Intelligent monitoring and control method for sedimentation of immersed tunnel based on multi-beam bathymetry

Abstract

This invention discloses an intelligent monitoring and control method for siltation in immersed tunnels based on multibeam bathymetry, belonging to the field of intelligent monitoring technology for siltation in immersed tunnels. This method includes the following steps: siltation acquisition interference assessment; siltation data input accuracy assessment; and siltation construction assessment. By performing siltation acquisition interference assessment during the siltation data acquisition process, and after the siltation acquisition interference assessment is satisfactory, performing siltation data input accuracy assessment, and after the siltation data input accuracy assessment is satisfactory, performing siltation construction assessment, this invention improves the accuracy of intelligent monitoring and control of siltation in immersed tunnels. It solves the problem in existing technologies where the accuracy of intelligent monitoring and control of siltation in immersed tunnels based on multibeam bathymetry is low due to interference with acoustic signals during the acquisition process.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for siltation in immersed tunnels, and in particular to an intelligent monitoring and control method for siltation in immersed tunnels based on multibeam bathymetry. Background Technology

[0002] With the advancement of marine engineering construction, the need to ensure the long-term stable operation of immersed tunnels and reduce maintenance costs is becoming increasingly prominent. Multibeam bathymetry technology boasts high precision and wide coverage, but its practical application in monitoring siltation in immersed tunnels faces challenges due to complex environmental interference and data processing difficulties. By constructing a siltation prediction model, the impact of different hydrological conditions and construction parameters on silt deposition can be assessed, thereby optimizing the layout of monitoring equipment and improving early warning accuracy and system response speed. The movement of sediment in the waters where immersed tunnels are located is influenced by multiple factors such as tides, waves, and currents. Traditional single-point monitoring suffers from data blind spots and information lag due to the scattering or absorption of bottom-reflected signals. Constructing an intelligent monitoring model can integrate multibeam bathymetry data, flow velocity and direction information, and turbidity variation characteristics to achieve dynamic reconstruction of the siltation situation in three-dimensional space, providing a scientific basis for formulating precise dredging strategies. In complex sea conditions, reducing resource waste and equipment wear caused by repeated measurements is a crucial consideration for engineering economics, ensuring monitoring accuracy while improving energy efficiency. With the development of IoT and edge computing technologies, the intelligent level of siltation monitoring in immersed tunnels is significantly improved by transmitting data to the platform in real time, providing technical support for extending the service life of undersea tunnels.

[0003] The commonly used monitoring methods are mostly single multibeam bathymetry or silt box observation. The transmission frequency, pulse width and absorption coefficient are adjusted according to the water depth and bottom characteristics, and the spacing of the measuring lines is set to ensure coverage of key areas such as the top of the pipe and the slope of the foundation trench.

[0004] For example, the invention patent announcement CN114167774B discloses a coal mine underground water reservoir siltation monitoring system, belonging to the field of underground water reservoir siltation monitoring. It includes: at least one set of water injection pipes, with one end located on the side of the fully mechanized mining face and the second end located upstream of the mine water flow direction in the underground water reservoir; the water injection pipes are used to inject high-suspended-solid mine water collected at the working face into the underground water reservoir; and at least one set of siltation monitoring pipes, with one end located on the side of the fully mechanized mining face and the second end located inside the underground water reservoir and downstream of the water injection pipes; the siltation monitoring pipes have several through holes on their walls inside the underground water reservoir, allowing suspended solids in the underground water reservoir to settle and enter the siltation monitoring pipes through these through holes; and several sets of monitoring devices are installed inside the siltation monitoring pipes, arranged at predetermined intervals by support components.

[0005] For example, the invention patent application with publication number CN119045394A discloses an integrated ecological dredging monitoring method and system for rivers and lakes based on a ship hull, belonging to the technical field of intelligent control systems applied in municipal engineering. It includes: acquiring real-time data on impurity removal status and obtaining impurity removal anomaly data; determining impurity removal anomaly points based on the impurity removal anomaly data and obtaining adjustment anomaly data; determining adjustment anomaly points based on the adjustment anomaly data; acquiring pressing status time-series data and obtaining pressing anomaly data; determining pressing anomaly points based on the pressing anomaly data and performing anomaly processing; and displaying and recording the impurity removal anomaly points, adjustment anomaly points, and pressing anomaly points in a visual form.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In existing technologies, due to the low bearing capacity and high compressibility of silty soil foundations, and the fact that water bodies in silty soil areas usually contain high concentrations of suspended sediment, the sound waves in multibeam bathymetry are scattered or absorbed during propagation, resulting in weak or even lost bottom reflection signals, causing data loss or errors. Consequently, the siltation situation in the foundation trench directly affects the construction progress and quality. Furthermore, the low accuracy of intelligent monitoring and control of siltation in immersed tunnels based on multibeam bathymetry is caused by interference with the sound wave signals during the acquisition process. Summary of the Invention

[0007] This application provides an intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry, which solves the problem of low accuracy in the existing technology due to interference with acoustic signals during the acquisition process, thereby improving the accuracy of intelligent monitoring and control of siltation backflow in immersed tunnels.

[0008] This application provides an intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry, including the following steps: During the data acquisition process for immersed tunnel siltation, an interference assessment is performed to quantify the degree of interference in the data acquisition. Based on the acquisition qualification criteria, it is determined whether to perform acquisition qualification optimization. Acquisition qualification optimization is used to improve the acquisition qualification of immersed tunnel siltation data during the data acquisition process. After the siltation interference assessment is passed, an accuracy assessment of siltation data input is performed to quantify the accuracy of the siltation data input to the preset data fusion and analysis platform. Based on the siltation data input accuracy criteria, it is determined whether to perform siltation data input accuracy optimization. Siltation data input accuracy optimization is used to improve the accuracy of the siltation data input process. After the siltation data input accuracy assessment is passed, a siltation construction assessment is performed to quantify the qualification of the siltation construction. Based on the predicted qualification criteria, it is determined whether to perform siltation construction optimization. Siltation construction optimization is used to improve the real-time performance of immersed tunnel siltation construction.

[0009] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By performing data acquisition of immersed tunnel siltation during the process of acquiring siltation data and determining whether the acquisition is qualified, then performing an accuracy assessment of siltation data input and determining whether the siltation data input is accurate, and finally performing a siltation construction assessment and determining whether the siltation construction is optimized, the qualification of intelligent monitoring and control of immersed tunnel siltation based on multibeam bathymetry is improved, thereby improving the accuracy of intelligent monitoring and control of immersed tunnel siltation. This solves the problem of low accuracy of intelligent monitoring and control of immersed tunnel siltation based on multibeam bathymetry in the existing technology due to interference of acoustic signals during the acquisition process.

[0010] 2. By multi-dimensionally coupling the input deviation value of the siltation data, the accurate input value of the siltation data is obtained. Based on the deviation value of the siltation sample, it is determined whether to optimize the siltation box acquisition interference. This enhances the qualification of siltation control of immersed tunnel based on siltation box, and further improves the qualification of intelligent monitoring and control of siltation in immersed tunnel based on multibeam bathymetry.

