous target period, and the channel fluctuation parameter = |total number of voyages - preset number of voyages| ÷ preset number of voyages × 100%.

[0088] In implementation, the preset fluctuation range ∈ [15%, 30%].

[0089] Specifically, the information analysis module determines the total number of voyages according to the channel information in the target period, and determines a channel fluctuation parameter according to the total number of voyages and a preset number of voyages to adjust the data collection frequency of each monitoring point, including:

[0090] According to the determination result that the channel fluctuation parameter is in the preset fluctuation range, it is determined not to adjust the data collection frequency of each monitoring point so that it is the data collection frequency of the previous target period.

[0091] It can be understood that the initial acquisition frequency is 10 days once, and the underwater robot is controlled to move to each monitoring point to acquire the measured water depth data at each monitoring point; when the channel fluctuation parameter is greater than the preset fluctuation range, the data acquisition frequency = channel fluctuation parameter ÷ 30% × data acquisition frequency of the previous target period; when the channel fluctuation parameter is less than the preset fluctuation range, the data acquisition frequency = channel fluctuation parameter ÷ 15% × data acquisition frequency of the previous target period;

[0092] It can be understood that the way of dynamically determining the data acquisition frequency according to the channel fluctuation parameter realizes the dynamic adaptation of the monitoring resources and the influence of ship navigation, optimizes the monitoring efficiency while ensuring the effectiveness of the siltation data; the application accurately identifies the influence strength of ship activity on siltation by quantitatively analyzing the difference in navigation times between the target period and the historical period (measured by the channel fluctuation parameter); when the fluctuation parameter exceeds the preset range, the acquisition frequency is automatically increased or decreased, for example, the acquisition frequency will be increased during the intensive ship navigation period (fluctuation parameter greater than 30%) to capture the dynamic changes of sediment caused by ship waves, propeller agitation, etc.; and the frequency is reasonably reduced during the low navigation period (fluctuation parameter less than 15%) to reduce resource consumption; this self-adaptive adjustment mechanism avoids the problems of data redundancy / monitoring lag caused by traditional fixed frequency monitoring, ensures the timeliness and integrity of siltation data in high-risk periods, reduces equipment operating costs in stable periods, and significantly improves the intelligence and economy of port siltation monitoring.

[0093] Please refer to Figure 3 As shown in the figure, it is a workflow diagram of the model control module of the embodiment model of the application. Specifically, the model control module determines the predicted water depth data of each monitoring point according to the three-dimensional water depth model to determine the water depth data deviation of each monitoring point in combination with the measured water depth data, and calculates the average value, standard deviation and average deviation of the water depth data deviation of each monitoring point to determine the location attribute of the corresponding monitoring point;

[0094] The location attribute includes abnormal monitoring points and normal monitoring points.

[0095] In implementation, the deviation fluctuation parameter is determined according to the ratio of the average deviation to the average value, and if the deviation fluctuation parameter is greater than the preset fluctuation value, the location attribute of the monitoring point is determined according to the average value and the standard deviation; if the deviation fluctuation parameter is less than or equal to the preset fluctuation value, the location attribute of the monitoring point is determined according to the average value; in implementation, the preset fluctuation value is usually between 0.05 and 0.15, and is preferably set to 0.1;

[0096] It can be understood that the deviation fluctuation parameter represents the difference between the measured data and the predicted data of each monitoring point. The larger the deviation fluctuation parameter, the greater the change in data deviation between each monitoring point, that is, the prediction effect of the model is not good, and therefore more measured data needs to be used to optimize and train the model. The smaller the deviation fluctuation parameter, the more similar the data deviation between each monitoring point, that is, the determined model has deviation but is more suitable for the measured terrain, and therefore a large amount of measured data is not needed to optimize and train the model.

[0097] It can be understood that if the deviation fluctuation parameter is greater than the preset fluctuation value, the position attribute of the monitoring point is determined according to the difference between the average value and the standard deviation. If the water depth data deviation of a single monitoring point is greater than the difference between the average value and the standard deviation, the monitoring point is determined to be an abnormal monitoring point. If the water depth data deviation of a single monitoring point is less than or equal to the difference between the average value and the standard deviation, the monitoring point is determined to be a normal monitoring point.

[0098] It can be understood that if the deviation fluctuation parameter is less than or equal to the preset fluctuation value, the position attribute of the monitoring point is determined according to the average value. If the water depth data deviation of a single monitoring point is greater than the average value, the monitoring point is determined to be an abnormal monitoring point. If the water depth data deviation of a single monitoring point is less than or equal to the average value, the monitoring point is determined to be a normal monitoring point.

[0099] It can be understood that the model regulation module accurately identifies the inaccuracy of the model prediction by scientifically and quantitatively analyzing the water depth data deviation of the monitoring points, and realizes intelligent and differentiated optimization of the siltation prediction model. The module intelligently judges the dispersion degree of the data deviation of each monitoring point by calculating the deviation fluctuation parameter. When the deviation fluctuation parameter is large, it indicates that the prediction effect of the model in different regions is significantly different. At this time, the monitoring points are screened loosely in combination with the average value and the standard deviation, more abnormal monitoring points are determined, and measured data is supplemented for depth optimization training. When the deviation fluctuation parameter is small, it indicates that the overall fitting degree of the model is good, and only a small amount of monitoring points with large deviation need to be fine-tuned based on the average value. This hierarchical processing mechanism avoids the waste of resources or insufficient optimization caused by the traditional “one-size-fits-all” model optimization, can quickly repair the prediction defects of the model in high-complexity areas, and can maintain the stability of the model in stable areas, significantly improving the accuracy and adaptability of the siltation prediction model, and providing more reliable data support for port dredging decisions.