[0011] 3. By using multi-dimensional coupling to obtain the accurate value of the siltation data input deviation, the system determines whether to optimize the siltation data input accuracy based on the accurate value of the siltation data input. This enhances the accuracy of qualified siltation data input and improves the accuracy of siltation construction through intelligent monitoring and control of immersed tunnel siltation based on multibeam bathymetry. Attached Figure Description

[0012] Figure 1 A flowchart of an intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry, provided for an embodiment of this application; Figure 2 A schematic diagram of the architecture of the intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry provided in the embodiments of this application; Figure 3 A logical framework diagram of the intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry provided in the embodiments of this application; Figure 4 A schematic diagram of the structure of the intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry provided in this application embodiment. Detailed Implementation

[0013] This application provides an intelligent monitoring and control method for siltation in immersed tunnels based on multibeam bathymetry, solving the problem of low accuracy in existing technologies due to interference with acoustic signals during data acquisition. The method involves performing an interference assessment during siltation data acquisition to quantify the degree of interference. Based on acquisition qualification criteria, it determines whether acquisition optimization is needed. If the monitored multibeam acquisition delay value meets the qualification criteria, the corresponding trench water depth data is marked as qualified multibeam data. If the monitored silt sample deviation value meets the silt sample qualification criteria, the corresponding silt sample data is marked as qualified silt box data. Then, after the siltation interference assessment is passed, the method executes... The accuracy assessment of siltation data input quantifies the accuracy of the pre-set data fusion and analysis platform for siltation data input into immersed tunnels. Based on the accuracy conditions of the siltation data input, it determines whether to optimize the siltation data input accuracy and whether the conditions are met. If the monitored siltation data input accuracy values ​​meet the conditions, the corresponding qualified immersed tunnel siltation data are fused. Finally, after the siltation data input accuracy assessment is qualified, a siltation construction assessment is performed to quantify the qualification of the siltation construction. Based on the predicted qualification conditions, it determines whether to optimize the siltation construction. If the monitored siltation trend prediction values ​​meet the predicted qualification conditions, the qualified immersed tunnel siltation data that meets the predicted qualification conditions are stored in the pre-set data fusion and analysis platform. This improves the accuracy of intelligent monitoring and control of immersed tunnel siltation based on multibeam bathymetry, which is affected by interference with acoustic signals during the acquisition process.

[0014] The technical solution in this application embodiment is to solve the problem mentioned above, where the accuracy of intelligent monitoring and control of siltation backflow in immersed tunnels based on multibeam bathymetry is low due to interference with acoustic signals during the acquisition process. The overall approach is as follows: By performing an interference assessment during the data acquisition process for immersed tunnel siltation, the degree of interference in the siltation data acquisition is quantified, and the appropriate optimization is determined based on the acquisition qualification criteria. Next, after the siltation data acquisition interference assessment is passed, a siltation data input accuracy assessment is performed to quantify the accuracy of the siltation data input to the preset data fusion and analysis platform. The appropriate optimization is determined based on the siltation data input accuracy criteria. Finally, after the siltation data input accuracy assessment is passed, a siltation construction assessment is performed to quantify the qualification level of the siltation construction. The appropriate optimization is determined based on the predicted qualification criteria. This approach effectively improves the accuracy of intelligent monitoring and control of immersed tunnel siltation.

[0015] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0016] like Figure 1 The diagram shown is a flowchart of an intelligent monitoring and control method for siltation in immersed tunnels based on multibeam bathymetry, provided in an embodiment of this application. The method includes the following steps: Interference Assessment for Siltation Data Acquisition in Immersed Tube Tubes: During the siltation data acquisition process in immersed tube tunnels, an interference assessment is performed to quantify the degree of interference. Based on the acquisition qualification criteria, it is determined whether to perform acquisition qualification optimization. Acquisition qualification optimization is used to improve the acquisition qualification of siltation data in the immersed tube tunnel siltation data acquisition process. Siltation Data Input Accuracy Assessment: After the siltation data acquisition interference assessment is passed, a siltation data input accuracy assessment is performed to quantify the accuracy of the siltation data input to the preset data fusion and analysis platform. Based on the siltation data input accuracy criteria, it is determined whether to perform siltation data input accuracy optimization. Siltation data input accuracy optimization is used to improve the accuracy of the siltation data input process. Siltation Construction Assessment: After the siltation data input accuracy assessment is passed, a siltation construction assessment is performed to quantify the qualification level of siltation construction. Based on the predicted qualification criteria, it is determined whether to perform siltation construction optimization. Siltation construction optimization is used to improve the real-time performance of siltation construction in immersed tube tunnels.

[0017] In this embodiment, as Figure 2 The diagram shown is an architectural schematic of the intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry provided in this application embodiment. Figure 3The logical framework diagram of the intelligent monitoring and control method for immersed tunnel siltation based on multibeam bathymetry provided in this application embodiment shows that the siltation acquisition interference assessment, siltation data input accuracy assessment, and siltation construction assessment are interconnected and progressively advanced. During the siltation data acquisition process, an siltation acquisition interference assessment is performed to quantify the degree of interference in the siltation data acquisition. This siltation acquisition interference assessment includes multibeam bathymetry interference assessment and siltation box silt sample acquisition interference assessment. If the monitored multibeam bathymetry... If the acquisition delay value meets the multibeam acquisition qualification criteria, the corresponding trench water depth data is marked as qualified multibeam data. If the monitored multibeam acquisition delay value does not meet the multibeam acquisition qualification criteria, multibeam acquisition interference optimization is performed. If the monitored silt sample deviation value meets the silt sample qualification criteria, the corresponding silt sample data is marked as qualified silt box data. If the monitored silt sample deviation value does not meet the silt sample qualification criteria, silt box acquisition interference optimization is performed. Following the successful completion of the interference assessment for siltation data collection in the immersed tunnel, an accuracy assessment of the siltation data input is performed to quantify the accuracy of the siltation data input to the preset data fusion and analysis platform. If the monitored siltation data input accuracy value meets the siltation data input accuracy conditions, the corresponding qualified immersed tunnel siltation data is fused and processed, and a siltation construction assessment is performed. If the monitored siltation data input accuracy value does not meet the siltation data input accuracy conditions, siltation data input accuracy optimization is performed. Finally, a siltation construction assessment is performed to quantify the qualification of the siltation construction. If the monitored siltation trend prediction value does not meet the prediction qualification conditions, siltation construction optimization is performed. If the monitored siltation trend prediction value meets the prediction qualification conditions, the qualified immersed tunnel siltation data that meets the prediction qualification conditions is stored in the preset data fusion and analysis platform. Through layer-by-layer optimization of siltation data collection interference assessment, siltation data input accuracy assessment, and siltation construction assessment, the qualification of intelligent monitoring and control of siltation in immersed tunnels based on multibeam bathymetry is improved, thereby enhancing the accuracy of intelligent monitoring and control of siltation in immersed tunnels.

[0018] It should be added that, Figure 4 This is a schematic diagram of the structure of an intelligent monitoring and control method for siltation in immersed tunnels based on multibeam bathymetry. First, an interference assessment for siltation acquisition in immersed tunnels is performed, which includes: multibeam acquisition interference assessment and siltation sample acquisition interference assessment. Second, an accuracy assessment of siltation data input is performed. Finally, an assessment of siltation construction is performed. The steps are progressively advanced, which improves the accuracy of intelligent monitoring and control of siltation in immersed tunnels based on multibeam bathymetry due to interference with acoustic signals during the acquisition process.

[0019] In the complex environment of multibeam bathymetry, the siltation data of immersed tunnels includes water flow, water velocity, and particle size distribution. During the process from multibeam bathymetry and siltation box measurement to the pre-set data fusion and analysis platform, interference from high-concentration suspended sediment in silty soil can occur, leading to interference with the acoustic signals reflected by the multibeam bathymetry measurement. This interference affects the data acquisition process of immersed tunnel siltation data and the accuracy of intelligent monitoring and control of immersed tunnel siltation based on multibeam bathymetry. In the process of acquiring and inputting immersed tunnel siltation data, interference assessment, accurate data input assessment, and siltation construction assessment are interconnected. Through the collaboration among these assessments, the stability of immersed tunnel siltation data acquisition and input, as well as the accuracy of immersed tunnel siltation construction, is improved during the monitoring process.

[0020] It should be added that, prior to the design of the intelligent monitoring and control method for siltation in immersed tunnels based on multibeam bathymetry in this application, a database storing various preset data was established. The database includes, but is not limited to, preset siltation data acquisition time, preset siltation box tilt angle range, preset siltation volume, preset siltation sample deviation value, preset siltation data input value, and preset siltation trend prediction value, etc., and various values ​​are directly set by technical personnel; for example, the preset siltation sample deviation value is represented by the average value of siltation sample deviation values ​​over a historical time period.

[0021] Furthermore, during the data acquisition process for immersed tunnel siltation, an interference assessment is performed to quantify the degree of interference in the siltation data acquisition. This assessment includes multi-beam acquisition interference assessment and siltation box silt sample acquisition interference assessment. The multi-beam acquisition interference assessment evaluates the timeliness of the trench depth data acquisition process based on the monitored multi-beam acquisition delay value. The multi-beam acquisition delay value reflects the accuracy of the trench depth data acquisition. The multi-beam acquisition delay value is represented by the average of the difference between the preset time period for siltation data acquisition and the actual siltation data acquisition time. The siltation box silt sample acquisition interference assessment evaluates the anti-interference capability of the silt sample data acquisition process based on the monitored silt sample deviation value. The silt sample deviation value reflects the qualification of the silt sample data acquisition.

[0022] In this embodiment, multibeam bathymetry can acquire water depth data over a large area of ​​the foundation trench, but it cannot directly obtain siltation sample data; siltation box measurement can provide direct siltation sample data, but its measurement range is limited. Using both together improves the accuracy of measurement data in silty areas. The combined effect of multibeam bathymetry and siltation box measurement allows for understanding the overall topography and water depth changes in silty areas, as well as the characteristics, thickness, and patterns of siltation materials in local areas, thereby improving the accuracy and reliability of the overall measurement data and ultimately enhancing the accuracy of intelligent monitoring and control of siltation in immersed tunnels.

[0023] Furthermore, the specific process for evaluating interference in the collection of sludge samples from the sludge return box is as follows: determine whether the monitored tilt angle of the sludge return box is within the preset tilt angle range; if the monitored tilt angle is not within the preset tilt angle range, then set the placement angle; setting the placement angle means sending a prompt to the preset personnel to adjust the tilt angle of the sludge return box; if the monitored tilt angle is within the preset tilt angle range, then obtain the sludge sample deviation value.

[0024] In this embodiment, by comparing the tilt angle of the silt return box with the preset tilt angle range of the silt return box obtained from the database in real time, the qualification of the silt return box placement can be effectively evaluated, the accuracy of the silt return sample data collection can be avoided, and the silt return box can be ensured to collect silt return sample data without interference, thereby improving the qualification of the silt return sample data and thus achieving the effect of improving the accuracy of intelligent monitoring and control of silt return in immersed tunnels.

[0025] Furthermore, the specific process for obtaining the deviation value of the sludge sample is as follows: After performing a ratio analysis on the change in sludge thickness and the preset sludge thickness (the ratio analysis means performing a ratio calculation), and then combining this with a weighted calculation based on the thickness of the medium, the sludge thickness analysis value is obtained. This value reflects the impact of the change in sludge thickness on the passability of the sludge sample collection from the sludge box. Specifically, the expression for the sludge thickness analysis value is: , This represents the analysis value of the silt thickness in the k-th preset time period. This represents the change in the thickness of the backfill material during the k-th preset time period. Indicates the preset thickness of the silt. Indicates the thickness of the medium; k represents the number of the preset time period, and n represents the total number of preset time periods. The thickness of the silt at the preset deep tunnel monitoring point in the preset time period is monitored by the silt box, and the average value corresponding to the absolute value of the difference between the silt thickness and the preset silt thickness is taken as the change in silt thickness.

[0026] A ratio analysis was performed on the change in backfill density and the preset backfill density. Then, a weighted calculation was performed using the density medium to obtain the backfill density analysis value. This value reflects the impact of the change in backfill density on the passability of backfill samples collected from the backfill box. Specifically, the expression for the backfill density analysis value is: , This represents the density analysis value of the backfill material in the k-th preset time period. This represents the change in the density of silt during the k-th preset time period. Indicates the preset silt density. The density of the medium is represented by the siltation box, which monitors the density of the siltation material at a preset deep tunnel monitoring point within a preset time period. The average value of the absolute value of the difference between the siltation material density and the preset siltation material density is taken as the change in siltation material density.

[0027] A proportional analysis is performed on the change in the particle size distribution ratio of the sludge and the preset particle size distribution ratio. Then, a weighted calculation is performed based on the particle size distribution ratio to obtain the sludge particle size distribution ratio analysis value. This value reflects the impact of the change in the sludge particle size distribution ratio on the passability of the sludge samples collected from the sludge box. The change in the sludge particle size distribution ratio is represented by the absolute value of the difference between the original sludge particle size distribution ratio and the preset sludge particle size distribution ratio. Specifically, the expression for the sludge particle size distribution ratio analysis value is: , This represents the particle size distribution ratio analysis value of the silt in the k-th preset time period. This represents the change in the proportion of particle size distribution of silt during the k-th preset time period. This indicates the preset particle size distribution ratio of the silt. The particle size distribution ratio of the medium is represented by the particle size distribution ratio of the silt at a preset deep tunnel monitoring point within a preset time period, which is monitored by the silt return box. The absolute value of the difference between this and the preset particle size distribution ratio of the silt is used as the average value of the change in the particle size distribution ratio of the silt.

[0028] The obtained silt sample analysis values ​​are coupled in multiple dimensions to obtain the silt sample deviation value.

[0029] The deviation value of the silt sample was obtained in the following way:

[0030] In the formula, This represents the deviation value of the silt sample in the k-th preset time period; It should be added that the sludge sample deviation value is used to reflect the combined influence of the sludge sample variation value and the preset sludge volume on the pass rate of the sludge sample deviation value; the sludge sample analysis values ​​include: sludge thickness analysis value, sludge density analysis value, and sludge particle size distribution ratio analysis value; the sludge sample variation values ​​include: sludge thickness variation, sludge density variation, and sludge particle size distribution ratio variation; the preset sludge volume includes: preset sludge thickness, preset sludge density, and preset sludge particle size distribution ratio; the media influence values ​​include: thickness media, density media, and particle size distribution ratio media.

[0031] The preset backfill thickness is represented by the average backfill thickness over a historical time period; the preset backfill density is represented by the average backfill density over a historical time period; the preset backfill particle size distribution ratio is represented by the average backfill particle size distribution ratio over a historical time period; the units for both the backfill thickness variation and the preset backfill thickness are meters; the units for both the backfill density variation and the preset backfill density are kilograms per cubic meter; and the units for both the backfill particle size distribution ratio variation and the preset backfill particle size distribution ratio are undefined.