[0100] Specifically, the model regulation module determines whether to use the measured data of each monitoring point to retrain the siltation prediction model to predict the three-dimensional water depth model of the siltation range again according to the position attribute of each monitoring point, and calculates the port siltation amount according to the re-predicted three-dimensional water depth model.

[0101] It can be understood that for the abnormal monitoring point, the coordinates and the measured water depth data of the abnormal monitoring point are all input into the three-dimensional water depth model to retrain the three-dimensional water depth model, so as to obtain a three-dimensional water depth model more suitable for the current actual situation, and the corresponding siltation amount is determined according to the three-dimensional water depth model.

[0102] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will all fall within the protection scope of the present application.

[0103] The above description is only the preferred embodiments of the present application and is not used to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A port siltation amount prediction system based on multi-source data fusion, comprising: a data acquisition module for acquiring siltation range and channel information of a port, collecting measured data and storing historical siltation data; an information analysis module connected to the data acquisition module, for determining monitoring points and their positions based on the siltation range; a model regulation module connected to the data acquisition module, for regulating a siltation prediction model; the model regulation module is provided with a siltation prediction model for predicting a three-dimensional water depth model of the siltation range based on historical siltation data; characterized in that it further comprises: the information analysis module confirms siltation channel information and determines whether to adjust monitoring points and their positions according to changes in the siltation channel information in a target period; and, based on channel information in a target period, a channel fluctuation parameter is calculated to adjust the data acquisition frequency of each monitoring point; the model regulation module determines the accuracy of the siltation prediction model based on the measured data of each monitoring point and the three-dimensional water depth model to determine whether to regulate it and then calculate the port siltation amount.

2. The port siltation volume prediction system based on multi-source data fusion according to claim 1, characterized in that, The data acquisition module comprises an information acquisition unit, a data monitoring unit and a data storage unit; the information acquisition unit is used to acquire the siltation range and channel information of the port; the data monitoring unit is used to collect measured data at each monitoring point; the data storage unit is used to store historical siltation data of the port; wherein the channel information includes the number of channels and the number of voyages of each channel, and the historical siltation data includes historical siltation amount and corresponding hydrological data.

3. The port re-silting quantity prediction system based on multi-source data fusion according to claim 1, characterized in that, The information analysis module confirms the channel information in the siltation range as siltation channel information, and determines whether to adjust the monitoring points according to the changes of each siltation channel in a target period; based on the number of different siltation channels in a target period, the monitoring point adjustment mode is determined, including: in response to the number of different siltation channels being less than a preset number, the information analysis module adds a second monitoring point based on the different siltation channels; in response to the number of different siltation channels being greater than or equal to a preset number, the information analysis module re-determines the monitoring points, including a first monitoring point and a second monitoring point; wherein the monitoring points include a first monitoring point and a second monitoring point.

4. The port re-silting quantity prediction system based on multi-source data fusion according to claim 3, characterized in that, The information analysis module determines the corresponding siltation influence area according to each siltation channel, and divides the siltation influence range into several monitoring areas according to the overlap degree between each siltation influence area; wherein the overlap degree between adjacent monitoring areas is different, and the siltation influence range includes the position range of all the siltation influence areas.

5. The port re-silting quantity prediction system based on multi-source data fusion according to claim 4, characterized in that, The information analysis module determines the number of first monitoring points in each monitoring area according to the overlap degree of each monitoring area to determine the position of the first monitoring points; the number of first monitoring points is positively correlated with the overlap degree of the corresponding monitoring area.

6. The port re-silting quantity prediction system based on multi-source data fusion according to claim 3, characterized in that, The information analysis module determines the turning points of each siltation channel according to the siltation channel information, and determines the position of the second monitoring points of the corresponding siltation channel according to the position and turning direction of the turning points.

7. The port re-silting quantity prediction system based on multi-source data fusion according to claim 1, characterized in that, The information analysis module determines the total number of voyages according to the channel information in a target period, and determines the channel fluctuation parameter according to the total number of voyages and a preset number of voyages to adjust the data acquisition frequency of each monitoring point, including: According to the determination result that the channel fluctuation parameter is greater than the preset fluctuation range, it is determined to adjust the data acquisition frequency of each monitoring point so that the adjusted data acquisition frequency is greater than the data acquisition frequency of the previous target period; According to the determination result that the channel fluctuation parameter is less than the preset fluctuation range, it is determined to adjust the data acquisition frequency of each monitoring point so that the adjusted data acquisition frequency is less than the data acquisition frequency of the previous target period.

8. The port re-silting quantity prediction system based on multi-source data fusion according to claim 7, characterized in that, The information analysis module determines the total number of voyages according to the channel information in the target period, and determines the channel fluctuation parameter according to the total number of voyages and the preset number of voyages to adjust the data acquisition frequency of each monitoring point, and further comprises: According to the determination result that the channel fluctuation parameter is in the preset fluctuation range, it is determined not to adjust the data acquisition frequency of each monitoring point so that it is the data acquisition frequency of the previous target period.

9. The port re-silting quantity prediction system based on multi-source data fusion according to claim 1, characterized in that, The model regulation module determines the predicted water depth data of each monitoring point according to the three-dimensional water depth model to combine the measured water depth data to determine the water depth data deviation of each monitoring point, and calculates the average value, standard deviation and average deviation of the water depth data deviation of each monitoring point to determine the position attribute of the corresponding monitoring point; Wherein, the position attribute includes abnormal monitoring point and normal monitoring point.

10. The port re-silting quantity prediction system based on multi-source data fusion according to claim 9, characterized in that, The model regulation module determines whether to use the measured data of the monitoring point to retrain the siltation prediction model to re-predict the three-dimensional water depth model of the siltation range according to the position attribute of each monitoring point, and calculates the port siltation amount according to the re-predicted three-dimensional water depth model.

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