[0032] It should be added that this embodiment provides a set of mappings extracted from a database. The mapping relationships in the mapping set can be one-to-one or many-to-one. This mapping set contains the mapping relationships between the analysis values ​​of silt samples and the corresponding media influence values, and this mapping set is obtained from the database. By inputting the real-time monitored analysis values ​​of silt samples into the mapping set, the corresponding media influence values ​​can be obtained. The mapping set is constructed by preset personnel who map the analysis values ​​of silt samples to the media influence values ​​one-to-one using preset mapping relationships; for example, in this embodiment, the range of the media influence values ​​is 0 to 1.

[0033] In this embodiment, further analysis of the sludge sample analysis values ​​yields the sludge sample deviation value. A larger sludge thickness analysis value indicates a stronger influence of the change in sludge thickness on the qualification of the sludge sample collection from the sludge box, resulting in a larger sludge sample deviation value. Similarly, a larger sludge density analysis value indicates a stronger influence of the change in sludge density on the qualification of the sludge sample collection from the sludge box, resulting in a larger sludge sample deviation value. Likewise, a larger sludge particle size distribution ratio analysis value indicates a stronger influence of the change in the particle size distribution ratio on the qualification of the sludge sample collection from the sludge box, resulting in a larger sludge sample deviation value. In summary, the sludge sample analysis values ​​and the sludge sample deviation value are positively correlated.

[0034] In this embodiment, the changes in the silt sample values ​​are not independent but interconnected, requiring comprehensive analysis. A greater change in silt thickness indicates increasing pressure on the lower silt layer from the upper layer. Under this pressure, the pores between the silt particles are compressed, water is squeezed out, and the silt particles move closer together, leading to a greater change in silt density. A greater change in silt thickness also leads to silt deposition as the water flow slows down. During water flow, the water carries silt, which is deposited in other locations, resulting in an increased particle size distribution ratio. By analyzing the interrelationships between the silt sample changes, the quality of silt sample data collected by the silt collection box is improved, thereby enhancing the accuracy of intelligent monitoring and control of siltation in immersed tunnels.

[0035] Furthermore, the specific process for determining whether to perform acquisition qualification optimization based on the acquisition qualification conditions is as follows: First, determine whether the acquired multibeam acquisition delay value meets the multibeam acquisition qualification conditions; the multibeam acquisition qualification conditions indicate that the multibeam acquisition delay value is greater than 0; if the monitored multibeam acquisition delay value does not meet the multibeam acquisition qualification conditions, then multibeam acquisition interference optimization is performed; if the monitored multibeam acquisition delay value meets the multibeam acquisition qualification conditions, the water depth data of the trench corresponding to the multibeam acquisition qualification conditions is marked as qualified multibeam data; second, determine whether the acquired silt sample deviation value meets the silt sample qualification conditions; silt... The sample qualification condition indicates that the deviation value of the silt sample is not greater than the preset silt sample deviation value. If the monitored silt sample deviation value does not meet the silt sample qualification condition, silt box acquisition interference optimization is performed. If the monitored silt sample deviation value meets the silt sample qualification condition, the silt sample data corresponding to the silt sample qualification condition is marked as qualified silt box data. Acquisition qualification conditions include: multibeam acquisition qualification conditions and silt sample qualification conditions. Qualified immersed tunnel silt data is input into the preset data fusion and analysis platform. Qualified immersed tunnel silt data includes qualified multibeam data and qualified silt box data.

[0036] In this embodiment, by comparing the real-time monitoring of the multibeam acquisition delay value with the preset multibeam acquisition delay value obtained from the database, the accuracy of the trench water depth data acquisition can be effectively evaluated, avoiding interference with the accuracy of the trench water depth data, ensuring that the multibeam acquisition is not interfered with, and thus improving the qualification of the trench water depth data. Similarly, by comparing the real-time monitoring of the silt sample deviation value with the preset silt sample deviation value obtained from the database, the qualification of the silt sample data collected by the silt box can be effectively evaluated, avoiding interference with the qualification of the silt sample data, ensuring that the silt box acquisition is not interfered with, and thus improving the qualification of the silt sample data. This ultimately achieves the effect of improving the accuracy of intelligent monitoring and control of siltation in immersed tunnels.

[0037] Further, the acquisition qualification optimization includes: multi-beam acquisition interference optimization and silt box acquisition interference optimization; the specific process of multi-beam acquisition interference optimization is as follows: First, perform adjustment pulse operation; performing adjustment pulse operation means sending a prompt to the preset personnel to increase the pulse width of the high-resolution multi-beam transducer by a preset multiple within a preset time period; the pulse width is less than the preset maximum pulse width; performing adjustment pulse operation also includes performing aperture sonar operation, performing aperture sonar operation means sending a prompt to the preset personnel to emit sound wave signals through a preset mobile platform during movement; if the multi-beam acquisition delay value re-acquired after performing adjustment pulse operation does not meet the multi-beam acquisition qualification conditions, proceed to the second step; the second step is to perform line density measurement operation; performing line density measurement operation means sending a prompt to the preset personnel to decrease the line spacing of the high-resolution multi-beam transducer by a preset multiple; the line spacing is greater than the preset minimum line spacing; if the multi-beam acquisition delay value re-acquired after performing line density measurement operation meets the multi-beam acquisition qualification conditions, stop. The specific process for optimizing interference in silt collection box acquisition is as follows: Step 1: Shorten the observation period. This means sending a prompt to the preset personnel to gradually shorten the observation period by a preset multiple obtained from the database. The observation period is greater than the preset minimum observation period. If the deviation value of the silt sample obtained after shortening the observation period does not meet the qualified conditions of the silt sample, proceed to Step 2. Step 2: Place the silt collection box. This means sending a prompt to the preset personnel to gradually increase the number of silt collection boxes at preset intervals according to the preset number of monitoring points in the immersed tunnel. The number of silt collection boxes placed is less than the preset maximum number of silt collection boxes. If the deviation value of the silt sample obtained after placing the silt collection box meets the qualified conditions of the silt sample, stop placing the silt collection box and perform an accurate assessment of the silt data input. Otherwise, send a warning to the preset personnel.

[0038] In this embodiment, the pulse width of the high-resolution multi-beam transducer is increased stepwise by a corresponding preset multiple, using the multi-beam acquisition delay value obtained from the database and a multiple relationship between the multi-beam acquisition delay value and a preset multiple obtained from the database as a standard. Simultaneously, an acoustic signal is emitted through a preset mobile platform during movement. When the multi-beam acquisition delay value re-obtained after adjustment pulse operation meets the multi-beam acquisition qualification conditions, the adjustment pulse operation is stopped. The adjustment pulse operation reflects the qualification level of the multi-beam acquisition. The preset multi-beam acquisition delay values ​​obtained from the database are used as a standard. The measurement line spacing of the high-resolution multi-beam transducer is gradually reduced by the corresponding preset multiples. When the multi-beam acquisition delay value re-acquired after the measurement line density operation meets the multi-beam acquisition qualification conditions, the measurement line density operation is stopped. Aperture sonar operation is performed to improve resolution and clarity to enhance the detection capability of underwater targets. Adjustment pulse operation is performed to increase the transmission power of multi-beam depth measurement to reduce multi-beam acquisition delay. The measurement line density operation reflects the qualification level of multi-beam acquisition. By analyzing the data... The deviation value of the silt sample obtained from the database and the multiple of the preset deviation value of the silt sample obtained from the database are used as the standard. The observation period is shortened step by step according to the corresponding preset multiple. When the deviation value of the silt sample obtained after shortening the observation period meets the qualified condition of the silt sample, the shortening of the observation period is stopped. The shortening of the observation period reduces the error of the silt sample data during the silt box collection process, thereby improving the qualification of the silt box silt sample collection. The shortening of the observation period can reflect the qualification degree of the silt box collection. The ratio between the obtained deviation value of the silt sample and the preset deviation value of the silt sample obtained from the database is used as a standard. The number of silt boxes is increased step by step according to the corresponding preset ratio. When the deviation value of the silt sample obtained after the silt box placement operation meets the qualified condition of the silt sample, the silt box placement operation is stopped. The silt box placement operation is used to reduce the error of collecting silt sample data at the preset number of monitoring points in the immersed tunnel. The silt box placement operation can reflect the qualified degree of the silt box collection, thereby improving the accuracy of intelligent monitoring and control of siltation in immersed tunnels.

[0039] It should be added that the adjustment pulse operation is performed first to match the water depth and bottom sediment conditions. Otherwise, the subsequent measurement line data may not reach the bottom or be excessively attenuated. The measurement line spacing can be reasonably planned to avoid measurement line overlap and reduce the delay of multi-beam acquisition. Shortening the observation cycle can filter out anomalies through the time smoothing effect, improve the real-time performance of the data, and thus accurately locate the position where a silt return box needs to be added.

[0040] Furthermore, after the interference assessment of the immersed tunnel siltation data collection is deemed satisfactory, an accuracy assessment of the siltation data input is performed to quantitatively evaluate the accuracy of the immersed tunnel siltation data input to the preset data fusion and analysis platform. The specific process is as follows: After analyzing the proportion between the preset qualified immersed tunnel siltation data collection analysis value and the qualified immersed tunnel siltation data collection value, a weighted calculation is performed based on the collection influence rate to obtain the qualified immersed tunnel siltation data collection deviation value, which reflects the impact of the qualified immersed tunnel siltation data input to the preset data fusion and analysis platform. The qualified immersed tunnel siltation data collection analysis value is represented by the absolute value corresponding to the difference between the qualified immersed tunnel siltation data collection value and the preset qualified immersed tunnel siltation data collection value. Specifically, the expression for the qualified immersed tunnel siltation data collection deviation value is: , This represents the acceptable deviation value for sedimentation data collection in the immersed tunnel during the t-th preset input time period. This represents the qualified analysis value of siltation collection in the immersed tunnel during the t-th preset input time period. This indicates the preset acceptable values ​​for sedimentation data collection in immersed tunnels. Indicates the impact rate of data collection. t represents the number of the preset initial mining time period, and m represents the total number of preset initial mining time periods; the preset input time period represents the preset time period during the process of inputting qualified immersed tunnel siltation data into the preset data fusion and analysis platform. The preset distributed control system monitors the amount of qualified immersed tunnel siltation data corresponding to the preset data fusion and analysis platform during the preset input time period, and uses its average value as the qualified value of immersed tunnel siltation collection.

[0041] After analyzing the proportion of the preset input time for immersed tunnel siltation data and the analysis value of the input time for immersed tunnel siltation data, a weighted analysis is performed based on the time deviation rate to obtain the siltation input time deviation value. This value reflects the impact of the analysis value of the input time for immersed tunnel siltation data on the accuracy of the preset data fusion and analysis platform for qualified immersed tunnel siltation data. Specifically, the expression for the siltation input time deviation value is: , This represents the sludge input time deviation value for the t-th preset input time period. This represents the input time analysis value of the immersed tunnel siltation data for the t-th preset input time period. This indicates the preset input time for immersed tunnel siltation data. The time deviation rate is represented by the timer monitoring the input time of the immersed tunnel siltation data in the preset data fusion and analysis platform within the preset input time period. The absolute value of the difference between the time deviation rate and the preset input time of the immersed tunnel siltation data is used as the average value of the input time of the immersed tunnel siltation data.

[0042] After performing a ratio analysis on the preset siltation data input frequency and the siltation data input frequency analysis value, a weighted analysis is conducted based on the frequency deviation rate to obtain the siltation data input frequency deviation value. This value reflects the impact of the siltation data input frequency analysis value on the accuracy of the qualified immersed tunnel siltation data input preset data fusion and analysis platform. The siltation data input frequency analysis value is represented by the absolute value of the difference between the siltation data input frequency and the preset siltation data input frequency. Specifically, the expression for the siltation data input frequency deviation value is: , This represents the input frequency deviation value of the siltation data in the t-th preset input time period. This represents the frequency analysis value of the siltation data input during the t-th preset input time period. This indicates the preset siltation data input frequency. The frequency deviation rate is represented by the frequency sensor monitoring the input frequency of the backfill data in the preset data fusion and analysis platform during the preset input time period. The absolute value of the difference between this and the preset backfill data input frequency, and the average value of the difference, are used as the backfill data input frequency analysis value.

[0043] The obtained siltation data input deviation value is coupled in multiple dimensions to obtain the accurate value of the siltation data input; The accurate values ​​for siltation data input are obtained through the following method:

[0044] In the formula, This represents the accurate value of the siltation data input for the t-th preset input time period; It should be added that the accuracy value of the siltation data input is used to reflect the combined influence of the siltation data input analysis value and the preset siltation data input value on the accuracy of the siltation data input. The siltation data input deviation value includes: the deviation value of the immersed tunnel siltation collection qualification value, the siltation input time deviation value, and the siltation data input frequency deviation value. The siltation data input analysis value includes: the immersed tunnel siltation collection qualification value, the immersed tunnel siltation data input time analysis value, and the siltation data input frequency analysis value. The preset siltation data input value includes: the preset immersed tunnel siltation collection qualification value, the preset immersed tunnel siltation data input time, and the preset siltation data input frequency. The deviation rate-related values ​​include: the collection influence rate, the time deviation rate, and the frequency deviation rate.

[0045] The preset qualified value for immersed tunnel siltation collection is represented by the average value of the qualified value for immersed tunnel siltation collection over a historical input time period; the preset input time for immersed tunnel siltation data is represented by the average input time for immersed tunnel siltation data over a historical input time period; the preset input frequency for siltation data is represented by the average input frequency for siltation data over a historical input time period; the unit for both the qualified value for immersed tunnel siltation collection and the preset qualified value for immersed tunnel siltation collection is bytes; the input time for both the immersed tunnel siltation data and the preset input time for immersed tunnel siltation data are seconds; and the input frequency for both the siltation data and the preset input frequency for siltation data are Hertz.

[0046] It should be added that this embodiment provides a set of mappings extracted from the database. The mapping relationships in the mapping set can be one-to-one or many-to-one. This mapping set contains the mapping relationships between the input analysis values ​​of siltation data and the corresponding deviation rate correlation values. This mapping set is obtained from the database. By inputting the real-time monitored siltation data input analysis values ​​into the mapping set, the corresponding deviation rate correlation values ​​can be obtained. The mapping set is constructed by preset personnel who map the siltation data input analysis values ​​to the deviation rate correlation values ​​one-to-one according to preset mapping relationships. For example, in this embodiment, the deviation rate correlation values ​​range from 0 to 1.

[0047] Further analysis of the input deviation values ​​of the siltation data yielded the accurate input values. A smaller deviation value indicates a stronger influence of the qualified siltation data collection analysis value on the accuracy of the pre-set data fusion and analysis platform, resulting in a higher accurate input value. Similarly, a smaller deviation value indicates a stronger influence of the siltation data input time analysis value on the accuracy of the pre-set data fusion and analysis platform, resulting in a higher accurate input value. Likewise, a smaller deviation value indicates a stronger influence of the siltation data input frequency analysis value on the accuracy of the pre-set data fusion and analysis platform, resulting in a higher accurate input value. In summary, the siltation data input deviation value and the siltation data input accurate value are inversely correlated.

[0048] In this embodiment, the changes in the silt samples are not independent but interconnected, requiring comprehensive analysis. A larger input time analysis value for immersed tunnel silt data means more qualified silt data can be input due to the longer input time, thus reducing incompleteness and ensuring accuracy. This improves the efficiency of silt data input, leading to an increase in silt data and consequently, a higher qualified silt collection analysis value. A higher silt data input frequency analysis value further enhances the ability to capture detailed information and accurately describe changes in the siltation process, resulting in a larger accumulated qualified silt data and consequently, a higher qualified silt collection analysis value. By analyzing the interrelationships between the silt sample changes, the accuracy of qualified silt data input is improved, thereby enhancing the accuracy of intelligent monitoring and control of immersed tunnel siltation.

[0049] In summary, the increased deviation in the data collection of sedimentation data for immersed tunnels is due to several factors. Firstly, as the amount of sedimentation data increases, the pre-set data fusion and analysis platform needs more time to input this data, leading to a larger input time deviation. Secondly, the increased input frequency deviation is caused by the platform caching the data, further lengthening the input time and resulting in a larger input time deviation.

[0050] Furthermore, the specific process for determining whether to optimize the accuracy of silt input data based on the accuracy conditions of the silt input data is as follows: Based on the monitored silt input accuracy value, determine whether it meets the silt input accuracy conditions; the silt input accuracy conditions indicate that the silt input accuracy value is greater than the preset silt input accuracy value; if the monitored silt input accuracy value meets the silt input accuracy conditions, then the corresponding qualified immersed tunnel silt input data is fused and processed, and a silt construction assessment is performed; fusion processing means fusing the qualified immersed tunnel silt input data with the preset data fusion and analysis platform; if the monitored silt input accuracy value does not meet the silt input accuracy conditions, then silt input accuracy optimization is performed; the specific process for silt input accuracy optimization is as follows: S1, perform an operation on the number of immersed tunnel monitoring points; performing an operation on the number of immersed tunnel monitoring points means sending a prompt to preset personnel to increase the number of immersed tunnel monitoring points in preset multiples step by step; the number of immersed tunnel monitoring points is less than the preset maximum number of immersed tunnel monitoring points. If the accuracy value of the siltation data re-acquired after the operation of increasing the number of monitoring points in the immersed tunnel does not meet the accuracy conditions for siltation data input, then proceed to S2; S2, perform the operation of adding relay nodes; performing the operation of adding relay nodes means sending a prompt to the preset personnel to increase the number of relay nodes at the receiving end in a preset multiple step by step; the number of relay nodes at the receiving end is less than the preset maximum number of relay nodes; performing the operation of adding relay nodes is used to improve the reliability of data transmission of qualified immersed tunnel siltation data; if the accuracy value of the siltation data re-acquired after the operation of adding relay nodes meets the accuracy conditions for siltation data input, then perform the siltation construction assessment, otherwise send a warning to the preset personnel; after the siltation data input accuracy is optimized, the qualified immersed tunnel siltation data is re-judged to determine whether the accuracy value of the siltation data input meets the accuracy conditions for siltation data input. If the accuracy value of the re-acquired siltation data input meets the accuracy conditions for siltation data input, then the corresponding qualified immersed tunnel siltation data is fused and processed, and the siltation construction assessment is performed, otherwise, a warning is sent to the preset personnel.

[0051] In this embodiment, the ratio between the accurate input value of the siltation data obtained from the database and the preset accurate input value of the siltation data obtained from the database is used as a standard. The number of siltation boxes is increased step by step according to the corresponding preset ratio. When the accurate input value of the siltation data obtained after the immersed tunnel monitoring point count operation meets the siltation data input accuracy condition, the immersed tunnel monitoring point count operation is stopped. By performing the immersed tunnel monitoring point count operation, the spacing between monitoring points can be reduced to ensure full coverage of silty areas. Dense monitoring points can capture more detailed siltation distribution characteristics, preventing localized siltation phenomena caused by sparse monitoring points from being overlooked. Calculating the number of monitoring points for immersed tunnels reflects the accuracy of the siltation data input for qualified immersed tunnels. This calculation captures more detailed siltation distribution characteristics, ensuring full coverage of the siltation area and preventing localized siltation phenomena from being overlooked due to sparse monitoring points. Using the ratio between the accurate siltation data input value obtained from the database and the preset accurate siltation data input value obtained from the database as a standard, the number of receiver relay nodes is increased incrementally at corresponding preset multiples. If the accuracy of the re-acquired siltation data after the relay node operation meets the accuracy conditions for siltation data input, the relay node addition operation is stopped. Adding relay nodes can effectively increase the number of channels for transmitting qualified immersed tunnel siltation data, thereby improving the reliability of the preset data fusion and analysis platform for qualified immersed tunnel siltation data input. Adding relay nodes can reflect the accuracy of qualified immersed tunnel siltation data input. Adjusting the number of monitoring points in the immersed tunnel can effectively reduce the input error of qualified immersed tunnel siltation data, improve the accuracy of deep tunnel siltation data, and thus achieve the effect of improving the accuracy of intelligent monitoring and control of immersed tunnel siltation.

[0052] Furthermore, after the siltation data input is accurately assessed and deemed qualified, a siltation construction assessment is performed to quantify the qualification level of the siltation construction. The specific process is as follows: Obtain the predicted siltation trend value. The predicted siltation trend value represents the result output from the pre-set data fusion and analysis platform, reflecting the qualification level of the siltation construction; determine whether the obtained predicted siltation trend value meets the predicted qualification conditions; if the monitored predicted siltation trend value meets the predicted qualification conditions, the qualified immersed tunnel siltation data meeting the predicted qualification conditions is stored in the pre-set data fusion and analysis platform; if the monitored predicted siltation trend value does not meet the predicted qualification conditions, siltation construction optimization is performed; the predicted siltation trend value represents the predicted siltation trend value obtained based on the pre-set data fusion and analysis platform; the predicted qualification condition indicates that the predicted siltation trend value is not greater than the pre-set predicted siltation trend value.

[0053] In this embodiment, by comparing and analyzing the real-time monitoring of the predicted siltation trend with the preset predicted siltation trend obtained from the database, the accuracy of the preset data fusion and analysis platform can be effectively evaluated. The preset data fusion and analysis platform can use algorithms, for example, input qualified immersed tunnel siltation data that meets the predicted qualification conditions into the preset data fusion and analysis platform. The preset data fusion and analysis platform can output the siltation volume per unit time of the immersed tunnel through a preset hydrodynamic model based on the Navier-Stokes equations and sediment transport equations. The siltation volume per unit time of the immersed tunnel is marked as the predicted siltation trend value. The predicted siltation trend value is used to reflect the siltation trend, which can effectively avoid interference with the real-time execution of siltation construction, ensure the accuracy of siltation construction, and thus improve the qualification of siltation construction, thereby achieving the effect of improving the accuracy of intelligent monitoring and control of immersed tunnel siltation.

[0054] Further, the specific process for optimizing the siltation construction is as follows: First stage: Perform siltation frequency operation; this involves sending prompts to designated personnel to gradually increase the siltation frequency by a predetermined multiple; the siltation frequency must be less than the predetermined maximum siltation frequency. If the predicted siltation trend value obtained after the siltation frequency operation meets the prediction qualification conditions, the qualified immersed tunnel siltation data meeting the prediction qualification conditions is stored in the predetermined data fusion and analysis platform; otherwise, proceed to the second stage. Second stage: Perform trench angle operation; this involves sending prompts to designated personnel to gradually decrease the trench angle by a predetermined multiple; the trench angle must be greater than the predetermined minimum trench angle. If the accurate value of the siltation data obtained after the trench angle operation still does not meet the prediction qualification conditions, an early warning is sent to designated personnel; otherwise, the qualified immersed tunnel siltation data meeting the prediction qualification conditions is stored in the predetermined data fusion and analysis platform.

[0055] In this embodiment, the dredging frequency is increased incrementally by a multiple of the predicted siltation trend value obtained from the database and a preset predicted siltation trend value obtained from the database. When the siltation frequency operation is performed and the newly acquired siltation data input accuracy meets the prediction qualification conditions, the dredging frequency operation is stopped. The dredging frequency operation is used to reduce the local suspended sediment concentration, indirectly inhibiting the subsequent siltation rate. It also helps maintain the flowability of the deep tunnel and avoids blockage by coarse-grained silt. Furthermore, the dredging frequency operation reflects the accuracy of the siltation construction. Using the ratio between the predicted siltation trend value obtained from the database and the preset predicted siltation trend value obtained from the database as a standard, the trench angle is gradually reduced by the corresponding preset ratio. When the accurate value of the siltation data re-obtained after trench angle operation meets the prediction qualification conditions, the trench angle operation is stopped. The trench angle operation is used to quantify the trench size to improve slope stability; it is also used to reduce the instability of deep tunnels; and it reflects the accuracy of siltation construction, thereby improving the accuracy of intelligent monitoring and control of siltation in immersed tunnels.

[0056] In summary, by performing the following steps during the data acquisition process for immersed tunnel siltation, including siltation data acquisition and evaluation to determine if data acquisition is qualified, siltation data input accuracy assessment and evaluation to determine if siltation data input accuracy is qualified, and finally siltation construction assessment and evaluation to determine if siltation construction is qualified, the pass rate of intelligent monitoring and control of immersed tunnel siltation based on multibeam bathymetry is improved. This, in turn, enhances the accuracy of intelligent monitoring and control of immersed tunnel siltation, solving the problem of low accuracy in existing technologies due to interference with acoustic signals during the acquisition process.

[0057] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0058] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0059] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0062] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry, characterized in that, Includes the following steps: During the process of collecting siltation data in immersed tunnels, an interference assessment of siltation data collection is performed to quantify the degree of interference in the data collection. Based on the qualified collection conditions, it is determined whether to perform qualified collection optimization. The qualified collection optimization is used to improve the qualified collection of siltation data in immersed tunnels during the data collection process. After the interference assessment of the siltation data collection in the immersed tunnel is qualified, the accuracy assessment of the siltation data input is carried out to quantitatively evaluate the accuracy of the preset data fusion and analysis platform for the siltation data input in the immersed tunnel. Based on the accuracy conditions of the siltation data input, it is determined whether to optimize the accuracy of the siltation data input. The siltation data input accuracy optimization is used to improve the accuracy of the siltation data input process in the immersed tunnel. After the siltation data is accurately input and assessed as qualified, a siltation construction assessment is performed to quantify the qualification level of the siltation construction. Based on the predicted qualification conditions, it is determined whether to optimize the siltation construction. The siltation construction optimization is used to improve the real-time performance of the immersed tunnel siltation construction.

2. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 1, characterized in that, The interference assessment for sedimentation in the immersed tunnel includes multibeam acquisition interference assessment and sedimentation box sediment sample acquisition interference assessment. The multibeam acquisition interference assessment refers to the evaluation of the timeliness of the water depth data acquisition process in the trench based on the monitored multibeam acquisition delay value; The interference assessment of sludge sample collection in the sludge box represents the assessment of the anti-interference capability during the sludge sample data collection process based on the monitored deviation value of the sludge sample.

3. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 2, characterized in that, The specific process for interference assessment of sludge sample collection from the sludge return box is as follows: Set the placement angle; The setting of the placement angle refers to adjusting the tilt angle of the sludge return box. Determine whether the tilt angle of the monitored sludge return box is within the preset tilt angle range; If the monitored tilt angle of the sludge return box is not within the preset tilt angle range, an early warning will be sent to the preset personnel. If the tilt angle of the monitored sludge return box is within the preset tilt angle range, the deviation value of the sludge sample is obtained.

4. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 3, characterized in that, The specific process for obtaining the deviation value of the silt sample is as follows: After analyzing the proportion of the change in backfill thickness and the preset backfill thickness, the backfill thickness analysis value is obtained by weighting the thickness medium. This value is used to reflect the impact of the change in backfill thickness on the qualification of the backfill sample collection in the backfill box. The ratio of the change in backfill density to the preset backfill density is analyzed, and then weighted calculation is performed in combination with the density medium to obtain the backfill density analysis value, which is used to reflect the impact of the change in backfill density on the qualification of backfill samples collected from the backfill box. The proportion analysis of the change in the particle size distribution ratio of the sludge and the preset particle size distribution ratio of the sludge is performed. Then, the particle size distribution ratio of the medium is weighted and calculated to obtain the particle size distribution ratio analysis value of the sludge. This value is used to reflect the impact of the change in the particle size distribution ratio of the sludge on the qualification of the sludge sample collected from the sludge box. The obtained silt sample analysis values ​​are coupled in multiple dimensions to obtain the silt sample deviation value; The deviation value of the silt sample is used to reflect the combined effect of the change value of the silt sample and the preset silt volume on the pass rate of the silt sample deviation value; The analytical values ​​of the silt sample include: silt thickness analysis value, silt density analysis value, and silt particle size distribution ratio analysis value. The changes in the silt sample include: changes in silt thickness, changes in silt density, and changes in the proportion of silt particle size distribution.

5. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 1, characterized in that, The specific process for determining whether to perform data collection qualification optimization based on the data collection qualification conditions is as follows: Determine whether the acquired multi-beam acquisition delay value meets the multi-beam acquisition qualification conditions; The multi-beam acquisition qualification condition indicates that the multi-beam acquisition delay value is greater than 0; If the monitored multi-beam acquisition delay value does not meet the multi-beam acquisition qualification conditions, then multi-beam acquisition interference optimization is performed; If the monitored multibeam acquisition delay value meets the multibeam acquisition qualification conditions, the water depth data of the trench corresponding to the multibeam acquisition qualification conditions will be marked as qualified multibeam data. Determine whether the deviation value of the obtained silt sample meets the qualification conditions for silt samples; The qualified condition for the silt sample means that the deviation value of the silt sample is not greater than the preset deviation value of the silt sample. If the deviation value of the monitored sludge sample does not meet the qualified condition of the sludge sample, then the interference of the sludge collection box will be optimized. If the deviation value of the monitored sludge sample meets the qualified conditions for sludge samples, the sludge sample data corresponding to the qualified conditions for sludge samples will be marked as qualified sludge box data. The acquisition qualification conditions include: multibeam acquisition qualification conditions and silt sample qualification conditions; Input qualified immersed tunnel siltation data into a preset data fusion and analysis platform; The qualified immersed tunnel siltation data includes qualified multibeam sonic logging data and qualified siltation box data.

6. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 5, characterized in that, The acquisition qualification optimization includes: multi-beam acquisition interference optimization and siltation box acquisition interference optimization; The specific process of multi-beam acquisition interference optimization is as follows: The first step is to adjust the pulse. The term "adjusting pulse operation" refers to increasing the pulse width of the high-resolution multi-beam transducer. The adjustment pulse operation also includes aperture sonar operation, wherein the aperture sonar operation means emitting a sound wave signal; The aperture sonar operation is used to improve resolution and clarity to enhance the detection capability of underwater targets; The adjustment pulse operation is used to increase the transmission power of multibeam depth measurement in order to reduce the delay of multibeam acquisition; If the multi-beam acquisition delay value re-acquired after adjusting the pulse operation does not meet the multi-beam acquisition qualification conditions, proceed to the second step; The second step is to measure the line density. The term "performing line density measurement operation" refers to reducing the line spacing of the high-resolution multibeam transducer. If the multibeam acquisition delay value re-acquired after the line density measurement operation meets the multibeam acquisition qualification conditions, stop the line density measurement operation and perform accurate evaluation of siltation data input; otherwise, send an early warning to the preset personnel. The specific process for optimizing interference collected by the sludge return box is as follows: Step 1: Perform the operation to shorten the observation cycle; The operation of shortening the observation period means shortening the observation period; If the deviation value of the silt sample obtained after shortening the observation period does not meet the qualified condition of the silt sample, then proceed to step two. Step two, place the sludge return box; The phrase "performing the placement of sludge return boxes" indicates increasing the number of sludge return boxes placed. The operation of placing the silt return box is used to reduce the error in collecting silt sample data at the preset number of monitoring points in the immersed tunnel. If the deviation value of the sludge sample obtained after the placement of the sludge box meets the qualified conditions for the sludge sample, the placement of the sludge box is stopped, and the sludge data input accuracy assessment is performed; otherwise, an early warning is sent to the preset personnel.

7. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 6, characterized in that, After the interference assessment of the siltation data collection in the immersed tunnel is deemed satisfactory, an accuracy assessment of the siltation data input is performed to quantitatively evaluate the accuracy of the siltation data input into the preset data fusion and analysis platform. The specific process is as follows: After analyzing the proportion of the preset qualified analysis value of immersed tunnel siltation collection to the qualified value of immersed tunnel siltation collection, the weighted calculation is performed in combination with the collection influence rate to obtain the qualified deviation value of immersed tunnel siltation collection. This value is used to reflect the impact of the input of qualified immersed tunnel siltation data into the preset data fusion and analysis platform on the accuracy. After analyzing the proportion of the input time of the pre-set immersed tunnel siltation data and the analysis value of the input time of the immersed tunnel siltation data, a weighted analysis is performed in combination with the time deviation rate to obtain the siltation input time deviation value, which is used to reflect the impact of the analysis value of the input time of the immersed tunnel siltation data on the accuracy of the qualified immersed tunnel siltation data input pre-set data fusion and analysis platform. After performing a ratio analysis on the preset siltation data input frequency and the siltation data input frequency analysis value, a weighted analysis is performed in conjunction with the frequency deviation rate to obtain the siltation data input frequency deviation value, which is used to reflect the impact of the siltation data input frequency analysis value on the accuracy of the qualified immersed tunnel siltation data input preset data fusion and analysis platform. The obtained siltation data input deviation value is coupled in multiple dimensions to obtain the accurate value of the siltation data input; The accuracy value of the siltation data input is used to reflect the combined influence of the siltation data input analysis value and the preset siltation data input value on the accuracy of the siltation data input. The siltation data input deviation values ​​include: the qualified deviation value of siltation collection for immersed tunnels, the siltation input time deviation value, and the siltation data input frequency deviation value; The siltation data input analysis values ​​include: qualified analysis values ​​for immersed tunnel siltation collection, siltation data input time analysis values ​​for immersed tunnel, and siltation data input frequency analysis values.

8. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 7, characterized in that, The specific process for determining whether to perform siltation data input accuracy optimization based on the condition of accurate siltation data input is as follows: Determine whether the conditions for accurate input of siltation data are met based on the accuracy of the input data. The accurate input condition for siltation data means that the accurate input value of siltation data is greater than the preset accurate input value of siltation data. If the monitored siltation data input accuracy value meets the siltation data input accuracy conditions, the corresponding qualified immersed tunnel siltation data will be merged and processed to perform siltation construction assessment. The fusion process refers to inputting qualified immersed tunnel siltation data into a preset data fusion and analysis platform for data fusion. If the accuracy value of the monitored siltation data input does not meet the accuracy conditions for siltation data input, then siltation data input accuracy optimization will be performed. The specific process for accurately optimizing the input of the siltation data is as follows: S1, to count the number of monitoring points for the immersed tunnel; The operation of increasing the number of monitoring points for the immersed tunnel means increasing the number of monitoring points for the immersed tunnel. The method of increasing the number of monitoring points for immersed tunnels is used to reduce the input error of qualified immersed tunnel siltation data. If the accuracy value of the siltation data re-acquired after the operation of increasing the number of monitoring points in the immersed tunnel does not meet the conditions for accurate siltation data input, then proceed to S2; S2, perform the operation to add a relay node; The operation of adding relay nodes means increasing the number of relay nodes at the receiving end; The operation of adding relay nodes is used to improve the reliability of data transmission for siltation in qualified immersed tunnels. If the accurate value of the siltation data re-acquired after adding a relay node meets the accurate siltation data input conditions, then the siltation construction assessment will be performed; otherwise, an early warning will be sent to the preset personnel. After the siltation data input is accurately optimized, the qualified immersed tunnel siltation data is re-input into the preset data fusion and analysis platform to determine whether the siltation data input accuracy value meets the siltation data input accuracy conditions. If the re-acquired siltation data input accuracy value meets the siltation data input accuracy conditions, the siltation construction assessment is performed; otherwise, an early warning is sent to the preset personnel.

9. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 8, characterized in that, After the siltation data is accurately input and assessed as qualified, a siltation construction assessment is performed to quantitatively evaluate the qualification level of the siltation construction. The specific process is as follows: Obtain the predicted value of siltation trend; Determine whether the predicted siltation trend value meets the prediction qualification criteria. If the predicted value of the monitored siltation trend meets the prediction qualification conditions, the siltation data of qualified immersed tunnels that meet the prediction qualification conditions will be stored in the preset data fusion and analysis platform. If the predicted value of the siltation trend does not meet the prediction qualification conditions, the siltation construction will be optimized.

10. The intelligent monitoring and control method for siltation backflow in immersed tunnels based on multibeam bathymetry as described in claim 9, characterized in that, The specific process for optimizing the siltation construction is as follows: The first phase involves frequent dredging operations. The dredging frequency operation is achieved by sending prompts to preset personnel to gradually increase the dredging frequency by preset multiples. The dredging frequency is less than the preset maximum dredging frequency; If the predicted value of the backfill trend obtained after the dredging frequency operation meets the prediction qualification conditions, the qualified immersed tunnel backfill data that meets the prediction qualification conditions will be stored in the preset data fusion and analysis platform; otherwise, the second stage will be carried out. The second stage involves adjusting the foundation trench angle. The operation of the foundation trench angle means sending a prompt to a preset person to gradually reduce the foundation trench angle by a preset multiple. The aforementioned trench angle manipulation is used to quantify the trench dimensions in order to improve slope stability; If the accurate value of the siltation data re-acquired after the foundation trench angle operation still does not meet the predicted qualification conditions, an early warning will be sent to the preset personnel. Otherwise, the qualified immersed tunnel siltation data that meets the predicted qualification conditions will be stored in the preset data fusion and analysis platform.

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